Master thesis in Human Resource Studies

Knowledge sharing behavior as a catalyst for innovative work behavior

The role of job autonomy and social media

Student: Indy Wijngaards

ANR: 736175

Date: August 5, 2016

Supervisor: Dr. M. (Marinus) Verhagen

Second assessor: Dr. M.C. (Christina) Meyers

Project period: January 2016 until August 2016

Project theme: On-the-job innovation

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Abstract

In today’s hypercompetitive and connected world, organizations are increasingly compelled to

promote workplace innovations and trigger knowledge sharing among employees. Yet, studies

linking these two essential work behaviors are scarce. In addition, ongoing debates continue

about why and how job autonomy and social media influence knowledge sharing behavior (KSB)

and innovative work behavior (IWB). This study hypothesized that job autonomy influences

IWB both directly and indirectly through knowledge sharing (i.e. donating and collecting)

behavior, wherein social media use strengthens the relationship between KSB and IWB. Besides,

this study built on the ability (A), motivation (M) and opportunity (O) model of knowledge

sharing and hypothesized that job autonomy affects KSB through an employee’s combined AMO

to share knowledge. Using a dataset of 292 employees from 62 organizations in the Netherlands

and Aruba, Hayes process regression analyses were conducted to test the hypotheses. This cross-

sectional study discovered that knowledge donating behavior partially mediates the relationship

between job autonomy and IWB, while knowledge collecting does not. The results showed that

the combination of AMO to share knowledge fully mediates the relationship between job

autonomy and KSB. Surprisingly, the combination of AMO to share knowledge also turned out

to mediate the relationship between job autonomy and IWB. Moreover, although social media

use’s theorized moderating effect was not found, direct positive relationships with knowledge

donating behavior and IWB were found. Finally, several limitations, suggestions for further

research and practical implications were discussed.

Keywords: Job autonomy, AMO model, knowledge sharing behavior, knowledge

donating behavior, knowledge collecting behavior, innovative work behavior, social media

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Content

Abstract ................................................................................................................................... 2

Introduction ............................................................................................................................. 4

Theoretical framework ............................................................................................................ 6

Job autonomy and innovative work behavior ....................................................................... 6

Job autonomy, knowledge sharing behavior and the mediating effect of AMO to share

knowledge ........................................................................................................................... 7

Job autonomy, innovative work behavior and mediating effect of knowledge sharing

behavior ............................................................................................................................... 9

The moderating role of social media use .............................................................................10

Method ...................................................................................................................................11

Research design ..................................................................................................................11

Procedure............................................................................................................................12

Sample ................................................................................................................................12

Instruments .........................................................................................................................13

Statistical analysis ...............................................................................................................16

Results ...................................................................................................................................17

Descriptive statistics ...........................................................................................................17

Hayes process regression analyses ......................................................................................18

Conclusion and discussion ......................................................................................................25

Conclusion ..........................................................................................................................25

Discussion ..........................................................................................................................25

Limitations and directions for future research .....................................................................28

Recommendations for practitioners .....................................................................................30

References ..............................................................................................................................32

Appendix A ............................................................................................................................42

Appendix B ............................................................................................................................43

Appendix C ............................................................................................................................46

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Introduction

In pursuit of survival in today’s competitive and ever-connected world, organizations pay

particular attention to the innovativeness of individual employees (De Spiegelaere, Van Gyes,

De Witte, Niesen & Van Hootegem, 2014a). This trend compels organizations to leverage more

and more on knowledge as key resource and facilitator of employee innovativeness (Lin, 2007a;

Wang & Noe, 2010). Consequentially, knowledge sharing behavior (KSB) –– the process of

exchanging knowledge and skills between colleagues (Van den Hooff & De Ridder, 2004) ––

turns out to be increasingly important (Wang & Noe, 2010).

This study addresses the growing interest of both practitioners and academics in the field

of workplace innovations (UWIN, 2012) by means of focusing on innovative work behavior

(IWB) and KSB as a potential antecedent. IWB differs notably from an organization’s innovation

capacity (Lawson & Samson, 2001; Aulawi, Sudirman, Suryadi & Govindaraju, 2009), as it

covers a broader range of behaviors. In contrast to innovation capability, IWB not only regards

the introduction of new and innovative products, but also concerns the development, promotion,

discussion, revision and, eventually, implementation of innovative ideas, processes and

procedures (De Spiegelaere, Van Gyes & Van Hootegem, 2014b). While literature points out

that KSB could act as a catalyst for individual-level and firm-level innovation capability (Mura,

Lettieri, Radaelli & Spiller, 2013; Lin, 2007b), the relationship between KSB and IWB remains

heavily understudied (Radaelli et al., 2014).

