Relationships among Span, Time Allocation, and Leadership of First-Line Managers and Nurse and Team Outcomes

by

Raquel Marie Meyer

A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy

Graduate Department of Nursing Science

University of Toronto

© Copyright by Raquel Marie Meyer 2010

ii

Relationships among Span, Time Allocation, and Leadership of First-Line Managers and Nurse and Team Outcomes

Raquel Marie Meyer

Doctor of Philosophy

Graduate Department of Nursing Science University of Toronto

2010

Abstract

Comparisons of raw span (i.e., number of staff who report directly to a manager) within and

across organizations can misrepresent managerial capacity to support staff because managers

may not allocate the same amount of time to staff contact. The purpose was to examine the

influence of alternative measures of managerial span on nurse satisfaction with manager’s

supervision and on multidisciplinary teamwork. The alternative measures were (a) raw span as a

measure of reporting structure and (b) time in staff contact as a measure of closeness of contact

by the manager. The main effects of the alternative measures, leadership, hours of operation, and

other covariates on outcomes were examined. The interaction effects of the alternative measures

with leadership and hours of operation were investigated. The study framework was based on

Open System Theory and the boundary spanning functions of managers. A descriptive,

correlational design was used to collect survey and administrative data from employees,

managers, and organizations. Managerial time allocation data were collected through self-

logging and validated through observation. Acute care hospitals were selected through purposive

sampling. For supervision satisfaction, the final sample size was 31 first-line managers and 558

nurses. For teamwork, the final sample size was 30 first-line managers and 754 staff. The

Leadership Practices Inventory, the Satisfaction with my Supervisor Scale, and the Relational

ii

iii

Coordination Scale were used. Hierarchical linear modeling was the main type of analysis

conducted. Raw span interacted with leadership and hours of operation to explain supervision

satisfaction. Teamwork was explained by leadership, clinical support roles, hours of operation,

total areas, and non-direct reports, but not by raw span or time in staff contact. Large acute care

hospitals can improve satisfaction with supervision and teamwork by modifying first-line

management positions.

iii

iv

Acknowledgements

Success as a doctoral student depends on the efforts of many people. I am deeply grateful to the many

individuals and organizations that enabled this journey and extend my sincerest thanks.

To my thesis supervisor and mentor Dr. Linda O’Brien-Pallas, for sharing your vast expertise and

experience, for believing in me, and for fostering my success. I will always hold you in high and fond

regard. To my committee members Dr. Diane Doran, Dr. David Streiner, Dr. Mary Ferguson-Paré, and

Dr. Christine Duffield, for sharing your expert knowledge and humour to skillfully guide this thesis. To

my thesis reviewers Dr. Linda McGillis Hall and Dr. Carol Brewer, for your constructive feedback. To

the staff at the Nursing Health Services Research Unit, for facilitating this thesis.

To the nurse managers, who graciously allowed me into their busy worlds, thank you so much for your

insights and for the vital contributions you make every day to our healthcare system. You made this

dissertation possible and I will always be grateful. To the front-line nurses and health care providers,

administrative staff, senior nurse leaders, and hospitals who also very generously participated, I am

extremely appreciative.

To the following, thank you for financial support: the Canadian Institutes for Health Research, the

Nursing Health Services Research Unit, the Canadian Health Services Research Foundation/Canadian

Institutes for Health Research Chair in Nursing/Health Human Resources, the Ontario Training Program

in Health Services and Policy Research, the Ontario Nursing Leadership Network, the Registered Nurses

Foundation of Ontario, the Nursing Research Interest Group, and the Ontario Nursing Informatics Group

as well as awards through the Lawrence S. Bloomberg Faculty of Nursing and University of Toronto.

To my father Ken Meyer and stepmother Lynn Brown, for your steadfast encouragement and for

supporting me in a myriad of ways. To my mother Cam Duhaime and stepfather Don Kishibe, for

instilling me with a strong work ethic and for cheering me on. To Amy, Louise, and Bob, for believing in

me and for the on-line support. To Barb Mildon, Christine Covell, Kim Sears, Joan Almost, Jessica

Peterson, and Doris Leung for friendship and laughter during the doctoral program adventure.

Finally and ever so importantly, to my husband Paul Barber, for unwavering love, support, and humour

and for weathering this journey alongside me – you are the wind beneath my wings.

iv

v

Table of Contents

Abstract.......................................................................................................................................... ii

Acknowledgements ...................................................................................................................... iv

Chapter 1: The Problem............................................................................................................... 1

Background of the Problem ............................................................................................. 1

Problem Statement............................................................................................................ 2

Purpose of the Study......................................................................................................... 3

Literature Review ............................................................................................................. 3

Managerial Span ............................................................................................................... 4

Measurement and Analytical Issues ....................................................................... 11

Studies of Span as Reporting Structure and Staff Outcomes .............................. 13

Studies of Span as Closeness of Contact and Staff Outcomes.............................. 14

Managerial Time Allocation .......................................................................................... 16

Measurement Issues ................................................................................................. 16

Leadership ....................................................................................................................... 22

Studies of Managerial Span, Leadership, and Staff Outcomes ........................... 25

Gaps in the Literature and Study Rationale ................................................................ 26

Chapter 2: Theory and Study Framework............................................................................... 28

Theory .............................................................................................................................. 28

Assumptions.............................................................................................................. 28

Open System Theory Applied to Large Scale Organizations............................... 29

Boundary Spanning ................................................................................................. 32

Outcomes................................................................................................................... 33

Study Framework ........................................................................................................... 38

First-Order Relationships ....................................................................................... 42

Manager Level Covariates ...................................................................................... 46

v

vi

Staff Level Covariates.............................................................................................. 51

Chapter 3: Method...................................................................................................................... 53

Design............................................................................................................................... 53

Design and Data Collection Overview.................................................................... 53

Power......................................................................................................................... 54

Setting and Sample................................................................................................... 55

Data Collection Procedures ..................................................................................... 57

Risk and Benefits...................................................................................................... 59

Administrative Data........................................................................................................ 60

Managerial Work Logs................................................................................................... 62

Instrumentation............................................................................................................... 64

Leadership Practices Inventory .............................................................................. 64

Nurse Satisfaction with Manager’s Supervision ................................................... 65

Relational Coordination Scale ................................................................................ 68

Data Analyses .................................................................................................................. 68

Data Entry and Cleaning......................................................................................... 68

Levels of Analysis ..................................................................................................... 69

Study Objectives....................................................................................................... 70

Knowledge Translation Plan.......................................................................................... 71

Chapter 4: Results....................................................................................................................... 73

Instruments...................................................................................................................... 73

Leadership Practices Inventory .............................................................................. 73

Satisfaction with my Supervisor Scale ................................................................... 74

Relational Coordination Scale ................................................................................ 74

Sample Description ......................................................................................................... 74

Descriptive Statistics of the Study Variables................................................................ 77

Satisfaction Findings....................................................................................................... 84

vi

vii

Objective 1: Main Effects for Satisfaction ............................................................. 84

Objective 2: Interaction Effects for Satisfaction ................................................... 87

Objective 3: Model Explaining Most Variation in Satisfaction ........................... 92

Teamwork Findings ........................................................................................................ 93

Objective 1: Main Effects for Teamwork .............................................................. 93

Objective 2: Interaction Effects for Teamwork .................................................... 96

Objective 3: Model Explaining Most Variation in Teamwork ............................ 98

Chapter 5: Discussion of Findings........................................................................................... 100

Descriptive Findings ..................................................................................................... 100

Manager Sample..................................................................................................... 100

Outcomes................................................................................................................. 102

Raw Span and Outcomes ............................................................................................. 102

Time in Staff Contact and Outcomes .......................................................................... 103

Leadership and Outcomes............................................................................................ 103

Hours of Operation and Outcomes ............................................................................. 104

Three-Way Interaction Effects .................................................................................... 105

Covariates and Outcomes............................................................................................. 107

Implications for Boundary Spanning.......................................................................... 109

Study Limitations and Strengths................................................................................. 110

Future Knowledge Translation Plan........................................................................... 112

Chapter 6: Summary, Recommendations, and Conclusions ................................................ 113

Summary........................................................................................................................ 113

Recommendations for Research .................................................................................. 114

Recommendations for Theory Development .............................................................. 116

Recommendations for Organizational Policy and Managerial Practice ................. 116

vii

viii

Conclusions.................................................................................................................... 120

References.................................................................................................................................. 121

viii

ix

List of Tables

Table 1. Span at the Manager Level ............................................................................................... 6

Table 2. Operational Definitions of Span ..................................................................................... 12

Table 3. Components of the Literature Used to Derive the Study Framework ............................ 41

Table 4. Data Collection Flow Chart ............................................................................................ 54

Table 5. Managers’ Average Raw Span, Total Areas Assigned, and Surveys Analyzed............. 75

Table 6. Managers’ Age, Tenure, Years of Experience, and Education....................................... 76

Table 7. Nurses’ Designation, Age, Years of Experience, and Education ................................... 76

Table 8. Team Surveys by Occupation ......................................................................................... 77

Table 9. Team Surveys by Highest Education.............................................................................. 77

Table 10. Descriptive Statistics of Study Variables...................................................................... 78

Table 11. One-Way Analysis of Variance Model for Satisfaction ............................................... 84

Table 12. Fixed-Coefficient Regression Model Level-1 for Satisfaction..................................... 85

Table 13. Fixed-coefficient Regression Model Level-1 for Satisfaction: Reduced Model .......... 85

Table 14. Raw Span. Intercepts-as-Outcomes Model for Satisfaction ......................................... 86

Table 15. Time in Staff Contact. Intercepts-as-Outcomes Model for Satisfaction....................... 86

Table 16. Raw Span with Two-Way Interactions for Extended Hours of Operation. Intercepts-as-

Outcomes Model for Satisfaction ................................................................................. 87

Table 17. Raw Span with Two-Way Interactions for Compressed and Mixed Hours of Operation.

Intercepts-as-Outcomes Model for Satisfaction ........................................................... 88

Table 18. Time in Staff Contact Two-Way Interactions for Extended Hours of Operation.

Intercepts-as-Outcomes Model for Satisfaction ........................................................... 88

Table 19. Time in Staff Contact with Two-Way Interactions for Compressed and Mixed Hours of

Operation. Intercepts-as-Outcomes Model for Satisfaction ......................................... 89

Table 20. Raw Span with Three-Way Interaction for Extended Hours of Operation. Intercepts-as-

Outcomes Model for Satisfaction ................................................................................. 89

Table 21. Holm Procedure for Three-Way Interaction for Two Alternative Measures for

Satisfaction.................................................................................................................... 90

Table 22. Raw Span with Three-Way Interaction for Compressed and Mixed Hours of Operation.

Intercepts-as-Outcomes Model for Satisfaction ........................................................... 91

ix

x

Table 23. Summary of Between-Manager Variance Explained in Satisfaction Models with

Alternative Span Measures ........................................................................................... 93

Table 24. One-Way Analysis of Variance Model for Teamwork................................................. 93

Table 25. Fixed-coefficient Regression Model Level-1 for Teamwork ....................................... 94

Table 26. 95% Confidence Intervals of Pairwise Differences in Mean Teamwork Scores.......... 95

Table 27. Raw Span. Level-2 Covariate Model Parameter Estimates for Teamwork.................. 96

Table 28. Time in Staff Contact. Level-2 Covariate Model Parameter Estimates for Teamwork 96

Table 29. Time in Staff Contact with Two-Way Interactions for Extended Hours of Operation.

Level-2 Covariate Model Parameter Estimates for Teamwork .................................... 97

Table 30. Time in Staff Contact with Two-Way Interactions for Compressed and Mixed Hours of

Operation. Level-2 Covariate Model Parameter Estimates for Teamwork .................. 98

Table 31. Summary of Between-Manager Variance Explained in Teamwork Models with

Alternative Measures .................................................................................................... 98

Table 32. Level-2 Covariate Model Parameter Estimates for Teamwork .................................... 99

Table 33. Characteristics of Study Managers Compared to Other Studies................................ 101

x

xi

List of Figures Figure 1. Large scale organization as open system (based on Katz & Kahn, 1978). ................... 30

Figure 2. Study framework. .......................................................................................................... 39

Figure 3. Distribution of raw span values..................................................................................... 79

Figure 4. Distribution of time in staff contact values. .................................................................. 80

Figure 5. Distribution of leadership scores. .................................................................................. 81

Figure 6. Distribution of level-1 nurse satisfaction with manager’s supervision scores. ............. 82

Figure 7. Distribution of level-1 teamwork scores. ...................................................................... 83

Figure 8. Plot of supervision satisfaction on raw span and leadership for extended hours of

operation........................................................................................................................ 91

Figure 9. Plot of supervision satisfaction on raw span and leadership for compressed and mixed

hours of operation.......................................................................................................... 92

xi

xii

List of Appendices

Appendix A. Studies of Span at the Manager Level and Staff Outcomes.................................. 139

Appendix B Information and Consent Letter and Survey for Managers .................................... 150

Appendix C. Information and Consent Letter and Survey for Employees ................................. 156

Appendix D. Pilot Work ............................................................................................................. 161

Appendix E. Pearson Correlations of Study Variables............................................................... 170

Appendix F. Letters of Permission to Use Instruments .............................................................. 172

xii

1

Chapter 1: The Problem

Background of the Problem

Although intended to streamline services and reduce costs, Canadian health care restructuring

initiatives during the 1990s were paradoxically accompanied by increases in the prevalence and

cost of employee turnover, casualisation, absenteeism, and injury (Baumann et al., 2001;

Canadian Nursing Advisory Committee, 2002; O’Brien-Pallas, Thomson et al., 2003; Shamian,

O’Brien-Pallas, Thomson, Alksnis & Kerr, 2003). These challenges, which are the domain of

managers, have been compounded not only by a growing shortage of health care professionals

(O’Brien-Pallas, Alksnis & Wang, 2003) but also by increasingly heavy managerial workloads

and by greater numbers of direct reports per manager (i.e., wider raw spans) despite a lack of

evidence to support these organizational changes (Baumann; Canadian College of Health Service

Executives, 2001; Canadian Nursing Advisory Committee; Commission on the Future of Health

Care in Canada, 2002; O’Brien-Pallas, Thomson et al., 2003). In Canada, the proportion of the

Registered Nurse workforce reporting employment in management positions across all levels

declined from 8.9% in 1997 to 7.1% in 2007 (Canadian Institute for Health Information, 2002,

2008). Until recently, the fiscal and human consequences of this trend and the effect of

increasing workloads and spans of first-line managers have remained relatively unexamined.

Although scientific inquiry has been directed to the workload and productivity of nurses

(O’Brien-Pallas, Meyer & Thomson, 2004; O’Brien-Pallas, Thomson et al., 2004), less attention

has been devoted to the span, workload, and productivity of managers. Evidence that explains

and optimizes the influence of managerial work design on patient, staff, manager, and system

outcomes can assist decision makers in organizations and in health care policy to evaluate

managerial deployment decisions. The span and workload of first-line managers in relation to

patient, staff, and system outcomes has yet to be studied in depth.

1

2

Problem Statement

Research on managerial span, for the most part, is limited by conflicting conceptual and

operational definitions and by the failure to consider both the complexity of the organizational

environment and the characteristics and competing demands of employees, consumers, and the

organization. Managerial span has yet to be considered in relation to the total workload of the

manager in order to more comprehensively measure and evaluate the contributions of managers

to the health care system. Measurement of managerial span and work allows the relationships

between managerial inputs and outputs to be investigated. Based on the literature reviewed,

studies examining managerial span in relation to employee and organizational outcomes are

emerging. However, limited research has been conducted on managerial span in the health care

sector in relation to patient, employee, and organizational outcomes.

This thesis aimed both to explore the influence of alternative measures of first-line managerial

span (i.e., raw span and time in staff contact) on nurse and team outcomes in the hospital sector

and to examine the curvilinear relationship between span and outcomes originally proposed by

Van Fleet and Bedeian (1977). A curvilinear relationship would indicate that when raw spans are

very narrow or very wide, outcomes may be subject to diminishing or increasing returns. Meier

and Bohte (2000) and Theobald and Nicholson-Crotty (2005) examined the curvilinear

relationship between span and outcomes in the education sector. However, preliminary pilot

work for this thesis in the acute care hospital setting demonstrated that an examination of the

curvilinear relationship was not feasible for the reasons below. Unlike the education sector,

hours of operation in acute care hospitals are not constant across managers and areas. For

example, clinics may run weekdays only whereas as inpatient units run 24 hours a day, 7 days a

week. Hours of operation are relevant to two concepts central to this study: raw span and time

allocation. Variation in hours of operation in health care alters the density of staff relative to the

manager’s workday and the coverage of service hours by the manager relative to his/her

workweek. Consequently, the curvilinear relationship between span and outcomes could not be

explored, and hours of operation was integrated into the study framework because of its potential

interaction with raw span and time allocation.

3

Purpose of the Study

The purpose of the study was to examine the influence of alternative measures of managerial

span on nurse and team outcomes in the hospital sector. The alternative measures were (a) raw

span as a measure of reporting structure and (b) time in staff contact as a measure of closeness of

contact by the manager. Specifically, the study objectives were:

1. to examine the main effects of the alternative measures of managerial span on outcomes

for nurses and teams;

2. to examine the interaction effects of the alternative measures of managerial span with

leadership and hours of operation on outcomes for nurses and teams; and

3. to determine the extent to which the alternative measures of managerial span explain

variation in outcomes for nurses and teams.

The focus was on the first-line manager’s capacity to supervise and support staff. The outcomes

were nurse satisfaction with manager’s supervision and teamwork. The design and execution of

the study was guided by an in-depth review of the literature.

Literature Review

The literature review to inform this study was inclusive of three specific areas. First, a critical

appraisal explored the pragmatic utility of the span of management concept. The historical,

conceptual, and measurement issues related to span of management at the manager level are

reported here as well as studies of managerial span and outcomes. The alternative measures of

managerial span proposed in this study were derived from this review. Next issues in the

measurement of managerial time allocation were appraised to select the time measurement

technique employed in this study. Finally, reviews of the leadership paradigms and studies of

leadership, span, and staff outcomes informed the choice of leadership model for this study and

the extent of progress in this topic area. Study outcomes are addressed in Chapter 2.

4

Managerial Span

A critical appraisal of the literature was first conducted to explore the pragmatic utility of the

span of management concept (Meyer, 2008). The goal of this type of analysis is to determine the

usefulness of an abstract concept to science by clarifying the purpose of the inquiry, ensuring

validity, maintaining bibliographic records, identifying significant analytical questions, and

synthesizing results (Morse, 2000). Literature was retrieved from the disciplines of business,

psychology, health care, and nursing. The sample was derived from online searches of Business

Source Premier, PsychINFO, and CINAHL. The following related terms were identified: span of

control, span of authority, span of responsibility, supervisory ratio, supervisory span, chain of

command, scalar chain, organizational structure, and work group size. After reviewing the

keywords in each database, the search terms were narrowed to span of control, span of

management, organizational structure, hierarchy, supervisory ratio, and work group size.

Ancestry and invisible college approaches were also used to track cited references and

unpublished works (Cooper, 1982). The search was limited to English language, peer-reviewed

publications between 1975 and 2007 in order to address conceptual and methodological advances

since Van Fleet and Bedian’s (1977) historical review. Relevant seminal works were also

included. Citations numbered 78 for Business Source Premier, 21 for PsychINFO, and 11 for

CINAHL. Using a computerized system, 51 manuscripts were reviewed and catalogued. The

search resulted in 17 citations related to conceptual or methodological issues about the span of

management. The critical appraisal revealed that conceptual approaches to span vary according

to the question posed, the purpose of the inquiry, and the level of analysis (i.e., organizational,

manager, work group, and employee levels), and the full concept analysis is reported elsewhere

(Meyer, 2008).

Consistent with the study purpose, the literature discussed in this chapter addresses the span of

management at the level of analysis of the manager (i.e., measures of ‘managerial span’). In this

section, a historical overview of span is presented; three conceptual approaches to span at the

manager level are highlighted; and, determinants of span at the manager level are identified.

Issues in the measurement and analysis of managerial span are discussed. Finally, studies of

managerial span and staff outcomes are reviewed.

5

Derived from the Germanic word ‘spanne’, span refers to breadth, extent, or range which can be

measured along different axes: spatially (e.g., distance), numerically (e.g., quantity, diversity), or

temporally (e.g., time period; Oxford University Press, 2005). Span of control may be described

as “the area of activity, number of functions or subordinates, etc., for which an individual or

organization is responsible” (Oxford University Press). Fervently debated since the advent of the

Industrial Revolution, the concept of span has been variously labeled span of control, span of

management, span of authority, span of responsibility, supervisory ratio, supervisory span, chain

of command, and scalar chain (Fayol, 1937; Gittell, 2001; Ouchi & Dowling, 1974; Van Fleet &

Bedeian, 1977).

Approaches to understanding span have evolved over the past century (Van Fleet & Bedian,

1977). During the industrial revolution, classical theorists debated the maximum number of staff

whose work and interactions the manager could supervise, direct, coordinate, and control

(Graicunas, 1937; Gulick, 1937; Urwick, 1937). This approach to span, also known as ‘limited

span’, delimits the maximum number of workers that one superior can oversee (Van Fleet &

Bedeian). For example, Graicunas quantified the potential exponential increase in direct-single,

direct-group, and cross relationships that a manager must supervise as the number of

subordinates increase. This approach, which reflected the growing emphasis on quantification

within scientific management circles, considered neither the influence of supervisory

effectiveness (Van Fleet & Bedian) nor the frequency and intensity of the interactions between

managers and their staff (Koontz, O’Donnell & Weihrich, 1980). Subsequently, span was

theorized in relation to organizational structures, with wider and narrower spans deemed

appropriate at lower and higher organizational levels respectively (Van Fleet & Bedian). Greater

diversity in the number of specialties supervised has been associated with narrower spans for

lower level managers (Dewar & Simet, 1981; Meier & Bohte, 2003). During the 1950s, studies

were conducted to validate the concept of an ‘optimum span’ which recognized that spans which

were too wide or too narrow could alter the effectiveness of supervision (Van Fleet & Bedian).

During the 1960s, the influence of contextual factors on span was acknowledged (Koontz et al.)

and later on, Ouchi and Dowling (1974) argued that it was necessary to factor in how much time

managers devote to employees.

6

Prior to the 1980s, most empirical studies of span were largely descriptive and measured span as

an outcome variable reflective of organizational characteristics (Van Fleet & Bedeian, 1977).

The question asked was: What organizational characteristics predict span within and across

different organizations? These studies examined the relationships between existing spans and

organizational characteristics. The organizations studied were generally assumed to be

successful, and objective performance measures were rarely used. More recently, researchers

have shifted the emphasis to span as an intermediary variable and its influence on organizational

outcomes. Span has been tested as a predictor, moderator, and outcome variable. Studies of span

have had limited success in explaining the influence of span on outcomes.

Ouchi and Dowling (1974) argued that conceptualization and measurement of span vary

according to the research question posed. Measures of span also reflect the level of analysis

(Meyer, 2008). The question, purpose of the inquiry and the level of analysis reflect underlying

conceptual approaches, each of which calls for a different measure of span. Table 1 presents

these facets of span at the manager level (Meyer, 2008).

Table 1. Span at the Manager Level

Question Purpose Level of Analysis Concept Measurement Example For how many employees is the

manager responsible? Lines of accountability Manager Reporting

structure number of employeesa

per manager

How much time does the manager spend supervising &

supporting employees?

Proxy for interaction between manager &

employee

Manager Closeness of contact by manager

number of employeesb % of time by manager

What is the breadth of the manager’s responsibilities &

roles?

Proxy for managerial capacity

Manager Scope of managerial role

multifactor toolsc

Notes. aMcCutcheon, 2004. bAdapted from Ouchi & Dowling, 1974. cMorash, Brintnell & Rodger., 2005; Stieglitz, 1962. The full article published by Blackwell Publishing is available in the Journal of Advanced Nursing (Meyer, 2008).

Span as reporting structure. Span of management has been conceptualized at the managerial

level as a measure of the reporting structure. Ouchi and Dowling (1974) referred to raw span,

which encompasses the “limits of hierarchical authority exercised by a single manager” (p. 357).

The question to be answered is: For how many employees does the manager have some

authority, control, or responsibility? (Ouchi & Dowling). The emphasis is on reporting

structures, lines of accountability, and communication. The measure is commonly

operationalized as the number of employees reporting directly to the manager (i.e., the number of

direct report staff). However, such measures fail to consider the frequency, intensity, purpose,

7

quality, amount, and outcomes of manager-staff interactions (House & Miner, 1969; Koontz et

al., 1980). Further, the supervisory demands associated with nonpermanent positions (e.g.,

nursing students) or independent contractors (e.g., agency nurses) are excluded from this

traditional definition. For this thesis, a measure of raw span (i.e., the number of employees

reporting directly to the manager) was used as a proxy for span as reporting structure.

Wider raw spans are theorized to be appropriate when workers are highly skilled or specialized,

because their extensive knowledge of the work process requires less supervision (Meier & Bohte,

2003). When first-line managers are responsible for teams of regulated healthcare professionals,

this argument is relevant. In contrast, unregulated workers may require more hands-on

supervisory activity by the nurse manager. Unit size in combination with mandatory staffing

ratios can determine the minimum number of direct report staff per manager. Overly wide spans

may hinder access to the manager, delay communication by staff, and overextend the manager

(Alidina & Funke-Furber, 1988). Wide spans may impede highly skilled workers from

communicating with and providing feedback to management to complete complex work

processes (Blau, 1968). Wide spans may also indicate increased managerial job complexity

because larger subunit size necessitates greater division of labor within the group and hence

greater coordination of interdependent tasks within and between subunits (Mia & Goyal, 1991).

Given that nurses and other healthcare professionals work in complex environments, the purpose,

amount, context, and outcomes of managerial activity need to be considered in addition to the

number of direct report staff.

Narrow raw spans are theorized to facilitate horizontal communication when coordination of the

main work processes occurs horizontally (Gittell, 2003). Indeed, nursing teams generally deliver

services with limited vertical coordination required across management layers (McCutcheon,

2004). At the extreme end, overly narrow spans, which may stifle worker autonomy through

increased supervision and reduced delegation, may contribute to lower worker autonomy and

morale (Alidina & Funke-Furber, 1988; House & Miner, 1969).

Span as closeness of contact by the manager. Theorists have recognized that in addition to

integrating and coordinating human resources, managers are often assigned other job functions

(Altaffer, 1998; Ouchi & Dowling, 1974). Competing demands for the manager’s time indicate

8

that, although important, span as a measure of manager-employee interaction may only be one

aspect of the manager’s total workload. Ouchi and Dowling argue that as managerial job

complexity increases, the amount of time managers have available for staff decreases. Even if

managers have the same number of direct report staff, the amount of support provided to

employees may vary relative to the amount of time each manager allocates to interaction with

staff. Comparisons of the number of staff who report directly to a manager within and across

organizations can misrepresent managerial capacity to support staff because managers may not

allocate the same amount of time to staff contact (Ouchi & Dowling). Measures that factor in

how much time managers spend in supervisory support facilitate comparisons of managers

across units and organizations (Ouchi & Dowling). How managers spend their time may

influence the relationship between span and outcomes when the outcome is sensitive to time

allocation across management activities.

With respect to span as closeness of contact, the question to be answered is: How much time

does a manager spend supervising employees? (Ouchi & Dowling, 1974). The emphasis is on the

relational aspects of the interactions between the manager and employees (Ouchi & Dowling).

Napier and Ferris (1993) suggested that span can limit the interaction between supervisors and

workers. In more contemporary terms, the question to be answered is: Given the number of

direct report staff assigned, how much attention, support, clinical supervision, and feedback can

the manager provide to each employee? For this thesis, time in staff contact (i.e., the average

daily amount of time spent by the manager interacting with staff) was used as a proxy for span as

closeness of contact by the manager.

Amount and allocation of managerial resources across an organization may also influence how

individual managers spend their time. Greater managerial resources, whether through taller

structures (i.e., multiple management layers) or through re-distribution of management functions

to other roles or departments, may influence the division of work amongst management

positions. For example, in a study by Drach-Zahavy and Dagan (2002), head nurses within a

four-tier nursing management chain focused their activities on internal unit coordination and

spent less time than anticipated in external coordination, which was taken on by higher level

nurse managers. A shift from clinical to management activities has been observed as first-line

management functions such as training, supervision, and care delivery management have

9

devolved to other roles (Duffield, Donoghue & Pelletier, 1996; Duffield, Pelletier & Donoghue,

1994). Not only do managers in healthcare navigate one of the most complex industries, but

restructuring and decentralization of the Canadian health system during the 1990s was associated

with increased responsibilities for managers. The accountability of first-line nurse managers for

patient care on a single unit broadened to the management of finances, operations, human

resources, and patient care across multiple units (Hollett, 2001; McGillis Hall & Donner, 1997).

Thus, the degree of contact that first-line nurse managers have with staff varies according to

competing work demands. How much time a manager spends interacting with employees is

partially dependent on other competing demands and the overall distribution of managerial

resources.

Span as managerial scope. Span has been also conceptualized as the scope of responsibility of

the manager. This approach considers the breadth of the manager’s position by weighting the

complexity and diversity of the assigned functions and responsibilities. The question to be

answered is: What is the scope of the manager’s responsibilities, in addition to direct report

staff? For example, Altaffer (1998) examined the budgetary responsibility and number of

assistants allocated to the manager, as well as employee full-time equivalents. In 1962, Stieglitz

described a weighted scale to optimize seven factors influencing managerial span in a

manufacturing setting. These factors included the diversity and complexity in the work and

workers, geographical proximity, coordinating and planning activities, as well as organizational

assistance received. Morash, Brintnell, and Rodger (2005) developed a tool based on literature,

focus groups and expert consensus in which factors influencing the roles, responsibilities, and

span of control of clinical hospital managers along unit, staff, and program dimensions were

weighted. Multiple variables were used to operationalize budgetary responsibility, program

diversity, complexity of material management, and characteristics of staff. A field study

demonstrated that the tool adequately reflected variation among managers in one hospital

(Morash et al.). These tools reflect the broader responsibilities assigned to a manager by

considering organizational characteristics and work demands in addition to the number of direct

report staff, but have yet to be evaluated in relation to outcomes.

A review of the literature revealed that factors influencing the scope of the manager’s role were

proposed in both the theoretical and empirical literature. As early as 1937, Gulick theorized three

10

key determinants of raw span: diversification of function, stability, and space. For Gulick,

diversification of function entailed differences in the technologies used by subordinates, as for

example when subordinates have different disciplines. Stability referred to the changing nature

of the organization. The element of space described the dispersion of subordinates relative to the

superior. It is important to note that Gulick was discussing span within the context of early 20th

century manufacturing industries. Meier and Bohte (2003) expanded on the determinants

described by Gulick. Variation and unpredictability along these dimensions are theorized to

place greater demands on the manager.

Diversification of function entails the different types of workers, functions, or inputs used. Little

variation in workers, inputs, and the work performed should allow the manager to supervise

more employees since the workplace technologies are constant and predictable (Meier & Bohte,

2003). Greater diversity in employee categories or disciplines may generate differing needs for

supervision, support, resources, and information and thus may place greater demands on the

manager.

Stability reflects the constancy of workers, organizational inputs, and the environment, which in

turn, supports routinization (Meier & Bohte, 2003). Worker experience is theorized to reduce the

need for supervision because workers have greater fluency in the job (Meier & Bohte). Stability

of organizational inputs, such as the inflow of patients and funding, contributes to routinization

and facilitates long term planning which reduces the demands on managers and allows for wider

spans. Organizations experiencing turbulent funding or restructuring may, for pragmatic reasons,

merge clinical areas or services, which could easily increase the number of direct reports, reduce

time available to interact with staff, or broaden the scope of the manager’s responsibilities, if

additional resources are not allocated to support the manager.

Space refers to the physical separation of the manager from workers. Greater distance between

the manager and the employees, as may be occasioned by supervising employees in different

locations or in large organizations, reduces the face-to-face interaction between the two parties.

Distance can influence the quality of manager-employee relationships and communication

(Stogdill & Bass, 1981) and the frequency of opportunities for task and social exchange (Stogdill

& Bass). Bearing in mind the manufacturing context assumed by Gulick (1937), greater physical

11

distance was presumed to increase requirements for supervision. Thus, when similar workers

perform routine work using standard inputs in a confined space, a manager will be able to

supervise a greater number of employees (Gulick; Meier & Bohte, 2003). However, in large

organizations that rely on specialists (e.g., regulated health care professionals), the need for

immediate supervision may be less than in organizations that employ generalists (Stogdill &

Bass; Meier & Bohte).

In terms of other factors influencing the scope of the manager’s role, authors have identified the

potential importance of variables such as administrative support and technology (Van Fleet &

Bedian, 1977) as well as the extent of supervision, coordination, and planning required of the

manager (Stieglitz, 1962). In the health care industry, several contextual factors that may

influence managerial span have been proposed. These variables include: characteristics of the

manager (leadership, skills, experience, training, knowledge in domains of responsibility,

amount of non-supervisory managerial work, support roles; proximity); employees (volume,

stability, skill mix, training, competence, education, satisfaction); units (care delivery models,

occupancy rates, operational complexity, hours of operation, demands for fiscal control and

efficiency); programs (reporting and accountability structures, budget); and patient populations

(acuity and immediacy of decisions, complexity and quality of care, degree of coordination;

Alidina & Funke-Furber, 1998; Altaffer, 1998; Doran et al., 2004; Kramer et al., 2007; Mahon &

Young, 2006; McCutcheon, 2004; Morash et al., 2005; Pabst, 1993). For this thesis, the

following variables were used to represent select characteristics of the manager’s role: manager

education, manager experience, manager position tenure, hours of operation, support roles,

number of areas assigned, diversity of jobs reporting to the manager, employee tenure, full-time

employment, and non-direct reports. The rationale for including these variables in the study

framework is detailed in Chapter 2.

Measurement and Analytical Issues

Managerial span is typically measured as a ratio (Ouchi & Dowling, 1974). Units of

measurement include headcounts, full-time equivalent positions or time spent in supervisory

activity by full-time equivalent position (Ouchi & Dowling; Pabst, 1993). Variation in

12

operational definitions has impeded comparisons of span across studies (Ouchi & Dowling).

Examples of operational definitions of span at the manager level are presented in Table 2. Table 2. Operational Definitions of Span

Author (Year) Operational Definition Altaffer (1998) Raw span:

number of full-time equivalent employees per manager

Graicunas (1937) Span of control: Series of formulas to calculate number of direct single, direct group, and cross relationships assigned to manager

Ouchi & Dowling (1974)

Raw span: number of employees

per manager

Ouchi & Dowling Adjusted span for time spent in supervisory activity by manager only:a number of employees

% time spent by manager on the selling floor Note. aAdapted from the organizational level to the managerial level

Ratios are advantageous because the calculations are transparent, easily compared, and serve as

rudimentary indicators of management structures. However, the numerator, denominator, and

context of the ratio are assumed to be standard. Simple span ratios assume that all employees

have similar needs for managerial support and that all managers provide equal amounts,

frequency, and quality of support within comparably resourced and spatially designed

environments. However, measures of span are complicated by several factors. For example,

allocation of managerial time to human resource activities will vary according to competing

workload demands. Also, employee outcomes sensitive to span may be influenced by

supervisory support provided by other roles (e.g., charge nurse, clinical educator). Thus

comparisons of simple span ratios across and within organizations may be less accurate and less

sensitive to the outcomes of interest (Ouchi & Dowling, 1974).

Use of full-time employee equivalents versus employee headcounts. When employees are

counted as full-time equivalents rather than headcounts in the calculation of span, Ouchi and

Dowling (1974) argued that this measure reflects the span of responsibility. The question to be

answered is: For how many 40 hour work weeks is the manager responsible? The authors

suggested that employee full-time equivalents represent an average number of employees for

whom management is responsible, but do not capture the resources allocated per employee

related to scheduling, evaluation, and orientation. Using full-time equivalents as the metric of

analysis is theorized to be less sensitive to outcomes that are based on closeness of contact

13

(Ouchi & Dowling) and may be less appropriate when the focus is on manager-employee dyads.

This thesis used a headcount as the metric for counting employees.

Use of full-time managerial equivalents. Some calculations of span use managerial full-time

equivalents, rather than headcounts, to partition out the proportion of time managers allocate to

supervisory activity or, when full-time equivalents are the only available data (e.g., Ouchi &

Dowling, 1974; Pabst, 1993). However, in salaried positions, managers’ worked hours may

exceed a standard full-time equivalent. Thus use of full-time equivalents may be influenced by

variation in the length of the manager’s work week. This thesis used a headcount as the metric

for counting the manager.

Relative versus absolute time spent. Ouchi and Dowling (1974) recommend calculating the

percentage of the manager’s full-time equivalent spent in supervisory activity. Use of a

percentage implies a relative value whereby time spent varies relative to the length of the

manager’s work week (i.e., not all managers work 40 hours per week). For this thesis, absolute

time spent by the manager was used and worked hours were examined as a covariate to control

for differences in the length of the workweek.

Analytical methods. For the most part, studies examining the relationships between span and

outcomes have used correlations or regression without attending to the hierarchical nature of the

data. More recently, hierarchical linear modeling has been applied to studies of span and

leadership to address the nested structure of the data and cross level interactions within data

structures (Castro, 2002). Hierarchical linear modeling was used in this thesis to account for the

nested structure of the data set.

Studies of Span as Reporting Structure and Staff Outcomes

Of 10 studies examining the main effects of managerial span on staff outcomes, 5 were

conducted in health care settings and 2 attended to hierarchical levels within the data (Appendix

A). The 2 studies attending to levels-of-analysis are reviewed here. The first study investigated

raw span (by headcount) among Canadian hospital nurse managers (Doran et al., 2004;

McCutcheon, 2004; McCutcheon, Doran, Evans, McGillis Hall & Pringle, 2009). Wide spans

were associated with a lower proportion of staff nurses surviving the first year on the unit

14

(McCutcheon), higher staff nurse turnover (Doran et al.; McCutcheon), and lower patient

satisfaction (Doran et al.; McCutcheon et al.). No association between span and staff nurse job

satisfaction was observed. For every 10 person increment in the manager’s span of control, unit

turnover of nursing staff increased by 1.6%. Thus a span of 100 would be expected to result in a

16% turnover rate (Doran et al; McCutcheon).

In a second study, in the airline industry, the raw spans (by full-time equivalent) of airline flight

departure supervisors were examined in relation to group performance (Gittell, 2001). Wide and

narrow spans were associated with lower and higher levels of group performance respectively.

Wide spans were also significantly associated with less timely communication; lower levels of

problem solving, shared goals, and shared knowledge; and with lower levels of helping, and

mutual respect. The level of teamwork mediated the relationship between span and outcomes.

The remaining eight studies that investigated the main effects of span on staff outcomes,

although not addressing levels-of-analysis issues, also generally supported this trend whereby

wider spans were associated with decreased performance (Bohte & Meier, 2001; Meier & Bohte,

2000), less employee engagement (Cathcart et al., 2004), lower staff empowerment (Spreitzer,

1994), more negative staff nurse perceptions of the work environment (McGillis Hall et al.,

2006) and increased accidents and unsafe behaviors (Hechanova-Alampay & Beehr, 2001).

However in a study of performance evaluation with nurse-supervisor dyads, a non-significant

relationship between raw span and performance ratings was observed (Judge & Ferris, 1993).

Theobald and Nicholson-Crotty (2005) also observed that when multiple outcomes were

considered, optimal span values conflicted.

Studies of Span as Closeness of Contact and Staff Outcomes

Variation in managerial time allocation has been documented across industries and management

positions (e.g., Carroll & Gillen, 1987; Hass, Porat & Vaughan, 1969; Mahoney, Jerdee &

Carroll, 1963; Penfield, 1974). In the healthcare sector, Dunn and Schilder (1993) using work

observations techniques found that head nurses spent 20% of their time in scheduling,

orientation, staff development, employee evaluation, and counseling activities. Fox, Fox, and

Wells (1999) used job description categories to determine that nurse managers in one hospital

logged nearly 8.6% of their time in personnel management activities.

