Post on 27-Mar-2015
PARENTHOOD AND ORGANIZATIONAL NETWORKS:
A RELATIONAL VIEW OF THE CAREER MOBILITY OF WORKING
PARENTS
DISSERTATION
Presented in Partial Fulfillment of the Requirements for the Degree of Doctor of
Philosophy in the Graduate School of The Ohio State University
By
Kyra Leigh Sutton
**************
The Ohio State University
2006
Dissertation Committee:
Professor, Raymond A. Noe, Adviser Approved by
Professor, David Greenberger __________________________
Professor, Howard J. Klein Adviser Graduate Program in Labor and Human Resources
Copyrighted Kyra Leigh Sutton
ABSTRACT
This dissertation examined how parental responsibilities impacted three
organizational networks characteristics, network size, network ties, and network content
across employees. The study was based on theory and research from sociology (i.e. social
networks), careers, and the work-family literature. The study was designed to understand
the potential moderators of the relationship between parental status and the three network
characteristics. This dissertation sought to understand the relationship between the three
network characteristics of interest and two career outcomes including career success and
career management perceptions. The career success measures included in this study were
salary, salary growth, promotions, and career satisfaction. The career management
perception measures included in this study were career planning, career tactics, and
career mobility preparedness. This study utilized network analysis and investigated the
organizational networks of working adults with children in comparison to the networks of
working adults without children. The goal of this dissertation was to understand the
following research questions:
Research Question: First, how do networks differ after the birth of a child for males vs females? Secondly, how do networks differ between working adults with and without children? Thirdly, what constraints produce those differences?
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The study results suggest that network characteristic, network content, may vary
across parental status, where working parents reported a higher percentage of their
network content (i.e. topics of conversation) to be non-work and kin relevant topics.
There were no significant interactions between parental status and the four moderators of
interest, including gender, family involvement, job involvement, and role segmentation.
However, significant main effects were found for both job involvement and role
segmentation on network ties and network content. Finally, the results suggest that
network size has a significant main effects on salary growth and career mobility
preparedness, individual and peer-related career satisfaction. Network content was also
significantly related to career tactics.
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DEDICATION Dedicated to my three favorite people, Mom (Wanda G. Sutton), Dad (James C. Sutton),
and Grandma (Gloria I Gibson)
and in Memory Of My Favorite Little Buddy, “Sweetie Pie Sutton” (7/11/89 – 6/04/04)
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ACKNOWLEDGMENTS First, I must thank God, the higher power that has sustained me throughout my life, this
program, and in all future endeavors.
In reflecting on this dissertation process, I have many people to thank. I will start by
thanking my undergraduate thesis committee at Spelman College including Dr. Romie
Tribble, Dr. Ann Hornsby, and Dr. Jack Stone. Each of you saw my potential to go to
graduate school and complete a doctoral program, and I thank for your guidance,
encouragement, and confidence that I would begin and successfully complete my
program. I also want to thank my current dissertation committee members including Dr.
Raymond A. Noe, Dr. Howard Klein, and Dr. David Greenberger. First, Ray, as my
advisor and committee chair, I thank you for your time, wonderful feedback, and
willingness to push to get the best out of me. I also thank Ray for being the first faculty
member to really introduce me to the work-family area and for continuously giving me
the encouragement to do my best throughout the program, and really being the first
professor to give me a chance in the program. Howard, I thank you for many things
including your willingness to help me by answering as I progressed through this
dissertation process and other research projects. I appreciate your willingness to include
myself and my cohort during our first year in a research project, and I thank you for
always being accessible. David, I thank you for being a person that always has a open-
door policy and for making sure that as doctoral students we are taking care of ourselves
as well as our minds. I also thank you for challenging me to look through a “social
identity lens”, dissertation not excluded. Finally, in working on my dissertation I also had
the help of two very special individuals including Dr. James (Jim) Moody (Duke
University) and Eddie Willett. Jim, I thank you for giving my first taste of the social
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networking! This is a fascinating field and I am so appreciative to have had your
guidance and knowledge as I worked on my dissertation. I also appreciate you for
encouraging me to take ownership of the dissertation and encouraging me to really think
about the data and questions I wanted to analyze. Finally, I was very fortunate to have
what I consider to be the best experience with a computer programmer, that is, Eddie.
Eddie, I thank you for your time and patience and willingness to respond so quickly as
we worked on this project.
In addition, I would also like to thank my family and friends. First, Mom, I absolutely
needed your support in this program and I thank you forever. You are always available to
listen to me, support me, give a hug when I needed one, and make me laugh when I
needed to do that. Also, you are my perfect role model, beautiful, intelligent, generous
and thoughtful. Dad, thank you for always reminding me of the bigger goal, and being
able to see the bigger picture. Also Dad, thank you for always checking on me; I always
appreciated your calls just to “see how your young lady is doing”, it always gave me
great comfort to talk to you. Grandma, thank you for just being you. It was so comforting
to talk to you on the phone and just hear your words of encouragement and praise. You
have and always will make such a difference in my life. Knowing that I have Mom, Dad,
and Grandma in my life, always let me know that everything will be more than okay, it’ll
be great!
Also, I want to thank all of my friends, and two very special friends, Dr. Michele Harvey
and Justina Richards. Michele and Justina, as an only child I could not have been so
lucky to be blessed with two beautiful people who I both consider my sisters. I thank you
both for your support and encouragement, and I thank you for just always being available
to talk and listen, even when I couldn’t always do the same. Throughout the program I
developed many colleagues and friends some of whom include
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Marie-elene Roberge, Dr. Monica Wang, Dr. Ed Tomlinson, Dr. Boyce Watkins,
Dr. Chris O.L. H. Porter, Janice Molloy, Dr. Hyondong Kim, Aden Heuser, and many,
many others. I thank you for your collegiality and friendship now and in the future.
Lastly, I wanted to thank the organization that allowed me to collect data, although the
name of the organization will be withheld. I also want to thank Shari-Mickey Boggs with
whom I worked very closely to collect data for the focus group portion of my
dissertation. Shari you are great to work with and I look forward to working with you in
the future. I also want to thank Heidi Dugger. Heidi, you are one in a million and the
Management and Human Resources Department at Fisher is fortunate to have you.
In closing, I am thankful for the financial support I received for my dissertation project
from two grants including the Coca-Cola Critical Difference for Women Graduate
Studies Grants for Research on Women, Gender, and Gender Equity (OSU) and the Ohio
State University, Graduate School’s Alumni Grants for Graduate Research and
Scholarship (AGGRS) Fund.
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VITA
September 25, 1976………………………… .....Born- USA 1998……………………………………………..B.A. Economics, Spelman College 1998-2000……………………………………….Business Analyst, AT Kearney
Consulting Firm 2000-2001…………………………………….....Senior Corporate Forecasting
Analyst, Delta Airlines 2001-present…………………………………….Graduate Research and Teaching Associate, The Ohio State University
PUBLICATIONS Sutton, K.L. & Noe, R.A. (2004). Family Friendly Programs and Work-Life Integration: More Myth Than Magic. In Kossek, E. E. & Lambert, S. (Eds.) Work And Life Integration: Organizational, Cultural and Psychological Perspectives. Mahwah, N.J.: Lawrence Erlbaum Associates. Referred Conference Publications Klein, H.J., Heuser, A. E., & Sutton, K.L. (April, 2006). “The Dimensions and Levels of Socialization Content. Paper accepted for presentation at the Annual Conference of the Society for Industrial and Organizational Psychology. Dallas, TX. Sutton, K.L. & Dunn-Jensen, L. (August, 2005). “Managing Work-Family Balance in the 21st Century: Do Informal Work Practices Help or Hinder Employees". Organizer, Co-chair and Presenter. Symposium accepted for presentation at the annual meeting of the Academy of Management, Honolulu, HW.
**Symposium nominated for Best Symposium Award, Careers Division** Sutton, K.L. & Noe, R.A. (2004). Work Family Practices: A Pragmatic Perspective: Do We Really Know How These Practices Work? Organizer and co-chair. Symposium accepted for presentation at the annual meeting of the Academy of Management, New Orleans, LA. Wang, C. and Sutton, K.L. (2004). "Nodding Along or Fighting for 'Us': Do Conflict Management Style and Propensity to Initiate Negotiations Influence Group Identification and Effectiveness?" Accepted for presentation at the International Association of Conflict Management, Pittsburgh, PA.
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Sutton, K.L., Klein, H., & Barnard, J.,& Noe, R. A. (2003). Distance Learning and Learning Preferences: Does Gender Matter? Presented at a poster session during the annual meeting of the Academy of Management, Seattle, WA. Technical Publications –Organizational Use Only Ellingson, J.E., Reichers, A., Molloy, J. & Sutton, K. (2005). Retaining Female Tenure-Track Assistant Professors. A Descriptive Evaluation of the Faculty Cohort Project Conducted at The Ohio State University. Department of Management and Human Resources. Fisher College of Business, The Ohio State University.
FIELDS OF STUDY Major Field: Labor and Human Relations
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TABLES OF CONTENTS
ABSTRACT................................................................................................................. ii DEDICATION............................................................................................................ iii ACKNOWLEDGMENTS ......................................................................................... iv VITA.......................................................................................................................... viii LIST OF TABLES…………………………………………………………………..xi LIST OF FIGURES………………………………………………………………....xv Chapters: 1. INTRODUCTION............................................................................................... 1 Problem Statement.................................................................................................... 11 Contributions of this dissertation............................................................................ 15 2. LITERATURE REVIEW ................................................................................ 18 Social Capital and The Relational Approach to Career Development ................ 27 Network Measures and Characteristics.................................................................. 33 The Importance of Organizational Networks ........................................................ 41 Gender and organizational networks..................................................................... 43 Parental Status and Organizational Networks....................................................... 52 Careers: An Overview of Various Approaches...................................................... 57 Careers and Organizational Networks ................................................................... 61 Careers and Gender.................................................................................................. 67 Careers and Parental Status .................................................................................... 70 Careers and Work-Family Concerns ...................................................................... 74 Job and Family Involvement.................................................................................... 81 Weak Tie Theory....................................................................................................... 84 Boundary Theory...................................................................................................... 88 3. CONCEPTUAL MODEL AND HYPOTHESES DEVELOPMENT .......... 95 Gender Constraint .................................................................................................. 102 Family Involvement Constraint............................................................................. 104
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Role Segmentation Constraint ............................................................................... 107 Job Involvement Constraint................................................................................... 110 Perceptions of Career Success ............................................................................... 113 Perceptions of Career Self-Management .............................................................. 116 4. METHOD ........................................................................................................ 119 Focus Group Study ................................................................................................. 120 Pilot Testing............................................................................................................. 127 Field Study............................................................................................................... 130 Survey Response Rate............................................................................................. 140 Measures .................................................................................................................. 145 Plan for Data Analysis ............................................................................................ 175 5. RESULTS ........................................................................................................ 177 Preliminary Analysis .............................................................................................. 177 Data Checks and Cleaning ..................................................................................... 186 Scale Reliability Analysis ....................................................................................... 192 Tests of Hypotheses................................................................................................. 210 6. DISCUSSION .................................................................................................. 281 Overview of Findings.............................................................................................. 281 Theoretical Implications......................................................................................... 309 Practical Implications............................................................................................. 314 Future Research Questions .................................................................................... 319 Study Limitations.................................................................................................... 321 Works Cited............................................................................................................ .330 Appendix A: Focus Group Interview Survey ............................................... .333 Appendix B: Wave 1 E-mail: Invitation to Participate in The Study ....... 346Appendix C, Wave 2 E-mail: Invitation to Participate in The Study ....... 348 Appendix D: Field Survey Wave 1 ........................................................................ 352 Appendix E: Organizational Networks and Careers Survey- Wave 2Error! Bookmark not defined.
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LIST OF TABLES Table 2.1: Frequently Measured Items Related to Network Ties ....................... 34 Table 2.2: Typical Social Network Measures Used to Describe Entire Networks36 Table 4.1: Non-Response Bias Analysis ................................................................ 144 Table 4.2: Calculation of Role segmentation ‘Actual’ Measure ......................... 157 Table 5.1: Descriptive Statistics for Demographic Variables ............................. 182 Table 5.2: Descriptive Statistics and Correlations on Career Success and Career Management……………………………………………………………………….180 Table 5.3: Family Involvement Scale .................................................................... 193 Table 5.4: Role segmentation Scale A ................................................................... 197 Table 5.5: Role segmentation Scale B ................................................................... 198 Table 5.6: Role segmentation Scale C ................................................................... 199 Table 5.7: Job Involvement Scale ......................................................................... 201 Table 5.8: 3-Factor Solution, Career Management ............................................. 206 Table 5.9: Career Success-Individual Career Satisfaction Scale........................ 208 Table 5.10: Career Success-Peer/Related Career Satisfaction Scale ................. 209 Table 5.11: One-Way ANOVAs between Parental Status and Ego Network Characteristics......................................................................................................... 214 Table 5.12: Regression Results of the Relationship between Parental Status and Network Constraint (Gender) on Network Size, Hypothesis 1a......................... 218 Table 5.13 Regression Results of the Relationship between Parental Status and Network Constraint (Gender) on Network Ties .................................................. 219
Table 5.14 Regression Results of the Relationship between Parental Status and Network Constraint (Gender) on Network Content............................................ 220 xi
Table 5.15: Regression Results of the Relationship between Parental Status and Network Constraint (Family Involvement) on Network Size ............................. 223 Table 5.16 Regression Results of the Relationship between Parental Status and Network Constraint (Family Involvement) on Network Ties ............................. 224 Table 5.17 Regression Results of the Relationship between Parental Status and Network Constraint (Family Involvement) on Network Content ...................... 225 Table 5.18 Regression Results of the Relationship between Parental Status and Role Segmentation (Perceptual) on Network Size, ....................................................... 230 Table 5.19 Regression Results of the Relationship between Parental Status and Role Segmentation (Perceptual) on Network Ties........................................................ 231 Table 5.20 Regression Results of the Relationship between Parental Status and Role Segmentation (Perceptual) on Network Content ................................................. 232 Table 5.21 Regression Results of the Relationship between Parental Status and Role Segmentation (Actual) on Network Size ............................................................... 233 Table 5.22 Regression Results of the Relationship between Parental Status and Role Segmentation (Actual) on Network Ties ............................................................... 234 Table 5.23 Regression Results of the Relationship between Parental Status and Role Segmentation (Actual) on Network Content ........................................................ 235 Table 5.24 Regression Results of the Relationship between Parental Status and Network Constraint (Actual) on Network Size .................................................... 238 Table 5.25 Regression Results of the Relationship between Parental Status and Network Constraint (Actual) on Network Ties .................................................... 239 Table 5.26 Regression Results of the Relationship between Parental Status and Network Constraint (Actual) on Network Content ............................................. 240 Table 5.27 Regression Results of the Relationship of Network Size on Salary . 243 Table 5.28 Regression Results of the Relationship of Network Size on Salary Growth................................................................................................................................... 244 xii
Table 5.29 Regression Results of the Relationship of Network Size on Promotions................................................................................................................................... 245 Table 5.30 Regression Results of the Relationship of Network Size on Individual Career Satisfaction.................................................................................................. 246 Table 5.31 Regression Results of the Relationship of Network Size on Peer-Related Career Satisfaction.................................................................................................. 247 Table 5.32 Regression Results of the Relationship of Network Ties on Salary. 250 Table 5.33 Regression Results of the Relationship of Network Ties on Salary. 251 Table 5.34 Regression Results of the Relationship of Network Ties on Promotions................................................................................................................................... 252 Table 5.35 Regression Results of the Relationship of Network Ties on Individual Career Satisfaction.................................................................................................. 253 Table 5.36 Regression Results of the Relationship of Network Ties on Peer-Related Career Satisfaction.................................................................................................. 254 Table 5.37 Regression Results of the Relationship of Network Content on Salary257 Table 5.38 Regression Results of the Relationship of Network Content on Salary Growth ..................................................................................................................... 258 Table 5.39 Regression Results of the Relationship of Network Content on Promotions............................................................................................................... 259 Table 5.40 Regression Results of the Relationship of Network Content on Individual Career Satisfaction.................................................................................................. 260 Table 5.41 Regression Results of the Relationship of Network Content on Peer-Related Career Satisfaction ................................................................................... 261 Table 5.42 Regression Results of the Relationship of Network Size on Career Planning ................................................................................................................... 264 Table 5.43 Regression Results of the Relationship of Network Size on Career 265 xiii
Table 5.44 Regression Results of the Relationship of Network Size on Career Mobility Preparedness............................................................................................ 266 Table 5.45: Regression Results of the Relationship of Network Ties on Career Planning ................................................................................................................... 268 Table 5.46: Regression Results of The Relationship of Network Ties on Career Tactics ...................................................................................................................... 269 Table 5.47 Regression Results of the Relationship of Network Ties on Career Mobility Preparedness............................................................................................ 270 Table 5.48 Regression Results of the Relationship of Network Content on Career Planning ................................................................................................................... 272 Table 5.49 Regression Results of the Relationship of Network Content on Career Tactics ...................................................................................................................... 273 Table 5.50 Regression Results of the Relationship of Network Content on Career Mobility Preparedness............................................................................................ 274 Table 5.51: Summary of Study Hypotheses and Findings .................................. 275 Table 6.1: Summary Results of Network Size on Career Objective Career Success Indicators ................................................................................................................. 292 Table 6.2: Summary Results of Network Ties on Career Objective Career Success Indicators ................................................................................................................. 297 Table 6.3: Summary Results of Network Content on Career Objective Career Success Indicators ................................................................................................... 300 Table 6.4: Summary Results of Network Size on Career Management Indicators302 Table 6.5: Summary Results of Network Ties on Career Management Indicators304 Table 6.6: Summary Results of Network Content on Career Management Indicators................................................................................................................................... 307
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LIST OF FIGURES
Figure 1: Conceptual Model of The Relationship Between Family Status Differences, Network Constraints, Job Involvement, Career Success, and Career Self-Management .................................................................................................... 100
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CHAPTER 1
INTRODUCTION
One of the recent pressures employees face within organizations includes the
responsibility of managing their own careers. Hall (2002) describes careers as “the
individually perceived sequence of attitudes and behaviors associated with work-related
experiences and activities over the span of the person’s life” (p.4). Recently, there have
been many changes in the concept of careers. Thus includes the trend that more
individuals are engaging in career mobility or frequent job changes (Hall, 2002).
Frequent job changes are often motivated by individuals “taking advantage of better job
opportunities and searching for a better match between job characteristics and personal
interests” (Hall, 2002). Also, frequent job changes are characteristic of what Hall (1976,
1996, 2002) describes as the “protean career”. The protean career is:
A process which the person, not the organization is managing… a process characterized by frequent change and self-invention, autonomy, and self-direction…The protean person’s own personal choices and search for self-fulfillment are the unifying or integrating elements in their life.. The criterion of success is internal (psychological success). (pp 36, 38, 43)
One outcome of the “protean career” is decreased job stability and increased job
mobility, both within and between organizations (Hall, 2002).
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This mobility across jobs is also characteristic of the “permeability” of organizational
boundaries, that is, intra-organizational mobility (“movement between levels, projects,
products, functions, and locations”) and inter-organizational mobility has become more
acceptable, frequent and essential (Hall, 2002). To increase mobility, an individual must
have access to information regarding job and career opportunities, even when that
individual chooses to remain with the same organization. Therefore, researchers must
consider how individuals gain information about job and career opportunities, which is
useful for them moving either within or across organizational boundaries.
Drawing from a social network framework, researchers can study networks within
organizations to understand how information is exchanged between individuals (or
actors) within the network (Higgins & Kram, 2001; Wasserman and Faust, 1994).
Although understanding the role of organizational networks as it relates to acquisition of
job and career-related information is important, researchers have often overlooked the
importance of (informal) networks on the career mobility of their employees (Podolny &
Baron, 1997). This is an important matter to consider, as now the burden (or
responsibility) for an employee’s career development has shifted from the organization to
the individual (Forret & Dougherty, 2004).
A few distinctions should be made clear about the terminology that is used in this
dissertation. First, the phrase social network refers to the set of actors (individuals) and
the ties among them (Wasserman and Faust, 1994). It should be noted that a single social
networking theory does not exist; rather a social networking perspective is used to
understand mostly patterns of behavior and interaction (Brass, 1995). Networking is a
specific behavior, that is, it’s a behavior where an individuals attempt to develop and
2
maintain relationships with others who have the potential to assist them in their work or
career (Forret & Dougherty, 2004). Network capital is a form of social capital that
makes resources available through interpersonal ties (Wellman & Frank, 2001).
Organizational networks are the set of nodes and ties representing some relationship or
the lack of a relationship between nodes, where nodes are actors (individuals, work units,
or organizations) (Brass et al, 2004). Finally, (individual) social capital can be defined
as investments into social relations which allow individuals to gain access to embedded
resources to enhance expected returns of instrumental or expressive actions; that is, social
capital is “how individuals access and use resources embedded in social networks to gain
returns in instrumental actions (e.g. finding better jobs)… individuals are said to invest in
social capital in hopes of some return to them” (Lin 2001, p.19).
The social networking perspective provides researchers with a framework to
understand both the (networking) behavior of individuals (actors) and how these
individuals (actors) exchange information within organizational networks. This
dissertation will use the social networking perspective to understand if specific network
characteristics, that is, network size, ties, and content influence career outcomes, career
success and career satisfaction. Also, the social networking perspective is used to
understand if there are specific moderators that cause differences in the network
characteristics between working adults without parental responsibility, and working
adults with parental responsibility.
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The career development literature discusses the importance of networking
behaviors, and the importance of organizational networks for employees’ careers. The
careers literature investigates the importance of the relationships that one can develop
through their participation in organizational networks. For example, in his 1996 book,
The Career is Dead-Long Live the Career. A Relational Approach to Careers, Hall
suggested that the primary resources for career development, which refers to how people
“grow” in their careers, include relationships with other people, and work challenges.
Although limited, empirical support confirms the notion that relationships at work
are positively related to career outcomes. For example, Higgins and Thomas (2001)
found that as the number of developmental (networking) relationships increased, the
individuals were more likely to remain with the organization and to experience higher
work satisfaction. Also, career advancement was positively related to multiple
hierarchical relationships, rather than just the development of one primary relationship
(Higgins and Thomas, 2001). That is, career advancement was related to the
development of multiple relationships in comparison to the development of a single,
primary relationship such as the one developed with a mentor. Seibert et al (2001) found
interpersonal relationships led to various career benefits including access to information,
access to material and financial resources, and visibility and sponsorship within the
organization. Bozionelos (2003) found network resources (e.g. relationship ties who’s
function is to promote the career interests of others) were associated with intra-
organizational career success, over and above human capital, demographics and
mentoring received. Podolny & Baron (1997) found results similar to Burt’s (1992)
finding. That is, informal social ties impacted advancement within organizations. Ties are
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defined as a relationship between two sets of nodes or actors within a network (Brass,
1995). Specifically, large information ties that lack indirect ties (or relationships)
promote upward mobility within organizations.
The relational view of careers links to the social network perspective of
relationships. That is, a social networking framework can be used to study the influence
of relationships (and how information is exchanged within these relationships) on various
career outcomes. Specifically, by analyzing patterns of independent relationships
researchers can gain an understanding of how information is exchanged across multiple
relationships (Wasserman & Galaskiewicz, 1994). This dissertation will use a
networking framework to explore how individual differences (i.e. parental status and
gender) impact the relationships people develop within their organizational networks.
Researchers have been successful in using networks to study individual
satisfaction, performance, job turnover, group structure and performance, and
organizational innovation and survival (Brass et al., 2004). Networks can also be used to
study how individuals leverage existing relationships while searching for new jobs.
Specifically, Granovetter’s (1973) weak tie argument suggests that people find jobs
through their acquaintances or weak ties. This phenomena occurs because an individual’s
weak ties or associates are not likely to know each other. Individuals benefit from weak
ties because they provide an individual with diverse and nonredundant information
(Granovetter, 1973; Brass et al., 2004). In addition to finding new jobs, organizational
networks also help individuals mobilize their career within their current organizations.
Described as social capital, that is, “who you know”, most managers owe a significant
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amount of their career success to their connections with others (this is especially true at
mid-level management) (Brass et al., 2004).
Burt’s (1992) structural hole argument suggests that individuals must not only be
concerned with developing weak ties (Granovetter, 1973), but they must be concerned
about the diversity of their contacts. Specifically, Burt (1992) argues that people should
have a network of abundant structural holes. Structural holes exist when there is an
absence of a link between two actors who are both linked to a common actor (Burt, 1992;
Brass et al., 2004). As a result of this structural hole, individuals are less likely to have
access to the same kind of information. In simpler terms, an individual that is interested
in maximizing the amount of job or career-related information should develop a large
number of weak ties, and they should make certain that the weak ties are not in contact
with each other. This can be achieved by an individual joining multiple organizations in
which the there is little overlap between the members across organizations. Consistent
with this notion, Podolny and Baron (1997) found that individuals whom have a large,
sparse informal network with many structural holes experienced enhanced career
mobility (i.e. higher amount of movement between jobs throughout their career).
Given the importance placed on the role of networks, (where networks are
operationalized as the development of multiple, nonredundant relationships) in
mobilizing an employee’s career, it also makes sense to identify specific network
properties that are likely to be related to career outcomes. The network properties of
interest to this study include network size, network ties, and network topics of
conversation. Network size refers to the number of individuals within a network (Brass,
1995). Network tie, is defined by the relationship between a specific tie and the ego in the
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network (e.g. friendship tie, coworker tie, family tie). Finally, network topics of
conversation, refers to the topics an ego discusses with their network members. This
study will operationalize network topics of conversation by asking individuals to identify
a set of conversation topics discussed with each member within their network and ask
them to identify which of those conversation topics they discuss most frequently from the
set of conversation topics they have identified.
This study suggests that four moderators may impact the three network properties
included in this study (network size, network ties, and network topics of conversation).
The four moderators this dissertation proposes will influence network properties include
gender, parenthood, family involvement, job involvement, and the extent to which
individuals want their home and work life integrated or segmented. Next, a brief
description of each of the factors thought to impact the development of multiple,
nonredundant relationships are provided. Chapter 3 provides a detailed discussion of how
each of these four factors will influence each of the three network properties.
Of the four factors mentioned, research has empirically tested the impact of
gender on an individual’s network and the advancement of one’s careers (e.g. Ibarra and
Smith-Lovin, 1997). A case can be made that men have better networks, that is broader
networks (i.e. weak ties) and more powerful contacts within their networks, hence
resulting in men having better access to job and career related information (e.g. Ragins &
Sundstrom, 1989) than women. This provides evidence that an individual’s gender may
impact at least two network characteristics network size and ties, which results in men
having better access to job and career-related information. However, the other variables
included in this dissertation research, that is parental status, family involvement, and role
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segmentation, are not gender specific, and are likely additional variables that impose
constraints on the networking behaviors of employees. Specifically, as traditional family
roles evolve and men become more involved with parental responsibilities, (e.g. dual-
earner families, an influx of single dads raising children), it is no longer sufficient to
primarily focus on gender differences within networks. Instead, it is important to consider
additional individual differences (e.g. parental status, family involvement) that impact the
networks within organizations. Thus this dissertation suggests that the work-family,
careers, and networking fields must evolve from merely focusing on gender differences
within networks to identifying additional individual characteristics (e.g. family
involvement, job involvement) that contributes to differences in the networking
experiences of employees.
Previous research findings regarding gender are consistent, that is, gender matters
in terms of explaining differences within organizational networks. Now the question
becomes, in addition to gender, what else matters? It is likely that, parental status, family
involvement, and role segmentation have an effect on networks. However, research has
not yet investigated how those factors influence an individual’s network and its
relationship to career management and career success.
A number of studies have investigated the relationship between gender,
parenthood, networks, and career outcomes. For example Valcour and Tolbert (2003)
found women’s intra-organizational career mobility was negatively related to the number
of children. Also, Smith-Lovin and McPhearson (1993) proposed that the networks of
unmarried career women and men are mostly similar, however, the birth of a child tends
to produce dramatic changes in the gender and kin composition of career women’s
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networks; changes which reduce the “career-related value” of the network (e.g. career
mobility). For example, women, after the birth of a child, have a tendency to use their
networks to gain and share information about family and households (Smith-Lovin and
McPhearson, 1993). In comparison, men seem to use their networks to exchange
information related to career, money and recreation pursuits (Smith-Lovin and
McPhearson, 1993). To date, this notion of changes within a woman’s network after the
birth of a child, has not been empirically tested.
Family involvement refers to the psychological involvement with and importance
of the family to a person (Parasuraman, Purohit, Godshalk & Beutell, 1996). Family
involvement is an important variable to consider because when an individual is highly
involved with the family, they are likely to devote more time to their family demands (in
comparison to their work demands). Most of the work-family research measures the
relationship between family involvement and work-family conflict and a negative
relationship is usually found between the two variables (e.g. Parasuraman et, al., 1996).
That is, the more an individual is involved with their family, the more likely they are to
experience work-family conflict (e.g. Eby et al., 2005). Work-family conflict occurs
when involvement in a work-related activity, interferes with participation in a competing
family activity (Greenhaus and Powell, 2002). Family involvement and parental status
have been tested separately in previous empirical studies. However, previous studies have
not investigated how both parental status and family involvement impacts an individual’s
network. This study will make a unique contribution to the field by investigating the
interaction between parental status and family involvement, and how that interaction
impacts network size, ties, and content.
9
Similar to family involvement, job involvement also refers the psychological
involvement with and importance of the job to the individual (Lodahl & Kejner, 1965).
Job involvement is an important variable to consider as the more an individual is
involved in their job, the less time and involvement that individual will have with things
that are not specific to their job (e.g. family). While research has investigated the
relationship between job involvement and work-family conflict, little is know about how
job involvement many influence the development of multiple, nonredundant
relationships.
Finally, the extent to which an individual wants their family and work life
segmented or integrated is drawn from Ashforth, Kreiner, & Fugate ‘s boundary theory
(2000). This study will argue that individuals that segment their role and family roles will
have organizational networks with different characteristics compared to individuals that
integrate their work and family roles. For example, those individuals that segment their
work and family roles would be less likely to prefer to bring their families to social
networking activities such as company picnics. In comparison, individuals that choose to
integrate their work and family roles, would seek out opportunities to bring their families
to work-sponsored events. It is important to note these differences, as information about
jobs and careers can be exchanged in multiple informal environments including social
gatherings where both employees and their families may be present.
For example in a recent empirical study, Scott (2001) describes how family, specifically
children and spouses, act as social capital. Taken from an in-depth semi structure
interview, one of the respondents reports:
10
“There are summer conferences that bring together state legislative officials, so at those conferences, you can get to meet the state senator as well as his wife and three kids. That has been helpful in terms of a network at the state level, where you can’t get to all these state capitals, and not being able to go as often as I used to. At least I get to see people once a year, and they know my husband. I don’t have children- I bring one of my nieces. So there’s a social element to it, very family oriented” (pp 23).
This remark “is an indication of the significance of family status, particularly parenthood,
for establishing work connections (relationships)” (Scott, 2001, pp 23).
In short, integrating work and family, specifically children and spouses seems to make a
difference in helping an employee further develop their work relationships. Therefore, an
employee whom prefers to segment their work and family lives, may be not fully
utilizing their family ties as social capital to help them establish relationships at work.
Individuals that do not leverage their family in the development of social ties may
experience a reduction in the overall size of their network, which according to
Granovetter’s strength of weak ties argument (1973), leads to individuals attaining less
job and career related information. This study contributes to the field, by investigating if
role segmentation will interact with parental status and contribute to differences in
network size, ties, and content.
Problem Statement
This study assumes one of the key functions of organizational networks is for
employees to leverage these networks to share information which will be helpful for
career mobility, that is, acquiring information related to job openings and career
opportunities within the organization. The purpose of this study is to understand if there
is evidence to support the notion that parental responsibility results in several key
changes within an employee’s organizational network.
11
Three major research questions will be addressed in this study include: First, how do
networks differ after the birth of a child for males vs females? Secondly, how do
networks differ between working adults with and without children? Thirdly, what
constraints contribute to differences in those networks?
This study contributes to the work-family literature in the following ways.
First, very little research has investigated the impact of parenthood on the networking
behaviors of employees. Most networking literature has looked at various factors
including gender and it’s impact on organizational networks (e.g. Ibarra and Smith-
Lovin, 1997). Also, most previous work-family research has focused on the relationship
between work-family concerns and career satisfaction (e.g. Sturges and Guest, 2004;
Almer et al., 2004), work-life and career issues specific to women (e.g. Harris, 2004; Ng
& Posh, 2004), the issues of role conflict among dual-career couples (e.g. Hall, 2002;
Elloy & Smith, 2003; Burke, 2000), and concerns related to protean careers and dual
career relationships (e.g. Hall, 2002). Some studies have also addressed the
relationships between intra and inter- organizational mobility and work-family concerns
(e.g. Valcour and Tolbert, 2003) and have focused on work-family challenges from the
life course perspective (e.g. Moen and Sweet, 2004). The life-course perspective views an
individual’s career as a series of stages characterized by the changing patterns of
developmental tasks, career concerns, activities, values, and needs which emerge as the
individual passes through various age ranges (Hall, 2002). Work-family researchers have
not yet addressed the various ways that developmental networks influence career choice
and decision making of parents (Hall, 2002). A recent search in Business Source Premier
yielded only 7 conference abstracts and zero research articles when the key words
12
“networks, career and work-family” were entered as search terms. Sullivan (1999)
adequately summarized the potential contribution of networking theory for studying
careers:
“social networking theory may provide an effective framework by which to conduct future research on the careers… networking theory may be useful in understanding the interactions of short-term and long-term careerists… and researchers should understand “the effect of gender, race, age and personal characteristics on the development of large, non-redundant networks” (pp 469).
This study investigates how parental responsibilities impacts employee
organizational networks, by comparing the network characteristics of working parents to
the experiences of working adults without parental responsibility. This study suggests
that parental responsibilities will impact three aspects of an employee’s organizational
network including, the size of their networks, the members (i.e. ties) within their
organizational networks, and what employees discuss amongst members of their
organizational networks (i.e. network content). Previous research suggests that the size
of one’s network will change after the birth of a child, that is, an individual’s network
will become smaller (e.g. Smith-Lovin & McPhearson, 1993). This change is thought to
be related to employee gender. That is, women will experience a greater change in their
network size than men. Therefore, this study expects to find that female parents have
smaller networks then male parents. According to Smith-Lovin & McPhearson (1993)
women have more childcare responsibilities, therefore resulting in smaller networks.
However, while gender may play a role in a change in network size, this study also
suggests that family involvement may impact the change in networks. Thus, in addition
to gender, the employee with the highest amount of family involvement may experience
the largest change in their organizational network.
13
In addition to a change in size of organizational network, this study proposes that
the membership of an employee’s network will be different for individuals that are
parents. Specifically, the members of the employee’s network with who they are in
frequent contact will be different from employees with no parental responsibilities.
Usually frequency of contact is a function of social closeness (intimate, active, latent),
spatial closeness (same neighborhood, metropolitan area), and kinship closeness (that is,
immediate vs extended kin) (Wellman, 1992). A network framework can be used to
identify the members of the network with whom the focal individual (ego) has active
relationships (e.g. kin ties, friend ties, work ties). Specifically, this study suggests,
parental responsibilities will increase the frequency of contact with family and friends for
working parents. Most people have contact at least once a week with their active network
members (Wellman, 1992). This dissertation will test the hypothesis that working
parents will have a higher proportion of kin ties in their network, than working adults
without parental responsibility.
In addition, this study also will investigate how network content differ across
parental status. Specifically this study suggests that working adults with parental
responsibility are likely to have a higher proportion of non-work/kin network content.
That is, working adults without parental responsibility will be more likely to discuss
topics related to children and other non-work issues among members of their networks.
This study assumes that people will discuss important matters among people within their
networks and we operationalize information exchanged by assessing the conversation
topics discuss among the ego and members within their network.
14
Finally, this study suggests that parental responsibilities will impose a large
constraint on employees who desire to segment their work and family lives. Boundary
theory suggests that employees differ in the degree to which they desire to integrate their
home and family life. Individuals whom segment their work and family lives want a clear
temporal and physical boundary established between their work and family roles. For
examples, these individuals prefer not do their work from home, most of their work will
be completed at the office. In addition, these individuals are reluctant to integrate their
family members in work activities, including work-related social activities (e.g. baseball
outings) that may occur during or after work hours. As a result of clearly establishing
boundaries between their work and home life, individuals who prefer to segment their
work and home lives are likely to have less time to dedicate to the maintenance of their
organizational networks. As a result, this dissertation expects role segmentation to
explain differences in the organizational networks of working adults with children and
working adults without children. For those individuals whom segment their work and
family life, it is expected that there will be differences in their network size, membership
and content exchanged within their network. This study makes a contribution using role
theory to understand the differences in network characteristics between working parents
with children and working parents without children.
Contributions of this dissertation
This study will make the following contributions to the field of human resources
management and organizational behavior. First, this study will utilize social networking
theory (e.g. weak ties argument) and social networking methods (e.g. ego (individual)
network size, ties, content) and apply it to research on careers, and work-family concerns
15
(i.e. working parents). Second, by considering the impact of family involvement and role
segmentation, in addition to gender and the onset of parenthood, this dissertation
examines additional factors that influence networks and the advancement of one’s career.
Third, this study contributes to the literature on careers, by taking a relational perspective,
to understand how an individual’s network influences their access to information and
subsequent perceptions of career success and attitudes toward network resources. In
addition, this study will also contribute to the research on networks and careers. That is,
most research on the relationship between networks and careers, investigates outcomes
such as career mobility or career advancement (e.g. Podolny & Baron, 1997; Granovetter,
1973; Burt, 1992). This study will use attitudinal measures (e.g. career satisfaction) to
understand the extent to which parents are constrained by various networking properties
(e.g. size, ties, and content) in utilizing their networks to gain career and job related
information. Finally, this research addresses a need identified by Higgins and Kram
(2001) to better understand individual’s developmental relationships.
Of note, social network analysis can be studied at various levels (Brass, 1995).
Two examples include network research at the individual level (i.e. ego networks) or
studying the entire systems of networks. This dissertation will study networks at the ego
or individual level. Studying network characteristics at the ego-level allows researches to
gather an individual’s perception of their network (and specific network characteristics,
inclduing network size) (Brass, 1995). Specifically this dissertation studies the relational
aspects of networks at the ego (i.e. individual) level. The relational aspect of social
networks is focused on the social characteristics (e.g. gender) of these network members
and of their ties (Wellman, 1992). The relational aspect of social networks can be also
16
used to study characteristics of the relationships between ties in a network (e.g. frequency
of contact, intimacy).
Chapter 2 presents the literature review that provides theoretical and empirical
support for the hypotheses tested in this dissertation. Chapter 3 introduces the theoretical
model used in the study and concludes by presenting hypotheses based on the model. The
methods used in this study are discussed in Chapter 4. Chapter 5 discusses the results of
the study. Finally, the practical and research implications, theoretical contributions, and
directions for future research resulting from this study will be discussed in Chapter 6.
17
CHAPTER 2
LITERATURE REVIEW
The relational approach to careers suggest that people will be responsible for their
own careers and can use various relationships to share information and acquire
information from others about various job and career opportunities. Also, research related
to the relational approach of careers helps provide insight into an individual’s entire
social environment (Hall, 1996). One common example of the study of relationships and
career development, is that of the mentoring relationship. Mentoring serves two
functions including providing support for career development (which contributes to the
mentee’s advancement in the organization; and “psychosocial support” which contributes
to the mentee’s personal and professional growth (Kidd et al., 2003). Mentoring has been
linked to many career outcomes including, enhanced career development, career
progress, higher rates of promotion, and career satisfaction (Higgins and Kram, 2001).
The literature on mentoring has generally described it as a single relationship, or
focused on primary mentoring relationships, where primary mentoring relationships are
those in which the mentor holds a higher-level or more senior level position (compared to
their mentee) (Higgins & Thomas, 2001). That is, the mentor-mentee relationship is
characterized by organizational members of unequal status, and it does not generally
focus on peer-peer mentoring relationships (Kram, 1988). Thus previous research has
restricted the study of the mentoring relationship, to a single relationship with a more 18
senior individual in the mentee’s organization (Higgins and Thomas, 2001). The
nature of these mentoring relationships is usually dyadic, where only the relationship
between the primary mentor and mentee are studied. Research typically does not study
concurrent, mentoring relationships.
More recently, researchers (e.g. Higgins and Kram, 2001; Kram, 1988) have
begun to investigate relationships beyond the single relationships or dyadic relationships
that were prevalent in the mentoring literature. Thus current research acknowledges that
individuals receive mentoring types of support from a set or “constellation” of
developmental relationships including peers, subordinates, friends, families and bosses,
that is, individuals receive support from more than one person (Kram, 1988). As
previously suggested, the relational model of career development is focused on
understanding how an employee’s interaction with people (both within and outside of the
organization) helps influence their career development. The relational perspective of
mentoring is also described by some researchers as a developmental constellation
(Higgins and Thomas, 2001; Kram, 1988). The developmental constellation is “the set of
relationships an individual has with people who take an active interest in the
advancement of an individual’s career by assisting with his/her personal and professional
development” (Higgins and Thomas, 2001). Constellations are operationalized as a set of
“multiple concurrent relationships”, that is, relationships an individual identifies at a
single point in time as having been important to their career development (Higgins and
Thomas, 2001). Previous research that has investigated multiple relationships generally
considers a series of relationships over time, instead of a set of relationships at one
specific time period (Higgins and Thomas, 2001). Therefore, this study will investigate a
19
set of relationships at one specific time, that is, relationships that are included in the
individual (or ego’s) network.
Higgins (1998) demonstrated that relationships impact an individual’s career by
influencing the extent to which one seeks information from nonredundant sources (hence
providing a variety of different information) and the extent to which the individual’s
sources are heterogeneous and provide exposure to different points of view. In addition,
the most effective organizations take a relational approach of the development of their
employees. That is, “the employer will provide opportunities and flexibility and
resources, particularly people resources, to enable the employee to develop identity and
adaptability and thus be in charge of their own career” (pp 40) (Hall, 2002). If
organizations are moving towards a relational approach of the development of their
employee’s careers, it makes sense that researchers understand more about the role of
relational networks in individual career experiences and their impact at the organizational
level (Hall, 2002). In fact, one can argue that the major source of power of networks lies
precisely in the information exchanges and collective learning that they can promote”
(Hall, 2002).
In a recent study, Parker, Arthur, & Inkson (2004) also demonstrate the value of
relationships in supporting an individual’s career. Parker et al (2004) draw from several
approaches (e.g. the relational approach to careers, the subjective career (i.e. an
individual’s perspective of their career) to describe career communities. Of note, the
concept of career communities differs slightly from the relational view of careers. When
career communities are studied, researchers investigate the career support that members
find within identifiable communities (Parker et al., 2004,). In comparison, the relational
20
view of careers investigates the career support individuals receive through their
interpersonal relationships. Career communities include three factors (knowing-why,
knowing-how, knowing-whom) through which individuals draw career support (Parker et
al., 2004). The three factors, knowing-why, knowing-how, and knowing whom describe
the investments that individuals make to their career over time. For example, knowing-
why describes the values (and various motivational factors) that impact an individual’s
career choice, especially their career adaptability and commitment (Parker et al., 2004).
Knowing-why is also related to an individual’s perception of non-work issues that impact
an individual’s motivation and values about their careers including personal interests and
changes in family status. Knowing-how is related to an individual’s career-related
expertise or skills, that is, those skills that are applied to an individual’s work. Of note, in
the traditional view of careers, an individual’s knowing-how skills were considered one
of the most important predictors of career success (Parker, et al., 2004). Finally knowing-
whom describes the relationships that support an individual’s career. This aspect of the
career communities, knowing whom, is most directly related to the relational aspect of
careers. Taken together, the knowing-why, knowing-how, and knowing-whom comprise
an individual’s career community and all three aspects represent investments an
individual makes in their career in hopes of achieving a successful career.
In the most recent Parket et al (2004) paper, the ideas of the career communities
were tested empirically. In this study, Parker et al (2004) selected three organizations in
which they expected to find a specific type of career community. That is, the authors
expected to be able to distinguish between a knowing why community, a knowing how
community and a knowing whom community. The results of the study suggest that the
21
three community types do not emerge as three pure types of communities (Parket et al.,
2004). Rather, the career communities described in each organization seems to suggest a
hybrid perspective of career communities. The study suggests that an investment into all
three areas of an individual’s career will be important, across different organizational
settings. In addition, the investment an individual makes into each of the three career
communities will increase their perception of perceived subjective career success.
Organizational networks
Networking, as defined by Forret and Dougherty (2004) is one’s ability to
develop and maintain relationships with others who have the potential to assist them in
their work or career. That is, networking is concerned with the specific behaviors an
individuals uses to obtain information. Further networking behaviors are developmental
relationships used to help one improve both their personal life and their professional
lives. Networking also plays a key role in regulating access to jobs, providing mentoring
and sponsorship, channeling the flow of information and increasing the likelihood and
speed of promotions (Ibarra & Smith-Lovin, 1997).
Previous research has found that networking behavior was more beneficial for the
career progress of males in comparison to females (Forret and Dougherty, 2004). For
example, increased internal visibility was related to the number of promotions and total
compensation for men, but not women. Forret and Dougherty (2004) found networking
behaviors were not as advantageous for women as for men. Men tended to engage more
frequently in networking and socializing activities. They suggest that men may engage in
networking behavior more frequently than women, as a result of men having more hours
to allocate to networking; specifically participating in networking events after work
22
hours. The researchers posit that women may have had less time to participate in after-
hours networking activities, as a result of their childcare responsibilities, which require
them to go directly home after work. Research needs to be conducted to understand why
networking behaviors are not as advantageous for men as they are for women. Further,
there has not been a comprehensive study on networking and career mobility for women
(Ibarra & Smith-Lovin, 1997).
Networking enables employees to effectively manage their career because as
suggested by Burt (1992), networks provide employees with at least two advantages.
First, employees whom are embedded in networks have access to information and people.
In addition, networks offer employees “just-in-time” information. Stated differently, Burt
(1992) suggests,
“Timing is a significant feature of information received by networks. Beyond making sure that you are well informed, personal contacts can make sure you are one of the people that is informed early. Therefore, personal contacts get your name mentioned at the right time in the right place so that opportunities are presented to you” (p 14).
Networking or informal interactions remain important because this is often
described as one strategy women can use to break through the glass ceiling (Forret &
Dougherty, 2004). However, research suggests that women are often challenged by
exclusion from or limited access to informal dialogues which occur within organizational
networks. Research also indicates that the “absence of women from informal networks
and the limitation of publicity of job openings may lead to restriction primarily to men of
information about high-level openings in organizations” (Ragins and Sundstrom, 1989).
Previous research also suggests that organizations may inadvertently or deliberately
23
exclude women from powerful positions through recruitment (Ragins and Sundstrom,
1989 and Plake et al., 1987).
Higgins and Kram (2001) also discuss the importance of networking on the career
mobility of employees. The researchers describe the developmental network as the set of
people an individual identifies as taking an active interest in and action to advancing their
career by providing developmental assistance (Higgins and Kram, 2001). Within a
developmental network, at least two types of support are meaningful, (1) career support
(such as exposure and visibility, sponsorship and protection), and, (2) psychosocial
support (e.g. friendship, counseling, acceptance, confirmation and sharing beyond work)
(Higgins and Kram, 2001). The developmental network differs from other organizational
networks (e.g friendship or social networks) in that it does not consists of all the members
of an individual’s networks, rather it consists of the people whom an individual identifies
at a particular point in time as being important to his/her career development.
In the article, Higgins and Kram (2001) describe a typology of 4 developmental
networks, entrepreneurial, opportunistic, traditional, and receptive. The two
developmental networks that seemed to be most relevant to this study are the
entrepreneurial and opportunistic developmental networks. The entrepreneurial network
was described as one where individuals have the “best of both worlds”. That is,
individuals have a network with strong ties and members in their networks that provide
access to a wide array of information. Strong ties are defined as relationships between an
individual and their closest friends and family members, and the individuals usually all
know each other or are somehow connected; this makes for a highly dense network
(Granovetter, 1973). The opportunistic networks differ from the entrepreneurial network,
24
in terms of the strength and breathe of ties that exist within an individual’s network. In
the entrepreneurial network, individuals have multiple ties or relationships with
individuals and very strong (or intimate) ties. In comparison, the strength of the ties or
relationships developed within the opportunistic network, tend to be weak. That is, within
the opportunistic network, an individual must cultivate, that is, communicate frequently
and actively seek help from members within their networks. If they do not, then the
individual may only receive help from members in their network when it is offered.
Specifically, they will not utilize their networks effectively to gain information, because
they are not willing to invest the time in maintaining these relationships. As a result, the
ties or relationships within an entrepreneurial network are likely to be strong. That is,
individuals can depend on relationships in the entrepreneurial network for specific
guidance and information. The ties within the entrepreneurial network are strong as a
result of the ego within that network making certain to have frequent contact with
individuals within that network that can help them.
Working parents are likely unable to have frequent contact with individuals
within their network, due to their parental responsibilities and subsequent time
constraints. Hence, this study suggests that working adults with no children are likely to
have more frequent contact with members within their networks, which will lead to them
have better access to job and career related information. In comparison, working adults
with parental responsibility may be more likely to have less frequent contact with
members in their networks, due to their responsibilities at home.
25
Specifically, they may be more likely to have less frequent contact with individuals that
would be able to provide them with career-related information. Therefore, working
parents may be more likely to have an opportunistic network.
Although the entrepreneurial or the opportunistic networks are not directly related
to this dissertation, they are worth mentioning for at least two reasons. The opportunistic
developmental network described by Higgins and Kram (2001) can lead one to conclude
that working parents are not able to use their networks to gain information because they
do not cultivate the relationships within their networks particularly after the birth of a
child. In comparison, working individuals without children may have a better chance of
developing an entrepreneurial developmental network. In comparison to parents, whom
have less time to cultivate relationships in their network, working individuals without
children could have more time to develop stronger relationships within their networks.
Also the networks of working adults without parental responsibility may be larger, and
individuals within their network will know them well enough (as a result of more
frequent contact) to speak on their behalf (Higgins and Kram, 2001), when they are
looking for new job or career opportunities. Consistent with this view, Higgins and Kram
(2001) propose that individuals with entrepreneurial developmental networks are more
likely to experience change in their careers than individuals who have opportunistic
networks. The entrepreneurial networks tend to be larger (i.e. more ties) and individuals
tend to have more frequent contact with the members in their networks. Therefore,
consistent with Burt’s (1992) and Granovetter’s (1973) arguments, entrepreneurial
networks will lead to larger networks where larger amounts of nonredundant information
will be shared between the ego and the members of their network.
26
Social Capital and The Relational Approach to Career Development.
Several different components of networks have been studied from a social capital
perspective. Similar to the relational perspective of career development, social capital is
described as the investment in social relations with expected return (Lin, 2001). Social
capital is suggested to provide a return on relationships for the following reasons: (1)
social capital facilitates the flow of information, specifically, social ties located in certain
strategic locations and/or hierarchical positions can provide an individual with useful
information about opportunities and choices which otherwise may not available, (2)
social capital often exerts influence on the agents (e.g. recruiters or supervisors of the
organization) who play a critical role in decisions (e.g. hiring or promotion) involving the
actor, (3) perceptually, an individual’s social capital can be viewed as their social
credentials or an indication of whom supports the individual within (and across)
organizations, that is, an indicator of their “network”, and, (4) social capital reinforces
identity and recognition, that is, it makes a member feel like they are part of the
organization and reinforces their efforts will be recognized by others within the
organization (Lin, 2001).
The notion of social capital can be studied at both a group and individual level of
analysis. Consistent with this study, researchers use social capital framework at the
individual level to understand how individuals gain access to and use the resources
embedded in their social networks to gain returns in instrumental actions (e.g. finding
better jobs) (Lin, 2001). In comparison, researchers studying social capital at the group
level may be interested in investigating how certain groups develop and maintain more or
less social capital as a collective asset (Lin, 2001).