Fortunately, evidence suggests that organizations have the ability to stimulate KSB and

trigger workplace innovations (Wang & Noe, 2010; Hammond, Neff, Farr, Schwall & Zhao,

2011). An often identified job characteristic that promotes these work behaviors, is job autonomy

(Foss, Minbaeva, Pedersen & Reinholt, 2009; Hammond et al., 2011). Although evidence is

already strong, this study addresses ongoing debates about how and why job autonomy impacts

KSB (Gagné, 2009; Kettinger, Li, Davis & Kettinger, 2015) and IWB (Battistelli, Montani &

Odoardi, 2013; de Spiegelaere et al., 2014a). For understanding these underlying mechanisms,

the meta-theoretical ability (A), motivation (M) and opportunity (O) model of knowledge sharing

is used. According to this model, work behavior (e.g. KSB) is determined by an employee’s

perceived capability, motivation and opportunity to do so (Siemsen, Roth & Balasubriamanian,

2008; Radaelli et al., 2014). It is argued that job autonomy particularly motivates employees

(Foss et al., 2009) and provides them the opportunity to share knowledge with colleagues

(Cabrera & Cabrera, 2005). In the same vein, job autonomy could give employees the

opportunity to experiment and come up with new work procedures to realize them (Hammond

et al., 2011). This study therefore concentrates not solely on the direct effect of job autonomy on

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IWB, but also on indirect effects via the combination of AMO to share knowledge and KSB.

KSB and workplace innovations can also be stimulated by means of information

communication technology (Burrus, 2010; Higón, 2012; Ravenscroft, Schmidt, Cook & Bradley,

2012). The rapid rise of social media (Ngai, Tao & Moon, 2015), in particular, yields new

approaches of doing business (Vaast & Kaganer, 2013) and initiating innovations (Aral et al.,

2013). However, solid evidence about the implications of using social media applications (e.g.

LinkedIn, Facebook and WhatsApp) for innovation purposes remains scarce (Anderson,

Potočnik & Zhou, 2014). Because social media provides employees the opportunity to virtually

share knowledge, brainstorm, refine ideas and built support (Paroutis & Al Saleh, 2009), this

study argues that the combination of KSB at the workplace and social media use results in

especially high engagement in IWB. Testing this claim, the present study aims at empirically

uncovering the effect of social media use in relation to IWB (Vaast & Kaganer, 2013; Gong, Lee

& Liu, 2015).

In terms of scientific relevance, the contribution of this study to the extant literature is

fourfold. First, it examines the barely investigated link between KSB and IWB (Radaelli et al.,

2014). Second, the study addresses research directions of Siemsen and colleagues (2008) by

incorporating the exchanging nature of KSB throughout the entire study. Third, following

directions posited by Kettinger et al. (2015) and De Spiegelaere et al. (2014a), the study provides

valuable insights by researching the manifold role of job autonomy in relation to KSB and

workplace innovations. Fourth, as the innovation literature left the area of social media use in

relation to IWB nearly untouched (Anderson et al., 2014), this study aims at serving as a starting

point for filling this gap.

From a managerial perspective, the study’s findings could improve the understanding and

practice of practitioners in the fields of human resource (HR), knowledge and innovation

management. If sharing knowledge with colleagues is in fact positively related to IWB, managers

from different fields (e.g. knowledge and innovation) are encouraged to combine forces and

jointly create strategy and policy (Aral et al., 2013). The same accounts for job autonomy; a

widely used job design tool for HR managers (Cabrera & Cabrera, 2005) and highly valued by

employees (Ryan & Deci, 2000; Barrick, Mount & Li, 2013; Lammers, Stoker, Rink & Galinsky,

2016). If significant effects are to be found, managers are offered an extra business case of why

to provide employees enough discretion in how they execute their tasks. Furthermore, as

practitioners in both the private (Blanchard, 2011; Aral, Dellarocas & Godes, 2013; Tuten &

Solomon, 2014) and public sector (Mergel, 2013) recognized the huge impact of social media

and struggle to exploit its benefits, the study´s results could provide organizations a concrete

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rationale of why to promote social media use for innovation purposes. Especially the

organizational tendency to perceive social media as “constraint” for value creation (Vaast &

Kaganer, 2013), could be reviewed and perhaps even be adapted into “enabler” for value

creation. Considering the above, the study’s aim is to answer two main research questions:

1. To what extent does job autonomy directly influence IWB and is this relationship

mediated through AMO to share knowledge and KSB?

2. To what extent does social media use moderate the relationship between KSB and IWB?

The next section will clarify the present study’s conceptual model in detail by discussing

the relationships between job autonomy, AMO to share knowledge, KSB, social media use and

IWB. In the subsequent section, a description of the adopted research design, procedure, sample,

instruments and statistical analyses will be provided. Hereafter, the results of the analyses will

be presented and hypothesis testing will be performed. To finish, the findings will be discussed

and limitations, future research directions and practical recommendations will be provided.

Theoretical framework

Job autonomy and innovative work behavior

Innovative work behavior (IWB) denotes “all employee behavior aimed at the generation,

introduction and/or application (within a role, group or organization) of ideas, processes,

products or procedures, new and intended to benefit the relevant unit of adoption” (De

Spiegelaere et al., 2014b, p.144). De Spiegelaere et al. (2014b) distinguish between four phases

within the concept of IWB: problem recognition, idea generation, idea promotion and idea

application. According to Dorenbosch, Van Engen and Verhagen (2005) and De Jong and Den

Hertog (2010), problem recognition is defined as the process of searching for ways to fine-tune

products, processes or services or attempting to think of them in different ways. Furthermore,

idea generation refers to recombination and reorganization of information and existing concepts

to resolve problems or enhance performance. Idea promotion refers to acquiring support,

building alliances by showing enthusiasm and confidence about the innovation, being

determinant and involving the right people. Finally, idea application refers to making innovations

part of standard work processes and behaviors (Dorenbosch et al., 2005; De Jong & Den Hertog,

2010; Baer, 2012).