15

Although time spent with employees by managers varies, no research was located that explicitly

quantified these variations in relation to staff outcomes. However, two studies offer indirect

support. In a hospital study of performance evaluations between supervisor and nurse dyads,

Judge and Ferris (1993) observed that variation in performance evaluations, although not

predicted by raw span, was predicted by staff nurse perceptions’ of their supervisors’

opportunities to observe their performance. The authors conjectured that raw span may be an

insufficient measure of the contact between supervisors and staff because supervisors may

differentially allocate their time to non-supervisory work and may enact their roles with varying

degrees of efficiency. Hence managerial time allocation could be a more sensitive predictor of

performance evaluations than raw span alone. In a study of airline departure teams, the positive

influence of narrow spans on teamwork was potentially attributed to supervisors having greater

time available to work alongside, coach, and provide feedback to team members based on

qualitative observations (Gittell, 2001).

In summary, three conceptual approaches are evident in the literature related to span at the level

of the manager. These include span as: reporting structure, closeness of contact by manager, and

managerial scope. Ouchi and Dowling (1974) proposed that measures of span as closeness of

contact would be more sensitive to staff outcomes than measures of span as reporting structure.

This thesis addressed this argument by determining the extent to which alternative measures of

managerial span explained variation in outcomes. Specifically, the two alternative measures in

this thesis were raw span as a measure of reporting structure and time in staff contact as a

measure of closeness of contact by the manager. Factors influencing the scope of the managerial

role were also included in the study framework. Measures of raw span using headcounts and

measures of time allocation using absolute measures of time were identified as appropriate for

this thesis. Despite differences in design, methods, and analytical approaches, the review of the

research suggested that span as reporting structure (e.g., raw span) is an important predictor of

staff outcomes. Research linking span as closeness of contact (e.g., time spent in staff contact)

with staff outcomes is limited.

16

Managerial Time Allocation

In this section managerial work is defined and approaches to describing managerial work are

overviewed. Time allocation is a key predictor in this study and therefore issues in the

measurement of managerial time allocation are examined.

Inquiry into the nature and purpose of managerial work has a long-standing history (Urwick,

1937). Mahoney et al. (1963) succinctly delineated the basic purpose of management. They

observed that:

some form of management is inherent in all formal group activity which involves

cooperation in the pursuit of a common goal. Cooperation in this setting involves the

allocation of labor or responsibilities for action among group members in such a way that

individual actions sum to meaningful group activity… Management is that ingredient

which … effectively translates the activities of a collection of individuals into purposeful

group activity. (p.3)

In the management literature, the work of those in management roles has typically been

described at the level of observable tasks (e.g., doing paperwork, phoning, attending meetings),

in relation to contacts (i.e., with whom) or, in relation to purpose (Hales, 1986; McCall,

Morrison & Hannan, 1978). The work activities of managers are often classified using structured

observation categories (e.g., Arman, Dellve, Wikström & Törnström, 2009; Mintzberg, 1970,

1971, 1973; Stewart, 1982), job descriptions (e.g., Fox et al., 1999; Dunn & Schilder, 1993), or

the classical management functions (e.g., Carroll & Gillen, 1984, 1987; Mahoney et al., 1963;

Singler, 1982). Studies indicate that managerial work is characterized by variety, interruption,

fragmentation, and brevity (Arman et al., 2009; Martinko & Gardner, 1985; McCall et al.).

Measurement Issues

Time measurement techniques for managerial work have included structured observation, work

sampling, and time studies. Additional considerations include: the classification system, data

collectors and reporting methods, as well as the measurement time frame. These measurement

issues are explored in this section.

17

In Mintzberg’s (1973) review of empirical studies from the 1950s and 1960s, descriptions of

managerial work were predominantly derived from self-report diaries in samples of senior and

middle managers. Beginning in the 1960s, structured observation techniques were introduced to

determine how managers spend their time. The trend amongst health services researchers has

been to use observational techniques such as work sampling and time studies to investigate how

practitioners spend their time (Finkler, Knickman, Hendrickson, Lipkin & Thompson, 1993;

Pelletier & Duffield, 2003). These techniques, which characterize industrial and management

engineering approaches, focus on the observable tasks or groups of tasks (i.e., activities)

performed, without necessarily considering the context (including power relations), quality,

purpose, and outcomes of the work, nor the skill proficiency and mental processes (e.g.,

problem-solving) of the worker (Kerlinger, 1986; Pelletier & Duffield; Willmott, 1987).

O’Brien-Pallas (2004) noted that researchers must consider the purpose of the work

measurement, the level of rigor required, and the resources available in selecting a work

measurement technique.

Structured observation, work sampling, and time studies. To determine which type of work

measurement technique to apply, the purpose of the research and the resources available for the

project should be considered (Finkler et al., 1993; O’Brien-Pallas, 2004; Pelletier & Duffield,

2003). If the study purpose is to describe managerial activity levels (e.g., frequency of activities,

contacts, and interruptions) and interaction patterns (e.g., type, recipient, and location of

communication) in relation to performance criteria and the organizational context, then

structured observation may be an appropriate technique (Martinko & Gardner, 1985;

Noordegraaf & Stewart, 2000). Structured observation has proven helpful for generating

inductive descriptions of the work of managers. This method relies on independent observation

to classify managerial work according to a structured categorization system without the use of

random sampling procedures (Martinko & Gardner) and is generally feasible for small sample

sizes (McCall et al., 1978). Structured observation has also been used to describe time spent by

activity (Martinko & Gardner); however, these descriptions focused on observable tasks (e.g.,

doing paperwork, phoning, and attending meetings; McCall et al.).

If the study purpose is to describe the types of and proportion of time spent per activity, then

work sampling is an acceptable method. This technique samples work activities using random or

18

systematic intervals to extrapolate a distribution of activities which are assumed to be

representative of the phenomena (Finkler et al., 1993; Kerlinger, 1986). Due to the instantaneous

nature of the observations, task duration cannot be determined (Finkler et al.). Infrequent

activities may not be captured unless the sampling net is sufficiently large (Kerlinger). The larger

sample size enhances external validity (Finkler et al.). Given the large number of observations

that may be required to achieve precision, work sampling may also be more feasible when the

average distribution of the work activities amongst a group of workers is of interest and

observations can be divided amongst many workers.

If the purpose is to describe the type, frequency, and duration of time per task as well as the

proportion of total time spent by task, then time studies are an appropriate approach. Time

studies involve continuous observation of work activity to record the number and duration of

activities performed (Finkler et al., 1993). Detailed data, including infrequent activities, are more

likely to be captured (Finkler et al.). Time studies are labor intensive, and usually only a small

sample size can be supported, which limits external validity. If the predictor variables of interest

or unit of analysis are at the level of the individual worker, then time studies can provide data

about the time allocation of a particular worker.

This thesis aimed to explore the relationship between the time allocation of first-line managers

and staff outcomes. Structured observation was not chosen for this thesis because of its focus on

observable behaviors and its feasibility with small samples only. Nor was a work sampling

design used in this study because dividing the observations of managerial work across managers

would provide information on how managers spend their time on average, but not on how

particular time allocation patterns may be associated with variation in outcomes between

managers. Instead, a time study was selected as the measurement technique because the

associations of interest between the time allocation patterns of particular managers and their

respective staff outcomes could be examined. Common issues in the reliability and validity of

time study techniques are now discussed. These include the classification system, data collectors,

observation method, and the time frame.

Classification system. Classification systems should comprise exhaustive and mutually exclusive

categories (Kerlinger, 1986), be readable (Burns & Grove, 2001), and clear (Pelletier & Duffield,

19

2003). In determining categories, broad definitions may improve validity because more aspects

of the construct are considered; however, greater observer judgment is required and reliability

may be lowered (Kerlinger). In contrast, more specific operational definitions which require less

inference, may improve reliability of observations but the operational list may exclude important

aspects of the construct (Kerlinger). Content validity is enhanced by using previously validated

tools, but researchers have tended to modify existing or create new tools which limit

comparisons across studies (Pelletier & Duffield). Literature reviews and pilot testing can

enhance content validity of the data collection instrument (Cardona, Tappen, Terrill, Acosta &

Eusebe, 1997; Ross, Rideout & Carson, 1994). Pre and post interviews with subjects can clarify

recording issues with self-report tools (Ross et al.). In this thesis, the classification system was

pre-tested.

Data collectors. Observations may be made by an independent rater or by self-observation using

a structured instrument (Burke et al., 2000; Pelletier & Duffield, 2003). Although reliability

increases as the work measurement approach advances from self-logging to work sampling to

direct observation, so too do costs (O’Brien-Pallas, 2004). Regardless of whether the

observations are self-reported or made by independent data collectors, participants are at risk for

the Hawthorne effect, whereby they alter their behaviors when under observation (Burns &

Grove, 2001; Burman, 1995; Finkler et al., 1993). To minimize this effect, the self-report method

should minimize disruptions to work flow and independent data collectors should be carefully

selected to be neutral, unobtrusive observers and trained using trial runs (McNiven, O’Brien-

Pallas & Hodnett, 1993; Pelletier & Duffield). Interrater reliability of greater than 90% randomly

tested throughout the data collection period is considered acceptable and should be conducted for

both self-observation and independent methods (Pelletier & Duffield). Although costly,

independent data collectors are effective when the activity is observable in nature.

Self-observation may enhance validity in situations when workers multi-task because workers

can identify the primary activity performed (Rutter, 1994), or when the activity is not directly

observable (Carroll & Gillen, 1987; McCall et al., 1978). Since managerial work is characterized

by mental, rather than observable activity, self-observation by managers is recommended in

order for the manager to identify the purpose or subject of the activity (Carroll & Gillen). In this

20

thesis, managers engaged in self-observation to collect data. This approach was supplemented by

examining inter-observer agreement.

Self-report methods. Common self-report methods in the management literature include

estimations and work diaries. Estimates of time allocation, although easily collected, are subject

to recall bias. Early seminal work by Stogdill and Shartle (1955) found high correlations between

logged and estimated times in a 3-day, continuous self-observation logging exercise by naval

officers. However, because the participants estimated their time in activities after completing the

logging exercise, the high correlations for observable activities may have resulted from a priming

effect because the data were easily retrieved from memory. Estimated and logged times did not

correlate highly when the activities were mental in nature (e.g., planning, reflecting) or

infrequent. In a study of time spent per activity by managers, a three way comparison of

estimates of self-report, self-observation, and work sampling by independent observer produced

comparable results across participants on average (Carroll & Taylor, 1968). Similarly, Penfield

(1974) reported an average agreement coefficient of .81 between managers’ estimates of time

spent and independent observations. In a review of approaches to studying managerial work,

McCall et al. (1978) found that managers poorly estimated their work activities. This finding is

supported by more recent studies in the computer industry and in employment surveys which

indicate that self-reported estimates of time allocation at the level of the individual tended to

regress towards the group mean with respondents who spent little time in the activity

overestimating time spent and those who engaged extensively in the activity underestimating

time spent (Collopy, 1996; Jacob, 1998).

Work logs are another self-report data collection technique used to identify and record job

activities (Freda, Senkewicz & AT&T, 1988). However, self-report techniques can be subject to

biases such as social desirability. Social desirability bias occurs because participants generally

want to appear as favorably as possible and tend to over- or under- report behaviors deemed to

be more or less acceptable by researchers and employers (Donaldson & Grant-Vallone, 2002;

Podsakoff, MacKenzie, Lee & Podsakoff, 2003). Varying data collection techniques in terms of

timing, sources, method, and location can assist in minimizing these biases (Podsakoff et al.;

Podsakoff & Organ, 1986). Also, assuring respondents of the confidentiality of responses and

21

that answers are neither right, nor wrong, may reduce the likelihood of socially desirable

responses (Podsakoff et al.).

Work log techniques may also increase the participant’s sensitivity and awareness about the

behavior under study which could potentially result in over-reporting or changes in behaviors

(Burns & Grove, 2001). Participants may become fatigued or bored which might result in

underreporting (Burns & Grove). During busy periods, logging may become onerous, entries

may be skipped, and participants could rely on memory to complete the missing entries, thus

hindering data accuracy (Pelletier & Duffield, 2003). The potential to falsify responses also

exists (Pelletier & Duffield).

In comparison to retrospective interviews, self-report diaries enhance internal validity by

generating more data and reducing recall error (Verbrugge, 1980). Work diaries are feasible for

larger samples, less costly than independent observation, and can track time distribution across

predetermined activities (McCall et al., 1978). In a synthesis of studies using health diaries,

Verbrugge reported high rates of patient participation (86-98%) and completion (88-100%).

Health diary data were found to be complete and of high quality when participants were

monitored and encouraged during the data collection period (Verbrugge). However, participant

sensitization and fatigue over time can negatively influence data quality of health diaries, with a

5-25% decline observed in responses over a 2 to 3 month period (Verbrugge). The burden on the

respondent must therefore be considered. To minimize fatigue and improve response rates, Freda

et al. (1998) recommend a maximum of two work logging days per week. Activity logging at

half hourly intervals is also advised to enhance the accuracy of the data and minimize recall bias

(Freda et al., 1988). In this thesis, time allocation work logs were completed prospectively by

managers to enhance the quality of the data as compared to data derived from estimates or

retrospective interviews. Strategies to reduce the burden of self-report work logs were considered

and are described in Chapter 3.

Time frame. Sampling error can be reduced by increasing the number of observations and by

ensuring the time frame is wide enough to capture all activities (Kerlinger, 1986; McNiven et al.,

1993). The time frame for work diary completion should consider the cyclical nature of the

22

behavior under study whereby the length of the time study should at least equal the longest

period of the cyclical behavior of interest (Brisley, 2001; Freda et al., 1988).

Studies of nurse managers indicate that cycles vary for different human resource activities.

Hospital managers in three U.S. teaching hospitals self reported the frequency of a set of

management activities along the following scale: at least annually, at least monthly, at least

weekly, at least daily, and repeatedly daily (Baskin, 1996; Furman, 1995; Hudak, 1995). The

frequency of human resource activity in these studies mostly occurred daily, weekly, or monthly

(Baskin; Furman; Hudak). Only orientation and planning of orientation occurred annually, with

managers in one hospital also reporting hiring on an annual basis (Baskin; Furman; Hudak). In

this thesis, the time frame for collecting time allocation data considered that the cyclical peaks in

managerial activity may occur on a wide time frame (e.g., monthly or yearly, rather than hourly).

Given the resources available for the study and the burden of data collection on the managers,

the time frame chosen for the study reflected a one month cycle of work activity.

In summary, approaches to measuring time allocation are diverse. Selection of a time allocation

measurement technique needs to consider the question to be answered, the unit of analysis, and

the resources available. For this thesis, a time study approach was selected to measure

managerial time allocation for a one month cycle of work activity. Prospective, self-reported

work logs were completed by managers and were supplemented by examining inter-observer

agreement.

Leadership

Leadership is one of the most studied attributes of managers and was considered in this study

because of its numerous associations with staff outcomes. In this section leadership is defined;

methodological issues are briefly considered; the main leadership paradigms are introduced; and

the rationale for selecting the leadership model used in this study is presented. Finally, studies

examining leadership in relation to managerial span and staff outcomes are reviewed.

A fundamental theme in the management and health care literature, leadership has been studied

along a variety of dimensions including leadership traits, styles, behaviors, skills, and

relationships (House & Aditya, 1997; Patrick & White, 2005; Vance & Larson, 2002).

Traditionally, leadership theories have mainly focused inward on the leader-follower

23

relationship, largely disregarding the type of organization, the context, and the network of

relationships in which leadership is enacted (House & Aditya). Most definitions reflect the idea

that leadership is a process which involves, motivates, and gains the commitment of people in

the completion of tasks to achieve mutual goals (Wylie, 1994).

Studies of leadership have also been plagued by conceptual and methodological challenges, often

resulting in conflicting or inconsistent findings (House & Aditya, 1997; Yammarino, Dionne,

Chun & Dansereau, 2005). A review of leadership research in organizational settings located

only 19 empirical publications in the last decade that appropriately theorized and analyzed the

data according to its hierarchical structure (Yammarino et al.). In a review of leadership studies

between 1970 and 1999 in health care and business settings, only 5.2% of studies progressed

beyond anecdotes and descriptive designs to examine correlations between leadership and

measurable outcomes (Vance & Larson, 2002). In this study, the relationships between

managerial leadership and staff outcomes were tested, and the hierarchical structure of the data

set was accounted for by using hierarchical linear modeling with staff outcomes nested under

managerial predictors.

Leadership theory has evolved along four main paradigms: trait, behavioral, contingency, and

neo-charismatic (House & Aditya, 1997). Early trait theories searched for universal

characteristics of individuals that were associated with effective leadership (Patrick & White,

2005). An emphasis on the personal abilities and characteristics of the leader implied that leaders

were born, rather than made; however, support for the heritability of personality traits is

inconsistent (House & Aditya). Positive leadership traits in managers have been associated with

nurse retention and job satisfaction (Patrick & White).

Under the behavioral paradigm, two broad categories of leader behaviors were identified and

studied in relation to leader effectiveness (House & Aditya, 1997). These comprised task- and

person-oriented behaviors. The former focus on the structure and organization of the work,

whereas the latter build relationships and address the concerns and job satisfaction of employees

(Ferguson-Paré, Mitchell, Perkin & Stevenson, 2002; Patrick & White, 2005; Wylie, 1994). The

search for universal traits and universally effective leadership behaviors met with limited success

because research on both trait and behavioral approaches to leadership was mainly inductive,

24

lacking a theoretical base, and fraught with measurement and study design limitations (House &

Aditya). Furthermore, the influence of context and constraints placed upon the leader was

ignored (House & Aditya).

Subsequently, contingency theories focused attention on the contextual influences (e.g.,

situational moderators) that interact with leader characteristics and behaviors (House & Aditya,

1997). Although the resulting findings were ambiguous, these studies stimulated further theory

development (House & Aditya). To date, only a few studies have examined the moderating

effects of organizational structures on the relationship between leadership and outcomes in

nursing. McCutcheon et al. (2009) reported a significant moderating effect for span on the

relationship between leadership and nurse job satisfaction and patient satisfaction. The influence

of nurse managers’ emotional intelligence on staff nurse empowerment has also been shown to

be conditional on wide or narrow spans (Lucas, Laschinger & Wong, 2008). Stordeur,

Vandenberghe, and D’Hoore (2000) observed a moderating effect for hierarchical level on the

relationship between leadership and perceived nursing unit effectiveness. Cummings et al.

(2008) observed indirect effects of relational leadership on nurse job satisfaction through work

environment variables related to organizational structure, namely staff development programs,

staffing levels, and staff participation in policy decisions.

Finally, House and Aditya (1997) describe a paradigm shift toward neo-charismatic leadership

theories during the 1970s. Theories within this genre explain how visionary, transformational,

and charismatic leader behaviors generate affective responses by followers, including high levels

of motivation and commitment, and foster outstanding accomplishments within organizations

(House & Aditya). These theories emphasize visionary and charismatic behaviors, but also

address person-oriented, and to a lesser extent, task-oriented behaviors (House & Aditya; Wylie,

1994). In the health care sector, measures of transformational and visionary leadership have been

associated with staff nurse and organizational outcomes, including job satisfaction (Dunham-

Taylor, 2000; McCutcheon, 2004; McCutcheon et al., 2009; McNeese-Smith, 1995; Medley &

Larochelle, 1995; Morrison, Jones & Fuller, 1997; Loke, 2001; Stordeur et al., 2000),

organizational commitment (Loke, 2001; McNeese-Smith), extra effort (Dunham-Taylor;

Stordeur et al.), perceived unit effectiveness (Stordeur et al.), self-reported productivity

(McNeese-Smith), and turnover (Houser, 2003; McCutcheon).

25

Given the plethora of approaches to leadership, Vance and Larson (2002) recommended that

researchers select a definition that corresponds with the theoretical, methodological, or

substantive aspect of leadership under study. Because of their specialized knowledge and

professional accountability, health care professionals may not require or benefit from high levels

of task structuring by managers, which could potentially hinder their autonomy and decision-

making capacity in the provision of patient care. Leadership that emphasizes visionary and

relationship oriented behaviors rather than task supervision and control may therefore be

particularly suited to the management of health care professionals. For these reasons, a theory of

transformational leadership was chosen for this study. Specifically, Kouzes and Posner’s (2002)

leadership model was selected and the instrument is described in Chapter 3.

Studies examining Kouzes and Posner’s (2002) leadership model in the health care industry have

reported significant associations between managerial leadership practices and staff outcomes.

Specifically, McNeese-Smith (1995) found that the variation in U.S. hospital staff outcomes

explained by managerial leadership practices ranged from 9 to 15% for self-reported

productivity, 11 to 27% for job satisfaction, and 16 to 29% for organizational commitment.

Similarly in a study of Singapore hospital nurses, Loke (2001) observed that 9% of self-reported

productivity, 29% of job satisfaction, and 22% of organizational commitment were explained by

managers’ leadership practices. Houser (2003) also determined that managerial leadership

practices had a moderate inverse effect on nurse turnover in long-term care facilities. These

studies provide empirical validation of Kouzes and Posner’s leadership model with regard to

predicting staff outcomes in the health care sector.

Studies of Managerial Span, Leadership, and Staff Outcomes

In the health care sector, two studies have examined the relationships among span as reporting

structure, leadership, and outcomes (Appendix A). McCutcheon et al. (2009) found that raw span

(by headcount) moderated the relationship between leadership style and nurse job satisfaction

and patient satisfaction, whereby even highly transformational leaders could not overcome spans

that were too wide. Wider spans reduced the positive effects of transformational and

transactional leadership on nurse and patient satisfaction and increased the negative effects of

management-by-exception and laissez-faire leadership on nurses’ job satisfaction. Similarly,

26

Lucas et al. (2008) observed that the beneficial influence of emotionally intelligent leadership by

nurse managers on staff nurse empowerment was lessened under wider raw spans (by

headcount).

In the airline industry, although teamwork mediated the relationship between raw span (by full-

time equivalent) and group performance, qualitative observations indicated that the benefits

achieved by narrow spans were dependent upon whether the supervisor’s style was facilitative or

coercive (Gittell, 2001). Performance was enhanced when supervisors provided coaching and

feedback to employees. In a banking sector study, raw span (by full-time equivalent) moderated

the relationship between quality of leader-member exchange and organizational commitment, but

not supervisors’ ratings of staff performance (Schriesheim, Castro & Yammarino, 2000). This

study may have been challenged by a small sample, with only 2 employees per manager

participating. Overall, these studies indicate that span interacts with leadership to influence staff

outcomes.

In summary, several approaches to leadership theory have evolved in the literature. Given the

study setting and sample, a theory of transformational leadership was identified as suitable to the

study purpose. In the research reviewed, managerial span has been shown to moderate the

influence of leadership on nurse outcomes.

Gaps in the Literature and Study Rationale

A review of the literature assists in ascertaining the extent of progress in the theoretical and

empirical domains of interest, while allowing an assessment of future areas for research and the

potential for methodological improvements. The literature revealed limited research in the areas

of managerial span and work, particularly in the health care industry. Although research is

emerging, further inquiry related to managers, managerial work, work contexts, and outcomes is

needed to build an evidence base that allows for the synthesis of findings to inform

organizational and heath system policy.

This thesis sought to advance the science related to managerial work and the management of

hospital services in several ways. Few studies have investigated managerial span and time

allocation in relation to objective outcome measures. This was one of the first studies to consider

time allocation at the level of the manager in relation to staff outcomes. This dissertation

27

examined the extent to which managerial span and time allocation explained variation in two

staff outcomes while assessing the manager’s leadership practices. In addition, the influence of

other factors and key determinants of managerial span were considered in the study framework.

Furthermore, to address the multi-disciplinary responsibilities of the first-line hospital manager,

a measure of multi-disciplinary team functioning was utilized. Given recent analytical advances

that address the hierarchical nature of data sets, the nested structure of the data in this

dissertation was also taken into account. This thesis aimed to explore the work of managers in a

manner that allows organizations to assess how best to deploy managers, given the capacity of

managers to produce positive outcomes under varying spans.

28

Chapter 2: Theory and Study Framework

Theory

The study framework of first-line manager span was based on Open System Theory as applied to

large scale organizations by Katz and Kahn (1978). The concept of boundary spanning, which

evolved from an open system view of organizations (Gittell, 2003), was used to examine the

work activities of managers within hospitals. This chapter first outlines the assumptions

underpinning the research inquiry and the basic principles of Open System Theory as applied to

large scale organizations (Katz & Kahn). The concept of boundary spanning and the

characteristics of effective boundary spanners are described. The two study outcomes, nurse

satisfaction with manager’s supervision and teamwork, are introduced and the influence of

boundary spanning by managers on these selected outcomes is explained. Finally, the study

framework is presented, the study variables are operationally defined, and the empirical rationale

for their inclusion in the framework is examined.

Assumptions

This research inquiry assumed the existence of an objective external reality whereby the

phenomenon of an “organization” was approached as an object with identifiable and measurable

characteristics (Hatch & Cunliffe, 2006). Consistent with a post-positivist paradigm, the goals

were to describe and explain relationships and variations in the phenomena of interest. Notably

these patterns are understood in a probabilistic sense; that is, relationships are subject to

contingencies and to context (Cook & Campbell, 1979; Ford-Gilboe, Campbell & Berman,

1995). Post positivists acknowledge that while the truth is never completely knowable, whether

through sensory or experiential modes, rigorous methodology and triangulation of different types

of evidence contribute to theory testing (Ford-Gilboe et al.). This study was intended to

contribute to the growing and eclectic understanding of organizations and organizational theory

that have been advanced through modern, symbolic, and post modern perspectives alike.

Additionally, although the work functions and characteristics of managers were important foci of

the study, it was recognized that the work of managers and managers themselves are embedded

within larger, complex environments. As such, their work and leadership practices may be

29

shaped or constrained by organizational values, priorities, resources, and power relations, as well

as by political and economic forces (e.g., labor market trends; Willmott, 1987). Thus, although

span, time allocation, and leadership were selected as significant predictors of managerial

influence on satisfaction and teamwork, other important predictors may also exist.

Open System Theory Applied to Large Scale Organizations

In this study, the organization was conceptualized as a social structure with the properties of an

open system (Katz & Kahn, 1978). Open System Theory recognizes the hierarchical nature of

entities whereby each level of a system consists of a ‘subsystem’ of interrelated parts. A large

scale organization is an open system comprised of supportive, maintenance, adaptive,

production, and management subsystems. A simplified representation is illustrated in Figure 1.

The supportive, maintenance, and adaptive subsystems import people, materials, and energies

through transactions at the organizational boundaries; balance internal work structures relative to

human inputs by formalizing activities and socializing and rewarding members; and deal with

problems of adjustment to external forces by recommending and incorporating change (Katz &

Kahn). Production subsystems transform the energy (e.g., people, material, resources) of the

organization by dividing the labor to accomplish tasks and generate output (Katz & Kahn).

Overall organizational functioning and adjustment to external demands are coordinated and

integrated by the management subsystem which crosscuts and directs all subsystems and

negotiates conflict across hierarchical levels.

30

Figure 1. Large scale organization as open system (based on Katz & Kahn, 1978).

In terms of functioning, the subsystems do not operate in isolation, but rather, are interdependent

and interact dynamically as part of a greater, complex whole. Wholeness implies that an entity is

a function of the behaviors of all the elements and that change in the entity is greater than the

sum of the changes amongst its elements (i.e., change is not necessarily linear; Bertalanffy,

1950). An organization consists of repeating cycles of interconnecting events and sub-events

within or between subsystems (Katz & Kahn, 1978).

A fundamental characteristic of an open system is its ability to import, transform, and export

energy; an open system is thus considered an ‘energic’ entity (Katz & Kahn, 1978). Katz and

Kahn proposed that a social organization constitutes an energic input-output system that must

import and renew energy to sustain its functioning. An organization is dependent on its

supporting environment for continued inputs to ensure its sustainability. Various forms of energy

(e.g., materials, information, resources, staff, and patients) are imported across the organization’s

31

external, semi-permeable boundary and are redistributed to its subsystems. To survive, an

organization must overcome entropy which is an inevitable process of disorder and dissolution

caused by the loss of inputs or by an inability to transform energies (Katz & Kahn). An open

system must acquire negative entropy, usually through some form of storage capacity, to ensure

its continued existence (Katz & Kahn). For organizations, negentropy can involve the renewal of

inputs, the storage of energy, the creation of slack resources, or the maximization of imported

energy relative to exported energy (Galbraith, 1974; Katz & Kahn). Organizations also

counteract entropy by adapting system functioning in response to informational signals and

feedback from the environment.

An energic input-output system must exchange energy to export outputs (Katz & Kahn, 1978).

The reorganization and transformation of energy into outputs is known as throughput. Within

production subsystems, the energic inputs are processed through the recurring and patterned

activities and interactions of individuals to yield outputs (Katz & Kahn). Thus an organization is

essentially a social structure that “consists of other people and their behavior and the products of

their activities” (Katz & Kahn, p. 5). These outputs must be used by an outside system or group

and must reactivate the organization itself (Katz & Kahn). Renewal of a social structure may be

generated by its output (e.g., revenues).

Regularity in energy inflow, transformation, and outflow allows the system to achieve both a

steady state, in that the character of the system is maintained, and a dynamic homeostasis, in that

the system continuously anticipates and adjusts to disturbances in energy inflow (Katz & Kahn,

1978). Adjustment in a social system often requires the importation of additional resources and

multiplication of subsystems which result in system growth (Katz & Kahn). An open system

becomes more complex as its subsystems multiply and specialize in function (Katz & Kahn).

Greater differentiation and proliferation of subsystems requires processes to unify system

functioning (Katz & Kahn). There is no single way for an open system to achieve its final state.

An organization can achieve its end state from various initial conditions and through different

means (Katz & Kahn). This suggests that there is no single right way to structure an

organization.

32

Boundary Spanning

The study was based upon an understanding of the boundary spanning activities of managers

within an organization. Boundary spanning evolved from a system view of organizations

whereby open systems with semi-permeable boundaries must negotiate the inflow and outflow of

energy between subsystems and with the external environment (Gittell, 2003). Boundaries exist

internally within an organization, such as those that occur between subsystems (Katz & Kahn,

1978), hierarchical levels (Ancona & Caldwell, 1992; Katz & Kahn, 1978), functional groups, or

spatial divisions (Gittell, 2003). The boundary function relates a unit or subsystem to its external

structure or environment and is often charged to those in leadership roles (Katz & Kahn).

Although “interconnected groups may form the technical or production subsystem of an

organization … these behavior patterns are crisscrossed by cycles of behavior from the

managerial subsystem” (Katz & Kahn, p. 3-4). In social structures, the managerial subsystem

serves to resolve conflicts between hierarchical levels, coordinates substructures, and balances

external demands with internal resources and needs (Katz & Kahn). At all levels in the hierarchy,

managers contribute to organizational functioning by coordinating and integrating system

functioning across suprasystem and subsystem boundaries. Senior executives are most likely to

negotiate the external suprasystem boundaries, whereas first-line managers are more likely to

manage internal and production subsystem boundaries. This study focused on first-line managers

of production subsystems.

Managers span boundaries by coordinating and integrating inputs (e.g., information, materials,

and human resources), throughput processes, and outputs across interrelated subsystems,

hierarchical levels, functional groups, and spatial divides. Boundary spanning can involve

acquiring, filtering, and importing information from outside entities for distribution to internal

users (Tushman & Scanlan, 1981); regulating the flow of inputs and negotiating for needed

resources (Ancona & Caldwell, 1992); and engaging in relationship management to buffer and

influence relations that occur externally and internally to the unit (Gittell, 2003; Ancona &

Caldwell).

Effective boundary spanning requires individuals to understand the vocabulary, semantics,

shared beliefs, and context of both the internal unit and its external environment (Tushman &

33

Scanlan, 1981). Effective boundary spanners are more likely to interpret and translate language

and contextual information on both sides of the unit boundary to meet the contrasting

information needs and demands of external areas (Tushman & Scanlon). Appropriate

interpretation and translation of messaging and contextual cues by the boundary spanner prevents

the distortion and bias that can occur as communication crosses boundaries (Tushman &

Scanlan). Boundary spanners with strong connections to and communication patterns with

external information areas are also more likely to effectively link an internal unit to external

areas (Tushman & Scanlon). Boundary spanners with technical competence and work expertise

are more likely to understand internal work processes, demands, and roles and to be consulted by

internal system users (Tushman & Scanlon).

Effective boundary spanners foster high quality communication and relationships with and

among people through physical movement, interpersonal skills, and conversation which require

time on the part of the boundary spanner (Gittell, 2003). Coordination of communication and

relationships is a time- and relationship-intensive endeavor for managers who provide coaching

and feedback to members through direct supervision (Gittell). Charnes and Tewksbury (1993)

suggested that “within each organizational unit, coordination among members of the unit is

facilitated by patterns of interaction and by common goals, rewards, cognitive and interpersonal

orientations, and supervision” (p. 25). Coaching alongside the employee is a boundary spanning

activity that offers opportunities to enhance worker respect for and knowledge about the work of

others and to reinforce how the work completed individually serves a larger, shared goal (e.g.,

patient centered care; Gittell).

Outcomes

The study examined the contributions of managers to care delivery with respect to two outcomes:

nurse satisfaction with manager’s supervision and multidisciplinary teamwork. This section

defines the outcomes and theorizes how boundary spanning by first-line managers influences

these outcomes. Empirical support for the influence of managers on the outcomes and the

relevance of the outcomes to health care organizations are presented.

Nurse satisfaction with manager’s supervision. Nurse satisfaction with manager’s supervision

was selected as an outcome of managerial work for this study. Nurses’ perceptions of their

34

manager’s abilities with respect to supervision were assessed. A facet specific measure, the

Satisfaction with my Supervisor Scale (Scarpello & Vandenberg, 1987) was used. Conceptually,

the scale reflects three areas of supervisory ability: technical, human relations, and administrative

skills (Mann, 1965). Effective supervision requires the manager to understand the technical

content of the work performed. Technically proficient supervisors can better assist in problem-

solving and work completion. Human relations skills reflect the manager’s ability to listen to

workers’ concerns and ideas, to deal with mistakes made by staff, to recognize staff

accomplishments, to treat staff in a consistent manner, and to back up staff with other

management. Effective supervisors also demonstrate administrative abilities in the assignment

and delegation of work, in the fair appraisal of worker performance, and in the communication of

organizational changes to workers.

Supervision generates boundary spanning work for managers across subsystem, functional,

spatial, and hierarchical boundaries. Nurses do not provide nursing care in isolation; their work is

reliant on the flow of inputs (e.g., patients, equipment), on other subsystems (e.g., admissions

department, laboratories), and on other healthcare providers (i.e., the multidisciplinary team) to

accomplish interdependent work goals. Managers coordinate energy flow and work processes

across departments and patient care areas, professions, and roles to facilitate nurses’ work.

Spatial boundaries are inherent in the physical space created by an organization. Managers travel

physically within the organization to engage in face-to-face communication, observation, and

direct supervision of nurses.

Managers also span hierarchical boundaries in relation to the supervision of nurses. By virtue of

the formal reporting structure of the organization (i.e., hierarchy), management positions are

vested with the authority to direct the actions and the norms expected of subordinate positions

(Weber, 1978) and are assigned responsibility for aspects of staff and system functioning and

performance. Reporting relationships are important to ensure employees are held accountable for

the work assigned (Jaques, 1990), to create channels of appeal (Weber), and to ensure that staff

can access organizational resources and managerial support (Blau, 1968; Kanter, 1977) thereby

ensuring the flow of information and resources.

35

Nurses’ satisfaction with the supervisory relationship is an increasingly important concern for

health care organizations that wish to retain nurses in an era of worsening shortages of nurses

(O’Brien-Pallas, Thomson et al., 2003). Many nurses do not feel respected or supported by their

managers (Laschinger, 2004; O’Brien-Pallas, Tomblin Murphy et al., 2005; Pellico, Brewer &

Kovner, 2009). Communication and relationship with supervisor are significant predictors of

nurse job satisfaction (Blegen, 1993; Buccheri, 1986; Cummings et al., 2008; Hall, 2007; Irvine

& Evans, 1995; Kovner, Brewer, Yow-Wu, Cheng & Suzuki, 2006). Evidence suggests that

managers can influence nurses’ satisfaction. For instance, nurse job satisfaction has been

significantly predicted by the manager’s leadership or management style (Duffield et al., 2009;

Dunham-Taylor, 2000; Leveck & Jones, 1988; Loke, 2001; McCutcheon, 2004; McCutcheon et

al., 2009; McGillis Hall et al., 2001; McGilton, McGillis Hall, Wodchis & Petroz, 2007;

McNeese-Smith, 1995; Medley & Larochelle, 1995; Morrison et al., 1997; Stordeur et al., 2000)

as well as by boundary spanning activities such as management communication (Blegen; Irvine

& Evans; Laschinger & Finegan, 2005) and performance feedback (Irvine & Evans; Larson,

1984; Tonges, Rothstein & Carter, 1998). In turn, nurse satisfaction is a pertinent indicator for

health care organizations because of its positive associations with patient satisfaction (McGillis

Hall, 2003; McGillis Hall et al., 2001) and nurse retention (Leveck & Jones) as well as its

negative associations with nurses’ intentions to leave and nurse turnover (Duffield et al., 2007;

Irvine & Evans; Price & Mueller, 1986; Shields & Ward, 2001).

For this study, the outcome of supervision satisfaction was examined only from the perspective

of nurses. As a result of the shift to program management in Ontario hospitals, health care

professionals and other non-nursing service providers often have dual reporting relationships

with both a first-line unit manager and a department head (Leatt, Lemieux-Charles & Aird,

1994). Ultimately, the satisfaction of these non-nurse employees with supervision may be related

to factors beyond the control and influence of the first-line manager. In contrast, staff nurses are

generally only supervised by one manager.

Teamwork. The Relational Coordination Scale for General Healthcare Settings (Gittell, 2006)

was used to measure teamwork. Teamwork is defined as the frequency, accuracy, timeliness, and

problem-solving orientation of the communication between care providers, as well as the shared

knowledge, shared goals, and mutual respect among group members (Gittell, 2003). A team is

36

composed of “multiple health disciplines with diverse knowledge and skills who share an

integrated set of goals and who utilize interdependent collaboration that involves

communication, sharing of knowledge and coordination of services to provide services to

patients and their caregiving systems” (Drinka & Ray, 1987, p. 44). Communication,

coordination, and shared decision-making are key sub-concepts underpinning definitions of

teams and teamwork (Doran, 2005). Coordination of workers’ activities, which is central to

achieving organizational goals, is a primary function of managers (Katz & Kahn, 1978; Mahoney

et al., 1963). Managers also integrate system functioning by fostering shared norms and values

(Katz & Kahn).

In developing a measure of teamwork, Gittell (2003) attended to the relational aspects of

teamwork. Known as ‘relational coordination’, the measure is premised on the theory that highly

interdependent work processes are most effectively coordinated through high quality

communication and high quality relationships. Relational coordination is theorized to improve

performance in settings characterized by interdependence, uncertainty, and time constraints

(Gittell, 2000; 2003). Unlike sequential or pooled work processes, interdependent work occurs

when work activities are inter-reliant and workers must engage in mutually supportive

interactions to accomplish the work goals (Gittell et al., 2000). Interdependency characterizes the

work of health care providers in hospital settings who often work in multidisciplinary teams to

provide patient-centered care (Gittell et al., 2004). Relational coordination is a competency that

is “carried out by front-line workers with an awareness of their relationship to the overall work

process and to other participants in that process” (Gittell, 2000, p. 518). The focus is on the

informal mechanisms of coordination, as reflected by communication patterns and relationships,

rather than formal structures such as routines (e.g., clinical pathways) or meetings (e.g., patient

rounds; Gittell, 2000; 2002a).

Teamwork generates boundary spanning work for managers across subsystem, hierarchical,

functional, and spatial boundaries. Subsystem boundary work is fundamental to the work of

managers because production subsystems do not function in isolation of the organization

suprasystem to produce patient care services. To accomplish work goals, health care teams rely

on and interact with other subsystems (e.g., to negotiate the flow of inputs and outputs) and must

respond to changing work demands and standards required by the organizational suprasystem.