27
Social capital is considered a resource for an individual’s career, and people
utilize their interactions with others in an organizational setting to share information
(Palgi and Moore, 2004). Two widely used mechanisms which foster the development of
social networks include mentors and organizational networks. Social capital is beneficial
to employees and employers mutually. For example, employers value employees with
social capital, because as a result of hiring these employees, the organization will
enhance its social capital by utilizing the new employees’ contact to reach various
organizational goals (Erickson, 2001). An example of how an organization leverages
their employee’s social capital is when the organization relies on an employee’s contacts
to develop new relationships with suppliers (Erickson, 2001). Of note, social capital is
usually a requirement/asset for employees at higher levels within the organization; in fact
employees with greater social contact get better higher-level jobs whether they were hired
through personal contacts or not. Lower level employees are not typically responsible for
recruiting new clients (although in the insurance industry this is an inaccurate
assumption) or making deals with clients (Erickson, 2001).
In short, social capital seems to be important within organizations for at least four
reasons: (1) social capital facilitates the flow of information (by providing an individual
with useful information about opportunities and choices not otherwise available), (2) the
social ties one develops, may be able to influence certain agents (e.g. recruiters or
supervisors) who play an important role in making critical decisions within organization,
(3) people social ties, that is their acknowledged relationships with others in an
organization, may help highlight an individual’s social credentials, and (4) social capital
should help reinforce an individual’s identity within an organization (Lin, 2001).
28
Taken together, both the social capital framework and the relational perspective of
career development demonstrate the importance of relationships between members in an
organization. Both social capital and the relational perspective of career development
suggest that employees (1) need access to information which will enable them to learn
about opportunities within the organization, and (2) employees need to invest time in
social relations that will enable them to gain access to information, and provide them
social support. Social network analysis then is a set of measurements that are used to
capture specific aspects of an individual’s network (e.g. network size, density, cohesion
and closeness within social networks). Specifically, at the network level of analysis,
researchers look at the composition of the networks (e.g. network size, network
heterogeneity, mean frequency of contact) and the structure of these networks; thus
network analysis is used to understand how the properties of networks affect what
happens in them (and to them) (Wellman and Frank, 2001).
Social network analysis can also be used to study various relationships within
organizations. For example, social network analysis can be used to study supporting
partnerships and alliances. Executives are increasingly employing cross-organizational
initiatives such as alliances or other forms of strategic partnerships to leverage the firm’s
unique capabilities. Thus social network analysis can illuminate the effectiveness of
information flow, knowledge transfer and decision making. In short, organizational
networks provide companies a diagnostic tool which can be used to assess specific
processes and flows of information (including information related to career mobility)
(Cross, Parker& Cross, 2004). Network analysis can also be used to investigate the extent
to which hierarchy conditions impact the flow of information across organizational levels
29
(Cross, Parker& Cross, 2004). Specific to this context, future research could investigate if
hierarchy amongst organizational networks impacts the flow of information within the
network. That is, are those more central in an organizational network more informed
about career opportunities and if so, what are the implications? In general, work-family
research has lacked a networking perspective; however suggestions have been made that
work-family research should begin to understand the relationship between networking
and career mobility (e.g. women’s work-related networks may be subject to disruption as
a result of participation in work-family programs).
The Formation of Networks and Network Data Collection Techniques.
Sociologists first begin thinking about networks by looking at Konigsberg’s
bridges of Prussia (Wasserman &Faust, 1994). This graph/map contained links and
nodes. Networks are formed through social links, and these links form as people interact
with each other. In a graphical representation, people are “nodes” and each encounter
with a new actor is represented by a social “link”. Networks are often represented
graphically on a histogram, where a series of links are drawn between various nodes.
Networks start from a small nucleus and expand with the addition of new nodes. Some of
the fundamental characteristics of networks include the following (Wasserman & Faust,
1994):
o Actor – Actors are discrete individual, corporate, or collective social units. Examples of actors are people in a group, departments within a corporation, public services agents in a city, or nation-states in the world system.
o Relational Tie- Actors are linked to one another by social ties. The defining feature of a tie is that it establishes a linkage between a pair of actors.
o Dyad- A dyad consists of a pair of actors and the (possible) ties between them. Dyadic analyses focus on the properties of pairwise relationships,
30
such as whether ties are reciprocated or not, or whether specific types of multiple relationships tend to occur together.
o Triad- Relationships among larger subsets of actors may also be studied. Many important social network methods and models focus on the triad: a sublet of three actors and the possible tie(s) among them.
o Subgroup- A set of actors that and all ties among them. o Group- The collection of all actors on which ties are to be measured. o Relation- The collection of ties of a specific kind among members of a
group is called relation. o Ego-Centric Network- An ego-centered network consists of a focal actor,
termed ego, as set of alters who have ties to the ego, and measurements on the ties among these alters. Ego-centered networks are also used quite often in the study of social support. The term “social support” has been used to refer to social relationships that aid the health or well-being of an individual.
Social networking data can be collected at multiple levels of analysis. The
multiple levels include different levels: the individual actor, the pair of actors or dyad,
the triple of actors or triad, or the network as a whole. The level of analysis is called
the modeling unit (Wasserman & Faust, 1994). Most social network data is collected
by observing, interviewing or questioning individual actors about the ties from these
actors to other actors in the set (Wasserman & Faust, 1994). The questionnaire
method of collecting data is most often used when the actors are people. The
questionnaire usually contains questions about the respondent’s ties to the other
actors. Questionnaires are most useful when the actors are people, and the relations
being studied are ones that the respondent can report (Wasserman & Faust, 1994).
The three types of questionnaire formats that can be used include (Wasserman &
Faust, 1994) :
o Roster vs Recall- Roster indicates actors are presented complete list of people in the set. If the researcher does not have a complete roster they can use the recall method which ask respondents to name those people with whom you (fill in specific tie)… The respondents generating a list a names is called free recall.
31
o Free vs Fixed Choice – If actors are told how many people to nominate on a questionnaire, then each person has a fixed number of choices. In a fixed design, each actor has a fixed max number of ties to the other actors in the set. If actors are not given any such constraints on how many nominations to make, the data are free choice.
o Ratings vs complete rankings – In some cases, actors are asked to rank or rate order all the other actors in the set for each measured relation. Ratings ask an actor to assign a value or rating to each tie.
There are at least three sampling techniques used to collect network data (1) the
saturation sampling technique, (2) the ego-networking technique and (3) the position-
generator technique (Lin, 2001). The ego-networking technique will be used in this
dissertation, but a brief description of each techniques has been included. The saturation
sampling technique is used when it is possible to map and define the boundaries of an
entire network. This technique is most often used within a single organization, or a small
network among organizations (Lin, 2001). The position name-generator technique is used
when a sample of positions within an organization are identified (e.g. marketing
representative, financial analyst), and the individual is asked to indicate if s/he know
anyone holding that position within the organization; based on these responses the
researchers is able to identify various network characteristics including the range of the
individual’s network (that is the distance between the individual with the highest and
lowest position within a network) or the heterogeneity of a given network. The last
technique is the ego-network technique, and the one that will be used in this dissertation.
The ego-network is the sampling method generally used when a researcher is
investigating a large or less definable network (e.g. the researcher is interested in
studying the networks of people across several organizations). The ego-network
technique uses a name-generator survey in which the ego identifies a list of alters within
32
their network and they are able to answer questions related to various characteristics of
their alters (frequency of contact, physical proximity of alters, etc). One common
shortcoming in using the ego-network technique is it usually elicits strong (people with
whom the ego is close too) rather than weak ties, that is, the ego usually names
individuals with whom they have more intimate relationships with or people with whom
they have more frequent contact (Lin, 2001). However, this bias may be beneficial if the
researchers are studying issues related to quality of life or social support, or various
perceptual or psychological outcomes (Lin, 2001).
Network Measures and Characteristics
There are many components of a network that can be measured and used to
describe an individual’s (ego) network. Below, are two tables of typical measures used to
describe networks among individuals.
33
Measure Definition Example
• indirect links Path between two actors is mediated by one or more others
A is linked to B, B is linked to C, thus A is indirectly linked to C through B
• frequency How many times, or how often the link occurs A talks to B 10 times per week
• stability Existence of link over time A has been friends with B for 5 years
• multiplexity Extent to which two actors are linked together by more than one relationship
A and B are friends, they seek out each other for advice, and work together
• strength Amount of time, emotional intensity, intimacy, or reciprocal services (frequency or multiplexity often used as measure of strength of tie)
A and B are close friends, or spend much time together
• direction Extent to which link is from one actor to another
Work flows from A to B, but not from B to A
• symmetry (reciprocity)
Extent to which relationship is bi-directional A asks for B for advice, and B asks A for advice
From D Brass (1995)
Table 2.1: Frequently Measured Items Related to Network Ties
34
The measures listed in Table 2.1 are usually used to describe a link between at least two
nodes (e.g. actors, groups). Of note, a network consists of nodes, and a set of relations
linking these points (Smith-Lovin and McPherson, 1993). Examples of relations that the
measures in the table could be used to study include Person A “gives orders to” (Smith-
Lovin & McPherson, 1993). This would be an example of a directional measures. In
comparison, Table 2.2 (which appears on the proceeding page) describes measures that
are used to capture an entire network, whereas the measures in Table 2.1 are used to
describe a specific link between nodes within a network.
35
Measure Definition
• Size Number of actors in the network
• Inclusiveness Total number of actors in a network minus the number of isolated actors (not connected to any other actors). Also measured as the ratio of connected actors to the total number of actors.
• Component Largest connected subset of network nodes and links. All nodes in the component are connected (either direct or indirect links) and no nodes have links to nodes outside the component.
• Connectivity (Reachability)
Extent to which actors in the network are linked to one another by direct or indirect ties. Sometimes measured by the maximum, or average, path distance between any two actors in the network.
• Connectedness Ratio of pairs of nodes that are mutually reachable to total number of pairs of nodes
• Density Ratio of the number of actual links to the number of possible links in the network.
• Centralization Difference between the centrality scores of the most central actor and those of other actors in a network is calculated, and used to form ratio of the actual sum of the differences to the maximum sum of the differences
• Symmetry Ratio of number of symmetric to asymmetric links (or to total number of links) in a network.
• Transitivity Three actors(A, B, C) are transitive if whenever A is linked to B and B is linked to C, then C is linked to A. Transitivity is the number of transitive triples divided by the number of potential transitive triples (number of paths of length 2).
From D Brass (1995) Table 2.2: Typical Social Network Measures Used to Describe Entire Networks
36
As demonstrated by Brass (1995), several characteristics of an individual’s network can
be studied. Of the network characteristics that were described in the Brass (1995) table,
two will be included in this dissertation. The two measures include network size (the
number of ties in the ego’s network) and network ties/frequency (the number of times the
ego has contact with a specific tie in their network, and the frequency of contact with
those ties). These measures were selected because they are the measures most relevant to
the study of work-family concerns, networks, and careers.
Specifically, previous research has suggested that the characteristics that change
the most in an individual’s network after the birth of a child, include network size and
network ties (e.g. Bost, Cox, & Payne, 2002; Belsky & Rovine, 1984). Network ties, refer
to the individuals with whom the ego has contact. The ties are usually described in terms
of a relational measure, that is, family or kin ties, friendship ties, coworker ties, neighbor
ties, etc. Kin ties include, but are not limited to immediate kin (e.g. parents, adult children
and siblings and in-laws) and extended kin (e.g. aunts, uncles, cousins). If individuals
have networks that are mostly composed of kin, the disadvantage of this network includes
kin are less likely to obtain job and career related information (Wellman, 1992).
However, working parents, specifically, those with children under six years of age are
likely to have a high number of kin in their intimate network. Immediate kin are likely to
be very close to working parents as they are more likely to trust and receive help
(especially with childcare responsibilities) from the kin ties in their network. In fact,
individuals, especially those with parental responsibilities, may want stronger ties.
According to Wellman (1992), strong, intimate ties provide more emotional support and
companionship, in comparison to weaker ties. Interestingly, aside from immediate kin,
37
most of the active ties (that is, those with whom the ego is in frequent contact with)
include friend ties. In fact, friends and neighbors make up nearly half of the most active
and intimate networks (Wellman, 1992).
A distinction is made in the literature between assessing social networks at two
different organizational levels, the system or organizational level and the personal
network level (Ibarra and Smith-Lovin, 1997). The organizational level of social
networking research is concerned with identifying networking characteristics among a
group of individuals who co-exist within defined boundaries (e.g. a specific organization
or a specific workgroup). This dissertation is concerned with measuring social network at
the personal or ego-centric level. That is, the “egocentric or personal networks” are
concerned with assessing the network at the individual level and are more interested in a
specific individual’s contacts.
The total number of actors (i.e. ties) in an ego’s network is a measure of network
size. Network size is an important, commonly measured variable included in studies
interested in investigating career-related outcomes. For example, Carroll and Teo (1996)
studied differences in the organizational networks of managers and non-mangers,
including overall network size. The researchers compared the organizational networks of
managers and non-managers. The study found several differences in the network
characteristics of mangers and non-managers including managers tended to have more
external network ties than non-managers (e.g. more membership in clubs and societies).
38
Also, managers seemed to have more nonredundant ties within their networks (i.e. people
that were total strangers within their network) and have larger networks. The results
from the Carroll and Teo (1996) study were not very surprising, as one would expect the
networks of managers, or people with higher level jobs to be larger, and networks with
nonredundant ties.
In addition, to the size and type/frequency of contact with ties, the third measure
that will be used to describe the ego’s network characteristics includes topics of
conversation or content of conversations within networks. The topics of conversation
within informal studies have been studied previously (e.g. Kidd et al. 2003). The findings
from previous studies suggest individuals experienced positive career-related outcomes
when employees engaged in informal conversations, such as those one would have within
their networks. Some of the positive outcomes reported included “future direction, self-
insight, awareness of opportunities and feel-good” (Kidd et al., 2003). In the Kidd et
al,.2003 study: “future directions” was described as identification or exploration of
particular career options relevant to self and it described career decision or clearer
direction in terms of a career path within a given organization, “self-insight” was greater
awareness of ambitions, lifestyle, values, skills, and strengths, “awareness of
opportunities” included knowledge of a range of career opportunities and general
knowledge of external opportunities, and “feel-good” was described as individuals who
reported feeling better or more reassured about their jobs and careers. In addition,
about one-third of the respondents reported that informal conversations with people led to
a job move and about one quarter of the conversations led to “developmental
opportunities, on-going dialogue, greater political awareness about internal processes, or
39
improved career skills” (Kidd et al., 2003). The Kidd et al (2003) study made a
contribution to the literature because it measured outcomes related to informal
conversations; most previous literature measured outcomes using measures such as
learning and career outcomes. Kidd et al (2003) also studied the following topics during
their semi-structured interviews: who the respondent had discussions with, the setting the
discussion took place, how the parties involved behaved during the discussion, what the
outcomes of the discussion were, and how the receiver felt after the discussion.
Therefore, this study, similar to the Kidd et al (2003) study, will contribute to the
field by measuring the topics discussed within the ego’s network and outcomes related to
career management perceptions and indicators of objective/subjective career success. In
addition, this dissertation will go beyond the Kidd et al (2003) study because it will
compare topics of conversations discussed across four groups, working mothers, working
fathers, working women with no parental responsibilities, and working men with no
parental responsibilities. Further, this study will analyze what topics were discussed
among the people that the respondents have identified as important to their professional
careers. In the Kidd et al (2003) study, the respondents were asked to identify anyone
with whom they had an effective career-oriented discussion. In comparison, this
dissertation will analyze the members within a network that are most important for
individuals to talk to, rather than just analyzing career-oriented conversations with
individuals that may or may not be included in their network. Further, the Kidd et al
(2003) study assessed only the career outcomes that resulted from these discussions.
40
This study will assess career-related outcomes and other important topics (e.g. family,
childcare) to gain an understanding of whether there are differences in conversations
topics across gender and parental status. The Kidd et al (2003) study does not compare
the conversation topics discussed across gender or family status; rather the study reports
12 overall categories of conversation, without accounting for individual differences (e.g.
gender) in topics/outcomes discussed.
In summary, it is important to consider the topics of conversation that are
discussed within networks. For example, this study suggests that women may not realize
the full advantages of networks in comparison to men for the following reasons. Men,
talk about work-related or career related topics when they initiate informal conversations
within their networks or in conversations with their immediate supervisors. In
comparison, it may be the case that women initiate dialogue about their work-family
concerns, in an effort to begin dialogues within their networks and during informal
conversations. The concern is if women initiate these informal work and family dialogues
within their networks, are they able to successfully shift their conversations within in
their networks from “family talk” to more relevant talk about jobs and careers?
The Importance of Organizational Networks
As mentioned previously, organizational networks are a key factor to securing
advancement within an organization. Networks offer many benefits including
information, career guidance, advocacy for promotion, and “having a relationship with
influential people within one’s organization provides entry into social networks that are
inaccessible through formal communications” (King, 2003, p120). Further, networking
41
or the development of informal relationships is important, because the burden (or
responsibility) for an employee’s career development has shifted from the organization to
the individual (Forret & Dougherty, 2004). In addition to networking being beneficial to
an employee’s career development, other benefits of organizational networks include
formal and informal information exchange, career planning, professional support and
encouragement, greater visibility with senior management, and personal and career
development, as reported by a sample of women in a recent empirical study (Linehan,
2001). In addition, networks were thought to be advantageous especially for women who
did not have a mentor throughout their career (Linehan, 2001).
Another advantage of organizational networks is the development of social
capital. There are two expected returns from investment into social capital, (1) returns to
instrumental action and (2) returns to expressive action (Lin, 2001). Returns to
instrumental capital includes elements such as career development opportunities,
inclusion in powerful organizational networks, access to information, access to higher-
level and higher paying jobs, and increases in power within organizations. Returns on
expressive action, refer to an individual’s effort to gain access to others whom share
common interests and control (access to) specific resources. For example, Lin (2001)
describes three types of return on expressive action that individuals usually seek
including physical health, mental health and life satisfaction. Of interest to work-family
researchers, one outcome related to social capital is the return related to expressive
action, that is, life satisfaction. Life satisfaction suggests optimism and satisfaction with
various life domains such as family, marriage, work and community and neighborhood
42
environments (Lin, 2001). Also, life satisfaction is an outcome variable that is also
commonly measured in the work-family literature.
Gender and Organizational Networks
Research using network analysis suggest that similar people tend to interact with
each other, where similarity can be defined as age, gender, prestige, tenure and
occupation (Brass et al,. 2004). This line of reasoning may explain why women have
been excluded from organizational networks, that is, the idea that the old boys network
where men generally congregate with each other. Of note, similarity is a relational
phenomena such that people are similar with each other only when they are dissimilar to
another group or person (Brass et al. 2004).
Within the literature, there have been some key differences noted in the
networking behaviors of men and women. For example, some argue that men tend to
have broader informal networks that often included top executives and outside contacts
(Ragins and Sundstrom, 1989). In addition, men in comparison to women generally have
higher position power. That is, power associated with the control over resources, rewards,
and punishments, information, the work environment, and work procedures (Ragins and
Sundstrom, 1989). As a result, men may be better informed and have a broader
networks, which helps them facilitate their career mobility. Broader networks are those in
which the ties within the network are at various levels within the organization, spanning
from entry-level to executive levels. Men generally have more fully developed
organizational networks, and men tend to utilize their organizational networks to bid for
jobs, in comparison to women (Cannings & Montmarquette, 1991).
43
Research has also suggested that women tend to benefit less than men from
participation in organizational networks (Davidson and Cooper, 1992; Broadbridge,
2004). Brass (1985) provides three explanations for why women benefit less from
networks than men including: (1) they are unaware of informal networks, (2) they interact
with people whom are like them (in respect to values, attitudes and experiences) and, (3)
they are intentionally excluded from informal networks by men. There has been little
support found for the later rationale, that is, women are intentionally excluded from
networks. Empirical evidence has found that women are included in informal networks,
however many women choose to participate in gender-based networks, that is they prefer
networking with other women (Brass, 1985).
One of the factors that may lead to homophily within groups, is the argument of
weak and strong ties. Homophily refers to interaction with similar others (Brass, 1995).
Similarity can be operationalized on many dimensions including age, gender, education,
social class, tenure, and occupation. Research suggests that in general, cross-gender and
cross-racial networks are perceived to be weaker than homophilous relationships (i.e.
interaction with similar others). This perception (and attraction) to the network is mostly
based on the perceived ability and power of a specific network group (Ibarra, 1993). In
addition, homophilous groups are often formed naturally, particularly at higher levels of
the organization. In senior manager and executive level jobs, people forms groups with
others within their functional groups. Thus homophilous groups sustain themselves in
organizations because organizational members have a disincentive to join cross-gender or
cross racial groups; thus providing minorities and women with less access to more
powerful networks within their organization (Ibarra, 1993). Both Brass (1985) and
44
Ibarra (1993) also found that many informal groups are also “male-only” groups. It is
important to consider the role of homophilous groups because the relationship between
two actors (individuals) within networks can vary due to actor similarity. For example, if
there are two individuals within a network and they are dissimilar (i.e. different ages,
gender, education) they are likely to have less frequent communication, there relationship
will be less stable over time, and they are more likely to have a weak in comparison to a
strong tie (Brass, 1995).
Thus, within organizations, informal networks are often segregated by gender,
leaving women at a disadvantage. However, one must take into account how male-only
groups are formed. These groups may not always intentionally exclude women. Previous
studies have indicated that there are fewer minorities and women in senior level
positions. Moreover, the number of employees at the very senior levels of management
within organizations is limited. That is, there are a higher number of female and minority
employees who hold entry and middle level positions in organizations. Therefore, people
within this relatively small executive group, that is people at the top levels of
organizations, naturally form a network amongst each other, without purposely excluding
women and minorities. Women and minorities can not be intentionally excluded if there
are none employed in a specific functional area, or in senior management levels within
organizations.
An additional factor that makes it difficult for women to join organizational
network groups is the topic of conversations during exchanges within organizational
groups. Conversation topics are often “male dominated” including sports or computer-
oriented conversations (Broadbridge, 2004). Further, other studies have suggested men
45
talk more in formal task-oriented contexts (i.e. workplace settings), while women are
more likely to talk in informal settings (James and Drakich, 1993). This leads one to
question to what extent are women disadvantaged within their organizational networks.
Thus, even if women are included in organizational networks, the James and Drakich
(1993) study suggests that women may not feel comfortable talking in these formal
settings. This provides additional evidence to suggest that men and women may have
different experiences within their organizational networks.
Some research has investigated acceptable and unacceptable conversations at
work. For example, Singh et al (2002) found that women deliberately avoid talking about
their families at work. Most women thought they should only speak about work-related
topics while they were on the job. In fact in the study some women perceived only a
work-focused individual would be promoted to the next level. This leads one to question
if women and working parents in general feel comfortable discussing their family
responsibilities within their informal networks, especially if their networks include
individuals who do not openly discuss family-oriented issues. This raises a question as to
whether or not working parents can comfortably discuss work-family topics at work.
Specifically, if working parents can only talk about work-family concerns in specific
forums, do they purposely exclude themselves from larger organizational networks,
where other job-oriented topics are discussed (e.g. career progression) and participate in
smaller (or non-work based) networks where they feel comfortable discussing work-
family issues (this may be especially true for working moms). In addition to discussing
work-family issues within networks, some studies suggest women, especially working
mothers do not have time to participate in organizational networks (e.g. Linehan, 2001).
46
For example, in a recent qualitative study a working female shared the following
comment about organizational networks:
“To be quite honest, I think women have less time than men for networking. Networking has to take place to a great extent after work and on top of your job. If you are a woman with a family, you have less time. Men have more time for networking. Working women are very busy”(Linehan, 2001, pp. 826).
The women that were found to benefit the most from informal networks were
those whom participated in integrated informal networks, where the majority of the
members were men (Brass, 1985). In general, the informal networks which include
mostly men seemed to be perceived by the organization as most influential. Personal
contacts have been found to have a major impact on hiring decisions especially for top-
level and upper managerial job openings. In short, various organizational barriers have
limited the career mobility of women. This study suggests that informal exchanges are
one of the key organizational barriers (i.e. insufficient knowledge about job openings)
that have limited the career mobility of women. This explanation provides an alternative
view to the notion that women are not promoted as a result of a supervisor’s perceptions
that women will be unable to fulfill job obligations (due to the role conflict they
experience in attempting to balance their home and family lives).
In general, although network size does not generally vary between men and
women, the key network members differ. For example, men seem to network the most
with coworkers and volunteer groups, while women tend to network with relatives and
neighbors (Mardsen, 1990).
47
Arguably, this trend is changing as more women enter the workforce. Another important
finding is there is an overall tendency of gender homophily, that is most networks are
same-gender, rather than cross-gender networks (Ridgeway & Smith-Lovin, 1999).
Therefore, it is presumable that women and men participate in organizational networks.
Ragins and Sundstrom (1989) suggest that women with children have less time to
devote to development of networks. Research has also found that the (generally
speaking) smaller representation of women in networks appeared to result from
exclusionary pressures in comparison to women’s preferences for female friends (Mehra,
et al., 1998). In addition to gender, differences within organizational networks also seem
to vary across levels. For example, Carroll and Teo (1996) found in comparing managers
and nonmanagers within the same sample, managers tend to participate in wider
organizational networks, that is, they belong to more clubs and societies. Further, this
study found managers tended to have networks with a greater number of people and more
“weak ties” or pairs of strangers included in their network in comparison to nonmanagers
(Carroll and Teo, 1996). The authors also found these differences in sub-groups
(managers vs non-managers) even when controlling for differences in education, age,
gender and ethnicity. The findings of these studies led Carroll and Teo (1996) to
conclude that the differences in organizational network patterns of managers and non-
managers may be a result of “the selective entry and retention of people with particular
types of networks into management” (p.437) or “the behavioral transformation of people
placed in the intense social structures surrounding managers” (p.437). In other words,
consistent with previous studies, one manner in which employees can enhance their
chances of career mobility is through attaining social capital, through the development of
48
multiple relationships (e.g. managers were found to belong to more clubs and societies in
comparison to non-managers in thee Carroll and Teo (1996) study). Therefore,
employees whom have successfully developed their social capital, by enhancing
networks, may be more likely to be promoted into management positions, than employees
with less social capital.
Burt suggests that building a network around the immediate supervisor is
unproductive. Instead Burt (1992) suggests that the greatest benefit of a network is with
people completely removed from the immediate workgroup. Burt also makes a distinction
between task and opportunity networks. Burt suggests that task networks are ones in
which contacts include your current workgroup. Task oriented networks suggest you
communicate only within your workgroups and are prominent among employees that
were recently promoted. Comparatively, opportunity networks are ones in which people
invest a lot of time outside of their immediate workgroup and they spend very little time
with people within their direct workgroups. When deciding between the two networks,
Burt (1992) suggests that the choice of networks to develop may differ by gender. For
example, high ranking men seem to benefit from opportunity networks, which will lead
to faster promotions within the organization. In comparison women (and entry-level men)
seem to benefit from a task-oriented network built around a significant organizational
member, besides their immediate boss. Although differences measured across
organizational level will not be included in this study, this is a factor that should be
investigated in future studies.
Ibarra and Smith-Lovin (1997) describe at least two contextual factors that
contribute to differences in men and women’s networks. The first factor is the
49
stratification of women and men in terms of their careers and occupations. In the past,
women tended to be concentrated in industries dominated by women. Alternatively
women held jobs in lower positions within male-dominated organizations. Also, women
have been challenged by individuals in organizations making attributions that they are
less powerful and have been excluded from participation in various networks. Another
contextual factor that contributes to the differences in men and women’s networks
include homophily, that is, the idea that individuals tend to network with others that are
similar to them. Gender has been one of the key elements that has contributed to
homophily, which is the tendency for men to network with men and women to network
with women. A distinction is made in the literature between induced homophily and
choice homophily. Induced homophily suggests that similar members are included in
organizational networks as a result of lack of variance amongst employees (e.g. auto
repair tends to be a male-dominated field). In comparison, choice homophily is the result
of preference for interaction with others (Ibarra and Smith-Lovin, 1997). Therefore, in
some organizations were there is a strong presence of both male and female employees,
some individuals may actively choose to network with others of their same gender,
thereby leading to choice homophily. Based on anecdotal evidence, this often happens in
such industries as banking (e.g. investment banking) and entertainment.
Research has also suggested that women tend to benefit less than men from
participation in organizational networks, that is, the impact or organizational networks on
career advancement creates a significant advantage for men; this also results in men’s
greater influence and centrality in (elite) networks (e.g. Brass, 1985; Cannings and
Montmarquette, 1991; Moore, 1988). In fact, as a result of women relying on informal
50
networks specifically for career promotion opportunities, women are often confronted by
an invisible ceiling that only can only be removed by social relationships being
restructured within organizations (i.e. the development of network or affinity groups
within organizations that are organized around common themes, for example, an Asian-
American affinity group that meets to discuss the concerns of Asian-American employees
across gender) (Cannings and Montmarquette, 1991).
Recently, studies have suggested new models of career success must include
situational and personal factors (Eddleston, Baldridge, & Veiga 2004). This study
suggests that a key organizational contextual factor worth reexamination is the influence
of networking behaviors across men and women and its influence on career mobility.
Eddleston, et al. (2004) found exposure to powerful (organizational) networks was
significantly related to number of promotions offered (number of promotions is a
commonly used measure of career mobility). Also, efficacy of mentoring, that is, an
individual’s belief that mentoring influenced their career progression, was also positively
related to both exposure to networks and number of promotions offered.
Specific to gender differences (i.e. a personal factor), Eddleston, et al. (2004)
found a significant relationship between exposure to powerful networks and promotions
offered for men, but not women (Eddleston, et. al 2004). The findings from this study
seem to suggest that interpersonal relationship, specifically exposure to powerful
networks, play a more important role in shaping the career progression of men in
comparison to women (Eddleston, et al, 2004). This finding suggests that women do not
have the same access to networks as men, and it suggests that women do not use their
networks as effectively as men (Eddleston, et al, 2004). Eddleston, et al. (2004)
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identified several areas for future research including (1) examining why exposure to
powerful networks benefits men’s careers, but not women’s careers, or (2) examining
why women do not utilize their connections (network) to top management to aid their
careers (e.g. are there subtle forms of discrimination that keep women from benefiting
from exposure to powerful networks) (Eddleston et al., 2004). The proposed dissertation
research will help to address the research needs by investigating differences in the
network experiences of men compared to women.
Parental Status and Organizational Networks
The birth of a child and family involvement impacts the organizational networks
of employees. The time and energy formally allotted for the development of relationships
within networks is now allocated to the tasks and duties of childbearing (Munch et, al.,
1997). Empirical evidence also suggests that the influence of the birth of a child may
differ by gender. For example, Campbell (1988) suggests that having young children at
home decreases women’s but not men’s job-related contacts. Munch et al (1997) found
the age of the child impacted network size, that is, women whose youngest child is 3 or 4
years old displayed significantly smaller networks than do their counterparts with adult
children. Further, having a young child did not produce a significant effect on men’s
network size and in fact, the men’s networks remain at a relatively constant size over the
childbearing years (Munch et al., 1997). Another interesting finding from this study was
women’s networks tend to be smallest when children are infants, and through the time
that their children reach the age of four (Munch et al, 1997). After children reach the age
of four, the network size of women tends to rebound (Munch et al, 1997). This finding
may be consistent with the age that children begin going to pre-school, that is, parents
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may have more time to develop their networks once their children begin attending school.
In addition, at the age of three, children begin to develop motor skills that require
increasing amounts of parent’s attention and time (e.g. when children begin to walk) in
comparison to an infant child that may spend a fair amount of time sleeping (Munch et
al., 1997). In short, parental status seems to be directly related to at least once change in
networks, that is, a reduction in network size (where size indicates the number of ties
within an individual’s network). Future research needs to investigate if the loss of
contacts (i.e. a reduction in network size) leads to a failure of parents to establish new
contacts, especially after the child passes the age of 3.
One of the reasons that the network size of a working parent may be smaller is
related to the multiplexity argument. The multiplexity argument suggests that individuals
that have strong ties may have smaller networks (Wellman, 1992). Specifically,
multiplexity occurs when an individual’s network members have multiple role relations
to that individual. For example, if a working parent hires their sibling to provide childcare
provision, this sibling is fulfilling multiple roles for the working parent, that is, the role of
sibling and the role of childcare provider. Research suggests that those individuals that
have multiplex ties, that is, when a person within your network fulfills at least two roles,
that these types of ties are stronger and are more supportive because these ties have a
detailed knowledge of each other’s needs (Wellman, 1992). As a result, working parents
may have a tendency to have stronger, but fewer ties, because they may have a tendency
towards spending time with individuals that are able to fulfill multiple roles for them (e.g.
when they are seeking childcare providers). The constraint that parents may face is,
although stronger ties are likely to be more supportive, they are also less likely to provide
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working parents with new information. That is, the weak tie argument, to be discussed in
detail later in this chapter, suggests that individuals should maximize the number of weak
ties, especially when it comes to planning their jobs or careers, as they want to ensure that
they maximize the amount of nonredundant information they receive, which is best
accomplished, when an individual seeks information from weak vs. strong ties.
In addition, many women leave the labor force at least temporarily when their
first child is born (Waite, et al., 1986). As a result, working parents are likely to
experience an interruption to their organizational networks. In addition, in an effort to
allow work flexibility, organizations now are offering options that allow working parents
to continue their career with the organization by providing teleworking, part-time job
opportunities and condensed work weeks. However, organizations and researchers have
given little thought to the impact these alternative works arrangements have on an
working parent’s organizational network (participation). In a study where the researchers
determined whether new parents differ (in terms of career aspirations) from adults who
forgo or delay the birth of a child, Waite et al., (1986) found the following: (1) work
expectations for women decrease at the onset of pregnancy and remain well below
expectations for the next two years, (2) the work orientation for fathers increased after the
birth of a child and, (3) the total earnings per week is higher for men whom are
expecting children in comparison to those who are not (Waite, et al., 1986). Similarly,
another study found that the average hours worked differed between employed women
and employed mothers, where employed mothers worked five hours less per week on
average (Kaufman & Uhlenberg, 2000). In addition, the effect of parental status on the
number of hours worked was most significant when the number of children increases
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(less hours are worked) and the age of the child increases (as age of the child increase,
moms work more hours) (Kaufman & Uhlenberg, 2000). Interestingly, the relationship
between hours worked and parental status differed between women and men in this study.
As the number of children increased, men tend to work more hours, but the amount of
hours works seems to vary by attitudes toward desirable childcare arrangements
(Kaufman & Uhlenberg, 2000). That is, the more satisfied men were with the childcare
arrangements, the more hours they were likely to work. If the men in the study were not
satisfied by the childcare arrangements, they were likely to work less hours.
The impact of the birth of a child on networks is important because it has been
suggested that time spent with family is thought to constrain the networks of working
employees, especially women. Thus time spent with family is viewed by social scientists
as limiting women’s success in the workplace (Scott, 2001). If women or working
parents in general are dedicating more of their time to their home responsibilities, they
are taking time away from their opportunities to participate in organizational activities.
Also researchers suggest the social ties formed at home are not as beneficial to women as
the ties formed in organizations (Scott, 2001).
In a recent article, Scott (2001) examined the gender differences in family
involvement and responsibilities and explores how these differences impact a women’s
ability to participate in organizational networks. This study was conducted among
workers in the public affairs, specifically, governmental relations divisions. Networking
with corporate executives, employees and the public is an important component for those
working in governmental affairs. Therefore this study tries to understand to what extent
marriage and family responsibilities create obstacles to interacting with government
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clients and with key people in business (e.g. are married women, or women with children,
limited in the particular kinds of networks in which they engage?) This dissertation
addresses a key research need identified in previous work; “researchers have examined
the relationship between gender and work networks, but few have included the effects of
families in their analysis” (Scott, 2001, p9).
There were several interesting findings reported in the Scott (2001) study. The
factor that seemed to impact a woman’s ability to network was not parental status, instead
it was marital status. Married women were found to be at a slight disadvantage when it
comes to socializing in non-work settings (e.g. concerts, theatre, sporting events). This
finding was inconsistent with the notion that women with children are at the greatest
disadvantage in terms of participating in organizational networks. Based on this finding,
The findings from Scott’s (2001) research may suggest that that once ‘boundaries’ of
marriage are formed and solidified, the presence of children produces little additive effect
in terms of negatively impacting a woman’s opportunity to socializing and fostering
relationships within their networks.
One factor that is believed to help working parents continue to participate in
organizational networks was the availability and quality of childcare. A comment made
by one of the individuals interviewed in Scott’s (2001) study suggested that women, after
the birth of child, will have a hard time maintaining their professional relationships unless
they have some kind of help at home (in terms of childcare). Also, some women may not
directly maintain their own professional relationships after the birth of a child. Often
women began attending the non-work events of their husbands, and do not attend the
non-work events of their own organizations. As a result women may loose several of
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their professional ties, especially if they are attending events where members of their
personal professional networks are not present.
Careers: An Overview of Various Approaches
In order to understand the role of careers within organizations, it is useful to know
how careers are defined. Hall (2002) describes career as “the individually perceived
sequence of attitudes and behaviors associated with work-related experiences and
activities over the span of one’s life”. The relational approach to careers, as described
previously, is concerned with how employees develop and leverage interpersonal
relationships to assist them obtaining information about new job and career opportunities.
Although interest in the relational approach to careers appears to be increasing, that is,
there is an increase in empirical and theoretical work (e.g. Higgins and Kram, 2001;
Seibert et al, 2001), the relational approach is not the only perspective used to study the
career behaviors of employees. The previous perspectives of careers, typically examined
the developmental aspect of careers (Kram, 1996). Therefore, if one knew a person’s age,
tenure, personality, values, and/or learning style one could accurately predict what the
person’s salient career concerns and developmental tasks might be (Kram, 1996).
One example of a developmental perspective of careers includes Hall and
Nougaim’s (1968) three stage model of organizational career development. In the first
stage, the establishment stage, the employee is at the beginning of their career and the
individual is mostly concerned with defining their position and feeling comfortable in this
position, that is, the individual is looking for a way to integrate themselves into the
organization (Hall and Nougaim, 1968). In the next stage, advancement, the individual is
no longer concerned about fitting into the organization, instead they are interested in
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looking for opportunities to advance their career. Further in this stage, individuals show a
higher need for achievement (Hall and Nougaim, 1968). In the final stage, maintenance,
successful individuals may be concerned with helping or mentoring others. In
comparison, for those individuals who are not successful, they may try to derail the career
progression of individuals newer to the organization, in an effort to maintain their
position in the organization, and to reduce competition for their specific job. Further, this
last stage represents a career plateau (Hall, 2001). Employees meet career plateaus when
they are stuck in their current jobs with little likelihood of promotion or with very few
opportunities for increased promotion (Greenhaus et al, 2000).
Another example of a developmental model of careers includes Super (1957) and
Levinson (1978). Using a life-span approach, Super’s (1957) model suggested that
individuals develop their self-concept through their choices in vocations. Described in
four stages, this model describes differences and behaviors across various career stages.
For example, the exploration stage (1) includes a period of self-examination, formal
education and the exploration of various career options (Super, 1990; Sullivan, 1999).
The second stage, establishment, is the period of finding employment and developing a
specific niche, and the third stage, maintenance, describes an individual upholding their
job and developing skills necessary to function in that job (Super, 1990; Sullivan, 1999).
Disengagement, the final stage, marks the retirement period for an employee (Super et al.
1988; Sullivan, 1999). Levinson’s (1978) model, in comparison, suggests a punctuated
equilibrium model, in which after a certain period, an individual will reassess their goals.
Specifically, the Levinson (1978) model suggests there are four eras of the human life
cycle: pre-adulthood, early adulthood, middle adulthood and late adulthood. The
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fundamental argument Levinson’s (1978) model made was an individual goes through
both stable and transitional period during their lives, which are closely related to the
individual’s age. Levinson (1978) further argues that the typical transition period about
five years and transitional periods last about five to seven years. During the periods of
stability, individuals focus on non-work issues, develop job-related skills and mentally
prepare for themselves for transition periods (Levinson, 1978, 1986).
One criticism of the both the Super (1990) and Levinson (1986) models is these
models may not apply to women. Very little empirical work has been done to tests these
models on women. Further it has been suggested that the traditional (developmental)
career stage models were developed to explain the careers of men and were tested
primarily with male samples (Sullivan, 1999). Therefore, researchers are calling for
future research to take a broader approach to and consider the interaction of multiple
factors, including the timing of parenthood, family responsibilities (e.g. childcare) the
career stage of the woman’s partner, and organizational support (e.g. flexible schedules).
The relational model, which suggests the value of relationships in the career development
of employees, is not specific to any gender and therefore may provide a more
comprehensive view of the factors that impact the careers of men and women, and
parents.
In addition, to the potential gender biased introduced by the use of stage models,
it has been suggested that the developmental models are not consistent with the average
tenure of employees. The career developmental models were developed at a time when an
employee, for the most part, stayed with one organization (Sullivan, 1999). Given the
trend of employees today, that is to move between organizations, on average, every four
59
to five years, the developmental models are no longer a means of understanding the
employee’s career behaviors or attitudes.
The most current perspective on careers is the protean career. The protean career
is defined as a self-directed career “in the pursuit of psychological success in one’s work”
(Hall, 2002), and a career that is “self-determined, driven by personal values rather than
organizational rewards, and serving the whole person, family, and life purpose” (Hall,
2004, p2). Some of the characteristics of the new protean career include: (1) the career is
managed by the employee, not the organization, (2) the career consists of a lifelong series
of experiences, skills, transitions, and identity changes, (3) career development is marked
by continuous, self-directed learning and the development of relationships, (4) career
development does not necessarily include formal training and upward mobility, and (5)
the employee expects their organization will provide challenging assignments, and
informational and other developmental resources (Hall, 2002; Hall and Mirvis, 1996). In
a recent article, Higgins and Kram (2001) suggested the way to make protean careers
work is through the development of career networks. That is, if an individual is going to
be successful in managing their own career and pursuing opportunities that meet their
subjective measures of career success, individuals need to have specific tools to manage a
protean career. Drawing from Higgins and Kram (2001) work, this dissertation suggests
that gender, family involvement, job involvement, and role segmentation will moderate
the relationship between parental status and the network characteristics size, ties, and
content. As a result, when compared to working adults without children, this dissertation
argues that working parents face several disadvantages that make pursuing a self-
managed or protean career difficult. Because of the constraints faced by working parents,
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they have a more homogenous set of relationships within their career networks. Although
not studied in this dissertation, future studies could also assess the role of the
organization in helping individuals develop relationships that would be useful in pursuing
a protean career. Some of the contributions of organizations in supporting employees
develop relationships may include encouraging employees to engage in career dialogues
with a manager, peer or career coach (Hall, 2004).
Careers and Organizational Networks
Previous research has examined the relationship between organizational network
and various career outcomes. Some of the outcome measures include career success,
career mobility, and career progression. A brief description of each of the career
outcomes will be included in this section, followed by examples of empirical findings
related to career outcomes. Career mobility is usually measured in terms of the number
of promotions an individual receives in a specific time period, the salary progression of
the individual (including salary, commission, and bonus), and the movement of
employees in various organizational roles (e.g. Vardi, 1980; Martin & Strauss, 1956). In
some empirical studies, career mobility is also called career progression (Turban and
Dougherty, 1994). In prior literature, some studies measured either salary progression (a
measure across years of salary and bonuses received) or promotions (number of new jobs
received in a specified amount of time) individually. That is, salary progression and
promotions are treated as two separate dependent variables (e.g. Wallace, 2001). In
comparison, when studies use both salary progression and promotions, these two
variables are combined into one larger dependent variable category, that is, career
mobility or career progression (e.g. Stroh, Brett, & Reily, 1992). Other measures of
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career mobility include intra-organizational mobility (i.e. the total number of job changes
with the same employer) and inter-organizational career mobility (i.e. the total number of
job changes that involved moving from one employer to another) (e.g. Valcour &
Tolbert, 2003). For example, Polodny and Baron (1997) studied the relationship between
networks and career mobility. They found having a large sparse informal network (that is
a large number of nonredundant ties) led to upward career mobility among individuals
studied.
Burt (1997) also examined career mobility. He studied the relationship between
network size and career mobility (e.g. promotability and salary increases). Burt (1997)
examined a group of managers whom had a large network of nonredundant ties. He found
the managers with structural holes, that is networks where the alters of the network do not
know each other (i.e. the alters are various ties or individuals within the ego’s network),
were promoted more quickly and received larger bonuses than individuals with networks
composed of strong ties (i.e. strong, but very few ties within their networks). In
addition, Saxenian (1996) studied the impact of networks on career mobility among
professionals in Silicon Valley. Saxenian (1996) found that those interested in inter-
organizational mobility (the Silicon Valley has a norm for high inter-organizational
mobility) and those individuals whose ties included members of professional
organizations (another example of nonredundant ties) were able to move easier between
organizations, than an individual whom had only organizational ties. Finally, other
studies have investigated the relationship between career mobility and a number of
attitudinal measures including career satisfaction and organizational commitment. For
example, Murrell et. al (1996) conducted a study to address the overall affect of job
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changes on career outcomes. The authors found frequent moves within an organization
had a negative impact on work-related attitudes, specifically overall job satisfaction and
commitment.
In addition to career mobility measures, several studies have used attitudinal or
perceptual measures, for example career satisfaction and career self-management. Career
success is based on psychological success. That is, career success is a person’s
perceptions of how successful their careers are, regardless of observable indicators such
as salary or hierarchical attainment (e.g. Valcour & Tolbert, 2003; Turban & Dougherty,
1994). Vardi (1980) characterized the psychological approach to careers as any work that
is based on understanding the:
“individual’s perspective, career attitudes, perceptions, expectations and behaviors; “thus a psychological approach to careers looks at both the antecedents of the career behavior (e.g. personality, ability, choices) and the consequences of the outcome of this behavior” (e.g. career success, career satisfaction) “ (p.344).
Consistent with this view, career success is based on individual perceptions.
Career success can be defined as “the accomplishment of desirable work-related
outcomes at any one point in a person’s work experiences over time” (Arthur, Khapova,
& Wilderom, 2005, p. 179). Therefore, career success is a perceptual measure and it can
be measured by items such as “I am in a position to do work that I like”,” I am respected
by my colleagues” or “I am pleased with the promotions I have received so far” (Gattiker
and Larwood, 1986). A common distinction made in the literature is between objective
and subjective career success. Objective career success is success that is directly
observable, measurable, and verifiable by a third party (Hughes, 1937; Hughes, 1958).
Examples of career success may include pay increases or promotion to a higher position
63
(e.g. from bank teller to loan officer within a commercial banking setting). In fact, the
most widely used measures of career success include “salary, salary growth, and
promotions” (Heslin, 2005, p.115). Consistent with this view, Arthur and Rousseau
(1996) found that more than 75 percent of empirical studies published between 1980 and
1994, used an objective career success measure. The large presence of objective career
success in published articles is a result of several methodological advantages; specifically
when objective success career measures are collected from a 3rd party (e.g. employee
records) there are no issues related to common method variance or self-serving bias
(Heslin, 2005).
Despite the prevalence of objective career success measures in the literature, some
studies (e.g. Greenhaus, 2003; Hall, 2002) have begun to investigate the antecedents of
subjective career success (Heslin, 2005). Subjective career success alludes more to an
internal satisfaction an individual experiences. Specifically, subjective career success is
defined by an individuals reactions (across any dimensions that are important to that
individual) to their career experiences and it is usually operationalized by a career
satisfaction or a job satisfaction measure (Hughes, 1937, 1958; Heslin, 2005; Arthur et
al., 2005). Career success is usually measured by items that assess global career success.
It can also be measured with items such as satisfaction with pay, promotions and the
development of specific skills (Turban & Dougherty, 1994). It is important to consider
multiple measures of career success (e.g. intrinsic and extrinsic career success) in the era
of the protean or boundaryless careers, because careers do not follow a template in the
same way traditional careers do (Valcour & Tolbert, 2003).
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An example of a study that measured the relationship between network and career
success was conducted by Bozionelos (2003). In this study, she found network resources
(e.g. a perceptual measure of the availability of intra-organizational network resources)
were associated with career success and networks were found to make an additive
contribution to career success beyond any mentoring received. In the Bozionelos (2003)
study, both extrinsic and intrinsic career success were measured. Extrinsic career success
was measured as current organizational grade. Intrinsic career success was measured
using ten items from the Gattiker and Larwood (1985) scale. The ten items include
measures of job success (e.g. “ I am in a position to do work I really like”), inter-personal
success (e.g. “I am well liked by my colleagues) and hierarchical success (e.g. “I am
pleased with the promotions I received so far”).
In another study measuring career success, Seibert et al (2001) found weak ties
and structural holes, that is factors to contributing to larger networks and networks with
nonredundant information, related to social resources (e.g. information), which led to
promotions and career satisfaction among the sample studied. In addition, Valcour
&Tolbert (2003) measured career success and the relationship between intra-and inter-
career mobility. They found when people move between jobs (inter-organizational
mobility), they tend to experience a decline in their pay, but this does not affect how
successful people feel in their jobs (subjective career success). In comparison intra-
career mobility, that is taking a new job in the same organization, resulted in an increase
in earning, but negative effect on perceived career success.
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A perceptual measure, career self-management is best described as performing
activities such as setting-career related goals and devising strategies to achieve them
(Noe, 1996). That is,
“career management is the process by which individuals collect information about values, interests, and skill strengths and weaknesses, identify a career goal, and engage in career strategies that increase the likelihood the goal will be achieved” (Noe, 1996, p.119).
One study that used a career-self management measure recently was conducted by
Sturges, Guest, & Davey (2000). In this study career self-management was measured by
asking the research participants the extent to which they had practiced or intended to
practice a set of career self-management behaviors (e.g. developing built contacts with
people in other areas that they would like to work). Further this study measured the
relationship between career self-management techniques and organizational commitment.
The findings of the study suggested that recent graduates perceived career-self-
management techniques should be used in addition to participation in organizational
career management techniques (e.g. training programs, formal mentoring programs).
Specifically, participation in organizational career management techniques, that is,
participation in more formal organizational activities was related to higher organizational
commitment, especially when the employees first join the organization. In comparison,
the employees that had been with the organization at least 8 years were more concerned
about participating in informal organizational practices (e.g. being introduced or
networking with people).
The Sturges et al (2000) study suggests that individuals need various kinds of
career self-management guidance depending on the length of tenure with the
organization. Perhaps the individuals in Sturges et al (2000) study desire to build their
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human capital skills (e.g. participation in training) when they initially enter the company
and develop their social capital (e.g. participation in networks) after becoming adjusted to
their work environment and job responsibilities.
Careers and Gender
For some time, researchers have studied the influence of gender on promotions
and career mobility. Career mobility, that is, a movement between jobs results in an
individual acquiring additional skills and broader job experience. Previous studies have
addressed why women and men experience variance in their career mobility patterns. For
example, research suggests that men are advanced at a faster rate than women (Ragins
and Sundstrom, 1989). Although differences related to gender have been noted, previous
research has not been able to clearly determine why those gender differences persist, nor
why gender (in comparison to tenure) explains a large amount of variance in promotion
rates. Thus, the interesting question becomes, why does this occur and what is the role of
networking in career mobility?
The two elements that appear to be important to en employee’s career
advancement include human and social capital. Human capital is the accumulation of
knowledge and skills over time (Becker, 1993). Examples of human capital variables
that are important to careers (i.e. career success) include mental ability and educational
attainment (Melamed, 1996). Mental ability is said to positively impact the pace at which
job knowledge is acquired and educational attainment enables individuals to access
higher paying and higher status jobs, thereby leading to career success (at least objective
career success, that is, when salary is used as an indicator of objective career success. In
comparison, social capital can be defined as the ability of individuals to reap the benefits
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of membership in social networks or other social structures (Portes, 1998). Networking
within organizations is one example of enhancing one’s social capital. Human capital (an
employee’s knowledge and skills) is expected to help employees gain entry into
organization, while social capital is expected to help employees to middle and senior
management (Metz and Tharenou 2001). Of interest, Metz and Tharenou (2001) found
little support for the notion that networks facilitated the career development of women.