Job autonomy – the extent to which an employee decides on work, procedures and

equipment use (Hackman & Lawler, 1971) – serves as an opportunity providing factor for

engaging in workplace innovations (Hammond et al., 2011). When employees are not provided

the opportunity to choose when and how to do their work, their ability to innovate may stifle. In

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accordance, job autonomy gives employees the freedom and independence to be proactive

(Parker, Williams & Turner, 2006) and experiment freely (e.g. Ramamoorthy, Flood, Slattery &

Sardessai, 2005; Radaelli et al., 2014). Evidence also suggests that job autonomy facilitates IWB

(e.g. Unsworth, Wall & Carter, 2005; Ohly, Sonnentag & Pluntke, 2006; Slåtten & Mehmetoglu,

2011; De Spiegelaere et al., 2014a). In line with former research, it is hypothesized that:

Hypothesis 1: Job autonomy enhances innovative work behavior.

Job autonomy, knowledge sharing behavior and the mediating effect of AMO to share

knowledge

Next to the direct effect of job autonomy on IWB, research indicates that job autonomy

also positively affects KSB (Cabrera, Collins & Salgado, 2006; Foss et al., 2009; Siemsen et al.,

2008). Knowledge sharing behavior (KSB) encompasses the process of mutually exchanging

information between individuals (Van den Hooff & De Ridder, 2004; He & Wei, 2009). KSB,

therefore, involves more than the dissemination of one’s acquired knowledge to others in the

organization (Hsu et al., 2007; Cavaliere, Lombardi & Giustiniano, 2015). It is characterized by

two active processes: knowledge donating behavior (i.e. granting knowledge to others) and

knowledge collecting behavior (i.e. seeking knowledge of others, Van den Hooff & De Ridder,

2004; Lin, 2007; Wang & Noe, 2010; Yan, Davison & Mo, 2013). Considering the exchanging

character, KSB is defined as “communicating to others what one’s personal intellectual capital

is, and consulting colleagues in order to get them to share their intellectual capital” (Van den

Hooff & De Ridder, 2004, p.118).

The ability (A), motivation (M) and opportunity (O) model (Macinnis & Jaworski, 1989)

serves as meta-theoretical underpinning for explaining the linkages between individual and

organizational determinants (e.g. job autonomy) and KSB within organizations (Siemsen et al.,

2008; Radaelli et al., 2014). The model assumes that the AMO components are interdependent,

yet conceptually distinct predictors of behavior (Macinnis & Jaworski, 1989). In general, the

model suggests that work behavior is determined by ability (‘can do’), motivation (‘will do’)

and opportunity (‘chance to do’). As substantiated in the knowledge literature (Siemsen et al.,

2008; Radaelli et al., 2014), the model asserts that individual KSB is a function of ability,

motivation and opportunity to share knowledge or, in short, KSB = f(A,M,O). Ability to share

knowledge refers to the skills and competence of an employee necessary to share knowledge

(Siemsen et al., 2008). Motivation to share knowledge refers to the deep-rooted drive to share

knowledge (Boudreau, Hopp, McClain & Thomas, 2003; Van den Hooff & De Ridder, 2004).

Opportunity to share knowledge refers to the factors in an employee’s environment that inhibit

or enable KSB (Siemsen et al., 2008).

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Hence, in order for an employee to share knowledge, they have to be willing, provided

the opportunity and capable to do so (Radaelli et al., 2014; Kettinger et al., 2015). Siemsen et al.

(2008) argue that AMO to share knowledge are highly interacting factors. They claim that the

absence of one AMO component potentially acts as bottleneck that completely inhibits an

employee to share knowledge at the workplace. For instance, an employee with a strong drive

and high competence to share knowledge, but with no time to share that knowledge, will not

likely engage in KSB at all. As empirical findings have yet to confirm or reject this claim, this

study investigates whether in fact synergy exists between the AMO elements. It is subsequently

hypothesized that:

Hypothesis 2a: The combination of ability, motivation and opportunity to share

knowledge enhances knowledge sharing behavior.

Hypothesis 2b: Synergy exists between ability, motivation and opportunity to share

knowledge.

The impact of job autonomy on KSB is explained using the AMO model of knowledge

sharing. First and foremost, job autonomy appears to serve as an opportunity providing factor,

as it gives employees the required freedom to invest time and effort in asking their colleagues

for knowledge and share their own knowledge (Cabrera & Cabrera, 2005; Siemsen et al., 2008).