37

As boundary spanners, managers coordinate the bidirectional flow of information, resources, and

work processes between the team and other subsystems. They also attempt to influence external

opinions and buffer the team from external demands (Ancona & Caldwell, 1992). At times, this

boundary work spans hierarchical levels as the manager interacts with more senior management

positions (e.g., to secure additional resources).

The professional and role boundaries inherent in multidisciplinary teams also create boundary

work for managers who must integrate interdependent work processes. Because health care

providers are generally educated in professional silos, their understanding of the roles of other

team members may be limited (Charnes & Tewksbury, 1993). Professional affiliations can

magnify functional divisions since members of a functional group (e.g., nursing) interact more

frequently, develop social relationships, are supervised and evaluated from within the group, and

conform to professional standards (Charnes & Tewksbury). Coordination of activities may be

challenging when group members have functionally based differences in “work goals, thought

worlds, and status” (Gittell, 2003, p. 294). When work is highly interdependent, managers also

assist in coordinating the efforts of team members by building connections with and among

workers (Gittell). In health care, managers may need to manage the relationships amongst

professional groups and roles to achieve integrated, patient-centered care (Charnes &

Tewksbury). Managers travel physically across spatial divides within the organization to engage

in face-to-face communication, observation, and direct supervision, including coaching. This

movement enables managers to create connections among team members, and between team

members and external parties.

Evidence suggests that managers can influence multidisciplinary teamwork and inter-

professional relationships. Manager leadership style and skills have been associated with

teamwork (Gittell, 2001; Kramer et al., 2007) and related concepts such as extra effort (Dunham-

Taylor, 2000; Stordeur et al., 2000), perceived unit effectiveness (Stordeur et al.), access to

resources, and nurse-physician relationships (O’Brien-Pallas, Tomblin Murphy et al., 2005).

Both Blau (1968) and Kanter (1977) theorized that managers enable access to information and

resources. Organizational support in the form of information, feedback, and resources has been

associated with improved team communication, cooperation, and decision-making (Kennedy,

Loughry, Klammer & Beyerlein, 2009).

38

Teamwork remains a salient concern for health care organizations. The demand for improved

interdisciplinary communication and collaboration can be attributed to reductions in hospital

stays, as well as increased patient acuity and complexity and increased frequency of exchanges

amongst multi-disciplinary health care providers (O’Brien-Pallas, Hiroz, Cook & Mildon, 2005).

Teamwork is an important informal coordinating mechanism in health care because of its

influence on patient, staff, and system outcomes. Higher levels of teamwork have been

associated with improved patient outcomes, including reduced postoperative pain, improved

postoperative functioning, shorter length of stay (Gittell et al., 2000), satisfaction and intent to

recommend (Gittell, 2002b), improved patient functional status (McGillis Hall et al., 2001),

lower fall rates (Kalisch, Curley & Stefanov, 2007), and fewer adverse events (Houser, 2003).

Strong group functioning has also been associated with improvements in outcomes for

continuous quality improvement projects by interdisciplinary health care teams (Doran et al.,

2002), lower nurse turnover (Kalisch et al.; Mohr, Burgess & Young, 2008; Shortell et al., 1994)

and lower nurse intent to leave (Estryn-Béhar et al., 2007).

Study Framework

This section presents the study framework, defines the variables, and provides a rationale for the

variables selected. The study purpose was to examine the influence of alternative measures of

managerial span on nurse and team outcomes. The alternative measures of managerial span were

(a) raw span and (b) time in staff contact. Figure 2 depicts the main and interaction effects

examined.

39

Figure 2. Study framework.

The specific study objectives were:

1. to examine the main effects of the alternative measures of managerial span on outcomes

for nurses and teams;

2. to examine the interaction effects of the alternative measures of managerial span with

leadership and hours of operation on outcomes for nurses and teams; and

3. to determine the extent to which the alternative measures of managerial span explain

variation in outcomes for nurses and teams.

The level-2 variables in the study framework were chosen based on the boundary spanning

function in large scale organizations, the characteristics of effective boundary spanners, and key

40

determinants of span (i.e., diversification of function, stability, and space), as well as other

factors influencing managerial span that were proposed in the health care literature. Level-2

covariates were also included. The level-1 variables were associated with variation in the study

outcomes as identified in the literature or were included as control variables. Table 3 identifies

the components of the literature used to derive the study framework.

41

Table 3. Components of the Literature Used to Derive the Study Framework Level Literature Reference(s) Manager

First-Order Relationships Raw Span Boundary spanning Ancona & Caldwell, 1992; Katz & Kahn, 1978 Empirical literature Alidina & Funke-Furber, 1988; Blau, 1968; Cathcart et al., 2004; Doran et al.,

2004; Bohte & Meier, 2001; Gittell, 2001; Hechanova-Alampay & Beehr, 2001; Jaques, 1990; McCutcheon, 2004; McCutcheon et al., 2009; McGillis Hall et al., 2006; Meier & Bohte, 2000; Spreitzer, 1994

Time in Staff Contact

Empirical literature Gittell 2001, 2003; Ferguson-Paré, 1997; Ouchi & Dowling, 1974

Leadership Boundary spanners Katz & Kahn; Tushman & Scanlan, 1981 Empirical literature Gittell, 2000, 2001; Kouzes & Posner, 2002; McCutcheon; McCutcheon et al.;

Lucas et al., 2008; Schriesheim et al., 2000 Hours of Operation Empirical literature Morash et al., 2005 Covariates Education Boundary spanners Tushman & Scanlan, 1981 Empirical literature Alidina & Funke-Furber; Duffield & Franks, 2001; Mahon & Young, 2006;

McGillisHall & Donner, 1997; Reyna, 1992; Smith & Friedland, 1998; Synowiez, 1987

Experience Boundary spanners Tushman & Scanlan Empirical literature Alidina & Funke-Furber; Doran et al.; Dunn & Schilder, 1993; Englebardt,

1993; Meier & Bohte, 2003; Reyna, 1992 Position Tenure Empirical literature Doran et al. Worked Hours Measurement issue Identified as potential confounder Administrative

Support Roles Empirical literature Alidina & Funke-Furber; Altaffer, 1998; Drach-Zahavy & Dagan, 2002;

Duffield et al., 1996; Kramer et al., 2007; Ouchi & Dowling; Pabst, 1993; Van Fleet & Bedian, 1977

Clinical Support Roles

Empirical literature Alidina & Funke-Furber; Altaffer; Drach-Zahavy & Dagan; Duffield et al., 1994; Gittell, 2002a; Kramer et al.; McCutcheon; McCutcheon et al.; Ouchi & Dowling; Pabst; Van Fleet & Bedian

Total Areas Boundary spanning Gittell, 2003; Katz & Kahn Key determinants Gulick, 1937; Meier & Bohte, 2003 Empirical literature Altaffer; Morash et al.

Occupational Diversity

Boundary spanning Gittell, 2003

Key determinants Gulick, 1937; Meier & Bohte, 2003 Empirical literature Alidina & Funke-Furber; Morash et al. Employee Tenure Key determinants Gulick; Meier & Bohte, 2003 Full-time

Employment Key determinants Gulick; Meier & Bohte, 2003

Empirical literature Edwards & Robinson, 2004; Grinspun, 2002; Kalisch & Begeny, 2005 Non-Direct Reports Boundary spanning Gittell, 2003 Key determinants Gulick; Meier & Bohte, 2003 Empirical literature Burke, 1996; Green et al., 1996; Hechanova-Alampay & Beehr, 2001; Mullen

et al., 1989 Staff

Covariates Nurse Age Empirical literature Blegen & Mueller, 1987; Kalleberg & Loscocco, 1983; O’Brien-Pallas,

Tomblin Murphy et al., 2005; Price & Mueller, 1986; Shields & Ward, 2001 Nurse Day Shift

Employment Empirical literature Blegen & Mueller, 1987; McCutcheon; Shields & Ward, 2001

Nurse Education Empirical literature O’Brien-Pallas, Tomblin Murphy et al.; Shields & Ward; Ward, 2002 Nurse Registration Empirical literature O’Brien-Pallas, Tomblin Murphy et al.; Shields & Ward; Ward, 2002 Occupational Group Empirical literature Manser, 2009 Full-time Status Empirical literature Edwards & Robinson, 2004; Kalisch & Lee, 2009

42

First-Order Relationships

Raw span was the number of assigned employees who report directly to a manager. Managers

who have larger raw spans are less effective boundary spanners by virtue of the number of staff

members for whom they have some authority and responsibility. The hierarchical structure of the

organization determines the number of direct reports assigned to a manager. Direct report

relationships enable managers to supervise and coordinate the work performed in assigned areas.

Managers can assign or delegate responsibility for work performance to staff, as well as coach

staff in the performance of their role. Direct report staff members are, in turn, liable for work

performance and accountable to the manager (Jaques, 1990). As discussed under boundary

spanning and outcomes, managers span the hierarchical boundaries (Ancona & Caldwell, 1992;

Katz & Kahn, 1978) within the organizational suprasystem to coordinate the supervision of staff

as well as teamwork.

Although very low spans have been posited to stifle worker autonomy (Alidina & Funke-Furber,

1988) and reduce staff empowerment (Spreitzer, 1994), first-line nurse managers in healthcare

typically do not have the very narrow spans observed in other industries (e.g., raw spans as low

as 1 to 10; Hales, 2005). In two Ontario studies of acute care hospitals, minimum raw spans for

first-line nurse managers numbered 40 (McGillis Hall et al., 2006) and 36 (Doran et al., 2004)

respectively. Because a truncated range of span values was anticipated in this study, the effects

of very narrow raw spans, as described in the business literature, were not expected in this study.

However, moderate and high raw spans were anticipated in the study sample. Overly wide spans

are theorized to negatively impact staff outcomes because they may hinder access to the

manager, delay communication by staff, and overextend the manager (Alidina & Funke-Furber,

1988). Wide spans may also impede skilled workers from accessing managerial support and

organizational resources needed to complete complex work processes (Blau, 1968). Wider

managerial raw spans have been associated with lower levels of staff stability (McCutcheon,

2004), consumer satisfaction (McCutcheon et al., 2009), teamwork (Gittell, 2001), performance

(Bohte & Meier, 2001; Gittell; Meier & Bohte, 2000), staff engagement (Cathcart et al., 2004),

and staff empowerment (Spreitzer, 1994). Wider raw spans have also been associated with

higher staff turnover (Doran et al., 2004; McCutcheon), increased accidents and unsafe behaviors

43

(Hechanova-Alampay & Beehr, 2001), and more negative staff perceptions of the work

environment (McGillis Hall et al., 2006).

Time in staff contact was the average daily amount of time spent by the manager interacting

with staff and physicians working in the area(s) assigned to the manager. Contact included

verbal, written, and email communication with staff and person-to-person interaction. Managers

who spend greater time in staff contact are more effective boundary spanners by virtue of their

efforts to communicate and to develop relationships with staff and team members. The

importance of interpersonal dynamics within a social structure can therefore not be

underestimated. Managers who engage in a holistic approach to people by showing personal

interest in people, addressing psychosocial and spiritual concerns, and using touch to interact

enhance professional nursing autonomy (Ferguson-Paré, 1997). The presence of the manager is

necessary to offer this support to staff. Similarly, Gittell (2003) proposed that supervisors must

spend sufficient time with staff to establish and maintain high quality communication and

relationships with and among workers.

Even if managers have the same number of direct report staff (i.e., raw span), the amount of

supervisory support provided to employees may vary relative to the amount of time each

manager allocates to interaction with staff. Ouchi and Dowling (1974) argued that a measure of

span adjusted for the time spent by the manager in contact with staff more accurately represents

the manager’s capacity to engage staff (i.e., closeness of contact) and thus facilitates

comparisons of managers across units and organizations. No studies were located that quantified

the associations between managerial time allocation and staff outcomes. However based on

qualitative observations of airline departure teams, Gittell (2001) surmised that narrow spans

increase teamwork levels by virtue of supervisors having greater time available to coach and

provide feedback to team members.

Leadership practices were the ratings of leadership behaviors of the manager as measured by the

Leadership Practices Inventory (Kouzes & Posner, 2002; Appendices H & I). The five leadership

practices are: challenging the process, inspiring a shared vision, enabling others to act, modeling

the way, and encouraging the heart. Katz and Kahn (1978) argued that human effort and

motivation are essential inputs to the continued existence of a social organization. Managers

44

integrate system functioning by fostering shared norms and values amongst its members. As

discussed under boundary spanning, Tushman and Scanlan (1981) theorized that effective

boundary spanners have excellent communication skills and understand contextual cues,

vocabulary, semantics, and shared beliefs both within and across subsystems. Furthermore,

effective boundary spanners are technically competent which allows them to meaningfully

coordinate work processes, roles, and demands among internal users. Consistent with the

characteristics of effective boundary spanners, Kouzes and Posner’s leadership practices enable

the manager to establish effective communication and relationships as described below.

Managers with highly transformational leadership styles are more effective boundary spanners

by virtue of their leadership behaviors which include challenging the process. This is because

managers who challenge the process seek and are positive about the ideas of others, encourage

action by setting clear work goals, responsibilities, and manageable time frames, allow staff to

master work activities before assigning new work, and discuss work changes ahead of time with

staff (Kouzes & Posner, 2002). These managers also allow staff to take risks, back up staff with

other management, and treat errors as learning opportunities. These practices enhance team

members’ “awareness of their relationship to the overall work process and to other participants in

that process” (Gittell, 2000, p. 518).

Managers with highly transformational leadership styles are more effective boundary spanners

by virtue of their leadership behaviors which include inspiring a shared vision and enabling

others to act. Managers who inspire a shared vision listen to nurses and get to know nurses by

understanding their career aspirations (Kouzes & Posner, 2002). Managers who inspire a shared

vision will create shared goals among team members. Managers who enable others to act foster

accountability and trust by not supervising work in an over-controlling manner, by engaging in

face-to-face interactions, and by building relationships (Kouzes & Posner). Enabling managers

listen to and coach staff. Enabling managers foster interdependence among team members by

ensuring each member understands how their role contributes to the larger goal; by establishing

norms of reciprocity to ensure fairness, predictability, and stability of relationships; and by

rewarding joint efforts (Kouzes & Posner). In this way, team members are more likely to

understand and respect each others’ roles. Durable and frequent face-to-face interactions,

informal interactions, and the sharing of influence, information, and resources also foster

45

collaboration and trust. Enabling managers strengthen the problem solving abilities and

confidence of staff through coaching, increasing choice, and delegating authority (Kouzes &

Posner).

Managers with highly transformational leadership styles are more effective boundary spanners

by virtue of their leadership behaviors which include modeling the way and encouraging the

heart. Managers who model the way demonstrate competence and align actions with values by

following through to solve problems and achieve results; by assisting staff in meeting work

requirements when needed; by behaving consistently towards staff; by using mistakes as learning

opportunities; and by providing feedback (Kouzes & Posner, 2002). Managers who model the

way exhibit and encourage high quality communication and shared values among team members.

Managers who encourage the heart do so by having clear expectations with appropriate

feedback; by providing frequent individualized recognition; and by creating a spirit of

community enhanced by public and social celebrations of shared successes, values, and

outcomes (Kouzes & Posner).

Empirical research has shown that the beneficial influence of positive leadership on outcomes

may be conditional on the number of staff reporting directly to the manager. McCutcheon et al.

(2009) and Lucas et al. (2008) observed that no matter how strong the leadership style, managers

with overly wide spans were unable to positively influence nurse job satisfaction and

empowerment, respectively. Similarly, Gittell (2001) also observed that the beneficial influence

of smaller spans on teamwork was dependent on whether the supervisor’s style with staff was

facilitative or coercive. In contrast, Schriesheim et al. (2000) found that higher levels of leader-

member exchange were associated with higher staff organizational commitment under wider raw

spans.

Hours of operation was the average weekly hours of operation per manager weighted by the

number of direct reports in each assigned area. Variation in the hours of operation alters the

density of staff relative to the manager’s workday and the extent to which the manager’s

workweek covers the serviced hours. Longer hours of operation are thought to reflect increased

complexity of the areas assigned to managers (Morash et al., 2005). Hours of operation are an

important contextual factor that alters the meaning of two concepts central to this study: raw

46

span and time allocation. Variation in hours of operation in health care alters the density of staff

relative to the manager’s workday and the coverage of service hours by the manager relative to

his/her workweek. For these reasons, hours of operation was included as a key predictor.

Manager Level Covariates

Manager education was measured as the highest educational qualification held by the manager

and was dichotomized as graduate degree versus less than graduate degree. Managers with

graduate education are more effective boundary spanners by virtue of their greater depth of

knowledge about organizations and health care systems. As discussed under boundary spanning,

Tushman and Scanlan (1981) theorized that effective boundary spanners have expertise that

enables them to negotiate external subsystems and to effectively communicate with and meet the

needs of internal and external users. Management training and knowledge related to financial and

human resource management are thought to influence the manager’s supervisory capacity

(Alidina & Funke-Furber, 1998; Mahon & Young, 2006). A graduate level degree serves as a

proxy for advanced organizational and leadership knowledge related to health care operations.

Educational preparation for the managerial role has been posited to influence health care

outcomes (Duffield & Franks, 2001; McGillis Hall & Donner, 1997). Managerial education level

has been associated with risk taking propensity (Smith & Friedland, 1998), greater autonomy

(Synowiez, 1987), and staff nurse motivation (Reyna, 1992).

Manager experience was the number of years’ of experience of the manager in a first-line

manager position. Managers with more first-line management experience are more effective

boundary spanners by virtue of their background experience and skills. As discussed under

boundary spanning, Tushman and Scanlan (1981) theorized that effective boundary spanners

have expertise that enables them to negotiate external subsystems and to effectively

communicate with and meet the needs of internal and external users. Management experience

and knowledge related to clinical operations, finances, and human resource management are

posited to influence managerial span (Alidina & Funke-Furber, 1998). Years of management

experience serves as a proxy for the background knowledge and practice that managers have

accumulated related to the management role.

47

Managerial experience has been associated with positive work unit climate and work relations

(i.e., work involvement, peer cohesion, and supervisory support; Englebardt, 1993); higher staff

nurse motivation (Reyna, 1992); and lower nurse turnover (Doran et al., 2004). Managerial

experience also has implications for differences in the work styles and skill levels of managers.

For example, although novice and expert head nurses allocated time similarly across work

activities, those with more than two years’ experience operated at a more controlled and calm

pace; were able to multi-task more fluidly; oversaw, rather than micro-managed unit activities;

and more effectively filtered and channelled information than their frantic novice counterparts

(Dunn & Schilder, 1993). Less experienced managers worked nearly twice the amount of

overtime as experts (Dunn & Schilder). Experienced managers have greater job fluency (Meier

& Bohte, 2003) and practice in terms of management skills, role demands, and organization

structures.

Manager position tenure was the years in the current position and served as a proxy for the

length of the relationship between the manager and staff. Managers with longer tenure in

position are more effective boundary spanners by virtue of greater opportunities to establish

working relationships with staff over time. Based on position tenure, a manager may alter the

amount of time spent in staff contact in a given area if, for example, previous investments of

time have successfully established good working relationships with staff. Unit tenure of the

manager has been associated with lower nurse turnover in hospitals (Doran et al., 2004). Position

tenure serves as a proxy for the length of the relationship between manager and staff and was

included in the study framework as a potential control variable.

Manager worked hours was the average number of hours worked by the manager daily.

Managers who work more hours per day on average have the potential to allocate more time to

staff contact than managers who work fewer hours. The amount of time in staff contact may vary

relative to the hours worked by the manager. Worked hours was included in the study framework

as a potential control variable.

Administrative support roles was the total number of full-time equivalent positions in the

manager’s assigned area(s) that enacted supervisory functions (e.g., secretary, scheduling

coordinator). Managers with more administrative support roles are more effective boundary

48

spanners by virtue of reduced supervisory demands (i.e., substitution effect) related to staffing

and scheduling activities. Subsequently, managers are assumed to reinvest their energy into other

managerial boundary spanning activities. The amount and types of work performed by managers

is partially dependent on the work performed by other roles with administrative functions

(Drach-Zahavy & Dagan, 2002; Duffield et al., 1996; Kramer et al., 2007).Variation in the

number of administrative support roles across managers has been observed in health care settings

(Altaffer, 1998; Pabst, 1993). Support roles are posited to allow for broader managerial spans

(Alidina & Funke-Furber, 1988; Altaffer; Ouchi & Dowling, 1974; Pabst; Van Fleet & Bedian,

1977) although this has yet to be linked to staff outcomes.

Clinical support roles was the total number of full-time equivalent positions in the manager’s

assigned area(s) that enacted clinical support functions (e.g., clinical nurse educator, discharge

planner). Managers with more clinical support roles are more effective boundary spanners by

virtue of reduced supervisory demands (i.e., substitution effect) related to activities such as

training, coaching, hands-on supervision and work coordination, and evaluation of staff. These

roles enhance interdependent work processes by fostering positive interactions amongst workers

across functional boundaries (Gittell, 2002a). Subsequently, managers are assumed to reinvest

their energy into other managerial boundary spanning activities. The amount and types of work

performed by managers is partially dependent on the work performed by other roles with clinical

functions (Drach-Zahavy & Dagan, 2002; Duffield et al., 1994; Kramer et al., 2007). The

number of clinical support roles tends to vary across managers in health care settings (Altaffer,

1998; McCutcheon, 2004; McCutcheon et al., 2009; Pabst, 1993). Support roles are thought to

allow for wider managerial spans (Alidina & Funke-Furber, 1988; Altaffer; Ouchi & Dowling,

1974; Pabst; Van Fleet & Bedian, 1977). Boundary spanner roles such as case managers have

been associated with improved relational coordination and patient care quality as well as reduced

lengths of hospital stay (Gittell). McCutcheon also observed that for front-line nurse managers,

an increasing number of staff resource roles (e.g., clinical nurse educators and specialists) was

modestly associated with more nurses surviving the first year on the nursing unit.

Total areas was the number of units, clinics, and services assigned to the manager. Managers

assigned fewer areas are more effective boundary spanners by virtue of reduced demands across

fewer subsystem boundaries. From a boundary spanning perspective, multiple assigned areas

49

divide the manager’s focus among production subsystems with varied relational dynamics, work

content, work processes, and physical locations. These represent increased functional and spatial

boundaries that the manager must negotiate (Gittell, 2003; Katz & Kahn, 1978). Based on

Gulick’s (1937) and Meier and Bohte’s (2003) proposed determinants of span, the grouping of

production subsystems within management positions creates variation in workplace technologies

(i.e., diversification of function) thereby increasing the demands on the manager. Managers with

fewer areas can investigate and resolve issues more easily than dealing with diverse issues across

multiple areas. The numbers of units, sites, or locations have been proposed as potential factors

influencing managerial span (Altaffer, 1998; Morash et al., 2005).

Occupational diversity referred to the number of different job titles that report directly to the

manager. Managers assigned lower occupational diversity are more effective boundary spanners

by virtue of reduced coordination and integration demands across functional boundaries. From a

boundary spanning perspective, job titles reflect functional boundaries. Managers must

coordinate work activities within and across job functions (Gittell, 2003). Diversity in staff

functions has been proposed as an influential factor on managerial span (Alidina & Funke-

Furber, 1988; Morash et al., 2005). In terms of span determinants, constant and predictable

workplace technologies (e.g., roles) allow the manager to supervise more employees (Gulick,

1937; Meier & Bohte, 2003). Although McCutcheon (2004) observed wider raw spans for front-

line nurse managers as the number of staff categories increased, no significant associations were

observed between number of staff categories and either nurse turnover or the proportion of

nurses surviving the first year on the nursing unit.

Employee tenure was the average number of years employees reporting to the manager have

worked for the organization. Managers assigned areas with greater employee tenure are more

effective boundary spanners because of reduced coordination and supervisory demands. Based

on Gulick’s (1937) and Meier and Bohte’s (2003) proposed determinants of span, staffing

stability enhances routinization of work processes because of reduced planning and coordination

demands on the manager and because workers have greater job fluency which lessen the need for

supervision (Meier & Bohte). Employee tenure serves as a proxy for staffing stability under the

manager.

50

Full-time employment was the percentage of employees reporting to the manager with full time

employment status. Managers assigned areas with higher proportions of full-time staff are more

effective boundary spanners because of reduced supervision and scheduling demands. Gulick

(1937) and Meier and Bohte (2003) suggested that stability of inputs is a key determinant of span

because it fosters routinization that reduces coordination demands on mangers. A full-time

staffing complement stabilizes the worker inputs under the manager and supports routinization.

Others also suggest that full-time employment by organizations enhances continuity of care

delivery (Grinspun, 2002). Greater continuity of care is likely to decrease coordination demands

on the manager. Research indicates that when group membership includes proportionately

greater part-time or casual staff, coordination needs are increased because the team composition

from shift-to-shift may be unstable and staff may be less familiar, less invested, and less

accountable for work group routines, standards, goals, and outputs (Kalisch & Begeny, 2005).

Roughly 40% of managers perceived that part-time employment of staff nurses resulted in

communication and information exchange difficulties with the team and co-workers (Edwards &

Robinson, 2004).

Non-direct reports was the number of employees, physicians, and medical residents routinely

working in the manager’s assigned area(s) but who did not report directly to the manager’s

position. Managers must coordinate work processes between direct and non-direct reports. From

a boundary spanning perspective, non-direct reports exemplify the functional and spatial

boundaries identified by Gittell (2003). Non-direct reports typically represent different

professions (e.g., allied health, medicine) which engender functional boundaries. The manager

may also need to travel physically within the organization because non-direct reports are not

always physically co-located in the production subsystem. Similarly in identifying space as a

determinant of span, Gulick (1937) and Meier and Bohte (2003) proposed that a lack of physical

proximity between the manager and workers can increase the supervisory demands on the

manager.

Although no studies of non-direct reports were located, research on work group size offers

indirect support for the influence of large numbers of workers on staff outcomes. Larger work

groups have been associated with lower employee ratings of supervisory competence, overall

satisfaction with the firm, unit morale, and due process (Burke, 1996) and lower quality of

51

leader-member exchange (Green et al., 1996) as well as increased employee intent to quit

(Burke), dissatisfaction (Mullen et al., 1989), and accidents and unsafe behaviours (Hechanova-

Alampay & Beehr, 2001).

Staff Level Covariates

Variations in nurse job satisfaction or in satisfaction with manager’s supervision have been

associated with age, day shift employment, and education. These level-1 nurse variables were

treated as control variables in the supervision satisfaction models. Nurse age was the age in

years of the nurse reporting to the manager. Older employees tend to report higher job

satisfaction (Blegen & Mueller, 1987; Kalleberg & Loscocco, 1983; O’Brien-Pallas, Tomblin

Murphy et al., 2005; Price & Mueller, 1986; Shields & Ward, 2001). Price and Mueller found

that other personal characteristics such as occupation, gender, full- and part-time status, tenure,

and opportunity did not explain significant variation in job satisfaction among hospital

employees.

Nurse day shift employment referred to the nurse’s regular employment on a day shift schedule

(as opposed to a rotating schedule). Day shift employment is a significant predictor of nurse job

satisfaction (Blegen & Mueller, 1987; McCutcheon, 2004; Shields & Ward, 2001). Day shift

employment may influence the amount of interaction between the manager and staff or staff

perceptions of accessibility to the manager.

Nurse education level was the highest level of nursing education achieved by the nurse.

Education level has been negatively associated with nurse job satisfaction (O’Brien-Pallas,

Tomblin Murphy et al., 2005; Shields & Ward, 2001). With respect to satisfaction with

supervisor specifically, Ward (2002) also reported variation by educational level but not by age,

gender, or race.

Nurse registration was the registration status of the nurse as a Registered Nurse or Registered

Practical Nurse. Registered Nurses have tended to report lower job satisfaction than Registered

Practical Nurses in Canada (O'Brien-Pallas, Tomblin Murphy et al., 2004).

Variation in teamwork was considered in relation to occupation and employment status of the

team member. These level-1 staff variables were treated as control variables in the teamwork

52

models. Occupation was the current occupation of the staff respondent which was categorized as

nursing (inclusive of registered practical nurses and registered nurses in staff and in advance

practice roles), other regulated health professional, or as unregulated care provider. Research

indicates that team members from different occupations tend to perceive the quality of teamwork

differently (Manser, 2009).

Full-time status was the employment status of the team member as full-time employee versus

other status (e.g., part-time or casual). Full-time employees coordinate work processes and

manage relationships on an ongoing basis. Full-time employees may take on the burden of care

continuity and thus may perceive greater challenges to teamwork. For example, part-time nursing

staff has been associated with communication and information exchange difficulties with the

team and co-workers (Edwards & Robinson, 2004), and part-time nurses are likely to rate

teamwork more highly (Kalisch & Lee, 2009).

Chapter 3 details the study design, data collection processes, instrumentation, data analyses, and

knowledge translation approach.

53

Chapter 3: Method

Design

This chapter begins by addressing study design considerations. These include an overview of the

design and data collection process, a discussion of power, settings, and sample sizes, and a

detailed description of the data collection procedures, including ethical considerations. Next, the

derivation of some of the study variables from administrative and managerial work log data is

explained. The established instruments utilized in the study are also examined. The approach to

data analysis is subsequently documented in regards to data preparation, levels of analysis,

analytical techniques, and the study objectives. Finally, knowledge translation processes are

considered.

Design and Data Collection Overview

A descriptive, correlational design with cross-sectional and longitudinal components was used to

collect survey and administrative data. Cross-sectional survey data were collected for leadership

practices, managerial job characteristics, supervision satisfaction, and teamwork. Data on raw

span, time allocation, and administrative data related to direct reports were collected

longitudinally.

Data collection was a phased process and data were collected from multiple sources (Table 4).

The separation of data collection for predictor and outcome variables, both temporally (i.e.,

Phases I and II) and methodologically (i.e., from different sources in different places using

different methods), helped to reduce common method bias (Podsakoff et al., 2003). Pilot work

was done to pre-test the work log method and to establish the metrics related to time allocation.

The pre-test assessed the feasibility of the work log procedures and classification system. The

researcher shadowed the managers and checked inter-observer agreement during the pre-test.

Phase I entailed a survey of managers related to demographics (i.e., age, occupation, education,

experience, tenure), job characteristics (i.e., number of areas, support roles, budget), and self-

reported leadership practices.

During Phase II, managers self-reported time allocation in work logs and tracked their worked

hours. As managers work logged, the researcher also completed inter-observer agreement

54

ratings. In addition, a subset of managers was shadowed by the manager to observe managerial

work flow. Staff surveys were collected during Phase II. All staff surveys included demographics

(e.g., age, occupation, education, employment status, tenure, day shift employment). Direct

report nurses completed a measure of supervision satisfaction. A separate subset of nurses and

directors completed a measure of the manager’s leadership practices. Nurses and other health

care providers working in the manager’s assigned area completed the teamwork measure. The

human resource departments provided administrative data (i.e., area, job title, date of hire, year

of birth, and employment status) about managers’ direct reports for three consecutive monthly

data points. Data on hours of operation and number and types of non-direct reports were

collected from the managers by the researcher. Table 4. Data Collection Flow Chart Phase Source Managers Employees Human Resource

Department Researcher

Pre-test • pre-test of work logs • observation of

managers • inter-observer

agreement Phase I • survey

• leadership practices (LPI-self)

Phase II • work logs of time in

staff contact • worked hours

• supervision satisfaction (SWMSS)

• leadership practices (LPI-other)

• teamwork (RCS) • demographics

• administrative data about direct reports

• inter-observer agreement

• observation of subset of managers

• hours of operation & non-direct reports

Note. LPI = Leadership Practices Inventory; SWMSS = Satisfaction with My Supervisor Scale; RCS = Relational Coordination Scale

The pre-test began in February 2007 with recruitment of first-line managers at the 4 participating

organizations occurring between May 2007 and February 2008. Phases I and II were completed

between June 2007 and June 2008. Data analysis finished in June 2009.

Power

Hierarchical linear modeling (HLM) is a relatively recent statistical technique. Although

increasing attention has been paid to the assumptions and applications of HLM, at the time this

study was proposed limited guidance was available for determining sample sizes needed to

achieve power, particularly in two level and three level models with more than one outcome

(Raudenbush & Bryk, 2002). In a review of two key HLM sampling studies, Kreft and De

55

Leeuw (1998) recommended a sample of at least 30 groups with 30 observations per group for

assessing cross-level interactions. Overall, collection of data from many groups, as opposed to

from many individuals, is preferable for detecting cross-level interaction effects, and as the

number of higher level units increases, fewer lower level units are needed (Kreft & De Leeuw).

This thesis proposed to examine interaction effects within the same analytical level, but not

between levels (i.e., cross-level interaction effects). To achieve power in this study, a minimum

sample of 30 managers was targeted. For cross-level interactions, Kreft and De Leeuw’s

guidelines indicated that fewer than 30 lower level units (i.e., staff respondents) were needed

given 30 upper level units (i.e., managers). However, the exact number of lower level units (i.e.,

less than 30) could not be determined. Target samples for outcome measures were set at 15

nurses for supervision satisfaction (target n = 450) and 15 nurses plus up to 5 other health care

providers for teamwork (target n = 600) where possible.

Setting and Sample

Hospitals. Acute care hospitals in a large urban city were selected through purposive sampling.

Of 6 hospitals invited to participate in the study, 4 (66.6%) agreed. The non-participating

organizations were also located in urban cities; one was an academic teaching hospital, the other

was a large academic-affiliated community hospital. Non-participating organizations indicated

that planned changes to the scope of first-line management positions were imminent. Three of

the participating hospitals were academic teaching hospitals and one was a large academic-

affiliated community hospital. Two hospitals were multi-site organizations.

Managers. A convenience sample of managers was recruited from managers of patient care

units, clinics, and services within the participating hospitals who met the inclusion criteria.

Inclusion criteria for managers included: (a) first-line management position (i.e., direct reports

were non-management, other than assistant managers), (b) employment in current position for at

least 3 months, and (c) management of at least one area where health care providers directly

deliver patient care services. The 3 month parameter was specified to reflect, at least in part, the

current manager’s influence on employee supervision satisfaction and teamwork (as opposed to

the previous manager’s influence).

56

Across the 4 participating organizations, from approximately 105 first-line management

positions, 35 (33.3%) managers agreed to participate. Of the 35 managers who consented to

participate, 31 (88.6%) completed the study. Managers who did not complete the study reported

insufficient time available in their work week to participate or misplaced work logs. The 31

managers were assigned 81 areas in total. Nurse satisfaction with manager’s supervision was

surveyed among 31 managers in 51 (63%) areas. Teamwork was surveyed among 30 managers

in 54 (67%) areas. Reasons for excluding assigned areas included: no nurses or direct reports

were employed in the area, or patient care was not delivered in a locally situated work group

context (e.g., hospital wide service).

Employees. A convenience sample of employees was recruited in each participating area where

outcomes could be collected. The inclusion criterion for staff members was employment by the

organization in the current position for at least 3 months. Agency staff and students were

excluded. Although the study planned to sample staff physicians to complete the measure of

teamwork, difficulties in accessing physicians across the participating sites resulted in this group

being excluded as well.

An examination of the administrative raw span data supplied by human resource departments

and data on non-direct reports supplied by managers provides an estimate of the total number of

potential staff participants working in the managers’ areas. However these estimates do not

exclude employees with less than 3 months experience in their current position. For supervision

satisfaction, of the estimated 1,786 nurses working under the study managers in areas where the

survey was administered (excluding the LPI-other subset described below), 31.2% completed

surveys (n = 558). The mean response rate per manager was 33.6% (range: 10.8 - 57.7%). For

teamwork, the estimated total number of potential staff participants included direct and non-

direct report nurses, allied health professionals, and other care providers working in the areas

assigned to managers where the survey was administered. Of the estimated 2,484 health care

providers, 30.4% completed teamwork surveys (n = 754). The mean response rate per manager

was 35% (range: 11.8 - 72.9%). Staff members who did not participate indicated that they were

too busy, did not wish to participate in research, or feared their employment would be placed at

risk. The final sample sizes numbered 558 for supervision satisfaction and 754 for teamwork.

57

A sub-sample of nurses as well as managers’ directors were recruited to complete ratings of

managerial leadership practices (LPI-other). The inclusion criterion for staff members was

employment by the organization in the current position for at least 3 months. The proposed target

sample was set at 9 observers which included the manager’s supervisor, one peer manager, and

seven randomly selected nursing staff across the manager’s areas. This target was higher than

other studies using subordinates to rate managers’ leadership styles in hospital (n = 3; Houser,

2003) and bank (n = 2; Schriesheim et al., 2000) settings. Due to difficulty recruiting managers

and the data collection demands imposed on participating managers, peer managers were not

recruited. Staff nurses who completed the LPI-other were excluded from completing the outcome

measures to reduce common method variance (Podsakoff & Organ, 1986). A subset of nurses,

rather than a subset of employees from other disciplines, was chosen to complete the LPI-other,

because nurses are typically the largest disciplinary cohort. This allowed other health care

professionals, who are typically fewer in number, to complete the outcome measure of

teamwork. Two to 7 LPI-other surveys were submitted for each manager (M = 3.8). Of the final

117 LPI-other instruments completed, 97% were rated by nurses.

Data Collection Procedures

Upon defense of the thesis proposal, this study was submitted to the participating hospitals and

the University of Toronto for ethical review prior to data collection. Each participating hospital

approved the research protocol. Ethical approval for data storage at the Nursing Health Services

Research Unit was received from the University of Toronto. Unless otherwise noted, all data

collection forms will be stored for 7 years in the Nursing Health Services Research Unit’s locked

data storage unit and then destroyed.

Managers were recruited by the researcher through manager staff meetings and third party email

distributions. Refreshments were provided at the meetings. The research protocol and nature of

the study were reviewed with managers prior to data collection. A letter of introduction attached

to the Phase I manager survey package highlighted the objectives and background of the study,

participants’ rights, and the confidentiality of both the respondents themselves and the data

(Appendix B). Managers who agreed to participate in the study completed a written consent form

(Appendix B) and were briefly oriented to the work log procedure by the researcher (e.g., less

58

than 30 minute session). A nominal, weekly thank you token (e.g., coffee coupon) was provided

to managers who completed work log data collection on a weekly basis during Phase II.

A 6 digit study number assigned to each manager was recorded on the manager survey and work

logs. Each area assigned to the manager was also given a study code. These codes were used to

link manager-level data with staff-level data without recording the manager’s name or area on

the surveys to enable data collection and analyses of the nested data set. The 6 digit study

number, names, and assigned areas for each manager were recorded in a confidential master log

that was kept and available only to the research team. Only code numbers appeared in the data

files. The master log was stored in a password protected file on a password protected server in a

locked office within the locked Nursing Health Services Research Unit until the final defense of

the thesis.

Given that work log data were self-recorded, managers were asked to carry and store the work

logs securely to ensure their confidentiality. Managers faxed the work logs to the Nursing Health

Services Research Unit on a weekly basis. This allowed the researcher to clarify work log entries

as needed with the manager, to monitor manager participation, and to distribute the weekly thank

you token for work log completion. The researcher was periodically on-site to collect the work

logs and worked hours data from the manager.

Employees were recruited by the researcher or research assistant through staff meetings or

information sessions and refreshments were provided. To minimize possible disruptions to care

delivery, staff recruitment sessions were offered in areas based on work levels (as determined by

attending morning bed meetings or by communicating with staffing coordinators or team leaders)

and on recruitment targets. For the sub-sample of staff completing the LPI-other, nurses were

recruited during staff information sessions in the participating areas and directors were recruited

via third party email. At staff recruitment sessions, the nature of the study was reviewed with

employees. Employees who indicated an interest in participating in the study received an

information and consent sheet attached to the Phase II employee survey package which

highlighted the objectives and background of the study, participants’ rights, and the assurance of

confidentiality of both the respondents themselves and the data (Appendix C). Submission of the

59

completed employee survey to the research team indicated that the employee had read,

understood, and consented to participate in the study.