Instead, Metz and Tharenou (2001) found internal networks were negatively related to
the career advancement of women at the middle and senior level management positions.
Consistent with this view, additional studies have found that women, specifically in
senior-level management, were excluded from informal groups, thereby having limited
access to information that was being exchanged (Davis-Netzley, 1998; Moore, 1988;
Swiss, 1996). Also, Lyness and Thompson (2000) found exclusion from informal
networks was a perceived barrier of success for female executives in comparison to
males. When asked to identify the factors necessary for success in executive level
positions, women reported that networking with men in powerful positions was the most
significant requirement for success in executive level positions (Davies-Netzley, 1998).
Researchers have suggested that the influence of organizational context on the
persistent of the glass ceiling, is a key area for future research (Chernesky, 2003). Glass
ceilings are one example of career barriers often described by researchers and
practioners. The glass ceiling is described as a non-job related barrier that individuals
face (London, 1993). Specifically, the glass ceiling occurs when people are denied access
to a career goal or negative decisions made about them on the basis of their race or
gender. Therefore reaching the glass ceiling suggests a career barrier of not being
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promoted beyond a certain point and it is a common barrier faced by women (London,
1993). In addition to the glass ceiling phenomena, another example of a career barrier
faced by women and parents is the multirole conflict barrier. The multirole conflict
barrier refers to problems or demands from one’s family (spouse or children) or friends
that may distract the individual’s attention from working toward a career goal (London,
1993). It is important for researchers to address various career barriers (e.g. the glass
ceiling phenomena), as research suggest individuals will have an emotional reaction to
career barriers. That is, strong emotions interrupt an individual’s thought process and
behaviors; this influences the extent to which people can rationally interpret a career
barrier and determine a constructive course of action (London, 1993). Thus we need to
develop a more comprehensive model of career barriers that considers challenges specific
to women. Although outside of the scope of this article, emotions should be included in a
more comprehensive model of career barriers (e.g. how to balance work-family demands
while also meeting individual career goals).
This dissertation seeks to understand the attitudes of working parents specific to
their ability to use organizational networks to learn specific job or career-relevant
information. An example of an attitudinal measure that will be used in this dissertation
draws from Parker’s et al. (2004) study. In this study, Parker measured individual’s
perceptions or attitudes towards members in their organizational networks and the extent
to which these members influence their careers. The measure used was called knowing
whom and it was used to assessed the relational aspect of the individual’s career
situation. Additional detail will be provided in Chapter 4, regarding all measures that will
be used to assess the attitudes of individuals toward their networks and their careers.
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Careers and Parental Status
Previous research has examined the impact of parental and marital status on
career advancement. In a recent literature review it was reported that work-family
conflict may be negatively associated with less career satisfaction and lower perceived
career success (Eby et al., 2005). Empirical evidence also seems to suggest career
outcomes may vary across parental status. For example, Tharenou (1995) found childless
singles experienced less career advancement than married employees with or without
children. Tharenou (1995) also found that single men advanced less than married men.
This finding was consistent with the commonly held notion that single men has less
financial need than married men (especially if married men are a part of a single-income
household). Thus, since single men have less financial need than married men, they may
experience less career advancement. In short, the key finding from the study indicated
that married men and women, regardless of parental status, advanced more in their
careers than childless singles. This finding contradicts some of the prior literature that
suggests that employees with children have less time to devote to their careers, will
therefore experience less career advancement, in comparison to employees without
children. Of note, Tharenou’s (1995) sample was limited to early and middle level
managers and professional employees.
Tharenou’s (1995) findings are consistent with other studies that have found that
career advancement, measured in terms of salary and salary progression, favor married
men (e.g. Schneer & Reitman, 2002). That is, married men tend to earn more than both
single men and women. Married men may earn more than single men and women,
because their family status (e.g. husband, father) signals to organizations that they are
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more stable (i.e. unlikely to leave the organization due to family responsibilities) and
demonstrate a higher financial need, especially if their spouse is unemployed.
Some studies have also measured the impact of children on salary progression,
that is, the career progression of individuals with children. For example, Miree & Frone
(1999) investigated the impact of children on career outcomes of MBA graduates. The
study found an effect for age. That is, working parents, regardless of gender, made higher
salaries (where salary is a commonly used measure for salary progression) when the
individuals had older children only (the study did not specify the age distribution of older
vs younger children). Therefore, working parents with younger children made less than
the employees with older children. One factor that may have contributed to the
differences in salaries between those workers with younger in comparison to older
children, was the age of the employee. It may be the case that the employees with older
children were older and had more work experience (hence higher pay) than the
employees with younger children. The interesting fact from this study was that the age of
the child matters. That is, parents seem to experience more challenges with younger
children in comparison to older children regarding career progression (i.e. salary
progression). As a result, this dissertation will focus on the challenges working parents
face with younger children as far as career outcomes are concerned.
Metz and Tharenou (2001) found that family responsibilities hindered the career
advancement of women more at lower levels of the organization in comparison to higher
levels within the organization. In other words, women at higher levels within
organizations do not perceive family responsibilities to burden their career advancement.
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This may give some insight into women that decide to wait until later in their careers to
have children, after obtaining the higher managerial position they desire.
Valcour and Tolbert (2003) studied the impact of family and gender on careers,
specifically intra-organizational and inter-organizational mobility. Their results suggested
both gender and family characteristics impact mobility. For example, men were more
likely to move to new careers within the same organization (intra-organizational
mobility), while women were more likely to move across organizations (inter-
organizational mobility). Also, men’s intra-organizational mobility was positively
influenced by number of children, while women’s intra-organizational mobility was
negatively influenced by children (and positively influenced by marriage). Inter-
organizational mobility was unaffected by the presence of children for men, while
children positively influenced the inter-organizational mobility for women. It was not
surprising that children positively influenced inter-organizational mobility for women, as
women sometimes move between organizations after the birth of child, in order to find
work-arrangements (e.g. part-time work) that help meet their needs to balance their work
and family demands. In conclusion, the Valcour and Tolbert (2003) study provides
evidence that gender and parental status matter in terms of career mobility. This
difference seems to be most prevalent for women, regardless if they are moving within or
between organizations. In fact the women who obtained the greatest career success, in
terms of mobility within a single employer, were those who had previously been
divorced, and who were either childless or had fewer children.
The empirical results related to the impact of parenthood on networks are
inconsistent. For example, in an empirical study conducted in the public sector, Scott
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(2001) found women with children were not significantly disadvantaged relative to their
colleagues in terms of networks. They were as likely as women without children to talk
with staff members and other government relations managers and top executives. Of
interest, one of the employees interviewed in the study reported that their organization
pays for baby-sitting services which allow the employees to attend social events after
traditional working hours (Scott, 2001). One possible interpretation of the Scott (2001)
study is that women are not disadvantaged by their parental status and in some cases
parental status may help working parents gain social capital, through their interaction
with other members of the organization who also have children.
Previous studies have shown that a change in family status does impact an
individual’s social network. For example, Kearns and Leonard (2004) found the social
networks of men and women were reshaped by marriage. Specifically, the networks of
married couples become more interdependent, as indicated by the increased overlap
between husbands’ and wives’ friends and family network. Arguably, this change in
networks, may lead to a reduction of nonredundant information shared within networks.
For example, if the husband and wife begin to interact primarily with an overlapping
group of individuals, this may suggest that they lose contact with individuals with whom
they communicated prior to marriage. This suggests that similar to a change in parental
status, a change in marital status may also reduce the number of direct ties within the
ego’s network. Consider a scenario where the wife loses contact with her college friends,
as a result of moving with her husband to a new city to take a job. The wife is no longer
in touch with her former college friends as a result of physical proximity. Further, from a
networking perspective it can be argued that wife has experienced a reduction in her ego
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network, thereby leading to a reduction in nonredundant information related to job or
career opportunities.
Careers and Work-Family Concerns
Previous research has examined the relationship between family and career issues.
Most of the previous research has focused on the relationship between dual-career
couples and role conflict and the protean career. (Hall, 2002). Previous research has also
investigated the relationship between work-family and family-work conflict on (career)
withdrawal behavior, and the moderating effect of family involvement and career
involvement (e.g. Greenhaus et al., 2001). Of note, work-to-family conflict occurs when
involvement in a work-related activity, interferes with participation in a competing family
activity (Greenhaus and Powell, 2002). In comparison, family-work-conflict occurs when
involvement in a family activity interferes with participating in a work activity
(Greenhaus and Powell, 2002). A recent study found when work-family conflict is high,
leaving the professions that interferes with family life, is likely to reduce the stress of that
individual (Greenhaus et al. 2001). In comparison, an individual experiencing family-to-
work stress is not likely to withdraw from that profession because the stressor is related at
home, not the workplace (Greenhaus et al., 2001). Therefore, an individual’s intention to
leave a specific profession is related to the direction of interference between work and
family roles (Greenhaus, et al., 2001).
In addition, the Greenhaus et al. (2001) study found career involvement impacted
the individual’s decision to leave the organization, but family involvement had no impact
on the decision to leave. Therefore, when the employees were highly involved with their
careers, they were not disturbed, greatly, by the interference of work with family life and
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they are willing to tolerate the interference for the sake of their careers (Greenhaus, et al.
2001). In contrast, the study did not find an interaction between family involvement and
intentions to leave the profession (Greenhaus et al., 2001). This may suggest that
employees who are highly involved with their families are concerned about maintaining
their position with the organization, providing them with the ability to financially support
their family.
Very little research has investigated the relationship between family status and a
relational approach to careers (as suggested in this dissertation). Most of the studies have
investigated the antecedents (and consequences) of work-family or family-work conflict.
For example, Evans and Bartolome (1981, 1984) found when work roles and home roles
come into conflict, work usually wins.
Some studies have looked at the relationship between family involvement and
career involvement. For example, Hall and Hall (1979) developed a typology based on
the four possible combinations of career and family involvement. This typology suggests
couples will experience conflict based upon two factors, career involvement and home
involvement. The group expected to experience the most stress are the couples that want
high involvement careers and high involvement family lives (referred to by Hall & Hall
(1979) as acrobats). Hall and Hall (1979) suggested that the typology is not static, that is,
a couple can move between various stages based on career and life stages development.
The Hall and Hall (1979) typology for dual career couple is based on role theory. Role
theory is often used in work-family studies to describe the conflict individuals experience
when they try to manage competing demands from both home and work. Thus, while
previous research has suggested the antecedents and consequences of role conflict among
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dual-career couples, future research needs to investigate the relational aspects of careers,
that is, the intersection (and tradeoffs) between family and the role of relational networks
in careers.
Other studies have looked at the relationship between family structures and career
satisfaction. For example, Schneer and Reitman (1993) investigated the relationship
between family structure (e.g. marital status, parental status, and spousal employment
status), career and income satisfaction, and gender. This study was conducted on a
sample of MBA students and self-report data was used to measure multiple variables of
interest including employment status, income, career satisfaction, parental status, marital
status, etc. Schneer and Reitman (1993) found that families where both spouses were
employed with children, earned more than families with only one spouse working. This
result is not surprising as the number of dual income earners have increased, and both
men and women are working full-time and balancing parental responsibilities. Further,
Schneer and Reitman (1993) found that families in which both adults were working and
children were present in the home, were more satisfied with their careers than families
with only one parent working and children are present in the home. According to Schneer
and Reitman (1993) this finding was surprising as these individuals, that is families with
both parents working, depart from the traditional successful manager model (i.e. where
one parents stays at home to raise the children and the other parent works to support the
family financially). The authors suggest that the career success of dual-earner families
with children may be attributed to individuals being able to fill multiple roles (i.e. spouse,
worker and parent), which leads them to be more satisfied with their careers.
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Another set of studies have examined the relationship between the birth of a child
and occupational mobility. These types of studies are important, because the birth of a
child is one of the major career interruptions for women (Brannen, 1989). That is, these
studies investigate if women experience a career interruption if they leave the workforce
temporarily after having children. For example, Brannen (1989) compared the careers of
two groups of women, those that resumed their full-time jobs after maternity leave with
their pre-birth employers to those women that moved to new employers after the birth of
a child. The study was a longitudinal study conducted over five years. Brannen (1989)
found that the women that remained with the same employer (even if they reduced their
work hours) were at “much less risk of downward mobility (Brannen, 1989, p186) and
were much more likely to be upwardly- mobile, that is, receiving a job promotion. In
comparison, for women that left their original (or pre-birth) employer and moved to a
new employer after returning from maternity leave, were more likely to experience lower
pay, than the women who returned to their original employer after returning from
maternity leave. Remaining with the same employer (i.e. pre-birth employer) after
returning from maternity leave appears to offer women an advantage (i.e. higher pay and
higher likelihood of an upward promotion). In addition to increasing the likelihood of an
upward promotion, the women who remained with their pre-birth employer after
returning from maternity leave were more likely to have more job security and a higher
number of paid holidays, than the women that took a job with a new employer after
retuning from maternity leave (Brannen, 1989). Based on the findings in the Brannen
(1989) study, it seems that the birth of a child may not impose as large of an interruption
on women’s careers as once thought. Instead the findings from the Brannen (1989) study
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seemed to suggest that women can be successful in their careers (e.g. experience an
upward promotion) if they remain with the same pre-birth employer. Given this finding,
it would be interesting to identify the factors that contribute to the career mobility of
women after returning from maternity leave. Although outside of the scope of this
dissertation, it would be interesting to study the role of interpersonal relationships in
facilitating the career mobility of women after they return from maternity leave.
Presumably, if women return to the same pre-birth employers they are likely to have a
large number of existing interpersonal relationships (i.e. a networks with numerous co-
worker ties), depending on the length of tenure with their pre-birth employer. However,
the women that take jobs at new employers after returning from maternity leave, would
have to develop new interpersonal relationships. Therefore, they would have fewer
interpersonal ties (and less access to job and career-related information) than the women
that are returning to their pre-birth employers who are more likely to have a larger
number of existing ties (and thereby more access to job and career-relevant information
and a higher chance of experiencing career mobility).
Based on the empirical studies, literature reviews, and popular press articles, it is
fair to suggest that working parents face many challenges, which may deter their career
progression. For example, women are challenged by career interruptions, or time taken
off from work to stay at home with children. Broadbridge (2003) offered insight into the
challenges faced by employees in organizations, specifically working mothers. In
general organizations lack both formal and informal policies that will help working moms
address the challenges they face in trying to advance their careers and engage in their
family responsibilities. Formal policies (e.g. lack of childcare facilities, lack of flex-time,
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long “after work” social hours) are held responsible for the perceived reasons why there
are so few women in senior management positions. However, organizational culture and
informal policies are also important in helping or hindering career development. In short,
work-family researchers often study to what extent formal policies impact an employee’s
ability to balance work and family responsibilities, without considering the role of
informal practices (e.g. developing or maintaining social capital), specifically related to
career development.
Moreover, it has been suggested that organizations and researchers may be
overlooking the significance of informal policies, specifically in understanding why the
myth of the glass ceiling phenomenon has persisted for women (Broadbridge, 2004). This
dissertation suggests that work-family researchers in the past may not have ignored the
challenges working parents face in terms of their ability to adhere to organizational
expectations set forth by informal policies (e.g. the importance of “face time” in
organizations, the importance of developing social networks in order to advance your
career). In short, employees’ use of work-family practices will hinder their career
advancement and success (e.g. taking an external parental leave or setting limits on the
number of hours worked) (Blair-Loy & Wharton, 2002). Also, many employees fear that
starting a family will also hinder their career.
This dissertation does not propose that all working parents participate in work-
family programs (e.g. condensed work weeks). Rather, it is likely that whether
employees participate in work-family programs or not, working parents will have less
time to allocate towards the development and socialization of organizational networks as
a result of career interruptions (taking time off from work soon after the children are
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born), will spend less time at work (especially networking functions that take place after
work, conferences, etc), and when engaging in their organizational networks may spend
more time discussing their family-life (in comparison to career-related information). It
has been suggested in the existing literature that perhaps professional and managerial
employees with fewer family responsibilities can focus more single-mindedly on their
careers, spend more energy cultivating critical networks, and thus ascend higher or more
quickly through organizational ranks (Blair-Loy and Wharton, 2002). As such,
organizations and work-family researchers must address both the importance and barriers
employees face as a result of not complying with both formal and informal organizational
policies. This topic requires further investigation. For example, one may study if the
ability to develop or participate in organizational networking activities is determined by
the type of work-family program in which the employee participates. For example, are
those employees who participate in teleworking less likely to have an opportunity to
participate in networking activities than an employee whom works a condensed work
week?
Previous work-family research has been concerned with the utilization of work-
family practices and the negative consequences utilizing these practices may have on the
career development of working parents. For example, Lewis and Taylor (2000) found
some of the women interviewed in their study raised concerns that their participation in
alternative working patterns would negatively impact their career progression,
specifically in organizations that measure productivity in terms of time spent at the office
(i.e. being seen at work). One activity employees may engage in while at work is
interacting within organizational network groups. The opportunity to network as
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previously stated allows employees to gain information, and it allows employees to be
seen as engaged in work activities by both peers and upper-level management. Also, if
working parents have less time to participate in organizational networks, they may have
less information readily available to them, providing that individuals leverage
organizational networks to learn about job/career opportunities. It is important for w-f
researchers to consider the impact of gender on the career advancement of working
parents. Empirical evidence suggests that working moms are penalized by slower
advancement and reduced wages in comparison to women without children, while
working fathers experience more advanced career and salary progression, then men
without children (Tharenou et al, 1994). In contrast, Tharenou (1999) found, in a
longitudinal assessment, that working moms experienced more career advancement than
women without children. It appears that the empirical evidence is inconclusive in terms
of the impact of children on the career advancement of employees. Therefore, the role of
organizational networks in advancing the careers of employees may differ by both gender
and parental status.
Job and Family Involvement
Previous research has investigated the work-family conflict individuals
experience as a result of participating in dual roles in their lives. Role involvement is
thought to lead to conflict among individuals because (1) high levels of involvement in
that role may lead to increased amount of time spent in that role, therefore allowing less
time to be allocated to the second role (Greenhaus & Beutell, 1985). In addition, high role
involvement in one role may lead to individuals being mentally involved with the most
prominent role in their life, even when one is physically trying to fulfill the demands of
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the second role in their lives (Greenhaus & Beutell, 1985). Two measures commonly
used to assess the importance of a specific roles in an individual’s life include job
involvement and family involvement (e.g. Lodahl and Kejner, 1965; Kanungo 1982). Job
involvement is a measure of how important a job is to an individual’s life. That is, a job
involved person is “one that views work as an important or central part of their lives”
(Lodahl & Kejner, 1965). In addition, family involvement is similar to the job
involvement measure, that is, it is a measure of how important an individual sees family
as important to their life. That is, job involvement is generally operationalized as the
extent to which one indicates job-related activities or the job itself to be of central and
unique importance in their lives, and a key source of personal identity (Reeve and Smith,
2001).
There are many factors that are thought to contribute to job involvement including
opportunities for promotion, financial reward, job security and supportive relationships
with coworkers and supervisors (Lodahl and Kejner, 1965; Lambert, 1991). Related to
job involvement, at least two themes have been identified in the literature. The first is the
notion of whether job involvement differs by gender, that is, are males or females more
likely to be involved with their job. Examples of this research include Golembiewski
(1977) who argued that work is perceived as more central by males in comparison to
females. This ascertain would suggest that males are more involved with their jobs than
females. A study by Lorence (1987) aimed to understand if gender differences exist by
gender. This research was carried out in two waves and was part of a measure, The
Quality of Employees Survey. Findings from the study included, (1) marital status and
the number of children did not affect job involvement among males or females, but (2)
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overall women were less involved with their jobs than men. Contrary to what the authors
expected, the gender differences found in job involvement did not have any relationship
with family responsibilities. Instead the job involvement differences by gender were
related to women having less autonomy in their jobs or poor working climate.
Another theme that is related to gender differences includes differences between
gender by marital status. That is, are married men more/less likely to be involved with
their jobs in comparison to single men; and are married women more/less likely to be
involved with their jobs in comparison to single women? Consistent with this view,
studies have found married men are more involved with their jobs than single men (e.g.
Agassi, 1982). However, other research suggests that women, especially married women,
are more committed to their jobs (especially the longer they work) (Haller & Rosenmayr,
1971). Consistent with this view, married women were found to be more committed to
their jobs than single women (Agassi, 1982). Taken together, these studies suggest that
although gender differences may exist, there may be additional factors that influence the
job involvement of individuals, marital status, notwithstanding.
Previous work-family studies have investigated the relationship between role
involvement and work-family conflict. For example, Frone and Rice (1987) investigated
whether family involvement moderates the relationship between work-family conflict and
job involvement. The authors found that job involvement was positively related to job-
parent conflict, regardless of parental involvement. In addition, in a recent literature
review Eby, Casper, Lockwood, Bordeaux & Brinley (2005) reported several studies that
measured job involvement and various career-related outcomes. For example, Gould and
Werbel (1983) found job involvement (and organizational identification) to be lower
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among males with working spouses in comparison to males without working spouses.
Further, for the individuals who had both working spouses and children, job involvement
was higher. Also, a study by Lambert (1991) also investigated the impact of several
extrinsic factors (e.g. opportunities for promotion) and intrinsic factors (e.g.
meaningfulness of work) on job involvement. The findings from the study suggest there
is very little difference between the job involvement of men and women. However certain
factors such as career-related rewards (e.g. opportunities for promotion) were more
important to men, but factors related to social rewards (e.g. having a good relationship
with co-workers) were more important to the job involvement of women.
These findings suggest (1) others factors besides gender are related to job
involvement and (2) job involvement is related to interpersonal relationships at work.
That is, it appears that one of the factors that contributes to high job involvement is an
individual’s relationships within their organizations. For the purposes of this dissertation,
job involvement will be measured as a moderator between the ego’s network
characteristics and parental status.
Weak Tie Theory
Granovetter’s (1973) weak tie argument is one of the most highly cited papers
regarding how individuals can best mobilize their networks. Granovetter’s (1973)
argument suggests individuals should maximize the number of nonredundant ties within
their network, as this will lead to the individual learning more information about future
job and career opportunities. Granovetter argues weak ties are sources of better
information, because they offer new information. In short, this study uses Graonovetter’s
(1973) paper as a basis to argue that the birth of a child will result in employees spending
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more time with kin and less time with organizational members, and their overall size of
their social networks will decrease. A reduction in the size of an ego’s networks will lead
to redundant information related to job and career opportunities being shared within
networks. That is, as the ego’s network size reduces, they will have less members in their
network. Granovetter argues that individuals should maximize the size of their networks,
to increase the likelihood that they will learn non-redundant information from the
members within their networks.
Although Granovetter’s recommendation suggests that an individual should
maintain multiple nonredundant weak ties, there is a challenge in maintaining weak ties
that should be addressed. Higgins and Kram (2001) suggest that an individual must
communicate or reach out frequently to their weak ties. If individuals do not reach out
frequently to their weak ties then they may only receive help from members in their
network when it is offered and they do not utilize their network effectively to gain
information (some people are simply not willing to dedicate the time it requires to
maintain contact with multiple individuals within their network). This is especially true
for parents whom have less time to invest in maintaining those multiple contacts.
Working parents are not likely to have time to dedicate to maintaining multiple weak ties
(meaning they are in touch with these individuals at least once a week).
In addition, to Granovetter’s weak ties argument, Burt (1992) describes the
benefits of networks through his structural holes argument. Burt argues individuals
should focus on the pattern of relationships within their network. Specifically, Burt
(1992) suggests that people create an efficient network by maximizing their structural
holes, or the distance between two alters (individuals) within a network. Burt (1992)
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defines structural holes as absence of connection between among those in the network,
suggesting that the more the ego (individual) is surrounded by structural holes, the more
likely they are to experience mobility. Burt (1992) similar to Granovetter argues that the
larger the network, that is, the larger number of direct, nonredundant ties, should lead to
an individual experiencing more upward career mobility. Therefore, both Granovetter
and Burt suggest that the ego should maximize the size and nonredundancy of their
network.
Burt argues that networks provide employees with at least two advantages. First,
employees who are embedded in networks have access to people and information. Also,
networks provide employees with just-in-time information. Stated differently, Burt
(1992) argues that timing is a significant feature of information received by networks.
Beyond making sure that you are well informed, personal contacts can make sure you are
one of the people that are informed early. Therefore, personal contacts get your name
mentioned at the right time in the right place so that opportunities are presented to you.
Burt’s theory also suggests that individuals should not build a network around an
immediate supervisor. Instead the greatest benefit of a network is with people completely
removed from the immediate group.
Burt (1992) argues that structural holes exist when the alters of a network do not
know each other. Thus, Burt’s structural holes argument suggests that structural holes
create a situation in which information is additive rather than overlapping. If you have a
number of weak ties but they all know the same information; then you will have
redundant information being shared rather than new information.
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Redundancy comes in two forms: (1) cohesive contacts – contacts strongly
connected to each other are likely to have the same information and (2) Structurally
equivalent contacts (contacts linked the same third party). In summary, the key tenets of
Burt’s Structural Hole theory are:
All individuals have potential to develop social capital
Focus on patterns of relationships within their network
Efficient networks maximizes nonredundant contacts
Structural hole exists when two alters within a network are not
connected with each other.
Individual should be connected to many alters who themselves are
not to connected to other alters in the ego’s network. Leads to
additive rather than redundant information
Previous studies have used the weak tie theory as a basis for understanding the
role of social capital and career outcomes. For example, a study by Seibert et al (2001),
examined a conceptual model where two measures of social network structure, weak ties
and structural holes, were thought to be related to two social resources, the number of
contacts in other functional areas and the number of contacts in at higher organizational
levels. This model used social network structure and social resources to predict career
success (i.e. current salary, the number of promotions received over the career and career
satisfaction). The findings from this study suggested that social capital is important to
career success. Specifically, there was a strong relationship between weak ties and a
greater number of organizational contacts at higher levels (r=.44). This suggests that
individuals with weak ties (i.e. multiple, nonredundant ties) are likely to have multiple
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contacts at higher levels. Further, social resources were positively related to current
salary, number of promotions over the career, and career satisfaction. Of note, none of the
paths from structural holes to network benefits (e.g. access to information, access to
resources, and career sponsorship) were significant. However, two of the three paths
from weak ties to structural holes (access to information and career sponsorship) were
significant. Therefore, this suggests that the weak ties argument supports the relationship
between social capital and various career outcomes, that is, the weak ties measure
appears to have a stronger and more robust effect on social resources (Seibert et al.,
2001). This finding is consistent with the use of the weak tie framework as a basis for this
dissertation. That is, this study demonstrates that the relationships between an
individual’s network and various career outcomes is significant and should continued to
be analyzed in future studies (hence this dissertation).
Boundary Theory
Boundary theory is useful for understanding how individuals move between their
work and family roles. Roles are specific forms of behavior associated with given
positions, and they develop from task requirements (Katz & Kahn, 1978). There are at
least three core characteristics of boundaries including they are flexible, permeable, and
directional (Hecht et al., 2004). Specifically, the flexibility characteristic of boundary
theory suggests that physical time and location markers can change (e.g. working hours).
The permeability characteristic of boundary theory describes the extent to which a person
that is physically present in one domain, is psychologically concerned with another
domain in their lives (e.g. a parent at work that is concerned about a sick child that is at
home with a babysitter). Finally, the directionality characteristic of boundary theory
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describes the extent to which an individual defines the strengths of their boundaries, that
is, there is variance in the extent to which an individual allows work to cross over to
home, in comparison to the extent to which an individual allows home to cross over to
work (Hecht et. al., 2004). For example, a person may have a strong boundary, where
they do not allow their personal life to enter their work environment. At the same time, an
individual may have a weak boundary between work and home, where they allow the
office to call them at work after regular business hours. In general, an individual with
strong boundaries, generally prefers to segment their work and family lives, and the
boundaries between work and home are usually inflexible and impermeable (Hecht et al.,
2004). Meanwhile, an individual that enjoys weak boundaries, that is, a person that
prefers to integrate their home and work life, establish boundaries between home and
work that are both permeable and flexible (Hecht et. al., 2004).
Boundary theory is a theory that demonstrates that individuals have preferences
for the extent to which they desire two roles in their life, work and home, to be integrated
or segmented. A individual’s preference for integration or segmentation are along what
researchers describe as the integration- segmentation continuum. Segmentation refers to
the separation of work and home roles, while integration refers to inclusion of work and
home roles. Examples of segmentation include not displaying pictures of families in
offices, while a person that desires to integrate their work and family roles would display
pictures of family in their offices. Also, segmentors would no likely take work home,
instead they would rather finish it in the office. Meanwhile, integrators, are ore likely to
take work home (Rothbard et al., 2005). Integration and segmentation lie on the same
continuum, and instances of complete segmentation or complete integration are rare
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(Rothbard et al, 2005). The primary reason individuals choose to integrate or segment
their work and home roles is to minimize the difficulty of participating in both roles
(Rothbard et al, 2005). That is, individuals are looking for a coping mechanism that
allows them to manage the difficulty of holding multiple roles in their lives.
The relevance of boundary theory to work and family research is to understand
how individuals engage in daily role transitions, and the psychological movement
between roles, from role exit to role entry (e.g. leave work and coming home to parenting
role) (Sutton and Noe, 2004). Role behavior is the recurring actions of an individual,
appropriately interrelated with the actions of others, and the best criterion for
understanding role behaviors is to study the expectations of a specific role (Katz & Kahn,
1978). Boundaries are physical and temporal limits that help individuals conceptualize
two entities, work and family, as separate from one another. Role boundaries specifically
describe how individuals make the distinction between various roles in which they are
engaged (i.e. employee, parent). Finally, role identity describes a social construction
where individuals use various cues (i.e. goals, values, beliefs) to identify their occupancy
in a particular role. Ashforth et al (2000) made four key assumptions regarding their
model of role transition. The first assumption was that assumed roles are relatively stable.
Second, there is variance among individuals in terms of the actual number of roles they
prefer to enact. Third, individuals vary in their preference for role segmentation or role
integration. Finally, people seek to minimize the difficulty associated with role
transitions.
Role segmentation suggests that there are large difference in the roles experienced
by individuals at work and home. Further, given the large discrepancies in roles, it is
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unlikely that the various roles will influence one another; a “mental fence is drawn
around each identity” (Ashforth et al, 2000; Clark, 2000). In integrated roles, there is
hardly any difference between the roles of individuals at home and in their workplace.
For example, an individual who enjoys having their work and family roles integrated
would likely enjoy working from home.
According to boundary theory, individuals have preferences for the extent to
which they want their work and home life integrated or segmented. For example, Kossek
et al. (1999) theorized that the preferences individuals have for the extent to which they
want their work and home roles segmented or integrated may vary as a result of gender or
family status. Examples of segmentation include not displaying pictures of families in
offices, while a person that desires to integrate their work and family roles would display
pictures of family in their offices. Also, individuals that segment their work and homes
lives, would no likely take work home, instead they would rather finish it in the office.
Meanwhile, integrators, are ore likely to take work home (Rothbard et al., 2005).
Integration and segmentation lie on the same continuum, and instances of complete
segmentation or complete integration are rare (Rothbard et al, 2005). The primary reason
individuals choose to integrate or segment their work and home roles is to minimize the
difficulty of participating in both roles (Ashforth et al. (2000) & Rothbard et al. (2005)).
That is, individuals are looking for a coping mechanism that allows them to manage the
difficulty of holding multiple roles in their lives. For example, individuals that desire
segmentation between their work and home roles may want to avoid the spillover of
negative emotions (e.g. tension at work) from one domain (e.g. work) to another domain
(e.g. home) (Rothbard et al, 2005). In addition to individual preferences, it should be
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noted that the current policies organizations offer to help employees manage their home
and work responsibilities also fall along the segmentation-integration continuum
(Rothbard et al, 2005). For example, an employer that offers on-site daycare is an
example of an organizational policy that allows for integration of work and home.
An interesting question that this study raises is whether an individual’s
preference for integration and segmentation of work and family roles, contribute to the
degree to which individuals share work-family concerns within their networks. This
dissertation draws on boundary theory to suggest that working parents may vary in the
extent to which they discuss family-related issues within their organizational networks.
As suggested previously, one of the elements within organizational networks that are
expected to change after the onset of parenthood, is the content of the discussion between
working parents and members within their networks.
This dissertation argues that the change in content of discussions of working
parents within their networks may be moderated by the extent to which the parents
choose to integrate or segment their work and family lives. This idea is consistent with
Ashforth et al’s (2000) proposition which stated that the greater the role segmentation,
the less difficult it tends to be to create and maintain role boundaries but the more
difficult it tends to be to cross boundaries. Consistent with boundary theory, for those
working parents that desire to integrate their work and home lives, the content of their
conversations within their organizational networks should change and reflect more
discussion related to family issues. In comparison, for those working parents whom
prefer to segment their work and home lives, the content of the discussion within their
organizational networks is not expected to change as a result of the birth of a child.
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Although, not directly related to working parents, this next illustration provides an
example of two individuals that have different levels of desirability to segment their work
and home lives. This illustration speaks to the point that there is variance in the degrees
to which individuals want their family and work lives to overlap. Further, this dissertation
will argue that these individual differences to segment work and family roles may be
more severe for working parents.
An example of the differences in conversation topics and the extent to which an
individual wants to segment their work and family lives is seen in this next illustrative
example found in the Nippert-Eng (1996) article.
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In this scenario, John clearly intermingles home and work dimensions, while Ed does not.
“John was married to his scientific collaborator. So not only did he freely do and discuss his work with his wife but also he shared much of what others might consider to be his private life with his coworkers. Also, John frequently could be found in his laboratory any day of the week, …and at other times he could be found working at home, reading work materials, puzzling out a problem, planning strategies, and discussing his work with his wife. His work days were also riddled with quick, random phone calls or in-person conversations with his wife about domestic issues and plans” (Nippert, 1996, pp 565-566). “Ed dislikes talking about work with her (his wife), seeing no reason to expose her to much of what goes on there (at work) or the boring details of work. When he gets home, he wants to forget about work, not drag it into the living room behind him. Ed also steadfastly refuses to divulge personal information to his coworkers. He firmly believes that the less other workers know about his life outside of the Lab, the less than can use this information against him. Ed, clocks in and out of work at the precise times mandated by his contract and vehemently insist on his right to evenings and weekends with his family. He never thinks (or talks) about work while he is at home and literally never socializes with colleagues, mush less invite them to his house. His wife only calls work and during breaks in the day, what Ed calls ‘my’ rather than the ‘Lab’s’ time (Nippert, 1996, pp 565-566).
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CHAPTER 3
CONCEPTUAL MODEL AND HYPOTHESES DEVELOPMENT
Drawing from the relational view of careers, individuals are largely responsible
for managing their own careers. Moreover the relationship(s) they develop, that is their
network, will be a key component of career self-management (Hall, 1996). Further,
Higgins and Kram (2001) argue that individuals must be focused on the development
(and maintenance) of multiple, concurrent relationships. That is, the more people or ties
an individual has, the better they will be able to manage their careers. Relationships are
important to an individual’s career because they are a key component by which an
individual becomes aware of job and career-related information. To maximize the amount
of information an individual has about job and career-related information, they must
develop weak ties, that is, a large number of individuals that provide then with
nonredundant job and career-related information (Higgins & Kram, 2001; Granovetter,
1973). Social networks enable an individual to develop multiple, concurrent
relationships, and they allow individuals to exchange job and career-related information
(Forret& Doughterty, 2001).
This dissertation argues that networks are key to an individual acquiring career
and job-related information that will assist them in managing their career. This same idea
is captured by the social capital argument, that is, individuals making an investment into
multiple relationships for the purpose of gaining information (Lin, 2001). To better 95
understand the relationship between networks and career mobility Sullivan (1999)
suggested using a social networks framework (e.g. Burt’s Structural Holes or
Granovetter’s Weak Ties theories) conduct future research on careers, specifically the
relational aspects of career mobility. This dissertation also speaks to a research need
addressed by Sullivan (1999) in which it was suggested that individuals characteristics,
such as gender, age, and race, need to be investigated in terms of the overall
development of large non-redundant networks. Further, Sullivan (1999) suggests that
researchers need a better understanding of the factors that contribute to an individual’s
success in managing a boundaryless career; this dissertation suggests networks (i.e. large
and nonredundant networks) are a key factor to helping an individual successfully
manage their careers.
One way to describe an individual’s network is to identify key measures that are
used to describe the network. The specific network characteristics of interest to this
dissertation include network size, network ties, and network content (i.e. the topics
discussed between the ego and important people in their network). Specifically, network
size is a measure of the absolute numbers of ties within an individual’s network. Network
ties, is a measure of the type of ties individuals have in their network (e.g. co worker-ties,
kin ties, friend ties). Finally, network content describes the conversations individuals
have with important people within their network (e.g. from “work talk- to “family-talk”).
In describing specific network characteristics, it also makes sense to discuss
various factors that may impact specific network characteristics, specifically network
characteristics that are important to an individual’s career. When careers are studied, one
of the most common network characteristics studied is network size, that is, the number
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of ties an individual identifies within their network. Several studies have demonstrated
that certain factors impact an individual’s network size. One of the most common factors
thought to impact network size is gender. For example, gender seems to impact the
composition of ties that comprise an individual’s networks. Mardsen (1990) found the
networks of men include mostly coworkers and volunteer ties, while the networks of
women tend to include more kin and neighbors ties. Thus, it can be concluded that
gender will interact with network size, which usually results in men having larger
networks than women.
In addition to gender, parental status also seems to impact specific networking
characteristics, especially network ties. Network ties, is the type of relationship (e,g, kin,
parent) that an ego has with the individuals they identify as part of the network. In
comparison to adults without parental responsibility, adults with parental responsibility
often have a higher proportion of kin ties in their network. For example, Bost, Cox,
Burchinal & Payne (2002) found that after the birth of a child, parents reported a decline
in the number of friend ties in comparison to the number of family ties within an
individual’s network. Consistent with this notion, as the number of kin ties (in
comparison to friend ties) increased, the respondents reported having more contact with
their kin ties in comparison to the friend ties. In short, after the birth of a child, the
respondents reported spending less time with their friends, in comparison to their family
members.
The examples provided above, suggests that network characteristics, such as
network size, tend to vary by individual differences including gender and parental status.
The first purpose of this dissertation is to understand the factors that cause the
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relationship between parental status and the three network characteristics size, ties, and
content to vary. To accomplish this, this dissertation will identify and test four
moderators believed to influence the relationship between parental status and the three
network characteristics. The four moderating variables that will be studied include
gender, family involvement, role segmentation, and job involvement. Secondly, this
dissertation seeks to examine the relationship between the three network characteristics
and career outcomes. That is, this dissertation seeks to understand if differences in the
network characteristics leads to differences in the career management and career success
indicators included in this study.
This study begins by identifying the variables that are thought to moderate the
relationship between parental status and the three network characteristics, network size,
network ties, and network content. As mentioned previously, those variables include
gender, family involvement, role segmentation, and job involvement. However, in order
to begin the discussion of the antecedents that will moderate the relationship between
parental status and network characteristics, it makes sense to briefly describe why the
three network characteristics (size, ties, and content) were included in this study.
Following that discussion, the next section will describe the four moderators thought to
impact the relationship between parental status and network characteristics.
To identify the network characteristics that should be included in this study, a
literature search was conducted to identify the network characteristics in previous
literature that varied across parental status. Most notably, the network characteristics that
appear to differ across parental status include network size and network ties. Specifically,
research has demonstrated that the network size of adults after the birth of a child
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decreases (e.g. Smith-Lovin). That is, the network size of adults with children, tends to be
smaller than the network size of adults without children. In addition, to network size,
another network characteristic that appears to differ across parental status is network ties.
Specifically, previous research also supports the notion that after the birth of a child,
contact with family increases and the percentage of kin ties within a network also
increases (e.g. Belsky & Rovine, 1984). Also, Munch et al., (1997) found that kin ties
accounted for 70% of the network ties (i.e. composition) within the ego’s network during
the first 4 years after the birth of a child. This increase in kin ties after the birth of a child
is usually consistent with a parent’s need, for additional emotional support from family
members. Thus, the proportion of kin ties is likely to be higher in the network of a
parent, in comparison to the network of an adult without parental responsibility.
In addition, to the two network characteristics network size and network ties
previous research has demonstrated are likely to differ across parental status, this
dissertation also investigates differences across parental status in network content; where
network content is a variable that has yet to be tested in empirical research. Network
content are the topics of conversation that individuals are discussing with the people they
have identified as part of the network (Bearman & Palgi, 2004). There is no previous
evidence to suggest that network content differs across parental status. Rather, network
content was included as an exploratory variable, as anecdotal evidence suggests that
parents tend to discuss family and children-related topics more than adults without
parental responsibility. Including network content into this dissertation, makes a
contribution to the field by introducing a new variable that is thought to vary across
parental status. Although the relationship between network content and parental status
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has not been studied, previous research does demonstrate that network content is
important. Specifically, network content is an important measure, as this dissertation
assumes that the topics of conversations an individual discusses with members of their
network, is an indicator of the type of information exchanged between the ego and the
members within their network, and it is an indictaor of the matters that individuals find
important. That is, individuals talk about matters that are important to them with
members in their network (Bearman & Parigi, 2004). Social capital theory (Lin, 2001)
tells us that there is a direct link between the type of information shared amongst a
network, and specific career outcomes (e.g. career success or career advancement).
Specifically, individuals that leverage their networks to gain job and career-relevant
information are likely to experience career success.
If the assumption is made that network characteristics will vary across parental
status, the real question to address is, what variables cause the differences between
parental status and the three network characteristics that have been identified in this
dissertation? To address this issue, a conceptual model was developed, see Figure 1. The
conceptual model developed for this dissertation, demonstrates the following
relationships:
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H2 H3
H4
H6
Family Status Differences Ego’s Org Network Characteristics
Size Ties Content
Role Segmentation
Gender Family Involvement
Career Management
Job Involvement
“Network Constraints”
H5
Career Success
Figure 1: Conceptual Model of The Relationship Between Family Status Differences, Network Constraints, Job Involvement, Career Success, and Career Self-Management
First, this model shows the relationship between parental status and network
characteristics (e.g. network size, ties, and content). This relationship suggests that
parental status will causes differences in the network characteristics included in the
model. That is, network size, network ties, and network content will differ for working
adults with parental responsibility in comparison to working adults without parental
responsibility. The model then shows the antecedents that are expected to moderate the
relationship between parental status and network characteristics. These antecedents
include gender, family involvement, role segmentation, and job involvement. The
hypotheses tested in this study assume that each of the antecedents will have a separate
relationship with each of the network characteristics, size, ties, and content. Thus, for
each of the four antecedents, there will be three hypotheses (e.g. 1a, 1b, and 1c) tested.
Further, the hypotheses tested in the study assume that parental status will interact with
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each of the antecedents, and impact the relationship that parental status has on each of the
network characteristics. Next, a discussion of each of the moderators and the expected
interaction with parental status is discussed. Following this discussion, the last section of
this chapter will describe the expected impact that each of the network characteristics of
interest, that is, network size, content, and ties, will have on the career success indicators
and career management perceptions included in the model.
Gender Constraint
Gender has received much attention in the career, social networking, and work-
family literatures (e.g. Brass, 1985; Ibarar, 1997; Burt, 1992). Some of the gender-related
conclusions presented earlier in the literature review, include the following observations.
First, networking behaviors tend to vary by gender. Men usually have larger, broader
networks and have more ties to top executives and people in higher positions (Ragins &
Sundstrom, 1989). Thus, men are more likely to leverage networks to meet specific
career goals (e.g. career mobility) (Ragins &Sundstrom, 1989; Cannings &
Montmarquetter, 1991). In comparison, women tend to benefit less than men from
participation in organizational networks (Brass, 1985; Davidson & Cooper, 1992); that is
networks do not seem to consistently offer women advantages in terms of career mobility
or advancement. Informal networks are often segregated by gender, leaving women at a
disadvantage.
Also, important topics of conversation within network groups, especially those
network groups formed within organizations, are often male-dominated topics (e.g.
sports). This leaves women feeling uncomfortable discussing topics related to family and
other female-gendered topics (Broadbridge, 2004). Women avoid talking about their
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families at work, as they believe topics unrelated to work, will make others perceive they
are not focused on their jobs. Finally, women are more likely to have (relational) ties with
relatives and neighbors, while men are more likely to have (relational ties) with to
coworkers and volunteer groups (Mardsen, 1990).
In this study, gender is expected to interact with parental status, and influence the
three network characteristics (network size, ties, and content) included in this study.
There is evidence in the literature that gender is related to network characteristics, and the
relationship is moderated by parental status. For example, previous work (e.g. Ibara &
Smih-Lovin 1997; Munch, et al., 1997) has demonstrated that women experience more
significant changes in their networks than men after childbirth. Also, Munch et al. (1997)
found having children impacts a woman’s network size negatively, but it does not impact
the network size for men. Many of the differences in network characteristics related to
gender, found in previous studies, were thought to have occurred because of women
being the primary childcare provider. Because gender has been shown in previous
research to impact networking behavior, it is expected that differences in organizational
networks will be more salient for women than men. Therefore, it is reasonable to expect
that the networking characteristics of female parents will differ from the networking
characteristics of male parents. That is, the relationship between family status and ego
involvement is moderated by gender. The following hypotheses will be tested.
Hypothesis 1a: Working adults with parental responsibility will have a smaller network
(i.e. network size) than working adults without parental status; also among working
parents, working mothers will have a smaller network than working fathers (i.e. the
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interaction between gender and parental status will result in a negative relationship with
network size).
Hypothesis 1b: Working adults with parental responsibility will have a higher proportion
of kin ties within their network than working adults without parental responsibility; also
among working parents, working mothers will have a higher proportion of kin ties within
their network than working fathers (i.e. the interaction between gender and parental status
will result in a negative relationship with network ties).
Hypothesis 1c: Working adults with parental responsibility will have a higher proportion
of non-work network content, than working adults without parental responsibility; also
among working parents, working mothers will have a higher proportion of non-work
content than working fathers (i.e. the interaction between gender and parental status will
result in a negative relationship with network ties).
Family Involvement Constraint
Recently, there has been an increase in the number of female workers, an influx of
dual-earner families, single-parent households, and employees managing the care of both
elder members and children. Also, the total numbers of hours worked by employees has
continuously increased over the last twenty years (Saltzstein, et al, 2001). As a result,
both women and men are participating in household responsibilities, including childcare.
Although gender has been investigated in relationship to differences in networking
characteristics (e.g. women have smaller networks than men, women have more ties to
family and men have more ties to coworkers), family involvement is an additional
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variable that is likely to influence an individual’s network characteristics. That is, family
involvement is expected to moderate the relationship between parental status and network
characteristics.
Family involvement is a measure of how important an individual sees family as a
prominent aspect of their lives (Lodahl & Kejner, 1965; Parasuraman, et al., 1996).
Family involvement is an important measure to consider because it is expected that the
more individuals become involved with their families, they will have less time and
energy to allocate to other roles in their lives (i.e. work roles). Parenthood or family
involvement is likely to greatly influence the amount of time an individual, regardless of
gender, devote to their family role, and they are likely to dedicate less time to their work
role (Parasuraman, et al., 1996); and subsequently less time to maintaining work-based
network ties. Also, research supports the notion that characteristics of an individual’s
network change after the birth of a child. For example, Belsky & Rovine (1984) found
that contact with family increases when a child is added to the family; this will likely lead
to an increased frequency of contact with kin ties.
Thus, in consideration of the changing nature of the workforce (e.g. an influx of
dual-earner careers, more women entering the workforce), the conceptual model shows
that gender is likely not to be the only factor that contributes to differences in the
characteristics in networks of working parents. Rather, it is hypothesized that family
involvement will help explain differences in the characteristics in the networks of
working parents in comparison to working adults without parental responsibility. That is,
the conceptual model suggests that the role of childcare provider no longer includes just
women. Instead, the childcare provider role is being shared by both men and women.
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Therefore, this model suggests that regardless of gender, any working parent highly
involved with their family will experiences differences in their network characteristics
(e.g. size, ties, and content of conversations).
Therefore, it is expected that the networking characteristics of individuals
involved with their families will differ from the networking characteristics of individuals
less involved with their families. That is, the relationship between family status and ego
involvement is moderated by family involvement. The following hypotheses will be
tested:
Hypothesis 2a: The parental status-network size relationship will be moderated by family
involvement. That is, family involvement will interact with parental status, such that
parents that are highly involved with their families will have smaller networks compared
to parents that are less involved with their families. (i.e. When family involvement and
parental status interact, there will be a negative relationship with network size).
Hypothesis 2b: The parental status-network ties relationship will be moderated by family
involvement. That is, family involvement will interact with parental status, such that
parents that are highly involved with their families will have a higher proportion of kin
ties in their network compared to parents that are less involved with their families (i.e.
when family involvement and parental status interact, there will be a positive relationship
with network ties).
Hypothesis 2c: The parental status-network content relationship will be moderated by
family involvement. That is, family involvement will interact with parental status, such
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that parents that are highly involved in their families will have a higher proportion of
kin/non-work network content compared to parents that are less involved with their
families (i.e. when family involvement and parental status interact, there will be a
positive relationship with network content).
Role Segmentation Constraint
Another variable expected to influence an individual’s networking characteristics
is role segmentation. Boundary theory suggests that individuals differ in the extent to
which they prefer to integrate or segment their work and family roles (Ashforth et al.,
2000). When work and home are fully integrated, this suggests that there are no
boundaries which separate the contents or meaning between the two, and all time is
allocated to multipurpose between these two very salient roles (Nippert-Eng, 1996). In
comparison when home and work are conceptually viewed as two separate roles, there is
no overlap between theses two roles and each task a person is responsible for completing
(e.g. maintaining the members within their network) is clearly designated to a home or
work category, that is, two categories that are mutually exclusive (Nippert-Eng, 1996).
There are four key assumptions related to boundary theory (Ashforth et al., 2000). The
first assumption is that roles are relatively stable. Second, there is variance among
individuals in terms of the actual number of roles they prefer to enact. Third, individuals
vary in their preference for role segmentation or role integration. Finally, people seek to
minimize the difficulty associated with role transitions. Thus, individuals who like to
segment their work and family roles, enjoy roles that are not only highly differentiated
but are tied to specific settings and permit few interruptions across roles. That is, those
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individuals that want their work and family roles segmented desire clear proximal and
psychological separation between the two roles.
Thus, drawing from role theory, the conceptual model suggests that individuals
vary on the extent to which they want their work and family roles segmented. The model
suggests that those individuals that desire to segment their work and family roles, will be
more likely to report the following about each of their network characteristics, compared
to those who do not desire to segment their work and family roles. First, individuals that
prefer to segment their work and family roles are likely to have smaller network sizes.
Also, individuals that prefer to segment their work and family roles, will have a higher
proportion of kin ties within their network. Lastly, individuals that prefer to segment their
work and family roles will have a higher proportion of non-work/kin-related conversation
topics (i.e. network content).
As suggested by the example provided in Chapter 2, (i.e., the example comparing
Ed and John), two individuals that vary on the degree to which they want their home and
work lives segmented, it’s clear that an individual’s desire to segment their work and
family lives will impact many of their daily practices. For example, in the comparison
made between Ed and John, several characteristics varied between them including their
topics of conversation among people. This illustration provides further evidence to
support the idea that an individual’s desire to segment their work and family roles will
impact various areas of their lives, conversation topics not withstanding. This dissertation
also argues that in addition to conversation topics that both an individual’s network size
and ties will also be impacted by their desire to segment their work and family roles.
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That is, the relationship between family status and ego network will be moderated by role
segmentation. The following hypotheses will be used to test this proposition.
Hypothesis 3a: The parental status-network size relationship will be moderated by role
segmentation. That is, role segmentation will interact with parental status, such that
parents that clearly segment their work and family roles will have smaller networks
compared to parents that do not clearly segment their work and family roles. (i.e. when
role segmentation and parental status interact, there will be a negative relationship with
network size).
Hypothesis 3b: The parental status-network ties relationship will be moderated by role
segmentation. That is, role segmentation will interact with parental status, such that
parents that clearly segment their work and family roles will have a higher proportion of
kin network ties compared to parents that do not clearly segment their work and family
roles. (i.e. when role segmentation and parental status interact, there will be a positive
relationship with network ties).