Besides, job autonomy functions as a motivational factor in two ways; intrinsically and

extrinsically. According to Foss and colleagues (2009), job autonomy serves as antecedent of

intrinsic motivation to share knowledge – finding knowledge sharing itself “interesting,

enjoying, and stimulating” (p.875). In line with Ryan and Deci (2000), the authors explain that

autonomy is a basic psychological need and therefore a crucial determinant of intrinsic

motivation to collect and donate knowledge. Job autonomy may also function extrinsic

motivator, as autonomous employees will perceive higher utility from sharing knowledge than

employees who cannot deviate from work instructions and procedures (Cabrera et al., 2006). The

scholars reason that autonomous employees do so, because they have more practical need for it

and have the liberty to actually utilize the newly acquired knowledge in their day-to-day work.

From a socio-economic perspective, employees with high job autonomy will thus be more likely

to view the donation and subsequent collection of knowledge as a behaviors that yield more

benefits than costs (Lin, 2007b). Finally, although less obviously related, job autonomy could

impact ability to share knowledge. Evidence suggests that autonomous jobs give employees the

opportunity to informally (i.e. on-the-job) develop their ability to work in teams (Sorohan, 1993;

Gallie, Zhou, Felstead & Green, 2012). This ability is commonly associated with employees’

capability to share knowledge (Leinonen & Blueminck, 2008).

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In consideration of job autonomy’s link with AMO to share knowledge and the evidence

that suggests that the combination of AMO to share knowledge effectively predicts KSB, it is

hypothesized that:

Hypothesis 3a: Job autonomy enhances the combination of ability, motivation and

opportunity to share knowledge.

Hypothesis 3b: Job autonomy enhances knowledge sharing behavior.

Hypothesis 3c: The combination of ability, motivation and opportunity to share

knowledge mediates the relationship between job autonomy and knowledge sharing

behavior.

Job autonomy, innovative work behavior and mediating effect of knowledge sharing

behavior

Switching from antecedents to consequences of KSB, studies indicate that KSB

positively affects workplace innovations. KSB enhances employees’ idea generating capacity by

(a) forcing them to explain, integrate and translate knowledge to required understandable and

relevant information for the receivers (i.e. knowledge donating, Nijstad & Stroebe, 2006;

Radaelli et al., 2014; Akhavan, Hosseini, Abbasi & Manteghi, 2015), and (b) evaluating

reflections and input of the receivers of the shared knowledge (i.e. knowledge collecting, Paulus

& Brown, 2007; Radaelli et al., 2014). Furthermore, KSB increases the likelihood of successful

idea promotion and application. Employees are forced to translate their ideas into attractive and

understandable potential innovations for their coworkers and supervisor. Thereby, they are urged

to disseminate the knowledge required for the routinization of the innovation (Radaelli et al.,

2014; Akhavan et al., 2015).

In conclusion, it is theorized that the more employees share knowledge with their

colleagues, the more they will engage in workplace innovations (Radaelli et al., 2014). As

discussed in the previous paragraphs, the amount of perceived job autonomy is expected to

influence employees’ innovation and knowledge sharing behaviors. Hence, besides the

hypothesized direct effect of job autonomy on IWB (Hammond et al., 2011; De Spiegelaere,

2014; Giebels, De Reuver, Rispens & Ufkes, 2016), IWB is expected to be indirectly affected

by job autonomy through KSB. Consequently, it is hypothesized that:

Hypothesis 4a: Knowledge sharing behavior enhances innovative work behavior.

Hypothesis 4b: Knowledge sharing behavior partially mediates the relationship between

job autonomy and innovative work behavior.

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Having considered two mediation effects, job autonomy is also presumed to affect IWB through

two sequential mediators. It is argued that employees who perceive high job autonomy will have

relatively more combined AMO to share knowledge. Moreover, for that reason, they are assumed

to engage in KSB and, in turn, IWB more often than employees who have less job autonomy. As

a result, it is hypothesized that:

Hypothesis 5: The combination of ability, motivation and opportunity to share knowledge

and knowledge sharing behavior sequentially mediate the relationship between job

autonomy and innovative work behavior.

The moderating role of social media use

The direct effect of KSB on IWB could be strengthened by means of an employee’s usage

of social media. Social media are “internet-based applications that build on the ideological and

technological foundations of Web 2.0, and that allow the creation and exchange of user-

generated content’’ (Kaplan & Haenlein, 2010, p.61). They are characterized by social sharing,

collaboration, social networking and participation (Vaast & Kaganer, 2013; Gachago & Ivala,

2015; Mwanza-Simwami, 2016). This study examines two potentially fruitful and publically

available types of social media: social network sites (e.g. Facebook, LinkedIn and Instagram)

and mobile instant messaging applications (e.g. WhatsApp and Facebook Messenger)

(Majchrzak, Faraj, Kane & Azad, 2013; Church & de Oliveira, 2013; Gachago & Ivala, 2015),

because of their particular societal importance and understudied natur,.