Staff surveys were distributed, completed, and collected during the recruitment session or on the

same day to ensure that surveys were correctly coded as follows. Staff participants identified

their area and manager to the researcher who noted this on a confidential, numbered list that

corresponded to the survey numbers. Once returned, the researcher discretely transcribed the

manager and area codes onto the survey which was then stored at the Nursing Health Services

Research Unit.

Administrative data provided by the human resource departments excluded employee names.

Once received, the electronic files were further anonymized using the manager and area study

codes and were password protected. These anonymized files were stored on the password

protected server within the locked Nursing Health Services Research Unit or on the researcher’s

password protected laptop computer.

Risk and Benefits

Voluntary participation, amount of risk, anticipated benefits, and assurance of confidentiality

were discussed with managers and employees. Opportunities to ask questions were available. All

managers and employees were made aware of their right to withdraw from the study at any time

without penalty. Subjects were assured that participation in research was voluntary and that they

could refuse to answer any question(s) or to stop responding to the survey or withdraw from the

study at any time. Potential participants were informed that although there were no direct

benefits related to their participation, the study would contribute to an understanding how

organizations and policy makers can optimize the work of managers in the hospital system.

Managers were informed that, beyond the period of time required to complete the survey,

orientation and work logs, there may be minimal discomfort associated with being shadowed by

the researcher and that there may be employment risks associated with participation (e.g., in the

event that the results of individual managers were to become known). Further by agreeing to

participate, the manager was allowing the research team to ask (a) nurses, a peer manager and

their director to consider completing a questionnaire about their leadership practices, and (b)

60

staff in the units, clinics and services that they manage to consider completing questionnaires of

nurse satisfaction with manager and teamwork. Managers were advised that the questionnaires

about leadership behaviours, nurse satisfaction with the manager’s supervision, and teamwork

may lead the respondents to reflect on issues they might not have otherwise.

Staff members were also informed that beyond the period of time required to complete the

survey, there were no known risks associated with their participation in this study. However, one

hospital also required employment risk to be declared in the event that the results of the

individual staff surveys were to become known.

All participants were informed that the research team would do everything possible to maintain

the confidentiality of all surveys. Only the research team would have access to the surveys and

raw data. No participant would be given access to the surveys of other participants (e.g.,

directors would not have access to the surveys of other staff). All findings would be rolled up to

group, hospital, or study levels to protect against identification. For example, if only 3 palliative

care units participated, the findings from these units would be rolled up into the results for

medical units. All paper surveys were stored at the Nursing Health Services Research Unit.

Administrative Data

During Phase II, anonymized data about direct report employees assigned to study managers

were extracted by the human resource department of each participating organization and

provided to the researcher in Excel spreadsheets. The data elements provided at a case level for

each direct report employee and extracted at 3 consecutive monthly time points were: area, job

title, date of hire, year of birth, and employment status. These data were used by the researcher to

derive an average mean (based on the 3 data collection time points) for the following manager-

level study variables: raw span, occupational diversity, employee tenure, and full-time

employment. The monthly counts of direct report employees stratified by area, job title, and

employment status were verified by participating managers. Two managers identified

discrepancies in that allied health professionals (n = 12) who formerly reported to the director

level now reported to their position. Thus nearly complete case level data were received (99.6%).

The area, job title, and employment status for the missing direct reports were obtained from the

managers and subsequently added to the data set.

61

Using the case level data provided by the human resource departments, raw span was calculated

as the monthly average of the number of direct report employees (by headcount) assigned to the

manager. Occupational diversity was calculated as the monthly average of the number of job

titles reporting directly to the manager. For employee tenure, the difference (in years) between

the date of hire and the date of data extraction was calculated and then used to compute the

monthly average of the organizational tenure (in years) of all direct report employees assigned to

the manager. Full-time employment was the monthly average of the percentage of full-time

direct report employees (by headcount) assigned to the manager. The percentage was derived by

dividing the number of full-time direct report employees by the total number of direct report

employees assigned to the manager.

The researcher also asked managers to identify the number of non-direct reports by job title (by

headcount). This variable was calculated as the sum of the number of physicians, medical

students, and other staff (e.g., allied health) not reporting directly to the manager but who

routinely worked in the manager’s assigned areas. Where non-direct reports rotated through the

assigned area(s), the estimated average number at any one time was used. That is, this was a

cross-sectional count, not a cumulative count. In some areas, a float pool service provided

coverage on an as-needed basis (e.g., respiratory therapy for intensive care units) and this staff

was excluded from the count of non-direct reports.

Hours of operation were obtained by the researcher from the managers. A measure of weekly

operational hours weighted by the number of direct reports per assigned area was created at the

manager level. Hours of operation was calculated as:

∑manager number of direct reports per area * number of hours of operation weekly per area

total number of direct reports

The weighted value was then classified as extended (i.e., 24 hours per day, 7 days a week with

less than 3.5% of direct reports working in a compressed area), compressed (<106 hours per

week), or mixed (i.e., combination of extended and compressed hours of operation assigned to

the manager with more than 3.5% of direct reports working in an area with compressed hours of

operation).

62

Also, two covariates initially proposed in the study framework were subsequently excluded due

to unavailability of data. These variables measured variation in patient case mix groups in the

manager’s assigned area(s) and the percent change in the budget assigned to the manager

between the previous and current fiscal year. These covariates would have served as proxies for

the stability of inputs which is a key determinant of raw span proposed by Gulick (1937) and

Meier and Bohte (2003).

Managerial Work Logs

A time study measurement technique was used to quantify the time allocation of managers for a

one month cycle of work activity. Prospective, self-reported work logs were completed by

managers and were supplemented by inter-observer agreement. The work logging method and

the classification system were pre-tested and this process is detailed in Appendix D.

Measures of time allocation and the associated metrics were piloted and this work is described in

Appendix D. Time in staff contact was the final measure of time allocation. Time in staff contact

was the average daily amount of time (in hours) spent by the manager interacting with direct and

non-direct report staff, physicians, and students working in the area(s) assigned to the manager.

Contact included verbal, written, and email communication and person-to-person interaction.

Time in staff contact was calculated as the sum of the daily total hours in staff contact divided by

the total number of work log days.

During Phase II, managers self-reported time spent in staff contact using the work logs. The

work logging time frame for this study was determined based on the availability of resources, the

data collection burden placed on managers, and the cyclical peaks in managerial activity. Ideally,

measurement of managerial time allocation would be stratified across a one year period to

capture seasonal variation in the work performed (e.g., performance review deadlines) in order to

improve accuracy. However, a 12 month sampling time frame was beyond the resources of this

thesis and would have placed a significant burden on managers. Instead the time frame chosen

for the study reflected a one month cycle of work activity.

Given that managers typically work weekdays only, a one month cycle consists of an average of

21.7 weekdays per month (i.e., 52 weeks x 5 weekdays / 12 months = 21.7). The target number

63

of work log days was set at 20 days. Twenty data collection days allowed for four weekdays each

to be sampled (i.e., four Mondays, four Tuesdays, etc.). To minimize fatigue and improve

response rates, Freda et al. (1998) recommend a maximum of two work logging days per week.

Therefore, managers were asked to complete the work logs for 2 days a week for 10 weeks. A

random stratified sample of weekdays for each manager was determined by the researcher so that

managers would not self-select heavier or lighter days in terms of staff contact thereby biasing

the work log data. Half hourly entries were used to minimize recall bias (Freda et al.) and work

flow interruptions. For each half hourly log entry, managers recorded the number of minutes

spent in staff contact. In terms of work log completion, the mean number of work log days per

manager was 18.7 (range: 13 - 20) with 71% of managers completing 19 or more work log days.

This generated a total of 578 manager work log days.

During Phase II, the researcher contacted the manager by phone, email, or in-person to answer

questions, to provide support regarding the work logging procedure, and to clarify weekly

submissions and work log entries as needed. Inter-observer agreement of the work log data was

also evaluated for each manager. The manager and the researcher completed the inter-observer

agreement ratings for 2 half days on days when the manager was assigned to work log; once near

the beginning and once near the mid-point of the manager’s work log period. Cohen’s kappa,

which factors in chance agreement, was used to assess inter-observer agreement (Norman &

Streiner, 2000). The manager and the researcher rated the presence or absence of time in staff

contact at half hourly intervals. Managers often engaged in multiple activities and interactions

during an observation interval. Disagreement on any one activity or interaction during the half

hour period resulted in the interval being coded as disagreement (i.e., agree/disagree or

disagree/agree). Based on Cicchetti’s (1981) guideline, for kappa sample sizes with two

categories (e.g., agree/disagree) approximately 16 observations are sufficient to estimate this

parameter. The mean number of half hourly observations per manager was 12.7 (range: 8 - 19).

Cohen’s Kappa averaged 0.82 (range: 0.43 - 1.00).

To supplement the work log data, a sub-sample of managers was observed for an additional day

to provide the researcher with an opportunity to understand the manager's workflow. The sub-

sample of managers had raw spans of less than 60 (n = 3), 60 to 100 (n = 3), and 100 to 175 (n =

3).

64

During Phase II, managers also self-reported the hours they worked on a daily basis for 10

weeks. Worked hours was the average number of hours worked by the manager per weekday.

Overtime hours were recorded also and included in the count for weekday hours. On-call hours

and lunch breaks were excluded. Vacation days were also excluded. If the manager worked on a

weekend, those weekend hours were divided by the number of weekdays worked for that

particular week and added to the weekday values. Worked hours was calculated as the sum of the

worked hours per weekday divided by the number of valid weekday entries. On average,

managers recorded worked hours for 34 weekdays (range: 12 - 50).

Instrumentation

Leadership Practices Inventory

The Leadership Practices Inventory (LPI) is a 30 item instrument rated using a ten point scale

that can be completed by either or both the manager (LPI-Self version) and other persons

familiar with the manager’s behavior (LPI-Other version; Posner & Kouzes, 1988). Both the self

and other versions of the LPI were used in this study. The LPI was initially derived from

qualitative case studies (n = 650) and in-depth interviews (n = 38) of managers’ personal best

experiences in leading a project (Posner & Kouzes). The qualitative data were subjected to

content analysis and validation by external raters (Posner & Kouzes). The five fundamental

leadership practices which emerged were: challenging the process, inspiring shared vision,

enabling others to act, modeling the way, and encouraging the heart (Posner & Kouzes). Each

leadership practice entailed two basic strategies. Challenging the process involved searching for

opportunities and experimenting and taking risks. Managers who inspired a shared vision were

able to envision the future and enlist the support of others. Those who enabled others to act both

fostered collaboration and strengthened others. By modeling the way, managers set the example

and planned small wins. Managers who encouraged the heart recognized contributions and

celebrated accomplishments. These five leadership practices accounted for 80% of the behaviors

and strategies described in the qualitative data (Posner & Kouzes).

The LPI scale was subsequently developed using iterative feedback from respondents, including

over 2,100 managers and their employees, and factor analyses of sets of behaviorally-based

statements (Posner & Kouzes, 1988). This was supported by two validation studies. The first

65

study involved 708 managers and 2,168 of their employees (Posner & Kouzes, 1988). The

second study included 5,298 managers and 30,913 of their subordinates (Posner & Kouzes,

1993).

Internal reliabilities of the LPI ranged from .70 to .90 in 1988 and from .80 to .91 in 1993

(Posner & Kouzes, 1988). LPI-self reliabilities ranged from .70 to .84 in 1988 and from .70 to

.85 in 1993 (Posner & Kouzes, 1993). LPI-other reliabilities ranged from .81 to .91 in 1988 and

from .81 to .92 in 1993 (Posner & Kouzes, 1988, 1993). However, use of the LPI-self with nurse

managers in an Ontario study resulted in Cronbach alpha values lower than .70 for two of the

leadership practices (Tourangeau, Lemonde, Luba, Dakers & Alksnis, 2003). Test-retest

reliabilities averaged approximately .94 in both the 1988 and 1993 studies (Posner & Kouzes,

1988, 1993). Social desirability response bias tests using the Marlowe-Crowne Personal Reaction

Inventory demonstrated no statistically significant correlations in a sample of 30 managers

(Posner & Kouzes, 1988). Factor analysis has consistently revealed 5 factors with eigenvalues

greater than 1.0 which explained 59.9% to 60.2% of the variance (Posner & Kouzes, 1988,

1993). When ratings by managers to the ratings of others were compared, LPI-self scores were

likely to be higher than LPI-other scores (p < .001), although the relative rank ordering of the

practices was identical by both managers and others in both studies (Posner & Kouzes, 1988,

1993). Research on the LPI indicates little variation in leadership behaviors among managers

related to demographic (e.g., age, marital status, experience, education) and organizational

characteristics (e.g., size; Posner, 2002).

For this study, exploratory factor analysis of the LPI was planned, and if more than one factor

(i.e., subscale) was observed, then confirmatory factor analysis (Norman & Streiner, 2000)

would be used to test a global score of leadership. Previous research that conducted confirmatory

factor analysis demonstrated that the LPI assessed an over-arching construct of transformational

leadership (Carless, 2001).

Nurse Satisfaction with Manager’s Supervision

Job satisfaction represents an employee’s affective reaction or cognitive appraisal of the job and

work context based on beliefs, feelings, and behavioral intentions (Fields, 2002; McShane,

2009). Facet, global, and composite measures are commonly employed to assess job satisfaction.

66

Selection of a job satisfaction scale should be based on the purpose of the measurement and the

specificity of the criterion (Ironson, Smith, Brannick, Gibson & Paul, 1989; Smith, 1976)

because facet, global, and composite measures are not necessarily equivalent (Ironson et al.;

Jackson & Corr, 2002; Rice, McFarlin & Gentile, 1991).

Global and composite scales of job satisfaction are intended to represent general, overall

reactions to the job (Ironson et al., 1989). These general measures are often used as an index of

organizational effectiveness (Ironson et al.). Global measures, which elicit a single, integrated

response, assume that an individual identifies, processes, and consolidates various aspects of the

job (Ironson et al.). An advantage of global scales is that the individual is free to select those

aspects of the job that are most relevant and important, thereby allowing individuals to view the

situation in their own unique way (Ironson et al.). Composite measures, which are constructed

using single items to represent each component of job satisfaction, assume that “the whole is

equal to the sum of its principal parts” (Ironson et al., p. 194). In a comparison of global, facet,

and composite scales, Ironson et al. found that composite measures represent a unitary construct

and differ from facet and global measures.

Facet scales assess employee reactions to separate, homogeneous aspects of the job. Although

mutually exclusive, facets tend to be intercorrelated (Ironson et al., 1989). Facet measures are

useful for distinguishing various aspects of job satisfaction, and can, for example, assist

organizations in identifying areas for improvement (Ironson et al.). However, facet measures

may underestimate job satisfaction by excluding important, or by including unimportant, aspects

of the concept (Ironson et al.). Linear addition of facets may also inaccurately reflect the

individual’s perceptions or weighting of various aspects of job satisfaction (Ironson et al.).

Respondents are likely to apply different cognitive heuristics to facet scales as compared to

global scales, leading to the non-equivalence of these different types of scales (Jackson & Corr,

2002; Rice et al., 1991).

Given that the main focus of the dissertation is managerial time spent in staff contact, a single

facet scale, the Satisfaction with My Supervisor Scale (SWMSS; Scarpello &Vandenberg, 1987)

was utilized. Nurses’ perceptions of the technical, administrative, and relational abilities of the

manager with respect to supervision were assessed. The SWMSS is an 18 item instrument rated

67

using a 5 point Likert scale that is completed by subordinates of the supervisor (Scarpello &

Vandenberg). The SWMSS was initially derived from focus groups with supervisors and

employees (n = 308) in a U.S. manufacturing plant. Of those factors identified by focus group

participants as contributing to job satisfaction, 23 items specific to the supervisor were used to

construct preliminary scale items. Conceptually, the scale reflects the three areas of supervisory

skill outlined by Mann (1965) which involve technical, human relations, and administrative

skills.

These preliminary items were subsequently embedded in a larger 180 item multi-facet scale of

job satisfaction (Satisfaction with the Quality of Employment Survey) along with 2 composite

items of supervisory satisfaction from the short-form Minnesota Satisfaction Questionnaire

(MSQ) and 2 global items each for general job satisfaction and general supervisory satisfaction

(Scarpello & Vandenberg, 1987). Over a 3 year period, data were collected from 2,101

employees in 7 U.S. manufacturing plants. Factor analyses across plants revealed a 2 factor

solution with the 2 composite items from the MSQ loading on the first factor. Predictive power

of the scale was assessed by regressing the items on the global item of supervisory satisfaction

and the 2 MSQ composite items and 5 of the preliminary SWMSS items were dropped, resulting

in an 18 item scale. Coefficient alpha across the plants ranged from .95 to .96. Convergent and

discriminant validity were demonstrated by assessing SWMSS items against 6 global measures

of various job satisfaction facets. Predictive and content validity were successfully evaluated by

regressing global supervisory satisfaction on the SWMSS items. The 18 item SWMSS was

subsequently tested with 1,104 employees of a U.S. insurance company and explained 85% of

the variance in supervisory satisfaction (p < .001; Scarpello & Vandenberg).

Although a two factor solution was obtained across all plants and the insurance company,

Scarpello and Vandenberg (1987) proposed that this reflected a primary general factor

accompanied by a lesser secondary factor which supported the integrated nature of the

supervisor’s technical, human relations, and administrative skills. Furthermore, in a repeated test

at one of the plants, adding the word ‘supervisor’ to the second factor items (which did not

contain the word supervisor unlike the items loading on the primary factor) resulted in a single-

factor solution.

68

Later validation of the scale among management information systems professionals (n = 100)

resulted in coefficient alphas of .95 and test-retest reliability of .78 with two administrations of

the scale at a 5 month interval (Vandenberg & Scarpello, 1992). Convergent validity of the scale

was supported. The SWMSS also differed from measures of departmental, organizational, and

occupational commitment supporting discriminant validity. In a study of over 600 government

employees, Cronbach’s alpha for the SWMSS was .88 (Jones, Scarpello & Bergmann, 1999).

Relational Coordination Scale

Teamwork was measured using the Relational Coordination Survey for General Health Care

Setting. This 7-item instrument by Gittell (2006) appraises healthcare providers’ perceptions of

the quality of communication and the extent to which goals, knowledge, and respect are shared

amongst team members. Self-report bias is minimized by having respondents rate how well other

group members communicate with them (rather than how well the respondent communicates

with others). As noted by Podsakoff and Organ (1986), the tendency toward self-attribution

among respondents can mean that ratings about self are likely to be more favorable than ratings

about others or external factors. Frequency, timeliness, accuracy, and problem-solving

orientation of communication are rated, as well as the levels of shared knowledge and goals and

mutual respect (Gittell). Respondents rate each type of care provider on the team using a 5-point

scale. Cronbach’s alpha ranged from .72 to .84 for the individual dimensions and was .85 for the

overall index in a study of relational coordination in health care teams (Gittell et al., 2000).

Data Analyses

Data Entry and Cleaning

Data were entered using double data entry to minimize errors and were analyzed using the

Statistical Package for the Social Sciences (SPSS) version 16.0. The univariate frequency

distributions of variables to be included in the models were examined. The variables were fairly

normally distributed. Outliers for predictor variables were identified by box plots and by

examining the distribution of z-scores. Z-scores that exceeded three standard deviations from the

mean were treated as outliers (Norman & Streiner, 2000). For worked hours, total areas,

administrative support roles, and non-direct reports, identified outliers were reduced (or

69

increased) to the value of the nearest score plus one unit (Tabachnick & Fidell, 1996). Missing

data for nurse day shift (0.5%), nurse education (0.5%), and nurse employment status (0.7%)

were assigned an average unit value. Missing values for nurse age (10%) were imputed by

regressing years in occupation on nurse age. Reliability and factor analyses of the instruments

were conducted.

Levels of Analysis

In this study, employees were nested within defined areas (e.g., units, clinics, labs, or services)

which in turn, were nested under managers. Although a three-level hierarchical linear model was

anticipated, nearly one-third of the managers in the sample had only one area in which outcomes

could be collected. Because variation in outcomes between areas could not be examined for these

managers, area could not be treated as a separate level in the models. Therefore, only two-level

models were tested with individual employees (level-1) nested under managers (level-2). For this

reason, one of the covariates initially proposed as a unit level variable in the study framework

was subsequently excluded. Distance between the manager’s office and each assigned area was

proposed as a proxy for space, one of the key determinants of raw span proposed by Gulick

(1937) and Meier and Bohte (2003). This measure could not be meaningfully aggregated to the

manager level and was therefore omitted.

The two alternative measures of managerial span, leadership, and hours of operation were level-2

predictors. The goal was to examine the main effects of level-2 managerial predictors on level-1

staff outcome variables. Hierarchical linear modeling (HLM) was used to analyse the nested data

set because it permits the simultaneous examination of relationships between and across

hierarchical levels (Raudenbush & Bryk, 2002). HLM resolves problems of analysis such as

aggregation bias, misestimated precision, and heterogeneity of regression which emerge when

the nested structure of the data set is ignored (Raudenbush & Bryk). Aggregation bias occurs if

the meaning or effect of a variable is assumed to be the same when measured at different

organizational levels (Raudenbush & Bryk). Misestimated precision results when dependence

between individual responses in clusters is not taken into account, and is resolved through HLM

by incorporating a unique random effect for each cluster (Raudenbush & Bryk). Heterogeneity of

regression is observed when relationships between individual characteristics and outcomes vary

70

across organizations (Raudenbush & Bryk). This difficulty is addressed through HLM by

estimating a separate set of regression coefficients for each cluster and then modeling the

variation among the clusters in their sets of coefficients as multivariate outcomes to be explained

by organizational factors (Raudenbush & Bryk).

Study Objectives

The study objectives included an examination of main effects, interaction effects, and amount of

variance explained. The analytical approach for each objective is explored below. In addition, the

treatment of covariates included in the analytical framework is considered.

For study objective 1, the main effects of the alternative measures of managerial span,

leadership, and hours of operation on both study outcomes were examined. The analytical

strategy for main effects typically follows a four step process (Hoffman, 1997; Raudenbush &

Bryk, 2002): (a) one-way analysis of variance (ANOVA), (b) random coefficient regression

model, (c) intercepts-as-outcomes model, and (d) slopes-as-outcomes model. In this thesis, the

first three steps were conducted to meet the study objectives. First, an ANOVA was conducted to

assess systematic within and between group variance in the outcome (Hoffman). Second, a

random coefficient regression model was conducted to determine if significant variance is

observed in the intercepts and slopes across groups (Hoffman). Assuming significant variance

was observed in the intercept term in the second step, the third step was to conduct an intercepts-

as-outcomes model to examine whether variance in the level-1 outcome was significantly related

to the level-2 predictor (Hoffman). Step 4, the slopes-as-outcomes model, was not conducted. A

slopes-as-outcome model determines whether the level-1 predictor (i.e., slope) varies in relation

to a level-2 predictor (Hoffman; Raudenbush & Bryk). Essentially, a slopes-as-outcome model

assesses whether a level-2 predictor moderates the relationship between a level-1 predictor and

the level-1 outcome. This model is appropriate for testing cross-level interactions. Step 4 was not

necessary to answer the study objectives.

For study objective 2, the three-way interaction effects between each alternative measure of

managerial span (i.e., raw span, time in staff contact) with leadership and hours of operation

were also examined. These interactions were among variables from the same level of analysis

(i.e., these interactions were not cross-level interactions). Testing of a three-way interaction first

71

involves a test of the main effects of the predictors on the outcomes, then a test of the products of

each pair of predictors on the outcome (i.e., two-way interactions), followed by the three-way

interaction (Aiken & West, 1991). All continuous variables were mean-centered prior to forming

cross-product terms to minimize collinearity between the main effects and cross-product terms

(Aiken & West). Categorical variables were dummy coded. The two-way and three-way

interaction terms were added to step 3, the intercepts-as-outcomes model, as described for

objective 1.

For study objective 3, to determine the extent to which the alternative measures of managerial

span explained variation in outcomes for nurses and teams, the main and interaction effects

models with significant effects for raw span or time allocation were compared to the

unconditional model for each outcome. For each outcome, the alternative measure of managerial

span in the model that most reduced the between-manager variance was determined to explain

more variation in the outcome.

The study framework also proposed other level-2 covariates to reflect characteristics of the

managerial role. Covariates consist of confounding, moderating, and control variables. A

confounding variable must affect variation in both the predictor and outcome variables.

Confounding and control variables were entered as covariates. A variable that affects only

variation in the outcome variable, but not the predictor variable, is a moderator. First-order

moderator variables were analyzed as interaction terms.

Knowledge Translation Plan

Engaging with key policy players at an early stage in the doctoral endeavor (and hence, early in

the researcher’s career) helps to build a relational foundation to facilitate the processes of

increasing public awareness, engaging political support, and activating interest groups. Effective

policy-research linkages are prerequisite to supporting success in each of these steps (Lomas,

1997; Shamian, Skelton-Green & Villeneuve, 2002). Fortunately, as a doctoral fellow at the

Nursing Health Services Research Unit, I was actively involved with policy makers. For

example, as a student intern at the Dorothy M. Wylie Nursing Leadership Institute the doctoral

thesis took on particular focus as senior management nurses identified a need to measure and

optimize managerial span. Indeed, Lomas argued these linkages should be part of scientific

72

training and are necessary to shaping the entire research process to effectively define, design,

conduct, and disseminate policy-relevant research. Lomas noted that engaging policy makers in

the entire research process fosters their investment in the research question and process,

outcomes, and uptake of findings. This approach has been adopted by the Canadian Health

Services Research Foundation (2005) which encourages policy makers to be involved in all

stages of the research process. Consistent with this approach, a decision-maker who is a Chief

Nurse Officer and actively involved in several professional associations was a thesis committee

member. Her input into the research process and advice on how to tailor messages for key

audiences will enhance knowledge exchange activities.

73

Chapter 4: Results

This study used a descriptive, correlational design comprising cross-sectional and longitudinal

components to collect survey and administrative data from employees, managers, and

administrative sources. The purpose was to examine the influence of alternative measures of

managerial span on nurse and team outcomes in the hospital sector. This chapter presents the

study results in four sections: 1) reliability analyses of instruments; followed by 2) sample

description; 3) descriptive statistics of study variables; as well as, 4) findings for each outcome

by study objective.

Instruments

Leadership Practices Inventory

Two versions of the LPI were used in the study; the LPI-self was completed by managers (n =

31) and the LPI-other was filled in by nurses and directors (n = 117). The correlation between

LPI-self and LPI-other scores was low (r = .24, p < .05) suggesting that managers were tapping

different phenomena than other raters. The sample of LPI-self scores was also very small (n =

31) and was therefore excluded from the analysis.

Of the 117 LPI-other surveys submitted, 88.0% were complete with the remaining surveys

missing 1 to 8 items. Exploratory factor analysis using principal components methods was then

conducted for the LPI-other scores. Individual responses (n = 117) used in the factor analysis

were not independent and may therefore be correlated; however, this process was consistent with

the analysis done by Posner and Kouzes (1993). Sampling was adequate as indicated by

acceptable values for the Kaiser-Meyer-Olkin measure. Varimax rotation was applied. The scree

plot indicated a single factor solution explaining 59.9% of the variance. Confirmatory factor

analysis was subsequently not conducted as much larger sample sizes are recommended for this

purpose (Dixon, 2001). A mean LPI-other score was calculated to represent an over-arching

construct of transformational leadership consistent with Carless (2001). Cronbach’s alpha was

.98. An intraclass correlation coefficient of .577 indicated that 57.7% of the item variation was

between raters, rather than within raters (i.e., inconsistently rated items by a given rater).

74

Satisfaction with my Supervisor Scale

Of the 558 Satisfaction with My Supervisor Scales filled in by nurses, 85.8% were complete and

9.7% were missing one item. Twenty-five responses (4.4%) were missing 2 to 9 items. A mean

overall score was calculated for each respondent (n = 558) based on the number of items for

which there were responses. Cronbach’s alpha was .97. This result is consistent with or higher

than the reliability alphas reported by Scarpello and Vandenberg (1987), Vandenberg and

Scarpello (1992) and Jones et al. (1999). An intraclass correlation coefficient of .653 indicated

that 65.3% of the item variation was between raters, rather than within raters (i.e., inconsistently

rated items by a given rater).

Relational Coordination Scale

Of the 754 Relational Coordination Scales submitted by nurses, other regulated health care

providers, and unregulated care providers, 95.4% were complete with the remaining teamwork

surveys missing 1 to 4 items. A mean overall teamwork score was calculated for each respondent

(n = 754) based on the number of items for which there were responses. Cronbach’s alpha for the

7 items was .89 which is consistent with the alpha reliability reported by Gittell et al. (2000). An

intraclass correlation coefficient of .543 indicated that 54.3% of the item variation was between

raters, rather than within raters (i.e., inconsistently rated items by a given rater).

Sample Description

Table 5 shows the raw span values of the managers as well as the numbers of areas assigned and

staff participants per manager.

75

Table 5. Managers’ Average Raw Span, Total Areas Assigned, and Surveys Analyzed Manager Average Raw Span Total Areas Nurse Surveys Team Surveys

1 60.3 2 13 24 2 104.7 3 25 43 3 68.7 4 15 20 4 76.3 2 15 18 5 29.0 1 12 12 6 90.0 3 23 30 7 89.0 2 25 26 8 136.0 2 15 21 9 78.0 1 14 20

10 38.0 3 12 17 11 59.7 3 18 24 12 157.3 1 15 21 13 163.0 5 36 44 14 54.0 1 14 16 15 174.3 11 15 N/A 16 123.3 1 14 18 17 76.0 3 29 36 18 75.7 2 24 32 19 75.7 1 15 20 20 66.7 3 15 21 21 64.0 1 15 20 22 85.0 2 27 34 23 63.3 3 10 16 24 91.7 5 17 25 25 127.0 1 15 30 26 77.7 2 21 27 27 106.7 3 20 23 28 102.3 4 43 51 29 34.7 2 8 13 30 84.0 1 12 17 31 52.7 3 6 35

Note. N/A = not applicable.

Of the 31 managers, all were Registered Nurses and 93.5% were female (Table 6). In terms of

highest educational credential achieved, 38.7% held Master degrees, 45.2% held undergraduate

degrees, and 16.1% held college diplomas. On average, managers were aged 46.1 years and had

worked for 23.5, 16.6, and 3.0 years in the profession, hospital, and position respectively.

Managers averaged 6.9 years of management experience. For the 2007/2008 fiscal year, the

average assigned budget was 6.7 million Canadian dollars.

76

Table 6. Managers’ Age, Tenure, Years of Experience, and Education Manager Characteristics (n = 31)

% Mean Range Age 46 29-63

Position Tenure 3 .3-9 Hospital Tenure 17 .5-35

Nursing Experience 24 6-39 Management Experience 7 .25-19

Registered Nurse Diploma 16 Undergraduate Degree 45

Graduate Degree 39

Nurse Supervision Satisfaction Respondents. Of the 558 respondents, 87.8% were Registered

Nurses and 12.2% were Registered Practical Nurses (Table 7). On average, respondents were

aged 42.4 years and had worked for 16.0, 10.6, and 7.6 years in the profession, hospital, and area

respectively. Of these nurses, 91.4% were female, 35.3% held a baccalaureate nursing degree or

higher, and 26.9% worked day shift only. Most were employed full-time (79.2%), followed by

part-time (16.3%) and casual (4.5%). The most common position held was staff nurse (93.28%),

followed by team leaders (4.9%) and other (1.9%).

Table 7. Nurses’ Designation, Age, Years of Experience, and Education Nurse Characteristics Satisfaction Models (n = 558)

% Mean Range Registered Nurse 88

Age 42 22-67 Unit Experience 8 .25-33

Hospital Experience 11 .25-37 Nursing Experience 16 .33-46

RPN Certificate/Diploma 9 Registered Nurse Hospital School 2

Registered Nurse Diploma 54 Undergraduate Degree 33

Graduate Degree 2

77

Team Respondents. The occupations of the 754 respondents reflected a variety of

multidisciplinary roles (Table 8). Nearly 74% were nurses.

Table 8. Team Surveys by Occupation Frequency % Registered Nurse 476 63.2 Registered Practical Nurse 72 9.6 Physiotherapist/Physiotherapist Assistant 39 5.2 Unregulated Care Provider 27 3.6 Technician/Technologist 25 3.3 Social Worker 23 3.1 Clerical 23 3.1 Occupational Therapist/Assistant & Speech Therapist 14 1.9 Registered Dietician/Registered Dietary Assistant 13 1.7 Pharmacist 11 1.5 Respiratory Therapist 10 1.3 Advanced Practice Nurse 8 1.1 Other 7 0.9 Counsellor/Case worker 5 0.7

As shown in Table 9, 48.7% of team respondents held a university degree and only 1.3% held a

secondary school credential as their highest level of education.

Table 9. Team Surveys by Highest Education Frequency % College Diploma 314 42.1 Undergraduate degree 211 28.3 Graduate degree 152 20.4 College Certificate 54 7.2 High School 10 1.3 Hospital School of Nursing 5 0.7

Team respondents were mainly female (86.1%). On average, respondents were aged 41.0 years

and had worked for 14.1, 9.92, and 6.9 years in the occupation, hospital, and area respectively.

Most were employed full-time (81.3%), followed by part-time (14.2%) and casual (4.5%).

Descriptive Statistics of the Study Variables

78

Table 10 presents the descriptive statistics for predictors and the outcome variables.

Table 10. Descriptive Statistics of Study Variables Predictors % Mean SD Level-2 (Manager) Raw Span 86.6 36.2 Time in Staff Contact (minutes per weekday) 192 84 Leadership Practices – Other 7.6 1.0 Hours of Operation (weekly hours) Extended 61.3 Compressed and Mixed 38.7 Education (graduate degree) 38.7 Experience (years) 6.9 5.5 Position Tenure (years) 3.0 2.4 Worked Hoursa (per weekday) 8.9 0.9 Total Areasa 2.5 1.3 Administrative Support Rolesa (full-time equivalents) 0.9 0.9 Clinical Support Roles (full-time equivalents) 3.4 1.8 Occupational Diversity 9.1 4.0 Employee Tenure (years) 9.5 2.4 Full-time Employment (%) 59.5 8.5 Non-Direct Reportsa 33.3 20.5 Level-1 (Staff) Nurse Age 42.4 10.5 Nurse Day Shift Employment (not day shift) 73.1 Nurse Education Level (college diploma) 53.9 Nurse Registration (Registered Nurse) 87.8 Occupational Group Nursing 73.7 Other Regulated Health Professional 14.6 Unregulated Care Provider 11.7 Full-Time Status 81.3 Outcome Variables Level-1 (Staff) Nurse Satisfaction with Manager’s Supervision 3.8 0.8 Teamwork 3.9 0.5 Note. aOutlier(s) corrected.

Raw span values ranged from 29.0 to 174.3. Direct report staff numbered less than 66.7 for one-

third of managers and 90 or more for one-third of managers. The distribution of raw span values

is presented in Figure 3.

79

Figure 3. Distribution of raw span values.

Daily time in staff contact ranged from 1.4 to 7.2 hours. On average, one-third of managers spent

less than 2.4 hours in staff contact per day and one-third spent 3.9 hours or more in staff contact

per day. The distribution of time in staff contact values is presented in Figure 4. Relative to the

mean daily worked hours of managers, 36% of the workday was spent in staff contact on

average.

80

Figure 4. Distribution of time in staff contact values.

Leadership scores ranged from 5.3 to 9.2. The mean leadership score was 7.6, indicating that on

average, managers fairly often or usually engaged in leadership behaviors as measured by the

Leadership Practices Inventory - Other (Kouzes & Posner, 2003). On average, one-third of

managers scored 7.1 or lower and one-third scored 8.2 or higher. The distribution of leadership

scores is presented in Figure 5.

81

Figure 5. Distribution of leadership scores.

Of the managers, 12.9% (n = 4) covered area(s) with compressed hours of operation only, 25.8%

(n = 8) covered areas that were a mix of compressed and extended hours of operation, and 61.3%

(n = 19) covered area(s) with extended hours of operation only. In terms of highest educational

qualification, managers had completed graduate degrees (38.7%), undergraduate degrees

(45.2%), or college diplomas (16.1%). Years of management experience ranged from 0.25 to 3.9

for the least experienced one-third of managers and 7.5 to 19 for the most experienced one-third

of managers. Years of position tenure ranged from 0.25 to 1.0 for the least tenured one-third of

managers and 4.17 to 9.25 years for the most tenured one-third of managers. Of the managers,

29% worked less than 8.5 hours per day and 19% worked more than 9.5 hours per day. The total

areas assigned to managers numbered from one (29%), two (26%), three (29%), or four or more

(15%). Thus 71% of managers had more than one assigned unit, clinic or service. The proportion

of managers with three or more administrative and clinical support roles full-time equivalents

(including direct and non-direct report staff) was 6.5% and 68% respectively. Overall, 74% of

managers had three or more administrative and clinical full-time equivalent positions combined.

Over three-quarters of managers (77%) had 6 or more job titles reporting directly to their

82

position. Average employee tenure of direct report staff was less than 9 years for 36% managers

and greater than 11 years for 39% managers. The proportion of full-time employment for direct

report staff averaged 60%, ranging from 40% to 74%. Non-direct reports working in the assigned

areas numbered 20 or fewer for one-third of managers and 37 or more for one-third of the

managers.

Nurse satisfaction with manager’s supervision scores ranged from 1.0 to 5.0. One-third of the

scores were at or below 3.53 and one-third of the scores were at or above 4.17. The distribution

of supervision satisfaction scores is presented in Figure 6.

Figure 6. Distribution of level-1 nurse satisfaction with manager’s supervision scores. Teamwork scores ranged from 1.88 to 5.0. One-third of the scores were at or below 3.68 and

one-third of the scores were at or above 4.14. The distribution of teamwork scores is presented in

Figure 7.

83

Figure 7. Distribution of level-1 teamwork scores.

A weak correlation (r = .29, p < .01, n = 558) between supervision satisfaction and teamwork

scores indicated that the scales measured different phenomena in this study sample. Bivariate

Pearson correlations of study variables are presented in Appendix E.

84

Satisfaction Findings

Objective 1: Main Effects for Satisfaction

In Step 1, a one-way ANOVA with random effects (i.e., unconditional means model) was

conducted to determine how much variation in supervision satisfaction scores existed within and

between managers and the proportion of total variance residing between groups. As shown in

Table 11, the supervision satisfaction grand mean was 3.82. Variance components indicated

significant variability at the between-manager (.12) and within-manager (.55) levels.

Table 11. One-Way Analysis of Variance Model for Satisfaction

95% Confidence Interval

Parameter Estimate SE df t Wald Z Significance Lower Bound

Upper Bound

Estimates of fixed effectsa INTERCEPT 3.82 .07 28.81 53.63 .001 3.67 3.96 Estimates of covariance parametersa Residual .55 .03 16.20 .001 .49 .63 INTERCEPT (subject variance) .12 .04 2.97 .003 .06 .24 aDependent variable = Satisfaction

A modest range of plausible values for the supervision satisfaction means among managers was

observed with 95% of the means falling between 3.13 and 4.5. Using the variance components,

the intraclass correlation coefficient was computed as (.121979/[.121979 + .554057]) = .18,

indicating that 18% of the variance in supervision satisfaction was between managers. On

average, the sample means were fairly reliable as indicators of the true manager means (λ hat =

.78).

In Step 2, a random-coefficient regression model was conducted. At level-1 (the nurse model),

the supervision satisfaction score for each nurse under a given manager was regressed on nurse

age, nurse day shift employment, nurse education level, and nurse registration status (Table 12).

85

Table 12. Fixed-Coefficient Regression Model Level-1 for Satisfaction 95% Confidence Interval

Parameter Estimate SE df t Wald Z Significance Lower Bound

Upper Bound

Estimates of fixed effectsa INTERCEPT 3.82 .072 28.84 53.15 .001 3.67 3.96 Nurse Age 3.02x10-4 3.34x10-3 549.80 .09 .928 -6.26x10-3 6.86x10-3 Nurse Day Shift .04 .08 555.55 .51 .613 -.12 .20 Nurse Education .03 .07 542.61 .47 .637 -.11 .18 Nurse Registration -.28 .11 554.87 -2.48 .013 -.50 -.06 Estimates of covariance parametersa Residual .55 .03 16.20 .001 .48 .62 INTERCEPT (subject variance) .13 .04 2.99 .003 .06 .24 aDependent variable = Satisfaction

Of these level-1 covariates, only nurse registration explained significant variation in the

supervision satisfaction outcome. Nurse registration was retained as a control variable in

subsequent models (i.e., the level-1 slope was fixed to be invariant across level-2 models). A

reduced model was estimated using nurse registration (Table 13). The inclusion of nurse

registration reduced the within-manager variance by 1.2%.