Hypothesis 3c: The parental status-network content relationship will be moderated by
role segmentation. That is, role segmentation will interact with parental status, such that
parents that clearly segment their work and family roles will have a higher proportion of
kin/non-work related network content compared to parents that do not clearly segment
their work and family roles. (i.e. when role segmentation and parental status interact,
there will be a positive relationship with network content).
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Job Involvement Constraint
As stated previously, the conceptual model assumes first a main effect for
parental status. That is, the model expects that the network characteristics, size, ties, and
content will differ across parental status. Secondly, this model assumes that several
moderating variables will contribute to the differences in network characteristics across
parental status. One of the variables identified by the model that is thought to moderate
the relationship between parental status and network characteristics is job involvement.
Job involvement is defined as a measure of how important a job is to an individual’s life.
That is, a job involved person is one that views work as an important or central part of
their lives (Lodahl & Kejner, 1965). As mentioned previously, job involvement is
generally operationalized as the extent to which one indicates job-related activities or the
job itself to be of central and unique importance in their lives, and a key source of
personal identity (Reeve and Smith, 2001).
Some of the factors identified in the literature that are related to high levels of job
involvement include, opportunities for promotion, financial reward, job security, and
supportive relationships with coworkers and supervisors (Lodahl and Kejner, 1965;
Lambert, 1991). Job involvement has been shown to be unrelated to gender (Lorence,
1987). However, job involvement has been shown to vary by marital status, but the
findings in previous studies have been inconsistent. That is, some studies have suggested
that married people were found to be more involved with their jobs than unmarried
individuals (Haller & Rosenmayr, 1971; Agassi, 1982). In comparison, Singh et al (1981)
examined workers in two separate agencies. Singh et al (1981) found that married
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individuals were less involved with their jobs than unmarried individuals. Therefore the
relationship between marital status and job involvement is still unknown.
It is expected that individuals married or with children are less likely to be
involved in their jobs, due to an increase in their family responsibilities. When
individuals are less involved in their job, network size, ties, and content will each be
impacted. Specifically, individuals that are less involved in their job are less likely to
engage in networking behaviors that would result in developing a larger network. That is,
individuals are not likely to show fewer proactive behavior that would lead to increases
within their network size. Further, individuals that are less involved in their jobs are
likely to have a higher proportion of non-work ties that are included in their network.
Finally, individuals that are less involved with their jobs are less likely to discuss their
jobs with members in their network. Therefore, they are likely to have a larger proportion
of non-work network content (i.e. they will have a smaller frequency of conversation
topics related to work when they speak to members within their network). As a result it
is hypothesized:
Hypothesis 4a: The parental status-network size relationship will be moderated by job
involvement. That is, job involvement will interact with parental status, such that parents
that are not highly involved with their jobs will have smaller networks compared to
parents that are highly involved with their jobs. (i.e. when job involvement and parental
status interact, there will be a negative relationship with network size).
Hypothesis 4b: The parental status-network ties relationship will be moderated by job
involvement. That is, job involvement will interact with parental status, such that parents
that are not highly involved with their jobs will have a higher proportion of kin ties
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within their network compared to parents that are highly involved with their jobs. (i.e.
when job involvement and parental status interact, there will be a negative relationship
with network ties).
Hypothesis 4c: The parental status-network content relationship will be moderated by job
involvement. That is, job involvement will interact with parental status, such that parents
that are not highly involved with their jobs will have a higher proportion of kin/non-work
network content compared to parents that are highly involved with their jobs. (i.e. when
job involvement and parental status interact, there will be a negative relationship with
network content).
In summary, this study suggests that three specific network characteristics will
differ between working adults without children and working adults with children. The
three network characteristics that will differ include size, type of ties, and topics of
conversations within networks. This study argues that four moderating variables will
contribute to the differences in the networks of working adults with children and working
adults without children. The moderating variables that contribute to the differences in the
networking characteristics of working adults with children and working adults without
children include gender, family involvement, role segmentation and job involvement.
Overall, the conceptual model the hypotheses were based on, suggests that in comparison
to working adults without children, working parents will have a smaller network size,
their networks will have a higher proportion of kin ties, and the content of the
conversations within their networks will include topics related to family matters (e.g.
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childcare). As a result of the differences in the network characteristics of working
parents (i.e. size, ties, and content), this study suggests that working adults with children
and working adults without children will perceive the following career outcomes
differently; those career outcomes include perceptions of career success, career
satisfaction, and career self-management.
Perceptions of Career Success
Career success is defined as the accomplishment of desirable work-related
outcomes at any point in an individual’s work experiences over time (Forrett
&Doughterty, 2004). In previous studies, objective career success, that is, success that is
directly observable, measurable, and verifiable by a third party, is the measure most often
used to operationalize objective career success. Typical measures of objective career
success include salary, salary growth, and promotions. In comparison, subjective career
success lends itself to more of an internal satisfaction that an individual experiences
(Heslin, 2003). Specifically, subjective career success is defined by an individual’s
reaction to their career experiences and it is usually operationalized by a career
satisfaction or job satisfaction measure. Examples of measures used to capture subjective
career success include global career success or satisfaction with pay and promotions.
This study is interested in understanding the relationship between the network
characteristics (network size, ties, and content of conversation), and career success. This
is consistent with the relational approach of studying careers which is concerned with
how employees develop and leverage interpersonal networks, and to what extent
individuals uses the networks to assist them in obtaining information about new and job
and career opportunities. Specific to this study, career success is one of the two outcome
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variables identified in the conceptual model. Previous research has demonstrated that
there is a relationship between specific network characteristics and career success. Seibert
et al. (2001) found weak ties and structural holes led to promotions and career
satisfaction. Other studies have found similar relationships between career success and
network ties (e.g. Granovetter, 1973; Burt, 1992). Consistent with previous research, this
study suggests that individuals with multiple work ties should have more career success;
resulting from the person having a greater number of resources from whom they can gain
information to help make decisions or job changes that will result in career success In
addition to network size, this study predicts that network ties and network content will
also have a relationship with career success. For example, if one considers the impact of
network content on career success, it is likely individuals whom speak with the members
of their network about career and job related topics will experience more career success.
However, if an individual speaks with members of their network about non-work or non-
career –oriented topics, these types of conversations are not likely to contribute to an
individual’s success.
Similarly, this model suggests that it is important to consider both what you’re
talking about with members in your network, and who you’re talking to within your
network. Stated differently, this model suggests that individuals that spend a lot of time
talking to non-work/kin ties, may have differences in the level of career success that is
achieved, when compared to an individual that spends time talking to members within
their network that are work ties, and when an individual is also talking about work-
oriented topics. This model suggests that parents are more likely than working adults
without parental responsibility to talk have a larger proportion of kin ties within their
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network. This model also suggests that working parents are more likely to talk about
non-work topics with the members within their network, and working parents are likely
to have smaller networks. Therefore, the following network characteristics, size, ties, and
content will have relationship with career success (objective and subjective). It is
hypothesized that:
Hypothesis 5a: Network size will negatively influence career success, such that as
network size decreases, the objective indicators of career success (i.e. salary, salary
growth, and promotions) will also decrease. Further, there will also be a negative
relationship between network size and career satisfaction; where if network size
decreases, career satisfaction will also decrease.
Hypothesis 5b: There will be a negative relationship between network ties and objective
career success. That is, as the proportion of kin ties increases, objective indicators of
career success (i.e. salary, salary growth, and promotions) will decrease. Also, there will
be a negative relationship between network ties and subjective indicators of career
success. That is, as the proportion of kin ties increases, subjective indicators of career
success (i.e. individual career satisfaction and peer-related career satisfaction) will
decrease.
Hypothesis 5c: There will be a negative relationship between network content and
objective career success. That is, as the proportion of non-work content (i.e. types of
conversations) increases, objective indicators of career success (i.e. salary, salary growth,
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and promotions) will decrease. Also, there will be a negative relationship between
network content and subjective indicators of career success. That is, as the proportion of
non-work content (i.e. types of conversations) increases, subjective indicators of career
success (i.e. individual career satisfaction and peer-related career satisfaction) will
decrease.
Perceptions of Career Self-Management
As shown in the conceptual model, a relationship is expected between the
network characteristics, and career self-management techniques (i.e. career planning,
career tactics, and career mobility preparedness). First, career self-management is a
process where employees gather information to help them make key decisions about their
careers; employees usually engage in career self-management to achieve one of two
behaviors, gaining developmental feedback or learning about career mobility
opportunities (Kossek et al., 1998). Developmental feedback involves individuals seeking
information about their performance and their career developmental needs. In order to
gather appropriate career-related information, as individual must first plan the type of
information that will be of help to their own career development (e.g. what are their
current career goals, what are their strengths and what kinds of jobs would leverage those
strengths), this aspect of career management is called career planning (Hall, 1990). In
addition to planning the type of information that an individual should gather, an
individual must next decide which specific tactics they will use to gather career-relevant
information and seek developmental feedback. This stage of career management is called
career tactics (Hall, 1990). While one career tactic that is commonly used is networking,
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there are many other tactics an individual can use to gather career-related information
and/or seek developmental feedback including making themselves visible to people at
higher levels within their organization.
The second career self-management behavior, learning about career mobility
opportunities, is achieved through informal networking with members inside and outside
of the organization. Specifically, job mobility preparedness is “the degree to which an
individual prepares his or herself to be ready to act on internal and external career
opportunities” (Kossek et al., 1998, pp 939). Therefore, one way of gathering job related
information is through participation in networks, that is, the development of relationships
that will help individuals learn new career-related information; the possession of new
information helps individuals prepare to move either laterally or horizontally and assists
them in moving between or within organizations (Kossek, et al, 1998). As a result, it
important to evaluate, perceptually, the extent to which individuals feel they have control
over their career self-management. Also, it is important to understand how their network
characteristics (i.e. network size, ties, and content) are related to career management
perceptions.
This study suggests that the there is a relationship between the three network
characteristics proposed in the hypothesized model, and perceptions of career self-
management. That is, the model suggest that an individual with a larger network, that is a
larger network work with a higher proportion of work ties, will have better perceptions of
their ability to manage their career. In addition to network size, this model also expects a
relationship to exist between network content and network kin, and career management
perceptions. Specifically, this model suggests that individuals with high proportion of kin
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ties in their network will be less likely to experience career satisfaction that an individual
with a smaller portion of kin ties within their network. This study expects that an
individual with a large percentage of kin ties in their network, are likely to engage in
activities that are non-work related, and spend a lot of time with their kin ties. As a result,
they will have less time to allocate to managing their careers, thereby reducing their
perceptions of their ability to manage their careers. Similarly, if an individual is
frequently discussing non-work topics amongst members of their networks, they are less
likely to gain career- or job-relevant information, which would be helpful in managing
their careers. Therefore, the following network characteristics, size, ties, and content
will have relationship with career management perceptions. It is hypothesized:
Hypothesis 6a: There will be a negative relationship between network size and career
management perceptions. That is, as network size decreases, an individual’s perceptions
of their ability to manage their career will also increase.
Hypothesis 6b: There will be a negative relationship between network content and career
management perceptions. That is, as the proportion of non-work/kin ties increases,
perceptions of career management will decrease.
Hypothesis 6c: There will be a negative relationship between network content and career
management perceptions. That is, as the proportion of non-work content (i.e. topics of
conversation) increases, perceptions of career management will decrease.
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CHAPTER 4
METHOD
This chapter describes the methods that were used to tests the hypotheses
discussed in Chapter 3. As a result, this chapter begins with a description of the focus
group study that was conducted prior to the field study. The purpose of the focus group
was to collect data that would help refine and develop the measures used in the field
study. The field study involved administering two separate web-based surveys to two
distinct groups, working adults with parental responsibility and working adults without
parental responsibility. The measures collected during the field study included
demographic variables (e.g. gender, parental status), attitudinal data related to feelings of
segmentation between work and family roles, job involvement, and family involvement.
Finally, measures of career success (e.g. salary, promotions, career success indicators)
and measures of career management perceptions (e.g. career mobility preparedness) were
collected during the field study. This chapter discusses how the variables included in the
conceptualized model were operationalized. Also, the chapter provides a brief description
of the pilot study that was conducted to refine some of the measures that would be
collected during the field survey. Finally, the chapter concludes by briefly describing the
analyses that will be used to test the study hypotheses.
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Focus Group Study
A focus group study, the first step of this dissertation, was conducted in the
summer of 2005. The purpose of the focus group was to collect data that would help
further define the final measures used in the field study. The specific measures collected
during the focus group study included network size, network ties, and network topics of
conversation. In addition, the individuals who participated in the focus groups were asked
to define career success. The responses to this question were used to verify that both
subjective and objective measures of career success should be included in the study. That
is, the notes from the focus group study were reviewed, and it was determined that when
individuals were asked to define career success, the criteria they described related to both
objective (e.g. salary, opportunities for promotion) and subjective indicators (e.g. job
satisfaction, flexibility to work from home) of career success. Next, a brief overview of
the focus group study is provided.
Focus group interviews are defined as a research technique in which data is
collected through group interaction on a topic determined by the researcher (Morgan,
1996). The aim of focus group research is to draw conclusions about the participants’
views, ideas or experiences (Hydon & Bulow, 2003). There are three criteria that
describe focus group interviews including (1) they have to be conducted in formal
settings, (2) the interviews use directive interviewing, and (3) the interviews use
structured question formats. Focus group interviews are often paired with other methods
including surveys. Further, studies that include both focus group data and surveys, are
one of the primary ways to combine qualitative and quantitative methods (Morgan,
1996). A final advantage of using focus group interviews is, the amount of interaction
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(and ideas shared) between the focus group participants. Finally, focus group interviews
are considered both generalizable and valid measures. The generalizability of the content
that is produced during focus interviews can be assessed through subsequent research
designed to test the implications learned from the focus groups, when tested using a
quantitative method. For this study, many of the components included in the focus group
interviews (e.g. number of ties, types of ties) will also be assessed through the field study.
To assess the importance of networking, specifically network characteristics and
the role they play in career mobility, the focus group interviews were conducted at a
Midwestern university. The focus of these interviews was to (1) gather information to
help identify the correct items that should be included in survey measures in future
empirical studies (e.g. measurement of the perceived usefulness of networks and its
relationship to career mobility) , (2) develop further the research hypotheses that will be
utilized in a future empirical study, and (3) collect information of the network-career-
management relationship based on gender and parental status. Focus group participanjts
were asked two types of questions Participants completed a survey at the beginning of
the focus group that was used to assess the three network measures including network
size, network ties, and network topics of conversation. After completing the survey
participants spent the remaining time in the focus group answering structured, open-
ended questions. These questions were used to determine participants opinions of
organizational networks. Additionally, several questions were asked about career success
and career management perceptions.
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Sample
The Work-Life Initiative Office, a division of The Office of Human Resources,
and the Office of Human Resources were responsible for providing the names of the
individuals that were invited by the researcher to participate in the focus groups.
Specifically, the individuals identified by the Work-Life Initiative Office were the
working parents that were invited to participate in the study. In comparison, the
individuals identified by the Office of Human Resources were the individuals that were
working adults without parental responsibilities that were invited to participate in the
study. A brief description of the sampling procedure for each of the two groups, which is
the working adults without parental responsibility and working adults with parental
responsibility, is provided.
The individuals identified by the Work-Life Initiatives office were those working
adults who had a child, and had subsequently taken a parental leave within the last year.
In total, just over 300 individuals were identified through the Work-Life Initiative Office,
and invited to participate in the focus group interviews. E-mails were sent to the 300
individuals who’d participate in the parental leave program, and the individuals were
invited to participate in the focus group studies.
The individuals identified by the Office of Human Resources were working adults
who were employed by the university on a full-time basis. A random sample of 500
individuals employed by the University, were sent an e-mail inviting them to participate
in the focus groups and share their experiences of networking and their careers. That is,
the e-mails sent to the 500 individuals were drawn from a random sample of names
supplied by the Office of Human Resources at the university.
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Four subsets of focus groups were created, and these focus groups were stratified
by gender and parental status. Those four groups included working fathers, working
mothers, working women without parental responsibilities and working men without
parental responsibilities. The stratification of focus group participants is consistent with
methods used in many fields (e.g. marketing and sociology) when the topic of interest
(e.g. gender and parental status and networks) is expected to vary by particular categories
(Morgan, 1996). Of note, focus group research studies are stratified across four to six
focus groups (Morgan, 1996).
Consistent with previous research, guidelines were followed such that twelve
participants were the maximum number allowed in each focus groups Previous research
suggests that focus groups should consist of 8 to 12 (homogenous) members (Fern,
1982). The criteria for inclusion in this study were the following: (a) on the job no less
than 30 working hours a week for a minimum of 9 months of the year (full-time
employee), and, (b) if parent, child less than the age of 6 years old.
In all, there were 10 focus group sessions offered. This included two sessions
each for working women without parental responsibility and working men without
parental responsibility. A total of 24 working women and men without parental
responsibility participated in the focus groups, conducted for individuals without parental
responsibility. Also, there were 40 working women and men with parental responsibility
participated in the focus groups. Total sample size for all participants in the 10 focus
group sessions was N=64.
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Procedure
The participants were contacted via e-mail and asked to participate in the focus
groups. At that time the participants were given a description of the purpose of the focus
groups and their role in the focus groups should they decide to participate. The
participants were also made aware that all focus groups were scheduled for 90 minutes.
After agreeing to participate, on a voluntary basis, the focus group participants were
asked to sign-up for a focus group session that best suited their schedule (there were
asked to choose from 2 or 3 time periods).
Once the participants arrived at the focus group sessions, the participants were
given an opportunity to read and sign the consent form, and the purpose and procedure of
the focus groups was reviewed with the participants. Next, the participants were asked to
complete a survey which included several measures related to network ties, network
content of conversation, and network size. Specifically the participants were asked to
name (and write down) up to the 10 most important people in their professional lives.
After writing down the initials (the initials will ensure confidentiality during this
process), the individuals were also asked to indicate their frequency of contact, and the
relationship or tie they had with the individuals named. Of note, the name generator
question has been used in previous career literature (e.g. Gersick, et al. 2000) and this
method is commonly used in the networking literature (e.g. Burt, 1992). Typically, the
empirical studies in the networking literature use the GSS survey. The GSS survey is one
in which individuals are asked to name the important people within their networks.
Therefore, the methods used in this phase of this focus group are consistent with those
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used in previous literature. After identifying up to the 10 most important people in their
professional lives, the third phase of the focus group study was initiated. During this
stage, the focus group interviews were still completing the surveys. Specifically, the
individuals were asked to look at the ‘name generator’ sheet they just completed on the
survey, and they were asked to identify the four most important topics they discuss with
these individuals they’d identified as part of their network. The participants were then
asked to name the topics they discuss with these individuals and provide an example of
each topic they discuss. The purpose of this step was to generate a list of themes the
individuals discuss with the people they have identified, and to understand the frequency
of the topics discussed.
Of note, the focus group participants were asked to identify four topics of
conversation in an attempt to simply the theme-oriented coding system that was used to
organize the topics of conversation. Examples some of the themes that emerged from the
focus group participant’s surveys included: health, family, work, personal finances,
sports, art/music/literature, politics. Examples of job-related topics mentioned on the
respondent’s surveys included: organizational politics, managers, employee
retention/loyalty (given the local job market). The analysis of the theme content and
frequency was conducted after all focus group interviews have been conducted.
The fourth and last phase of the focus group session began after the participants
completed the sections of the survey described previously. The purpose of this phase was
to allow participants to share their experiences with networks and career practices.
During the fourth phase of the focus group sessions, respondents were asked to respond
to a series of open-ended questions. Of note, the open-ended questions and the entire
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interview guide can be found in Appendix A of this dissertation. Of note, questions 2-3
(Topic: Networks) were asked in the focus groups that included parents. The remainder
of the questions (Topic Networks: Q1and Topics: Careers Q 4-7) were asked of all focus
group participants. In total, the focus group interviews lasted 90 minutes. Lunch was
provided as an incentive for individuals to participate.
Finally, there were several outcomes analyzed from the focus group interviews.
First, the average number of ties was assessed by gender, parental status, and overall
group. The average number of ties is the indicator of network size, and is derived by
counting the number of ties each participant selects. In order to calculate average network
size, focus group participants were randomly selected where one-half of the participants
were asked to name up to 100 names of people that were important to them and the other
one-half were asked to name up to 10 names of people that were important to them.
Therefore an average and range (min and max) number of ties were developed. This
resulted in the respondents in the field survey being asked to identify up to 20 names of
people within their network. That is, 20 people were the average number of network ties
identified during the focus group study.
In addition, to size, this study also generated the type of ties that are included in
an individual’s network. Specifically, the participants were asked to describe the
relationship they had with the people whom they identified as part of their network (e.g.
co-worker). From those relationships identified, 10 categories of relationship types were
created and used in the field study. Also, frequency with each tie was assessed amongst
the focus group participants.
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Finally, this study generated important topics of conversation discussed within
networks. Specifically, the interviews were transcribed; the written text from the
interviews was reviewed to identify common themes from the questions included in the
interview. The interview transcriptions were coded by two individuals to ensure
interrater reliability. The themes derived from the transcript were exploratory and
therefore did not need to be identified a priori. Nine categories of conversations topics
were identified and used in the field study.
Pilot Testing
Prior to the launch of Surveys 1 and 2, a pilot study was conducted on the
test instruments. Approximately fifteen individuals were contacted and asked to
participate in the pilot testing. Those fifteen individuals included a group which consisted
of university professors, graduate students, and working professionals. The pilot tests
were conducted to achieve two main purposes. First, the pilot tests were conducted to
ensure the readability of the survey instruments. That is, the individuals that participated
in the pilot testing were given specific instructions to read the Welcome Page, Consent
Form, and the Test Instrument (that includes both the survey and the directions for each
section of the survey) and provide feedback regarding the clarity and readability of all
sections mentioned previously. Secondly, the respondents were asked, to take the entire
survey, that is reading all directions and answering all questions, and provide feedback
about the total amount of time it took to complete the survey.
While reviewing the survey, pilot study participants were asked to comment on
the face validity of the survey measures, specifically the scales used to measure family
involvement, job involvement, role segmentation, career management perceptions, as
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well as the career success measures. Acceptable face validity is apparent when
individuals review the operationalization of a measure, and determines the measure is a
good translation of the construct that is being measured (Schmidt & Hunter, 1998). To
assess the face validity of an instrument, researchers will look at the specific items used
to measure the construct, and determine if the items appear to measure the construct of
interest.
Face validity differs from content validity, because when content validity is
assessed, the items used to measure a construct are assessed against a detailed definition
of the construct (Schmidt & Hunter, 1998). For example, if an individual was interested
in determining the content validity of an instrument written to measure career success, an
individual would need to compare the items written in the instrument, to a list of criteria
that are used to define career success. An individual would then look at the list of criteria
that define career success, and determine which items tap those specific criteria. Any
items in the measure that did not tap the criteria listed would be eliminated from the
career success measure.
In comparison, if an individual was interested in determining the face validity of a
career success instrument, they would simply look at each of the items and determine if
they items seemed to reasonably measure career success. Face validity is typically the
least reliable way to assess the validity of the measure (as there is no criteria which is
compared when the measure of interest is investigated). However, one way to enhance
the face validity of a measure is to have experts who are familiar with the construct the
measures is trying to assess, review the measure.
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That is, to ensure the accuracy of the face validity assessment of a measure, researchers
would use subject-matter experts to review their measure. For example, to conduct an
accurate assessment of face validity of a career success measure, you would want an
individual that specializes in career research or an individual that works as a career
counselor to verify the face validity of your instrument.
The feedback provided from the respondents was reviewed by the researcher and
changes were made (e.g. clarifying directions) to the test instruments, if deemed
appropriate. Of note, while minor changes were made to the survey instruments and/or
welcome page or consent forms, there were no changes made to the actual questions in
terms of removing or adding additional items. Rather any changes made were specific to
the questions wording or directions.
Finally, the average time to complete the survey was measured. The average tine
to complete the first survey was 20 -30 minutes .and 10-15 minutes to complete survey 2.
This information was used in the field study. The average time to complete the survey
was communicated to field study participants on Welcome Page, which was the first page
of survey that participants saw after they selected the link to the survey (see Appendix D:
Field Survey Wave 1 Welcome Page, Consent Form, and Wave 1 Survey Questions).
Finally, since this study was a web-based survey, the pilot testing allowed the researcher
to ensure that all data was being stored properly prior to the survey being launched in the
field.
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Field Study
Sample
The sample used for this dissertation included a pool of respondents who were
either working adults, or working adults with children. The primary data collection
source for this survey was a large, multinational, manufacturing organization whose
headquarters is located in Michigan. This organization engages in the development,
manufacture, distribution, and sale of various automotive products, primarily passenger
cars, light trucks, and commercial vehicles worldwide.
The organization agreed to participate in the study after being contacted by the
author. The organization agreed to participate in the study because they were interested in
learning about their employees’ perceptions of usefulness of the professional
organizational networks that the organization had recently created. The newly created
organizational-based networking groups were reportedly developed to minimize
unwanted turnover within the organization. Therefore, the organizational-based network
groups were developed by the organization as a way to reach out to specific groups
within the organization. For example, organizational-based networking groups were
created for individuals with physical disabilities, working mothers, individuals from
underrepresented minority groups (e.g. African-American and Latinos), and other groups
targeted by the organization’s diversity program.
Specifically, the organization was interested in understanding if the members of
their organization communicated with members outside of their network group. The
network groups within this organization were created based on various demographic
categories (e.g. African-American Professional Group). As a result, the organization
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provided the researcher with the names of 1000 employees who worked throughout the
organization. The names of these 1000 employees were randomly selected from a larger
list of employees that were active in one of the organization-based networking groups.
That is, this list of names, while it included individuals from across the organization, was
restricted to individuals that were in some way active in one of the organizational-based
networking groups.
Although the organization created the employee networking groups for groups
within the organization targeted by their diversity program, the organization was
interested in learning to what extent individuals within the organization networked with
members outside of their interest group. Specifically the organization was interested in
learning if employees had cross-racial networking ties. Thus, in addition to the data that
was of interest for this dissertation, additional data was collected on the surveys related to
race and ethnic groups. Although additional questions were added, this did not increase
the amount of time the respondents had to spend completing the survey, for information
was gathered related to the race/ethnicity of their ties was not gathered on all ties the
respondent listed in their network.
It should be noted that individuals that did not fall into one of the organization’s
diversity groups (which was were not exclusive to racial and gender) did not have the
opportunity to participate in the study. That is, the organization only agreed to provide
the names of individuals whom both represented some diversity group within the
organization for whom an organizational-based network group had been created. This
restriction in sample size, may have resulted in the small representation of male
respondents. Also, it should be noted, that this sampling restriction may have produced
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another bias among the respondents that agreed to participate in the study. Specifically,
because all of the respondents that participated in the study were in someway connected
to an organizational-based networking group, these individuals may see the advantage of
participating in organizational networks for the purpose of career enhancement and for
social support. Although there were no specific questions which asked the respondents to
provide feedback on relationship between organizational networks and career outcomes,
the individuals that participated in the networks may have felt some social desirability to
respond favorably to the survey questions related to either their careers or their network
characteristics. Also, during the time this data was collected, the organization made four
layoff announcements. The layoff announcements may have created feelings of
uncertainty among the survey respondents, which again may have introduced a biased in
their responses. Evidence of any kind of response bias would have produced limited
variability (i.e. a small standard deviation) in the surveys responses.
Per the requirements set forth by the study’s researcher, the participants in this
sample had to meet specific criteria to be included in this sample. The criteria for
inclusion in this study was the following: (a) on the job no less than 30 working hours a
week for a minimum of 9 months of the year (full-time employee), and (b) if parent, child
less than the age of six years old. As it turns out, there were many individuals who had
children that were not under the age of six. It was later decided that their responses (i.e.
survey respondents with children over the age of six) would remain in the study. This
decision was made based on the following criteria. Although previous research suggested
parents may experience the most significant interruptions in specific network
characteristics (e.g. network size) when their children are under the age of six,
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there was no indication in the research that parents with children over the age of
six would not also experience significant interruptions in their network characteristics.
That is, parents with children over the age of six, also experience interruptions in their
networks. However, previous research does suggest that the most significant interruption
is most likely to occur for parents with children under the age of six (Smith-Lovin &
McPherson, 1993). As result, the decision was made to keep all working adults with
parental responsibility in the sample as long as they met the requirements of a full-time
employee.
The full-time employment measure (where individuals had to be on the job no
less than 30 working hours/per week, and work a minimum of 9 months per/year) used
in this study is consistent with the full-time employment measure used in previous studies
(e.g. Cron, 2001). Employees who are self-employed or working for a family business
were excluded from the study; individuals working in these conditions are “likely to
exhibit different patterns of career-related behaviors” (Forret & Dougherty, 2004, pp
424). This exclusion is consistent with previous studies (e.g. Forret & Dougherty, 2004).
Procedure
Consistent with the literature, surveys were used to collect data (See Appendices
D and E). Surveys and questionnaires soliciting self-reports are the predominant research
used when networking patterns are investigated (Marsden, 1990). The data collected was
self-reported data. Self-report data have two key advantages. First, self-report data is the
key way to obtain information related to an individual’s inner state (e.g. mood), beliefs
(e.g. beliefs related to careers), interpretations (e.g. is my behavior perceived as friendly
or unfriendly), cognitive processes (e.g. such as how was a decision reached), and
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behaviors that can not be directly observed (Whitley, 2002). The second advantage of
self-report data is it is easy to collect, that is, questionnaires can be administered to
multiple individuals at one time (Whitley, 2002). Self-reports are also useful for
gathering information related to a psychological state of a respondent, (e.g. job attitudes
or motivation) (Podsakoff & Organ, 1986).
A common issues that arises with self-reported data is common method variance,
that is, variance that is attributable to the measurement method (e.g. survey) rather than
the constructs being measured (e.g. networking behaviors) (Podsakoff, Mackenzine, Lee
& Podsakoff, 2003). Common method bias is an issue in research because it is one of the
main sources of measurement error. Measurement error suggests that an individual may
make a mistake about the conclusions they draw about the relationships between
measures, that is, a researcher can draw misleading conclusions. To study the effect of
common method variance, some researchers have looked at the strength of relationships
between variables and control for common method variance (CMV). CMV can inflate or
deflate observed relationships between constructs (Podsakoff, et al., 2003). CMV is a
problem because it can lead to the artificial co-variance between self-reported measures.
Sources of CMV include: social desirability, item social desirability, item
complexity (e.g. double-barreled questions). It appears that one of the key ways to control
the possibly of CMV is to design the study well. This includes separating predictor and
criterion questions, making sure the wording of questions is clear (and there is no
presence of double-barreled questions), using existing measures- as these measures are
probably written clearly, including negatively worded or reverse-coded items, and using a
common rater. Ways to avoid CMV include: (1) Obtaining measures of the predictor and
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criterion variables from different sources, (2) separating the measurement of the predictor
and criterion variables –this was completed in the dissertation, hence the 2-wave survey
(you can do this by introducing a time lag,), and using different response formats (e.g.
Likert scales, open-ended questions).
One recommendation for reducing common method variance is to
methodologically separate the measure of the predictor and criterion variables
(Podsakoff, et al., 2003). Benefits of separating predictor and criterion variables include
reducing the respondent’s ability and/or motivation to use previous answers to fill in
gaps, and prior responses are less salient available or relevant (Podsakoff, et al., 2003).
However, there are also challenges in trying to separating the predictor and criterion
variables. For example, if the time lag between measuring the predictor and criterion
variables is too long, this could mask a relationship that actually exists (Podsakoff, et al.,
2003). Also, if the time lag is too long, you may have an attrition problem. Therefore the
time lag between the measurement of the predictor and criterion has to be carefully
accounted for when designing the project. Other ways of avoiding CMV include
protecting the anonymity of the respondents. Also, you can ensure there are no right and
wrong answers as this is a reflection of question wording. Other suggestions for
improving CMV include being mindful of the manner in which questions are ordered and
to carefully construct scale items.
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Consistent with the suggestion on how to reduce CMV, the predictor and criterion
variables were collected separately. That is, this dissertation used a 2-wave survey. In the
fist wave of the survey, all of the predictor variables were collected, and in the second
wave of the survey all of the criteria variables were collected. The first and second
waves of the surveys were collected approximately two months apart from each other.
The surveys used in this dissertation were web-based. The decision was made for
use web-based surveys for several reasons. The advantages of web-based surveys
include: (1) control over physical appearance, (2) web-base surveys can include radio
buttons and drop down lists that permit only one choice for the response, (3) check boxes
allow multiple answers, (4) data from web-based surveys can be easily imported into data
analysis programs, (5) data collection can be monitored (e.g. response rate), (6) it
eliminates costs associated with paper, postage, mail out, and data entry costs, (7)
reminder and follow-up for non-respondents is relatively easy, and (8) they can be
designed to allow for more dynamic interaction with respondents (Archer, 2005). Other
advantages of web-based surveys include cost efficiency (i.e. web-based surveys costs
between 5 and 20% of e-mail surveys), survey return speed (e-mail and web-based
surveys are usually completed within 8 days, while mail surveys are usually completed
within 12 days), tracking (i.e. researchers can do sample adjustments and timely follow-
up), better and more accurate responses), and web-based surveys can be easily down-
loaded (Kalaian & Kasim, 2005).
The disadvantages of web-based surveys include: (1) not everyone has access to
the internet, so this method will not work with all populations, (2) even if everyone was
connected, not all individuals are equally computer literate, (3) screen configurations may
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appear significantly different from one respondent to another dependent on settings of
individual computers, (4) decision not to respond may be made more quickly, often
people are flooded with multiple e-mails, and (7) invitations to web-based surveys may
be detected as junk mail (Archer, 2005).
As a result, specific recommendations have been suggested when researchers are
designing web-based surveys. These recommendations include: (1) utilize a multiple
contact strategy much like the one that is used for regular mail surveys, (2) personalize all
e-mail contacts, (3) keeping the invitation brief, (4) beginning with an interesting, but
simple to answer questions, (4) using skip logic when possible, (5) making it possible for
each question to be visible on the screen at one time, (6) shortening the timing between
notice and reminders – e,g, reminder should be sent within 10-14 days of initial e-mail
inviting individuals to participate, (7) keeping the questionnaires short, (8) keep the
questionnaires short, (9) making limited used of open-ended questions, and (10) pilot
testing the survey before launching. Finally, research suggests that sending 2 reminders
(during an eleven day period) produces hat maximum number of responses.
Kalaian and Kasim (2005) suggest the best way to deal with non-response rates
on web-based surveys is to avoid them. In order to avoid them, they suggest that
researchers should be mindful of survey length, respondent contacts, compensation,
ensuring confidentiality, and questionnaire design (Kalaian & Kasim, 2005). All efforts
were made during the pilot testing phase to ensure that the survey length was as short as
possible and the survey responses were kept confidential (the responses were stored on a
secured server that was only accessible to researchers.
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In inviting 1000 random selected employees to participate, an initial e-mail was
sent by the researcher which described the purpose of the project and the eligibility
requirements individuals had to meet in order to participate in the project. The researcher
was responsible for screening and performing applicant selection when the individuals
respond to the e-mail. The e-mail to the respondents stated "please respond if you meet
the following qualifications (1) working at least 30 hrs per week and (2) no parental
responsibility or (3) parental responsibility of children under the age of 6. In addition,
these same questions were asked on the survey. Although it was later decided that an
individual with a child would be allowed to participate in the project, that is people with
children over the age of six were not excluded from the study, individuals were still
disqualified if they did not work at least 30 hours per week.
The respondents were able to gain access to each survey via the link or URL
address that was included in the e-mails inviting the respondents to participate during
both the 1st and 2nd waves of the survey. Specifically, during the first wave of the survey,
the respondents were initially contacted via e-mail and they were told about the purpose
of the study, and the criteria that had to be met in order to be eligible to participate in the
study. They respondents were told to participate in the survey by selecting the web-link
or URL addresses that was embedded in the e-mail, if they were interested in voluntarily
participating in the survey and if they met the eligibility requirements to participate in the
study (See Appendix B, Wave 1 E-mail: Invitation to Participate in The Study).
For the second wave of the survey, the respondents were sent an e-mail with the
link/URL address also embedded within the survey. The participants were reminded in
the e-mail that they were only allowed to complete the second wave of the survey if
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they’d completed they’d completed the first wave of the survey. The respondents were
told that their unique password they would need to complete the second wave of the
survey had been provided to them when they completed the first wave of the survey.
Specifically at the end of the first wave of the survey the respondents were instructed to
write down their unique password, which was provided on the last screen of the survey.
In addition, the respondents received an e-mail which made them aware that the answers
to the first wave of the survey had been recorded in the database and their unique
password was also provide in the e-mail. However, in the e-mail that was sent inviting
the respondents to participate in the 2nd wave of the survey, they were reminded that they
could contact the researcher for their unique password if they’d misplaced the e-mail
they’d received after completing the first wave of the survey, or they did not write down
their unique password at the close of the first wave of the survey. (See Appendix C,
Wave 2 E-mail: Invitation to Participate in The Study).
The first phase of the survey was used to collect the demographic variables, the
control variables (e.g. organizational tenure, job tenure, number of hours worked per
week, employment status, total number of years worked, and educational level), and all
independent variables of interest. The independent variables of interest included all
networking measures (e.g. network size, network ties, and network topics of
conversations), a family involvement measure, a job involvement measure, and a role
segmentation measure. The networking measures were being collected to potentially
identify some of the differences that may exist between working adults without children
and working adults with children specific to their network characteristics. Those
differences in parental status were expected in the following areas: family involvement,
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job involvement, and role segmentation.
The 2nd phase of the field survey was distributed via e-mail approximately 6-8
weeks after the first phase of the survey was completed. The measures included in the
2nd phase of the survey are directly related to the key outcome measures of the study.
Those outcome measures include subjective/objective measures of career success and
career management perceptions. Career success can be defined as “the accomplishment
of desirable work-related outcomes at any one point in a person’s work experiences over
time”. A common distinction made in the literature is between objective and subjective
career success. Objective career success is success that is directly observable,
measurable, and verifiable by a third party. Examples of career success may include pay
increases or promotion to a higher position (e.g. from bank teller to loan officer within a
commercial banking setting). Subjective career success alludes more to an internal
satisfaction an individual experiences. Specifically, subjective career success is defined
by an individuals reactions (across any dimensions that are important to that individual)
to their career experiences and it is usually operationalized by a career satisfaction or a
job satisfaction measure. For the purposes of this survey both subjective and objective
measures of career success were collected. The perceptions of career self-management
measure were used to assess to extent to which individuals perceived they were able to
manage their own careers.
Survey Response Rate
The section describes the response patterns of the subjects that participated in this
study. The specific response rates that will be included in this discussion are the attrition
rate between Wave 1 and Wave 2 of the field surveys, and any reported response bias
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between Wave 1 and Wave 2 of the survey. To determine if any response biases existed
between Wave 1 and Wave 2 response rates, a series of t-tests were conducted on the
demographic characteristics of the respondents whom completed only Wave 1 of the
survey in comparison to those respondents who completed both waves of the survey.
As mentioned previously, this survey was conducted in two separate waves. 1000
employees were contacted over e-mail and invited to participate in the study. The
participants were sent an initial e-mail and two reminder e-mails during the study. The
use of three total e-mails sent to those asked to participate is consistent with existing
research which suggests that the highest response rate on for survey-based data collection
efforts occurs when one initial e-mail is sent and two reminder e-mails are sent (Archer,
2005). Of the total 1000 participants invited to participate in the study, 441 respondents
participated in the first wave of the survey, which indicates a participation rate of 44.1%
for the first wave of the survey. Consistent with the methodologies explained previously,
only those individuals that participated in the first wave of the survey were invited to
participate in the second and final wave of the survey. After completing the first wave of
the survey, the respondents were asked to voluntarily submit their e-mail addresses,
which would allow the researcher to contact the participants and invite then to participate
in the 2nd wave of the survey. Of the 441 respondents who participated in the first wave
of the survey, 405 individuals submitted an e-mail address where they could be contacted
to complete the final wave of the survey. 36 participants whom completed the first wave
of the survey could not be contacted to participate in the second wave of the survey. A
total of 405 participants were contacted via e-mail and asked to participate in the second
and final wave of the survey. Of the 405 individuals contacted, 6 were no longer
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employed with the company. Thus the total number of individuals invited to participate in
the second wave of the survey were 399. The participants were contacted a total of 3
times, where one initial e-mail was sent, and two reminders e-mails were sent within two
weeks of the initial e-mail. Of the 399 individuals contacted to participate in the survey,
295 individuals completed the second wave of the survey, which led to an attrition rate of
27.1% between Wave 1 and Wave 2 of the surveys (i.e. 295/405 equals 27.15).
Next, a series of T-tests were conducted to determine if there was any response
bias between those individuals that completed only the first wave of the survey (n=441)
and those individuals that completed both waves of the survey (n=295). A series of t-tests
were run on various demographic characteristics of the sample. The F-values and
significant levels are provided below in the table. In general, there very few differences
between those individuals that participated in the first wave of the survey, and those that
completed both waves of the survey. The two variables in which significant differences
were found between those participants that completed Wave 1 and 2 and those that
completed only Wave 1, included number of hours worked per week, average number of
ties, and child care responsibility of the ego. The average number of hours worked per
week was slightly lower for those individuals that completed both waves of the survey
(M=44.87, SD =6.85), in comparison to those individuals that completed only the first
wave of the survey (M=46.47, SD =8.74). Given that the group that did not complete the
second wave of the survey worked slightly more hours, they may have had less time to
complete the second wave of the survey. The mean number of ties per ego was slightly
higher for those individuals that completed both waves of the survey (M=8.61, SD
=5.11), in comparison to the mean number of ties for those individuals that completed
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only the first wave of the survey (M=7.35, SD =5.84). Finally, for those individuals that
completed both waves of the survey, they reported higher frequencies of childcare
responsibility. That is, they were more likely to take care of their children themselves,
with the help of their spouses, or they were more likely to get help with childcare from a
third party (e.g. daycare). Overall, the results from the analysis of any response bias
between wave 1 and wave 2 of the survey suggested that because there were very few
differences between the respondents, the characteristics of the sample were consistent
across both waves of the survey, despite a slight decrease in response rate between Wave
1 and Wave 2 of the surveys. Table 4.1 shows the results of the response bias analysis.
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F-Value Significance
Marital Status 9.048 .060
Gender .216 .643
Parental Status .760 .859
Child Living at Home 3.603 .059
Child Born in Last Five Years
2.022 .156
Number of Children .984 .323
Child Care Responsibility -Ego
9.876 .007**
Number of Hours Worked Per Week
4.169 .042*
Ego’s Age .638 .425
Employment Tenure Years
.055 .814
Job Tenure Years
.643 .423
Total Employment .821 .365
Total Number of Ties 4.766 .030*
Education 7.308 .063
* p= >.050 ** This is the Pearson Chi-Square value, not the F Value. Table 4.1: Non-Response Bias Analysis – Wave 1,2 and Wave 1 (Only) Participants
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Measures
Control Measures
There were multiple control variables included in this study. The purpose in
including the control variables was to statistically account for factors other than the
independent variables of interest, that are known to influence the dependent variables
included in this study. In order to identify the control variables included in this study, a
literature review was conducted to identify those variables that had been statistically
controlled in previous studies where career outcomes have been investigated (e.g. Noe,
1996; Kossek et al., 1998; Eby et al., 2005; Gunz &Heslin, 2005; Ng et al., 2005). The
criteria for including the specific control variables used in this study included: (1) control
variables that were common across multiple studies related to network and career
research were included (e.g. age), (2) variables that were indicators of an individual’s
human capital, that is, an individual’s personal experiences (e.g. education, hours
worked) that are likely to enhance their careers (Ng et.al, 2005) were included as control
variables, and (3) socio-demographic variables (e.g. age) that have been found to
influence career outcomes in previous studies were included.
Of note, it is important to isolate an individual’s human capital factors, because
human capital variables may influence career success in addition to any networking
characteristics (e.g. size, ties). Based on the criteria described previously, the following
control variables were included in this study: age, organizational tenure (i.e. the total
amount of time an individual has spent in their current organization), job tenure (i.e. the
total amount of time an individual has spent in their current job), total employment (i.e.
the total amount of time an individual has been employed over their lifetime), number of
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hours worked per week, number of work interruptions, organizational size, and
education level. In the following paragraph(s) a description will be provided as to how
each of the control variables included in this study were measured.
The first control variable included in this study was age. Age was measured with
the following open-ended question: “What is your age_________?”. In a recent meta-
analysis age was found to have a large statistically significant effect on multiple career
outcomes (e.g. age, and promotions) (Ng et al., 2005). Therefore, the decision to control
for age statistically is substantiated by results from pervious studies. The next control
variable included in this study was organizational tenure, which measured the amount of
time an individual has been with the current organization. Organizational tenure was
measured with the following open-ended question: “How many years and months have
you been with your current employer”? The next control variable included in this study
was job tenure. Job tenure was measured with the following open-ended question: “How
many months have your been employed in your current job_______?”. Next, total
employment experience, that is, the number of years an individual has been employed
over the career was used as a control variable. Total employment experience was
measured with the following open-ended question: “How many total years and months
have your worked (type your best estimate) _________”? Similar to findings related to
the variable age, recent findings in the Ng et al (2005) meta-analysis found organizational
tenure, job tenure, and total employment experience to all have a large statistically
significant effect on multiple career outcomes (e.g. promotions, salary). Thus
organizational and job tenure, and total employment experience were included as control
variables in this study
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Level of education was another variable controlled in this dissertation. Similar to
many of the other human capital variables (e.g. job tenure, organizational tenure), level of
education was found to have a large, statistically significant effect on multiple career
outcomes (e.g. promotions and salary) (Ng, et. al, 2005). In this study, level of education
was measured using the following prompt: “Please indicate the highest level of education
achieved: (1) high school diploma, (2) associates degree, (3) bachelor degree, and (4)
graduate degree”. Organizational size is another control variable commonly included on
career-related studies. In this dissertation, the question used to measure organizational
size was: “Please indicate the approximate size of the organization in which you were
employed; where Small (2.500 employees or less), Midsize (2.501 employees to 10,000
employees), and Large (10,001 employees or more)”. Of note, although organizational
size was included as a control variable, it was not entered in the final analyses, as all data
was collected from a single organization.
Employment Status was also measured as a control variable. In measuring this
variable, employment status was coded (1) for exempt employees and (2) for non-exempt
employees. Number of hours worked per week, a control often included in many career-
related studies was measured by using the following open-ended question: “Please
indicate the number of hours you work per week (on average)____”. Finally, the last
control variable included in this study was work interruptions. This variable was
measured on a 4 point scale, where 0= I have not had any work interruptions to 4= I have
taken more than 3 months off from work. The specific question used was: “How many
work interruptions have you had over your career?”
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A work interruption includes taking a leave from work for at least 30 days. This excludes
vacation time or time allocated for training and development. For example, if you had
two work interruptions which resulted in missing work for more than a total of 40 days,
you would select answer choice ‘2’ ”.
Family Status
There were many additional variables collected in this study that were related to
family status. These variables were not included as the control variables in the study, but
they were collected to gather a better perspective of the respondents completing this
survey. Also, some of the questions related to family status were included to verify the
parental status of the respondents. In addition, this study included a measure related to
elder care, previous studies have indicated that elder care is very similar to childcare in
that it takes away an individual’s time and may also impose a similar role constraint as
childcare.
Marital status was measured by asking the survey respondents to indicate their
marital status ( single, partnered, married, divorced, or widowed). As mentioned, there
were several questions used to verify the parental status of the respondents. This step was
taken, as the primary interest of this dissertation was to learn if network characteristics
and career outcomes differed by parental status. Therefore, it was clearly important to be
able to identify the respondent’s parental status. In addition, these questions were also
included to learn more about the ages of the children of those respondents whom were
identified as parents. Thus, if the respondent indicated they were a parent, they were
asked to answer the following questions: (1) “If you have at least one child, are these
children living at home - Yes (1) or No (2)”, (2) “If you have at least one child, was this
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child born within the last five years”, (3) If you have at least one child, please indicate the
number of children that are in your home UNDER the age of six”, (4) “If you are a
parent, do you share parental responsibility with another adult”, and (5) If you share
parental responsibility with another person, please indicate their employment status”.
Many of the questions related to family status have been used in previous research (e.g.
Lyness and Thompson, 1997).
Network Constraint Measures (Gender, Family Involvement, Role Segmentation, and Job
Involvement)
As mentioned previously, the network constraint measures identified in this study,
including gender, job involvement, family involvement, and role segmentation are the
variables that have been hypothesized to create differences in the network characteristics
size, ties, and content across parental status. This next section provides a description of
how the network constraint variables were operationalized in this study.
Gender
The first network constraint variable, gender, was a dummy-coded variable in this study.
Therefore, Gender was be coded, (1) for females and (2) for males.
Job Involvement
The job involvement scale measure included in this study was drawn from the
Kanungo (1982) JIQ, or Job Involvement Questionnaire. The Kanungo JIQ scale was
developed to construct distinct measures of specific job involvement in comparison to
distinct measures of general work involvement. Further, the JIQ measure was developed
to address a key issue that Kanungo (1982) identifies about the widely used Lodahl &
Kejner job importance measure. In short, Kanungo (1982) argues that the items used in
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Lodahl & Kejner scale (1965) actually tap a person’s “psychological identification”
(Kanungo, 1982, pp341) with the job (e.g. “I live, eat, and breathe my job”) and an
individual’s “intrinsic motivation at work for fulfilling self-esteem needs” (Kanungo,
1982, pp341). Taken together, Kanungo argues that previous measures of job
involvement have confused job involvement with intrinsic motivation on the job (e.g.
Lodahl & Kejner, 1965) , or the measures have confused identifying the antecedents of
job involvement rather than identifying job involvement as a state and identifying it’s
subsequent affects (e.g. Lodahl & Kejner, 1965). As a result, Kanungo developed a new
measure of job and work involvement. In developing the “distinct measures of specific
job and work involvement, three different measurement formats, including questionnaires
(e.g. Lodahl & Kejner, 1965) semantic differential and graphic techniques,” were used
(Kanungo, 1982, pp342).
The final JIQ scale contained 10 items including: “The most important things that
happen to me involve my present job role”; “To me, my job is only a small part of who I
am”; “I am very much involved personally on my job”; “I live, eat, and breathe my job”;
“Most of my interests are centered around my job”; “I have very strong ties with my
present job which would be difficult to break”; “Usually I feel detached from my job”;
“Most of my personal life goals-are job oriented”; “I consider my job to be central to my
existence”; and “I like to be absorbed in my job most of the time”. These items were
measured on a 6-point agreeability scale. The alpha reliability for these 10 items was
alpha =.87. The JIQ scale was found to be correlated with overall job satisfaction (r=.43).
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Family Involvement
The measures for the family involvement scale were similar to the measures used
in the job involvement scale; except, the words “job” were replaced by “parent or
family”. This measure was taken from Kanungo (1982). The Kanungo (1982) scale
included 10 items, measuring job do you mean “family” involvement. The alpha
reliability for the 10-item Kanungo scale was alpha=.87. Of the 10 items included in the
scale, 4 items were included in this dissertation. The four items that were included in the
measure were: “The most important things that happen to me involve my present parental
role”; “Most of my interests are centered around my family”; “Most of my personal life
goals are family-oriented”; “I consider my family to be very central to my existence”.
The items were measured on a six-point agreeableness scale, where (1=Strongly disagree,
3= Neutral, and 6=Strongly Agree). The decision to made to use four items for the
family involvement scale is consistent with the items used to measure family involvement
in previous studies (e.g. Greenhaus et al., 1989).
Role Segmentation
Two measures were used to assess role segmentation, a perceptual measure of
role segmentation and an actual measure of role segmentation. The perceptual measure of
role segmentation was used account for an individual’s preferences to segment their work
and family roles/lives. The items used to measure role segmentation were drawn from
two scales inclduing the PEBS (Person-employment Boundary Scale) scale, and Edwards
and Rothbard’s (1999) four-item role segmentation scale. This section will begin with a
discussion of the PEBS scale.