Social media allows employees to build a virtual social network of people inside and

outside the organization. For innovation purposes, people in the employees’ network with the

same educational or professional background could especially act as valuable sources of

knowledge, feedback and support (Paroutis & Al Saleh, 2009; Gachago & Ivala, 2015). It is

argued that after initial KSB at work, a second round of communication via social media could

enhance the odds of successful idea generation and implementation. For instance, when

employees share their ideas with their colleagues at work and then search for additional feedback

and support on social media, the idea is likely to be further customized and better realizable than

an idea for which no social media was used to optimize it. Interacting (i.e. donating and collecting

knowledge) about work-related ideas and innovations with contacts via social media may

therefore serve as an extra booster (i.e. strengthener) for KSB, which could, consequently, result

in more frequent engagement in IWB (Chiu, Hsu, & Wang, 2006; Paroutis & Al Saleh, 2009).

As such, it is hypothesized that:

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Hypothesis 6: Social media use strengthens the relationship between knowledge sharing

behavior and innovative work behavior.

On basis of the arguments in the previous paragraphs, it is expected that the sequential

mediating effect through the combination of AMO to share knowledge and KSB, is stronger for

employees who frequently use social media than for employees who do not (i.e. moderated

mediation effect, Preacher, Rucker & Hayes, 2007). In other words, employees who show KSB

due to their job autonomy, will be more likely to have enhanced AMO to share knowledge and

subsequent KSB. As a result, KSB is more probable to be translated into workplace innovations

if employees attain high levels of social media use to further strengthen their initial KSB. As

such, it is hypothesized that:

Hypothesis 7: Social media use moderates the strength of the, through the combination

of ability, motivation and opportunity to share knowledge and knowledge sharing

behavior, sequentially mediated relationship between job autonomy and innovative work

behavior, such that the indirect effect of knowledge sharing behavior is stronger under

high levels of social media than under low levels of social media use.

Based on the above reasoning and hypothesizes, the current study’s conceptual model is

presented in Figure 1. All arrows are hypothesized to represent positive relationships.

Figure 1

Present study’s conceptual model

Note. H = Hypothesis; AMO = Ability, motivation and opportunity.

Method

Research design

An explanatory study was designed to test the formulated hypotheses and consequently

answer the research questions. This study concentrated on employees who are doing paid work

in organizations. Cross-sectional, quantitative research was performed within multiple

organizations and industries within the Kingdom of the Netherlands, the Netherlands and Aruba

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in specific. Under supervision of Dr. M. Verhagen, the study was conducted within a broader

master thesis circle consisting of two master students in HR Studies from Tilburg University.

The period of data collection ranged from mid-April to mid-May 2016. The collective effort of

obtaining data yielded a total of 292 respondents.

Procedure

In collaboration with a fellow student, a questionnaire was designed that measured the

respective studies’ variables of interest. As Aruba and the Netherlands have different languages,

the questionnaire was published in two languages: English and Dutch. The questionnaires were

translated by the researchers and hereafter checked for potential flaws (e.g. language mistakes

and unclear questions) using a pilot study with native English and Dutch speakers. Both students

were responsible for collecting at least 100 respondents. Respondents were obtained in two ways:

approaching organizations and using students’ social network. Various organizations were

approached and motivated to participate to the present study. Subsequently, the participating

organizations sent the questionnaire to employees of their choice; filling in the questionnaire was

not mandatory. The students’ (virtual) social networks (i.e. LinkedIn and Facebook) were also

used to gather additional respondents for the study.

Regarding the format of the questionnaire, the participating Aruban organizations

received hard-copy questionnaires and accompanying information letters, which in turn were

distributed to individual employees. The questionnaire was also prepared in the software

program Qualtrics. All Dutch respondents received a link to this online questionnaire via email.

In both questionnaires, the anonymity of the respondents was guaranteed, as there was no

possibility to fill in name and address details. Afterwards, the completed questionnaires were

obtained, either in person or digitally, and entered in 22nd version of SPSS.

Sample

This study considered two notably different target groups: the Dutch and Aruban working

population. Therefore, the demographics within the data were benchmarked to the respective

nations. The complete overview of the demographics characteristics of the combined sample is

to be found in Appendix A.

The Aruban sample (N=143) contained 31.9 percent male and 68.1 percent female

respondents with an average age of 40 years. The results regarding gender were considered

unrepresentative, while those regarding age were considered representative. The Central Bureau

of Statistics (CBS) in Aruba (CBS, 2010) indicated that the Aruban working population consists

of 50.2 percent male and 49.8 percent female workers with an average age ranging between 40

and 44 years. The average education level in the sample was middle and considered as relatively

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high, as secondary school is the average education level (CBS, 2010).

The Dutch sample (N=149) consisted of 63.8 percent male and 36.2 percent female with

an average age of 40.7 years. Recent data of the Dutch CBS indicated that the working population

consists of 53.8 percent male and 46.2 percent female (CBS, 2016a) with an average age of 41.9

years (CBS, 2015). Therefore, results in terms of gender in the Dutch sample were regarded

unrepresentative, whereas the results for age were regarded representative. The average

educational level in the sample was middle/higher and was considered as relatively high, as

secondary school/middle is the average education level (CBS, 2016b). The Aruban and Dutch

respondents both had an average tenure of 5-10 years.

To boost the power of the study, it was decided to combine the data from both nations

for the analyses and control for the country in which the organization was located. In terms of

the whole sample, the combined dataset was characterized by a representative average age (i.e.