Table 13. Fixed-coefficient Regression Model Level-1 for Satisfaction: Reduced Model

95% Confidence Interval

Parameter Estimate SE df t Wald Z Significance Lower Bound

Upper Bound

Estimates of fixed effectsa INTERCEPT 3.82 .07 28.90 53.32 .001 3.67 3.96 Nurse Registration -.27 .11 551.61 -2.42 .016 -.48 -.05 Estimates of covariance parametersa Residual .55 .03 16.21 .001 .49 .62 INTERCEPT [subject = mgr] .12 .04 2.99 .003 .06 .24 aDependent variable = Satisfaction

In Step 3, intercepts-as-outcome models were conducted to determine whether the level-1

intercept varied in relation to level-2 predictors. Fixed level-2 raw span and time in staff contact

covariates were added and examined in separate intercepts-as-outcome models; the level-1

model remained the same. Predictors were held constant and centered on the grand mean. All

proposed covariates were first examined in separate models. Following the step-up strategy

recommended by Raudenbush and Bryk (2002), five manager level covariates, namely

leadership, hours of operation, administrative support roles, clinical support roles, and worked

hours were tested separately and in combination with raw span and time in staff contact.

Raudenbush and Bryk recommend that level-2 predictors with small estimated effects and t

ratios near or less than 1 be excluded. On this basis, administrative support roles, clinical support

86

roles, and worked hours variables were excluded. Hours of operation, leadership, and nurse

registration were included in all supervision satisfaction models (Tables 14 and 15). As shown in

Table 14, no significant main effects were observed for raw span and operational hours.

Leadership practices were positively associated with supervision satisfaction. Registered Nurses

were less satisfied than Registered Practical Nurses with their manager’s supervision.

Table 14. Raw Span. Intercepts-as-Outcomes Model for Satisfaction

95% Confidence Interval

Parameter Estimate SE df t Wald Z Significance Lower Bound

Upper Bound

Estimates of fixed effectsa INTERCEPT 3.80 .08 30.73 47.50 .001 3.64 3.96 Raw Span -8.47x10-4 1.83x10-3 33.45 -.46 .647 -4.58x10-3 2.88x10-3 Leadership .22 .07 33.14 3.20 .003 .08 .36 Hours of Operation .039 .13 30.94 .30 .767 -.23 .30 Nurse Registration -.24 .11 548.82 -2.22 .027 -.46 -.03 Estimates of covariance parametersa Residual .55 .03 16.23 .001 .48 .62 INTERCEPT (subject variance) .09 .03 2.82 .005 .04 .17 aDependent variable = Satisfaction

As shown in Table 15, no significant main effects were observed for time in staff contact and

operational hours. Leadership practices were positively associated with supervision satisfaction,

and Registered Nurses were less satisfied than Registered Practical Nurses.

Table 15. Time in Staff Contact. Intercepts-as-Outcomes Model for Satisfaction

95% Confidence Interval

Parameter Estimate SE df t Wald Z Significance Lower Bound

Upper Bound

Estimates of fixed effectsa INTERCEPT 3.80 .08 31.14 46.91 .001 3.64 3.97 Time in Staff Contact 3.69x10-3 .05 28.01 .08 .936 -9.03x10-3 .10 Leadership .22 .07 32.87 3.14 .004 .08 .36 Hours of Operation .03 .13 30.88 .22 .825 -.24 .30 Nurse Registration -.25 .11 545.20 -2.28 .023 -.46 -.03 Estimates of covariance parametersa Residual .55 .03 16.23 .001 .48 .62 INTERCEPT (subject variance) .09 .03 2.83 .005 .04 .18 aDependent variable = Satisfaction

In summary for Objective 1, main effects on supervision satisfaction were observed for

leadership, but not for raw span, time in staff contact, and hours of operation.

87

Objective 2: Interaction Effects for Satisfaction

Following the procedure outlined by Aiken and West (1991), the two-way interactions were first

examined in a combined model for each alternative measure of managerial span. Of the two-way

interactions tested, only raw span by hours of operation (Tables 16 and 17) and time in staff

contact by hours of operation (Table 18) were significant. As shown in Table 16, raw span

interacted with extended hours of operation and of the conditional first order effects, only

leadership had a significant positive effect on supervision satisfaction.

Table 16. Raw Span with Two-Way Interactions for Extended Hours of Operation. Intercepts-as-Outcomes Model for Satisfaction

95% Confidence Interval

Parameter Estimate SE df t Wald Z Significance Lower Bound

Upper Bound

Estimates of fixed effectsa Intercept 3.82 .07 29.16 52.67 .001 3.67 3.97 Raw Span 4.80x10-3 2.73x10-3 31.82 1.76 .088 -7.54x10-4 .01 Leadership .19 .09 29.57 2.20 .036 .01 .36 Hours of Operation (extended) .08 .12 29.81 .66 .517 -.16 .32 Raw Span * Leadership -3.41x10-3 2.47x10-3 28.24 -1.38 .179 -8.46x10-3 1.65x10-3 Raw Span * Hours of Operation -8.86x10-3 3.52x10-3 30.29 -2.51 .017 -.02 -1.67x10-3 Leadership * Hours of Operation -.06 .13 30.91 -.45 .656 -.33 .21 Nurse Registration -.24 .11 534.93 -2.24 .025 -.45 -.03 Estimates of covariance parametersa Residual .55 .03 16.21 .001 .49 .62 INTERCEPT (subject variance) .06 .03 2.49 .013 .03 .14 aDependent variable = Satisfaction

With compressed and mixed hours of operation as the referent, raw span interacted with hours of

operation and none of the conditional first order effects were significant (Table 17).

88

Table 17. Raw Span with Two-Way Interactions for Compressed and Mixed Hours of Operation. Intercepts-as-Outcomes Model for Satisfaction

95% Confidence Interval

Parameter Estimate SE df t Wald Z Significance Lower Bound

Upper Bound

Estimates of fixed effectsa Intercept 3.90 .10 30.23 40.65 .001 3.70 4.09 Raw Span -4.06x103 2.25x103 30.95 -1.80 .082 -8.66x103 5.51x104 Leadership .13 .12 29.04 1.08 .289 -.12 .37 Hours of Operation (compressed

& mixed) -.08 .12 29.81 -.66 .517 -.32 .16

Raw Span * Leadership -3.41x103 2.47x103 28.24 -1.38 .179 -8.46x103 1.65x103 Raw Span * Hours of Operation 8.86x103 3.52x103 30.29 2.51 .017 1.67 .02 Leadership * Hours of Operation .06 .13 30.91 .45 .656 -.21 .33 Nurse Registration -.24 .11 534.93 -2.24 .025 -.45 -.03 Estimates of covariance parametersa Residual .55 .03 16.21 .001 .49 .62 INTERCEPT (subject variance) .06 .03 2.49 .013 .03 .14 aDependent variable = Satisfaction

As shown in Table 18, time in staff contact also interacted with extended hours of operation. Of

the conditional first order effects, only leadership had a significant positive effect on supervision

satisfaction.

Table 18. Time in Staff Contact Two-Way Interactions for Extended Hours of Operation. Intercepts-as-Outcomes Model for Satisfaction

95% Confidence Interval

Parameter Estimate SE df t Wald Z Significance Lower Bound

Upper Bound

Estimates of fixed effectsa Intercept 3.82 .08 31.50 50.06 .001 3.66 3.97 Time in Staff Contact .10 .06 29.71 1.61 .118 -.03 .23 Leadership .25 .09 32.48 2.81 .008 .07 .43 Hours of Operation (extended) .03 .12 30.59 .25 .803 -.22 .28 Time in Staff Contact *

Leadership .06 .08 28.90 .72 .476 -.11 .22

Time in Staff Contact * Hours of Operation

-.20 .10 28.19 -2.14 .041 -.40 -8.64x10-3

Leadership * Hours of Operation -.09 .15 31.73 -.64 .528 -.39 .20 Nurse Registration -.25 .11 536.80 -2.30 .022 -.46 -.04 Estimates of covariance parametersa Residual .55 .03 16.23 .001 .48 .62 INTERCEPT (subject variance) .07 .03 2.64 .008 .03 .15 aDependent variable = Satisfaction

With compressed and mixed hours of operation as the referent, time in staff contact interacted

with hours of operation; however, none of the conditional first order effects were significant

(Table 19).

89

Table 19. Time in Staff Contact with Two-Way Interactions for Compressed and Mixed Hours of Operation. Intercepts-as-Outcomes Model for Satisfaction

95% Confidence Interval

Parameter Estimate SE df t Wald Z Significance Lower Bound

Upper Bound

Estimates of fixed effectsa Intercept 3.85 .09 29.67 40.68 .001 3.66 4.04 Time in Staff Contact -.10 .07 27.11 -1.56 .130 -.24 .03 Leadership .16 .11 32.15 1.49 .146 -.06 .37 Hours of Operation (compressed

& mixed) -.03 .12 30.59 -.25 .803 -.28 .22

Time in Staff Contact * Leadership

.058 .08 28.90 .72 .476 -.11 .22

Time in Staff Contact * Hours of Operation

.20 .10 28.19 2.14 .041 8.64x103 .40

Leadership * Hours of Operation .09 .15 31.73 .64 .528 -.20 .39 Nurse Registration -.25 .11 536.79 -2.30 .022 -.46 -.04 Estimates of covariance parametersa Residual .55 .03 16.23 .001 .48 .62 INTERCEPT (subject variance) .07 .03 2.64 .008 .03 .15 aDependent variable = Satisfaction

The interaction effects were explored further in a three-way interaction. Three-way interactions

between hours of operation, leadership, and both alternative measures (i.e., raw span and time in

staff contact) were tested. The three-way interaction term was statistically significant for the raw

span model only (Table 20). Data are not shown for the time in staff contact model.

Table 20. Raw Span with Three-Way Interaction for Extended Hours of Operation. Intercepts-as-Outcomes Model for Satisfaction

95% Confidence Interval

Parameter Estimate SE df t Wald Z Significance Lower Bound

Upper Bound

Estimates of fixed effectsa Intercept 3.80 .07 29.16 56.49 .001 3.66 3.94 Raw Span 2.33x10-3 2.72x10-3 32.85 .86 .398 -3.21x10-3 7.88x10-3 Leadership .25 .08 29.68 3.00 .005 .08 .42 Hours of Operation (extended) .16 .11 29.41 1.43 .163 -.07 .40 Raw Span * Leadership 1.66x10-3 3.11x10-3 32.21 .53 .599 -4.69x10-3 8.00x10-3 Raw Span * Hours of Operation -6.31x10-3 3.42x10-3 31.65 -1.85 .074 -.01 6.56x10-4 Leadership * Hours of Operation -.28 .15 25.58 -1.84 .077 -.59 .03 Raw Span * Leadership * Hours

of Operation -.01 4.54x10-3 27.28 -2.39 .024 -.02 -1.56x10-3

Nurse Registration -.24 .11 517.10 -2.22 .027 -.45 -.03 Estimates of covariance parametersa Residual .55 .03 16.20 .001 .48 .62 INTERCEPT (subject variance) .05 .02 2.24 .025 .02 .12 aDependent variable = Satisfaction

To account for multiple comparisons (i.e., between raw span and time in staff contact), the Holm

procedure was used to determine a more stringent alpha level (Table 21; Norman & Streiner,

2000) for the significant three-way interaction. The Holm procedure applies a graduated

correction based on the number of tests. The smallest p value is compared to the most stringent

90

alpha level (α/total number of tests), the next larger p value is compared to critical value

factoring in the total number of tests less one (α/(total number of tests-1)), etc. The observed p

value for the three-way interaction between raw span, hours of operation, and leadership was

below the critical number and was thus considered significant.

Table 21. Holm Procedure for Three-Way Interaction for Two Alternative Measures for Satisfaction Model p Levels Calculation Critical Number Corrected p Level Raw Span (Table 22) .024 α/T .025 Significant Time in Staff Contacta .817 α/(T-1) .05 Not Significant Note. α = .05; T = number of tests was two. aModel not presented.

With extended hours of operation as the referent (Table 20), the conditional first order effects for

raw span and hours of operation were not significant. The conditional first order effect of

leadership was significant and positive at mean values for raw span and for extended hours of

operation. The conditional two-way interaction effects were not significant. The three predictor

interaction was casted into a series of simple regression equations to plot the interaction, using

values one standard deviation above and below the mean (Aiken & West, 1991). Figures 8 and 9

illustrate the two-dimensional view of the three-way interaction for extended hours of operation

versus compressed and mixed hours of operation respectively.

As shown in Figure 8, when managers were assigned extended hours of operation, nurses were

more satisfied under higher leadership in combination with higher raw span. The relationship

between extended hours of operation, leadership, raw span, and supervision satisfaction tended

to be stronger for managers with higher leadership. In other words, when managers were

assigned extended hours of operation, higher leadership enhanced nurse satisfaction with

manager’s supervision and this effect was more pronounced under higher raw spans.

91

3.91

3.353.67

3.30

1.00

2.00

3.00

4.00

5.00

Low (1 SD below = 6.7) High (1 SD above = 8.6)Leadership Practices

Sat

isfa

ctio

n

High Raw Span (1 SD above = 115.5)Low Raw Span (1 SD below = 53.2)

Figure 8. Plot of supervision satisfaction on raw span and leadership for extended hours of operation.

With mixed and compressed hours of operation as the referent (Table 22), none of the

conditional first order effects for raw span, leadership, and hours of operation were significant.

Of the conditional two-way interactions, only raw span by leadership was significant.

Table 22. Raw Span with Three-Way Interaction for Compressed and Mixed Hours of Operation. Intercepts-as-Outcomes Model for Satisfaction

95% Confidence Interval

Parameter Estimate SE df t Wald Z Significance Lower Bound

Upper Bound

Estimates of fixed effectsa Intercept 3.96 .09 29.35 42.97 .001 3.77 4.15 Raw Span -3.97x10-3 2.08x10-3 30.59 -1.91 .066 -8.21x10-3 2.71x10-4 Leadership -.03 .13 24.15 -.24 .816 -.29 .23 Hours of Operation (compressed

& mixed) -.16 .11 29.41 -1.43 .163 -.40 .07

Raw Span * Leadership -9.21x10-3 3.30x10-3 23.77 -2.79 .010 -.02 -2.39x10-3 Raw Span * Hours of Operation 6.31x10-3 3.41x10-3 31.65 1.85 .074 -6.56x10-4 .01 Leadership * Hours of Operation .28 .15 25.58 1.84 .077 -.03 .59 Raw Span * Leadership * Hours

of Operation .01 4.54x10-3 27.28 2.39 .024 1.56x10-3 .02

Nurse Registration -.24 .11 517.10 -2.22 .027 -.45 -.03 Estimates of covariance parametersa Residual .55 .03 16.20 .001 .48 .62 INTERCEPT (subject variance) .05 .02 2.24 .025 .02 .12 aDependent variable = Satisfaction

As shown in Figure 9, when managers were assigned compressed and mixed hours of operation,

nurse supervision satisfaction varied by raw span and leadership. Under low raw spans,

supervision satisfaction was higher with higher leadership. Under high raw spans, supervision

92

satisfaction was lower with higher leadership. In other words, no matter how highly

transformational the leadership style, when managers were assigned compressed and mixed

hours of operation, they could not overcome high raw spans to positively influence nurse

satisfaction with manager’s supervision.

3.10

4.294.00

3.51

1.00

2.00

3.00

4.00

5.00

Low (1 SD below = 6.6) High (1 SD above = 8.6)Leadership Practices

Sat

isfa

ctio

n

High Raw Span (1 SD above = 134.5)Low Raw Span (1 SD below = 45.7)

Figure 9. Plot of supervision satisfaction on raw span and leadership for compressed and mixed hours of operation.

In summary for Objective 2, the three-way interaction for supervision satisfaction was significant

with raw span, but not with time in staff contact.

Objective 3: Model Explaining Most Variation in Satisfaction

A summary of the models with the alternative measures of managerial span is presented in Table

23. Of the alternative measures of managerial span, only raw span in a three-way interaction with

leadership and hours of operation (Table 20) explained significant variation in supervision

satisfaction. Compared to the unconditional model (Table 11), this model reduced the between-

manager variance by ([.121979- .048062]/.121979) = .606, indicating that the model explained

60.6% of the variation in supervision satisfaction between managers. For this final model, the

residuals were normally distributed across level-1 and level-2 units and level-1 residuals were

fairly homogeneous across level-2 units.

93

Table 23. Summary of Between-Manager Variance Explained in Satisfaction Models with Alternative Span Measures Objectives & Terms (Table) Variance Explained by Model Main Effects Raw Span (16) NS Time in Staff Contact (17) NS Three-Way Interactions Raw Span x Leadership Practices x Hours of Operation (18) 60.6% Time in Staff Contact x Leadership Practices x Hours of Operationa NS Note. NS = Alternative measure was not statistically significant in the model. aModel not presented.

In summary for Objective 4, raw span interacted with leadership and hours of operation to

explain the most variation in supervision satisfaction between managers. Time in staff contact

did not explain significant variation in supervision satisfaction between managers.

Teamwork Findings

Objective 1: Main Effects for Teamwork

The analytical steps used for the supervision satisfaction models were repeated for the teamwork

models. In Step 1, a one-way ANOVA with random effects (i.e., unconditional means model)

was conducted to determine how much variability in teamwork scores existed between and

within managers and the proportion of total variance residing between groups. As shown in

Table 24, the teamwork grand mean was 3.92. Variance components suggested significant

variability at the between-manager (.03) and within-manager (.25) levels.

Table 24. One-Way Analysis of Variance Model for Teamwork

95% Confidence Interval

Parameter Estimate SE df t Wald Z Significance Lower Bound

Upper Bound

Estimates of fixed effectsa INTERCEPT 3.92 0.04 30.5 107.93 .001 3.85 4.00 Estimates of covariance parametersa Residual 0.25 0.01 19.04 .001 0.22 0.27 INTERCEPT (subject variance) 0.03 0.01 2.86 .004 0.01 0.06 aDependent variable = Teamwork

A modest range of plausible values for the teamwork means among managers was observed with

95% of the means falling between 3.59 and 4.26. Using the variance components, the intraclass

correlation coefficient was computed as (.028856/[.028856 + .247929]) = .10, indicating that

94

10% of total teamwork score variability occurred between managers. On average, the sample

means were fairly reliable as indicators of the true manager means λ hat = .73.

In Step 2, a random-coefficient regression model was conducted. At level-1 (the individual team

member model), the teamwork score for each team member under a given manager was

regressed on occupational group and full-time status (data not shown). Because teamwork scores

varied by occupational group and by full-time status of the respondents, these variables were

retained as control variables in subsequent models (i.e., the level-1 slopes were fixed to be

invariant across level-2 models; Table 25). The inclusion of occupational group and full-time

status reduced the within-manager variance by 7.4% compared to the unconditional model.

Table 25. Fixed-coefficient Regression Model Level-1 for Teamwork 95% Confidence Interval

Parameter Estimate SE df t Wald Z Significance Lower Bound

Upper Bound

Estimates of fixed effectsa INTERCEPT 3.9 .04 30.72 104.86 .001 3.85 4.00 Full-Time Status -.14 .05 741.85 -2.95 .003 -.23 -.05 Occupational Group -.20 .03 750.25 -7.06 .001 -.25 -.14 Estimates of covariance parametersa Residual .23 .01 19.04 .001 .21 .25 INTERCEPT (subject variance) .03 .01 3.00 .003 .02 .06 aDependent variable = Teamwork

Full-time respondents perceived lower levels of teamwork than their part-time and casual

counterparts. A one-way analysis of variance was conducted to evaluate the relationship between

occupational group and teamwork. The occupational group factor included three levels: nurses,

other regulated health care providers, and unregulated care providers. The ANOVA was

significant, F(2, 751) = 18.64, p < .001. The strength of the relationship between occupational

groups and teamwork was small as assessed by η2 with the occupational group factor accounting

for 4.7% of the variance in teamwork. Follow up tests using Dunnett’s C test were conducted to

evaluate pairwise differences among the means. There were no significant differences between

other regulated health professionals and unregulated care providers. However, nurses differed

significantly from other regulated health professionals and from unregulated care providers.

Nurses reported significantly higher levels of teamwork in comparison to the other occupational

groups (Table 26). Both occupational group and full-time status were retained as level-1

covariates in subsequent models.

95

Table 26. 95% Confidence Intervals of Pairwise Differences in Mean Teamwork Scores Occupational Group M SD Nursing Other Regulated Health Professional Nursing 3.97 .50 Other Regulated Health Professional 3.73 .45 .13 to .35* Unregulated Care Provider 3.69 .69 .10 to .47* -.16 to .25 Note. An asterisk indicates that the 95% confidence interval does not contain zero, and therefore the difference in means is significant at the .05 significance using Dunnett’s C procedure.

In Step 3, intercepts-as-outcome models were conducted to determine whether the level-1

intercept varied in relation to level-2 predictors. Fixed level-2 raw span and time in staff contact

predictors were added and examined in separate models; the level-1 model remained the same.

Predictors were held constant and centered on the grand mean. Proposed covariates were

examined in separate models. Following the step-up strategy recommended by Raudenbush and

Bryk (2002), nine manager level covariates were tested separately and in combination with span

and time in staff contact. The manager level covariates were: hours of operation, leadership,

experience, worked hours, total areas, clinical support roles, occupational diversity, employee

tenure, full-time employment, and non-direct reports. Raudenbush and Bryk recommend that

level-2 predictors with small estimated effects and t ratios near or less than one be excluded. On

this basis, experience, worked hours, occupational diversity, employee tenure, and full-time

employment variables were excluded. Five level-2 covariates consistently explained significant

variation in teamwork and were retained (Tables 27 and 28).

As shown in Table 27, raw span had no main effect on teamwork. Total areas was negatively

associated with teamwork. Teamwork was positively associated with leadership and with

compressed and mixed hours of operation. Clinical support roles and non-direct reports had no

significant main effects on teamwork in this model.

96

Table 27. Raw Span. Level-2 Covariate Model Parameter Estimates for Teamwork 95% Confidence Interval

Parameter Estimate SE df t Wald Z Significance Lower Bound

Upper Bound

Estimates of fixed effectsa INTERCEPT 3.82 .05 33.15 75.84 .001 3.72 3.92 Raw Span -2.99x10-4 1.51x10-3 33.45 -.20 .844 -3.36x10-3 2.76x10-3 Leadership .08 .04 37.94 2.17 .037 5.28x10-3 .15 Hours of Operation (extended) .17 .08 33.28 2.10 .044 5.36x10-3 .34 Total Areas -.08 .03 33.68 -2.32 .026 -.15 -9.92x10-3 Clinical Support Roles .04 .02 33.32 1.84 .074 -4.61x10-3 .09 Non-Direct Reports -3.40x10-3 1.80x10-3 31.29 -1.89 .068 -7.07x10-3 2.73 x10-4 Full-Time Status -.14 .05 745.43 -3.00 .003 -.23 -.05 Occupational Group -.20 .03 746.42 -7.02 .001 -.25 -.14 Estimates of covariance parametersa Residual .23 .01 19.09 .001 .21 .25 INTERCEPT (subject variance) .02 6.70x10-3 2.73 .006 8.93x10-3 .04 aDependent variable = Teamwork

As shown in Table 28, time in staff contact had no main effect on teamwork. Total areas and

non-direct reports were negatively associated with teamwork. Teamwork was positively

associated with leadership, compressed and mixed hours of operation, and clinical support roles.

Table 28. Time in Staff Contact. Level-2 Covariate Model Parameter Estimates for Teamwork

95% Confidence Interval

Parameter Estimate SE df t Wald Z Significance Lower Bound

Upper Bound

Estimates of fixed effectsa INTERCEPT 3.82 .05 33.48 77.72 .001 3.72 3.92 Time in Staff Contact -.01 .02 32.81 -.58 .565 -.06 .03 Leadership .08 .03 38.10 2.32 .026 .01 .15 Hours of Operation (extended) .18 .08 34.71 2.24 .031 .02 .35 Total Areas -.08 .03 34.56 -2.31 .027 -.15 -9.32x10-3 Clinical Support Roles .04 .02 33.95 2.29 .028 4.98x10-3 .08 Non-Direct Reports -3.54x10-

3 1.63x10-3 31.49 -2.17 .038 -6.87x10-3 -2.13 x10-4

Full-Time Status -.14 .05 745.80 -3.01 .003 -.23 -.05 Occupational Group -.20 .03 742.84 -7.00 .001 -.25 -.14 Estimates of covariance parametersa Residual .23 .01 19.09 .001 .21 .25 INTERCEPT (subject variance) .02 6.63x10-3 2.72 .007 8.76x10-3 .04 aDependent variable = Teamwork

In summary for Objective 1, main effects on teamwork were observed for leadership and hours

of operation, but not for raw span and time in staff contact.

Objective 2: Interaction Effects for Teamwork

Following the procedure outlined by Aiken and West (1991), the two-way interactions were first

examined in a combined model for each alternative measure of managerial span. None of the

97

two-way interaction terms in the raw span model was significant (data not shown). For time in

staff contact, the two-way interaction for time in staff contact by extended hours of operation

was conditional on the first order effect of leadership at mean values for raw span and for

extended hours of operation (Table 29).

Table 29. Time in Staff Contact with Two-Way Interactions for Extended Hours of Operation. Level-2 Covariate Model Parameter Estimates for Teamwork

95% Confidence Interval

Parameter Estimate SE df t Wald Z Significance Lower Bound

Upper Bound

Estimates of fixed effectsa INTERCEPT 3.90 .04 35.58 92.61 .001 3.82 3.99 Time in Staff Contact .045 .03 33.66 1.29 .206 -.03 .11 Leadership .12 .05 36.35 2.40 .022 .02 .22 Hours of Operation (extended) .07 .07 32.74 1.02 .314 -.07 .21 Time in Staff Contact *

Leadership .07 .04 32.02 1.51 .142 -.02 .16

Time in Staff Contact * Hours of Operation

-.13 .05 31.11 -2.41 .022 -.24 -.02

Leadership * Hours of Operation

-.14 .08 34.01 -1.76 .088 -.30 .02

Full-Time Status -.14 .05 743.13 -2.98 .003 -.23 -.05 Occupational Group -.20 .03 744.61 -7.01 .001 -.25 -.14 Estimates of covariance parametersa Residual .23 .01 19.06 .001 .21 .25 INTERCEPT (subject variance) .02 .01 2.74 .006 .01 .04 aDependent variable = Teamwork

However with compressed and mixed hours of operation as the referent (Table 30), the

significant two-way interaction for raw span by compressed and mixed hours of operation was

conditional on the first order effect of time in staff contact. These findings were explored further

in a three-way interaction.

98

Table 30. Time in Staff Contact with Two-Way Interactions for Compressed and Mixed Hours of Operation. Level-2 Covariate Model Parameter Estimates for Teamwork

95% Confidence Interval

Parameter Estimate SE df t Wald Z Significance Lower Bound

Upper Bound

Estimates of fixed effectsa INTERCEPT 3.97 .05 30.60 74.16 .001 3.87 4.08 Time in Staff Contact -.08 .04 29.61 -2.24 .033 -.16 -7.47x10-3 Leadership -.02 .06 32.60 -.39 .701 -.14 .09 Hours of Operation (compressed

or mixed) -.07 .07 32.74 -1.02 .314 -.21 .07

Time in Staff Contact * Leadership

.07 .04 32.02 1.51 .142 -.02 .16

Time in Staff Contact * Hours of Operation

.13 .05 31.11 2.41 .022 .02 .24

Leadership * Hours of Operation .14 .08 34.01 1.76 .088 -.02 .30 Full-Time Status -.14 .05 743.13 -2.98 .003 -.23 -.05 Occupational Group -.20 .03 744.61 -7.01 .001 -.25 -.14 Estimates of covariance parametersa Residual .23 .01 19.06 .001 .21 .25 INTERCEPT (subject variance) .02 7.64x10-3 2.74 .006 .01 .04 aDependent variable = Teamwork

The three-way interactions between each alternative measure (i.e., raw span and time in staff

contact) and hours of operation and leadership were examined. The three-way interaction term

was not significant in either model (data not shown). In summary for Objective 2, the three-way

interactions for raw span and for time in staff contact were not significant for teamwork.

Objective 3: Model Explaining Most Variation in Teamwork

A summary of the models with the alternative measures is presented in Table 31. Of the

alternative measures of managerial span, neither raw span nor time in staff contact explained

between-manager variation in teamwork.

Table 31. Summary of Between-Manager Variance Explained in Teamwork Models with Alternative Measures Objectives & Key Terms (Table) Variance Explained Main Effects Raw Span (29) NS Time in Staff Contact (30) NS Three-Way Interactions Raw Span x Hours of Operation x Leadership Practicesa NS Time in Staff Contact x Hours of Operation x Leadership Practicesa NS Note. NS = Alternative measure was not statistically significant in the model. aModel not presented.

However, other level-2 manager variables explained significant variation in teamwork between

managers. As shown in Table 32, total areas and non-direct reports were negatively associated

with teamwork. Leadership, compressed and mixed hours of operation, and clinical support roles

99

were positively associated with teamwork. Compared to the unconditional model (Table 24), this

model reduced the between-manager variance by ([.028856 - .018342]/.028856) = .364,

indicating that the model explained 36.4% of the variation in teamwork between managers. For

this final model, the residuals were normally distributed across level-1 and level-2 units and the

level-1 residuals were homogeneous across level-2 units.

Table 32. Level-2 Covariate Model Parameter Estimates for Teamwork

95% Confidence Interval

Parameter Estimate SE df t Wald Z Significance Lower Bound

Upper Bound

Estimates of fixed effectsa INTERCEPT 3.82 .05 33.51 77.27 .001 3.72 3.92 Leadership .08 .03 38.08 2.24 .031 7.46x10-3 .15 Hours of Operation (extended) .18 .08 34.64 2.20 .035 .01 .34 Total Areas -.08 .03 33.92 -2.44 .020 -.15 -.01 Clinical Support Roles .04 .02 34.60 2.21 .034 3.25x10-3 .08 Non-Direct Reports -3.54x10-3 1.64x10-3 31.56 -2.16 .039 -6.89x10-3 -1.94x10-4 Full-Time Status -.14 .05 745.66 -3.00 .003 -.23 -.05 Occupational Group -.20 .03 742.87 -7.02 .001 -.25 -.14 Estimates of covariance parametersa Residual .23 .01 19.09 .001 .21 .25 INTERCEPT (subject variance) .02 6.71 x10-3 2.73 .006 8.96x10-3 .04 aDependent variable = Teamwork

In summary for Objective 4, of the alternative measures of managerial span, neither raw span nor

time in staff contact explained significant variation in teamwork. However, leadership, hours of

operation, and other level-2 covariates (i.e., total areas, clinical support roles, and non-direct

reports) explained significant variation in teamwork.

100

Chapter 5: Discussion of Findings

The purpose of the study was to examine the influence of alternative measures of managerial

span (i.e., raw span and time in staff contact) on nurse and team outcomes in the hospital sector.

Raw span interacted with leadership and hours of operation to explain significant variation in

supervision satisfaction between managers. Time in staff contact and most other manager level

covariates, with the exception of leadership, did not explain variation in supervision satisfaction.

Neither span nor time in staff contact explained variation in teamwork. Leadership, hours of

operation, total areas, clinical support roles, and non-direct reports explained variation in

teamwork between managers. In this chapter, the study findings are discussed in relation to

previous research and literature. Theoretical implications for boundary spanning and the

strengths and limitations of the study are also considered.

Descriptive Findings

Manager Sample

The study sample was compared to other published samples of first-line nurse managers to

determine the extent to which it reflected the population of interest. Difficulties in recruiting

managers may have been associated with self-selection biases. In terms of manager

characteristics, the sample was of similar age and gender but had less position tenure, less

management experience, and more graduate level education compared to first-line nurse

managers in academic and community hospitals in recent studies in Ontario and Canada (Table

33). Urban versus rural location of hospitals was not reported in these comparative studies. The

participating hospitals in this thesis were located in a large urban city. Higher levels of graduate

education in the sample could reflect increasing organizational support for or demand for more

educated managers, or possibly greater access to education programs in urban areas.

Alternatively, more highly educated managers may be more willing to participate in research.

101

Table 33. Characteristics of Study Managers Compared to Other Studies Sample Characteristic Thesis Sample McCutcheon et al. (2009) Laschinger et al. (2008)

Region Ontario Ontario Canada N 31 41 780

Manager Characteristic Mean (%) Age 46 45 47

Female (94%) n/a (95%) Position Tenurea 3 5 7

Management Experience 7 10 11 Registered Nurse Diploma (16%) (22%) (11%)

Undergraduate Degree (45%) (51%) (71%) Graduate Degree (39%) (27%) (17%)

Note. n/a = unavailable; a. For McCutcheon et al. = unit experience; For Laschinger et al. = role experience

Comparisons with other studies also indicate that managers in this sample tended to be assigned

wider raw spans, more varied hours of operation, greater administrative and clinical support

roles, and greater diversity in the number of job titles reporting to their position. Specifically, the

mean (86.6) and median (78) raw spans in this study were slightly higher than other reported

Ontario means of 77 (n = 40; McCutcheon et al., 2009) and 77.5 (n = 16; Lucas et al., 2008; H.

K. S. Laschinger, personal communication, March 16, 2009), as well as the Ontario median

value of 70 (Laschinger et al., 2008). The raw span values were substantially higher than those

sampled by McGillis Hall et al. (2006) where 81% (n = 13) of acute care managers had 40-59

direct reports. The median span value in this study was also higher than those reported for

Western Canada (65), Atlantic Canada (55), and Quebec (63; Laschinger et al.).

A higher proportion of managers in the sample (38.7%) were assigned compressed or mixed

hours of operation compared to 10% (Doran et al., 2004) and 2% (Lucas et al., 2008) in other

Ontario studies. Overall, 74% of participating managers had three or more administrative and

clinical support full-time equivalent positions combined, which was higher than the 47%

reported by McCutcheon et al. (2009). Likewise, 77% of study managers had more than 6 job

titles reporting directly to their position compared to 60% in McCutcheon et al.’s Ontario study.

Finally, managers worked an average of 8.9 hours per weekday which was similar to the mean

9.0 hours (n = 10) reported by Arman et al. (2009) in a Swedish study. No other reports of

worked hours by managers were located.

Overall the study sample differed in terms of managerial experience, tenure, and education.

Participating managers also tended to be assigned higher raw spans, greater numbers of support

102

roles and job categories as well as more varied hours of operation. Of these variables, only raw

span, hours of operation, and support roles explained variation in the study outcomes.

Outcomes

In order to contextualize the findings, the mean supervision satisfaction and teamwork scores

were compared to findings in the literature. Out of a possible range of 1 to 5, the mean

supervision satisfaction score in this study was 3.82 (SD = 0.8). This was consistent with the

mean score of 3.8 (SD = 0.8) reported in Ward’s (2002) study of direct care providers in mental

health settings. The study scores for supervision satisfaction are somewhat higher than mean

scores of 3.5 and 3.6 for a computer company (Keller & Dansereau, 1995), multinational firm

(Vandenberg & Nelson, 1999), and county government (Jones et al., 1999). These comparisons

suggest that supervision satisfaction may be higher among health care workers than workers in

other industries.

For teamwork, the average relational coordination score in this study was 3.9 (SD = 0.5) out of a

possible range of 1 to 5. This score was similar to the mean of 4.0 (SD = 0.5) reported by Gittell

et al. (2000) in a multi-site study of acute care health care professionals. This is higher than mean

relational coordination scores reported between formal health care providers and informal

caregivers for postsurgical knee replacement patients (M = 2.8 on a 5 point scale; Weinberg,

Lusenhop, Gittell & Kautz, 2007), nursing home staff (M = 2.0 on a 4 point scale; Gittell,

Weingberg, Pfefferle & Bishop, 2008) and airline staff (M = 3.0 on a 5 point scale; Gittell,

2000). These comparisons indicate that relational coordination may be higher in acute care

hospital settings and that teamwork ratings in this study were comparable to those of other acute

care hospital staff.

The study framework examined main and interaction effects among raw span, time in staff

contact, leadership, and hours of operation on supervision satisfaction and on teamwork. The

study findings provide partial support for the proposed relationships which are discussed below.

Raw Span and Outcomes

Raw span did not have main effects on supervision satisfaction or on teamwork. The main effect

result specific to raw span and satisfaction with supervision is consistent with McCutcheon

103

(2004), Doran et al. (2004) and McCutcheon et al. (2009) who did not observe a direct

relationship between raw span and nurses’ job satisfaction in acute care hospitals. However,

McGillis Hall et al. (2006) found that nurses’ in acute care were more satisfied with the quality

of their work and working conditions when first-line unit managers had narrower raw spans.

Because the direct report supervisory relationship is fundamental to the employment contract,

managers must attend to each direct report staff regardless of their raw span. Thus satisfaction

with supervision may be less likely to be influenced by the total number of direct reports

assigned to a manager. Alternatively, the relationship between span and supervision satisfaction

may be more complex and this is discussed in the next section on interaction effects.

Raw span was also not a significant predictor of teamwork. This finding differs from Gittell

(2001) who documented lower levels of teamwork with increasing numbers of full-time

equivalent staff per supervisor in the airline industry. However, in acute care hospitals settings,

the team frequently includes significant numbers of non-direct report team members (e.g.,

physicians, allied health professionals). Because the unit-level of analysis was not examined in

the models, the influence of work group size (i.e., the number of direct and non-direct report

team members in an area) on teamwork in the presence of wide raw spans was not tested.

However, the influence of non-direct reports under the manager is considered in the section on

covariates and outcomes.

Time in Staff Contact and Outcomes

Time in staff contact did not have main effects on satisfaction with supervision or on teamwork.

Ouchi and Dowling’s (1974) proposition that time allocation to staff contact is a more sensitive

predictor (than raw span) of staff outcomes was not supported in the sample studied.

Observations of managerial work flow in the acute care hospital setting revealed that time in

staff contact varies relative to the hours of operation assigned to the manager which influence the

density of staff on the manager’s workday and the manager’s coverage of the service hours.

Potential interaction effects among time allocation, leadership, and hours of operation were

examined and did not explain variation in outcomes. No comparative studies were found that

examined the relationship between time in staff contact and satisfaction or teamwork.

Leadership and Outcomes

104

Leadership had positive main effects on supervision satisfaction and on teamwork. As boundary

spanners, when managers usually engaged in transformational leadership practices, nurses were

more likely to be satisfied with their manager’s supervision and team members were more likely

to rate teamwork highly. These findings are consistent with recent research on the relationship

between leadership and nurses’ job satisfaction (Duffield et al., 2007; Hall, 2007; McCutcheon et

al., 2009; McGillis Hall & Doran, 2007; McGilton et al., 2007), nurses’ psychological

empowerment (Laschinger, Finegan & Wilk, 2009), and nurses’ ability to work to full scope of

practice (Oelke et al., 2008) as well as between leadership and teamwork (Gittell, 2001;

Oandasan et al., 2006). Transformational leadership practices enable managers to establish

personal connections and build trust with staff, to support professional autonomy, and to

strengthen interdependent work processes among team members.

Hours of Operation and Outcomes

Hours of operation had no main effect on supervision satisfaction, but were associated with

teamwork. In this study, hours of operation were classified as extended or as compressed and

mixed. Comparable studies examining the relationship between hours of operation and nurse

satisfaction were not located. Teamwork was lower when managers were assigned extended

hours of operation (i.e., 24 hours, 7 days a week). No comparative studies examining the

relationship between hours of operation and teamwork were found. Two alternative explanations

for the teamwork finding are explored. First, a manager covers much less of the serviced hours

when hours of operation are extended. The manager’s availability to staff seeking access to

information, resources, support, or problem-resolution is also constrained by a lower density of

staff during the manager’s workday. In contrast, managers assigned compressed and mixed hours

of operation provide greater coverage because they are more likely to be working at the same

times as staff and staff density is higher during the manager’s workday.