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The first set of items used to measure role segmentation were drawn from Person-
employment boundary scale (PEBS) developed by Hecht & Allen (2002). The PEBS
scale was developed to provide a measure of boundary strength between work and
nonwork domains. Specifically, segmentation between work and home boundaries occurs
when what belongs to work and what belongs to home are clearly distinct. That is, work
and home activities are clearly differentiated and these activities are not carried out in the
same time or space (Hecht & Allen, 2002; Nippert-Eng, 1996). People who prefer to
segment their work and family lives, see these two aspects of their lives as mutually
exclusive and they allocate their time and space to one activity or the other (Hecht &
Allen, 2002; Nippert-Eng, 1996). Because there are no other measures of segmentation
available in published literature, the purposes of this scale were to develop a measure of
boundary strength, and to examine its psychometric properties (Hecht &Allen, 2002).
The second goal of this research was to explore the relationship between boundary
strength and an individual’s well-being.
Within the scale, items were worded such that high scores reflected weak
boundaries. In addition to measuring for an individual’s preference for work and home
life to be integrated or segmented, this measure also assessed to what extent people prefer
strong or weak boundaries. A strong boundary was defined when the two domains home
and work, do not overlap (Ashforth et al., 2000). In comparison a weak boundary existed
when the two domains work and home overlap (Ashforth et al., 2000).
The Work and Home preferences scale is a newly developed measure. The PEBS
measure includes 19 items in total. The scale is divided into two factors, WTOH (work-
to-home) and HTOW (home-to work). An individual that prefers integrating their work
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and home lives, should have high scores on the WTOH items. Nine items tapped the
strength of home-to-work boundary (HTOW) and the remaining 10 items tapped the
strength of the work-to-home (WTOH) boundary.
The WTOH items included: “I often do work at home”, “I do not work on
personal time (Reverse Coded)”, “I get work-related correspondence at home”, “I work
after hours”, “Work spills over to personal life”, “I never take work out of the office
(Reverse Coded)”, “My personal time is my own (Reverse Coded)”, “It is difficult to
keep work issues from coming home”, “It is not unusual to work over breakfast/dinner”,
and “I have no problem leaving work in the workplace (Reverse Coded)”. The reliability
for the WTOH scale was alpha=.94. The nine items measuring HTOW included: “I
rarely deal with personal matters when working (Reverse Coded)”, “I communicate with
friends & family during business hours”, “I often do personal errands on work time”,
“The office is reserved for doing work (Reverse Coded)”, “I often think of personal life
when working”, “When working, I am completely focused on work (Reverse Coded)”,
“My personal issues spill over to work”, “I schedule personal activities during business
hours”, and “I have no problem leaving personal life outside of the workplace”. The
reliability for the HTOW scale was alpha =.91. All items were measured on a seven-point
agreeableness scale, where 1= Strongly disagree and 7 =Strongly agree. Of note, there
were no other measures of boundary strength available at the time that Hecht & Allen
(2002) published this scale. Therefore, the authors did not measure convergent validity
with other boundary measures. In other words, the authors had no opportunity to
determine of there measure of boundary strength was similar to other measures of
boundary strength reported in published literature.
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Of the 19 total items included in the PEBS scale, 15 items were included in this
study. Those 15 items included: “I often do work at home”, “I work after hours”, “I
schedule personal activities during business hours”, “I communicate with family and
friends during business hours”, “I think of personal or family-related issues while I am
working”, “I do not work on personal time”, “I take work out of the office”, “My
personal time is my own time”, “When working I am completely focused on work”, I
leave my personal life outside of the workplace”, “I rarely deal with personal matters
when working”, and “The office is reserved for doing work”.
The four items not included in this scale were the following items: “I get work-
related correspondence at home”, “It is not unusual to work over breakfast/dinner”,
“Work spills over to personal life”, and “I often do personal errands on work time”. The
items “Work spills over to personal life” although included in the work and home
preferences scale, really seems to be items that measure work-family conflict. Because
the focus of this study was to understand the preferences an individual makes to integrate
or segment their work and family lives, a construct that is separate and distinct from the
work-family construct, the decision was made to eliminate this item from the measure
used in this study. The items “I get work-related correspondence at home”, “It is not
unusual to work over breakfast/dinner”, and “I often do personal errands on work time”,
do seem to suggest some preferences for creating boundaries between work and home.
However, these items seem to be a better measure of an individual that prefers to
integrate their work and homes lives. That is, an individual that prefers to integrate their
work and home lives has few, if any, boundaries between work and home and would be
likely to do work while eating breakfast, complete personal errands while working, or
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receive work-related correspondence at home. The purpose of this study was to measure
an individual’s preference for segmenting their work and home lives. Thus, these later 3
items were not included in the segmentation scale, because they appear to be items that
measure the extent to which an individual chooses to integrate their work and home lives.
Of note, there were some items included in the measure used in the study that did
assess an individual’s preference for integrating work and home (e.g. “I often do work at
home”). Thus, this measure did include items that reflected both an individual’s
preference to integrate and segment their work and home lives. The measure used in this
dissertation had to include items that measured both preferences for integration and
segmentation, because these items are on the same continuum. However, because a
majority of the items were included in the Hecht & Allen (2002) scale, the decision was
made to eliminate some of the items (as described previously) that measured an
individual’s preference for integrating their work and home lives, as the purpose of this
study was really to understand an individual’s preference to segment their work and
home lives.
The second measure used to assess an individual’s desire to for segmentation
between their work and home roles was drawn from Edwards and Rothbard (1999). This
scale is an established scale in the literature, and it measures an individual’s desire for
segmentation by asking individuals to identify acceptable, rather than ideal, amounts of
segmentation between work and family roles (Rothbard et al., 2005; Edwards &
Rothbard, 1999). This scale was presented similar to the way in which it has been
presented in other published studies (i.e. Rothbard et al., 2005). Thus, the items were
assessed using a six-point scale ranging from strongly undesirable to strongly desirable.
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The instructions which appeared prior to the actual items read: “When responding to
these questions consider how much of that characteristic you personally feel is
acceptable. Some people prefer more or less of some job characteristics than others”. The
items that followed the directions included: “I do not want to be required to work while at
home”, “I want to be able to forget work while I am at home”, “I do not want to think
about the work once I leave the workplace”, and “I do not want to be expected to take
work home”. The internal consistency reliability estimate for this scale was 0.77.
In addition to the work and home preferences scale, a perceptual measure of an
individual’s preference to segment their work and home lives, A separate network
measure was used to assess the actual measure of role segmentation among the
respondents. In order to assess this, the following steps were taken. First, within the
survey, the respondents were asked to identify both who they talk to (e.g. spouse) and
what topics they discuss (e.g. children/household). An actual measure of role
segmentation was calculated then to understand the proportion of the number of ties
within their network that the ego discusses both work and home conversation topics. The
proxy used to calculate this actual measure of role segmentation was N(work & family) /
n(Work OR family), where N is number of individuals with whom the respondents talk
about both work and family topics and n is the total number of people sampled in each of
the respondent’s network. This measure was developed specifically for this study, as
there are no other none measures of netrole segmentation measures of work and family
topics, available in published literature.
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The outcome for this role segmentation variable ranged from 0 to 1. A score of
zero indicates that there is no overlap in topics of conversation that the ego discusses with
their ties. In other words, the ego has a completely segmented network, where they
discuss work topics and family topics with different people within their network. In
comparison, a score of one indicates that there is complete overlap in the types of topics
that the ego discusses within their network. That is, the ego discusses both family and
work topics with all members within their network. This suggests that the ego wants to
integrate both their work and family lives. Next, an illustrative example of the role
segmentation (actual) measure is provided.
User ID Either* Both** Overlap***
15 6 6 1.00
57 6 4 0.66
24 5 2 0.40
97 6 0 0.00 *Either-Number of people in respondent’s sample (Also, it’s possible for ego to discuss either work or family with each tie within their sample) **Both- Intersection of work and family topics. That is, these are individuals within the ego’s network with whom they discuss both work and family. ***Overlap (Work and Family)/Work or Family or Both/Either (in Table 4.2)
Table 4.2: Calculation of Role segmentation ‘Actual’ Measure
Organizational Network Size
A single-item question was used for individuals to list the individuals with whom
s/he has a direct tie. The single item measure was drawn from the General Social Survey
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(GSS), and has been used in previous empirical research (e.g. Polodony & Baron, 1997).
The GSS is an ongoing survey that measures the social and political attitudes and various
behaviors of adults living in the United States; the GSS is administered by the National
Opinion Research Center (NORC) and is given every other year to a random sample of
households (Marsden, 2003). Data for the GSS survey is collected in person (interviews)
from a household representative at least eighteen years of age (Marsden, 2003).
For the purposes of this dissertation, the respondents were asked the following
(name generator) question (See Appendix for Full Survey):
“Please type BOTH the initials and first name (e.g. KLS – Kyra) or (e.g. KS – Kyra) of the most important people in your professional and personal life (up to the 20). This includes both people inside and outside of your organization, family members, friends, neighbors, members of professional organizations, supervisors, colleagues, and anyone else with whom you discuss important matters including your career plans and various aspects of your professional life”
The single item measure is called the name generating technique and it used to help the
respondent define their network, and it is a commonly used measure across social
network research (e.g. Polodony & Baron, 1997; Granovetter, 1972).
The name generator is the best known and one of the most widely used measures
for the collection of egocentric, that is, personal network data (Bailey & Marsden, 1999).
This question is consistent with the “important matters” name generator questions that
has been used in previous GSS surveys (e.g. Marsden, 1987; Marsden, 2003). Name
generator questions are usually followed by name interpreter questions that are used to
yield additional information on the individuals identified by the name generator
questions, and they are used to measure specific network characteristics (e.g.
composition, density) (Marsden, 2003).
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For the purposes of this study, there are six name interpreter questions that were
included. The purpose of these name generator questions is to measure specific network
characteristics, including, network size, network ties, and network content of
conversation. First, network size will be measured by counting the number of direct ties
connected to the ego. That is, network size will be measured as the number of distinct
names given in response to the name generator question (Marsden, 2003). b Network
size has a reasonably high stability over short periods of time (Mardsen, 1990; Mouton, et
al, 1995). Both the gender and the total number of organizational members will be
counted. Networks can include work groups, associations, neighborhoods, and family
members.
When estimating network size, research indicates that often the number of
reported interactions will be an imperfect indicator of the number of interactions that an
individual has with the ties within their network (Feld & Carter, 2002). However,
research also suggests that there is a strong correlation between reported and actual
number of interactions that an individual has with their specific ties, thus the number of
ties an individual reports is not very far from the actual number of reported interactions
with their ties (Feld & Carter, 2002).
Calculation of Network Size:
Network size was simply calculated by summing the number of ties each
respondent identified within their network. This calculation of network size is consistent
with how network size is calculated in the social networking literature (Wasserman &
Faust, 1994). The range of network ties was from 1 to 20 with a M = 8.60 and SD =
5.123.
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Organizational Members (Ties)
The average North American is said to have 1500 ties (Wellman, 1992), of which
4-8 are typically identified as close and supportive ties. Using the GSS survey instrument,
the respondent was asked to indicate up to the 20 people closest to them. They named the
individual(s) using initials only, and also identified the type of tie (e.g. kinship tie, friend
tie, work tie). The specific question used to measure the ego’s network ties was:
“Please indicate the type of relationship that you have with this person (e.g. KLS-Mentor)
as their name appears on the screen”. The answer choices included Supervisor/Boss
(Former or Current), Colleague/Coworker (Former or Current), Employee/Subordinate
(Former or Current), Mentor, Work Friend, Non-work Friend, Spouse/Partner,
Sibling/Parent, Other relative (e.g. daughter/son, in-laws), Neighbor and Other. The
answer choices were based on pilot data that was collected prior to the field survey being
initiated. In the field study, respondents were asked to identify the specific individuals
with whom they had conversations about important matters in their life. They were asked
to provide the individual’s name and their relationship to the ego. Based on this pilot
data, 10 categories/labels of relationship types were derived (e.g. Work Friend, Non-work
Friend) and used as the answer choices for this name interpreter question. This data was
interpreted as a dichotomous variables where (1) was used to code work ties and (2) was
used to code non-work/family ties. In addition to the relationship type, the survey
respondent was asked to provide the gender, age, parental status, and race of the
individuals with whom they have a relationship.
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As mentioned previously, network matrix data was collected. That is, the survey
respondent described the extent to which each member within their network knows each
other. This measure is consistent with measures used in previous studies (e.g. Burt, 1992)
where individuals have been asked to respond to specific questions about the alters in
their network. The specific question stem used to generate the network matrix data was
discussed in the previous section, in which the measure for the network constraint,
segmentation, was described.
Operationalization – Network Ties
In this study, network ties were operationalized as the percent of network ties that
were kin. It was expected that the number of kin ties would be higher for working parents
than it would be for working adults without parental responsibility.
To calculate network ties, the following procedure was completed. This
procedure is consistent with other measures used in the social networking literature, when
the researcher is trying to determine what proportion of an individual’s network, a certain
type of tie (e.g. friendship tie or kin tie) comprises within the ego’s overall network (e.g.
Moody, 2001). First, by user, all network ties were coded into one of two broad
categories, work ties and non-work ties. The ties that were included in the work ties were:
supervisor/boss (former/current), colleague/coworker (former/current),
employee/subordinate (former or current), mentor, and work friend. The remaining ties
including non-work friend, spouse/partner, sibling/parent, other relative, and neighbor
were classified as non-work ties. Next all sampled ties were counted per respondent (this
was completed in SPSS). The amount of sample ties per respondent ranged from 0-6.
Next all kin ties were counted per respondent (this was also computed using SPSS). The
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ratio of kin ties/all possible ties (sampled) was calculated. This ratio ranged from zero to
one, and the mean and standard deviation for this ratio were M = 0.566 and SD=.273.
Network Content
The conversation topics used to measure ego network content were first identified
during the focus group study described in Appendix A. As mentioned previously, during
the focus group study, individuals were provided with an open-ended question which
asked them to name 1-3 topics of conversation they discussed with each of the members
within their network. The purpose of including this measure in the focus group study was
to generate a pre-determined list of conversation topics, organized around multiple
themes, which would later be used in the field study. Specifically, all conversation
topics, by respondent, identified during the focus group study were recorded. After a full
set of conversation topics was generated per respondent, a conversation topic frequency
table was developed. The frequency table demonstrated by conversation topic, the
number of times it had been named across all participants in the focus group study. The
conversation topics with the highest frequencies were then categorized into one of two
broad themes, work and non-work. This pre-determined list of work and non-work
topics, was the final list of topics used during the field study. The list of conversation
topics used in the field study included, work-general (e.g. work expectations,
assignments), career/career progress (e.g. job hunting, career planning), continuous
education/training, work-related projects (e.g. new projects), networking, children/family
household, spouse/partner, marriage/relationship, health, and other. That is, the
participants in the field study selected conversation topics from a pre-determined list of
topics, where each topic fell into one of the two broader categories.
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Specifically, on the field survey, the two specific question stems were used to
measure topics of conversations (i.e. network content). The two questions read as
follows: (1) “Please consider the topics of conversation discussed with this individual.
Thinking back to the most recent discussions you had about an important matter,
inclduing your career plans, please indicate all of the conversation topics you discussed
with this individual (you may select more than one conversation topic)”, and (2) “Of the
conversation topics you selected in the previous question, select the TWO topics that you
discuss most frequently when you discussed an important matter, including your career
plans with this individual”. In actuality, the variable of interest for this study was the later
questions, that is, this study was interested in gaining an understanding of what each
respondent talks about most frequently within their network. However, in order to avoid a
double-barreled question, that is, what do you talk about and what do you talk about most
frequently, these two questions were separated on the field survey.
This content of conversations measure has been used in previous studies to
investigate the conversation topics within the social networks of individuals (e.g.
Bearman &Parigi, 2004). Also, the content of conversations is directly related to the
name generator technique (described earlier). That is, when interpreted literally, the name
generator technique, asks respondents to indicate the important matters they had in mind
when they identified the list of alters within their network (Bailey & Marsden, 1999).
Thus when the name generator technique is used (i.e. identify up to twenty people with
whom you discuss important life matters), the respondents considers both the individuals
and the topics of conversations with these individuals, when answering this question.
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Operationalization – Network Content (i.e. Network Topics of Conversation)
In this study, network content was operationalized as the percent of network
content that was family-oriented. It was expected that the number of family conversation
topics would be higher for working parents than it would be for working adults without
parental responsibility.
To calculate network content (i.e. topics of conversation), the following procedure
was completed. First, by user, all network content were coded into one of two categories,
work topics and non-work topics. The topics that were included in the work topics
category included work general (e.g. work expectations, assignments), Career/Career
Progress (e.g. job hunting, career planning), Continuous Education/Training, Work-
related Projects (e.g. new projects), and Networking. The remaining topics inclduing
children/family household, spouse/partner, marriage/relationship, health, and other were
included in the non-work topics. Next all ties sampled network content topics were
counted per respondent (computed in SPSS). Next all non-work topics were counted per
respondent (computed in SPSS). The total amount of topics discussed per respondent
ranged from 0-9. This procedure was followed by the number of non-work topics
counted per respondent. The total amount of non-work conversation topics discussed
ranged from 0-4. Finally, the ratio of non-work topics/all possible topics (sampled) was
calculated. This ratio ranged from zero to one, and the mean and standard deviation for
this ratio were M = 0.473 and SD=.198.
Of note, the procedure used to calculate proportion of non-work content is very
similar to the procedure used to calculate the percentage of kin ties. In this vain, the
procedure followed to calculate proportion of non-work content is very similar to
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procedures followed in previous studies (e.g. Moody, 2001) where the researcher is
interested in calculating the proportion of friendship ties that exist within a network (i.e.
proportion of friendship ties/total network ties).
Objective Career Success
The perceptions of career success, both subjective and objective career success,
were used to assess the extent to which individuals are happy with their current career
progress. As mentioned previously, current research in the careers literature (e.g. Huselid,
2004) calls for more career-orientated research to be focused on both the subjective (e.g.
criteria to reflect an individual’s values and preferences for things such as level of pay,
challenging jobs, job security) and objective (e.g. attainments in various areas including
work performance, pay, and promotions) indicators of career success.
In total, five items were used to measure objective career success in this study.
Those five items included two items used to measure salary/salary progression, and the
other three items were used to measure promotion/likelihood of promotions. The two
salary items were drawn from the Forret & Dougherty (2004) study on networking
behaviors and career outcomes. The specific items used in the study to measure total
compensation and number of salary increases were: “ Please indicate your total
INDIVIDUAL pretax income (e.g. salary, bonus, stock, profit sharing) in 2005 and in US
Dollars”; and the answer choices included (a) less than 30,000 (b) 30,000- 45,000 (c)
45,000-60,000 (d) 60,000 – 75,000 (e) 75,000-90,000 (f) 90,000 – 105,000 (g) 105,000 -
120,000 (h) 120,000 – 135,000, (i) greater than 135, 000. The last item used to asses the
individual’s salary included: “Throughout your career, please indicate how many salary
increases you have received. Of note a salary increase includes both (a) changes in annual
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salary; (b) qualifying for a performance-based company bonus, incentive, or stock plans”
Answer choices include 0, 1, 2, 3, 4, 5, and greater 5.
Three items were used on the survey to measure promotions, the second indicator
of objective career success. The first item based on Forret & Dougherty (2004) and
James (2000) included: “Since you’ve joined your current organization/company, please
indicate how many promotions you have received. Note: Promotions includes (a)
significant changes in salary; (b) lateral or horizontal promotions; (c) changes in offices
and/or type of furniture/décor in office; (d) significant changes in job scope or
responsibilities; and (e) changes in company level. The answer choices in this question
include 0,1,2,3, and 4, where 0 indicated zero promotions, 1 indicated one promotion, 2
indicated two promotions, 3 indicated three promotions, and 4 indicated four promotions.
The next question used to measure promotions included: “Please indicate how many
promotions you have received in your entire career. Note: Promotions include (a)
significant changes in salary; (b) lateral or horizontal promotions; (c) changes in office
and/or type of furniture/décor in office; (d) significant changes in job scope or
responsibilities; and (e) changes in company level. The answer choices for this question
included 0,1,2,3,4,5, and Greater Than 5, where the answer choices indicated, by number,
(e.g. 3 is 3 promotions) the number of promotions that individual received in their entire
career. The last question related to a promotions was drawn from the Stout, et al. (1988)
study , the question read: “How likely is that you will receive a promotion within the next
five years?” A five point Liker scale was used, where 0= No chance and 5= Very good
chance.
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Subjective Career Success
As mentioned previously, the second set of career success items, included those
used to assess perceptions of subjective career success. There were two sets of items used
to measure subjective career success, which included the individual’s perspective of their
career success, and their career success when compared to other-referent criteria.
The first set of items used to measure intrinsic career success, focused on the
individual’s perspective of their career success. These items were drawn from the
Greenhaus et al. (1990) career satisfaction scale. The Greenhaus et al (1990) measure was
developed to measure both satisfaction with career success and the extent to which an
employee has made satisfactory progress towards goals for income level, advancement
and development of skills. The Greenhaus et al (1990) career satisfaction scale has been
shown to correlate positively with having a job in general management, salary level,
number of promotions received, perceptions of upward mobility, sponsorship within an
organization, acceptance, job discretion, supervisory support, career strategies, perceived
person-organizational value congruence, presence of an internal labor market, and job
performance (Fields, 2002). It has been shown to correlate negatively with having
reached a career plateau (Fields, 2002). Finally, a confirmatory factor analysis
demonstrated that general perceptions of career satisfaction are empirically distinct from
financial success and hierarchical success in an organization (Field, 2002).
In total there are seven items in the Greenhaus et al (1990) scale. The alpha
reliability for these items was 0.89 in the Greenhaus et al (1990) study. This study used
six of the seven items from the Greenhaus et al (1990) scale. The items, measured on an
agreeableness scale where 1 = strongly disagree to 6 = strongly agree, included: “Relative
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to my career aspirations, I am satisfied with the progress I have made towards meeting
my goals for advancement”, “Relative to my career aspirations, I am satisfied with the
overall success I have achieved in my career”, “Relative to my career aspirations, I am
satisfied with the progress I have made toward meeting my goals for income”, “Relative
to my career aspirations, I am satisfied with the skill development I have attained”,
“Relative to my career aspirations, I am satisfied with the autonomy I have attained”, and
“Relative to my career aspirations I am satisfied with the intellectual stimulation I have
attained”. A decision was made after the pilot test was conducted to exclude one of the
items from the scale. The item, Relative to my career aspirations, I am satisfied with the
progress I have made toward meeting my overall career goals”, appeared to tap the same
dimension as the item “Relative to my career aspirations, I am satisfied with the overall
success I have achieved in my career”.
The second set of items measured the individual’s career success in comparison to
the career success of members within their peer group. These items were from Heslin
(2003) study. Peer-related career success really draws from Festinger’s (1954) social
comparison and Adam’s (1965) equity theories. These theories suggests that individuals
are motivated to evaluate outcomes they achieve and they attempt to do so by comparing
their outcomes to those of other people. Drawing from social comparison theory, Heslin
(2003) argued that the Greenhaus et al (1990) career success scale, although widely used,
is not the most accurate measure of assessing each respondent’s career success for at least
two reasons. First, Heslin (2003) argues that the Greenhaus et al (1990) scale is not
applicable to individuals who work on a contract basis, who run their own small business,
or individuals that value other features of their career besides hierarchical advancement.
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Thus, Heslin (2003) argues that researchers need to do a better job of assessing what
really matters to each person in terms of their career satisfaction. The second criticism of
the Greenhaus et al (1990) draws from social comparison theory. Heslin (2003) argues
that individuals make social comparison when they evaluate career outcomes, including
perceptions of their career success. The Heslin (2003) peer-related career satisfaction
scale was adapted from Greenhaus et al (1990) career satisfaction scale.
In total there are seven items drawn included in the Heslin (2003) measure. The
coefficient alpha level for these seven items was 0.95. In addition, success relative to
other-referent criteria was found to be positively correlated with career success relevant
to self-referent criteria. That is, an individual’s perception of how successful they were in
their own career when compared to someone within their peer groups, was highly related
to their own perceptions of the success they achieved in their careers.
Six of the seven items from the Heslin (2003) study were included in this
measure. Those six items measured on a six-point agreeableness scale where 1=strongly
disagree to 6=strongly agree, included: “Relative to the people who I perceive as peers in
my career/profession, I am satisfied with the progress I have made towards meeting my
goals for advancement”, “Relative to people who I perceive as peers in my
career/profession, I am satisfied with the overall success I have achieved in my career, “
Relative to people who I perceive as peers in my career/profession, I am satisfied with the
progress I have made toward meeting my goals for income”, “Relative to the people who
I perceive as peers in my career/profession, I am satisfied with the skill development I
have attained”, “Relative to people who I perceive as peers in my career/profession, I am
satisfied with the autonomy I have attained”, and “Relative to people who I perceive as
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peers in my career/profession, I am satisfied with the intellectual stimulation I have
received”. The seventh item, “Relative to people who I perceive as peers in my
career/profession, I am satisfied with the progress I have made towards meeting my
overall career goals”, was excluded as pilot test participants did not believe this item was
distinct from the item “relative to the people who I perceive as peers in my
career/profession, I am satisfied with the overall career success I have achieved in my
career)”.
Career Self-Management Perceptions
As described in the literature review, individuals bear the responsibility of
managing their own careers. The perceptions of career self-management measure were
measured by two specific set of items related to perceptions of career management and
job mobility preparedness (Kossek et al., 1998). Job mobility preparedness is a measure
assessing the extent to which an individual is proactive in gathering information about
new career opportunities. The later measure is a valid measure of career self-
management, as this study argues that individuals will have to manage their own careers
within the new protean career, which implies that individuals will proactively have to
gather information about jobs and be prepared to move between jobs frequently.
The first set of items, those related to perceptions of career management, was
drawn from Sturges et al. (2000) and Hall (1990). In total, there was a combined total of
19 items used from the two scales to measure the individual’s perception of career self-
management. Those 19 items were measured on a six point agreeableness scale, where 1=
strongly disagree to 6= strongly agree. Next a discussion is provided of the specific scales
from which the 19 items were drawn.
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Eleven of the nineteen items used to assess career management perceptions were
drawn from the Hall (1990) scale. This scale was designed to assess career management
practices related to career planning and tactics. The eleven items used from the Hall
(1990) study were drawn from two subscales. The first five items drawn from the Hall
(1990) career subscale, had an alpha of 0.70 and they were measured on a 5-point likert
type scale (where 1= very untrue of me to 5=very true of me). Those items measured
career planning and they included: “I have definite goals for my career over my lifetime”;
“When I think of changing my job, I always consider whether the new job leads to
another one I want”, “I give a lot of thought to plans and schemes for achieving my
career goals”, “I know what my strengths and weaknesses are in relation to my career”,
and “Achieving my career goals is very important to me”. The later six items were
drawn from the Hall (1990) career tactics subscale where the coefficient alpha was 0.68.
These six items were developed to assess the career management tactics that seemed to
be the most widely generalizable across organizations. Those six items measured on a 5-
point likert-type scale (where 1 =very untrue of me to 5 = very true of me) included: “ I
am always very careful to avoid dead-end career paths”, “I try to have as much visibility
and exposure to my bosses as I can”, “I go out of my way to find a mentor or sponsor to
help in my career in the firm”, “I cultivate friendships with influential people for my
career outside of work”, “I actively seek opportunities, rather than wait to be chosen”.
Of note, Hall’s (1990) eleven item measure of career management was used in at
least one other study (Orpen, 1994). Orpen (1994) found that there was a positive
relationship between career self-management and career effectiveness. Orpen (1994)
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also found (individual) career self-management to be positively correlated with salary
growth, promotions, career performance and career satisfaction.
The next set of items used to create the career management scale for this study
(where there were a total of 19 items) were drawn from the Sturges et al. (2000) study.
The Sturges et al (2000) were seeking to understand the relationship between an
individual’s experiences with multiple career management practices as it relates to their
organizational commitment. The Sturges et al. (2000) study was an exploratory study,
where the authors presented the respondents with a list of multiple career management
practices where they were asked about the frequency of their participation in these career
practices. The list of career management practices that appeared on the Sturges et al.
(2000) survey, included both existing career management practices found in the career
literature (e.g. Gould & Penley, 1984; Noe, 1996), and additional items developed by the
authors for the study. Sturges et al (2000) collected the data at two points in time (using
the same measure each time), and the items loaded on four factors including networking,
mobility-oriented behavior, practical things, and drawing attention. The coefficient alphas
for each scale were reported, where networking was 0.72, mobility-oriented behavior
0.80, practical things was 0.62, and drawing attention was 0.82.
The specific items used in this study were drawn from the items that loaded on
networking factor in the Sturges et al. (2000) study. The decision was made to include
the items from networking, because the researcher wanted to include a measure that was
directly related to networking. Specifically, a scale directly tapping networking behavior
was warranted in this study, as this attempted to better understand the relationship
between specific network characteristics, and how they relate to an individual’s to
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manage their career, including their ability to engage in networking behaviors. The items
loading on the remaining three factors, that is, mobility-oriented behavior, practical
things, and drawing attention in the Sturges et al (2000) study were either not relevant to
this study or they appeared to tap dimensions of career management behaviors that were
better represented by other scales (e.g. Kossek et al., 1999 and Hall, 1990).
During the field survey, the respondents were asked to indicate to what extent
theyhad engaged in each of the networking behaviors, and these items were measured on
a 5-point agreeableness scale (where 1=strongly disagree to 5= strongly agree). The items
the respondents responded to on the survey included: “I have gotten myself introduced to
people who can influence my career”, “I have talked to senior management at the
company’s social gatherings”, “I have built contacts with people in areas where I would
like to work”, “I have taken the initiative to be involved in high profile projects”, “I have
asked for career advice from people even when it has not been offered”, “I have asked for
feedback on my performance even when it was not given”, “I have refused to accept a
new role because it would not help me to develop new skills”, and “I have monitored job
advertisements to see what is available outside of the organization”.
The last set of items that appeared on the career management scale in this study
were drawn from the Kossek et al (1998) career mobility preparedness scale. The scale
was developed from information gathered during interviews with supervisors, employees,
and human resource managers at a major large auto company with operations in the U.S.
and Canada. The purpose of this measure was to understand the extent to which an
individual gathers information about new career opportunities, and the degree to which
an individual prepares him/her-self to act on internal and external career opportunities
173
(Kossek et al., 1999). Examples of behaviors include proactiveness in obtaining job
information, keeping a resume current, developing internal and external networks of
contacts who provide job information, and keeping a current resume (Kossek et al.,
1999). The specific items used in this study were drawn from a larger scale used to assess
career management in a quasi-experimental setting. The items were measured on a five-
point likert-type scale (where 1=not at all to 5=a great deal). As reported in the Kossek et
al (1999) study, the coefficient alpha level for these items was 0.84. Further Kossek et al.
(1999) found career mobility preparedness to be positively correlated with career self-
efficacy, adaptability, and feedback seeking behaviors.
The nine items, drawn from the Kossek et al (1999) measure, used in this study
included: “Over the past 6 months to what extent have you reviewed internal postings”,
“Over the past 6 months to what extent have you discussed future job openings within
your network (where internal means your network within your current organization (e.g.
co-workers, supervisors)” , “Over the past six months to what extent have you discussed
future job openings within your external network (where external means members in your
network outside of your current organization)”, “Over the past six months to what extent
have you thought about what position you would like to have next”, “To what extent do
you actively seek out information about job opportunities outside your current
organization”, “To what extent have you sought out any new personal connections AT
WORK in the past 6 months for the purpose of furthering your career?”, and “To what
extent have you sought out any new personal connections outside of work for the purpose
of furthering your career”. These items were measured on a five point likert-type scale
(where 1=not at all to 5 = a great deal). The ninth item was measured on a separate five-
174
point scale (where 1=not at all current and 5=very current), and the item read “How
current is your resume”.
Plan for Data Analysis
The data analysis for this dissertation began by first transferring all data that had
been collected over the internet into a usable file developed in SPSS. During this process,
the raw data was manually scanned for completeness, and any large amount of data
missing for an individual respondent was noted. The raw data was organized such that
the each respondent was given a unique row in the SPSS program, where all data that
corresponded with that respondent was entered into the same row.
Next, an analysis was conducted to analyze both the overall response rate to both
waves of the survey, and the attrition response rate between Wave 1 and Wave 2 of the
survey. Also, a series of steps were conducted to clean the data, including running a
series of one-wave ANOVAS and Chi-square tests to determine if there was non-
response bias-present between those that completed wave 1 of the survey and those that
completed both waves of the survey. Specifically, several of the key control variables
including, marital status, gender, parental status, number of hours worked per week,
ego’s age, job tenure and others, were selected to be included in the non-response bias
analysis. The variables that were selected to be included in the non-response bias analysis
met one of two characteristics: (1) they were the variables used as control variables in the
field study, or (2) they were variables measuring specific network characteristics. That
is, these variables were likely to influence the career outcomes measures. After the
variables were selected, a series of Chi-Square tests (for the categorical variables, e.g.
education) and One-Way Anova (for the continuous variables) were conducted. After the
175
non-response bias analysis was completed, a series of descriptive and frequency analysis
were conducted which were used to generate the sample descriptive (e.g. average age of
sample, number of respondents by gender).
Next, validity and reliability analysis was conducted on each of the scales used in
the dissertation. This included reviewing the measures used in the study, and determining
if any of those measures had items that needed to be reverse-coded. Next factor analysis
and reliability checks were conducted on each of the scales. During this process it was
determined the extent to which items from the scales loaded on the appropriate factors,
and the reliabilities were double-checked to determine if the reliabilities were fairly
consistent with the reliabilities reported in the published literature. Next, a series of
network-related measures (e.g. network content and network ties) were generated from
the raw data. Correlations, means and standard deviations were generated among the
study variables. Finally, the hypotheses proposed in the conceptual model were tested
using multiple regression. Specifically, multiple regression analysis was used to tests the
hypotheses regarding the main and interaction effects of the independent variables on the
dependent variables. This procedure was conducted to test all hypotheses, proposed in the
conceptual model.
176
CHAPTER 5
RESULTS
The purpose of this chapter is to report the results of the tested hypotheses
presented in Chapter 3. In completing these tasks, several preliminary analyses were
conducted prior to testing the hypotheses. This chapter will begin with a description and
results found during the preliminary analysis and will conclude with the formal testing of
the hypotheses.
Preliminary Analysis
Descriptive Statistics
Of the 295 participants that completed both waves of the survey, 245 respondents
were female and 50 respondents were male. It may be argued that the small sample of
male respondents, n= 50, does not warrant including them in the analyses of the study.
However, given that at least 3 of the proposed hypotheses are directly related to gender, it
makes sense to keep the male respondents in this sample, despite the small
representation. The mean age of the respondents was 39.04 (9.561 SD).
The marital status of the respondents in the sample included those who were
Single (n=58), Married (N=194), Partnered (N=18), Divorced (N=20), and Widowed
(N=3). Two of the 295 respondents did not report their marital status. Of the 295
respondents, 171 reported having children, this represents 58% of the sample.
177
Of those that have children, 61 (36%) had at least one child, 82 (48%) had at least two
children, and 28 (16%) had at least 3 children. The average number of children per
household was 1.40 (SD .492). There were 147 (86%) respondents who reported their
child lived at their home, and 100 respondents (58%) reported at least one child being
born within the last five years. Most of the respondents reported that they shared
childcare responsibility with another person (n=144) or 84%. Usually childcare
responsibility was shared between the ego and their spouse/partner n=78(54%). A smaller
amount of the respondents shared primary childcare responsibilities with a third person
provider (e.g. daycare) n=21 (15%), or a family member, besides their spouse or partner
n=7 (5%). Of those parents that shared childcare responsibility, 119 (83%) of the
individuals with whom they shared childcare responsibility were also employed at least
30 hours per week. In addition to childcare, a small amount of the respondents reported
they had elder care responsibility (n=11).
All but four of the respondents (n=291) reported working at least 30 hours per
week. The average amount of time individuals reported they had been with their current
employer (i.e. employment tenure) was 10 years and 5 months. The average amount of
time respondents reported they had been employed in their current job (i.e. job tenure)
was 4 years and 5 months. Total employment, that is the amount of time an individual has
been employed over their career was 19.73 years (10.272 SD). Almost half of the
respondents reported a salary between 45,000-60,000 (48%). Most of the respondents
have either a bachelor degree (n=108) 36.6% or a graduate degree (n=164) 55.5%.
The average number of network size across respondents was 8.61 (5.109 SD). The
minimum number of reported network members was 1 and the maximum number of
178
reported network members was 20, as participants were not allowed to name more than
20 network members. In addition to network size, respondents were asked to provide
additional information describing the relationship they hold with the network members
identified (e.g. work-friend), the gender of those members, and the age of the network
members and the content of conversation. As mentioned previously, respondents could
identify up to 20 members within their network. Of those members, respondents were
asked to answers a series of additional questions on up to 6 members within their
network. Of those 6 network members, the first 3 members were consistent with the first
three members that the respondents identified as part of network. The remaining three
members were randomly selected from the larger set of network ties identified. Across all
respondents, information (e.g. tie type, gender, age, conversation topic) was shared for
approximately 1,340 network members. This total of 1,340 network members reported
was lower than the total number of network ties expected to be generated by this study.
If all 295 members had shared information about 6 members (i.e. the maximum number
of network members which the respondents had to answer follow-up questions), there
would have been information reported about 2,360 network members. There are several
reasons why a higher total number of ties was not reported. First, during the data
collection process there was a glitch in the computer program. As a result, the individual
network member data for 65 individuals was lost. All of the 65 individuals were
contacted and asked to retake the section of the survey that was lost, that is Section B of
the network relationship, portion of the survey, and 31 individuals did retake that portion
of the survey. In addition to the computer glitch, some individuals may have experienced
respondent fatigue and may not have fully completed all questions related to their
179
network members (up to six members). However, all precaution was taken to minimize
respondent fatigue. This was accomplished by asking respondents to only answer
questions for up to 6 members within their network.
More than half of the network members (56.19%) identified by survey
respondents were non-work ties (n=753). The types of family ties most often identified as
a network member included Sibling/Parent ties (n=287), followed by Spouse/Partner Ties
(n=191) and Non-Work Friend Ties (n=151). Very few ties were identified as neighbor
ties (n=4). Work ties (n= 487) represented about 44% of the total ties identified by the
respondents. The two types of work tie most often identified were the supervisor (former
or current) n= 228 ties and the colleague (former or current) ties n=214. Surprisingly,
very few respondents identified mentors (n= 39) as members of their network with whom
they discuss important matters including their career plans. In identifying the gender of
ties, the distribution across gender was about even, where males ties made up 49% of all
ties identified in this data set and female ties made up the remaining 51% of all ties
identified in this sub-sample. This was an interesting finding given that the distribution
of female and male egos was not even represented in this dissertation. As mentioned
earlier of the 295 respondents, 245 of those individuals were female. That is, the gender
across network ties was fairly equal across gender. However, the gender across ego was
not even, where there were more female egos or respondents represented in this sample.
Therefore, if the distribution of gender across network ties had been consistent with the
distribution of gender across ego, there would have been a larger number of female
network ties (as over 80% of the respondents in this sample were female). Thus it appears
that females are likely to be networking with cross-gender ties (i.e. males) while males
180
may be more likely to network with same-sex gender ties. The average age of the ties
identified by the respondents was 44 years old (13.40 SD), just slightly higher than the
average age of the egos (i.e. 39 years old). When the egos were asked to share the
conversation topics they talk about most frequently amongst the ties in their network,
nearly 61% of the time, individuals are discussing work-related topics with the members
of their networks. The most common work-related topics discussed included general
work topics (e.g. work expectations and assignments) and topics related to career/career
progress (e.g. job hunting and career planning). One of the factors that may have
contributed to these discussion topics was the members of this organization were
experiencing a very turbulent work environment, as while data was being collected this
organization announced two rounds of layoffs. In addition, the work-ties most often
identified were supervisors and colleagues. Therefore it is likely that individuals would
discuss general work topics including work assignments and expectations with
supervisors and other colleagues. The remaining 39% of the topics discussed were
family-related topics, especially topics related to children and general household
concerns. See Table 5.1 for a full detail of the sample characteristics described in this
section.
181
Demographic Characteristic(s)
Ego Gender Female (n=245) Male (n=50)
Ego Age Mean= 39 (SD=9.561)
Ego Marital Status Single (n=58) Married (n=194) Partnered (n=18) Divorced (n=20) Widowed (n=3)
Ego Parental Status Child Living at Home Child Born Within Last Five Years Average Number of Children Shared Parental Responsibility Shared Primary Childcare (i.e. “If you have someone with whom you share parental responsibility with, who has the primary childcare responsibility”) Job Status (person with whom ego shares childcare responsibility)
Zero Children (n=124) One child (n=61) Two children (n=82) Three Children (n=28) Yes (n=146) No (n=21) Yes (n=100) No (n=68) Mean= 1.24 (SD =.429) Yes (n=144) No (n=17) Myself (n= 26) Both Myself and Spouse/Partner (n=78) My Spouse (n=16) Third Party Care Provider (n=21) Family Member (non spouse/partner) (n=7) Employed (n=119) Self-Employed (n=11) Unemployed (n=15)
Continued
Table 5.1: Descriptive Statistics for Demographic Variables –Wave 1 and 2 Participants
182
Table 5.1 continued Demographic Characteristic(s)
Elder Care Responsibility Yes (N=11) ; No (N=281)
Job Characteristics Average Number of Work Interruptions Working Greater than 30 Hrs Per Week Average Number of Hours Per Week Employment Status Employment Tenure (years) Employment Tenure (months) Job Tenure (years) Job Tenure (months) Total Employment (years)
Mean=1.09 (SD=1.234)
Yes (n=291); No (n=4)
Mean=44.87 (SD =6.854)
Exempt (N=275); Nonexempt (N=18)
Mean= 13.53 (SD =9.546)
Mean= 5.51 (SD =3.289)
Mean= 4.32 (SD =5.398)
Mean= 5.32 (SD =3.206)
Mean= 19.73 (SD =10.272)
Education High School Diploma (n=6) Associates Degree (n=17) Bachelor Degree (n=108) Graduate Degree (n=164)
Continued
183
Table 5.1 Continued
Members within Networks By Type (Work Ties) Colleague Mentor Work Friend Supervisor Employee Member 1 15 9 10 42 Member 2 33 8 17 53 Member 3 34 8 17 40 2 Member 4 48 6 16 30 5 Member 5 39 2 13 43 5 Member 6 45 6 16 20 5 Column Totals
214 39 89 228 17
Sum of Column Totals
587
Members within Networks By Type (Family Ties) Neighbor Spouse/
Partner Non-Work Friend
Other Relative
Sibling
Member 1 136 14 11 30 Member 2 18 22 29 82 Member 3 21 29 28 67 Member 4 1 9 26 20 55 Member 5 2 5 29 22 28 Member 6 1 2 31 14 25 Column Totals
4 191 151 124 287
Sum of Column Totals
753*
*Respondents were allowed to select an “other” when identifying their relationship with a tie. There were a total of 23 ‘other’ ties identified. The above totals do not reflect the ties identified as ‘other’.
Continued
184
Table 5.1 continued Frequency of Conversation By Topics (Work) Career Networking Work-related
(e.g. New Projects)
Work-general (e.g. Expectations Assignments)
Continuous Education
Sum
629 80 167 705 68 1649
Frequency of Conversation By Topics (Family) Children/ Household
Marriage Health
Spouse/ Partner
Other
Sum 588 257 125 63 11 1033
185
Data Checks and Cleaning
Prior to testing the hypotheses proposed in the conceptual model, several analyses
were conducted to insure the integrity of the data. First, as mentioned in Chapter 5, all
data was collected over the internet. Therefore all data was entered electronically through
syntax on the Web-based questionnaire. Participants had to respond to the survey in the
order that the questions appeared and they were provided reminders at the bottom of the
web-page which made them aware of when they had completed a given section. Per the
consent form, the respondents were given the option of skipping any questions that they
did not feel comfortable answering. As the respondents responded to each of the
questions, the data was stored in a series of Excel files. The data was stored in multiple
Excel files because there were multiple variables collected per respondent, especially
given the network measures that were collected.
The first step of preliminary analysis that was conducted was synthesizing the
data collected across Wave 1 and Wave 2 of the survey, into a single Excel spreadsheet,
where each user was given a unique row where all variables collected from that user were
stored. This single Excel spreadsheet was then uploaded into SPSS, where again, all
variables collected in each unique respondent were entered into a single row which was
identified by the first cell in that row where the respondent’s unique user ID appeared.
After the data had been uploaded to SPSS, a series of analyses were conducted to
ensure the accuracy of the data. First, the entire data set was scanned for completeness
and coherence. In conducting this analysis, the total amount of missing data was assessed
on all of the primary variables collected across both waves of the study. Overall, it was
discovered that just less than 2% of the data was missing across Wave 1 and Wave 2 of
186
the surveys. Specifically, in Wave 1, there was 306 (or 1.98%) missing data points out of
15,383 data points (this 15,383 data points excludes the specific network questions such
as content of conversation with network member, as there were great variance in the
number of responses addressing specific follow-up questions per network member). In
the second wave of the survey, there were 167 (or 1.27%) missing data points out of a
possible 13,275 data points. These outcomes suggest a reasonably high level of
completeness and accuracy of data in this dataset. Further, with each wave of the survey
reporting less than 2% of missing data this was especially impressive given that the
organization from which the data was collected experienced three rounds of layoffs
during the time the data was being collected. In addition, the missing data was evenly
distributed across all primary study variables (e.g. 2-3 responses were missing on average
per variable), therefore, there was no reason to exclude the data from one specific
primary variable included in the study. Finally, although this will be discussed later in
this section, research guidelines suggests that the pairwise deletion method is appropriate
to use when there is less than 2% of missing data (Roth, 1994). Consistent with this
notion, the pairwise deletion method was used during the hypotheses testing.
Every precaution was taken to ensure that all data was properly stored in the
Excel files as the respondents completed the survey. However, approximately 65
respondents were impacted by a glitch in the computer system. This resulted in a loss of
data on one specific question in the first wave of the study. Specifically, the data being
collected over the internet was stored in a series of Excel files. In the MS Excel program,
there is a default of the number of rows into which data can be entered. In the specific file
where the data related to network ties was being stored, the limit was not initially
187
removed. Therefore, although the respondents completed that section of the survey, their
answers were not recorded. This occurred until the error was recognized at which time
the maximum number of rows row default in that specific Excel file was removed.
Because the data was collected across a series of Excel files, other data collected for
those individuals was not impacted by the this error. All 65 individuals were contacted
and asked to retake the portion of the survey where there was missing data. Of the 65
individuals contacted, 31 individuals retook the missing portion of their survey. Although
the remaining 34 individuals had missing data, it was determined that those cases would
remain in the data set, as the missing data was applicable to only one questions on the
survey. The missing data was coded appropriately.
Next, all scale ranges were analyzed to ensure that the responses fell within the
correct parameters that were consistent with the scales and anchors used to assessed the
variables of interest. All responses were found to fall within the correct parameters.
Finally, there were two control variables that were included in the survey that were used
to ensure that the respondents met the eligibility criteria that was presented on the set of
directions that accompanied the survey. Those specific set of criteria included that
individual must be working at least 30 hours per week and no less than 9 months per
year. These criteria were used to assess whether or not that individual was working full-
time. In total 4 of the respondents worked less than 30 hours per year. Specifically two of
the respondents worked 28 hours per week and 2 worked only 24 hours per week. Of
those 4 network members that worked less than 30 hours, 2 network members also
worked less than 9 months per year (that is, the two that worked 24 hours per week). It
was determined that the individuals that worked 28 hours per week would be kept in the
188
data set. In comparison the individuals that worked 24 hours per week also worked less
than 9 months per year. Therefore it was determined those two respondents would be
excluded from the data set. This resulted in the total sample size for this study of 293.
Next, a series of analyses were run on the data including an analysis of response
bias between waves was conducted (i.e. a series of one-way ANOVAs and chi-square
tests were run), sample descriptives and frequencies were generated, and an analyses of
the scale reliabilities were performed on the study data.
189
16
.44**
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12
(..86
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11
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8 -.0
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Var
iabl
esC
ON
TRO
L1.
Age
2.O
rgTe
nure
3.Jo
bTe
nure
(Mth
s)4.
Empl
oym
ent
5.Ed
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ion
6.Em
ploy
men
t7H
ours
Wor
ked
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ork
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9..P
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talS
tatu
s10
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der
11.F
amily
12.S
egm
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tion-
13.S
egm
enta
tion-
14.J
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volv
emen
t15
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oN
etw
ork
16.
Ego
Net
wor
k17
.Eg
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DV
s18
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M-C
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r19
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M-C
aree
r20
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M-M
obili
ty21
.CS-
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ry22
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rwth
23. C
S- P
rom
otio
ns
24.
Sat-
Indi
vidu
al
25.
Sat
-. -P
eers
Con
tinue
d
T
able
5.2
Des
crip
tive
Stat
istic
s and
Cor
rela
tions
on
Car
eer S
ucce
ss a
nd C
aree
r Man
agem
ent
Not
e: N
=29
3
Alp
ha re
liabi
litie
s in
diag
onal
.
*
p<.0
5;**
p<.0
1
190
Tab
le 5
.2 D
escr
iptiv
e St
atis
tics a
nd C
orre
latio
ns o
n C
aree
r Suc
cess
and
Car
eer M
anag
emen
t N
ote:
N =
293
A
lpha
relia
bilit
ies i
n di
agon
al.
*p<
.05;
**p<
.01
Tabl
e 5.
2 C
ontin
ued
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(.79)
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18
(.82)
.6
8**
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00
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17
-.10
-.17**
-.10
.02
.04
.01
-.09
-.07
SD
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191
Scale Reliability Analysis
The coefficient alphas for the scales used ranged from .79 to .93 (see diagonals on
Table 5.2). In addition, factor analysis was used to ensure that the items loaded on the
same factors that they did in the published literature. Overall, all items loaded on the
appropriate factors for each of the scales of interest. To this end, the following
observations were made about each of the scales.
The first scale used in the study was the family involvement scale. As mentioned
previously, the original scale consisted of ten items. Of those ten, four items were used to
create the family involvement scale for this study. The family involvement items were
taken from the Kanungo (1982) scale. The original scale consisted of ten items, of which
four were used in this study. Of note, none of the 4 items had to be reverse coded.
Consistent with existing research, results from the factor analysis suggested that the four
items loaded one factor. This four item scale had an internal consistency reliability of .93.
The alpha reliability estimated in this study was slightly higher than the alpha reported in
the study from which this measure was drawn (alpha =0.87). The mean of this scale was
M=4.74. This suggests that most of the sample moderately agrees that family is important
to them. The factor loadings from the family involvement scale are presented in Table
5.3.
192
RESULTS OF FACTOR ANALYSIS – SCALE ITEMS
Item
Factor Loadings
1. The most important things that happen to me involve my present parental role
2. Most of my interests are centered around my family
3. Most of my personal life goals are family-oriented
4. I consider my family to be very central to my existence
Eigenvalue (3.332) Percent of variance explained (83.289%)
.857
.886
.882
.902
Table 5.3: Family Involvement Scale, Items and Loadings
193
The next two scales used in this dissertation were scales measured work and home
preferences, that is, this measure was used to understand the extent to which individuals
wanted their home and work lives segmented. Two scales were used to tap the work and
home preferences measure. Those two scales included Hecht and Allen (2002) PEBS
scale and the Edwards and Rothbard (1999) role segmentation scale. Prior to conducting
both the factor analysis and reliability analysis on the Hecht and Allen (2002) scale,
several items had to be reverse coded as was done in the original scale (e.g. I do not
work on personal time, When working, I am completely focused on my work. Consistent
with the original Hecht and Allen (2002) scale the twelve items loaded on two separate
scales. The five items that loaded on the WHOT scale (i.e. those items that suggest that
work is spilling over to home) had an internal consistency reliability of 0.85. This alpha
is slightly lower than the alpha reported in the study from which these items were drawn
(where alpha =0.94 for the WTOH scale). In comparison the seven items that loaded on
the HTOW scale (i.e. those items that suggest home is spilling over to work) had an
internal consistency of 0.82. This reliability estimate was also slightly lower than the
reliability reported in the study from which these items were drawn (HTWO alpha =.91).