40 years old), divergent gender distribution (i.e. 52.1 percent female and 47.1 percent male) and

a relatively high educational level (i.e. middle) of the respondents. Studying a total of 63

organizations in both the public and private sector in Aruba and the Netherlands, a variety of

industries was incorporated in this study (e.g. health care, engineering, hospitality and higher

education).

Instruments

All respondents were asked to rate themselves or their job in terms of job autonomy,

motivation, opportunity and ability to share knowledge, KSB, social media use and IWB.

Considering the cross-national nature of the study, the items and answer categories were

translated from English into Dutch (if no bilingual version of the scale was available). Before

any hypotheses testing was conducted, all scales were tested on construct validity and reliability.

Regarding construct validity, Principal Axis Factoring (PAF) analyses were conducted and

oblique rotation was performed. As criteria for factor analysis, it was decided that the Kaiser-

Meyer-Olkin (KMO) measure had to exceed .6 (Cramer, 2004) and the scales’ Eigenvalues (1

or higher) were to determine the amount of components. In terms of scale reliability, Cronbach’s

⍺ and Cronbach’s ⍺ if-item-deleted were considered. Cronbach’s ⍺ of >.6 and >.7 respectively

were considered as sufficient and good (Evers, Vliet-Mulder & Groot, 2000) and Cronbach’s ⍺

if-item-deleted had to be lower than the scale’s Cronbach’s ⍺. In the next paragraphs, only the

specific items that did not suffice the criteria mentioned above were discussed. All scales (Dutch

and English) with corresponding factor loadings, Cronbach’s ⍺ and Cronbach’s ⍺ if-item-deleted

were presented in Appendix B. The original English questionnaire is depicted in Appendix C.

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Job autonomy. Job autonomy was assessed using the 4-item ‘independence in the job’

scale originating in the Questionnaire on the Experience and Evaluation of Work 2.0 (QEEW

2.0) (Van Veldhoven, Prins, Van der Laken & Dijkstra, 2015). Respondents were asked to rate

themselves on a 4-point Likert scale (1 = Always, 2 = Often, 3 = Sometimes, 4 = Never). An

example question was: “Can you organize work yourself?”. Scale reliability was good (⍺ = .808).

Ability, motivation and opportunity to share knowledge. Motivation, opportunity and

ability to share – collect and donate – knowledge were respectively addressed using three 3-item

scales from Radaelli et al. (2014). Since the authors above solely studied AMO-components with

respect to knowledge donating dimension, the scales was transformed so that they considered

the exchanging nature of KSB. This was done by providing a short description of knowledge

donating and knowledge collecting behavior in the scale introduction. The scale had answer

categories ranging on a 5-point Likert scale (1 = I totally disagree, 2 = I disagree, 3 = Neutral, 4

= I agree, 5 = I totally agree). Example questions of respectively the ability, motivation and

opportunity to share knowledge scales were: “I believe I am fully capable of sharing knowledge

at any time”, “I intend to frequently share knowledge” and “I can devote enough time to sharing

knowledge”.

The criteria for factor analysis were not satisfied, as PAF of the three 3-item scales only

indicated two factors (Eigenvalue factor 1 = 3.655; Eigenvalue factor 2 = 1.418), which were not

in line with Radaelli et al.’s conceptualization of the AMO elements. Therefore, it was decided

to disregard the separate elements in the remainder of the study and consider the average score

on the summation of ability, motivation and opportunity to share knowledge. For this 9-item

scale, the remaining criteria for factor and reliability analysis were satisfied.

KSB. KSB was assessed using a 6-item scale adapted from Van den Hooff and De Ridder

(2004) and Lin (2007a). In an effort to further contextualize their scales, the respondents were

asked how often they related to the statements in the past six months. Respondents were invited

to rate themselves both on the knowledge donating (4-item) and knowledge collecting (2- item)

subscales on a 5-point Likert scale (1 = Never, 2 = Seldom, 3 = Neutral, 4 = Often, 5 = Always).

An example question was: “I shared my knowledge with people in my organization”. Factor

analysis suggested two components in the scale (Eigenvalue factor 1 = 3.356; Eigenvalue factor

2 = 1.512). The Cronbach’s ⍺ if-item-deleted of the two last items (.837 and .839) were

exceeding the value of Cronbach’s ⍺ (⍺ = .827). These results were in accordance with the

conceptualization of KSB of Van den Hooff and De Ridder (2004), as the two factors

respectively measured knowledge donating and knowledge collecting behavior. Likewise, the

two items that were exceeding the value of Cronbach’s ⍺ belonged to the collecting subscale.

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This study, therefore, split up the KSB scale and separately considered the two dimensions (i.e.

donating and collecting) within the analyses.

Social media use. Social media use was assessed using an 8-item scale, developed

especially for this research and based on the conceptualization of IWB of De Jong and Den

Hartog (2010). It measured the extent to which respondents utilized social media for their IWB

and distinguished between two dimensions: social media use for idea generation and social media

use for idea implementation. Again, for contextualization purposes, the respondents were asked

to rate their social media use within a six months period. As introduction to the questions, a

description of the social media use objective (i.e. usage for work innovations), applications (e.g.