A second alternative explanation is that the lower teamwork scores associated with extended

hours of operation may reflect more challenging internal team work conditions. Team members

are spread out across differing and rotating shifts resulting in temporal and physical dislocation.

This may increase the coordination demands across team members and across interdependent

roles, making it more difficult for managers and team members to establish and consistently

105

engage in high quality communication and relationships. With compressed and mixed hours of

operation, team members reported higher teamwork scores. It may be that the benefits of

temporal and physical co-location compensated for higher coordination demands associated with

high volume throughput (e.g., clinic visits, procedures) in these areas, as compared to lower

volume throughput (e.g., in-patients) in areas with extended hours of operation.

Three-Way Interaction Effects

A three-way interaction between raw span, leadership, and hours of operations was found for

supervision satisfaction, but not for teamwork. The significant three-way interaction for

satisfaction with supervision lends partial support to the two-way interactions observed between

raw span and leadership on nurse job satisfaction (McCutcheon et al., 2009) and on nurse

empowerment (Lucas et al., 2008). McCutcheon et al. observed that no matter how highly

transformational the leadership style, managers could not overcome raw spans that were too wide

to positively influence nurse job satisfaction. Similarly, Lucas et al. found that no matter how

emotionally intelligent the leadership style, managers could not overcome raw spans that were

too wide to positively influence nurse empowerment. Consistent with these findings, a two-way

interaction was observed in this study for nurse satisfaction with supervision when managers

were assigned compressed and mixed hours of operation (but not for managers assigned

extended hours of operation). When managers were assigned compressed and mixed hours of

operation, nurse satisfaction with supervision was lower under transformational managers with

wide raw spans. This interaction makes sense because with compressed and mixed hours of

operation, staff density is high relative to the manager’s workday and the manager covers more

of the serviced hours. Two alternative explanations are explored. One interpretation is that high

numbers of staff may overwhelm the capacity of even highly transformational managers to

positively influence supervision satisfaction when they are assigned compressed and mixed

hours of operation. That is, no matter how highly transformational the leadership style, when

managers were assigned compressed and mixed hours of operation, they could not overcome raw

spans that were too wide to positively influence nurse satisfaction with supervision.

However, this interaction also suggests that some nurses were more satisfied with supervision

when managers assigned compressed and mixed hours of operation had less transformational

106

leadership styles in combination with wide spans. An alternative interpretation is that under wide

spans, managers could have engaged in other types of behaviors (e.g., more task-oriented

leadership) to resolve structural work process and relational issues that impeded nurses’

workflow, hence enhancing nurses’ satisfaction with supervision. Managers assigned

compressed and mixed hours of operation often oversaw areas characterized by high volume

throughput (e.g., clinic visits, procedures). Shorter and frequent work cycles intensify

coordination demands between work roles and groups. Nurses may have been more satisfied

with supervision by managers who were more focused on tasks or who managed by exception,

which could have in turn, enhanced nurses’ workflow and productivity.

An important consideration is that the Leadership Practices Inventory was initially developed in

the context of managers’ best experiences leading projects, not managing high intensity

operations. A possible conjecture is that leadership styles other than transformational leadership

may be effective under these conditions. In their meta-analysis, Mullen, Symons, Hu, and Salas

(1989) noted that increases in either consideration (person-focused) or initiating structure (task-

focused) leadership behaviors were associated with high staff job satisfaction. It may be that

task-oriented behaviors, rather than the person-oriented behaviors associated with

transformational leadership, were conducive to meeting nurses’ needs for supervision in these

settings when managers had wide raw spans.

However, this interpretation is not consistent with McCutcheon’s (2004) finding that the positive

effect of transactional leadership (i.e., more task-focused orientation) on job satisfaction was

lessened under wide spans. Of interest however, is that although McCutcheon (2004) also

observed the overall negative influence of management-by-exception leadership on job

satisfaction, in some cases, the negative relationship was attenuated under wider raw spans. It

may be that in fast paced environments, managing by exception enabled nurses to maintain high

volume throughput or, that managers delegated authority thus enhancing nurse autonomy and

supervision satisfaction. These conjectures would require further inquiry.

In contrast, the three-way interaction with regard to extended hours of operation indicated that

more transformational leadership was associated with higher nurse supervision satisfaction in

combination with wider raw spans. More transformational leadership enhanced nurse satisfaction

107

with manager’s supervision and this effect was more pronounced under higher raw spans. This

finding is similar to a study in the banking industry by Schriesheim et al. (2000) whereby higher

supervisor ratings of leader-member exchange were associated with higher staff organizational

commitment under wider raw spans. However, the thesis result differs from the constraining

effect of wide raw spans that has been observed on leadership and nurse job satisfaction

(McCutcheon et al., 2009) and on leadership and nurse empowerment (Lucas et al., 2008).

Managers in these 2 studies were predominantly assigned extended hours of operation in hospital

settings. Similarly, McGillis Hall et al. (2006) reported less nurse satisfaction with the work

environment with wider raw spans.

A possible explanation for this contrary thesis finding is that the outcomes of nurse job

satisfaction, empowerment, and work environment satisfaction reflect broader constructs than

nurse satisfaction with manager’s supervision. That is, job satisfaction, empowerment, and work

environment satisfaction represent multi-faceted aspects of nurses’ work experiences. In contrast,

nurse supervision satisfaction is a narrower construct focused on the direct-report relationship

which is enhanced by higher staff density under extended hours of operation. This is consistent

with Blau (1968) who theorized that access to managerial support for resolution of complex

work problems is a central underlying mechanism of an effective manager-staff reporting

structure, especially when employees are skilled professionals. However, this study finding is

inconsistent with previous nursing studies and warrants future investigation and replication. This

finding also suggests that trade-offs may exist in the staff outcomes associated with wide versus

narrow raw spans (i.e., some outcomes may improve under wider raw spans).

Covariates and Outcomes

No other covariates were significant predictors of nurse satisfaction with manager’s supervision.

Teamwork however was significantly associated with other manager-level covariates, namely

clinical support roles, total areas, and non-direct reports. Each of these is discussed below.

A greater number of full-time equivalent clinical support roles working in the manager’s

assigned area(s) was significantly associated with higher teamwork scores (i.e., more frequent,

timely, accurate, and problem-solving communication and greater likelihood of shared goals,

shared knowledge, and mutual respect). Examples of clinical support roles in this study included

108

permanent team leaders, advanced practice nurses, and case managers. These roles enact

boundary spanning functions that create linkages among people and that move across functional,

professional, spatial, and organizational boundaries. For instance, permanent team leaders and

advanced practice nurses (e.g., nurse practitioners, clinical nurse educators) frequently liaise with

physicians, families, allied health professionals, and nursing staff to coordinate clinical care and

to educate team members about new clinical advances and organizational directives. Case

managers establish and maintain relationships, exchange information, and negotiate resources

with parties internal and external to the organization to facilitate patient care across occupations,

services, sectors, funding agencies, and locations. The study finding is consistent with emerging

research suggesting a positive association between clinical support roles and team processes. For

example, specialist nursing support or nurse educators or clinical leader roles have been

associated with fewer interventions left undone or delayed, fewer adverse events on units

(Duffield et al., 2007), more nurses surviving the first year on the nursing unit (McCutcheon,

2004), and improved patient and physician satisfaction with nursing care (Smith, Manfredi,

Hagos, Drummond-Huth & Moore, 2006). Case manager roles have been positively associated

with enhanced physician collaboration and satisfaction with nursing care, care outcomes, access

to resources, inter-professional relationships, and teamwork (Gittell, 2002a; Reimanis, Cohen &

Redman, 2001). Thus clinical support roles have the potential to enhance teamwork in acute care

settings by virtue of enacting boundary spanning functions across functional, spatial, and sub-

system boundaries.

In addition, the greater the number of units, clinics, and services assigned to the manager, the

lower the level of teamwork under that manager. Studies examining the scope of responsibility

assigned to managers in relation to outcomes were not located. This study finding supports

Gittell’s (2004) contention that focus in working relationships can enhance performance, as well

as Meier and Bohte’s (2003) proposition that variation in workplace technologies increases

demands on managers. Multiple assigned areas divide the manager’s focus among production

subsystems with varied relational dynamics, work content, work processes, and physical

locations. These represent increased functional and spatial boundaries that the manager must

negotiate. Under these conditions, managers may find it more challenging to foster shared norms

and values (Katz & Kahn, 1978) and high quality communication and relationships with and

among staff (Gittell, 2003).

109

The higher the number of non-direct reports working in the areas assigned to the manager, the

lower the level of teamwork under that manager. This suggests that work functions,

relationships, and information communication were more effectively integrated by managers

when fewer non-direct reports were involved in interdependent work processes. Direct report

relationships enable managers to supervise and coordinate the work performed in assigned areas.

Managers can assign or delegate responsibility for work performance to staff, as well as coach

staff in the performance of their role. Direct report staff members are, in turn, liable for work

performance and accountable to the manager (Jaques, 1990). Non-direct reports engendered

functional and spatial boundaries for the managers in this study as these workers often

represented distinct professional groups (e.g., social work, occupational therapy, medicine) and

were not likely to be continuously co-located in the production subsystem. Physical co-location

can foster spontaneous interactions and relationships across functional and professional divides,

resulting in knowledge sharing, problem-solving, and innovation (Galbraith, Downey & Kates,

2002). As boundary spanners, fostering shared norms and values (Katz & Kahn, 1978), building

high quality relationships amongst team members, and communicating information (Ancona &

Caldwell, 1992; Gittell, 2003; Tushman & Scanlan, 1981) may be more difficult when managers

are required to negotiate relationships with increasing numbers of non-direct reports. Although

no directly comparable studies were located, the finding is consistent with previous health care

studies which found inverse associations between work group size and work responsibility,

problem solving, goal setting, and conflict resolution (Stahelski & Tsukuda, 1990) as well as

increased intent to quit and lower morale (Burke, 1996), less employee engagement (Cathcart et

al., 2004), declines in organizational commitment (Green et al., 1996), and less satisfaction

(Burke, 1996; Green et al.; Mullen et al., 1989).

Implications for Boundary Spanning

Many authors have theorized that the personal characteristics and activities of boundary spanners

can influence their effectiveness across boundaries which occur between interrelated subsystems,

hierarchical levels, functional groups, and spatial divides (Ancona & Caldwell, 1992; Gittell,

2003; Katz & Kahn, 1978; Tushman & Scanlan, 1981). In this study, the capacity of managers to

influence supervision satisfaction and teamwork varied not only in relation to their leadership

110

practices, but also in relation to subsystem, hierarchical, functional, and spatial boundaries.

These boundaries reflect the scope of responsibility assigned to managers by the organization.

In addition, the study results suggest that managers span another type of boundary, namely time.

The purpose of the management subsystem is to coordinate work processes and to integrate

shared norms and values within and across organizational subsystems. As observations of

managerial work flow revealed, these functions were enacted over time. That is, managers

coordinated work processes and maintained relationships with staff across time. This was

particularly evident for managers assigned extended hours of operation. Even though these

managers typically covered only 26% of the weekly hours of operation (relative to their

workweek), they were responsible for work processes and staff at all times (i.e., 24 hours a day,

7 days per week). The study results suggest that time is another type of boundary that managers

must span to ensure organizational functioning. Indeed, temporal boundaries, as measured by

hours of operation, influenced both supervision satisfaction and teamwork. Thus, the personal

attributes of boundary spanners and the types of boundaries encountered by managers remain

theoretically and empirically relevant to future studies of managerial work.

Study Limitations and Strengths

A convenience sample of managers and staff participated voluntarily in the study. Participants

may have differed in significant ways from non-participants. For example, managers in this

study were less tenured, less experienced, and more likely to hold graduate degrees, than their

counterparts in recent Canadian studies. However, these covariates were examined and did not

explain variation in either study outcome. Study results should only be generalized to first-line

managers working in similar settings, namely academic teaching hospitals or large teaching-

affiliated community hospitals in urban centres.

This study was limited by a small manager (i.e., level-2) sample size. Small sample sizes lower

statistical power and increase the likelihood of a Type II error (i.e., the conclusion that a

difference does not exist, when it does; Norman & Streiner, 2000). That is, non-significant

findings could be related to the small number of participating managers. It is possible that

because of the small sample size, time in staff contact did not vary sufficiently across levels of

leadership and categories of hours of operation. At the time this study was proposed, limited

111

information was available for determining sample sizes needed to achieve power in hierarchical

linear models (Raudenbush & Bryk, 2002). Since then, additional guidance about sample sizes

for these models has been advanced. For multilevel models examining fixed effects, Scherbaum

and Ferreter (2009) estimated that, with a sample size of 30 level-2 units, a minimum of 15 level-

1 units would be required to detect a medium effect size with a power of .80. Further they note

that the inclusion of level-1 covariates can increase power. In this thesis, the level-2 units (i.e.,

managers) numbered 31 for supervision satisfaction and 30 for teamwork. The number of level-1

units (i.e., employees) averaged 18 for supervision satisfaction and 25 for teamwork and level-1

covariates were included. Relative to Scherbaum and Ferreter’s (2009) estimates, this suggests

that the sample size was adequate to achieve a power of .80 in this study.

As well, because multiple statistical tests were conducted, the risk of a Type 1 error (i.e., the

conclusion that a difference exists, when it does not) must also be considered (Norman &

Streiner, 2000). Although a more stringent alpha was applied for multiple comparisons, the

three-way interaction effect for supervision satisfaction must be interpreted cautiously as this

interaction may have occurred by chance. On the other hand, the power to detect differences is

lessened with higher order terms in interactions (Aiken & West, 1991) and the three-way

interaction for supervision satisfaction achieved statistical significance. Future replication of this

study with a larger sample size is warranted given the number of covariates and relationships as

well as the higher order interaction effects tested in the analytical models.

Common method bias, which is a source of measurement error, was reduced by collecting

predictor and outcome data from different sources using different methods at different times.

Williams, Cote, and Buckley (1989) cautioned that common method bias may account for up to

25% of shared variance between self-reported predictor and outcome variables. Good reliability

of the Satisfaction with My Supervisor Scale, the Relational Coordination Scale, and the

Leadership Practices Inventory – Other in this sample also enhanced the ability to detect true

differences. The work log data were collected prospectively to avoid recall error and were fairly

reliable in this sample. However, the expectancies of the researcher as the observer could have

influenced inter-observer agreement. Self-report bias, maturation effects, or hypothesis-guessing

could also have occurred among managers, with managers changing their time allocation

patterns or altering their responses.

112

Although some data were collected prospectively, the study design was cross-sectional. Thus

cause and effect relationships cannot be established. Level-of-analysis issues were encountered.

Too few hospitals participated to consider the influence of contextual variables at the hospital

level. As well, unit level covariates were not tested in the hierarchical linear models resulting in

the loss of potentially meaningful unit level variation in the outcomes. For the teamwork

outcome in particular, which is dependent on the members and context of a particular work

group, this loss of information may have reduced the variability in teamwork. Indeed, after

controlling for level-1 covariates, variability in teamwork was higher between units (16%) than

between managers (12%).

Future Knowledge Translation Plan

The goal of public awareness is to broadly and specifically target audiences to inform them of

the issue and the solutions proposed by the research (Shamian et al., 2002). For this doctoral

work, the Canadian Council of Health Services Accreditation represents a key legislative

audience with the potential to publicly regulate management standards (Lomas, 1990).

Administrative audiences such as employers, Boards of Directors, Chief Nursing Officers, and

managers will be targeted as these groups would be responsible for adopting and implementing

policies (Lomas). Finally, linkages with interest groups such as the Academy of Canadian

Executive Nurses, Nursing Leadership Network of Ontario, and nursing and allied health

associations will be explored to find supportive audiences. Key knowledge translation vehicles

will include a two page fact sheet, presentations at conferences and organizations, as well as

academic publications. A summary of the study, as well as recommendations and conclusions,

are presented in the next chapter.

113

Chapter 6: Summary, Recommendations, and Conclusions

Summary

De-layering of management structures is a frequent strategy to reduce costs in healthcare

organizations. As a result, managers remaining in the organization are typically assigned broader

responsibilities, increased work demands, and wider raw spans although evidence is lacking to

support these work redesign strategies. The overall goal of this study was to examine factors

influencing the capacity of first-line managers in acute care hospitals to support front-line staff.

The literature review revealed that studies of managerial work have often over-emphasized the

personal attributes of the manager without due consideration of the organizational context and

the demands placed on managers. Time allocation of managers was identified by Ouchi and

Dowling (1974) as a key factor impeding comparisons of span. Specifically, they argued that

comparisons of raw span within and across organizations have the potential to misrepresent

managerial capacity to support staff because managers may not allocate the same amount of time

to interaction with staff.

The purpose of the study was to examine the influence of alternative measures of managerial

span on nurse and team outcomes in the hospital sector. The two alternative measures of

managerial span were raw span as a measure of the reporting structure and time in staff contact

as a measure of closeness of contact by the manager. The framework was based on Open System

Theory and the boundary spanning functions of managers. The main effects of the alternative

measures of managerial span on supervision satisfaction and teamwork were investigated. The

interaction effects of the alternative measures of managerial span with leadership and hours of

operation on supervision satisfaction and teamwork were also examined.

A descriptive, correlational design comprising cross-sectional and longitudinal components was

used to collect survey and administrative data from employees, managers, and administrative

sources. Managerial time allocation data were collected prospectively through self-logging and

validated through inter-observer agreement. Acute care hospitals were selected through

purposive sampling. For supervision satisfaction, the final sample size was 31 first-line managers

114

and 558 nurses. For teamwork, the final sample size was 30 first-line managers and 754 staff.

The Leadership Practices Inventory, the Satisfaction with my Supervisor Scale, and the

Relational Coordination Scale were used. Hierarchical linear modeling was the main type of

analysis conducted.

Span interacted with leadership and hours of operation to explain 60.6% of the between-manager

variation in nurse satisfaction with supervision. Hours of operation were classified as extended or

compressed and mixed. With extended hours of operation, higher transformational leadership

enhanced supervision satisfaction and this effect was more pronounced under wider spans. With

compressed and mixed hours of operation, supervision satisfaction varied by span and

leadership. When managers were assigned compressed and mixed hours of operation, the

positive effects of transformational leadership on supervision satisfaction were observed under

narrower spans. In other words, no matter how highly transformational their leadership style,

managers assigned compressed and mixed hours of operation could not overcome wide raw

spans to positively influence nurse satisfaction with manager’s supervision. Variation in

teamwork was not explained by raw span or by time in staff contact. Of the between-manager

variation in teamwork, 36.4% was explained by the negative effects of total areas and non-direct

reports and the positive effects of transformational leadership, compressed and mixed hours of

operation, and clinical support roles.

Based on the study results, the following recommendations are advanced related to research,

theory development, and organizational policy and managerial practice.

Recommendations for Research

1. Replication of study. Replication of this study using a larger sample size is warranted to

overcome the error rate problem, to examine interactions among variables, and to adequately

sample managers assigned varied hours of operation.

2. Study other staff outcomes sensitive to managerial coverage and to staff density at the

manager level. Hours of operation was as a key variable influencing the density of staff

relative to the manager’s workday and the coverage provided by the manager relative to the

serviced hours. In areas with extended hours of operation, the study findings suggested that

115

the greater the density of the staff during the manager’s weekday, the more satisfied the

nursing staff is with supervision. However, in areas with compressed and mixed areas of

operation, high staff density (i.e., wide raw spans) impeded highly transformational

managers from positively influencing nursing staff satisfaction with supervision. Given that

the manager is the interface between the nurse and the organization, other outcomes may also

be sensitive to the interaction between hours of operation, raw span, and leadership. Potential

staff outcomes sensitive to managerial coverage and staff density that could be considered

include: supervisor-related commitment, organizational commitment, and organizational

citizenship behaviors.

3. Time allocation. Future studies testing the time allocation of managers need to control for

varied operational hours by sampling managers with the same hours of operation, or

alternatively, by increasing the sample size across all categories of operational hours.

4. Leadership effectiveness relative to area type. Studies are needed to test which leadership

style or management skills are effective under different operational conditions. In this study,

less transformational managers assigned compressed and mixed areas in combination with

wide raw spans achieved higher satisfaction with supervision than more transformational

managers with wide raw spans. The leadership and management skills needed to effectively

oversee areas with high volume throughput or high uncertainty may differ by area type.

5. Shift work. The influence of staff shift work on the capacity of managers to support staff

could be considered in future research. Managers whose staff work 12 hour shifts or

permanent nights or weekends are disadvantaged because these staff work fewer days

through the year or may work opposing shifts to the manager. Nursing work groups limited

to one shift length as well as nurses working 8- or 10-hour shifts or night shift only also tend

to report higher teamwork than nursing groups with mixed shift lengths or nurses working

12-hour shifts and combinations of 8- and 12-hour shifts (Kalisch, Begeny & Anderson,

2008; Kalisch & Lee, 2009). Future studies could examine the impact of staff shift work on

managers’ ability to supervise staff and to foster teamwork.

6. Teamwork. Future studies of teamwork incorporating key unit level predictors such as work

group size, the proportion of full-time staff, the mix of regulated and unregulated staff, and

116

care delivery models are warranted as these unit level variables influence communication and

care coordination processes (Kalisch & Begeny, 2005; McGillis Hall & Doran, 2004). Also,

for areas with extended hours of operation, there is a need to identify specific mechanisms or

supports to enhance teamwork across work group members who may work infrequently or

inconsistently with colleagues because of rotational shifts.

7. Subsequent studies in other settings. Future studies examining the work of first-line

managers are also needed for other hospital types (e.g., community, complex continuing

care) and in other sectors (e.g., home care, public health) to extend the generalizability of the

results. The context for other hospital types may differ in terms of resources (e.g., limited

clinical support roles) and organizational structure (e.g., layers of management). In other

sectors such as home care, geography may pose special challenges for managers.

Recommendations for Theory Development

1. Theory development. The study framework was based on the boundary spanning functions of

managers in open systems. However, more theoretical work is needed to explicate the work

of managers within an open systems approach to large scale organizations and to integrate

existing research related to concepts such as empowerment.

2. Manager sensitive outcomes. Further exploratory and theoretical work is needed to identify

the outcomes sensitive to managerial work (e.g., outcomes related to quality assurance, risk

management, and change management) and to clarify the theoretical mechanisms by which

managers influence these outcomes.

Recommendations for Organizational Policy and Managerial Practice

Open system theory posits that to survive, an organization must acquire negentropy to offset the

loss of inputs or the inability to transform energies, which otherwise lead to disorder and to the

dissolution of the organization (Katz & Kahn, 1978). For organizations, negentropy can involve

renewing inputs, storing energy, creating slack resources, or maximizing imported energy

relative to exported energy (Galbraith, 1974; Katz & Kahn). Based on the study findings, the

following recommendations for organizations and managers offer strategies by which

117

organizations can acquire negentropy with the goals of improving throughput efficiency (i.e.,

teamwork) and human resource outcomes (i.e., supervision satisfaction).

1. Leadership. In this study, transformational leadership had an overall positive influence on

satisfaction with supervision and on teamwork. Given that transformational leadership

practices can be learned (Kouzes & Posner, 2002), managers and organizations need to

continue to seek opportunities to enhance the leadership skills of first-line managers. This

strategy maximizes existing managerial inputs without increasing the number of managerial

inputs.

i. With extended hours of operation, the positive influence of transformational leadership

on satisfaction with supervision was even more pronounced under wider spans. Thus,

highly transformational managers assigned extended hours of operation can more

positively enhance satisfaction with supervision when assigned wider raw spans. Under

these specific conditions, wider raw spans maximize existing managerial inputs.

However, caution is warranted given that previous research indicates that wider raw

spans negatively influence other staff outcomes (e.g., staff engagement, work

environment satisfaction; Cathcart et al., 2004; McGillisHall et al., 2006) even when

managers have strong leadership skills (e.g., work empowerment, job satisfaction; Lucas

et al., 2008; McCutcheon et al., 2009).

ii. In contrast, with compressed and mixed hours of operation, wider spans for highly

transformational managers were detrimental to satisfaction with supervision. Hence there

are limits to maximizing managerial inputs when managers are assigned compressed and

mixed hours of operation. Organizations and managers are encouraged to identify

whether staffing issues sensitive to staff density or managerial coverage are occurring in

these areas and if so, to consider ways in which these issues could be alleviated by

altering the work assigned to the manager. Potential strategies are offered below.

2. Increased support by front-line management and clinical support roles.

i. Increases to first-line management support through co-management models could

enhance managerial coverage and accessibility during hours of operation. Co-manager

118

models could be considered in the following situations: (a) when teamwork levels are

low and managers are assigned extended operating hours and high numbers of non-direct

reports, and (b) when nursing staff are dissatisfied with supervision and managers are

assigned compressed and mixed hours of operation in combination with wide raw spans.

This role design strategy has been proposed by others with the goals of enhancing

manager role satisfaction, coping, health outcomes, empowerment, and clinical

competence and of reducing staff turnover (Carroll, Lacey & Cox, 2004; Shirey, 2009).

This study extends the recommendation under specific conditions to improve

multidisciplinary teamwork and nurse satisfaction with supervision. The trade-offs of co-

manager models are likely to include dual reporting relationships, new coordination

demands between co-managers (e.g., communication, consistency), and added up-front

costs to employ more managers. On the other hand, the potential cost savings related to

throughput and output improvements (e.g., teamwork, staff retention) would also need to

be considered. Organizations that implement co-manager models are essentially creating

slack resources by importing additional managerial inputs.

ii. If the issue is managerial coverage and staff density during the manager’s workday, then

managers who can bridge shifts during their workday can interface with more staff.

Similarly, managers who work shifts and weekends could also increase coverage and

access. This strategy maximizes existing managerial inputs without increasing the

number of managerial inputs. However, a co-manager model (which increases the

number of managerial inputs) would likely better support shift work rotations by

managers.

iii. Alternatively, increasing the number of full-time equivalent clinical support roles could

also enhance teamwork processes. This strategy requires organizations to import

additional staffing inputs to production subsystems to intensify the boundary spanning

function within and among subsystems, functional groups, and spatial boundaries.

3. Non-direct reports. Reducing the number of non-direct reports working in a manager’s

assigned area(s) can potentially enhance teamwork.

119

i. In the case of non-direct reports who are employees (e.g., allied health professionals or

unregulated workers with no direct report relationship to the manager), this might be

accomplished by establishing direct reporting relationships with the manager. The

underlying rationale is that when more work group members are accountable to the

manager, they are more likely to receive consistent communication about expectations

and feedback, and the manager has the authority to coordinate and integrate the work

processes and outcomes of diverse team roles. This strategy allows organizations to

benefit from the boundary spanning function of the first-line management subsystem

thereby maximizing existing managerial inputs without increasing managerial inputs.

However the trade-offs could include dual reporting relationships (e.g., in matrix

structures) which can be ambiguous and lead to conflict (Charnes & Tewksbury, 1993) or

the isolation of allied health professionals (e.g., in program management structures)

which has been associated with lower job satisfaction and fewer professional

development opportunities (Young, Charnes & Heeren, 2004).

ii. In the case of non-direct reports who are not employees of the organization (e.g.,

physicians, students), efforts could be directed at enhancing team continuity by limiting

the numbers of physicians and student rotations (i.e., reducing inputs), by extending the

length of physician and student rotations (i.e., creating slack resources by extending lead

times), or by promoting consistency in student rotations through dedicated learning units

(i.e., creating slack resources by reducing the number of exceptions). Alternatively, when

the number of non-direct reports is high and the manager is assigned more than one area,

the number of areas assigned to the manager could be reduced or co-manager models

could be considered (i.e., slack resources could be created).

4. Areas assigned to managers. The greater the number of areas assigned to managers, the

lower the teamwork, suggesting that splitting the focus of the manager may be detrimental to

fostering inter-professional collaboration. The scope of managerial roles could be narrowed

by assigning fewer areas. Organizations that subdivide managerial assignments are again

creating slack resources to increase throughput effectiveness.

120

Conclusions

This study contributes to the evidence base for designing effective managerial roles and is one of

the first studies to examine managerial time allocation in relation to outcomes. Overall, this study

offers emerging support that the effectiveness of first-line managers in acute care hospitals with

respect to nurse supervision satisfaction and to teamwork is influenced by the many hierarchical,

functional, spatial, and temporal boundaries that managers must negotiate to coordinate and

integrate organizational functioning. Further research is needed to better understand the

complexity of and the outcomes sensitive to managerial work. By enhancing the leadership skills

of first-line managers and by designing first-line management positions to factor in not only

reporting structures, but also other determinants of managerial span, organizations can positively

influence throughputs in production subsystems (i.e., teamwork) and human resource outcomes

(i.e., supervision satisfaction) which are important to efficient health care delivery in acute care

hospitals.

121

References

Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. Newbury Park, CA: Sage Publications.

Alidina, S., & Funke-Furber, J. (1988). First line nurse managers: Optimizing the span of

control. Journal of Nursing Administration, 18(5), 34-39. Altaffer, A. (1998). First-line managers: Measuring their span of control. Nursing Management,

29(7), 36-39. Ancona, D. G., & Caldwell, D. F. (1992). Bridging the boundary: External activity and

performance in organizational teams. Administrative Science Quarterly, 37, 634-665. Arman, R., Dellve, L., Wikström, E., & Törnström, L. (2009). What health care managers do:

Applying Mintzberg's structured observation method. Journal of Nursing Management, 17, 718-729.

Baskin, K. (1996). Nurse manager and staff nurse perceptions of the work of the first-line

manager in a metropolitan hospital. Unpublished Master of Science in Nursing, Duquesne University, Pittsburg, PA.

Baumann, A., O’Brien-Pallas, L., Armstrong-Stassen, M., Blythe, J., Bourbonnais, R., et al.

(June, 2001). Commitment and care: The benefits of a healthy workplace for nurses, their patients and the system. Ottawa, Ontario, Canada: Canadian Health Services Research Foundation and The Change Foundation.

Bertalanffy, L. (1950). An outline of General System Theory. British Journal for the Philosophy

of Science, 1(2), 134-165. Blau, P. M. (1968). The hierarchy of authority in organizations. American Journal of Sociology,

73(4), 453-467. Blegen, M. A. (1993). Nurses' job satisfaction: A meta-analysis of related variables. Nursing

Research, 42(1), 36-40. Blegen, M. A., & Mueller, C. W. (1987). Nurses' job satisfaction: A longitudinal analysis.

Research in Nursing and Health, 10, 227-237. Bohte, J., & Meier, K., J. (2001). Structure and the performance of public organizations: Task

difficulty and span of control. Public Organization Review, 1(3), 341-354. Brisley, C. L. (2001). Work sampling and group timing technique. In K. B. Zandin (Ed.),

Maynard's industrial engineering handbook (5th ed., pp. 17.47-17.64). New York: McGraw-Hill.

122

Buccheri, R. C. (1986). Nursing supervision: A new look at an old role. Nursing Administration Quarterly, 11(1), 11-25.

Burke, R. J. (1996). Unit size, work experiences and satisfactions: An exploratory study.

Psychological Reports, 78, 763-767. Burke, T. A., McKee, J. R., Wilson, H. C., Donahue, R. M. J., Batenhorst, A. S., & Pathak, D. S.

(2000). A comparison of time-and-motion and self-reporting methods of work measurement. Journal of Nursing Administration, 30(3), 118-125.

Burman, M. E. (1995). Health diaries in nursing research and practice. Image - The Journal of

Nursing Scholarship, 27, 147-152. Burns, N., & Grove, S. K. (2001). The practice of nursing research: Conduct, critique, and

utilization (4th ed.). Philadelphia: W. B. Saunders Company. Canadian Institute of Health Information. (2002). Registered Nurses Database: Supply and

distribution of Registered Nurses in Canada, 2001. Ottawa, Ontario, Canada: Canadian Institute of Health Information.

Canadian Institute for Health Information. (2008). Regulated Nursing Workforce by Position and

Province/Territory of Registration, 2007 [Data file]. Available from Canadian Institute for Health Information Web site, http://secure.cihi.ca/cihiweb/dispPage.jsp?cw_page=statistics_nursing_2007_e#k.

Canadian College of Health Service Executives. (2001, October). The leadership imperative:

Clarity, consistency and collaboration required [electronic version]. Submission to the Commission on the Future of Health Care in Canada.

Canadian Health Services Research Foundation. (2005). Knowledge exchange. Retrieved January

17, 2005, from http://www.chsrf.ca/knowledge_transfer/index_e.php Canadian Nursing Advisory Committee. (2002). Our health, our future: Creating quality

workplaces for Canadian nurses. Final report of the Canadian Nursing Advisory Committee. Ottawa, Ontario, Canada: Advisory Committee on Health Human Resources.

Cardona, P., Tappen, R. M., Terrill, M., Acosta, M., & Eusebe, M. I. (1997). Nursing staff time

allocation in long term care: A work sampling study. Journal of Nursing Administration, 27(2), 28-36.

Carless, S. A. (2001). Assessing the discriminant validity of the Leadership Practices Inventory.

Journal of Occupational and Organizational Psychology, 74, 233-239. Carroll, C. A., Lacey, S. R., & Cox, K. S. (2004). Comparing variations in labor costs for two vs.

one full-time nurse manager. Nursing Economics, 22, 254-257.

123

Carroll, S. J., & Gillen, D. J. (1984). The classical management functions: Are they really outdated? Academy of Management Proceedings, 132-136.

Carroll, S. J., & Gillen, D. J. (1987). Are the classical management functions useful in describing

managerial work? Academy of Management Review, 12(1), 38-51. Carroll, S. J., & Taylor, W. H. (1968). A study of the validity of a self-observational central-

signaling method of work sampling. Personnel Psychology, 21, 359-364. Castro, S. L. (2002). Data analytic methods for the analysis of multilevel questions: A

comparison of intraclass correlation coefficients, rwg(j), hierarchical linear modeling, within- and between-analysis, and random group resampling. The Leadership Quarterly, 13, 69-93.

Cathcart, D., Jeska, S., Karnas, J., Miller, S. E., Pechacek, J., & Rheault, L. (2004). Span of

control matters. Journal of Advanced Nursing, 34, 395-399. Charnes, M., & Tewksbury, L. (1993). The continuum of organization structures. In

Collaborative management in health care: Implementing the integrative organization (pp. 20-43). San Fransisco: Jossey-Bass.

Cicchetti, D. V. (1981). Testing the normal approximation and minimal sample size requirements

of weighted kappa when the number of categories is large. Applied Psychological Measurement, 5, 101-104.

Collopy, F. (1996). Biases in retrospective self-reports of time use: An empirical study of

computer users. Management Science, 42, 758-767. Commission on the Future of Health Care in Canada. (2002, November). Building on values:

The future of health care in Canada - Final report. Ottawa, Ontario, Canada. Commissioner: R. J. Romanow.

Cook, D. T., & Campbell, D. T. (1979). Causal inference and the language of experimentation.

In Quasi-experimentation: Design and analysis issues for field settings (pp. 1-36). Chicago: Rand McNally College Publishing Company.

Cooper, H. M. (1982). Scientific guidelines for conducting integrative research reviews. Review

of Educational Research, 52, 291-302. Cummings, G. G., Olson, K., Hayduk, L., Bakker, D., Fitch, M., Green, E., et al. (2008). The

relationship between nursing leadership and nurses' job satisfaction in Canadian oncology work environments. Journal of Nursing Management, 16, 508-518.

Dewar, R. D., & Simet, D., P. (1981). A level specific prediction of spans of control examining

the effects of size, technology, and specialization. Academy of Management Journal, 24(1), 5-24.

124

Dixon, J. K. (2001). Confirmatory factor analysis. In B. H. Munro (Ed.), Statistical methods for

health care research (4th ed., pp. 331-354). Philadelphia, PA: Lippincott Williams & Wilkins.

Donaldson, S. I., & Grant-Vallone, E. J. (2002). Understanding self-report bias in organizational

behavior research. Journal of Business and Psychology, 17, 245-260. Doran, D. (2005). Teamwork – Nursing and the multidisciplinary team. In: McGillis Hall, L.

(Ed.), Quality work environments for nurse and patient safety (p. 39-66). Sudbury, MA: Jones and Bartlett.

Doran, D. I., Baker, G. R., Murray, M., Bohnen, J., Zahn, C., Sidani, S., et al. (2002). Achieving

clinical improvement: An interdisciplinary intervention. Health Care Management Review, 27(4), 42-46.

Doran, D., McCutcheon, A. S., Evans, M. G., MacMillan, D., McGillis Hall, L., Pringle, D., et

al. (2004). Impact of the manager's span of control on leadership and performance. Ottawa, Ontario, Canada: Canadian Health Services Research Foundation.

Drach-Zahavy, A., & Dagan, E. (2002). From caring to managing and beyond: An examination

of the head nurse's role. Journal of Advanced Nursing, 38(1), 19-28. Drinka, T., & Ray, R. O. (1987). An investigation of power in an interdisciplinary health care

team. Gerontology and Geriatrics Education, 6(3), 43-53. Duffield, C., & Franks, H. (2001). The role and preparation of first-line nurse managers in

Australia: Where are we going and how do we get there? Journal of Nursing Management, 9, 87-91.

Duffield, C., Pelletier, D., & Donoghue, J. (1994). Role overlap between clinical nurse

specialists and nursing unit managers. Journal of Nursing Administration, 24(10), 54-63. Duffield, C., Donoghue, J., & Pelletier, D. (1996). Do clinical nurse specialists and nursing unit

managers believe that the provision of quality care is important. Journal of Advanced Nursing, 24, 334-340.

Duffield, C., Roche, M., O’Brien-Pallas, L., Catling-Paull, C., & King, M. (2009). Staff

satisfaction and retention and the role of the nursing unit manager. Collegian, 16, 11-17. Duffield, C., Roche, M., O’Brien-Pallas, L., Diers, D., Aisbett, C., King, M., et al. (2007).

Glueing it together: Nurses, their work environment and patient safety. Broadway, New South Wales, Australia: Centre for Health Services Management, University of Technology, Sydney.

125

Dunham-Taylor, J. (2000). Nurse executive transformational leadership found in participative organizations. Journal of Nursing Administration, 30, 241-250.

Dunn, K., & Schilder, E. (1993). Novice and expert head nurses: A comparative study of work

activities and behaviours. Canadian Journal of Nursing Administration, 6, 10-15. Edwards, C., & Robinson, O. (2004). Evaluating the business case for part-time working

amongst qualified nurses. British Journal of Industrial Relations, 42(1), 167-183. Englebardt, S. P. (1993). The relationship of nurse manager behaviors and characteristics to

subordinates' perceptions of the work unit climate. Unpublished Doctor of Philosophy, Virginia Commonwealth University, Richmond, Virginia.

Estryn-Béhar, M., Van der Heijden, B. I. J. M., Oginska, H., Camerino, D., Le Nezet, O.,

Conway, P. M., et al. (2007). The impact of social work environment, teamwork characteristics, burnout, and personal factors upon intent to leave among European nurses. Medical Care, 45, 939-950.

Fayol, H. (1937). The administrative theory in the state (S. Greer, Trans.). In L. Gulick & L.

Urwick (Eds.), Paper on the science of administration (pp. 99-114). New York: Institute of Public Administration, Columbia University.

Ferguson-Paré, M. (1997). Leadership that supports professional autonomous practice of

Registered Nurses. Unpublished Doctor of Philosophy, Fielding Institute. Ferguson-Paré, M., Mitchell, G., Perkin, M., & Stevenson, L. (2002). Academy of Canadian

Executive Nurses Background Paper on Leadership. Fields, D. L. (2002). Taking the measure of work: A guide to validated scales for organizational

research and diagnosis. Thousand Oaks, California: Sage Publications, Inc. Finkler, S. A., Knickman, J. R., Hendrickson, G., Lipkin, M. J., & Thompson, W. G. (1993). A

comparison of work-sampling and time-and-motion techniques for studies in health services researchers. Health Services Research, 28, 577-597.