The mean for the WHOT scale was M= 3.49 (SD= 1.31) , and the mean for the HTOW
scale was M= 3.12 (SD= 0.92). In interpreting the scale, the items were worded such that
high scores reflect weak boundaries between work and home, and low scores reflect
strong boundaries between work and home. Given that the mean of the scales was M=
3.5 and M=3.1 on a 6-point scale for the WHOT and HTOW scales respectively, this
would suggest moderately weak boundaries between work and home.
194
In anticipation of not knowing exactly how well the PEBS scales would
accurately measure an individual’s desire for segmenting their work and home
preference, that is, the construct of interest, another scale was included to measure an
individual’s desire for segmenting their home and work lives. As mentioned previously,
this third scale measured preferences for segmenting work and home lives (see Edwards
& Rothbard 1999). This measure included four items (e.g. I do not desire to be expected
to take work home) and loaded on one factor, which is consistent with the original scale.
The reported internal consistency for the scale in this study was 0.86, which was slightly
higher than the estimate reported in the original study (alpha =0.77). In addition, the
mean on this scale was M =4.9 (SD= 1.13) on a six point scale. In this case, the items in
the original scale were written such that high scores suggest that individuals desire to
have a clear segmentation between their work and home lives.
Of the three scales used to measure an individual’s preferences to segment, the
decision was made to use the Edwards & Rothbard (1999) preferences scale in the final
analyses. The rationale for this decision was based on two key ideas. First, of the three
scale used to measure work and home preferences, the Edwards & Rothbard scale had the
highest estimated internal consistency (alpha =.86) in comparison to the other two scales
drawn from the Hecht & Allen (2002) scales (WHOT =0.85 and HTOW =0.82). Further,
once the data was collected, a further examination of the items used in the three scales,
revealed that the items drawn from the Edwards & Rothbard (1999) scale (e.g. “I do not
desire to be required to do work while I am at home) were really a better measure of an
individual’s desire to segment their work and home roles.
195
The Hecht & Allen (2002) HTOW and WHOT scales are really a better measure of an
individual’s perception of which role, that is work or home, seems to have the most
spillover into the opposite role. The factor loadings from all three segmentation scales are
presented in Tables 5.4, 5.5 and 5.6.
196
RESULTS OF FACTOR ANALYSIS – SCALE ITEMS CONT.
Item
Factor Loadings
1. I often do work at home 2. I work after hours 3. I do not work on personal time (R) 4. My personal time is my own (R) 5. I take work out of the office
Eigenvalue (3.138) Percent of variance explained (62.752%)
.812
.739
.700
.599
.793
Table 5.4: Role segmentation Scale A- “Work to Home Boundary”, Items and Loadings.
197
RESULTS OF FACTOR ANALYSIS – SCALE ITEMS CONT.
Item Factor Loadings
1 2
1. I schedule personal activities during business hours
.189 .363
2. I communicate with family and friends during work hours
.161 .973
I 3. I think of personal or family-related issues
while I am working. .336 .521
4. When working I am completely focus on work (R)
.565 .185
5. I leave my personal life outside of the workplace (R)
.744 .213
6. I rarely deal with personal matters while working (R)
.785 . 362
7. The office is reserved for doing work(R) .695 .239
Eigenvalue (3.427) Percent of variance explained (48.962%) Table 5.5: Role segmentation Scale B- “Home to Work Boundary”, Items and Loadings
198
RESULTS OF FACTOR ANALYSIS – SCALE ITEMS CONT.
Item Factor Loadings
1. I do not desire to be required to work while at home
.641
2. I desire to be able to forget work while I am at home
.739
3. I do not desire to have to think about work once I leave the workplace.
.921
4. I do not desire to be expected to take work from home
.852
Eigenvalue (2.852) Percent of variance explained (71.310%) Table 5.6: Role segmentation Scale C- “Work and Home Desirability Scale”, Items and Loadings.
199
This items that comprise the job importance scale, as mentioned previously, were
drawn from the Kanungo (1982) scale. Prior to completing the factor analysis, two of the
ten items had to first be reverse coded (i.e. “To me, my job is only a small part of who I
am”; “Usually I feel detached from my job”).In this study, the job involvement items
loaded on two factors. However, a reliability analysis was conducted and the results
revealed this scale had the highest internal consistency when all items were loaded
together. That is the internal consistency for this scale was higher when all items were
loaded together rather than when the items were spilt between two scales. The internal
consistency for this scale was 0.87, which is consistent with the reliability reported in the
original study (Kanungo, 1982). In addition, the mean score on this scale was M= 3.07
(SD =0.94). This suggests that the respondents on this survey slightly disagree that their
jobs are very important in their lives. The factor loadings from the job involvement scale
are reported in Table 5.7.
200
RESULTS OF FACTOR ANALYSIS – SCALE ITEMS
Item Factor Loadings
1 2
1. The most important things that happen to me involve my present job
.436 .471
2. To me, my job is only a small part of who I am (R)
.371 .335
3. I am very much involved with
personally with my job .285 .652
4. I live, eat, and breathe my job. .536 .534 5. Most of my interests are centered
around my job .678 .395
6. I have very strong ties with my present job which would be difficult for me to break.
.310 .531
7. Usually I feel detached from my job (R).
.064 .607
8. Most of my personal life goals are job-oriented
.769 .080
9. I consider my job to be very central to my existence
.646 .253
10. I like to be absorbed in my job most of the time.
.609 .303
Eigenvalue 1 Factor – (4.554) Variance Explained 1 Factor – (45.542%)
Table 5.7: Job Involvement Scale: Items and Loadings
201
The next set of scales used in this dissertation included those scales used to
measure career management. In total there were twenty seven items used to assess
perceptions of career management. A factor analysis was conducted on all twenty seven
items. There were multiple factor analysis conducted on the career management scales.
Specifically, an exploratory factor analysis was conducted, and a one factor, three factor,
and four factor solutions were each tested separately. The exploratory factor analysis was
conducted using the maximum likelihood extraction method, and the rotated factor matrix
was conducted using the orthogonal rotation method. In addition, a separate reliability
analysis was conducted on the one-factor, three factor, and four factor tests. The initial
factor analysis resulted in a 3 factor solution, as evidenced by a scree plot analyses which
indicated three factors. After this initial factor analysis was conducted, a one-factor and
four-factor solution were forced. All items were found to load during the one-factor
solution, and the internal consistency reliability estimate for the one-factor solution was
0.90. However, the decision was made not to use the one-factor solution, as this solution
was not consistent with the manner in which the original scales were used in previous
research. That is, the career management scale was created by combining four separate
career management scales (i.e. career planning scale (Hall, 1990); career tactics scale
(Hall, 1990); career networking scale (Sturges et al., 2000), and career mobility
preparedness scale (Kossek et al., 1999). Next, the four factor solution was analyzed.
The conclusion drawn from this analysis was, although the career management measure
written for the survey used in this dissertation was done by combining four separate
scales, there was no evidence to suggest that these scales loaded on four separate factors.
202
That is, in investigating the scales, there was no evidence to support that each of the 27
items was loading uniquely on one of the four factors. That is, several of the items loaded
on multiple factors, and there was no evidence to suggest that the entire set of items (i.e.
B1,B2, B3, B15, B16, B17, B18, and B19) that loaded on multiple factors loaded on any
one factor better than the other (i.e. several of the items would load the same on more
than one factor). As a result, the decision was made to use a three-factor solution. The
three factor solution was used for this data set for several reasons. First, this solution
allowed consistency between the manner in which the original scales were written, that
is, this solution allowed for multiple career management scales to be tested in the final
analyses. In addition, the outcome of the three factor solution allowed the items drawn
from Hall (1990) and Kossek et al (1999) study, that is, career planning, career tactics,
and career mobility preparedness, to load separately on one of the three factors. There
was one exception to this rule. The items drawn from the Sturges et al (2000) study did
not load uniquely on a single. Rather, the items from the Sturges et al (2000) study loaded
on multiple factors. Further, the reliability of the items from the Sturges et al (2000)
study, that is, the career networking scale had the lowest reliability of the four scales used
to create the career management measure used in this study (internal consistency
reliability estimate was 0.76). Finally, the career networking scale was highly correlated
with the remaining three scales (e.g. career mobility preparedness). As a result, the
decision was made not to use the career networking scale in the final analyses. Thus, the
scales used to measure career management in the final analyses included the Career
Planning (Hall, 1990), Career Tactics (Hall, 1990), and Career Mobility Preparedness
(Kossek et al., 1999).
203
The first career management scale, career planning, included five items from
(Hall 1990). This scale measured the extent to which an individual knows their
individual strength and weaknesses and makes specific plans that will allow them to
achieve their career goals. The internal consistency reliability estimate for the career
planning scale was 0.82. This alpha is slightly higher than the one reported in the study
from which these items were drawn (alpha =0.70). The mean for this scale was M=4.618
(SD=0.92) on a six-point scale which suggests that individuals in the sample moderately
agree that they are able to plan their own careers. The factor loadings from the career
planning scale reported in this study appear in Table 5.8. Of note, the factor loadings
reported are the factor loadings from the 3-factor solution that was used in the final
analyses.
The second scale, career tactics, was also drawn from Hall (1990), and it
comprised of six items. The scale really assesses the extent to which the individual goes
out of their way to find mentoring and to take positions that will help enhance their
career. The internal consistency reliability estimate for the career tactics scale was 0.79.
This alpha was slightly higher than the original study where the reported alpha was 0.68.
The mean for this scale was M=3.976 (SD=0.93) on a six-point scale. The factor
loadings from the career tactics scale reported in this study appear in Table 5.8 As
mentioned previously, the factor loadings reported are the factor loadings from the 3-
factor solution that was used in the final analyses.
The final scale used to assess perceptions of career management, that is career
mobility preparedness, included eight items from Kossek et al. (1999). This scale was
used to assess an individual’s perceptions that they were engage in mobility preparedness
204
behaviors (e.g. to what extent do you actively seek out information about job
opportunities outside of your organization). The internal reliability estimate for this scale
was 0.86. The reliability estimate found is identical to the reliability estimate reported in
the study from which the items were drawn (where alpha =0.84). The mean for this scale
was M=3.041 (SD=0.95) on a five-point scale. This suggests to moderate extent,
individuals are engaging in mobility preparedness behaviors in order to manage their
careers. The factor loadings from the career mobility preparedness scale reported in this
study appear in Table 5.8.
205
FACTOR
1 2 3 “Career Tactics” “Career Mobility
Preparedness” “Career Planning”
Items B1 .413 .107 .152 B2 .578 .050 .108 B3 .458 .152 .329
.488 B4 .302 .120
.622 B5 .345 .255
.889 B6 .292 .203
.306 B7 .246 .080
.581 B8 .345 .128 .445 B9 .088 .582 .652 B10 .016 .283 .583 B11 .148 .366 .493 B12 .205 .309 .507 B13 .230 .252 .431 B14 -.053 .150
B15 .729 .129 .149 B16 .642 .264 .288 B17 .577 .130 .176 B18 .181 .204 .187 B19 -.005 .520 .129
206
C1 .100 .524 .162 C2 .048 .615 -.019 C3 .186 .610 .091 C4 .045 .770 .084 C5 .154 .595 .269 C6 -.007 .774 .069 C7 .335 .601 .106 C8 .170 .624 .099 *Scale Reliability 0.79 0.86 0.82
Eigenvalue 4.360 4.045 3.053 Variance Explained (%) 16.149% 14.981% 11.306% *Scale Reliabilities (Loadings from Rotated Factor Matrix) Table 5.8: 3-Factor Solution, Career Management Items
The last set of scales that were analyzed included two scales used to measure
career satisfaction, which is subjective career success. The scales described previously all
measured career management, a construct treated separately from subjective career
success in the hypothesized model in this study. As mentioned in Chapter 4, there were
two separate scales used to assess career success. The first scale used to assess career
success measured an individual’s perceptions of their own career success. This measure
drawn from Greenhaus et al. loaded on a single factor and the internal reliability estimate
was 0.86. The reliability estimate found in this study is identical to the reliability
estimate reported in the Greenhaus et al (1990) study. The mean for this scale was M =
4.12 (SD = 1.054) on a 6-point scale.
The second sets of items used to measure perceptions of career success (i.e.
subjective career success) were drawn from the Heslin (2003) study. These items
measured an individual’s perception of career success in comparison to others in their
peer group. Consistent with the scale originally published in the Heslin (2003) study, all
six items loaded on one factor and the internal consistency reliability estimate for this
scale was 0.89. This estimate is slightly lower than the alpha reported in the Heslin
(2003) study where the reported estimate was 0.95. The mean for this scale was M =
4.203 (SD = 1.065) on a six-point scale. Taken together, both scales suggest that the
respondents in this sample slight agree that they are satisfied with the career success.
The factor loadings from the Individual Career Satisfaction and Peer-Related Satisfaction
appear in Tables 5.9 and 5.10, respectfully.
207
RESULTS OF FACTOR ANALYSIS – SCALE ITEMS CONT.
Item Factor Loadings
1. Relative to my career aspirations, I am satisfied with the progress I have made towards meeting my goals for advancement.
.767
2. Relative to my career aspirations, I am satisfied with
the overall success I have achieved in my career. .860
3. Relative to my career aspirations, I am satisfied with
the progress I have made towards meeting my goals for income.
.690
4. Relative to my career aspirations, I am satisfied with
the skill development I have attained. .681
5. Relative to my career aspirations, I am satisfied with
the autonomy I have attained. .659
6. Relative to my career aspirations, I am satisfied with
the intellectual stimulation I have attained. .658
Eigenvalue (3.620) Percent of Variance Explained (60.335%) Table 5.9: Career Success-Individual Career Satisfaction Scale, Items and Loadings
208
RESULTS OF FACTOR ANALYSIS – SCALE ITEMS CONT.
Item Factor Loadings
1. Relative to the people I perceive as peers, I am satisfied with the progress I have made towards meeting my goals for advancement.
.886
2. Relative to the people I perceive as peers, I am satisfied with the overall success I have achieved in my career.
.924
3. Relative to the people I perceive as peers, I am satisfied with the progress I have made towards meeting my goals for income.
.698
4. Relative to the people I perceive as peers, I am satisfied with the skill development I have attained.
.665
5. Relative to the people I perceive as peers, I am satisfied with the autonomy I have attained.
.671
6. Relative to the people I perceive as peers, I am satisfied with the intellectual stimulation I have attained.
.660
Eigenvalue (3.983) Percent of Variance Explained (66.376 %) Table 5.10: Career Success-Peer/Related Career Satisfaction Scale, Items and Loadings
209
Tests of Hypotheses
Main Effects of Network Constraints on Ego’s Network
Hypotheses 1-4 predicted that there are differences in the networks of working
adults with and without children. Specifically, theses hypotheses suggested that the
network characteristics that differ between working adults and working adults with
children include network size, network composition (i.e. network ties), and conversation
topics discussed within networks. The variables that were thought to contribute to theses
differences in network characteristics across parental status included gender, family
involvement, role segmentation, and job involvement would moderate the relationship
between parental status and ego’s network.
Prior to testing the hypotheses, a set of one-way ANOVAs were run to denote if
there were differences in network characteristics across parental status. Of note, parental
status was a dummy-coded variable, where 0 was used to identify working adults without
parental responsibilities and 1 was used to denote working adults with parental
responsibility. The results from these ANOVAs appear in Table 5.11. Of note, the
sample sizes across network size, network ties, and network content are not the same, as
some of the survey respondents did not questions related to all three network
characteristics. Similar to the missing data within the entire dissertation, any missing data
within the ANOVA analysis was coded appropriately. As noted previously, there was
less than 2% missing data, and the missing data was evenly distributed across all primary
study variables (e.g. 2-3 responses were missing on average per variable). Therefore,
there was no reason to exclude the data from one specific primary variable included in
the study.
210
Differences across parental status were not found among network size. Instead, it
was found that regardless of parental status, the average network size for the respondents
included in this sample was 8.61 ties. This finding suggests, that despite the prevailing
notion (e.g. Smith-Lovin & McPhearson, 1993) that the network size for parents will be
smaller than the networking size for adults without parental status, the results of this
dissertation suggests that network size of parents is not significantly different from the
networking size of adults without parental responsibility. This may suggest that parental
status alone will not impact network size, and working parents should not expect to have
a smaller network than individuals without parental responsibility.
In addition, differences across parental status were not found among network ties.
However, it should be noted that the average proportion of kin ties among the individuals
sampled in this dissertation was 0.56. This finding suggests that more than 50% of the
ties within each respondent’s network were kin ties. As a result, one can conclude that
parental status has little to do with the ties within an individual’s network. In fact,
research suggests that most North American networks are usually kin-centered networks
(Bearman & Parigi, 2004). Prevailing notion suggests that the nature of kin-centered
networks exists in the US because individuals make personal choices to talk to kin ties
about matters that are of great importance to them (Bearman & Parigi, 2004).
Differences across parental status were among the remaining network
characteristics, that is, network content. Specifically, it was found working adults with
parental responsibility were more likely to discuss family-oriented topics among
members of their network. Differences in network content also occurred within parental
status groups. Specifically, parents with only one child reported that 50 percent of the
211
conversations among the members of network were related to family topics (e.g.
children/household, marriage, health). In comparison, working adults with two or at least
three children reported 45% and 44%, respectively, as family-oriented conversation
topics, which is less than that reported by the working adults with one child.
After the initial set of one-way ANOVAs were conducted, a series of moderated
regressions were used to test Hypotheses 1-4. Moderated regression analyses were used
to tests the hypotheses; as this type of analysis, in comparison to ANOVA, allows the
researcher to include control variables in the analyses. Moderated regressions were also
used to test for both the main effects and interactions of the network constraints (i.e.
gender, family involvement, role segmentation, and job involvement) and their
relationship with parental status, on the network characteristics of interest (i.e. network
size, network content, and network ties). The specific interaction variables (e.g. gender x
parental status) were created in SPSS. Specifically, interaction terms were created by
multiplying parental status by each of the by each independent variable (e.g. gender,
family involvement). In total five interactions variables were created (parental status x
gender, parental status x family involvement, parental status x role segmentation (actual),
parental status x role segmentation (perceptual), and parental status x job involvement
from the existing data, prior to beginning the hypotheses testing. Of note, these variables
were not centered prior to the interaction terms being formed. The variables were not
centered prior to the interaction action terms being created because centering only needs
to be done for interactions between variables that do not have meaningful zero values. In
this case, parental status can have a meaningful zero category (either working adults
without kids or working adults with kids can be recoded as 0 and the other category 1).
212
Therefore, the main effect and any interactions with parental status (i.e. parental status x
gender, parental status x family involvement, parental status x role segmentation (actual),
parental status x role segmentation (perceptual), and parental status x job involvement)
should not be centered.
The control variables included in these analyses were respondent age, number of
hours worked per week, number of work interruptions, and highest level of education
completed. The purpose in including the control variables was to statistically account for
factors other than the independent variables of interest, that are known to influence the
dependent variables included in this study. Also, the decision was made not include
organizational tenure, job tenure, and employment tenure (see Table 5.2). Each of these
variables was highly correlated with age (i.e. correlations were greater than r=.70). As a
result the decision was made to keep age as one of the control variables, and eliminate
organizational tenure, job tenure, and employment tenure. Age is control variable that is
included is most studies related to careers and networks, thus is it made sense to keep this
variable. Finally, the decision was made to exclude employment status as one of the
control variables. Employment status was not correlated with any of the variables
included in the study thus it was unnecessary to keep this as one of the control variables
in the study.
213
Variable
214
N = Working Adults Without Children
N= Working Adults With Children
Total MeanN
STD Df F Sig
Network Size
118 152 270 8.60 5.123 269 1.098 .350
Network Ties
116 150 266 0.57 0.28 265 1.97 .120
Network Content
116 151 267 0.44 0.21 266 3.05 .029**
Table 5.11: One-Way ANOVAs between Parental Status and Ego Network Characteristics **p > 0.05
215
Parental Status and Gender on Network Size, Ties and Content
Hypotheses 1a predicted that working adults with parental responsibility will have
a smaller network (i.e. network size) than working adults without parental status; and
among working parents, working mothers will have a small network than working fathers
(i.e. the interaction between gender and parental status will result in a negative
relationship with network size). Tables 5.12 – 5.14 illustrates the results of the regression
analyses used to investigate this hypothesis.
Hypothesis 1a was not supported. That is, there was no support found for the main
effect of parental status on network size. Also, there was no support found for the
interaction of gender x parental status on network size. Results from this are shown in
Table 5.12.
Hypothesis 1b suggested that working adults without parental responsibility will
have more kin ties within their network than working adults without parental
responsibility; and among working parents, working mothers will have more kin ties
within their network than working fathers (i.e. the interaction between gender and
parental status will result in a negative relationship with network ties). Hypothesis 1b
was not supported. That is, there was no support found for the main effect of parental
status on network size. In addition, there was no support found for the interaction of
gender x parental status on network ties. Results from this are shown in Table 5.13.
Lastly, Hypotheses 1c suggested that working adults with parental responsibility
would have more non-work network content than working adults without parental
responsibility; and among working parents, working mothers would have a higher
proportion of non-work content than working fathers (i.e. the interaction between gender
and parental status would result in a negative relationship with network ties). Hypothesis
1c was not supported. That is, there was no support found for the main effect of parental
status on network content. Also, there was no support found for the interaction of gender
x parental status on network content.
Overall, there was no support found that the interaction of gender with parental
status had any impact on network size, ties, or content. Further, there was no support
found for the main effect of gender on any of the network characteristics (i.e. network
size, network ties, and network content). While, Hypotheses 1a,1b, and 1c were not
supported, this outcome is interesting and meaningful. Specifically, several empirical
studies within the networking literature have found that demographic variables,
especially gender, do effect network size and network ties. Specifically, previous research
has suggested that men tend to have larger networks than women (e.g. Ragins and
Sundstrom, 1989), men tend to have a higher proportion of co-worker ties, while women
tend to have a higher proportion of kin ties (Mardsen, 1990), and women have been
found to avoid talking about their families at work (e.g. Singh et al. 2002). While,
previous research has found that gender predicts network size, network ties, and network
content, this study suggests that gender does not produce significant differences in
network characteristics. This finding suggests that while initial research found that gender
was helpful in explaining variance in networks, other factors may now be more important
in explaining the differences found across network characteristics. Consistent with this
notion, more recent research has begun to examine the role of personality traits in
predicting differences in networks (e.g. Bozionelos, 2003).
216
The regression analyses results for Hypotheses 1a, 1b, and 1c are shown in Tables
5.12, 5.13, and 5.14. All tables show each of the steps included in the regression
analysis. The first step includes the control variables, the second step tested for the main
effects, and the third step tested the hypothesized interactions (e.g parental status x
gender on network ties).
217
218
Table 5.12: Regression Results of the Relationship between Parental Status and Network Constraint (Gender) on Network Size, Hypothesis 1a
DV(s) Beta R2 Adjusted R
Df Change R
F2 2
change
F Overall
Step 1: .015 .000 4, 263 .015 1.033 1.033 Control Variables
Education .024 Hours
Worked Per Week
.059
Work Interruptions
.026
Ego’s Age -.091 Step2: Independent Variables
.021 Parental Status
-.197 -.002 6,261 .005 .697 .919
Gender -.118 Step3: Interaction Term Parental Status x Gender
.023 .181 -.003 7, 260 .003 .695 .886
2DV(s) Beta R Adjusted
RDf Change
RF
219
Table 5.13 Regression Results of the Relationship between Parental Status and Network Constraint (Gender) on Network Ties, Hypothesis 1b (* = p< 0.05)
2 2 change F Overall
Step 1: .075 .061 4, 259 .075 5.237 5.237 Control Variables
Education -.117 Hours
Worked Per Week
-.066
Work Interruptions
.014
Ego’s Age -.274 Step2: .088 .067 6,257 .013 1.884 4.143 Independent Variables
Parental Status
.188
Gender .178 Step3: .092 .067 7,256 .004 .994 3.693 Interaction Term Parental Status x Gender
-.210
220
Table 5.14 Regression Results of the Relationship between Parental Status and Network Constraint (Gender) on Network Content, Hypothesis 1c (* = p< 0.05)
DV(s) Beta R2 Adjusted R
Df Change R
F2 2
change
F Overall
Step 1: .029 .014 4,260 .029 1.920 1.920 Control Variables
Education -.043 Hours
Worked Per Week
-.116
Work Interruptions
.014
Ego’s Age -.043 Step2: .035 .013 6, 258 .006 .854 1.563 Independent Variables
Parental Status
.207
Gender -.020 Step3: .037 .011 7, 257 .002 .448 1.401 Interaction Term Parental Status x Gender
-.145
Parental Status and Family Involvement on Network Size, Ties and Content
Hypothesis 2a suggested that the parental status-network size relationship will be
moderated by family involvement. That is, family involvement will interact with parental
status, such that parents that are highly involved with their families will have smaller
networks compared to parents that are less involved with their families (i.e. when family
involvement and parental status interact, there will be a negative relationship with
network size). Hypothesis 2a was not supported. That is, there was no support found for
the interaction of family involvement and parental status on network size.
Hypothesis 2b suggested that the parental status-network ties relationship will be
moderated by family involvement. That is, family involvement will interact with parental
status, such that parents that are highly involved with their families will have a higher
proportion of kin ties in their network compared to parents that are less involved with
their families. Hypothesis 2b was not supported. That is, there was no support found for
the interaction of family involvement x parental status on network ties.
Finally, Hypothesis 2c suggested that the parental status-network content
relationship will be moderated by family involvement. That is, family involvement will
interact with parental status such that parents that are highly involved with their families
will have a higher proportion of kin/non-work network content compared to parents that
are less involved with their family. Hypothesis 2c was not supported. That is, there was
no support found for the interaction of family involvement x parental status on network
content.
Overall, there was no support found that interaction of parental status with family
involvement had any impact on network size, ties, or content. Further, there no support
221
found for the main effect of family involvement on any of the network characteristics. As
a result, it appears that family involvement, alone, is not a useful variable in explaining
the variance in networks. As mentioned in the literature review, family involvement is
measure used to assess the importance of a specific role in one’s life. Further, role
involvement is thought to lead to conflict among individuals because (1) high levels of
involvement that role may lead to increased amount of time spent in that role, therefore
allowing less time to be allocated to the second role (Greenhaus & Beutell, 1985).
Although family involvement is often measured in work-family studies, the impact of
family involvement on the criterion variable(s) of interest is not studied independently.
Rather, usually work-family research studies the interaction between role involvement
and work-family conflict, and the impact of that interaction on various outcomes (e.g. job
satisfaction). Thus, future research that is interesting in examining if family involvement
impacts networks, should look at the interaction of family involvement with work-family
conflict, and determine if that interaction explains variance across network
characteristics.
The regression analyses results for Hypotheses 2a, 2b, and 2c are shown in Tables
5.15, 5.16, and 5.17. The results shown in Tables 5.15 -5.17 present each step of the
regression analyses, where the first step of the analyses included the control variables, the
second step tested for the main effects, and the third step tested the hypothesized
interactions (e.g. parental status x family involvement on network size).
222
223
Table 5.15: Regression Results of the Relationship between Parental Status and Network Constraint (Family Involvement) on Network Size, Hypothesis 2a (* = p< 0.05)
DV(s) Beta R2 Adjusted R
Df Change R
F2 2
change
F Overall
Step 1: .015 .000 4,263 .015 1.033 1.033 Control Variables
Education .023 Hours
Worked Per Week
.061
Work Interruptions
.026
Ego’s Age -.089 Step2: .019 -.004 6,261 .004 .470 .843 Independent Variables
Parental Status
-.052
Family Involvement Composite
.053
Step3: Interaction Term
.019 Parental Status x Family Involvement Composite
.006 -.007 7,260 .000 .001 .720
224
Table 5.16 Regression Results of the Relationship between Parental Status and Network Constraint (Family Involvement) on Network Ties, Hypothesis 2b (* = p< 0.05)
DV(s) Beta R2 Adjusted R
Df Change R
F2 2
change
F Overall
Step 1: .075 .061* 4, 259 .075 5.237 5.237* Control Variables
Education -.100 Hours
Worked Per Week
-.065
Work Interruptions
.004
Ego’s Age -.246 Step2: .076 .054 6,257 .001 .158 3.521 Independent Variables
Parental Status
.049
Family Involvement Composite
.052
Step3: .076 .051 7,256 .000 .083 3.019 Interaction Term Parental Status x Family Involvement Composite
-.067
225
Table 5.17 Regression Results of the Relationship between Parental Status and Network Constraint (Family Involvement) on Network Content, Hypothesis 2c (* = p< 0.05)
DV(s) Beta R2 Adjusted R
Df Change R
F2 2
change
F Overall
Step 1: .029 .014 4,260 .029 1.920 1.920 Control Variables
Education -.044 Hours
Worked Per Week
-.121
Work Interruptions
.044
Ego’s Age -.054 Step2: .035 .012 6,258 .006 .779 1.537 Independent Variables
Parental Status
.241
Family Involvement Composite
.105
Step3: .037 .011 7,257 .003 .793 1.430 Interaction Term Parental Status x Family Involvement Composite
-.212
Parental Status and Role Segmentation (Actual and Perceptual) on Network Size, Ties
and Content
As mentioned previously, two measures of role segmentation were used in this
study. The perceptual measure of role segmentation was used to determine the
respondent’s desirability for clear segmentation between their work and home lives (e.g.
“I do not desire to be required to do work while at home”). In comparison, the actual
measure of role segmentation was a network measure created by looking at the overlap
between the types of conversation topics an ego discusses among the ties within their
network. A case of segmentation occurred when the ego did not have any overlapping
work and family conversation topics among the members in their network. That is, the
ego talked with some members in their network about work topics and they talked with
other members in their network about family topics, and there was no overlap between
the two. If an ego spoke with members within their network about both work and family
topics, this was an indication that the ego prefers to integrate the work and family lives.
As such, both actual and perceptual measures of role segmentation were examined to
understand the influence these variables have with parental status on the three network
characteristics (size, content, and size).
Hypothesis 3a suggested the parental status-network size relationship will be
moderated by role segmentation. That is, role segmentation will interact with parental
status, such that parents that clearly segment their work and family roles will have
smaller networks compared to parents that do not clearly segment their work and family
roles. Hypothesis 3a was not supported. That is, there was no support found for the
226
interaction of role segmentation (i.e. perceptual role segmentation or actual role
segmentation) x parental status on network size.
Hypothesis 3b suggested that the parental status-network ties relationship will be
moderated by role segmentation. That is, role segmentation will interact with parental
status, such that parents that clearly segment their work and family roles will have a
higher proportion of kin/network ties compared to parents that do not clearly segment
their work and family roles. Hypothesis 3b was not supported. That is, there was no
support found for the interaction of role segmentation (i.e. perceptual role segmentation
or actual role segmentation) x parental status on network size.
However, there was support found for the main effect of role segmentation
(perceptual) on network ties. That is, the reported beta between role segmentation
(perceptual) was significant (B= .245), and it indicated a positive relationship between
role segmentation (perceptual) and network ties. In other words, as role segmentation
increases, the proportion of kin ties within an individual’s network also increases.
Therefore, individuals that clearly segment their work and home lives, such that there is
no overlap between these two domains, tend to have a higher proportion of kin ties within
their network. Results from this analysis are displayed in Table 5.19. Of note, there was
no significant relationship found for the main effect of actual role segmentation on
network ties (See Table 5.22).
Finally, Hypothesis 3c suggested the parental status-network content relationship
will be moderated by role segmentation. That is, role segmentation will interact with
parental status, such that parents that clearly segment their work and family roles will
have a higher proportion of kin/non-work related network content compared to parents
227
that do not clearly segment their work and family roles. Hypothesis 3c was not
supported. That is, there was no support found for the interaction of role segmentation
(i.e. perceptual role segmentation or actual role segmentation) x parental status on
network content.
However, there was support found for the main effect of role segmentation
(perceptual) on network content. That is, the reported beta between role segmentation
(perceptual) was significant (B= .219), and it indicated a positive relationship between
role segmentation (perceptual) and network ties. Results from this are displayed in Table
5.20. In other words, as role segmentation increases, the proportion of non-work content
also increases. Therefore, individuals that clearly segment their work and home lives,
such that there is no overlap between these two domains, tend to have a tendency to
discuss a higher proportion of non-work content that they discuss with members of their
network. Thus, these individuals have very little overlap in their work and non-work
conversations. Therefore, they talk to certain members in their network about work topics
(e.g. career, current projects), and the talk to a separate and distinct group of members
within their network about non-work topics (e.g. children, household issues).
A similar finding was discovered for the main effect of actual role segmentation
on network content. That is, there was a positive and significant relationship found for the
main effect of actual role segmentation found on network content. Specifically, the
reported beta between role segmentation (actual) was significant (B= .166). This result
also suggests that individuals that clearly segment their work and family lives, speak with
certain members within their network about work topics, and they speak to a separate and
distinct group of individuals about non-work content. Results from this analysis are
228
displayed in Table 5.23. Of note, both actual and perceptual measures of role
segmentation were included in this study. Perceptual measure was used to assess an
individual’s attitude toward separating their work and home lives, while the actual role
segmentation measure was a true measure of the extent to which there is overlap between
work and family topics a member discusses with each member within their network.
The regression analysis results for Hypotheses 3a, 3b, and 3c are shown in Tables
5.18 thru Tables 5.23. The results shown in Tables 5.18 thru Tables 5.23 are
demonstrative of each step of the regression analyses, where the first step of the analyses
included the control variables, the second step tested for the main effects, and the third
step tested for the proposed interactions (e.g. actual segmentation x network content).
229
230
Table 5.18 Regression Results of the Relationship between Parental Status and Role Segmentation (Perceptual) on Network Size, Hypothesis 3a (* = p< 0.05)
DV(s) Beta R2 Adjusted R
Df Change R
F2 2
change
F Overall
Step 1: .015 .000 4,262 .015 1.029 1.029 Control Variables
Education .023 Hours
Worked Per Week
.060
Work Interruptions
.026
Ego’s Age -.103 Step2: .017 -.005 6,260 .002 .254 .767 Independent Variables
Parental Status
-.166
Perceptual Role Segmentation
-.065
Step3: .018 -.008 7,259 .001 .232 .688 Interaction Term Parental Status x Perceptual Role Segmentation
.140
231
Table 5.19 Regression Results of the Relationship between Parental Status and Role Segmentation (Perceptual) on Network Ties, Hypothesis 3b (* = p< 0.05)
DV(s) Beta R2 Adjusted R
Df Change R
F2 2
change
F Overall
Step 1: .075 .060 4,258 .075 5.217 5.217 Control Variables
Education -.117 Hours
Worked Per Week
-.063
Work Interruptions
-.002
Ego’s Age -.235 Step2: .112 .092* 6,256 .037* 5.404* 5.398* Independent Variables
Parental Status
.215
Perceptual Role Segmentation
.245*
Step3: .115 .090 7, 255 .002 .670 4.717 Interaction Term Parental Status x Perceptual Role Segmentation
-.228
232
Table 5.20 Regression Results of the Relationship between Parental Status and Role Segmentation (Perceptual) on Network Content, Hypothesis 3c (* = p< 0.05)
DV(s) Beta R2 Adjusted R
Df Change R
F2 2
change
F Overall
Step 1: .029 .014 4,259 .029 1.913 1.913 Control Variables
Education -.065 Hours
Worked Per Week
-.120
Work Interruptions
.039
Ego’s Age -.048 Step2: .061 .039* 6,257 .032* 4.435* 2.787* Independent Variables
Parental Status
.269
Perceptual Role Segmentation
.219*
Step3: .063 .038 7,256 .002 .559 2.465 Interaction Term Parental Status x Perceptual Role Segmentation
-.214
233
Table 5.21 Regression Results of the Relationship between Parental Status and Role Segmentation (Actual) on Network Size, Hypothesis 3a (* = p< 0.05)
DV(s) Beta R2 Adjusted R
Df Change R
F2 2
change
F Overall
Step 1: .015 .000 4,259 .015 1.017 1.017 Control Variables
Education .018 Hours
Worked Per Week
.056
Work Interruptions
.019
Ego’s Age -.092 Step2: Independent Variables
.018 Parental Status
-.262 -.005 6,257 .002 .291 .771
Actual Role Segmentation
-.035
Step3: .024 -.003 7, 256 .006 1.526 .881 Interaction Term Parental Status x Actual Role Segmentation
.255
234
Table 5.22 Regression Results of the Relationship between Parental Status and Role Segmentation (Actual) on Network Ties, Hypothesis 3b (* = p< 0.05)
DV(s) Beta R2 Adjusted R
Df Change R
F2 2
change
F Overall
Step 1: .075 .061 4,259 .075 5.237 5.237 Control Variables
Education -.099 Hours
Worked Per Week
-.060
Work Interruptions
.005
Ego’s Age -.229 Step2: .091 .069 6,257 .016 2.241 4.272 Independent Variables
Parental Status
-.039
Actual Role Segmentation
.122
Step3: .091 .066 7,256 .000 .014 3.650 Interaction Term Parental Status x Actual Role Segmentation
.024
235
Table 5.23 Regression Results of the Relationship between Parental Status and Role Segmentation (Actual) on Network Content, Hypothesis 3c (* = p< 0.05)
DV(s) Beta R2 Adjusted R
Df Change R
F2 2
change
F Overall
Step 1: .029 .014 4,259 .029 1.913 1.913 Control Variables
Education -.046 Hours
Worked Per Week
-.113
Work Interruptions
.046
Ego’s Age -.034 Step2: .056 .034* 6,257 .028* 3.765* 2.557* Independent Variables
Parental Status
.067*
Actual Role Segmentation
.166*
Step3: .056 .031 7,256 .000 .013 2.185 Interaction Term Parental Status x Actual Role Segmentation
-.023
Parental Status and Job Involvement on Network Size, Ties and Content
Hypothesis 4a proposed that the parental status-network size relationship will be
moderated by job involvement. That is, job involvement will interact with parental status,
such that parents that are not highly involved with their jobs will have smaller networks
compared to parents that are not highly involved with their jobs. Hypothesis 4a was not
supported. That is, there was no support found for the interaction of job involvement x
parental status on network size.
Hypothesis 4b suggested that the parental status-network ties relationship will be
moderated by job involvement. That is, job involvement will interact with parental status,
such that parents that are not highly involved with their jobs will have a higher proportion
of kin ties within their network compared to parents that are not highly involved with
their jobs. Hypothesis 4b was not supported. That is, there was no support found for the
interaction of job involvement x parental status on network ties. However, there was
support found for the main effect of role job involvement on network ties. Specifically,
there was a significant, negative relationship found for the main effect of job involvement
on network ties, (B= -.260). Results for this can be found in Table 5.25. This outcome
suggests that there is a negative relationship between job involvement and network ties,
or when job involvement increases, proportion of kin ties within an individual’s network
decreases. That is, it suggests that when individuals are highly involved with their jobs
they will have less kin ties in their network, and vice versa . In sum, job involvement and
proportion of kin ties are negatively related.
236
Finally, hypothesis 4c suggested that the parental status-network content relationship will
be moderated by job involvement. That is, job involvement will interact with parental
status, such that parents that are not highly involved with their jobs will have a higher
proportion of kin/non-work network content compared to parents that are highly involved
in with their jobs. Hypothesis 4c was not supported. That is, there was no support found
for the interaction of job involvement x parental status on network ties. However, there
was a significant, negative relationship found for the main effect of job involvement on
network content. The beta for job involvement on network contact was (-.235). Results
from this can be seen in Table 5.26. This suggests that as job involvement are negatively
related. That is, as job involvement increases, the proportion of non-work content
decreases and vice-versa.
The regression analyses results for Hypotheses 4a, 4b, and 4c are shown in Tables
5.24, 5.25, and 5.26, respectfully. The results shown in Tables 5.24 -5.26 present each
step of the regression analyses, where the first step of the analyses included the control
variables, the second step tested for the main effects, and the third step tested for the
proposed interactions.
237
238
Table 5.24 Regression Results of the Relationship between Parental Status and Network Constraint (Actual) on Network Size, Hypothesis 4a (* = p< 0.05)
DV(s) Beta R2 Adjusted R
Df Change R
F2 2
change
F Overall
Step 1: .015 .000 4,263 .015 1.033 1.033 Control Variables
Education .016 Hours
Worked Per Week
.037
Work Interruptions
.034
Ego’s Age -.107 Step2: .024 .001 6,261 .008 1.074 1.047 Independent Variables
Parental Status
.046
Job Involvement
.111
Step3: .024 -.002 7,260 .000 .133 .914 Interaction Term Parental Status x Job Involvement
-.078
239
Table 5.25 Regression Results of the Relationship between Parental Status and Network Constraint (Actual) on Network Ties, Hypothesis 4b (* = p< 0.05)
DV(s) Beta R2 Adjusted R
Df Change R
F2 2
change
F Overall
Step 1: .075 .061 4,259 .075 5.237 5.237 Control Variables
Education -.104 Hours
Worked Per Week
-.020
Work Interruptions
-.010
Ego’s Age -.244 Step2: .102 .082 6,257 .028* 3.961* 4.891* Independent Variables
Parental Status
-.307
-.260* Job Involvement
Step3: Interaction Term Parental Status x Job Involvement
.111 .314 .086 7,256 .008 2.312 4.544
240
Table 5.26 Regression Results of the Relationship between Parental Status and Network Constraint (Actual) on Network Content, Hypothesis 4c (* = p< 0.05)
DV(s) Beta R2 Adjusted R
Df Change R
F2 2
change
F Overall
Step 1: .029 .014 4,260 .029 1.920 1.920 Control Variables
Education -.049 Hours
Worked Per Week
-.062
Work Interruptions
.019
Ego’s Age -.044 Step2: .082 .061 6,258 .053* 7.474* 3.835* Independent Variables
Parental Status
.010
-.248* Job Involvement
Step3: .082 .057 7,257 .000 .053 3.283 Interaction Term Parental Status x Job Involvement
.048
Network Characteristics and Career Success
Hypotheses 5a, 5b, and 5c proposed there would be a relationship between each
of the network characteristics and each of the career success indicators. This section
begins with a discussion of Hypothesis 5a, that is, the relationships between network size
and salary, network size and salary growth, network size and promotions, network size
and individual career success, and network size and peer-related career success.
Specifically Hypothesis 5a proposed that network size will negatively influence
career success, such that as network size decreases, objective indicators of career success
(i.e. salary, salary growth, and promotions) will also decrease. Further, there will also be
a negative relationship between network size and career satisfaction; where if network
size decreases, career satisfaction will also decrease. Hypothesis 5a was partially
supported. That is, there was one significant relationship found between network size and
objective/subjective indicators of career success. Network size was related to salary
growth (see Table 5.29, Beta = -.122; change R2 = .015, p<.05). Although this
relationship was significant, the direction of this relationship was different from that
proposed in the hypothesized model. That is, this finding suggests that there is a negative
relationship between network size and salary growth, such that smaller network size
results in larger salary growth. This finding is counterintuitive to several findings in
previous studies (e.g. Burt, 1992), as most literature suggests that the relationship
between network size and salary are positive. That is, as network size increases, salary
also increases.
241
The regression analysis results for Hypothesis 5a are shown in Tables 5.27 (i.e.
network size on salary), 5.28 (i.e. network size on salary growth), 5.29 (i.e. network size
on promotions), 5.30 (i.e. network size on individual career satisfaction), and 5.31 (i.e.
network size on peer-related satisfaction). All control variables were entered into Step 1
of the regression and the independent variable of interest (e.g. network content) was
entered during Step 2 of the regression analysis.
242
243
Table 5.27 Regression Results of the Relationship of Network Size on Salary (Hypothesis 5a)
DV(s) Beta R2 Adjusted R
Df Change R
F2 2
change
F Overall
Step 1: .393 .383 4, 261
.393 42.170 42.170 Control Variables
Education .294 Hours
Worked Per Week
.311
Work Interruptions
.045
Ego’s Age .465 Step2: Independent Variables
.393 Network Size .030 .382 5, 260
.001 .376 33.731
244
Table 5.28 Regression Results of the Relationship of Network Size on Salary Growth (Hypothesis 5a) * p= <.05
DV(s) Beta R2 Adjusted R
Df Change R
F2 2
change
F Overall
Step 1: .342 .332 4, 261
.342 33.950 33.950 Control Variables
Education .131 Hours
Worked Per Week
.026
Work Interruptions
.164
Ego’s Age .514 Step2: Independent Variables
.357 Network Size -.122* .345 5,260 .015* 5.916* 28.855*
245
Table 5.29 Regression Results of the Relationship of Network Size on Promotions
(Hypothesis 5a) * p= <.05
DV(s) Beta R2 Adjusted R
Df Change R
F2 2
change
F Overall
Step 1: .126 .112 4,261 .126 9.374 9.374 Control Variables
Education .057 Hours
Worked Per Week
.179
Work Interruptions
.151
Ego’s Age .251 Step2: Independent Variables
.126 Network Size .050 .111 5,260 .002 .737 7.639
246
Table 5.30 Regression Results of the Relationship of Network Size on Individual Career Satisfaction (Hypothesis 5a) * p= <.05
DV(s) Beta R2 Adjusted R
Df Change R
F2 2
change
F Overall
Step 1: .029 .014 4,263 .029 1.958 1.958 Control Variables
Education -.098 Hours
Worked Per Week
.103
Work Interruptions
.045
Ego’s Age -.116 Step2: .031 .012 5,267 .002 .500 1.664 Independent Variables
Network Size .043
247
Table 5.31 Regression Results of the Relationship of Network Size on Peer-Related Career Satisfaction (Hypothesis 5a) * p= <.05
DV(s) Beta R2 Adjusted R
Df Change R
F2 2
change
F Overall
Step 1: .033 .018 4,261 .033 2.238 2.238 Control Variables
Education -.128 Hours
Worked Per Week
.088
Work Interruptions
.043
Ego’s Age -.121 Step2: .035 .017 5,260 .002 .608 1.909 Independent Variables
Network Size .048
Hypothesis 5b proposed a negative relationship between network ties and the
objective indicators of career success. That is, as the proportion of kin ties increases,
objective indicators of career success (i.e. salary, salary growth, and promotions) will
decrease. Also, Hypothesized 5b proposed a negative relationship between network ties
and subjective indicators of career success. That is, as the proportion of kin ties increases,
subjective indicators of career success (i.e. individual career satisfaction and peer-related
career satisfaction) will also decrease.
There was partial support found for Hypothesis 5b. Specifically, two significant
relationships were found between network ties and the objective/subjective indicators of
career success. First, a significant relationship was found between network ties and
individual career satisfaction (Beta = -.174; change R2 = .028, p<.05). The results for the
regression analysis of network ties on individual career satisfaction are shown in Table
5.35. The results from this analysis suggest that individual career satisfaction declines as
the proportion of kin ties increases.
Next, a significant relationship was found between network ties and peer-related
career satisfaction (Beta= -.138; change R2 = .018, p<.05). The regression results are
presented in Table 5.36. The results from this analysis suggest that peer-related career
satisfaction declines, as the proportion of kin ties increases within a network.
As mentioned previously, the network ties measure was operationalized as the
percentage of non-work/kin ties within an individual’s network. The results of these
findings are consistent with Hypothesis 5b which suggested a negative relationship
between network ties and career satisfaction.
248
That is, as the proportion of kin or non-work ties increase within an individual’s network,
the less satisfied they will be with the careers; this includes the satisfaction an individual
has for their career when they consider their own career goals, and the satisfaction they
have with their career when they compare their career relative to individuals within their
peer group. The regression analysis results are shown in Tables 5.32 (network ties on
salary), 5.33 (network ties on salary growth), 5.34 (network ties on promotion), 5.35
(network ties on individual career satisfaction), and 5.36 (network ties on peer-related
career satisfaction). All control variables were entered into Step 1 of the regression and
the independent variable of interest (e.g. network content) was entered during Step 2 of
the regression analysis.
249
250
Table 5.32 Regression Results of the Relationship of Network Ties on Salary (Hypothesis 5b) * p= <.05
DV(s) Beta R2 Adjusted R
Df Change R
F2 2
change
F Overall
Step 1: .393 .383 4,257 .393 .000 41.524 Control Variables
Education .291 Hours
Worked Per Week
.311
Work Interruptions
.454
Ego’s Age .046 Step2: .393 .382 5,256 .001 .371 33.212 Independent Variables
Network Ties -.031
251
Table 5.33 Regression Results of the Relationship of Network Ties on Salary Growth (Hypothesis 5b) * p= <.05
DV(s) Beta R2 Adjusted R
Df Change R
F2 2
change
F Overall
Step 1: .342 .332 4,257 .342 33.430 33.430 Control Variables
Education .131 Hours
Worked Per Week
.020
Work Interruptions
.533
Ego’s Age .162 Step2: .343 .330 5,256 .001 .195 26.699 Independent Variables
Network Ties .023
252
Table 5.34 Regression Results of the Relationship of Network Ties on Promotions (Hypothesis 5b) * p= <.05
DV(s) Beta R2 Adjusted R
Df Change R
F2 2
change
F Overall
Step 1: .126 .112 4,257 .126 9.230 9.230 Control Variables
Education .055 Hours
Worked Per Week
.180
Work Interruptions
.236
Ego’s Age .151 Step2: .127 .110 5,256 .001 .369 7.440 Independent Variables
Network Ties -.037
253
Table 5.35 Regression Results of the Relationship of Network Ties on Individual Career Satisfaction (Hypothesis 5b) * p= <.05
DV(s) Beta R2 Adjusted R
Df Change R
F2 2
change
F Overall
Step 1: .029 .014 4,263 .029 1.929 1.929 Control Variables
Education -.116 Hours
Worked Per Week
.094
Work Interruptions
-.165
Ego’s Age .046 Step2: .057 .039 5,258 .028* 7.650* 3.113* Independent Variables
Network Ties -.174*
254
Table 5.36 Regression Results of the Relationship of Network Ties on Peer-Related Career Satisfaction (Hypothesis 5b) * p= <.05
DV(s) Beta R2 Adjusted R
Df Change R
F2 2
change
F Overall
Step 1: .033 .018 4,261 .033 2.204 2.204 Control Variables
Education -.141 Hours
Worked Per Week
.082
Work Interruptions
.044
Ego’s Age -.161 Step2: .018 .032 5,256 .018 4.777 2.744 Independent Variables
-.138* Network Ties
Hypothesis 5c proposed there will be a negative relationship between network
content and objective career success. That is, as the proportion of non-work content (i.e.
types of conversations) increases, objective indicators of career success (i.e. salary, salary
growth, and promotions) will decrease, that is, there is a negative relationship expected
between network content and the objective indicators of career success. In addition,
Hypothesis 5c proposed a negative relationship between network content and subjective
indicators of career success. That is, as the proportion of non-work content (i.e. types of
conversations) increases, subjective indicators of career success (i.e. individual career
satisfaction and peer-related career satisfaction) will decrease.
There was partial support found for Hypothesis 5c. Specifically, one significant
relationship was found between network content and the objective/subjective indicators
of career success. First, a significant relationship was found between network content and
salary (Beta = .104; change R2 = .010, p<.05). Results are presented in Table 5.37. The
results of the finding between network content and salary are not consistent with the
relationship proposed in the hypothesized model. Rather, it was expected that as the
proportion of non-work topics increased, salary would decrease. However, this finding
does demonstrate that network content effects salary. Research suggests that is important
for individuals to have access to the vocabulary and topics of conversation that are
relevant to the group (Tonsing & Alant, 2004). That is, an employee needs to be able to
talk about topics that are of interest to their coworkers in order to build and maintain
relationships at work (Tonsing & Alant, 2004). Therefore, one can conclude that
individuals that are interested in building their careers do not have to limit their network
content to work topics. Perhaps, speaking about topics other than work content helps the
255
individual identify topics that are of interest to their coworkers, inclduing topics related to
family-issues. Interestingly, a study found that when individuals were asked to identify
the topics of conversation they shared with coworkers, family, was not a topic that was
discussed with great frequency among coworkers (Tonsing & Alanta, 2004). Instead, the
topics that were discussed with most frequency among coworkers included food,
interpersonal relations, and work (e.g. work processes, work activities, and work
equipment) (Tonsing & Alanta, 2004). Thus, it appears that selecting or being mindful of
conversation topics among the members of your network is important. It appears that
individuals find success in building and maintaining relationships (i.e. which leads to the
gathering of job and career-related information) when they select topics that are familiar
to all members within a given group (Tonsing & Alanta, 2004).