LinkedIn, WhatsApp, Facebook and Facebook Messenger) and contacts (i.e. same educational

or professional background) was provided. Answer categories ranged on a 4-point Likert scale

(1 = Never, 2 = Seldom 3 = Sometimes, 4 = Often). An example question was: “I used social

media to mobilize support for my work-related ideas and solutions”. Factor analysis indicated

only one component and scale reliability was very good (⍺ = .943). Yet, the first item contributed

marginally to the scale’s construct validity (.141) and its Cronbach’s ⍺ if-item-deleted (.972)

exceeded the value of Cronbach’s ⍺. Therefore, this item was excluded from the scale.

IWB. IWB was assessed with a 10-item scale from De Jong and Den Hartog (2010). All

dimensions – problem recognition, idea generation, idea promotion and idea application – were

examined using scale. The answer categories ranged on a 5-point Likert scale (1 = Never, 2 =

Rarely, 3 = Sometimes, 4 = Often, 5 = Always). An example question was: “I find new

approaches to execute tasks”. The Cronbach’s ⍺ if-item-deleted of the first item (.922) was

exceeding the value of Cronbach’s ⍺ of the scale (⍺ = .921). However, regarding the marginal

difference (∆α = .01) and the solid theoretical and empirical foundation behind the scale, this

item was not excluded from the scale.

Control variables. In total, five control variables were included in this study: age, gender

(0 = Male, 1 = Female), tenure (0 = Short tenure, 1 = Long tenure) educational level (0 = Lower

education, 1= Higher education) and country (0 = Aruba, 1 = the Netherlands). The latter four

were included as dummy-variables in the analyses, as they were either measured on a nominal

(i.e. gender and nationality) or an ordinal (i.e. tenure and educational level) scale. Lower

education consisted of primary, secondary and middle (i.e. MBO) level education and higher

education of higher (i.e. HBO) and university level of education. Short tenure entailed a tenure

lesser than 10 years, whereas long tenure meant tenure longer than 10 years.

The choice for these control variables was based on a variety of research findings. First,

older people seem to use social media less frequently (Correa, Hinsley & De Zuniga, 2010) and

16

possess lower idea generation capacity (Cohn, Emrich & Moscovitch, 2008; Bender, Naveh-

Benjamin & Raz, 2010). Second, evidence indicated that females perceive less job autonomy

(Adler, 1993), share knowledge more frequently (Lin, 2008), use social media differently than

males (Correa et al., 2010) and present IWB less frequently (Janssen, 2000). Third, higher

educated individuals tend to share knowledge more frequently (Sveiby & Simons, 2002). Fourth,

employees with a longer tenure are more inclined to share knowledge (Watson & Hewett, 2006)

and exhibit IWB (Dorenbosch et al., 2005). Finally, the country the organization was located in

was included as control variable, as differences in national culture may interfere with the results

(Hofstede, Hofstede & Minkov, 2010).

Statistical analysis

As a starting point, the data was checked for completeness using data screening. Missing

values were then recoded in ‘-99’ and pairwise excluded from the dataset. The scales were

checked for construct validity and reliability. Hereafter, the scale averages were computed. A

bivariate correlation (Pearson’s r) analysis was performed to check for multicollinearity between

the variables (i.e. dependent, independent and control).

Hayes Process Regression Analysis (HPRA) (Hayes, 2013) was used to test all

hypothesizes. Five models were tested: a moderated parallel mediation model (Model 14), two

simple mediation models (Model 4) and two sequential mediation models (Model 6). Notably, it

was not possible to test all hypotheses in one single model, as there is no macro that covers a

sequential mediation with two parallel mediators (i.e. the two KSB dimensions) and an

interaction term (i.e. social media use). The model’s hypothesized effects were computed using

the bootstrap method. The number of bootstrap was 5000 and the confidence interval (CI) was

determined to be 95 percent. There were multiple advantages of using HPRA. First, considering

the rather small sample size of this study, it was useful that the HPRA did not by definition

assume normality of the data distribution (Hayes, 2013). Second, by using the HPRA’s

bootstrapping method the power of the data was boosted. Third, an additional advantage of using

HPRA was that it presented the significance of effects on different levels of the interaction

variable. All analyses were performed, while controlling for gender, age, country, educational

level and tenure.

Firstly, a moderated parallel mediation analysis was conducted to examine whether

knowledge donating and knowledge collecting behavior mediate the relationship between job

autonomy and IWB in a parallel fashion. In addition, it was investigated whether social media

use moderated the relationships between the two KSB dimensions and IWB and, if so, the above

17

mediation model proceeded conditionally (i.e. different for lower and higher levels of social

media use).

Secondly, this study investigated the mediating effect of the combination of AMO to

share knowledge in the relationship between job autonomy and KSB. Regrettably, it was not

possible to check for synergy between the AMO elements, as factor analysis did not show a clear

three-factor structure in the scales. It was therefore decided to combine the items of the three

scales and use the combined variable in the analysis. In specific, two simple mediation analyses

were performed to find out whether the combination of AMO to share knowledge mediates the

relationship between job autonomy and, respectively, knowledge donating and knowledge

collecting behavior.