Ford-Gilboe, M., Campbell, J., & Berman, H. (1995). Stories and numbers: Coexistence without

compromise. Advances in Nursing Science, 18(1), 14-26. Fox, R. T., Fox, D. H., & Wells, P. J. (1999). Performance of first-line management functions on

productivity of hospital unit personnel. Journal of Nursing Administration, 29(9), 12-18. Freda, L. J., Senkewicz, J. J., & AT&T. (1988). Work diaries. In S. Gael (Ed.), The job analysis

handbook for business, industry, and government (Vol. 1, pp. 446-452). White Plains, NY: NYNEX Corporation.

126

Furman, A. (1995). Nurse manager and staff nurse perceptions of the work of the first line nurse manager in a community teaching hospital. Unpublished Master of Science in Nursing, Duquesne University, Pittsburg, PA.

Galbraith, J. R. (1974). Organization design: An information processing view. Interfaces, 4(3),

28-36. Galbraith, J., Downey, D., & Kates, A. (2002). How networks undergird the lateral capability of

an organization – Where the work gets done. Journal of Organizational Excellence, 21(2), 67-78.

Gittell, J. H. (2000). Organizing work to support relational co-ordination. International Journal

of Human Resource Management, 11, 517-539. Gittell, J. (2001). Supervisory span, relational coordination and flight departure performance: A

reassessment of postbureaucracy theory. Organization Science, 12, 468-483. Gittell, J. H. (2002a). Coordinating mechanisms in care provider groups: Relational coordination

as a mediator and input uncertainty as a moderator of performance effects. Management Science, 48, 1408-1426.

Gittell, J. H. (2002b). Relationships between service providers and their impact on customers.

Journal of Service Research, 4, 299-311. Gittell, J. H. (2003). A theory of relational coordination. In K. S. Cameron, J. E. Dutton & R. E.

Quinn (Eds.), Positive organizational scholarship: Foundations of a new discipline (pp. 279-435): Berrett-Koehler Publishing.

Gittell, J. H. (2004). Achieving focus in hospital care: The role of relational coordination. In R.

E. Herzlinger (Ed.), Consumer-driven health care: Implications for providers, payers, and policymakers (pp. 683-695). San Francisco: Jossey-Bass.

Gittell, J. H. (2006). Relational coordination: Coordinating work through relationships of shared

goals, shared knowledge and mutual respect. In O. Kyriakidou & M. Ozbilgin (Eds.), Relational perspectives in organizational studies: A research companion. Edward Elgar Publishers.

Gittell, J. H., Fairfield, K. M., Bierbaum, B., Head, W., Jackson, R., Kelly, M., et al. (2000).

Impact of relational coordination on quality of care, postoperative pain and functioning, and length of stay. Medical Care, 38, 807-819.

Gittell, J. H., Weinberg, D. B., Pfefferle, S., & Bishop, C. (2008). Impact of relational

coordination on job satisfaction and quality outcomes: A study of nursing homes. Human Resource Management Journal, 18(2), 154-170.

127

Graicunas, V. A. (1937). Relationship in organization. In L. Gulick & L. Urwick (Eds.), Papers on the science of administration (pp. 181-188). New York: Institute for Public Administration, Columbia University.

Green, S. G., Anderson, S. E., & Shivers, S. L. (1996). Demographic and organizational

influences on leader-member exchange and related work attitudes. Organizational Behavior and Human Decision Processes, 66, 203-214.

Grinspun, D. (2002). A flexible nursing workforce: Realities and fallouts. Hospital Quarterly.

6(1), 79-82, 84. Gulick, L. (1937). Notes on the theory of organization. In L. Gulick & L. Urwick (Eds.), Papers

on the science of administration (pp. 1-46). New York: Institute of Public Administration, University of Columbia.

Hales, C. (2005). Rooted in supervision, branching into management: Continuity and change in

the role of the front-line manager. Journal of Management Studies, 42(3), 471-506. Hales, C. P. (1986). What do managers do? A critical review of the evidence. Journal of

Management Studies, 23(1), 88-115. Hall, D. S. (2007). The relationship between supervisor support and Registered Nurse outcomes

in nursing care units. Nursing Administration Quarterly, 31(1), 68-80. Hatch, M. J., & Cunliffe, A. L. (2006). Organization theory: Modern, symbolic, and postmodern

perspectives (2nd ed.). New York: Oxford University Press Inc. Hechanova-Alampay, R., & Beehr, T. A. (2001). Empowerment, span of control, and safety

performance in work teams after workforce reduction. Journal of Occupational Health Psychology, 6, 275-282.

Hertzog, M. A. (2008). Considerations in determining sample size for pilot studies. Research in

Nursing and Health, 31, 180-191. Hoffman, D. A. (1997). An overview of the logic and rationale of hierarchical linear models.

Journal of Management, 23(6), 723-744. Hollett, J. E. (2001). The experience of managing from the perspective of first-line nurse

managers working in program management. Unpublished Master of Science in Nursing, University of Toronto, Toronto.

House, R. J., & Aditya, R. N. (1997). The social scientific study of leadership: Quo vadis?

Journal of Management, 23, 409-473. House, R. J., & Miner, J. B. (1969). Merging management and behavioral theory: The interaction

between span of control and group size. Administrative Science Quarterly, 14, 451-464.

128

Houser, J. (2003). A model for evaluating the context of nursing care delivery. Journal of

Nursing Administration, 33(1), 39-47. Hudak, V. A. (1995). The work of the first-line nurse manager in a community teaching hospital.

Unpublished Master of Science in Nursing, Duquesne University, Pittsburg, PA. Ironson, G. H., Smith, P. C., Brannick, M. T., Gibson, W. M., & Paul, K. B. (1989).

Construction of a Job in General scale: A comparison of global, composite, and specific measures. Journal of Applied Psychology, 74, 193-200.

Irvine, D., & Evans, M. (1995). Job satisfaction and turnover among nurses: Integrating research

findings across studies. Nursing Research, 44, 246-253. Jackson, C. J., & Corr, P. J. (2002). Global job satisfaction and facet description: The moderating

role of facet importance. European Journal of Psychological Assessment, 18(1), 1-8. Jacob, J. A. (1998). Measuring time at work: Are self-reports accurate? Monthly Labor Review,

(December), 42-51. Jaques, E. (1990). In praise of hierarchy. Harvard Business Review, (January-February), 127-

133. Jones, F. F., Scarpello, V., & Bergmann, T. (1999). Pay procedures - what makes them fair?

Journal of Occupational and Organizational Psychology, 72, 129-145. Judge, T. A., & Ferris, G. R. (1993). Social context of performance evaluation decisions.

Academy of Management Journal, 36(1), 80-105. Kalisch, B. J., & Begeny, S. M. (2005). Improving nursing unit teamwork. Journal of Nursing

Administration, 35(12), 550-556. Kalisch, B. J., Begeny, S., & Anderson, C. (2008). The effect of consistent nursing shifts on

teamwork and continuity of care. Journal of Nursing Administration, 38(3), 132-137. Kalisch, B. J., Curley, M., & Stefanov, S. (2007). An intervention to enhance nursing staff

teamwork and engagement. Journal of Nursing Administration, 37(2), 77-84. Kalisch, B. J., & Lee, H. (2009). Nursing teamwork, staff characteristics, work schedules, and

staffing. Health Care Management Review, 34(4), 323-333. Kalleberg, A. L., & Loscocco, K. A. (1983). Aging, values, and rewards: Explaining age

differences in job satisfaction. American Sociological Review, 48(February), 78-90. Kanter, R. M. (1977). Men and women of the corporation. New York: Basic Books, Inc.

129

Katz, D., & Kahn, R. L. (1978). The social psychology of organizations (2nd ed.). New York: John Wiley & Sons.

Keller, T., & Dansereau, F. (1995). Leadership and empowerment: A social exchange

perspective. Human Relations, 48, 127-146. Kennedy, F. A., Loughry, M. L., Klammer, T. P., & Beyerlein, M. M. (2009). Effects of

organizational support on potency in work teams: The mediating role of team processes. Small Group Research, 40(1), 72-93.

Kerlinger, F. N. (1986). Foundations of behavioural research (3rd ed.). New York: Holt,

Rinehart and Winston. Koontz, H., O'Donnell, C., & Weihrich, H. (1980). Management (7th ed.). New York: McGraw-

Hill. Kouzes, J. M., & Posner, B. Z. (2002). The leadership challenge (3rd ed.). San Francisco, CA:

Jossey-Bass. Kovner, C., Brewer, C., Yow-Wu, W., Cheng, Y., & Suzuki, M. (2006). Factors associated with

work satisfaction of Registered Nurses. Journal of Nursing Scholarship, 38(1), 71-79. Kramer, M., Maguire, P., Schmalenberg, C., Brewer, B., Burke, R., Chmielewski, L., et al.

(2007). Nurse manager support: What is it? Structures and practices that promote it. Nursing Administration Quarterly, 31(4), 325-340.

Kreft, I., & De Leeuw, J. (1998). Introducing multilevel modeling. London: Sage Publications. Larson, E. (1984). The performance feedback process: A preliminary model. Organizational

Behavior and Human Performance, 33, 42-76. Laschinger, H. K. S. (2004). Hospital nurses perceptions of respect and organizational justice.

Journal of Nursing Administration, 34, 354-364. Laschinger, H. K. S., & Finegan, J. (2005). Using empowerment to build trust and respect in the

workplace: A strategy for addressing the nursing shortage. Nursing Economics, 23(1), 6-13.

Laschinger, H. K. S., Finegan, J., & Wilk, P. (2009). Context matters: The impact of unit

leadership and empowerment on nurses' organizational commitment. Journal of Nursing Administration, 39(5), 228-235.

Laschinger, H. K. S., Wong, C., Ritchie, J., D'Amour, D., Vincent, L., Wilk, P., et al. (2008). A

profile of the structure and impact of nursing management in Canadian hospitals. Healthcare Quarterly, 11(2), 85-94.

130

Leatt, P., Lemieux-Charles, L., & Aird, C. (1994). Program management: Introduction and overview. In L. L.-C. P. Leatt, C. Aird (Ed.), Program management and beyond: Management innovations in Ontario hospitals (pp. 1-10). Ottawa, Ontario, Canada: Canadian College of Health Service Executives.

Leveck, M. L., & Jones, C. B. (1996). The nursing practice environment, staff retention, and

quality of care. Research in Nursing and Health, 19, 331-343. Loke, J. C. F. (2001). Leadership behaviours: Effects on job satisfaction, productivity and

organizational commitment. Journal of Nursing Management, 9, 191-204. Lomas, J. (1990). Finding and changing beliefs: The structure of research use in Canadian

policy. Journal of Health Politics, Policy and Law, 15, 525-542. Lomas, J. (1997). Improving research dissemination and uptake in the health sector: Beyond the

sound of one hand clapping. Policy Commentary C97-1. Hamilton, Ontario, Canada: McMaster Centre for Health Economics and Policy Analysis.

Lucas, V., Laschinger, H. K. S., & Wong, C. (2008). The impact of emotional intelligent

leadership on staff nurse empowerment: The moderating effect of span of control. Journal of Nursing Management, 16, 964-973.

Mahon, A., & Young, R. (2006). Health care managers as a critical component of the health care

workforce. In C. Dubois, M. McKee & E. Nolte (Eds.), Human resources for health in Europe (pp. 116-139). Berkshire, England: Open University Press.

Mahoney, T. A., Jerdee, T. H., & Carroll, S. J. (1963). Development of managerial performance:

A research approach. Cincinnati: South-Western Publishing Co. Mann, F. C. (1965). Toward an understanding of the leadership role in formal organization. In L.

Broom (Ed.), Leadership and productivity: Some facts of industrial life (pp. 68-103). San Francisco, CA: Chandler Publishing.

Manser, T. (2009). Teamwork and patient safety in dynamic domains of healthcare: A review of

the literature. Acta Anaesthesiologica Scandinavica, 53, 143-151. Martinko, M. J., & Gardner, W. L. (1985). Beyond structured observation: Methodological

issues and new directions. Academy of Management Review, 10, 676-695. McCall, M. W., Morrison, A. M., & Hannan, R. L. (1978). Studies of managerial work: Results

and methods (No. 9). Greensboro, NC: Center for Creative Leadership. McCutcheon, A. S. (2004). Relationships between leadership style, span of control and

outcomes. Unpublished Doctor of Philosophy, University of Toronto, Toronto.

131

McCutcheon, A. S., Doran, D., Evans, M., McGillis Hall, L., & Pringle, D. (2009). Effects of leadership and span of control on nurses' job satisfaction and patient satisfaction. Canadian Journal of Nursing Leadership, 22(3), 48-67.

McGillis Hall, L. (2003). Nursing outcome: Nurses’ job satisfaction. In D. M. Doran (Ed.),

Nursing-Sensitive Outcomes: State of the science (pp. 283-318). Sudbury, MA: Jones and Bartlett.

McGillis Hall, L., & Donner, G. J. (1997). The changing role of hospital nurse managers: A

literature review. Canadian Journal of Nursing Administration, 10(2), 14-39. McGillis Hall, L., & Doran, D. (2004). Nurse staffing, care delivery model, and patient quality

care. Journal of Nursing Care Quality, 19(1), 27-33. McGillis Hall, L., & Doran, D. (2007). Nurses’ perceptions of hospital work environments.

Journal of Nursing Management, 15, 264-273. McGillis Hall, L., Doran, D., O’Brien Pallas, L., Tranmer, J., Tregunno, D., Rukholm, E. et al.

(2006). Quality worklife indicators for nursing practice environments in Ontario: Determining the feasibility of collecting indicator data. Toronto, Ontario, Canada: Ontario Ministry of Health and Long-Term Care.

McGillis Hall, L., Irvine, D., Baker, G. R., Pink, G., Sidani, S., O'Brien-Pallas, L., et al. (2001).

A study of the impact of nursing staff mix models and organizational change strategies on patient, system and nurse outcomes. Toronto, Ontario, Canada: Faculty of Nursing, University of Toronto and Canadian Health Services Research Foundation/Ontario Council of Teaching Hospitals.

McGilton, K. S., McGillis Hall, L., Wodchis, W. P., & Petroz, U. (2007). Supervisory support,

job stress, and job satisfaction among long-term care nursing staff. Journal of Nursing Administration, 37, 366-372.

McNeese-Smith, D. K. (1995). Job satisfaction, productivity, and organizational commitment:

The result of leadership. Journal of Nursing Administration, 25(9), 17-26. McNiven, P., O'Brien-Pallas, L. L., & Hodnett, E. (1993). Work sampling revisited: A technique

for understanding nursing work. Canadian Journal of Nursing Administration, 6(1), 20-23.

McShane, S. (2009). Canadian organizational behaviour (7th ed.). Toronto, Ontario, Canada:

McGraw-Hill Ryerson Ltd. Medley, F., & Larochelle, D. R. (1995). Transformational leadership and job satisfaction.

Nursing Management, 26(2), 64JJ-64NN.

132

Meier, K., J., & Bohte, J. (2000). Ode to Luther Gulick: Span of control and organizational performance. Administration and Society, 32(2), 115-137.

Meier, K., J., & Bohte, J. (2003). Span of control and public organizations: Implementing Luther

Gulick's research design. Public Administration Review, 63(1), 61-70. Meyer, R. M. (2008). Span of management: Concept analysis. Journal of Advanced Nursing,

63(1), 104-112. Mia, L., & Goyal, M. (1991). Span of control, task interdependence and usefulness of MAS

information in not-for-profit government organizations. Financial Accountability & Management, 7(4), 249-266.

Mintzberg, H. (1970). Structured observation as a method to study managerial work. The Journal

of Management Studies, 87-104. Mintzberg, H. (1971). Managerial work: Analysis from observation. Management Science, 18(2),

B97-B110. Mintzberg, H. (1973). The nature of managerial work. New York: Harper & Row. Mohr, D. C., Burgess, J. F., & Young, G. J. (2008). The influence of team culture on physician

and nurse resignation rates in hospitals. Health Services Management Research, 21(1), 23-31.

Morse, J. M. (2000). Exploring pragmatic utility: Concept analysis by critically appraising the

literature. In B. L. Rodgers & K. A. Knafl (Eds.), Concept development in nursing: Foundations, techniques, and applications (2nd ed., pp. 333-352). Philadelphia, PA: Saunders.

Morash, R., Brintnell, J., & Rodger, G. L. (2005). A span of control tool for clinical managers.

Canadian Journal of Nursing Leadership, 18(3), 83-93. Morrison, R., Jones, L., & Fuller, B. (1997). The relation between leadership style and

empowerment on job satisfaction. Journal of Nursing Administration, 27(5), 27-34. Mullen, B., Symons, C., Hu, L., & Salas, E. (1989). Group size, leadership behavior, and

subordinate satisfaction. Journal of General Psychology, 116(2), 155-169. Napier, B. J., & Ferris, G. R. (1993). Distance in organizations. Human Resource Management

Review, 3(4), 321-357. Noordegraaf, M., & Stewart, R. (2000). Managerial behaviour research in private and public

sectors: Distinctiveness, disputes and directions. Journal of Management Studies, 37, 427-443.

133

Norman, G. R., & Streiner, D. L. (2000). Biostatistics: The bare essentials (2nd ed.). Hamilton, Ontario, Canada: B. C. Decker Inc.

Oandasan, I., Baker, G. R., Barker, K., Bosco, C., D'Amour, D., Jones, L., et al. (2006).

Teamwork in healthcare: Promoting effective teamwork in healthcare in Canada. Ottawa, Ontario, Canada: Canadian Health Services Research Foundation.

O'Brien-Pallas, L. (2004). Workload measurement issues. Lecture presented at NUR 1061

Patient Information/Workload Measurement Systems Course, University of Toronto, Toronto, Ontario, Canada.

O’Brien-Pallas, L., Alksnis, C., & Wang, S. (2003). Bringing the future into focus: Projecting

RN Retirement in Canada. Toronto, Ontario: Canadian Institute for Health Information. O'Brien-Pallas, L., Hiroz, J., Cook, A., & Mildon, B. (2005). Nurse-physician relationships:

Solutions and recommendations for change (Grant #06221). Toronto, Ontario, Canada: Nursing Health Services Research Unit, University of Toronto.

O’Brien-Pallas, L., Meyer, R., & Thomson, D. (2004). Workload and productivity. In L.

McGillis Hall (Ed.), Quality work environments for nurse and patient safety (pp. 105-138). Sudbury, MA: Jones and Bartlett.

O’Brien-Pallas, L., Thomson, D., Alksnis, C., Baumann, A. O., Luba, M., Pagniello, A., et al.

(2003). Stepping to success and sustainability: An analysis of Ontario’s nursing workforce. Toronto, Ontario, Canada: Nursing Effectiveness, Utilization & Outcomes Research Unit.

O’Brien-Pallas, L., Thomson, D., McGillis Hall, L., Pink, G., Kerr, M., Wang, S., et al. (2004).

Evidence based standards for measuring nurse staffing and performance. Final Report (RC1 - 0621 - 06) submitted to Canadian Health Services Research Foundation, Ottawa, Ontario, Canada.

O'Brien-Pallas, L., Tomblin Murphy, G., Laschinger, H. K. S., White, S., Wang, S., &

McCullogh, C. (2005). Canadian survey of nurses from three occupational groups. Ottawa, Ontario, Canada: The Nursing Sector Study Corporation.

O'Brien-Pallas, L., Tomblin Murphy, G., White, S., Hayes, L., Baumann, A., Higgin, A. et al.

(2004). Building the future: An integrated strategy for nursing human resources in Canada. Research synthesis report. Ottawa, Ontario, Canada: The Nursing Sector Study Corporation.

Oelke, N. D., White, D., Besner, J., Doran, D., McGillis Hall, L., & Giovannetti, P. (2008).

Nursing workforce utilization: An examination of facilitators and barriers on scope of practice. Canadian Journal of Nursing Leadership, 21(1), 58-71.

134

Ouchi, W. G., & Dowling, J. B. (1974). Defining the span of control. Administrative Science Quarterly, 19, 357-365.

Oxford University Press. (2005). Oxford Concise Dictionary. Retrieved May 11, 2005, from

http://dictionary.oed.com.myaccess.library.utoronto.ca/cgi/entry/50232085?query_type=word&queryword=span&first=1&max_to_show=10&sort_type=alpha&result_place=1&search_id=v9XK-7TVzqx-4329&hilite=50232085

Pabst, M. K. (1993). Span of control on nursing inpatient units. Nursing Economics, 11(2), 87-

90. Patrick, A., & White, P. (2005). Scope of nursing leadership. In L. McGillis Hall (Ed.), Quality

work environments for nurse and patient safety (pp. 181-212). Sudbury, MA: Jones and Bartlett Publishers.

Pelletier, D., & Duffield, C. (2003). Work sampling: Valuable methodology to define nursing

practice patterns. Nursing and Health Sciences, 5, 31-38. Pellico, L. H., Brewer, C., & Kovner, C. (2009). What newly licensed registered nurses have to

say about their first experiences. Nursing Outlook, 57(4), 194-203. Penfield, R. V. (1974). Time allocation patterns and effectiveness of managers. Personnel

Psychology, 27, 245-255. Podsakoff, P. M., MacKenzie, S. B., Podsakoff, N. P., & Lee, J. (2003). Common method biases

in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879-903.

Podsakoff, P. M., & Organ, D. W. (1986). Self-reports in organizational research: Problems and

prospects. Journal of Management, 12, 531-544. Posner, B. Z. (2002). The Leadership Practices Inventory: Theory and evidence behind the five

practices of exemplary leaders. Retrieved December 16, 2005, from http://media.wiley.com/assets/463/74/lc_jb_appendix.pdf.

Posner, B. Z., & Kouzes, J. M. (1988). Development and validation of the Leadership Practices

Inventory. Educational and Psychological Measurement, 48, 483-496. Posner, B. Z., & Kouzes, J. M. (1993). Psychometric properties of the Leadership Practices

Inventory - updated. Educational and Psychological Measurement, 53, 191-199. Prescott, P. A., & Soeken, K. L. (1989). The potential uses of pilot work. Nursing Research,

38(1), 60-62. Price, J. L., & Mueller, C. W. (1986). Absenteeism and turnover of hospital employees (Vol. 5).

Greenwich, CT: JAI Press.

135

Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data

analysis methods. Thousand Oaks, CA: Sage Publications. Reimanis, C. L., Cohen, E. L., & Redman, R. (2001). Nurse case manager role attributes: Fifteen

years of evidence based literature. Lippincott's Case Management, 6(6), 230-242. Reyna, S. L. S. (1992). Leadership styles of nurse managers and how they affect the motivation

level of nursing staff. The University of Texas at Austin, Austin, Texas. Rice, R. W., McFarlin, D. B., & Gentile, D. A. (1991). Facet importance and job satisfaction.

Journal of Applied Psychology, 76(1), 31-39. Ross, M. M., Rideout, E. M., & Carson, M. M. (1994). The use of the diary as a data collection

technique. Western Journal of Nursing Research, 16, 414-425. Rubin, R. S., Munz, D. C., & Bommer, W. H. (2005). Leading from within: The effects of

emotion recognition and personality on transformational leadership behavior. Academy of Management Journal, 48(5), 845-858.

Rutter, R. (1994). Work sampling: As a win/win management tool. Industrial Engineering,

26(2), 30-31. Scarpello, V., & Vandenberg, R. J. (1987). The Satisfaction With My Supervisor Scale: Its utility

for research and practical applications. Journal of Management, 13, 447-466. Schriesheim, C. A., Castro, S. L., & Yammarino, F. J. (2000). Investigating contingencies: An

examination of the impact of span of supervision and upward controllingness on leader-member exchange using traditional and multivariate within- and between-entities analysis. Journal of Applied Psychology, 85, 659-677.

Shamian, J., O’Brien-Pallas, L., Thomson, D., Alksnis, C., & Kerr, M. S. (2003). Nurse

absenteeism, stress and workplace injury: What are the contributing factors and what can/should be done about it? International Journal of Sociology and Social Policy, 23(8/9), 81- 103.

Shamian, J., Skelton-Green, J., & Villeneuve, M. (2002). Policy is the lever for effecting change.

In Realities of Canadian Nursing (pp. 83-104). Canada: Lippincott, Williams & Wilkins. Shields, M. A., & Ward, M. (2001). Improving nurse retention in the National Health Service in

England: The impact of job satisfaction on intentions to quit. Journal of Health Economics, 20, 677-701.

Shirey, M. R. (2009). Stress and coping in nurse managers: A qualitative description.

Unpublished Doctorate, Indiana University, Indianapolis.

136

Shortell, S. M., Zimmerman, J. E., Rousseau, D. M., Gillies, R. R., Wagner, D. P., Draper, E. A., et al. (1994). The performance of intensive care units: Does good management make a difference? Medical Care, 32(5), 508-525.

Smith, P.C. (1976). Behaviors, results, and organizational effectiveness: The problem of criteria.

In M. D. Dunnette (Ed.), Handbook of industrial psychology. Chicago: Rand McNally. Smith, S. L., & Friedland, D. S. (1998). The influence of education and personality on risk

propensity in nurse managers. Journal of Nursing Administration, 28(12), 22-27. Smith, L. S., Manfredi, T., Hagos, O., Drummond-Huth, B., & Moore, P. D. (2006). Application

of the clinical nurse leader role in an acute care delivery model. Journal of Nursing Administration, 36(1), 29-33.

Spreitzer, G. M. (1994). Social structural characteristics of psychological empowerment.

Academy of Management Journal, 39(2), 483-504. Stahelski, A. J., & Tsukuda, R. A. (1990). Predictors of cooperation in health care teams. Small

Group Research, 21, 220-233. Stewart, R. (1982). A model for understanding managerial jobs and behavior. Academy of

Management Review, 7(1), 7-13. Stieglitz, H. (1962). Optimizing span of control. Management Record, (September), 25-29. Stogdill, R. M., & Bass, B. M. (1981). Stogdill's handbook of leadership: A survey of theory and

research (Rev. ed.). New York: The Free Press. Stogdill, R. M., & Shartle, C. L. (1955). Methods in the study of administrative leadership.

Columbus, Ohio: The Ohio State University. Stordeur, S., Vandenberghe, C., & D'Hoore, W. (2000). Leadership styles across hierarchical

levels in nursing departments. Journal of Advanced Nursing, 35, 533-542. Synowiez, B. B. (1987). The relationship of educational preparation, managerial experience,

and organizational structure to nurse managers' attitudes to professionalism. Unpublished Doctor of Philosophy, University of Pennsylvania, Pittsburg.

Tabachnick, B. G., & Fidell, L. S. (1996). Using multivariate statistics (3rd ed). New York:

HarperCollins College Publishers. Theobald, N. A., & Nicholson-Crotty, S. (2005). The many faces of span of control:

Organizational structure across multiple goals. Administration and Society, 36(6), 648-660.

137

Tonges, M. C., Rothstein, H., & Carter, H. K. (1998). Sources of satisfaction in hospital nursing practice. Journal of Nursing Administration, 28(5), 47-61.

Tourangeau, A., Lemonde, M., Luba, M., Dakers, D., & Alksnis, C. (2003). Evaluation of a

leadership development intervention. Canadian Journal of Nursing Leadership, 16(3), 91-104.

Tushman, M. L., & Scanlan, T. J. (1981). Boundary spanning individuals: Their role in

information transfer and their antecedents. Academy of Management Journal, 24(2), 289-305.

Urwick, L. (1937). Organization as a technical problem. In L. Gulick & L. Urwick (Eds.),

Papers on the science of administration (pp. 47-88). New York: Institute of Public Administration, Columbia University.

Vandenberg, R. J., & Nelson, J. B. (1999). Disaggregating the motives underlying turnover

intentions: When do intentions predict behavior? Human Relations, 52, 1313-1336. Vandenberg, R. J., & Scarpello, V. (1992). Multitrait-multimethod validation of the Satisfaction

With My Supervisor Scale. Educational and Psychological Measurement, 52, 203-212. Van Fleet, D. D., & Bedeian, A. A. (1977). A history of the span of management. Academy of

Management Review, 2(3), 356-372. Vance, C., & Larson, E. (2002). Leadership research in business and health care. Journal of

Nursing Scholarship, 32, 165-171. Verbrugge, L. (1980). Health diaries. Medical Care, 27, S128-147. Washington, C. C., & Moss, M. (1988). Pragmatic aspects of establishing interrater reliability in

research. Nursing Research, 37(3), 190-191. Ward, L. W. (2002). A survey and description of direct care providers' satisfaction with their

supervisors: A look at race education, gender, and age characteristics of direct care providers. Unpublished Doctor of Science, Boston University, Boston.

Weber, M. (1978). Economy and society: An outline of interpretive sociology (E. Fischoff, H.

Gerth, A. M. Henderson, F. Kolegar, C. W. Mills, T. Parsons, M. Rheinstein, G. Roth, E. Shils & C. Wittich, Trans. Vol. 2). Berkely, CA: University of California Press.

Weinberg, D. B., Lusenhop, R. W., Gittell, J. H., & Kautz, C. M. (2007). Coordination between

formal providers and informal caregivers. Health Care Management Review, 32(2), 140-149.

Willmott, H. (1987). Studying managerial work: A critique and a proposal. Journal of

Management Studies, 24, 249-270.

138

Wylie, D. M. (1994). Leadership theory and practice. In J. M. Hibberd & M. E. Kyle (Eds.),

Nursing management in Canada (pp. 175-190). Toronto, Ontario, Canada: W. B. Saunders Canada.

Yammarino, F. J., Dionne, S. D., Chun, J. U., & Dansereau, F. (2005). Leadership and levels of

analysis: A state-of-the-science review. The Leadership Quarterly, 16, 879-919. Young, G. J., Charnes, M. P., & Heeren, T. C. (2004). Product-line management in professional

organizations: An empirical test of competing theoretical perspectives. Academy of Management Journal, 47(5), 723-734.

139

Appendix A. Studies of Span at the Manager Level and Staff Outcomes Author & reference

Theoretical Framework, Hypotheses

Design, Setting, Sample

Measures Analyses & Results Strengths Limitations

Bohte, J., & Meier, K., J. (2001). Structure & the performance of public organizations: Task difficulty & span of control. Public Organization Review, 1(3), 341-354.

Span is a measure of organizational structure; i.e., how relationships are structured between leaders & subordinates The relationship between span & outcomes may be linear, but as span exceeds capacity of leaders (e.g., because of environmental constraints), it is subject to diminishing returns 1. Is span

conditioned by task difficulty?

Design: Ex post facto Setting: Public education system Sample: 678 Texas school districts >500 students data sets: 1994 – 1997 Control Variables Inputs – minority & low-income students Resources – average teacher salary & per student spending on education Technology – not applicable; assumed to be similar for schools over a certain size

Span Ratios 1. top level mgmt: school

administrator: teachers 2. mid level mgmt: district

administrator: school administrators

3 levels of task difficulty Easy – tasks can be performed based on rules & procedures (3rd grade); decreases to span are not likely to improve performance Moderate – discretion required as to how to apply rules & procedures in particular circumstances; problems are solvable (7th grade); decreases to span are most likely to improve performance Extreme – technology to solve extremely difficult problems is unknown; decreases to span provide few benefits relative to costs (10th grade) Student performance: % of students in each school district who pass standard math & reading tests each year

Negative relationships with performance: inputs Positive relationships with performance: resources Regression Estimation of organization production function (non-linear relationship) Moderate task difficulty – top-level span ratio explained variation in performance Easy task difficulty – top level span ratio explained modest variation in performance Extreme task difficulty – neither span ratio explained variation in performance

Examined the non-linear relationship between span & outcomes

Raw measure of span Single outcome evaluated Class size & school size are not measures of managerial span Hierarchical structure of the data set not accounted for in the analysis

140

Author & reference

Theoretical Framework, Hypotheses

Design, Setting, Sample

Measures Analyses & Results Strengths Limitations

Cathcart, D., Jeska, S., Karnas, J., Miller, S. E., Pechacek, J., & Rheault, L. (2004). Span of control matters. Journal of Advanced Nursing, 34(9), 395-399.

1. What is the relationship between span & employee engagement scores?

2. Does reduction of

span by 30-50% improve employee engagement on 4 units with > 80 employees?

Design: Descriptive, correlational survey Setting: Large integrated health system, Midwest US Sample: 651 work groups

Span: number of direct reports assigned to a manager Employee engagement: Gallup instrument; 12 questions measure employee engagement & strength of workplace; 5 point scale Staff survey variables not specified Control variables: demographics (tenure, employment status, contract status, management/non-management, patient care/non-patient care) Levels of employee engagement by percentiles: 1. bottom 25th 2. 25th – 60th 3. 61st – 90th 4. top 10th

Type of analyses not always specified. Discriminant analysis to differentiate variables influencing 4 levels of employee engagement Relationship between span & employee engagement reported as work group size Improvements in employee engagement scores were observed on the 4 units where span was reduced by 30-50%

Large sample of work groups & employees Health care population

Methodology & analyses inadequately described; hierarchical structure of the data set not accounted for in the analysis Refer to both span & work groups; number of managers for the 651 work groups not explicitly stated Validity & reliability of Gallup instrument & other measures not reported Linear relationship assumed between span & outcome Single outcome evaluated

141

Author & reference

Theoretical Framework, Hypotheses

Design, Setting, Sample

Measures Analyses & Results Strengths Limitations

Gittell, J. H. (2001). Supervisory span, relational coordination & flight departure performance: A reassessment of postbureaucracy theory. Organization Science, 12(4), 468-483.

Flight departure process requires high levels of task interdependence Group process mediates the relationship between span & group performance 1. Wide span

strengthens group process, in turn improving group performance

2. Narrow span

strengthens group process, in turn improving group performance

Design: ex post facto research; prospective cross sectional & longitudinal components Setting: airline industry Sample: convenience sample of 4 airlines for a total of 9 groups to maximize differences in coordination; 354 group members (Response rate = 89%)

Span from administrative records: number of full-time equivalent front-line employees per supervisor on a monthly basis Survey instrument for group process (relational coordination) which considered the respondent’s perception of how other group members currently interact with them in terms of frequency & timeliness of communication, strength of problem solving, helping, mutual respect, shared goals, & shared knowledge among group members; 84 questions on strength of interactions on a 5 point scale; Cronbach’s alpha = 0.84 Performance & control variables from monthly reports over 12 months Performance variables: customer complaints, baggage handling, late arrivals; equally weighted index; Cronbach’s alpha = 0.81 Control variables: number of flights per month, average length of flight, number of passengers per flight, tones of cargo per flight & % passengers connecting Qualitative interviews (n = 48) & non-structured observation (n = 13 days)

ANOVA: cross-airlines & cross-groups differences in relational coordination (p < .001) Hierarchical linear modeling Wide & narrow span associated with lower & higher levels of group performance (p < .05) respectively Regression of relational coordination on performance: Relational coordination Is significantly associated with better performance (p < .01) Relational coordination mediates the influence of span on performance Regression of span on each dimension of relational coordination: Wide span associated with weaker relational coordination: less timely communication, & lower levels of problem solving, shared goals, shared knowledge (p < .01); & with lower levels of helping & mutual respect (p < .05) Qualitative data suggested that narrow spans encouraged feedback & coaching by the supervisor & resulted in better group performance, provided that the supervisor was facilitative, rather than coercive (i.e., did not engage in pressuring & blaming)

Self-report, observational & objective measures; some prospective longitudinal data Reduced common method bias Used hierarchical linear modeling Measured span monthly

Raw measure of span; used full-time equivalents Linear relationship assumed between span & group process

142

Author & reference

Theoretical Framework, Hypotheses

Design, Setting, Sample

Measures Analyses & Results Strengths Limitations

Judge, T. A., & Ferris, G. R. (1993). Social context of performance evaluation decisions. Academy of Management Journal, 36(1), 80-105.

1. Supervisory opportunities to observe job performance will positively influence the performance rating of the subordinate

2. Large span will

negatively influence performance rating of the subordinate

Design: Descriptive, correlational survey Setting: 238 bed hospital in US Sample: 81 registered nurses; 27 supervisors; resulting in 81 nurse-supervisor dyads

Span: number of employees directly reporting to the supervisor Supervisor’s opportunity to observe subordinates’ performance: How often do you think your supervisor regularly has the opportunity to observe your job performance and thus knows how you are doing? ; 5 point scale

Structural equation modeling Opportunity to observe positively & significantly influenced performance ratings Span did not influence performance ratings

Limitation of span measure identified; indirectly accounted for managerial time allocation Nursing population

Hierarchical structure of the data set not accounted for in the analysis Linear relationship assumed between span & outcome Single outcome evaluated

143

Author & reference

Theoretical Framework, Hypotheses

Design, Setting, Sample

Measures Analyses & Results Strengths Limitations

Hechanova-Alampay, R., & Beehr, T. A. (2001). Empowerment, span of control, & safety performance in work teams after workforce reduction. Journal of Occupational Health Psychology, 6(4), 275-282.

Safety performance is influenced by span & empowerment 1. Work group size

is positively correlated to unsafe behaviors & accidents

2. Level of

empowerment is negatively correlated to unsafe behaviors & accidents

3. Unsafe behaviors

are positively correlated to more accidents

Design: Descriptive, correlational survey Setting: Chemical company Sample: 1 company, 3 sites, 24 work teams selected bases on best & worst safety performance Group size: n = 24; M = 47 (SD = 24; range 12-110) Employees: n = 531; 46% response rate Data collected at group level; safety collected by self-report survey

Span: number of employees who report directly to the work group leaders Empowerment: delegation of power & authority to employees; team based assessment of 21 factors (e.g., formulation of team goals, communication outside the team, decision making on team rules, resolving conflict within teams, problem-solving customer interactions, work scheduling, providing feedback to team members) on a 4-point scale (0 - leader centered; 1- shared leadership; 2 - self-directed; 3 - fully empowered) Safety measures: Unsafe behaviors: average team score of the frequency of 18 individual behaviors rated on a 5-point likert scale Safety accidents: % of employees in each work team, in calendar year 1999, with accidents

Empowerment: M = 0.96 (SD = .81; range 0-2) Safety accidents: M = 2.6% (range 0-10%) One-tailed correlations at group level Span correlated positively with unsafe behaviors (r = .43, p < .05) & accidents (r = .44, p < .05) Empowerment negatively correlated with unsafe behaviors (r = -.48, p < .05) & accidents (r = -.51, p < .05) Unsafe behaviors positively correlated with accidents (r = .35, p < .05) Multiple regression analysis Span & empowerment explained 33% of variance in unsafe behavior & 34% of variance in safety accidents

Large sample of employees Self-report & objective measures used

Raw measure of span Data aggregated to resolve levels-of-analysis issues

144

Author & reference

Theoretical Framework, Hypotheses

Design, Setting, Sample

Measures Analyses & Results Strengths Limitations

Lucas, V., Laschinger, H. K. S., & Wong, C. (2008). The impact of emotional intelligent leadership on staff nurse empowerment: The moderating effect of span of control. Journal of Nursing Management, 16, 964-973.

Kanter’s Theory of Structural Empowerment 1. The influence of

managerial leadership on staff structural empowerment is moderated by raw span.

Design: Descriptive, correlational survey Setting: Community hospitals, Ontario Sample: 2 hospitals Staff nurses: n = 203; 68% response rate Managers: n = 16

Span: number of direct reports Emotional intelligent leadership: nurse ratings of their managers’ self-awareness, self-management, social awareness & relationship management as measured by the Emotional Competence Inventory, Version 2 (ECI 2.0) Structural empowerment: nurses’ perceptions of access to opportunity, information, support & resources, & formal & informal power as measured by the Conditions of Work Effectiveness-II (CWEQ-II)

Correlational analyses & moderated regression analyses Span: M = 77.5 (SD 38.56) with a range of 5-151 The moderating influence of span on leadership & empowerment was significant. The interaction term (leadership x span) was ß = -0.711, t = -2.71, p = .007). The final model explained 45% of variance in empowerment.