The regression analysis results are shown in Table(s) 5.37 (network content on
salary), 5.38 (network content on salary growth), 5.39 (network content on promotions),
5.40 (network content on individual career satisfaction), and 5.41 (network content on
peer-related career satisfaction). All control variables were entered in the first step of the
regression, the predictor variable of interest (e.g. network size, network content, or
network ties) was entered during the second step of the regression.
256
257
Table 5.37 Regression Results of the Relationship of Network Content on Salary (Hypothesis 5c) * p= <.05
DV(s) Beta R2 Adjusted R
Df Change R
F2 2
change
F Overall
Step 1: .393 .383 4,258 .393 41.686 41.686 Control Variables
Education .300 Hours
Worked Per Week
.326
Work Interruptions
.467
Ego’s Age .039 Step2: Independent Variables
.403 .391* 5, 257 .010 4.516* 34.706* Network Content
.104*
258
Table 5.38 Regression Results of the Relationship of Network Content on Salary Growth (Hypothesis 5c) * p= <.05
DV(s) Beta R2 Adjusted R
Df Change R
F2 2
change
F Overall
Step 1: .342 .332 4,258 .342 33.560 33.560 Control Variables
Education .132 Hours
Worked Per Week
.025
Work Interruptions
.158
Ego’s Age .529 Step2: .345 .332 5,257 .003 1.055 27.065 Independent Variables
Network Content
.053
259
Table 5.39 Regression Results of the Relationship of Network Content on Promotions (Hypothesis 5c) * p= <.05
DV(s) Beta R2 Adjusted R
Df Change R
F2 2
change
F Overall
Step 1: .126 .112 4,258 .126 9.266 9.266 Control Variables
Education .061 Hours
Worked Per Week
.188
Work Interruptions
.148
Ego’s Age .248 Step2: .128 .111 5,
257 .002 .566 7.514
Independent Variables
Network Content
.044
260
Table 5.40 Regression Results of the Relationship of Network Content on Individual Career Satisfaction (Hypothesis 5c)
DV(s) Beta R2 Adjusted R
Df Change R
F2 2
change
F Overall
Step 1: .029 .014 4,260 .029 1.936 1.936 Control Variables
Education -.102 Hours
Worked Per Week
.094
Work Interruptions
.051
Ego’s Age -.125 Step2: .008 .018 5,259 .008 2.170 1.990 Independent Variables
Network Content
-.091
261
Table 5.41 Regression Results of the Relationship of Network Content on Peer-Related Career Satisfaction (Hypothesis 5c) * p= <.05
DV(s) Beta R2 Adjusted R
Df Change R
F2 2
change
F Overall
Step 1: .033 .018 4,258 .033 2.212 2.212 Control Variables
Education -.131 Hours
Worked Per Week
.082
Work Interruptions
.049
Ego’s Age -.130 Step2: .039 .020 5,257 .006 1.501 2.074 Independent Variables
Network Content
-.076
Network Characteristics and Career Management
The network characteristics size, ties, and content were expected to have a
relationship with each of the career management perceptions (i.e. career planning, career
tactics, and career mobility-preparedness). As mentioned previously, career management
is a process where employees gather information to help them make key decisions about
their careers. In order to assess this process, career management perceptions must be
measured by a set of items that reflect the multiple dimensions of this construct. As a
result, there were three different dimensions of career management (career planning,
career tactics, and career mobility preparedness) measured in this study.
Hypothesis 6a, 6b, and 6c suggested that network size, network ties, and network
content, will each have a relationship with career management perceptions. Specifically,
Hypothesis 6a proposed there will be a negative relationship between network size and
career management perceptions. That is, as network size decreases, an individual’s
perceptions of their ability to manage their career will also decrease. Also, if network size
increases, an individual’s perception of their ability to manage their career will also
increase.
Partial support was found for Hypothesis 6a. Specifically, one significant
relationship was found between network size and career management perceptions. There
was a significant relationship found between network size and career mobility
preparedness (Beta = .152; change R2 = .023, p<.05). Results from this finding are
presented in Table 5.44. This finding suggested that as network size increases an
individual’s perception of their ability to prepare to mobilize their career also increases
(e.g. actively discuss internal career opportunities with members within their network).
262
The regression analysis results are shown in Tables 5.42 (network size on career
planning), 5.43 (network size on career tactics), and 5.44 (network size on career
mobility preparedness). The results in Table 5.42 – 5.44 are demonstrative of all steps
included in the regression analysis. All control variables were entered into Step 1 of the
regression and the independent variable of interest (e.g. network size) was entered during
Step 2 of the regression analysis.
263
264
Table 5.42 Regression Results of the Relationship of Network Size on Career Planning (Hypothesis 6a) * p= <.05
DV(s) Beta R2 Adjusted R
Df Change R
F2 2
change
F Overall
Step 1: .022 .007 4,263 .022 1.446 1.446 Control Variables
Education .012 Hours
Worked Per Week
.047
Work Interruptions
-.019
Ego’s Age -.124 Step2: .025 .006 5,262 .003 .829 1.322 Independent Variables
Network Size .056
265
Table 5.43 Regression Results of the Relationship of Network Size on Career Tactics (Hypothesis 6a) * p= <.05
DV(s) Beta R2 Adjusted R
Df Change R
F2 2
change
F Overall
Step 1: Control Variables
.054 Education -.034 .039 4,263 .054 3.725 3.725 Hours
Worked Per Week
.111
Work Interruptions
-.041
Ego’s Age -.175 Step2: Independent Variables
.065 Network Size .108 .047 5,262 .012 3.247 3.655
266
Table 5.44 Regression Results of the Relationship of Network Size on Career Mobility Preparedness (Hypothesis 6a) * p= <.05
DV(s) Beta R2 Adjusted R
Df Change R
F2 2
change
F Overall
Step 1: .100 .086 4,263 .100 7.270 7.270 Control Variables
Education .175 Hours
Worked Per Week
.118
Work Interruptions
.036
Ego’s Age -.196 Step2: .023 .106* 5,262 .023 6.783* 7.300* Independent Variables
.152* Network Size
Hypothesis 6b proposed a negative relationship between network ties and career
management perceptions. That is, as the proportion of non-work/kin ties (in comparison
to non-work ties) increases, perceptions of career management will decrease. Hypothesis
6b was not supported. That is, there was no support found for the main effect of network
content on career planning, career tactics, or career mobility preparedness.
The regression analyses results are shown in Tables 5.45 (network ties on career
planning), 5.46 (network ties on career tactics), and 5.47 (network ties on career mobility
preparedness). All control variables were entered into Step 1 of the regression and the
independent variable of interest (e.g. network ties) was entered during Step 2 of the
regression analysis.
267
268
Table 5.45: Regression Results of the Relationship of Network Ties on Career Planning (Hypothesis 6b) *p= <.05
DV(s) Beta R2 Adjusted R
Df Change R
F2 2
change
F Overall
Step 1: .022 .006 4,259 .022 1.424 1.424 Control Variables
Education .007 Hours
Worked Per Week
.047
Work Interruptions
-.018
Ego’s Age -.143 Step2: .024 .005 5,258 .002 .607 1.259 Independent Variables
Network Ties -.050
269
Table 5.46: Regression Results of The Relationship of Network Ties on Career Tactics (Hypothesis 6b) *p=<.05
DV(s) Beta R2 Adjusted R
Df Change R
F2 2
change
F Overall
Step 1: .054 .039 4,259 .054 3.668 3.668 Control Variables
Education -.038 Hours
Worked Per Week
.115
Work Interruptions
-.039
Ego’s Age -.199 Step2: .056 .038 5,258 .002 .635 3.058 Independent Variables
Network Ties -.050
270
Table 5.47 Regression Results of the Relationship of Network Ties on Career Mobility Preparedness (Hypothesis 6b)
DV(s) Beta R2 Adjusted R
Df Change R
F2 2
change
F Overall
Step 1: .100 .086 4,258 .100 7.159 7.159 Control Variables
Education .169 Hours
Worked Per Week
.121
Work Interruptions
.039
Ego’s Age -.235 Step2: .107 .090 5,258 .008 2.200 6.194 Independent Variables
Network Ties -.091
Finally, Hypothesis 6c proposed a negative relationship between network content
and career management perceptions. That is, as the proportion of non-work content (i.e.
topics of conversation) increases, perceptions of career management will decrease. There
was partial support found for Hypothesis 6c. Specifically, one significant relationship
was found between network content and career management perceptions. A significant,
negative relationship was found between network content and career tactics (Beta= -.165;
change R2 = .026, p<.05). Results are presented in Table 5.49. The results of this analysis
are consistent with those made in the hypothesized relationships. The negative beta
between (-.165) and career mobility tactics suggested that as the proportion of non-work
topics (in comparison to work topics) increases, an individual’s perception of their ability
to engage in career tactic behaviors (e.g. networking, seeking developmental feedback), a
facet of career management, will decrease.
The regression analyses results for the relationships proposed in Hypothesis 6c
are shown in Tables 5.48 (network content on career planning), 5.49 (network content on
career tactics), and 5.50 (network content on career mobility preparedness). All control
variables were entered into Step 1 of the regression and the independent variable of
interest (e.g. network content) was entered during Step 2 of the regression analysis.
Finally, Table 5.51 presents an overview of the hypotheses and results presented
in Chapter 5. A discussion of the findings will be presented in Chapter 6.
271
272
Table 5.48 Regression Results of the Relationship of Network Content on Career Planning (Hypothesis 6c) * p= <.0 5
DV(s) Beta R2 Adjusted R
Df Change R
F2 2
change
F Overall
Step 1: .022 .006 4,260 .022 1.430 1.430 Control Variables
Education .007 Hours
Worked Per Week
.038
Work Interruptions
-.011
Ego’s Age -.135 Step2: .032 .013 5, 529 .010 2.671 1.685 Independent Variables
Network Content
-.101
273
Table 5.49 Regression Results of the Relationship of Network Content on Career Tactics (Hypothesis 6c) * p= <.0 5
DV(s) Beta R2 Adjusted R
Df Change R
F2 2
change
F Overall
Step 1: .054 .039 4,260 .054 3.683 3.683 Control Variables
Education -.041 Hours
Worked Per Week
.097
Work Interruptions
-.028
Ego’s Age -.194 Step2: Independent Variables
.080 Network Content
-.165* .062* 5,259 .026 7.451* 4.509*
274
Table 5.50 Regression Results of the Relationship of Network Content on Career Mobility Preparedness (Hypothesis 6a) * p= <.0 5
DV(s) Beta R2 Adjusted R
Df Change R
F2 2
change
F Overall
Step 1: .100 .086 4,260 .100 7.187 7.187 Control Variables
Education .173 Hours
Worked Per Week
.116
Work Interruptions
.044
Ego’s Age -.217 Step2: .107 .089 5,259 .007 2.066 6.186 Independent Variables
Network Content
-.086
Number Hypothesis Result 1A Not Supported Hypothesis 1a: Working adults with parental
responsibility will have a smaller network (i.e. network size) than working adults without parental status; also among working parents, working mothers will have a smaller network than working fathers (i.e. the interaction between gender and parental status will result in a negative relationship with network size).
1B Not Supported Hypothesis 1b: Working adults with parental responsibility will have a higher proportion of kin ties within their network than working adults without parental responsibility; also among working parents, working mothers will have a higher proportion of kin ties within their network than working fathers (i.e. the interaction between gender and parental status will result in a negative relationship with network ties).
1C Not Supported Hypothesis 1c: Working adults with parental responsibility will have a higher proportion of non-work network content, than working adults without parental responsibility; also among working parents, working mothers will have a higher proportion of non-work content than working fathers (i.e. the interaction between gender and parental status will result in a negative relationship with network ties).
Continued
Table 5.51: Summary of Study Hypotheses and Findings
275
Table 5.51 continued
2A Not Supported Hypothesis 2a: The parental status-network size relationship will be moderated by family involvement. That is, family involvement will interact with parental status, such that parents that are highly involved with their families will have smaller networks compared to parents that are less involved with their families. (i.e. When family involvement and parental status interact, there will be a negative relationship with network size).
2B Not Supported Hypothesis 2b: The parental status-network ties relationship will be moderated by family involvement. That is, family involvement will interact with parental status, such that parents that are highly involved with their families will have a higher proportion of kin ties in their network compared to parents that are less involved with their families (i.e. when family involvement and parental status interact, there will be a positive relationship with network ties).
2C Not Supported Hypothesis 2c: The parental status-network content relationship will be moderated by family involvement. That is, family involvement will interact with parental status, such that parents that are highly involved in their families will have a higher proportion of kin/non-work network content compared to parents that are less involved with their families (i.e. when family involvement and parental status interact, there will be a positive relationship with network content).
Continued
276
Table 5.51 continued
3A Not Supported Hypothesis 3a: The parental status-network size relationship will be moderated by role segmentation. That is, role segmentation will interact with parental status, such that parents that clearly segment their work and family roles will have smaller networks compared to parents that do not clearly segment their work and family roles. (i.e. when role segmentation and parental status interact, there will be a negative relationship with network size).
3B Not Supported Hypothesis 3b: The parental status-network ties relationship will be moderated by role segmentation. That is, role segmentation will interact with parental status, such that parents that clearly segment their work and family roles will have a higher proportion of kin network ties compared to parents that do not clearly segment their work and family roles. (i.e. when role segmentation and parental status interact, there will be a positive relationship with network ties).
3C Not Supported Hypothesis 3c: The parental status-network content relationship will be moderated by role segmentation. That is, role segmentation will interact with parental status, such that parents that clearly segment their work and family roles will have a higher proportion of kin/non-work related network content compared to parents that do not clearly segment their work and family roles. (i.e. when role segmentation and parental status interact, there will be a positive relationship with network content).
Continued
277
Table 5.51 continued
4A Not Supported Hypothesis 4a: The parental status-network size relationship will be moderated by job involvement. That is, job involvement will interact with parental status, such that parents that are not highly involved with their jobs will have smaller networks compared to parents that are highly involved with their jobs. (i.e. when job involvement and parental status interact, there will be a negative relationship with network size).
4B Not Supported Hypothesis 4b: The parental status-network ties relationship will be moderated by job involvement. That is, job involvement will interact with parental status, such that parents that are not highly involved with their jobs will have a higher proportion of kin ties within their network compared to parents that are highly involved with their jobs. (i.e. when job involvement and parental status interact, there will be a negative relationship with network ties).
4C Not Supported Hypothesis 4c: The parental status-network content relationship will be moderated by job involvement. That is, job involvement will interact with parental status, such that parents that are not highly involved with their jobs will have a higher proportion of kin/non-work network content compared to parents that are highly involved with their jobs. (i.e. when job involvement and parental status interact, there will be a negative relationship with network content).
Continued
278
Table 5.51 continued
5A Partial Support Network size will negatively influence career success, such that as network size decreases, objective indicators (i.e. salary, salary growth, and promotions) will also decrease. Further, there will also be a negative relationship between network size and career satisfaction; where if network size decreases, career satisfaction will also decrease.
5B Partial Support There will be a negative relationship between network ties and objective career success. That is, as the proportion of kin ties increases, objective indicators of career success (i.e. salary, salary growth, and promotions) will decrease. Also, there will be a negative relationship between network ties and subjective indicators of career success. That is, as the proportion of kin ties increases, subjective indicators of career success (i.e. individual career satisfaction and peer-related career satisfaction) will decrease.
5C Partial Support There will be a negative relationship between network content and objective career success. That is, as the proportion of non-work content (i.e. types of conversations) increases, objective indicators of career success (i.e. salary, salary growth, and promotions) will decrease. Also, there will be a negative relationship between network content and subjective indicators of career success. That is, as the proportion of non-work content (i.e. types of conversations) increases, subjective indicators of career success (i.e. individual career satisfaction and peer-related career satisfaction) will decrease.
Continued
279
Table 5.51 continued
6A Partial Support There will be a negative relationship between network size and career management perceptions. That is, as network size decreases, an individual’s perceptions of their ability to manage their career will also decrease.
6B Not Supported There will be a negative relationship between network content and career management perceptions. That is, as the proportion of non-work/kin ties increases, perceptions of career management will decrease.
6C Partial Support Hypothesis 6c: There will be a negative relationship between network content and career management perceptions. That is, as the proportion of non-work content (i.e. topics of conversation) increases, perceptions of career management will decrease.
280
CHAPTER 6
DISCUSSION
This chapter provides a discussion of the findings presented in Chapter 5. The
chapter begins with a brief summary of the results, which includes a discussion of the
theoretical implications. Next the practical implications of this study are discussed. This
section closes with a discussion of future research questions, study limitations, and a brief
discussion of post-hoc statistical analysis.
Overview of Findings
As discussed in Chapter 1, the purpose of this dissertation was to determine if
there were differences in the three network characteristics, (network size, ties, and
content) across parental status. In addition, this study sought to understand the
relationship that each of the three network characteristics had with one’s ability to
achieve career success and their relationship with career management perceptions.
The reason is it is important to understand networks, is because research suggests
that employees are now working in the age of the protean career, where the responsibility
to manage an individual’s career, has now shifted from the employer to the employee
(Hall, 2002). Therefore, one way an individual can manage their career is through their
networks.
281
If it is the case that networks are important to careers, it is important to understand (1) are
their any constraints that may lead to differences in network characteristics across
employees (i.e. parental status), and (2) it’s important to understand the relationship that
network characteristics has with various career outcomes. As a result this dissertation
used a social networking perspective to understand how individuals exchange
information for the purpose of advancing their careers. That is, a social networking
framework was used to study the influence of relationships (and how information is
exchanged within these relationships) on various career outcomes.
In an attempt to address these issues, this study began by seeking answering for
the following three questions: First, how do networks differ after the birth of a child for
males vs females? Second, how do networks differ between working adults with and
without children? Third, what constraints produce those differences? In order to address
these questions, a series of One-Way ANOVAs were run and series of hypotheses were
tested, per the results presented in Chapter 5.
First, a series of One-Way ANOVAs were run to determine if there were any
differences in the network characteristics, network size, network ties, and network
content, across parental status. The results from the One-Way ANOVAs presented in
Chapter 5 (see Table 5.14), and the conclusion drawn from this analysis is, that working
adults with children and working adults without children, only differ on one of the three
network characteristics studied, that is, network content. As discussed in Chapter 5,
working adults with children, reported a higher percentage of their network content (i.e.
topics of conversation) to be related to non-work topics (e.g. children/household, health).
An interesting finding from this study was that the percentage of non-work content
282
individuals discuss with the members of their network, decreases as the number of
children increases. Specifically, parents with only one child reported that 50 percent of
the conversations among the members of network were related to family topics (e.g.
children/household, marriage, health). In comparison, working adults with two or at least
three children reported 45% and 44% , respectively, as family-oriented conversation
topics, which is less than that reported by the working adults with one child. Thus it
appears that working parents reported a higher proportion of non-work content if they
had just one child. As the number of children for which the working parent is responsible
for increases, the proportion of non-work content (e.g. family-related conversation topics)
also decreases.
The results showed no significant differences found across parental status for
network size and network ties. Overall, the average network size for the respondents
included in this sample was 8.61 ties. This is interesting to because it suggests that
despite the prevailing notion (e.g. Smith-Lovin & McPhearson, 1993) that the network
size for parents will be smaller than the networking size for adults without parental status,
the results of this study suggest that the network size of parents is not significantly
different from the networking size of adults without parental responsibility. This suggests
that parental status alone will not impact network size, and working parents should not
expect to have a smaller network than individuals without parental responsibility. In
addition, differences across parental status were not found among network ties. However,
it should be noted that the average proportion of kin ties among the study participants
was 0.56. This suggests that more than 50% of the ties within each respondent’s network
were kin ties. As a result, one can conclude that parental status has little to do with the
283
ties within an individual’s network. In fact, research suggests that most North American
networks are usually kin-centered networks (Bearman & Parigi, 2004). Prevailing notion
suggests that the nature of kin-centered networks exists in the US because individuals
make personal choices to talk to kin ties about matters that are of great importance to
them (Bearman & Parigi, 2004).
A series of regressions were run to test Hypotheses 1-4. Each of these hypotheses
proposed tested first for the main effect of parental status on each of the three network
characteristics (e.g. network size, ties, and content). Next, the hypotheses proposed tested
for the interaction effects of each of the 4 moderators with parental status (i.e. gender x
parental status, family involvement x parental status, job involvement x parental status,
and role segmentation x parental status) on the network characteristics included in the
study (i.e. network size, network ties, and network content).
As discussed in Chapter 5, when Hypotheses 1a, 1b, and 1c were tested, there was
no support found for the main effects of gender found for any of the three network
characteristics, size, ties, or content. Nor was there any support found for the interaction
between gender and parental status on any of the three network characteristics. That is,
there was no support found for Hypotheses 1a, 1b, and 1c. While, Hypotheses 1a,1b, and
1c were not supported, this outcome is interesting and meaningful. Specifically, several
empirical studies within the networking literature have found that demographic variables,
especially gender, relate to network size and network ties. Specifically, previous research
has suggested that men tend to have larger networks than women (e.g. Ragins and
Sundstrom, 1989), men tend to have a higher proportion of co-worker ties, while women
tend to have a higher proportion of kin ties (Mardsen, 1990), and women have been
284
found to avoid talking about their families at work (e.g. Singh et al. 2002). While
previous research has found that gender predicts network size, network ties, and network
content, this study suggests that gender does not produce significant differences in
network characteristics. This finding suggests that while initial research found that gender
was helpful in explaining variance in networks, other factors may be more important in
explaining the differences found across network characteristics. Consistent with this
notion, recent research has begun to examine the role of personality traits in predicting
differences in networks (e.g. Bozionelos, 2003).
In addition to gender, three other moderators, including family involvement, job
involvement, and role segmentation preferences, were hypothesized (Hypotheses 2-4) to
result in differences in network characteristics (i.e. network size, network ties, and
network content) across parental status. These hypotheses were not supported. That is,
the interaction between parental status and family involvement, job involvement, or role
segmentation, were not found to be related to network size, network ties, and network
content.
In examining the results from Hypotheses 2a-c, that is, the interaction between
parental status and family involvement on the three network characteristics, size, ties, and
content, the following observations can be made. As mentioned previously, no support
was found for the main effect of family involvement on any of the network
characteristics. As a result, it appears that family involvement alone is not a useful
variable for explaining the variance in networks. As mentioned in the literature review,
family involvement is used to assess the importance of a specific role in one’s life.
Further, role involvement is thought to lead to conflict among individuals. This occurs
285
among individuals because high levels of involvement in a role may lead to an increased
amount of time spent in that role, therefore allowing less time to be allocated to a second
role (Greenhaus & Beutell, 1985). Although family involvement is often measured in
work-family studies, the impact of family involvement on the criterion variable(s) of
interest is not studied independently. Rather, work-family research studies typically
investigate the interaction between family involvement and work-family conflict, and the
impact of that interaction on various outcomes (e.g. job satisfaction). Thus, future
research that is interested in examining if family involvement impacts networks, should
look at the interaction of family involvement with work-family conflict, and determine if
that interaction explains variance across network characteristics.
Related to the findings from Hypotheses 3a-c, which tested for the main effects of
role segmentation on the three network characteristics, and it tested for the interaction
between parental status and role segmentation on the three network characteristics, the
following observations can be made. There was support found for the main effect of two
of the moderating variables, that is, role segmentation and job involvement, network ties
and network content. First, there was a significant and positive main effect of perceptual
role segmentation on network ties. This finding suggests that there is a positive
relationship between role segmentation and network ties, such that as role segmentation
increases, the proportion of kin ties within a person’s network also increases. This
suggests that individuals that clearly want their work and family lives segmented, are
likely to have a higher number of kin ties within their network. In addition, there was also
a significant, main effect found for both actual and perceptual segmentation on network
content. The perceptual measure of role segmentation measures an individuals attitudes
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or preferences for segmenting their work and home lives (e.g. “I desire to be able to
forget work while I am at home”). In comparison, the actual segmentation measure is
used to understand the extent to which an individual separates their work and home lives,
by considering their topics of conversations with members within their network. The
actual segmentation measure ranges from 0 to 1, where 0 indicates an individual that
talks to some people within their network about work only. Meanwhile, a person scoring
a one on the role segmentation measure, completely integrates their work and family
lives, and they talk about both work and family matters with all members within their
network. The positive significant relationship between role segmentation (both actual and
perceptual) and network content, suggests that the more an individual segments their
work and family lives, they will have a higher proportion of non-work/kin related topics
that they discuss among the members of their network. Thus, individuals that segment
their work and family lives are more likely to talk about non-work issues (e.g. parental
responsibility, health) with the people in their network. What is interesting about these
findings is there was no significant interaction found between role segmentation x
parental status on network content or network ties. Thus, it may be the case that an
individual’s parental status does not directly impact the types of ties that they have within
their network (kin vs non-kin ties), nor does it seem to impact what they discuss with the
people in their network (i.e. network content). Rather, the preferences for segmentation
between work and family lives, an individual difference receiving a lot of attention in the
work-family literature (e.g. Rothbard et al., 2004) may help increase understanding of
specific network characteristics, network ties and network content. As a result, future
studies may want to measure role segmentation preferences in future studies using
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network analyses, if the researcher is interested in identifying other individual differences
besides gender, (something that is assumed to impact various network characteristics (e.g.
network size) ) to really develop a clearer picture of the type of individual differences
that cause variance in network characteristics.
In addition to the significant main effect found for role segmentation, a significant
main effect was also found for job involvement on both network ties and network
content. As expected, the relationship between job involvement and network ties was
found to be significant, but negative (as discussed in Chapter 5). The negative
relationship between job involvement and network ties, suggests that an individual that is
highly involved in their job, will have a smaller number of kin ties within their network.
This is not unexpected because an individual that is highly involved with their job is not
likely to spend a lot of time with kin ties outside of those in their immediate household
(e.g. wife, parent, in-law, child). In fact, previous research suggests that when individuals
are highly involved in one role, this will lead to an increased amount of time spent in that
role (e.g. Greenhaus & Beutell, 1985). Moreover, this finding does not suggest that
individuals highly involved with their jobs have zero kin ties within their network, they
are just likely to have fewer kin ties within their network than an individual that is not
highly involved with their job. One explanation for why individuals that are highly
involved in their jobs is directly related to one of the key factors thought to contribute to
high job involvement. Specifically, a factor that often contributes to high job involvement
is the supportive relationships an individual develops with their coworkers and
supervisors (Lodahl & Kejner, 1965). Therefore, if an individual is highly involved with
their job and is benefiting from the supportive relationships they have developed with
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their coworkers and supervisors, than they are less likely to have a high proportion of kin
ties within their network; instead they are likely to have a higher proportion of work ties
within their network. Finally, there was also a significant main effect of job involvement
on network content (i.e. topics of conversation). The beta for job involvement on network
content was negative. This suggests that as job involvement increases the non-work
network content (i.e. non-work topics of conversation) also decline. The direction of this
relationship is understandable, as one would expect that an individual that is highly
involved with their job will also spend a lot of time discussing their jobs or other topics
related to their jobs (e.g. specific projects, management styles). In conclusion, individuals
that are highly involved with their jobs are likely to have smaller proportions of kin ties
within their network, and they will discuss fewer non-work/kin topics amongst members
of their network.
As mentioned previously, there were no main effects found for parental status any
of the three network characteristics included in this study, that is, network size, network
ties, or network content. However, prior to the hypotheses being tested, a series of One-
Way ANOVAs were conducted on the data. The ANOVA results shown in Table 5.14
suggests that network content differs across parental status. That is, of the three network
characteristics proposed to differ across parental status, network content was the only
characteristic found to have significant differences across parental status. In addition,
although network content differed across parental status, the difference across parental
status was not moderated by the four variables suggested in this study, that is, gender,
family involvement, role segmentation, and job involvement. Future research needs to
determine what antecedents contribute to the differences in network content across
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parental status. One antecedent that may contribute to the differences found in network
content and in differences in other network characteristics of interest may be work-family
conflict.
Work-family conflict is the conflict individuals feel related to the pressure they
experience in balancing dual roles, that is their role at work and their role at home. That
is, work-family conflict is the role conflict individuals feel that makes compliance with
one role, difficult to comply with the other role (Kahn et al., 1964). Also, related to the
idea of work-family conflict is the theory of role strain, which suggests that individuals
experience a felt difficulty in fulfilling multiple role obligations (Goode, 1960). Finally,
Greenhaus & Beutell (1985) describe work-family conflict as three different types of
conflict an individual can face, which includes times-based conflict (where involvement
in one role is impeded by pressures in the other role), strained-based conflict (where
performance in role is impacted by performance in another role), and behavior-based
conflict (where performance in one role is made more difficult by the behavior required
in another role). In conclusion, the argument can be made that researchers interested in
using network analysis as a way of understanding the career challenges of working
parents should consider work-family conflict as one of the key antecedents in future
research. Work-family conflict is probably one of the key variables that is studied in the
work-family literature, and existing research demonstrates that work-family conflict is
empirically linked to other variables, for example job satisfaction (Kossek & Ozeki,
1998). Thus, in integrating the network, careers, and work-family literature, future
research should consider including antecedents from the work-family literature (i.e.
work-family conflict) in an attempt to explain the unique challenges working parents may
290
have in managing their careers, including maintaining their social capital (networks),
when they are compared to working adults without parental responsibility.
This study also sought to understand if a relationship exists between the network
characteristics size, ties, and content, and two specific career outcomes, career success
and career management perceptions (see Hypotheses 5-6). Hypotheses 5a – 5c predicted
the relationships between the three network characteristics and objective career success
indicators (e.g. salary, salary growth), while Hypotheses 6a-6c predicted the relationships
between the thee network characteristics and career management perceptions (e.g. career
planning, career tactics).
First, Hypotheses 5a, 5b, and 5 c proposed that network size (5a), network
ties(5b), and network content(5c) would have a relationship with each of the five career
success indicators (i.e. salary, salary growth, promotions, individual career success and
peer-related career success). Partial support was found for Hypotheses 5a, 5b, and 5c.
Specifically, network size, that is, the average number of ties in an individual’s network
was found to have a significant relationship with salary growth. The beta weight for
network size predicting salary growth was (-.122). This indicates for every -.122 unit
decrease in network size, is accompanied by a unit increase in salary growth. It was
expected that salary growth would increase as network size increases. A summary of the
regression results for Hypotheses 5a, 5b, and 5c can be seen in Table 6.1.
291
Independent Variables
Salary Salary Growth
Promotions Individual Peer Career Sat Career Sat
Step 1
292
Education .294 .131 .057 -.098 -.128
Number of Hours/Week
.311 .026 .179 .103 .088
Ego’s Age .465 .514 .251 -.116 -.121
Work Interruptions
.045 .164 .151 .045 .043
Step 2
Network Size .030 -.122* .050 .043 .048
Change in R2 .001 .015* .002 .002 .002 (Step 2)
Overall Adjusted R
.382 .345* .111 .012 .017 2
Table 6.1: Summary Results of Network Size on Career Objective Career Success Indicators (H5a) *p >= 0.05
Hypotheses 5b proposed that network ties would be related to salary, salary
growth, promotions, and career satisfaction indicators. Network ties was found to have a
significant relationship on both individual career satisfaction and peer-related career
satisfaction. The beta weight for network ties predicting individual career satisfaction was
(-.174). This indicates for every -.174 unit decrease in network ties, is accompanied by an
unit increase in individual career satisfaction. The beta weight for network ties predicting
peer-related career satisfaction was (-.138). This indicates for every -.138 decrease in
network ties, is accompanied by an unit increase in peer-related career satisfaction.
Further this finding suggests that a high number of kin ties in an individual’s network
leads to negative relationship with career satisfaction. Finally, it is also important to
point out that this that this relationship between network ties and career satisfaction is
true regardless of parental status, as both working parents and working adults without
parental responsibility reported that greater than 50% of the network, on average, is
composed of kin ties.
One factor that may contribute to the relationship found between network ties and
it’s negative effect on career satisfaction is a result of what some researchers describe as
network position (Smith-Lovin & McPherson, 1993). Specifically, an individual’s
network ties, that is the types of relationship one develops through their network,
determines the roles that that an individual enacts in their daily lives (Smith-Lovin &
McPherson, 1993). In other words, if an individual has a network comprised of a higher
proportion of kin ties and fewer coworker ties, that individual is likely to spend more
time occupying the identity(s) that are closely tied to their family-oriented roles. That is,
an individual that has a greater amount of kin ties in comparison to non-kin ties will have
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a stronger identity and spend more time occupying their roles that are associated with
their kin ties. For example, a woman may spend more time and identify with their role of
mother or daughter-in-law, as that is the role that is reinforced by the relationships they
hold with the people that are dominant in their network. In this case, an individual with a
higher proportion of kin ties, will spend less time occupying their role as coworkers, and
therefore will have less career satisfaction, as they are less likely to spend a large amount
of time or set goals related to their role and identity as a member of an organization.
In addition to network position, another factor that can help explain the negative
effect that network ties had on career satisfaction draws from Granovetter’s Weak Ties
Theory. It was expected that individuals with a higher proportion of kin ties within their
network would experience a decline in both individual career satisfaction and career-
related career satisfaction. Specifically, previous research has found a relationship
between career satisfaction and weak ties. That is, individuals that experience high levels
of career satisfaction also have a larger number of weak ties, as described in Chapter 2
and 3. As a result, if an individual has a greater proportion of kin ties, these ties are likely
to be strong ties. Strong ties, are good for providing an individual social and emotional
support, but they are typically less beneficial in providing an individual with
nonredundant information, as that is the role of weak ties. Thus, an individual with a high
proportion of kin ties, will likely have strong ties to those individuals. Empirical research
shows that the relationship between ties and career satisfaction is positive (perceived as
helpful) when an individual has weak ties.
Further, the study results are consistent with ideas expressed in previous research
related to kin ties. Specifically, previous research indicates that when network are
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comprised of a high percentage of kin ties, kin ties are less likely to acquire new,
nonredundant information as it related to jobs and careers (Wellman, 1992). Thus, the
study results suggests that as the percentage of kin ties increases, individual will be less
satisfied with their careers. This is likely because as the number of kin increase,
individuals will acquire very little new information (Wellman, 1992). Further kin ties
tend to be strong ties, and previous research suggests that individuals will gather the most
job and career –related information from weak ties (Granovetter, 1972). That is,
instrumental actions, such as job searching, require diverse resources and are more likely
to be accomplished through weak ties (Wellman, 1992), and extensive, weak ties to
nonfamilial sources are most useful for finding a job and achieving higher income. One is
likely to feel more satisfied with their jobs if they believe they have access to both
information and social capital. This becomes difficult to achieve as the percentage of kin
ties increase within an individual’s network. Thus, this study demonstrated that it is
important to consider an individual’s network ties, as the nature of type of network tie
affects the type of support that an individual receives from that member in their network
(i.e. strong vs weak ties, where kin ties tend to be strong ties).
Lastly, very little research on career success has included a measure for peer-
related career success (Heslin, 2005). Therefore, this study contributes to the careers
management literature by finding empirical support for the idea that individuals use
multiple referent points, including peers, to evaluate their career success (Heslin, 2005).
Specifically, this study demonstrated that individuals do evaluate their career success
relative their personal criteria and relative to the outcomes attained by other people (i.e.
the significant, main effect found of network ties on peer-related career satisfaction).
295
This finding suggests that future studies that include career success should measure both
individual and peer-related career success. In other words, the meaning of career success
to an individual is more likely to be explained if both their self-referent and other-referent
career success are measured (Heslin, 2005). A summary of the results can be seen in
Table 6.2.
296
Independent Variables
Salary Salary Growth
Promotions Individual Peer Career Sat Career Sat
Step 1
297
Education .291 .131 .055 -.116 -.141
Number of Hours/Week
.311 .020 .180 .094 .082
Ego’s Age .454 .533 .236 -.165 -.161
Work Interruptions
.046 .162 .151 .046 .044
Step 2
Network Ties -.031 .023 -.037 -.174* -.138*
Change in R2 .001 .001 .001 .028* .018* (Step 2)
Overall Adjusted R
.382 .330 .110 .039* .032* 2
Table 6.2: Summary Results of Network Ties on Career Objective Career Success Indicators (H5b) *p >= 0.05
Finally, network content (i.e. Hypothesis 5c) was predicted to have a relationship
with both the career success indicators and career satisfaction indicators, that is, the
objective indicators of career success. Network content influenced salary, where the beta
was (Beta = .104). It was surprising that network content had a positive relationship with
salary, as it was expected that network content would have a negative relationship with
salary. Network content was measure of the amount of non-work content that individual
discussed with members in their network. Thus it was expected that individuals that
experienced salary growth would discuss a high proportion of work-content with their
network members. A summary of the regression results for Hypotheses 5a, 5b, and 5c can
be seen in Table 6.3.
One rationale for the finding between network content and salary maybe related to
what members discuss within their network and the composition of their network
members. Specifically, if an individual discusses their family responsibilities with
members of their networks, and if that individual’s network is comprised of powerful
individuals, that is, individuals that can influence their salary, then an individual may be
able to leverage conversations with members in their networks to express a need for
greater income due to changes within their family obligations. For example, suppose an
employee expresses to a manager who is also a member of their network a concern they
have related to their ability to complete work at home. The employee may suggest they
would be a more productive employee if they were able to work in the office. However,
in order to work in the office they will need additional money to pay for their child to go
to daycare. In this case, if an individual is able to make a link between their ability to
298
improve their productivity, a manager may be willing to increase the salary of this
individual, which in turn will allow them to work at the office and use the additional
money to pay for childcare. In sum, network content appears to be related to salary
growth. This seems most likely to occur when an individual directly links an increase in
their salary to work performance improvements, especially when the increase in salary
will allow the worker to be more efficient and be able to spend more time in the office,
without neglecting their childcare responsibilities.
299
Independent Variables
Salary Salary Growth
Promotions Individual Peer Career Sat Career Sat
Step 1
300
Education .300 .132 .061 -.102 -.131
Number of Hours/Week
.326 .025 .188 .094 .082
Ego’s Age .467 .529 .248 -.125 -.130
Work Interruptions
.039 .158 .148 .051 .049
Step 2
Network Content
.104* .053 .044 -.091 -.076
Change in R2 .010* .003 .002 .008 .006 (Step 2)
Overall Adjusted R
.391* .332 .128 .018 .020 2
Table 6.3: Summary Results of Network Content on Career Objective Career Success Indicators (H5c) * p >= .005
Hypotheses 6a,b,and c proposed a relationship between the network
characteristics and each of the career management perceptions. Hypothesis 6a proposed
that network size would influence career planning, career tactics. Network size was
found to significantly predict career mobility preparedness where the beta for network
size on career mobility preparedness was (.152). This suggests that a .152 increase in
career mobility preparedness will lead to an increase in network size. This relationships is
consistent with that propose in Hypothesis 5c which suggested that network size will
positively influence career mobility preparedness. See Table 6.4 for a summary of the
regression analyses.
Network size was successful in predicting both salary (Beta= -.122) and career
mobility preparedness ( Beta= .152). This study contributes to the career literature by
examining the impact that network size has on both career success indicators and career
management perceptions. In most previous studies, both career management and career
success indicators have not been investigated in the same study.
301
Independent Variables
Career Planning
Career Tactics
Career Mobility Preparedness
Step 1
302
Education .012 -.034 .175
Number of Hours/Week
.047 .111 .118
Ego’s Age -.124 -.175 -.196
Work Interruptions
-.019 -.041 .036
Step 2
Network Size .056 .108 .152*
Change in R2 .003 .012 .023* (Step 2)
Overall Adjusted R
.006 .047 .106* 2
Table 6.4: Summary Results of Network Size on Career Management Indicators (H6a) * p >= .005
Hypothesis 6b proposed that network ties would influence career planning, career
tactics. Network ties was not found to significantly predict any of the career management
indicators. This finding seems to suggests that while network ties, does seem to influence
the objective success career indicators (e.g. salary), network ties do not appear to
perceptions of career management. See Table 6.5 for a summary of the regression
analyses. One can conclude that while network ties does have a small, but significant
relationship with subjective career success indicators (i.e. individual career satisfaction
and peer-related satisfaction), network ties do not explain any variance in career
management perceptions.
303
Independent Variables
Career Planning
Career Tactics
Career Mobility Preparedness
Step 1
304
Education .007 -.038 .169
Number of Hours/Week
.047 .115 .121
Ego’s Age -.143 -.199 -.235
Work Interruptions
-.018 -.039 .039
Step 2
Network Ties -.050 -.050 -.091
Change in R2 .002 .002 .008 (Step 2)
Overall Adjusted R
.005 .038 .090 2
Table 6.5: Summary Results of Network Ties on Career Management Indicators (H6a) * p >= .005
Finally, there was one significant relationship found when network content
predicts career management perceptions. Specifically, network content was found to
predict career tactics (Beta =-.165). Career tactics are specific behaviors (e.g. networking
or seeking developmental feedback) that an employee engages in, when they are trying to
collect job and career-related information. The summary results from the relationships
proposed by Hypothesis 6c are shown in Table 6.6.
As mentioned previously, one significant relationship was found between network
content and career management indicators. That is, a significant relationship was found
between network content and salary (Beta = .104). Also, network content, had a
relationship with career tactics (Beta= -.165). However, in comparing the beta weights
(and the variance explained) network content seems to have a stronger relationship with
career management perceptions.
The negative relationship found between network content and career tactics was
consistent with the notion that topics of conversation matter. Specifically, research
suggests that is important for individuals to have access to the vocabulary and topics of
conversation that are relevant to the group (Tonsing & Alant, 2004). That is, an
employee needs to be able to talk about topics that are of interest to their coworkers in
order to build and maintain relationships at work (Tonsing & Alant, 2004). Therefore,
individuals must be selective about the conversation topics they discuss among people
from who they want to gather career or job-related information, as it is important for
individuals to discuss topics that are of relevance and interest to the individuals with
whom they want to build or maintain relationships. Interestingly, Tonsing and Alanta
(2004) found that when individuals were asked to identify the topics of conversation they
305
shared with coworkers, family, was not a topic that was discussed with great frequency
among coworkers, Instead, the topics that were discussed with most frequency among
coworkers included food, interpersonal relations, and work (e.g. work processes, work
activities, and work equipment) (Tonsing & Alanta, 2004). Thus, it appears that selecting
or being mindful of conversation topics among the members of your network is
important. It is likely the case that individuals find success in building and maintaining
relationships (i.e. which leads to the gathering of job and career-related information)
when they select topics that are familiar to all members within a given group (Tonsing &
Alanta, 2004).
306
Independent Variables
Career Planning
Career Tactics
Career Mobility Preparedness
Step 1
307
Education .007 -.041 .173
Number of Hours/Week
.038 .097 .116
Ego’s Age -.135 -.194 -.217
Work Interruptions
-.011 -.028 .044
Step 2
Network Content
-.101 -.165* -.086
Change in R2 .010 .026* .007 (Step 2)
Overall Adjusted R
.013 .062* .089 2
Table 6.6: Summary Results of Network Content on Career Management Indicators (H6a) * p >= .005
In sum, the statistically significant relationships found between the three network
characteristics size, ties, and content, and the career outcomes career success indicators
and career management perceptions were all relatively small (that is the change in R-
squared did not exceed (.028). However, it does appear that each of the network
characteristics, that is, size, ties, and content were useful in predicting either a career
success indicator and/or a career management indicator. Specifically network size
predicted both salary and career mobility preparedness. Network ties predicted career
satisfaction, and network content predicted salary and career tactics. Therefore, based on
the study results in the influence of all three network characteristics on career outcomes
are worthy of future investigation . Alternatively, of the five career success indicators
included in the model (i.e. salary, salary growth, promotions, individual career
satisfaction, and peer-related career satisfaction), the network characteristics were not
useful in explaining any variance in promotions. Further, of the career management
perceptual measures included in the model (i.e. career planning, career tactics, and career
mobility preparedness), the network characteristics were not useful in predicting career
planning. Thus, if these same three network characteristics were tested in future studies
related to careers, it does not appear that it would be necessary to test for a relationship
between the three network characteristics and promotions, or the three network
characteristics and career planning.
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Theoretical Implications
The hypothesized model described in Chapter 3 was developed by drawing from
two key theoretical frameworks. Those two theoretical frameworks include the weak ties
theory and boundary theory. Granovetter’s (1973) weak tie theory argues that individuals
should maximize the number of nonredundant ties within their network, as this will lead
to the individual learning more information about future job and career opportunities. For
the purposes of this study, the weak tie framework was used to identify the network
characteristics that were important to career outcomes (i.e. network size and network
ties), and this framework was used to understand and identify the relationships that were
likely to exist between the network characteristics (network size, network ties) and the
various career indicators (e.g. career mobility preparedness) included in this study (i.e.
the relationships proposed in Hypotheses 5-6).
Specifically, the components of the weak tie theory that are important to this
study are the distinction Granovetter makes between weak and strong ties, that is, the
weak tie theory is useful in this study in distinguishing the types of relationships that may
exist in an individual’s network. Weak ties are defined as relationships with individuals
that lack intimacy, where contact may be infrequent, and they usually provide an
individual with nonredundant information. Typical examples of weak ties include
neighbors, co-workers, etc. In comparison, strong ties are usually defined by close,
intimate relationships and ones where individuals usually rely on their ties for social
and/or emotional support. Examples of strong ties include spouse, parents, siblings, ect.
The network kin measure used in this studied, was a measure of the types of relationships
309
that individuals have with members of their network. Specifically network kin, was a
measure of the proportion of network ties, that were kin ties. The findings from the study
suggest that network ties did predict career success, specifically individual and peer-
related career satisfaction, and specifically network kin had a significant negative effect
on the career success indicators. Thus, it appears that using the weak tie theory to
distinguish among the types of ties that individuals have within their network, does
appear to be a sound theoretical framework to use when making this distinction, as
empirical evidence support that types of ties matter. In addition, another distinction made
in the weak tie theory is related to network size. The weak ties theory argues that bigger
is better, and individuals should maximize their network size in order to maximize the
amount of nonredundant career and job-related information they receive from members
in their network. This notion of network size was supported empirically in this study.
Specifically, network size was found to have a significant positive relationship with
career mobility preparedness. That is, the finding between network size and career
mobility preparedness suggested that network size is able to predict career mobility
preparedness, that is, larger network results in individuals having positive perceptions of
their ability to move within their career. Thus, it appears that the weak tie theory is at
least a good starting place for future empirical work that is interested in investigating the
relationships between network characteristics and career outcomes; especially if network
size and network ties are included as network characteristics in the study.
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Thus, it appears that it was rationale to use the weak tie theory to develop some of the
relationships at the back-end of the conceptual model, that is, the portion of the model
that investigated the relationships between the network characteristics and career
outcomes.
It should be noted that in addition to network size and network ties, network
content was also found to predict several of the career outcome measures. However,
network content, that is, identifying what people are discussing with various members of
their networks is not a variable that can be directly explained by using the weak tie
theory. Therefore, it makes sense to investigate other theories that may be useful in
understanding the relationship between network content and career outcomes. In
conducting a literature search in Business Source Premier, fewer than 20 hits were found
when the key words communication (or conversation) and careers were entered. The
articles that were retrieved were from this search were practioner articles and they mostly
described how individuals should have conversations about careers with mentors or other
people if influence within organizations. Thus, there appears to be a conceptual need for
understanding the relationship between conversation content and career outcomes. There
has been some initial research done in sociology related to conversation topics.
Specifically, Bearman and Parigi (2004) looked at what people talk about when they
discuss important matters. This article presents empirical support for the idea that people
talk about different topics amongst the people in their network. However, it does not
provide any theoretical support for why these differences in conversation occur.
In addition to the weak tie theory, this study also used boundary theory as a
framework to guide the development of some of the hypothesized relationships displayed
311
in the model. Boundary theory was used to understand how one of the antecedents, role
segmentation, may moderate the relationship between parental status and network
characteristics. Boundary theory suggests that meeting individual preferences to integrate
or segment their work and family roles is a key determinant of role conflict. Further,
according to boundary theory, individuals have preferences for the extent to which they
want their work and home life integrated or segmented. Support was found to suggest
that role segmentation was useful in predicting two network characteristics, network
content and network ties. Specifically, a main effect was found for role segmentation on
network content and network ties. The main effect of role segmentation on network
content suggested that individuals that separate their work and family lives, tend to talk
about more non-work topics. Also, the main effect of role segmentation on network ties,
suggested that individuals that segment their work and family lives, have a higher
proportion of kin ties in their network. Given these findings, it made sense to use role
segmentation as a theoretical framework to hypothesis the relationship parental status
may have on network characteristics. However, although role segmentation had a main
effect on network content and network ties, it did not interact with parental status. Thus,
it can be concluded that role segmentation, regardless of parental status, is important in
determining the network characteristics size and content. As mentioned earlier, to truly
understand the unique challenges that parents face in maintaining their social networks
for the purposes of managing their careers, it makes sense to understand the relationship
between parental status and network characteristics using role theory. As mentioned
previously, role theory and work-family conflict (which is derived from role theory)
appears to be a key theoretical framework that should be used in future research related to
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parents, networks, and careers. Work-family conflict is probably one of the key variables
that is studied in the work-family literature, and existing research demonstrates that
work-family conflict is empirically linked to other variables (Kossek & Ozeki, 1998).
Thus, in integrating the network, careers, and work-family literature, it makes sense to
include antecedents from the work-family literature (i.e. work-family conflict) in an
attempt to explain the unique challenges working parents may have in managing their
careers, including maintaining their social capital (networks), when they are compared to
working adults without parental responsibility.
Lastly, this dissertation also speaks to a research need initially addressed by
Sullivan (1999) who suggested that individual characteristics, such as gender, age, and
race, need to be investigated in terms of the overall development of large non-redundant
networks. The findings from this study do seem to suggest that while demographic
variables such as age and marital status do not explain variance in networks, there is
some evidence to suggest that role involvement and role segmentation are two
preferences that help explain variance in network characteristics. Specifically, individuals
that are highly involved with their jobs do seem to have a smaller proportion of kin ties
and they are less likely to discuss kin/and non-work related content among the members
of their network. Also, those individuals that clearly prefer to segment their work and
family roles, do have a higher proportion of kin ties within their network, and they are
more likely to discuss non-work/kin-related topics among the members in their network.
Individuals that have a higher proportion of kin ties in their network, are also individuals
that have a larger percentage of strong ties within their network. Social identity theory
would suggest that as an individual’s social identity becomes stronger they are more
313
likely to pick individuals within their network who are more similar to themselves. Thus,
one could argue that individuals that have a strong identity and prefer to segment their
work and family roles, view their family role as the most prominent identity in their lives.
As a result, they will have a tendency to have a higher proportion of kin ties, as they will
likely interact with individuals that are family members, that is, they select individuals
that are also part of their families.
Thus, it appears that one area of fruitful research is to further understand why an
individual’s role or identity impact specific network properties. This also suggests that
future research can benefit from the inclusion of psychological variables (Kalish &
Robbins, 2006) including role involvement. As of now, very few empirical studies have
examined the impact of various individual differences and psychological variables on
network characteristics. Thus far, it appears that the individual differences that have been
studied to understand their impact on network characteristics includes the Big 5 traits (i.e.
neuroticism & extraversion), self-monitoring, locus of control, and social identity.
Practical Implications
There are several practical implications of the study results. First, it does appear
that network size matters. Specifically, network size was related to salary growth, where
salary growth was measured as the number of salary increases an individual has
experienced over their career. However, in this study, network size had a negative
relationship with salary growth. This finding was surprising, as one would expect that as
network size increases salary growth would also increase. Network size did have a
positive relationship with career mobility preparedness, which was a measure of career
management. This finding suggests that individual should increase their network size, as
314
they will be in a better position to engage in multiple activities which are related to
managing their careers (e.g. seeking external contacts to learn about new job
opportunities). In addition, network ties were found to have a negative relationship with
both individual career satisfaction and peer-related career satisfaction. The practical
implication from this finding was individuals have to be mindful of the proportion of kin
ties in their network, in comparison to the proportion of work ties. An individual with a
higher proportion of kin ties within their network may feel less satisfied with the career,
because they are unable to leverage the kin ties in their network for various kinds of kin
support. Specifically, an individual receives different types of support from different ties
within their network. Often kin ties offer an individual a lot of social support, while work
ties may offer an individual career/job-related support. Perhaps, when an individual
receives a lot of social support when compared to job/career support, they are less
satisfied with their overall careers. Finally, network content, that is, what topics are being
discussed amongst the members of the network was related to both career success and
career management perceptions. This finding provides evidence that individuals must be
aware not only of who they are talking to, but what they are talking about when they
interact with members of their network. This is especially true for working parents, where
they are likely to have a higher percentage of non-work topics they discuss with members
within their network. This finding is consistent with the view, that one of the key
purposes of networks, as it relates to careers, are these relationships allow individuals to
gather and receive job and career-related information. Therefore, if an individual is
interested in better managing their own careers and experiencing higher levels of career
315
success, they must be cognizant of making an effort to discuss career-related topics with
members within their network.
From the organization’s perspective, there should be an effort made to help
employees join and become active in organizational networks, as there do seem to be
several benefits related to networks, as it relates to career management. Currently, many
organizations do have employee-based network groups present. However, a lot of these
network groups were developed in an effort to help individuals from underrepresented
groups (e.g. women) meet each other and provide social support. An example of this kind
of organizational group is found at AT Kearney Consulting Firm, where there was an
African-American Networking Group that was started by several of the partners in the
firm. The mission of this network group was to provide social support for current
African-American employees, and to address issues of diversity within the firm, for
example increasing the number of African-American partners within the firm. Another
example of an organizational-based networking group is found at McKinsey & Company,
a strategic consulting form. Within this firm, a networking group was formed among the
female partners to foster support among the limited number of female partners within the
firm. Secondly, the group works to help other women interested in becoming partners
within the firm, meet the goals necessary to make partner.
As mentioned, several organizations have similar organizational-based
networking groups. While, this effort is something that organizations can continue,
organizations should also consider forming organizational-based networking groups
around other themes. Specifically, the results from this research are consistent with
previous findings where network size was found to be important. In several of the current
316
organizational-based network groups, while a need may be met of a specific population
within the firm, organizations should consider creating groups that meet the needs of a
larger set of employees. Thus, perhaps they may develop organizational-based
networking groups and attract all members of the organization that are golf and tennis
fans/players. The point is, organizations should strive to create groups that will attract
large numbers of individuals. In doing so, each employee that willingly joins these
groups has the potential to increase their network size, through meeting a number of
employees, and in increasing their network size they also will have an opportunity to
receive a larger amount of non-redundant information (another benefit of Granovetter’s
weak ties theory). By forming organizational-based networks around themes/interests that
will attract a larger group of employees, would be an example of organizations taking a
relational approach to the development of their employees. That is, “the employer will
provide opportunities and flexibility and resources, particularly people resources, to
enable the employee to develop identity and adaptability and thus be in charge of their
own career” (pp 40) (Hall, 2002).
Finally, individual employees are not the only ones that would benefit from
organizations assisting them in forming networks or social capital. Organizations also
benefit from their employee’s participation in networks. As mentioned previously,
organization benefit from their employee’s participating in network in at least four ways:
(1) social capital developed among an organization’s employees facilitates the flow of
information (by providing an individual with useful information about opportunities and
choices not otherwise available), (2) the social ties one employees develop, may be able
to influence certain agents (e.g. recruiters or supervisors) who play an important role in
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making critical decisions within organization, (3) an employee’s social ties, that is their
acknowledged relationships with others in an organization, may help highlight an
individual’s social credentials, and (4) social capital should help reinforce an individual’s
identity within an organization (Lin, 2001), thereby reducing the likelihood of turnover.
In addition, in helping employees develop their social capital through
organizational network, organizations are also helping employees manage their own
careers. This notion of organizations helping employees manage their careers was also
discussed in Kossek et al. (1998). In this case, if organizations help employees join and
participate in networks, they can also encourage employees to manage their own careers
by training employees on how to seek job and career-related information, especially for
those individuals that are interested in hiring internally, and individuals that are seeking
to mobilize their careers within their current organizations. Organizations will benefit
from training their employees in how to seek career and job-related information, by
eliminating the costs that are associated with external recruiting. Also, training
employees to seek job and career-related information internally should also reduce the
employee’s likelihood of leaving the organization. This is especially true for individuals
that are highly committed to their jobs, but may be dissatisfied with their current jobs.
While the argument can be made that organizations should become more involved in
training employees to manage their own careers through their participation in networking
groups, implementing these training programs may be difficult. Some of the challenges
that organizations may face include determining if this training should be mandatory or
voluntarily, determining the timing of this training (when in an employee’s career is it
318
appropriate to teach them how to manage their own careers), and determining which
employees might benefit the most form this training (Kossek et al., 1999).
Finally, there is a prevailing notion that suggests if employees receive support
from their employers, then employees will in turn feel obligated to reciprocate, that is
provide help to their organization. In this case, if organizations provide employees with
help in managing their careers and establishing networks they can join within the
organization, the employees are likely to feel motivated to help the organization (Sturges
et al., 2005). That is, career management support and opportunity to join organizational
networks that will benefit the employees, is viewed by the employees as perceived
organizational support. Perceived organizational support has been positively related to
job performance and negatively related to absenteeism and turnover (Sturges et al.,
2005).
Future Research Questions
The key issues related to conducting research on parents, networks and careers
are discussed below. First, it in order to identify some of the key antecedents that may
cause differences in network characteristics across parental status, future research should
draw variables from existing work-family literature. This would begin by examining two
key factors in work-family literature, role theory, and work-family conflict. Role theory
can be used a theoretical framework to describe the difficulty individuals experience in
attempting to balance multiple roles, that is, their work and family role. Drawing from
role theory, future research can also use boundary theory to explain the preferences
individuals have for segmenting their work and family roles. By drawing on these two
theoretical frameworks, many antecedents discussed in the work-family literature could
319
be used to describe factors that may impact network characteristics. Some of those
antecedents discussed in the work-family literature include work-family conflict, role
conflict, life satisfaction, absenteeism, parental demands (e.g. hours spent on housework),
and stress.
In addition, to variables that have been previously been studied in the work-
family literature and/or are directly related to role and boundary theories, a variable that
is often important to understand when investigating working parents is the element of
time. Time is an important variable to understand, as anecdotal evidence suggests that
working parents shared that they have limited time to participate in activities outside of
the immediate job responsibilities. In order to assess how time might impact the
relationship between parental status and network characteristics, one could study how an
individual allocates their time on a daily basis, including any time they allocate to
maintaining their social capital. Alternatively a longitudinal design also allows
researchers to study the impact of time, and very few longitudinal studies have been
conducted within the work-family literature (Macdermid, 2005).
In addition to time, it also appears that individual differences may play a role in
explaining the relationship between parental status and network characteristics.
Specifically, extroversion may play a role in determining differences in network
characteristics, especially network size. Extroverts usually prefer to spend time with other
people and do not enjoy being alone for longs amounts of time (Lee & Tsang, 2001). For
example, Van de Ven et al. (1984) found that individuals that were highly extroverted
tended to maintain a broad and complex network of multiple, concurrent organizations
relationships with ties both internal and external to their organization. As a result,
320
individuals that are generally outgoing and enjoy interacting with people may tend to
have a larger network size, regardless of parental status.
Finally, this study demonstrated there is a negative relationship between network
ties and career indicators, as the percentage of kin ties increase within a network.
However, previous research suggests that this increase of kin ties within the working
parents network, does not remain high. Rather, results from a longitudinal study suggest
that the percentage and contact with kin ties within an adult’s network seems to increase
the most within the first 24 months after the birth of a child, and begins to decline after
24 months (Bost et al., 2002). Thus, similar to most studies on social networks, the
findings in this study suggest research should be conducted using a longitudinal design.
That is, one should examine the how the relationship between network size, network ties,
and network content, varies with the multiple career indicators across time. This would
be helpful in understanding, for example, if the percentage of non-work topics changes,
as the age of the child increases.
Study Limitations
This study has several strengths helping it make a contribution to both the work-
family literature and the literature on careers, using network analysis. First, this study was
able to demonstrate that role segmentation, a variable in the work-family that although
discussed has had little empirical support, was able to predict network content and
network size. This finding is significant, as role segmentation is often discussed in the
work-family literature, but it has not been widely tested. Further, this study makes a
contribution to the literature on careers, as it was able to provide evidence that the
network characteristics ties and content are important to career outcomes. This was an
321
important contribution, as most previous research on career has focus on network size as
one of the key predictors of career-related outcomes. Finally, this study made a
contribution by introducing a new variable, network content, and demonstrated that
topics of conversation are related to career outcomes.
While this study made a contribution to the field, there were several study
limitations that should be noted. First, this data was collected from an organization that
was experiencing a significant amount of layoffs and turnover. Thus, when the
individuals were answered questions related to career satisfaction and career success,
there may have been some response bias introduced into their answers. That is, individual
may have responded positively to the career outcome scales, as they were working in a
very uncertain, unstable environment, and they may have been concerned about the true
nature of the study, that is, they may have been concerned that this survey was being
constructed to learn about their specific career attitudes with that organization.
Secondly, a limitation of this study was related to sample restriction. Individuals
that did not fall into one of the organization’s diversity group were not included in the
study. That is, the organization only agreed to provide the names of individuals whom
both represented some diversity group within the organization for whom an
organizational-based network group had been created. This restriction in sample also
resulted in a small representation of male respondents (only 50 respondents (or 17%)
were men). As a result, this may have made it difficult to assess any of the gender-related
assumptions proposed in the model (see Hypotheses 1a, 1b, and 1c).
322
Evidence of any kind of response bias would have produced limited variability (i.e. a
small standard deviation) in the surveys responses. In closing, a larger sample size may
have also produced larger effects sizes and increased the power of the study. Another
limitation of the study was the used of a cross-sectional design. When a cross-sectional
design is used, no assumptions can be made about causality. Rather only inferences can
be made about the relationships found between the variables included in the study.
In addition, another limitation of this study is related to the generalizability of the
study’s findings. Specifically, this sample was restricted to exempt white collar workers.
It is not certain that these same findings would generalize to other populations. In
addition, this study relied solely on self-reported data. Thus, there may have been a bias
that was introduced when the respondents were completing the survey. Finally, this
sample of respondents was drawn from a single organization, in comparison to the
general working population. Thus, the results of this study may not generalize to all
organizations. However, it should be noted that while this data was collected from a
single organization, the organization does have multiple sites which should lend itself to
some variance in response rate, that is, all data was not collected from employees in a
single location within the organization.
Another study limitation is the use of the ego-network technique. The ego-
network technique is a sampling method generally used when a researcher is
investigating a large or less definable network (e.g. the researcher is interested in
studying the networks of people across several organizations). The ego-network
technique uses a name-generator survey in which the ego identifies a list of alters within
their network and they are able to answer questions related to various characteristics of
323
their alters (frequency of contact, physical proximity of alters, etc). One common
shortcoming in using the ego-network technique is it usually elicits strong (people with
whom the ego is close too) rather than weak ties, that is, the ego usually names
individuals with whom they have more intimate relationships with or people with whom
they have more frequent contact (Lin, 2001). In order to overcome this challenge, that
name generator used in this study was written to say: “Please type BOTH the initials and
first name (e.g. KLS-Kyra) or (e.g. KS-Kyra) of the most important people in your
professional and personal life (up to 20). This includes BOTH people inside and outside
of your organization, family members, friends, neighbors, members of professional
organizations, supervisors, colleagues, and anyone else with whom you discuss important
matters INCLUDING your career plans and various aspects of your professional life”.
That is, the name generator questions was written to help the respondent elicit a broad set
of ties, including both weak (e.g. members of professional organizations, colleagues) and
strong ties (e.g. family members, friends).
It is important to speculate how future studies in this area could be improved.
First, future studies should include the work-family conflict measure, as one of the
antecedents that would moderate the relationship between parental status and network
characteristics. As described previously, it makes sense to include a variable well
established in the work-family literature and also one found to be related to various career
outcomes (e.g. career satisfaction) if the researcher is interested in understanding the
unique challenges working parents face in terms of managing their careers. In addition, a
longitudinal study should be conducted. In doing so, it would allow one to observe the
specific changes individuals face in their network characteristics. The cross-sectional
324
model used in this dissertation does not permit observing changes in network
characteristics pre- and post childbirth. Future studies should increase the sample size in
at least two ways. First, there should be a larger representations of males included in the
sample. Given that there has been a growth in the number of dual career couples, it is
quite possible that working fathers are facing some of the same challenges in maintaining
contact with members within their network, especially since childcare responsibility is
often shared between both parents. The overall study sample size should be increased oby
soliciting the participation of a larger number of people. This could easily be done by
conducting the study across multiple organizations, which would also be helpful in
generalizing the findings from the studies across populations. Lastly, from those
respondents that have parental responsibility, the children’s ages should be collected as
an additional variable. This would be useful for understanding if in fact there is a
difference in the network characteristics of parents across age of the child. That is, further
tests could be conducted to learn if network characteristics vary, and if so how, as the age
of the children change.
325
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Appendix A
Focus Group Interview Survey
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Code Number____________
1. Please indicate your martial status
_______ Single ________ Married _________Divorced
__________Widowed
2. Please indicate your parental status
______ no children _______ 1 child _______ 2 children______ 3+ children_______
3. If you have at least one child, are these children living in your home? If
not applicable, please skip this question.
Yes___________ No___________
4. Please indicate the number of children in your home that are under the
age of four? If not applicable, please skip this question.
____________ (write exact number)
5. What is your job title?
________________________________________________________
6. Please write down the initials (e.g. KLS) of up to the 10 most important
people in your professional lives (this includes members inside/outside of
your organization (e.g. family, neighbors, members of professional
organizations, supervisors, colleagues).
1. _____________ 2.________________ 3._______________
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4. _______________ 5. _______________6. _______________
7.________________8._______________ 9.
________________10._____________
Code Number____________
7. Please record, in the spaces below, the initials of these individuals.
Beside each set of initials, please indicate the type of relationship (Ex. KLS
– Wife)
Initials Relationship
1. _________________ _______________________
2. _________________ _______________________
3. _________________ _______________________
4. _________________ _______________________
5. _________________ _______________________
6. _________________ _______________________
7. _________________ _______________________
8. _________________ _______________________
9. _________________ _______________________
10. _________________ _______________________
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Code Number____________
8. This question asks you to consider the frequency of interaction with the
individuals named previously (Person 1-10). For each individual, please
indicate how often you interact with that person either in-person or over
the phone. Please circle the number that indicates the number of times you
are in contact with that individual.
Person 1: 1= Daily 2=Weekly 3=Monthly 4=Less than Monthly 5= Never
Person 2: 1= Daily 2=Weekly 3=Monthly 4=Less than Monthly 5= Never
Person 3: 1= Daily 2=Weekly 3=Monthly 4=Less than Monthly 5= Never
Person 4: 1= Daily 2=Weekly 3=Monthly 4=Less than Monthly 5= Never
Person 5: 1= Daily 2=Weekly 3=Monthly 4=Less than Monthly 5= Never
Person 6: 1= Daily 2=Weekly 3=Monthly 4=Less than Monthly 5= Never
Person 7: 1= Daily 2=Weekly 3=Monthly 4=Less than Monthly 5= Never
Person 8: 1= Daily 2=Weekly 3=Monthly 4=Less than Monthly 5= Never
Person 9: 1= Daily 2=Weekly 3=Monthly 4=Less than Monthly 5= Never
Person 10: 1= Daily 2=Weekly 3=Monthly 4=Less than Monthly 5= Never
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Code Number____________
9. This question asks you to consider the topics of conversation that are
discussed among the members of your network. Thinking back to the more
recent discussions you had about an important matter, including your
career plans, would you describe briefly, what was the general topic of
discussion (e.g. community issue, news/economy, kids and education,
politics, life and health, relationships, money & house), ideology/religion,
work, career)”. List up to 5 topics that you have discussed with each of the
members of your network.
Person Topics of Conversation Person 1: Person 2: Person 3: Person 4: Person 5: Person 6: Person 7: Person 8: Person 9:
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Person 10:
Code Number____________ 10. Please list the name of professional associations/organizations/groups to which you belong or have been active in within the last 3 years. ____________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________
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APPENDIX B
WAVE 1 E-MAIL: INVITATION TO PARTICIPATE IN THE STUDY
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Department of Management and Human Resources 700 Fisher Hall
2100 Neil Avenue Columbus, OH 43210-1144
fisher.osu.edu
To: Organization(s) X From: Kyra Sutton, Co-Investigator Re: Organizational Networks My name is Kyra Sutton and I am writing this letter on behalf of myself and my investigator, Ray Noe of the Fisher College of Business at The Ohio State University. The purpose of our project is to learn about the experiences individuals have with their informal/formal networks, and understand if there is any relationship between their networking and career experiences. Specifically, we are interested in comparing the networking and career experiences of working adults with parental responsibility to the experiences of working adults without parental responsibility. We are writing today to ask that you take our survey on a voluntary basis. The survey should be a fun, interesting experience for you, as we will ask you a number of questions related to your informal/formal networks (including the members of your network, the frequency of contact you have with these individuals). The survey will be conducted in two separate waves, in order to minimize participant fatigue. Thus, the first wave of the survey is available at the website provided at the end of this note. The second wave of the survey will be available at the same website approximately 4 weeks after the closing date of the first survey. Of note, the dates of the survey will be provided on the website. In the second wave of the survey, you will be asked question related to your perceptions of you careers. In exchange for your time, the employees that complete both waves of the survey will have a chance to enter a drawing. The winners of the drawing will be randomly selected and they will have a chance to select from one of the following prizes each valued at $100.00. If selected, the choices in prizes include (1) 100 gift certificate to Toys R’Us, (2) 100 gift certificate to Best Buy, (3) IPOD, or (4) an opportunity to donate 100.00 to their favorite charity. The participants will enter the drawing on a voluntary basis and they will not be penalized if they choose not to enter the drawing. Briefly, I will describe the survey, inclduing your time commitment, should you decide to voluntarily participate in this survey. As mentioned, this survey is a web-based survey. The link for this survey is http://fisher.osu.edu/~sutton_162/survey/. At the beginning of the survey you will find a consent form which you will be required to read (and we also suggest you print the consent form) prior to beginning the survey. After reading the consent form, you will begin Wave 1 Survey which consists of five parts. At
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most, you will commit 30 minutes of your time in completing this survey. We ask that you complete the entire survey and answer the questions on the survey to the best of your ability. After completing the first wave of the survey, you will receive an e-mail notification approximately 3-4 weeks later, stating that the 2nd wave of the survey is now available. The second wave of the survey is shorter than the first wave, and you expect to commit no more than 15 minutes of your time in completing the second survey. We have made this survey available on-line in order to facilitate the ease of completing the survey. You are free to complete this survey at your convenience and please note that your answers will remain confidential. Further, we do not ask you to provide any identifying information on either wave of the survey, and the only individuals will access to the survey are the principle and co-investigator, Ray Noe and Kyra Sutton, respectively. Once again, we appreciate your assistance in this study. Please proceed to the link below to begin the first wave of the survey if you choose to voluntarily participate in our study. If you have any questions, please feel free to contact Kyra Sutton at Sutton_162@cob.osu.edu or 614-538-8839 regarding the study. The survey is now available at the following link: Thank you again for your time. Warm regards, Kyra Sutton
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APPENDIX C
WAVE 2 E-MAIL: INVITATION TO PARTICIPATE IN THE STUDY
348
Department of Management and Human Resources
700 Fisher Hall 2100 Neil Avenue
Columbus, OH 43210-1144 fisher.osu.edu
Good Afternoon! Thank you very much for time in and interest in participating in the first wave of the Organizational Networks and Careers survey. I am appreciative of your willingness to participate in our study and it is in this vain that I write you today! As mentioned in the instructions of the first wave, we are now ready to launch the second and final wave of the survey. This part of the survey is specifically related to your career experiences and you will find this wave of the survey to be much shorter!! In total you should be able to complete this wave of the survey in no more than 10 minutes. Similar to the first wave, your answers will remain confidential. Specifically, all answers are stored in a main database and no personal identification is stored with the answers. Further, to assist in maintaining your confidentiality, we have provided you with a password that you will have to enter (along with the e-mail address you provided upon completing the first wave of the survey) in order to begin the survey. This unique ID was provided to you after you completed the first wave of the survey. The link for the second and final wave of the survey can be found at: http://fisher.osu.edu/~sutton_162/survey2/ After selecting the link to the survey, you will read the survey instructions and the consent form. Following those first two screens you will be prompted to enter your e-mail address (the one you provided after completing the 1st wave of the survey) and unique ID/password, which is a 6 digit-code (as previously mentioned). You will then begin the survey. Since I am aware that this e-mail may be passed along to other colleagues, I have not included your password/unique ID within the text of this e-mail. However, if you need a copy of your unique ID I am happy to share that with you via e-mail or telephone! As you know, your participation in this survey is meant to:
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• Assist in a research project conducted by Ohio State’s Fisher College of Business in which we want to learn about the experiences individuals have with their informal/formal networks, and understand if there is any relationship between their networking and career experiences.
As always, should you have any questions please feel free to reach out to Kyra (Sutton) at Sutton_162@cob.osu.edu or 313-600-7935. I am pleased to answer any questions that you may have. Thank you in advance for your time and willingness to complete this 2nd and final wave of the survey! Kind regards, Kyra
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APPENDIX D
FIELD SURVEY WAVE 1
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Welcome Page, Consent Form and Wave 1 Survey Questions
Department of Management and Human Resources
700 Fisher Hall 2100 Neil Avenue
Columbus, OH 43210-1144 fisher.osu.edu
We thank you for your interest in our study, Organizational Networks and Careers. What is the purpose of the study?
1. Understand the experiences you've had with participating in informal/formal networks either within your organization or outside of your organization.
2. Understand to what extent you see a relationship between your participation in formal/informal organizational networks and any relationship that may have with your career (e.g. your ability to manage your career, your happiness with your career).
How do I participate in the study?
1. Complete two surveys 2. First survey is attached to this link. 3. Second survey will be available in 4-6 weeks. You’ll receive an e-mail
notification asking you to complete the survey. How long will it take me to complete each survey?
1. The first survey will take no longer than 20-30 minutes. 2. The second survey (available in 4-6 weeks) will take no longer than 10-15
minutes. How do I know when I have completed the survey?
1. You will complete the survey once you arrive at the Thank You page. This page follows the screen where you will be given a Unique ID Number.
2. Remember to close your browser after you reach and read the Thank You Page.
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Is there any reward for my participation in the study?
1. After completing both surveys, you will be eligible to participate in a drawing (the directions will appear after you complete the second and final survey).
2. There will be five prizes awarded each valued at $100.00. 3. The winners of the drawing will be able to select from one of four prizes
including: (1) An Ipod, (2) A Best Buy Gift Certificate, (3) A Toys “R” Us Gift Certificate, or (4) we will donate $100.00 to your favorite charity on your behalf.
4. Also, in exchange for your participation we will send you a copy of the survey results. If you are interested in receiving a summary copy of the results, please e-mail Kyra Sutton at Sutton_162@cob.osu.edu. No individual survey results will be shared
How do I know if I am eligible to participate in the study?
1. 18 years or older 2. Currently employed at least 30 hours/per week. 3. Both working adults with and without children are eligible to participate. 4. However, if you are a working adult with a child, at least one child must be
under the age of 5. 5. Be able to read and understand this web page. 6. Participation in study is completely voluntary.
Before proceeding to the study, please read (and print) the Consent form on the next screen.
353
Department of Management and Human Resources
700 Fisher Hall 2100 Neil Avenue
Columbus, OH 43210-1144 fisher.osu.edu
Consent for Participation in Social and Behavioral Research Protocol title: Organizational Networks and Career Mobility, A Relational View Protocol Number: 2005B0369 Principal Investigator: Raymond A Noe I consent to my participation in research being conducted by Raymond Noe and Kyra Sutton of The Ohio State University. The description provided on the previous screen explained the purpose of the study, the procedures that will be followed, and the amount of time it will take to complete this survey. I understand the possible benefits, if any, of my participation. I know that I can choose not to participate without penalty to me. If I agree to participate, I can skip any question I do not want to answer and I can withdraw from the study at any time, and there will be no penalty. I have had a chance to ask any questions and to obtain answers to my questions. I can contact investigator Kyra Sutton at (614) 538-8839 or Sutton_162@cob.osu.edu if I have any further questions about this research. If I have questions about my rights as a research participant, I can call the Office of Responsible Research Practices of The Ohio State University at (614) 688-4792. I have read this form or I have had it read to me. I voluntarily agree to participate in this study. If you disagree with any of the previous statements and do not wish to participate, simply close this browser to end this session. If you agree to all of the above statements, print a copy of this page by selecting the print button on your web browser or by pressing the "Ctrl" key and the "P" key at the same time. Once the page has been printed, click the "submit" button below to continue the survey.
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Part A A1. What is your gender? Male � Female � A2. What is your martial status? Single � Partnered � Married � Divorced � Widowed � A3. What is your parental status? Zero Children � At least one child � Two children � Three children � If you are not a parent, please proceed to Question A10. If you are parent, please answer questions A4-A9. A4. If you have at least one child, are these children living at home? If not applicable, please skip this question. Yes � No � A5. If you have at least one child, have you had this child (children) within the last five years? Yes � No � A6. If you have at least one child, please indicate the number of children in your home that are UNDER the age of five? If not applicable, please skip this question. 1� 2� 3� 4� 5� 6� 7� 8� 9� A7. If you are a parent, do you share parental responsibility with another adult? Yes � No �
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A8. If you answered yes to Question A7, please answer the following question. Please indicate the job status of the person with whom you share parental responsibility? Employed (Full-Time; at least 30 hrs/week) � Employed (Part-Time; less than 30 hours/week) � Self-Employed � Unemployed � A10. If you are a parent, please indicate who has primary care giving responsibility for your child/children? If not applicable, please skip this question. Myself � My Spouse/Partner � Both myself and my spouse/partner � Family Care Provider (e.g. mother, father, sibling) � Third Person Care Provider (e.g. Daycare, Nanny, Au Pair) � A11. Do you work at least 30 hours or more per week (on average) Yes � No � A12. Please indicate the number of hours you work per week (on average) ____________ (type/write your best estimate ) A13. Do you work at least 9 months or more out of the year (on average)? Yes � No � A14. Please indicate your employment status. Exempt � Non-exempt �
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A15. Please indicate the approximate size of the organization in which you are employed. Small (2,500 employees or less) � Midsize (2,501 employees to 10,000 employees) � Large (10,001 employees or more) � A16. Which of the following best describes your Race/Ethnicity? White or Caucasian � African-American or African Descent � Native American � Native Hawaiian � Spanish � Mexican � Puerto Rican � Cuban � Other Hispanic � Chinese � Japanese � Korean � Other Asian � Other race � A17. What is your age? __________________________(years) A18. How many months have you been with your current employer? _________________ (type/write your best estimate )
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A19. How many months have you been employed in your current job (i.e. company/organization)? _________________ (type/write your best estimate ) A20. How many total years have you worked? _____________________________________(type/write your best estimate ) A21. Please indicate the highest level of education achieved? High school Diploma � Associates Degree � Bachelor Degree � Graduate Degree � A22. Do you have a physical disability? Yes � No �
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Part B: Relationships Overview/Directions: In this section we ask you to identify individuals that are important to your professional life. After identifying those individuals, we ask you to respond to a series of questions about each of the individuals. B1. Please type BOTH the initials and first name (e.g. KLS- Kyra) of the most important people in your professional and personal life (up to 20). This includes both people inside and outside of your organization, family members, friends, neighbors, members of professional organizations, supervisors, colleagues, and anyone else with whom you discuss important matters including your career plans and various aspects of your professional life. Initials First Name 1. _______ _______________ 2. _______ _______________ 3. _______ _______________ 4. _______ _______________ 5. _______ _______________ 6. _______ _______________ 7. _______ _______________ 8. _______ _______________ 9. _______ _______________ 10. _______ _______________ 11. _______ _______________ 12. _______ _______________ 13. _______ _______________ 14. _______ _______________ 15. _______ _______________ 16. _______ _______________ 17. _______ _______________ 18. _______ _______________ 19. _______ _______________ 20. _______ _______________
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Part B: Relationships (continued) Overview/Directions B2A – B2F
• The next set of questions will relate to the group of people that you have named (on the previous screen) as important people in your life, including those people with whom you discuss your career plans.
• At the top of each screen you will see the first name and the initials of the most
important people in your professional life. The first name and initials appear to the left of the phrase Network Member.
• On this same screen you will answer a series of questions related to the individual
whose name and initials appear at the top of the screen.
• After you answer the questions related to that specific network member, please press NEXT at the bottom of the page. Each time you proceed to the next screen- you will answer a set of questions about a DIFFERENT person in your network.
• The previously mentioned activity will repeat itself until you arrive at the screen
for question B3.
• Please note, you will not answer these questions for all individuals you have identified as part of your network. Rather you will answer a series of questions of a randomly selected set of important people you identified in question B1.
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Part B: Relationships (continued) Network Member: KLL (Katie) B2. Please answer the following questions about members you have identified in your network. The categories of questions include the type of relationship, gender, parental status, and the topics of conversation you discuss with these individuals. B2a. Please indicate the type of relationship you have with this person (e.g. KLL – Mentor) as their name appears on the screen. Supervisor/Boss (Former or Current) Colleague/Coworker (Former/Current) Employee/Subordinate (Former or Current) Mentor Work Friend Non-Work Friend Spouse/Partner Sibling/Parent Other Relative (e.g. daughter/son, in-laws) Neighbor Other B2b. Please indicate the gender of this individual. Male � Female � B2bb. Please indicate the approximate age of this individual. ____________ Years (type your best estimate) B2bbb. Please indicate the race of this individual. You are free to select more than one race. White or Caucasian � African-American or African Descent � Native American � Native Hawaiian � Spanish � Mexican � Puerto Rican � Cuban � Other Hispanic � Chinese � Japanese �
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Korean � B2c. Please indicate the parental status of this individual. Zero children � At least one child � At least two children � At least three children � B2d. If this individual has at least one child, please indicate if their child/children are ________ (select one below) than your children. Younger � About the same age � Older � I don’t have children � B2e. This question asks you to consider the topics of conversation discussed with this individual. Thinking back to the most recent discussions you had about an important matter, including your career plans, please indicate all of the conversation topics you discussed with this individual (you may select more than one topic): Work - General (e.g. work expectations, assignments) � Career/Career Progress (e.g. job-hunting, career planning) � Continuous Education/Training � Work-related Projects (e.g. new projects) � Networking � Children/Family Household � Spouse/Partner � Marriage/Relationship � Health � Other � B2f. Of the conversation topics you selected in question B2e, select the TWO topics that you discuss most frequently when you discussed an IMPORTANT matter, including your career plans with this individual. Work - General (e.g. work expectations, assignments) � Career/Career Progress (e.g. job-hunting, career planning) � Continuous Education/Training � Work-related Projects (e.g. new projects) � Networking � Children/Family Household � Spouse/Partner � Marriage/Relationship � Health � Other �
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Part B Relationships (continued) Overview/Directions: In this section we ask you to identify individuals that are important to your professional life. After identifying those individuals, we ask you to respond to a series of questions about each of these individuals. B3. Who knows who within your network? Directions (B3). This next set of questions asks you to consider how well the contacts within your network know one another. Type/write the number that best describes how well each pair of individuals in your network know each other. Please type the following number per the following guidelines:
Enter [ 0 ] for people within your network who are total strangers as far as you know.
Enter [ 1 ] for people within your network who are ‘distant’. A distant relationship is best described as one in which two people rarely spend time together.
Enter [ 2 ] for people within your network who are ‘close’. A close relationship is best described as one in which the two people know each other but are not in frequent contact, or their interaction may be specific to a certain setting (i.e. work, professional)
Enter [ 3 ] for people within your network who are ‘especially close’. An especially close relationship is best described as one in which people work very closely together, or have a high level of friendship, and they are in touch with each other on a regular basis.
Complete this table by beginning at the far left corner with Person 1. Indicate his or her relationship with the person in each column (from left to right) in one of four ways: ‘0-> no contact’, ‘1->distant’, ‘2->close’, ‘3->especially close’.
For example, first consider the relationship Person 1 in your network has with person 2. If Person 1 and Person 2 within your network are personal friends, you would identify this relationship as [3] or especially close. In comparison if Person 1 and Person 2 within your network work at different organizations, do not share the same profession, and have only met once (briefly) you would identify this relationship as [1] distant. Next, consider the relationship Person 1 had with Person 3 and the remaining persons in your network. Complete the table for each pair of relationships. For example, Your contacts WGG KLL (0)
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Part C: Families We want you to share your experiences of your involvement with your family. Indicate the extent of your agreement with each of the following statements: 1 = Strongly Disagree, 2 Moderately Disagree, 3 Slightly Disagree, 4 Slightly Agree, 5 Moderately Agree, 6 = Strongly Agree.
Strongly Disagree
Moderately Disagree
Slightly Disagree
Slightly Agree
Moderately Agree
Strongly Agree
C1. 1=� 2=� 3=� 4=� 5=� 6=� The most important things that happen to me involve my present parental role C2. Most of my interests are centered around my family
1= � 2=� 3=� 4=� 5=� 6=�
C3. Most of my personal life goals are family-oriented
1= � 2=� 3=� 4=� 5=� 6=�
C4. I consider my family to be central to my existence
1= � 2=� 3=� 4=� 5=� 6=�
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Part D: Work and Home Preferences
The following questions ask about your experiences with managing your workload in the office and at home. Indicate the extent of your agreement with each of the following statements: 1 = Strongly Disagree, 2 Moderately Disagree, 3 Slightly Disagree, 4 Slightly Agree, 5 Moderately Agree, 6 = Strongly Agree. Strongly
Disagree Moderately Disagree
Slightly Disagree
Slightly Agree
Moderately Agree
Strongly Agree
D1. I often do work at home.
1= � 2=� 3=� 4=� 5=� 6=�
D2. I work after hours.
1= � 2=� 3=� 4=� 5=� 6=�
D3. I schedule personal activities during business hours.
1= � 2=� 3=� 4=� 5=� 6=�
D4. I communicate with family and friends during business hours.
1= � 2=� 3=� 4=� 5=� 6=�
D5. I think of personal or family-related issues while I am working.
1= � 2=� 3=� 4=� 5=� 6=�
D6. I do not work on personal time.
1= � 2=� 3=� 4=� 5=� 6=�
D7. I take work out of the office.
1= � 2=� 3=� 4=� 5=� 6=�
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D8. My personal time is my own.
1= � 2=� 3=� 4=� 5=� 6=�
D9. When working, I am completely focused on my work.
1= � 2=� 3=� 4=� 5=� 6=�
D10. I leave my personal life outside of the workplace.
1= � 2=� 3=� 4=� 5=� 6=�
D11. I rarely deal with personal matters when working.
1= � 2=� 3=� 4=� 5=� 6=�
D12. The office is reserved for doing work.
1= � 2=� 3=� 4=� 5=� 6=�
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Part D: Work and Home Preferences (continued)
Below we ask you to consider additional questions related to managing your workload between the office and home. When responding to these questions consider how much of that characteristic you personally feel is acceptable. Some people prefer more or less of some job characteristics than others.
Indicate the extent to which you desire each of the following statements:
1= Strongly Undesirable, 2=Moderately Undesirable, 3=Slightly Undesirable, 4=Slightly Desirable, 5=Moderately Desirable, 6= Strongly Desirable.
Strongly Undesirable
Moderately Undesirable
Slightly Undesirable
Slightly Desirable
Moderately Desirable
Strongly Desirable
D13. I do not desire to be required to work while at home.
1=� 2=� 3=� 4=� 5=� 6=�
D14. I desire to be able to forget work while I am at home.
1=� 2=� 3=� 4=� 5=� 6=�
D15. I do not desire to have to think about work once I leave the workplace.
1=� 2=� 3=� 4=� 5=� 6=�
D16. I do not desire to be expected to take work home.
1=� 2=� 3=� 4=� 5=� 6=�
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Part E Job Importance
The last set of questions asks you to consider how important your job is to your life.
Indicate the extent of your agreement with each of the following statements: 1 = Strongly Disagree, 2 Moderately Disagree, 3 Slightly Disagree, 4 Slightly Agree, 5 Moderately Agree, 6 = Strongly Agree.
Strongly Disagree
Moderately Disagree
Slightly Disagree
Slightly Agree
Moderately Agree
Strongly Agree
E1. The most important things that happen to me involve my present job.
1=� 2=� 3=� 4=� 5=� 6=�
E2. To me, my job is only a small part of who I am.
1=� 2=� 3=� 4=� 5=� 6=�
E3. I am very much involved personally with my job.
1=� 2=� 3=� 4=� 5=� 6=�
E4. I live, eat, and breathe my job.
1=� 2=� 3=� 4=� 5=� 6=�
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E5. Most of my interests are centered on my job.
1=� 2=� 3=� 4=� 5=� 6=�
E6. I have very strong ties with my present job which would be difficult for me to break.
1=� 2=� 3=� 4=� 5=� 6=�
E7. Usually I feel detached from my job.
1=� 2=� 3=� 4=� 5=� 6=�
E8. Most of my personal life goals are job-oriented.
1=� 2=� 3=� 4=� 5=� 6=�
E9. I consider my job to be very central to my existence.
1=� 2=� 3=� 4=� 5=� 6=�
E10. I like to be absorbed in my job most of the time.
1=� 2=� 3=� 4=� 5=� 6=�
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APPENDIX E
ORGANIZATIONAL NETWORKS AND CAREERS SURVEY- WAVE 2
370
Department of Management and Human Resources 700 Fisher Hall
2100 Neil Avenue Columbus, OH 43210-1144
fisher.osu.edu Welcome! We thank you for your involvement with the first wave of our survey and we thank your for returning to complete the second wave of our study, Organizational Networks and Careers. What is the purpose of the second wave of the survey? In the second wave of the survey we ask you to complete a series of questions related to your careers (e.g. career satisfaction, career management) How do I know if I can participate in the second wave of the survey? After completing the first wave of the survey you were given a unique password. This password was sent to you via e-mail immediately after you completed the first wave of the survey. You must enter your unique password in order to complete the second wave of the survey. If I lost my Unique ID Number, what should I do?
1. If you have misplaced your Unique ID Number, please e-mail Kyra Sutton at Sutton_162@cob.osu.edu.
2. If you need to e-mail Kyra, please include the e-mail address (only) that you provided when your unique password was provided.
3. Of note, your survey responses were not stored in the same file as your password. Therefore, your answers from both the first and second survey remain confidential.
How long will it take me to complete the second wave of the survey? This wave of the survey will take no longer than 10-15 minutes. Is there any reward for my participation in the study?
1. If you complete both waves of the survey, you will be eligible to participate in a drawing.
2. There will be several prizes awarded. 3. The winners of the drawing will be able to select from one of four
prizes including: (1) An Ipod, (2) A Best Buy Gift Certificate, (3) A Toys “R” Us Gift Certificate, or (4) we will donate $100.00 to your favorite charity on your behalf.
4. Also, in exchange for your participation we will send you a copy of the survey results. If you are interested in receiving a summary copy of the results, please e-mail Kyra Sutton at Sutton_162@cob.osu.edu. No individual survey results will be shared.
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How do I know if I am eligible to participate in the study? 1. Completed the first wave of the survey. 2. 18 years or older 3. Currently employed at least 30 hours/per week. 4. Both working adults with and without children are eligible to
participate. 5. However, if you are a working adult with a child, at least one child must
be under the age of 5. 6. Be able to read and understand this web page. 7. Participation in study is completely voluntary. How do I know when I have completed the survey?
1. You will complete the survey once you arrive at the Thank You page. 2. If you are interested in entering the drawing, you will be prompted to
provide your name and e-mail address. 3. Remember to close your browser after you reach and read the Thank You
Page! Before proceeding to the study, please read (and print) the Consent form on the next screen.
372
Department of Management and Human Resources
700 Fisher Hall 2100 Neil Avenue
Columbus, OH 43210-1144 fisher.osu.edu
Consent for Participation in Social and Behavioral Research Protocol title: Organizational Networks and Career Mobility, A Relational View
Protocol Number: 2005B0369 Principal Investigator: Raymond A Noe I consent to my participation in research being conducted by Raymond Noe and Kyra Sutton of The Ohio State University. The description provided on the previous screen explained the purpose of the study, the procedures that will be followed, and the amount of time it will take to complete this survey. I understand the possible benefits, if any, of my participation. I know that I can choose not to participate without penalty to me. If I agree to participate, I can skip any question I do not want to answer and I can withdraw from the study at any time, and there will be no penalty. I have had a chance to ask any questions and to obtain answers to my questions. I can contact investigator Kyra Sutton at (614) 538-8839 or Sutton_162@cob.osu.edu if I have any further questions about this research. If I have questions about my rights as a research participant, I can call the Office of Responsible Research Practices of The Ohio State University at (614) 688-4792. I have read this form or I have had it read to me. I voluntarily agree to participate in this study. If you disagree with any of the previous statements and do not wish to participate, simply close this browser to end this session. If you agree to all of the above statements, print a copy of this page by selecting the print button on your web browser or by pressing the "Ctrl" key and the "P" key at the same time. Once the page has been printed, click the "submit" button below to continue the survey.
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Part A: Work Interruptions Overview: The following asks you to share your work history. A1.
How many work interruptions have you had over your career? A work interruption includes taking a leave from work for at least 30 days. This excludes vacation time or time allotted for training and development. (Next Add a Space between F1 description and example). Example: For example, if you have two work interruptions (e.g. maternity leave, personal leave) of at least 30 days each, you would select number 3. � I have had no work interruptions of 30 days � I have had one work interruption for at least 30 days � I have had two work interruptions for at least 30 days each � I have had three work interruptions for at least 30 days each � I have had four work interruptions for at least 30 days each � I have had five work interruptions for at least 30 days each � I have had six work interruptions for at least 30 days each
374
Part B: Career Management Overview: The following questions ask you about how you manage your career. Indicate the extent of your agreement with each of the following statements: 1 = Strongly Disagree, 2 Moderately Disagree, 3 Slightly Disagree, 4 Slightly Agree, 5 Moderately Agree, 6 = Strongly Agree.
Strongly Disagree
Moderately Disagree
Slightly Disagree
Slightly Agree
Moderately Agree
Strongly Agree
B1. 1=� 2=� 3=� 4=� 5=� 6=� I have gotten myself introduced to people who can influence my career. B2. 1=� 2=� 3=� 4=� 5=� 6=� I have talked to senior management at the company's social gatherings. B3. 1=� 2=� 3=� 4=� 5=� 6=� I have built contacts with people in areas where I would like to work. B4. 1=� 2=� 3=� 4=� 5=� 6=� I have definite goals for my career over my lifetime.
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1=� 2=� 3=� 4=� 5=� 6=� B5.
When I think of changing my job, I always consider whether the new job leads to another one I want. B6. 1=� 2=� 3=� 4=� 5=� 6=� I give a lot of thought to how the specific plans I make for my career, are going to be useful in achieving my career goals. B7. 1=� 2=� 3=� 4=� 5=� 6=� I know what my strengths and weaknesses are in relation to my career. B8. 1=� 2=� 3=� 4=� 5=� 6=� Achieving my career goals is very important to me. B9. 1=� 2=� 3=� 4=� 5=� 6=� I am always very careful to avoid dead-end career paths.
376
B10. 1=� 2=� 3=� 4=� 5=� 6=� I try to have as much visibility and exposure to my bosses as I can. B11. 1=� 2=� 3=� 4=� 5=� 6=� I go out of my way to find a mentor or sponsor to help in my career in the firm. B12. 1=� 2=� 3=� 4=� 5=� 6=� I cultivate friendships with influential people for my career outside of work.
377
B13. 1=� 2=� 3=� 4=� 5=� 6=� I actively seek opportunities, rather than wait to be chosen. B14. 1=� 2=� 3=� 4=� 5=� 6=� I try to help my superiors achieve things that are important to them, even if it is not what I want. B15. 1=� 2=� 3=� 4=� 5=� 6=� I have taken the initiative to be involved in high profile projects. B16. 1=� 2=� 3=� 4=� 5=� 6=� I have asked for career advice from people even when it has not been offered. B17. 1=� 2=� 3=� 4=� 5=� 6=� I have asked for feedback on my performance even when it was not given. 1=� 2=� 3=� 4=� 5=� 6=� B18. I have refused to accept a new role because it would not help me develop new skills. B19. 1=� 2=� 3=� 4=� 5=� 6=� I have monitored job advertisements to see what is available outside the organization.
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Part C: Career Management- Part Two Overview: In this section, the following questions ask about how you feel about your career.
In this first section, we ask you to consider your resume.
Not at All
Current Very
Current 1* 2* 3* 4* 5* C1 How
current is your resume?
� � � � �
In this section reflect on whether you have done the following activities over the past 6 months?
Not at All Somewhat A Great Deal 1 2 3 4 5 C2 Over the past 6
months to what extent have you reviewed internal postings?
1=� 2=� 3=� 4=� 5=�
1=� 2=� 3=� 4=� 5=� C3 Over the past 6 months to what extent have you discussed future job openings within your INTERNAL network (where internal network members include people working at your current organization including co-workers, supervisors, etc.)?
379
1=� 2=� 3=� 4=� 5=� C4 Over the past 6
months to what extent have you discussed future job openings within your EXTERNAL network (where external includes members in your network outside of your current organization)
C5 Over the past 6 months to what extent have you thought about what position you would like to have next?
1=� 2=� 3=� 4=� 5=�
C6 To what extent do you actively seek out information about job opportunities outside your current organization?
1=� 2=� 3=� 4=� 5=�
C7 To what extent have you sought out any new personal connections AT WORK in the past 6 months for the purpose of furthering your career?
1=� 2=� 3=� 4=� 5=�
C8 1=� 2=� 3=� 4=� 5=� To what extent have you sought out any new personal connections outside of work for the purpose of furthering your career?
380
Part D: Career Satisfaction Overview: The following questions ask about YOUR INDIVIDUAL attitudes toward your career.
Indicate the extent of your agreement with each of the following statements: 1 = Strongly Disagree, 2 Moderately Disagree, 3 Slightly Disagree, 4 Slightly Agree, 5 Moderately Agree, 6 = Strongly Agree.
Strongly
Disagree Moderately Disagree
Slightly Disagree
Slightly Agree
Moderately Agree
Strongly Agree
D1 Relative to my career aspirations, I am satisfied with the progress I have made towards meeting my goals for advancement.
1=� 2=� 3=� 4=� 5=� 6=�
D2 Relative to
my career aspirations, I am satisfied with the overall success I have achieved in my career.
1=� 2=� 3=� 4=� 5=� 6=�
1=� 2=� 3=� 4=� 5=� 6=� D3 Relative to
my career aspirations, I am satisfied with the progress I have made toward meeting my goals for income
381
D4 Relative to
my career aspirations, I am satisfied with the skill
1=� 2=� 3=� 4=� 5=� 6=�
development I have attained.
D5 Relative to my career aspirations, I am satisfied with the autonomy
1=� 2=� 3=� 4=� 5=� 6=�
I have attained.
D6 Relative to my career aspirations, I am satisfied with the intellectual
1=� 2=� 3=� 4=� 5=� 6=�
stimulation I have attained.
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Part E : Career Satisfaction And My Peer Group Overview: The following questions ask how you feel about your career compared to your peers.
Indicate the extent of your agreement with each of the following statements: 1 = Strongly Disagree, 2 Moderately Disagree, 3 Slightly Disagree, 4 Slightly Agree, 5 Moderately Agree, 6 = Strongly Agree.
Strongly
Disagree Moderately Disagree
Slightly Disagree
Slightly Agree
Moderately Agree
Strongly Agree
1=� 2=� 3=� 4=� 5=� 6=� E1 Relative to people who I perceive as peers in my career/profession, I am satisfied with the progress I have made towards meeting my goals for advancement.
E2 Relative to people who I perceive as peers in my career/profession, I am satisfied with the overall success I have achieved in my career.
1=� 2=� 3=� 4=� 5=� 6=�
1=� 2=� 3=� 4=� 5=� 6=� E3 Relative to
people who I perceive as peers in my career/profession, I am satisfied with the progress I have made toward meeting my goals for income.
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E4 Relative to
people who I perceive as peers in my career/profession, I am satisfied with the skill
1=� 2=� 3=� 4=� 5=� 6=�
development I have attained.
E5 Relative to people who I perceive as peers in my career/profession, I am satisfied with the autonomy
1=� 2=� 3=� 4=� 5=� 6=�
I have attained.
E6 Relative to people who I perceive as peers in my career/profession, I am satisfied with intellectual
1=� 2=� 3=� 4=� 5=� 6=�
stimulation I have attained.
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F1. Please indicate your total INDIVIDUAL pretax income (e.g. salary, bonus, stock, profit sharing) in 2005 and in US Dollars. Less than 30,000 30,000- 45,000 45,000-60,000 60,000- 75,000 75,000 - 90,000 90,000-105,000 105,000- 120,000 120,000-135,000 Greater than 135,000 F2. Throughout your career, please indicate how many salary increases you have received. Note: Salary increases include (a) changes in annual salary; and/or (b) qualifying for a performance-based company bonus, incentive or stock plan.
0 1 2 3 4 5 Greater than 5 F3. Since you've joined your current organization/company, please indicate how many promotions you have received. Note: Promotions includes (a) significant changes in salary; (b) lateral or horizontal promotions; (c) changes in offices and/or type of furniture/décor in office; (d) significant changes in job scope or responsibilities; and (e) changes in company level.
0 1 2 3 4 5 Greater than 5
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F4. Please indicate how many promotions you have received in your entire career. Note: Promotions includes (a) significant changes in salary; (b) lateral or horizontal promotions; (c) changes in offices and/or type of furniture/décor in office; (d) significant changes in job scope or responsibilities; and (e) changes in company level. 0 1 2 3 4 5 Greater than 5 F5. How likely is that you will receive a promotion within the next five years? 0 - No Chance 1 2 3 4 5 - Very Good Chance
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Department of Management and Human Resources
700 Fisher Hall 2100 Neil Avenue
Columbus, OH 43210-1144 fisher.osu.edu
Thank you for your participation in our study, Parenthood and Organizational Networks: A Relational View of the Career Mobility of Working Parents- Part 2. In appreciation of completing both waves of the survey, we would like to give you the opportunity to enter a drawing. Five individuals will be randomly selected to receive a prize each worth 100 US Dollars. Please read the disclaimer below and enter this drawing should you be interested in your name being included in the drawing. Of note, all winners will be notified directly over e-mail by the Co-Investigator, Kyra Sutton. Disclaimer: I understand that I am entering this drawing on a voluntary basis. I have completed Wave 1 AND Wave 2 of the surveys from the Parenthood and Organizational Networks: A Relational View of the Career Mobility of Working Parents- Part 2 study. I understand that 5 winners will be randomly selected, and that each winner will be awarded a prize worth 100 US dollars. I understand there will be a choice in the prizes awarded. Specifically, the winners will have a choice of (1) an Ipod, (2) a Toys R’ Us Gift certificate, (3) a Best Buy Gift Certificate, or (4) an opportunity to donate 100.00 US Dollars to their favorite charity. I am aware that I am entering this drawing on a voluntary basis, and I do not have to enter this drawing if I do not desire to be considered for the drawing. I have given my name and e-mail addresses in the boxes below, and I am aware that all winners will be contacted directly by e-mail. Name (First, Last): E-mail Address (Preferred):
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