Nursing population

Raw span measure Linear relationship assumed between span & outcome Single outcome evaluated Hierarchical structure of the data set not accounted for in the analysis

145

Author & reference

Theoretical Framework, Hypotheses

Design, Setting, Sample

Measures Analyses & Results Strengths Limitations

McCutcheon, A. S. (2004). Relationships between leadership style, span of control & outcomes. Unpublished Doctor of Philosophy, University of Toronto, Toronto. Also: Doran, D., McCutcheon, A. S., Evans, M. G., MacMillan, D., McGillis Hall, L., Pringle, D., et al. (2004). Impact of the manager's span of control on leadership & performance. Ottawa, Ontario, Canada: Canadian Health Services Research Foundation. Also: McCutcheon, A. S., Doran, D., Evans, M., McGillis Hall, L., & Pringle, D. (2009). Effects of leadership and span of control on nurses' job satisfaction and patient satisfaction. Canadian Journal of Nursing Leadership, 22(3), 48-67.

1. Span has a main effect on outcomes

2. Span moderates

the relationship between leadership & outcomes

Design: descriptive correlational Setting: hospitals in Ontario, Canada Sample: 717 nurses (Response rate = 88%); 41 nurse managers; 51 patient care units; 7 hospitals

Span: total number of nurses on a unit reporting directly to the manager Leadership: Multifactor Leadership Questionnaire – adapted (Bass & Avolio, 2000) Nursing Job Satisfaction: Mueller & McCloskey (1990) Patient Satisfaction: nursing section of the Patient Judgments of Hospital Quality Questionnaire (Rubin, Ware & Hayes, 1990) Unit turnover rate: % of nurses who left their position during a one-year period Unit labor stability rate: % of nurses who survived the first year in the unit Other variables: nurse & manager demographics, & unit characteristics (number of units per manager, manager roles, number of staff resources reporting to & not reporting to the manager, categories of staff, unit type, & unit unpredictability) Unit level: span, turnover, labor stability Individual level: Mgr leadership style, nurse job satisfaction

Hierarchical linear modeling Multiple regression for each outcome Leadership style, but not span, predicted job satisfaction Span moderated leadership & job satisfaction Span had a main effect on turnover & labor stability Wider span associated with higher turnover & lower stability; for every 10 person increment in span, turnover increased by 1.6%. A span of 100 would be expected to result in a 16% turnover rate Wider span reduced the positive effects of transformational & transactional leadership on job satisfaction Wider span increased the negative effects of management-by-exception & laissez-faire leadership on job satisfaction Wider span reduced patient satisfaction & reduced the positive effects of transformational & transactional leadership on patient satisfaction

Power analysis conducted Nursing population Hierarchical linear modeling used to account for nesting of nurses within units. Multiple outcomes evaluated, including patient outcome

Raw span measure Linear relationship assumed between span & outcome

146

Author & reference

Theoretical Framework, Hypotheses

Design, Setting, Sample

Measures Analyses & Results Strengths Limitations

McGillis Hall et al. (2006). Quality worklife indicators for nursing practice environments in Ontario: Determining the feasibility of collecting indicator data. Toronto, Ontario, Canada: University of Toronto.

1. Does a relationship exist between unit manager span of control and nurses’ perceptions of the work environment?

Design: descriptive correlational Setting: acute (AC), complex continuing (CCC), long-term (LTC) & home (HC) care settings in Ontario, Canada Sample: 451 nurses; 53 nurse managers; 65 units; 20 sites

Span: number of direct reports Work Environment: Revised Nursing Work Index (NWI-R) & Work Quality Index (WQI) Note: High scores on NWI-R indicate lower satisfaction with autonomy, control over the work environment, relationships with physicians & organizational supports High scores on the WQI indicate higher satisfaction with the professional work environment, autonomy, work worth, professional relationships, role enactment & benefits

Span: AC: 81.2% had 40-59 CCC: 86.7% had 30-59 LTC: highly variable Overall, 76% managed more than one unit AC: span correlated positively with NWI-R (rho = .25, p = .01) & negatively WQI (rho = -.19, p = .03) scores CCC: no correlations LTC: span correlated positively with NWI-R (rho = .22, p = .01) HC: no correlations Overall: span correlated positively with NWI-R (rho = .26, p = .01)

Nursing population

Raw span measure Linear relationship assumed between span & outcome Hierarchical structure of the data set not accounted for in the analysis

147

Author & reference

Theoretical Framework, Hypotheses

Design, Setting, Sample

Measures Analyses & Results Strengths Limitations

Meier, K., J., & Bohte, J. (2000). Ode to Luther Gulick: Span of control & organizational performance. Administration & Society, 32(2), 115-137. Note: same data set Bohte & Meier (2001) reported above

Span is a measure of organizational structure The relationship between span & organizational performance is quadratic; as span exceeds capacity of leaders, it is subject to diminishing returns; a linear relationship is expected, except when organizations have incentives to add additional subordinates or supervisors 1. What is an

optimal span for a set of organizations using the same mode of production toward the same goal?

2. Does the effect of

span on performance differ between high- & low-performing districts?

Design: Ex post facto analysis of secondary data Setting: Public education system Sample: 678 Texas school districts with >500 students 2712 pooled cases data set: 1994 – 1997

Span Ratios 1. mid level mgmt: district

administrators: school administrators

2. first level mgmt: school administrators: teachers

Student performance: % of students in each school district who pass standard math & reading tests each year Control Variables Inputs – minority & low-income students Resources – average teacher salary & per student spending on education Technology – not applicable; assumed to be similar for schools over a certain size

Examined linear relationship & estimated organization production function (non-linear relationship) of span ratios & student performance using regression Mid & first level management ratios had a positive linear relationship with performance on average; however this relationship was quadratic for low performing schools (authors speculate that this may be related to the quality of leadership)

Examined non-linear relationship between span & outcome

Class size & school size are not measures of managerial span Hierarchical structure of the data set not accounted for in the analysis Single outcome evaluated

148

Author & reference

Theoretical Framework, Hypotheses

Design, Setting, Sample

Measures Analyses & Results Strengths Limitations

Schriesheim, C. A., Castro, S. L., & Yammarino, F. J. (2000). Investigating contingencies: An examination of the impact of span of supervision and upward controllingness on leader-member exchange using traditional and multivariate within- and between-entities analysis. Journal of Applied Psychology, 85(5), 659-677.

Supervisors in large work units have more demands & fewer opportunities for interaction with subordinates When the work unit is large, subordinates are more likely to value their relationship with their supervisor 1. Wide spans will

positively moderate the relationships between leader-member exchange & outcomes

2. The strongest

span moderator effects exist at the between-groups level

Design: Descriptive correlational surveys Setting: Branch banks Sample: matched data set of 75 managers & 150 staff (one high & one low performing staff per manager)

Span: number of staff full-time equivalents supervised Quality of leader-member exchange: 7 item scale based on 4 point likert; coefficient alpha = .83 (supervisors) & .86 (staff) Staff performance rated by manager: modified Mott’s (1972) scale on quantity & quality of performance over past 6 months; coefficient alpha = .77 Organizational commitment of staff: Porter, Steers, Mowday & Boulian (1974) Organizational Commitment Questionnaire; extent of employee attachment to the organization; 15 item scale; 7 point likert; coefficient alpha = .83

Spans ranged from 5 – 21 full-time equivalents (M = 11.12) Comparison of 2 alternatives to WABA: multiple relationship analysis & multivariate WABA Span did not moderate the relationship between leader-member exchange & performance Wide spans positively moderated ratings of leader-member exchange & organizational commitment of staff

Raw measure of span; used full-time equivalents rather than headcount to study dyadic relationships Managers evaluated the performance of only two staff Small number of subordinates used

Spreitzer, G. M. (1994). Social structural characteristics of psychological empowerment. Academy of Management Journal, 39(2), 483-504.

Compared to supervisors with wider spans, those with narrow spans tend to closely control & monitor subordinates thereby diminishing psychological empowerment (greater feelings of incompetence & reduced meaning). 1. Wide spans will

positively influence staff empowerment

Design: Descriptive correlational surveys Setting: Fortune 50 organization Sample: 393 middle managers; staff M = 4 per manager

Span: number of staff supervised by one middle manager Psychological empowerment: An individual’s orientation to his/her work role in terms of meaning, competence, self-determination & impact; 18 items on 7-point likert scale; coefficient alpha = .81

Spans average 5.36 (SD = 5.92) Correlations: Span correlated weakly with competence (r = 0.12, p < .05) Multiple regression analysis: Wide spans positively associated with staff empowerment (ß = .09, p < .01)

Large sample of managers.

Raw measure of span Few employees per manager Hierarchical structure of the data set not accounted for in the analysis

149

Author & reference

Theoretical Framework, Hypotheses

Design, Setting, Sample

Measures Analyses & Results Strengths Limitations

Theobald, N. A., & Nicholson-Crotty, S. (2005). The many faces of span of control: Organizational structure across multiple goals. Administration and Society, 36(6), 648-660. Note: same data set used by Meier & Bohte (2000) & Bohte & Meier (2001) reported above

1. Do tests of the functional form of span create conflicting goals for organizations when tested on three outcomes?

Design: Ex post facto analysis of secondary data Setting: Public education system Sample: 678 Texas school districts with >500 students 2712 pooled cases data set: 1994 – 1997

Span Ratios 1. mid level mgmt: district

administrators: school administrators

2. first level mgmt: school administrators: teachers

Performance: % of students in each school district who pass standard math & reading tests each year SAT performance: average score on the SAT in a district Dropout rate: 100 – district dropout number Control Variables Inputs – minority & low-income students Resources – average teacher salary & per student spending on education Technology – not applicable; assumed to be similar for schools over a certain size

Estimated production function (quadratic relationship) of span ratios & three outcomes using regression Goal conflict not evident for mid level management ratio Optimal first-level management ratios conflict when the three outcomes are considered together

Examined non-linear relationship between span Multiple outcomes

Raw measure of span Class & school size are not measures of managerial span Hierarchical structure of the data set not accounted for in the analysis

150

Appendix B Information and Consent Letter and Survey for Managers You are being asked to voluntarily take part in a research study. This is not a quality assurance study. Before agreeing to participate in this research study, it is important that you read & understand the proposed study procedures. The following information describes the purpose, procedures, benefits, discomforts, risks & precautions associated with this study. It also describes your right to refuse to participate or withdraw from the study at any time. In order to decide whether you wish to participate in this research study, you should understand enough about its risks & benefits to be able to make an informed decision. You may consult with anyone you like about your decision of whether or not to participate: family members; friends; human resources; your union; any knowledgeable person may be consulted. This is known as the informed consent process. Please ask the study staff to explain any words you don’t understand before signing this consent form. Make sure all your questions have been answered to your satisfaction before signing this document. Background - Research has studied the work of hospital staff but has not looked closely the work of managers. In a redesigned health care system, first-line managers are faced with managing larger programs & more clinical areas & services. We have little understanding of how the work of managers affects their support to staff & how their span of control & leadership shape staff, patient & system outcomes. This research builds on other studies that have shown that the span & leadership style of managers is related to job satisfaction, patient satisfaction & teamwork. For the purposes of this study, “span” could refer to: a) the number of people supervised by a manager (this is called “raw span”); b) time spent in human resource (HR) activity; c) time spent in contact with staff. Purpose - You are being asked to consider participating in this research study because you hold a first-line management position. A total of 60 managers across at least three hospitals will be sought to participate in the research study. The study will last up to 18 weeks. This research looks at how managers’ spans of control, work & leadership practices shape nurse satisfaction with manager & multidisciplinary teamwork. This research study aims to understand:

1. how span & time spent in human resource activities shape outcomes; 2. how leadership shapes outcomes under different spans; &, 3. which span levels improve outcomes.

Procedures - The table below outlines the three phases of the research study & your role, or the role of a delegate that you assign, in collecting data.

151

Manager Manager

(or delegate that you assign) Phase

1 During 2-4 week period • a 30-45 minute survey of work activities,

leadership practices & demographics • 15 minute orientation about work logs

(general session or personal appointment)

During 2-4 week period • assist in collecting & verifying baseline

span data (*see below) on direct report employees from payroll & human resources

• identify budget amounts assigned to you in the previous & current fiscal years

• locate your office in relation to the units, clinics & services managed

Phase 2

10 week period • on a work log day, record at half hourly

intervals the number of minutes spent in HR activity & in contact with staff; each entry will take less than 10 seconds to complete as you gain proficiency. This will take place for 20 work log days (i.e., 2 randomly assigned days per week over 10 weeks)

• interrater reliability; the researcher will shadow you for two half days

• record your daily worked hours

10 week period • record daily worked hours by those in

other supervisory roles (e.g., team leaders)

• fax work logs to researcher weekly • 2 monthly updates of administrative span

data (initially collected during Phase I)

Phase 3

Additional 2-4 week period • 1 final monthly update of administrative

span data (initially collected during Phase I)

* data = number; occupation; clinical areas; year of birth; employment start date; employment status

During Phase 2, you may also be invited to participate in 2 full days (instead of 2 half days) of job shadowing by the researcher. The purpose is to improve the researcher’s understanding of work flow issues experienced by managers. A small sub-set of managers will be invited & a separate consent will be sought. Eligibility - To be eligible, managers need to have been employed in a first-line management position for at least 3 months & manage at least one patient care unit where health care providers directly deliver patient care services. Risks - Beyond the period of time required to complete the survey, orientation & work logs, there may be minimal discomfort associated with being shadowed during Phase 2. There may be employment risks associated with your participation. If you agree to participate, you are allowing the research team to ask:

i. nurses, a peer manager & your director to consider completing a questionnaire about your leadership practices, &

152

ii. nurses, other health care providers & physicians in the units, clinics & services that you manage to consider completing questionnaires of nurse satisfaction with manager & teamwork.

The questionnaires about leadership behaviours, nurse satisfaction with the manager & teamwork may lead the respondents to reflect on issues they might not have otherwise. This also means that nursing staff, health care providers & physicians on the units, clinics & services that you manage & manager peers & your director will know that you have agreed to participate in the research & that they will be asked to consider filling in questionnaires about your leadership practices. The research team will do everything possible to maintain the confidentiality of all surveys. No participant will be given access to the surveys of other participants (e.g., directors will not have access to the surveys of other staff). All findings will be rolled up to group, hospital or study levels to protect against identification. For example, if only 3 palliative care units participated, the findings from these units would be rolled up into the results for medical units. Benefits - There are no direct benefits to you. However, your participation will assist in understanding how organizations & policy makers can optimize the work of managers in the hospital system. Participation & Withdrawal - Participation in research is voluntary. If you choose to participate in this research study, you can withdraw from the study at any time. You may also refuse to answer any question(s) or choose to stop responding to the survey or filling in the work logs at any time. Withdrawal from the research study does not necessarily include withdrawal of any data complied up to that point. Cumulative research findings will be available to participants. Confidentiality - All information obtained during the study will be held in strict confidence. A 6 digit study number will be assigned to you to enable data collection & analyses & to protect your confidentiality. This study number will be linked to your name & assigned clinical areas in a confidential code book which will only be accessible to the research staff & which will be destroyed at the completion of the thesis. Your study number will be imprinted in small font on your work logs. As you will be self-recording work log data, you are asked to carry & store the work logs securely to ensure your confidentiality. Your submitted survey & work logs are completely confidential. Your name will not be written on the questionnaires given to other study participants (i.e., nurses, peer manager, director, health care providers & physicians). After participants return their questionnaires to the researcher, the manager study number & unit study number will be written onto the questionnaires by the researcher & the questionnaires will be stored. Only the research team will have access to the raw data. No names or identifying information will be used in any publication or presentations. Hospital level findings will be provided to the University Health Network on the condition that a minimum of 10 managers from the University Health Network participate in order to ensure confidentiality of the participating managers. All forms will be stored for 7 years in the Nursing Health Services Research Unit’s locked data storage unit & then destroyed. No information identifying you will be transferred outside the investigators of this research study.

153

Reimbursement - During Phase 2, for each week that the work logs are completed & returned, you will receive a small, weekly thank you token (e.g., coffee coupon). There is no other reimbursement for your participation in this research study. Compensation - In no way does signing this consent form waive your legal rights nor does it relieve the investigators, sponsors or involved institutions from their legal & professional responsibilities. Questions - If you have concerns or general questions about the research study, please call the study personnel in charge of this study, Raquel Meyer at 416-946-7154. If you have any questions about your rights as a research participant, please call the Chair of the Research Ethics Board. This person is not involved with the research project in any way & calling him/her will not affect your participation in the study. Consent I have had the opportunity to discuss this research study & my questions have been answered to my satisfaction. I consent to take part in the study with the understanding I may withdraw at any time. I have received a signed copy of this consent form. I voluntarily consent to participate in this research study. ________________________ ________________________ ________________________ Study Subject’s Name Study Subject’s Signature Date I confirm that I have explained the nature & purpose of the research study to the subject named above. I have answered all questions. ________________________ ________________________ ________________________ Name of Person Obtaining Signature Date Consent

154

Manager Survey 1. What is your profession?

Business/Management Nursing Occupational Therapy Physiotherapy Social Work Other, specify your profession: __________________________________

2. How long have you worked in your profession?

Years:

3. How long have you worked as a first-line manager? Years:

4. How long have you worked in your current position?

Years:

5. Please indicate your highest educational credential: college diploma, specify qualification: _______________________________ undergraduate degree, specify qualification: ___________________________ masters degree, specify qualification: ________________________________ doctoral degree, specify qualification: ________________________________

6. Do you have any other management related credentials? Please spell out acronyms.

___________________________________________________________________________________________________________________________________________________________________________________________________

7. Please list the professional management associations to which you belong. Please spell out

acronyms. ___________________________________________________________________________________________________________________________________________________________________________________________________

8. What is your gender?

Female Male

9. What year were you born? Year: 19

155

10. List the units you manage and how long you have managed each one:

Unit ID Unit Name Number of years & months you have managed this unit:

11. List the clinics you manage and how long you have managed each one:

Clinic ID Clinic Name Number of years & months you have managed this clinic:

12. Do you manage other departments or services distinct from the units and clinics you listed

in 10 & 11? No, skip to 13 Yes, please list: _______________________________________________________

Service ID

Service Name Number of years & months you have managed this service:

156

Appendix C. Information and Consent Letter and Survey for Employees

Informed Consent to Participate in a Research Study TITLE: Relationships between span, time allocation & leadership of first-line

managers & nurse & team outcomes (Short Title: Manager Study) INVESTIGATORS: Raquel Meyer, RN, PhD(c)

Dr. Linda O’Brien-Pallas, RN, PhD, FCAHS SPONSOR: Canadian Institutes of Health Research & the Nursing Health Services

Research Unit. You are being asked to voluntarily take part in a research study. This is not a quality assurance study. Before agreeing to participate in this research study, it is important that you read & understand the proposed study procedures. The following information describes the purpose, procedures, benefits, discomforts, risks & precautions associated with this study. It also describes your right to refuse to participate or withdraw from the study at any time. In order to decide whether you wish to participate in this research study, you should understand enough about its risks & benefits to be able to make an informed decision. You may consult with anyone you like about your decision of whether or not to participate: family members; friends; human resources; your union; any knowledgeable person may be consulted. This is known as the informed consent process. Please ask the study staff to explain any words you don’t understand before signing this consent form. Make sure all your questions have been answered to your satisfaction before signing this document. Background - Research has studied the work of hospital staff but has not looked closely at the work of managers. In a redesigned health care system, first-line managers are faced with managing larger programs & more clinical areas & services. We know little about how the work of managers affects their support to staff & how their span of control & leadership shape staff, patient & system outcomes. This research builds on other studies that have shown that the span & leadership style of managers is related to job satisfaction, patient satisfaction & teamwork. For the purposes of this study, “span” could refer to: a) the number of people supervised by a manager (this is called “raw span”); b) time spent in administrative human resources activities; c) time spent in relational human resource activities. Purpose - You have been asked to consider participating in this research study by completing a one-time survey of nurse satisfaction with manager & multidisciplinary teamwork. Approximately 800 nurses across at least three hospitals will be sought to participate in the study. This research looks at how managers’ spans of control, work & leadership practices shape nurse satisfaction with manager & multidisciplinary teamwork. This research study aims to understand:

4. how span & time spent in human resource activities shape outcomes;

157

5. how leadership shapes outcomes under different spans; &, 6. which span levels improve outcomes.

Procedures - If you agree to participate, you will be asked to 1) identify your main clinical area & your first-line nurse manager to the researcher & 2) complete a 15-25 minute survey. You are invited to complete this survey only once, even if you work in more than one clinical area. If you are asked again, please let us know if you have already participated. Eligibility - To be eligible, health care providers need to have worked on the participating unit for at least 3 months. Risks - Beyond the period of time required to complete the survey, there may be employment risks associated with your participation if the results of the surveys became known. For this reason the research team will do everything possible to maintain the confidentiality of all surveys. No names will be collected with surveys. No participant will be given access to the surveys of other participants (e.g., managers will not have access to the surveys of other staff). All findings will be rolled up to group, hospital or study levels to protect against identification. For example, if only 3 palliative care units participated, the findings from these units would be rolled up into the results for medical units. Benefits - There are no direct benefits to you. However, your participation will assist in understanding how organizations & policy makers can optimize the work of managers in the hospital system. Participation & Withdrawal - Participation in research is voluntary. If you choose to participate in this research study, you can withdraw from the study at any time. You may also refuse to answer any question(s) or choose to stop responding to the survey at any time. Withdrawal from the research study does not necessarily include withdrawal of any data complied up to that point. Cumulative research findings will be available to participants. Confidentiality - Your name will not be recorded. Your submitted survey is completely confidential. Only the research team will have access to your survey & the raw data. All forms will be stored for 7 years in the Nursing Health Services Research Unit’s locked data storage unit & then destroyed. Reimbursement - There is no reimbursement for your participation in this research study. Compensation - In no way does participating in this research study waive your legal rights nor does it relieve the investigators, sponsors or involved institutions from their legal & professional responsibilities. Questions - If you have concerns or general questions about the research study, please call the study personnel in charge of this study, Raquel Meyer at 416-946-7154.

158

If you have any questions about your rights as a research participant, please call the Chair of Research Ethics Board. This person is not involved with the research project in any way & calling him/her will not affect your participation in the study. Consent I have had the opportunity to discuss this research study & my questions have been answered to my satisfaction. I consent to take part in the study with the understanding I may withdraw at any time. By returning my filled-in survey to the researcher, I hereby voluntarily consent to participate & have been given a copy of this consent form.

159

Employee Survey For each question, please check the box that best describes you or enter the information asked. 1. What is your occupation?

Clerical staff (e.g., unit clerk, secretary) Health Care Aide, Personal Support Worker Occupational Therapist Physiotherapist Recreational Therapist Registered Nurse Registered Practical Nurse Respiratory Therapist Social Worker Other, specify: ___________________________

2. How long have you worked in this occupation? (Including years worked for other

employers) Years: Months:

3. How long have you worked in this hospital?

Years: Months: 4. How long have you worked on this unit?

Years: Months: 5. What is your highest educational credential?

High school diploma College certificate College diploma Undergraduate degree Graduate degree

6. What is your highest nursing credential?

Not applicable. I am not a nurse. College practical nursing certificate/diploma College nursing diploma Undergraduate nursing degree Masters of Nursing degree

7. What is your gender?

Female Male

8. What year were you born?

Year: 19

160

9. What is your work status, as defined by your employer?

Full-time Part-time Casual

161

Appendix D. Pilot Work

This appendix presents the pilot work undertaken to pre-test the work log procedures and work

log classification system for time allocation as well as the metrics related to time allocation.

Consistent with Prescott and Soeken (1989), this pilot work assessed the adequacy of the

instrument as well as problems in data collection strategies and methods and developed

operational definitions in the study setting. The work log methodology was pre-tested to

establish the feasibility of the work log procedures and to clarify the classification scheme for

time allocation. A convenience sample of managers (n = 3) was recruited via third party email

from one hospital participating in the study. The information and consent form are presented

near the end of this appendix. The work log pretest consisted of four days, with one manager

participating twice. A pilot sample size of less than 10 is considered acceptable for purposes such

as assessing the ease of administration, wording, or acceptability (Hertzog, 2008).

Managers were asked to complete the log for a work day in the presence of the investigator in

order to assess any difficulties in completing the work diaries. The investigator asked managers

the extent to which the work activities were easily classified and mutually exclusive and

exhaustive as recommended by Ross et al. (1994). Difficulties in classifying work activities,

reasons for the reported difficulties, omissions in logging, and clarity of the wording and

instructions were noted. These difficulties are described below. The investigator revised the work

log procedure and categories accordingly (Washington & Moss, 1988). Inter-observer agreement

was assessed.

Work Log Procedure

For each log entry, managers recorded the number of minutes spent in the specified categories.

Twenty and thirty minute work log entry intervals were trialed. Half hourly intervals were

deemed acceptable to the managers as shorter intervals were more disruptive, especially during

meetings. Managers were provided with a pager that automatically alarmed (silent vibration or

auditory alarm) at 30 minute intervals. Instructions for the use of the pager and the work logging

procedure were clarified.

Classification System

162

As discussed in Chapter 1, Ouchi and Dowling (1974) proposed that measures of span as

closeness of contact that factor in time allocation would be more sensitive to staff outcomes than

measures of span as reporting structure (i.e., raw span). This thesis addressed this argument by

determining the extent to which alternative measures of managerial span explained variation in

outcomes. Ouchi and Dowling (1974) further specified that it matters how much time managers

allocate to the supervision and support of staff. This suggested two facets reflecting

administrative responsibilities and interpersonal relationships. Using the employee subject

dimensions of management performance delineated by Mahoney et al. (1963), the initial

proposed classification scheme used 2 categories to reflect these facets: administrative human

resource activity and relational human resource activity. Activities in the employee subject

dimension were classified as either administrative or relational in nature. Administrative human

resource activities included: staffing, scheduling, evaluating and observing performance, wage

and salary administration, collective bargaining, and dispute resolution. Relational human

resource activities included: training, counseling, coaching, providing informal feedback,

recognition, and rewards, and social exchange at work.

The first pre-test day for the work logs revealed considerable subjectivity by the manager in

interpreting the initial classification scheme (i.e., administrative versus relational human resource

activity). For example, speaking to a charge nurse about staffing and scheduling for the next shift

was classified by the manager as a relational activity because the purpose from the manager’s

perspective was to support and coach the charge nurse. However, the purpose inferred by the

researcher, as an independent observer, was that the activity was administrative given the focus

on staffing and scheduling. The researcher was unable to infer the manager’s intention and thus

these classification categories were problematic.

In a second iteration of the classification scheme, the categories were redefined as staff contact

and indirect human resource activity. Time in staff contact consisted of verbal and written

communication and person-to-person interaction with direct report and non-direct report

employees working in the area(s) assigned to managers. Manager feedback was that substantial

communication also occurs with staff through email as most managers now carry a personal

digital assistant device (PDA). Staff contact through email was subsequently added to the

definition for staff contact. As well, managers indicated that contact with physicians was an

163

important aspect of their responsibilities in managing their assigned area(s). Physician contact

was subsequently included. Indirect human resource activity included any of the following

activities as long as there was no direct contact with staff: staffing, scheduling, evaluating and

observing performance, wage and salary administration, collective bargaining, dispute

resolution, and recognition and rewards. The 2 categories were mutually exclusive. Pre-testing

with 2 managers revealed that this second classification scheme was confusing, and that the

required concentration and time by the manager to classify the activities exceeded the managers’

resources as they were operating under pressure and juggling multiple activities.

Subsequently in a third iteration, the classification categories were redefined as staff contact and

human resource activity. Staff contact, emphasized the manager’s interactions with others.

Human resource activity focused on the content of the manager’s work. These categories were

consistent with the traditional types of categories used in structured observations of managerial

work (Martinko & Gardner, 1985). The categories were not mutually exclusive. Staff contact

consisted of verbal and written communication, email, and person-to-person interaction with

direct and non-direct report employees and physicians working in the area(s) assigned to

managers. Human resource activity included: updating staff on initiatives, policies, protocols;

staffing; scheduling; assigning or delegating work; administering wages, salaries, benefits;

evaluating staff performance; disciplining; handling staff complaints and disputes; developing or

giving orientation, training, and in-services; promoting, transferring, and terminating; recruiting,

interviewing, and hiring; and collective bargaining. Time spent in a human resource activity that

also involved interaction with staff (e.g., staff meeting) was counted in both categories. Inter-

observer agreement was assessed with one manager. The manager and the researcher rated the

presence or absence of time in staff contact at half hourly intervals. Disagreement on any one

activity or interaction during the half hour period resulted in the interval being coded as

disagreement (i.e., agree/disagree or disagree/agree). Based on Cicchetti’s (1981) guidelines, for

kappa sample sizes with two categories (e.g., agree/disagree) approximately 16 observations are

sufficient to estimate this parameter. Cohen’s Kappa was 0.75 for human resource activity and

0.86 for staff contact (n = 16 half hourly observations). The final work log is presented at the end

of this appendix.

164

Two additional issues arose regarding the metrics used in the operational definitions of the time

allocation measures as well as the overlap between classification categories. In terms of metrics,

the adjusted span measures initially proposed were operationally defined as the average daily

amount of time per employee. This metric adjusted the manager’s time allocation per direct

report and served to standardize the time values across managers. Values for time adjusted span

for human resource activity and for time adjusted span for staff contact are presented in Table 1

(rows 2 and 3). Table 1. Alternative Span Measures Metric Mean SD Min Max 1. Raw Span number of direct reports 86.6 36.2 29 174.3 2. Time Adjusted Span for Human Resource Activity minutes per direct report 2.0 1.2 .51 5.6 3. Time Adjusted Span for Staff Contact minutes per direct report 2.6 1.5 .78 6.5 4. Time in Human Resource Activity minutes per weekday 150 84 36 420 5. Time in Staff Contact minutes per weekday 192 84 84 426

However, the ‘time adjusted span’ metrics emphasized how much support and supervision are

received per direct report, rather than the manager’s capacity to support and supervise staff on a

typical weekday and led to non-meaningful values in this sample. For example, using the mean

time value for time adjusted span for staff contact (2.6 minutes per direct report; Table 1, row 3),

a manager with an average raw span of 87 would spend a minimum of 3.8 hours to a maximum

of 9.4 hours in staff contact daily. A manager with a wide raw span of 175 would spend a

minimum of 7.6 hours to a maximum of 19 hours in staff contact daily. These values are

nonsensical because the manager cannot engage solely in these aspects of his/her assigned work.

As well, when a manager covers an area with extended hours of operation, he/she cannot

interface with all staff during his/her workday and therefore a metric of daily time per employee

is not meaningful.

Because the raw spans observed in this study were very wide and because the phenomenon of

interest was the manager’s capacity to supervise and support staff (not the amount of supervision

and support received by each staff member), this metric was redefined as: (a) “the average daily

amount of time spent by the manager in human resource activity” (i.e., time in human resource

activity); and (b) “the average daily amount of time spent by the manager in staff contact” (i.e.,

time in staff contact).

165

However, the categories with the revised metrics were highly correlated (r = .78, p < .01; Table

2) indicating significant overlap. During observations of managers, the researcher noted that

managers spent substantial amounts of time in human resource activities that involved only a few

staff (e.g., union grievances or disciplinary issues) or that were not likely to influence the

supervision satisfaction or teamwork of current staff (e.g., seeking new hires). Time in staff

contact was the more fundamental concept of interest in terms of closeness of contact between

the manager and his/her staff. Therefore time in human resource activity was excluded from the

analysis.

Table 2. Pearson Correlations of Revised Alternative Span Measures Metric 1 2 1. Raw Span number of direct reports 2. Time in Human Resource Activity minutes per weekday 0.25 3. Time in Staff Contact minutes per weekday 0.26 0.78** * p < .05, ** p < .01

The final alternative measures of managerial span are presented in Table 3. To ease the

interpretation of time allocation relative to the manager’s workday, time in staff contact was

defined using hours instead of minutes.

Table 3. Final Alternative Measures of Managerial Span Metric Mean SD Min Max 1. Raw Span number of direct reports 86.6 36.2 29 174.3 2. Time in Staff Contact hours per weekday 3.2 1.4 1.4 7.1

166

Information and Consent Form for Managers for Pre-Test of Work Logs You are invited to participate in a pre-test for the research study entitled: Relationships between span, time allocation & leadership of first-line managers & nurse & team outcomes. Purpose of the Research - The pre-test of the work log & interrater reliability methods will help make sure that ‘time spent in human resource activities’ is well measured. The pre-test objectives are to get manager feedback about:

A. the work log instructions; B. the list for sorting work activities; C. the work log forms; D. the binders used to carry the work logs; E. the frequency of the work log entries F. the use of silent pagers; & G. ways that the researcher can stay out of the manager’s way when job shadowing the

manager while still understanding the general purpose of the manager’s activity. The goal of the overall research study is to understand how managers’ spans of control, time spent in management activities & leadership practices shape nurse satisfaction & multidisciplinary teamwork. To be eligible, you need to be a first-line manager who has worked in this position for at least 3 months. Research Funding – The study is being done for research purposes & is funded through a personnel award from the Canadian Institutes of Health Research & the Nursing Health Services Research Unit. This study is part of the requirement for doctoral student Raquel Meyer to complete a Doctor of Philosophy degree at the Faculty of Nursing, University of Toronto under the supervision of Dr. Linda O’Brien-Pallas. Description of the Research - Up to 3 managers will be sought to participate. The pre-test will last up to one work day for each manager. The researcher will job shadow you for the day. To fill in the work log, every 30 minutes you will record on paper the number of minutes spent in human resource activities & the percentage of those minutes spent in administrative or relational human resource activities. A silent pager to remind you to work log every 30 minutes will be tested. You can provide feedback at any time to the researcher. When the purpose of your work activities is unclear to the researcher (e.g., doing paperwork or email), the researcher will ask you to briefly explain the subject of your activity in general terms (e.g., budgeting, updating a clinical protocol). The researcher will remain as unobtrusive as possible by remaining at a distance or walking away when you require privacy or are approached by staff. After the interaction, the researcher will ask you to describe in general terms the subject of the activity (e.g., to discuss patient care, equipment issues, procedures or a personal issue). The researcher will discretely ask you questions about any observed difficulties & will ask to compare work log entries & to discuss reasons for differences between the manager’s & the researcher’s entries to improve the work log method.

167

Potential Harms, Risks & Inconveniences – There may be minimal discomfort associated with being shadowed by the researcher. Because the researcher will be shadowing you during your daily work activities, this means that other people at the Toronto East General Hospital may know that you have agreed to participate in the research. No individual manager will be identifiable in the study results. Potential Benefits – There are no direct benefits to you. However, your participation will assist in understanding the work flow issues faced by managers. Confidentiality & Privacy – All information obtained during job shadowing will be held in strict confidence. Your name will not be attached to the field notes. Only the research team will have access to the raw data. No names or identifying information will be used in any publication or presentations. All forms will be stored for 7 years in the Nursing Health Services Research Unit’s locked data storage unit & then destroyed. No information identifying you will be transferred outside the investigators of this research study. Publication of Results – Information obtained from the pre-test will be rolled up to the group level when reported. The pre-test process will be described in publications & presentations. No individual will be identifiable. Reimbursement – There is no reimbursement for your participation in this study. However all participation will occur during working hours & time away from your duties will be covered as necessary. Compensation for Injury – There is no risk for injury associated with participation in this study. Participation & Termination – Participation in research is voluntary. If you chose not to participate, it will not affect your job in any way. If you choose to participate in the pre-test for this study, you can withdraw from the pre-test at any time without any effect on your job. You may also refuse to answer any question(s) or choose to stop filling in the work logs at any time. Withdrawal from the pre-test does not necessarily include withdrawal of any data complied up to that point. Research findings from the overall study will be made available to participants. Questions Regarding Participation & Contact Information – If you have any questions as a research participant, you may contact the Chair of the Research Ethics Committee. If you have any questions about the study, you may contact the Student Investigator or Thesis Supervisor.

Faculty of Nursing, University of Toronto (Monday to Friday 9:00 – 5:00)

Toronto East General Hospital(Monday to Friday 9:00 – 5:00)

Doctoral Student Investigator: Raquel Meyer, RN, PhD(c)

Chair, Research Ethics Committee: Dr. Donald Borrett

Phone: 416-946-7154 Phone: 416-469-6580 x6639Thesis Supervisor: Linda O’Brien-Pallas, RN, PhD, FCAHS

Phone: 416-978-1967

168

If you choose to participate, please sign the consent form & return it to the investigator. Thank you. Raquel Meyer, RN, PhD(c) Faculty of Nursing, University of Toronto Phone: 416-946-7154 raquel.meyer@utoronto.ca

169

Date: Start time: Finish time: Worked time (exclude breaks):

Time HR Activity Notes & Tally

HR Min.

Staff Contact Notes & Tally

Staff Min.

Time HR Activity Notes & Tally

HR Min.

Staff Contact Notes & Tally

Staff Min.

7:00- 7:30 1:00-

1:30

7:30- 8:00

1:30- 2:00

8:00- 8:30

2:00- 2:30

8:30- 9:00

2:30- 3:00

9:00- 9:30

3:00- 3:30

9:30- 10:00

3:30- 4:00

10:00- 10:30

4:00- 4:30

10:30- 11:00

4:30- 5:00

11:00- 11:30

11:30- 12:00

12:00- 12:30

12:30- 1:00

HR Activity What HR work did I do? • Staff meetings & updates to staff on initiatives, policies, protocols • Staffing, scheduling; assign/delegate work • Administer wages, salaries, benefits • Evaluate staff performance; discipline • Handle staff complaints & disputes • Develop/give orientation, training, in-services • Promote, transfer, terminate; recruit, interview, hire • Collective bargaining

Staff Contact Who did I have contact with? Who: • Staff who report to you • Staff who work in your areas but may not report to you • Physicians who work in your areas Contact: • Verbal, written & email communication with staff (exclude brief greetings) • Person-to-person interaction with staff

170

Appendix E. Pearson Correlations of Study Variables

Table 1. Pearson Correlations of Level-2 Variables & Aggregated Level-2 Outcomes 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 1 Satisfaction (Aggregated) 2 Teamwork (Aggregated) .38* 3 Raw Span -.02 .06 4 Time in Staff Contact .07 -.05 .26 5 Leadership Practices - Other .54** .29 .18 .10 6 Hours of Operation .04 .02 .08 .15 -.02 7 Education -.09 .13 -.10 -.05 -.28 -.14 8 Experience -.14 -.38* .02 -.19 -.07 -.15 .27 9 Position Tenure .04 .17 -.05 -.17 -.14 .10 .06 .17 10 Worked Hours .23 .04 .36* .58** .26 -.08 .10 -.08 -.24 11 Administrative Support Roles -.29 -.25 .42* .25 -.04 .16 .10 .24 -.33 .19 12 Clinical Support Roles -.17 .19 .60** .27 -.14 .07 .30 -.03 .20 .23 .06 13 Total Areas -.15 -.27 .31 .14 -.08 .63** -.04 .25 .28 -.02 .43* .10 14 Occupational Diversity -.03 -.05 .16 .21 -.03 .30 -.10 .23 .11 -.01 .34 .07 .48** 15 Employee Tenure -.33 -.06 .08 .28 -.25 .19 -.04 -.30 .08 .24 .08 .20 .28 -.11 16 Full-time Employment -.13 -.28 .13 .15 -.18 .19 -.11 .15 -.15 .28 .42* .02 .21 .34 .26 17 Non-Direct Reports -.16 -.22 .54** .12 .19 .11 -.12 -.10 -.17 .09 .34 .34 .08 -.11 .14 .32 * p < .05, ** p < .01

Table 2. Pearson Correlations of Level-1 Variables & Level-1 Satisfaction Outcome 1 2 3 4 1 Satisfaction 2 Nurse Age -.01 3 Nurse Day Shift -.02 .10* 4 Nurse Education .01 -.32** .06 5 Nurse Registration -.08 -.08 .12** .22** * p < .05, ** p < .01 Table 3. Pearson Correlations of Level-1 Variables & Level-1 Teamwork Outcome 1 2 1 Teamwork 2 Occupational Group -.21** 3 Full-time Status -.08* -.06 * p < .05, ** p < .01

172

Appendix F. Letters of Permission to Use Instruments

173

174