To test the whole model, two sequential mediation analyses were performed. The first

one examined the impact of job autonomy on IWB via two sequential mediators, the

combination of AMO to share knowledge and knowledge donating behavior. This analysis was

performed, while controlling for the moderating effect of social media use. The second analysis

was nearly identical, but instead considered knowledge collecting behavior as second mediator.

As there is no predefined HPRA macro available for estimating moderated sequential mediation

models, this study followed Hayes’ (2015) recommendation to include the hypothesized

interaction term (i.e. social media use) as a control variable within the conventional sequential

mediation analyses. This interaction term was created by first standardizing the concerned

variables and then multiplying them. Specifically, two interaction terms were created: (i) social

media use*knowledge donating behavior and (ii) social media use*knowledge collecting

behavior. In contrast to the moderated mediation model that was tested first, the outcomes of

these analyses did not display the indirect effects for different levels of social media use.

Results

Descriptive statistics

Within this section, the results of the bivariate correlation analyses were described. An

overview of the mean scores, standard deviations and Pearson correlations of the independent,

dependent and control variables was presented in Table 1.

The data showed that IWB is positively correlated with knowledge donating behavior,

job autonomy, social media use and the combination of AMO to share knowledge. Notably, IWB

was not related to knowledge collecting behavior; implying that asking for knowledge does not

incline showing enhanced IWB. Knowledge donating and knowledge collecting behavior

correlated significantly with the combination of AMO to share knowledge. Besides, while

knowledge donating behavior was significantly related to job autonomy and social media use,

18

no significant bivariate correlations were found between knowledge collecting behavior and,

respectively, job autonomy and social media use. In addition, respondents perceiving high job

autonomy scored higher on the combination of AMO to share knowledge compared to

respondents with less autonomous jobs.

Furthermore, respondents from Dutch organizations were more apt to show IWB,

perceived higher job autonomy and used social media for innovation purposes more frequently

compared to their Aruban counterparts. Interestingly, within Dutch organizations knowledge

donating occurred more frequently than in Aruban organizations, whereas respondents working

in Aruban organizations were relatively more eager to collect knowledge. Female respondents

were significantly more inclined to donate knowledge and use social media. Older respondents

scored higher on the combination of AMO to share knowledge, were more prone to donate

knowledge and perceived more job autonomy than younger respondents. In general, respondents

with a long tenure donated more knowledge, collected less knowledge and perceived more job

autonomy compared to respondents with a shorter tenure. Finally, respondents with a higher

educational level donated less knowledge than respondents with a lower level of education.

Table 1

Means, standard deviations, and correlations of variables (Pearson’s r) (N=275–292)

Note. ** = Correlation is significant at the .01 level (2-tailed); * = Correlation is significant at the .05 level (2-tailed); Reference category gender

= male; Reference category country = Aruba; AMO = Ability, motivation and opportunity; SD = Standard deviation; M = Sample mean; N =

Total number of cases.

Hayes process regression analyses

The testing of the theorized direct, indirect and conditional effects was based on HPRA.

Firstly, the relationships between job autonomy, KSB, social media use and IWB were examined

in a moderated parallel mediation model. Secondly, the mediating role of AMO to share

knowledge in the relationship between job autonomy and the KSB dimensions was investigated

in two simple mediation models. Lastly, the full model was tested in two sequential mediation

analyses. In contrast to the correlation matrix, the displayed coefficients below are

M SD 1. 2. 3. 4. 7. 8. 9. 10. 11. 12. 13.

1. Innovative work behavior 3.592 0.695

2. Knowledge donating behavior 3.836 0.755 .493**

3. Knowledge collecting behavior 3.983 0.772 .033 .251**

4. Combination of AMO to share knowledge 3.946 0.488 .392** .564** .396**

7. Job autonomy 3.020 0.616 .430** .233** -.014 .202**

8. Social media use 2.097 0.923 .427** .396** .010 .195** .121*

9. Gender .520 .089 .223** .006 .072 .070 .196**

10. Age 40.030 12.545 -.008 .148* -.083 .150* .160** .060 .072

11. Country .490 .429** .352** -.174** .107 .198** .479** .319** -.004

12. High level of education .374 .047 -.229** .084 -.040 .101 -.129* -.177** -.120* -.295**

13. Long tenure .239 .072 .140* -.133* .075 .167** -.024 .047 .512** .088 -.045

19

unstandardized. A coefficient was considered significant, if 0 was not within the confidence

interval, it had a p-value smaller than .05, or both. All hypotheses were assessed controlling for

gender, age, country, level of education and tenure.

First of all, the postulated effects between job autonomy (𝑋), knowledge donating (𝑀1)

and knowledge collecting behavior (𝑀2), social media use (𝑊) and IWB (𝑌) were assessed using

a moderated parallel mediation analysis (Model 14) (Hayes, 2013). The model was displayed in

conceptual form in Figure 2 and the estimated regression coefficients were presented in Table 2.

Figure 2

Conceptual model visualizing the hypothesized moderated parallel mediation model

Note. X = Independent variable; M = Mediator; W = Moderator; Y = Dependent variable.

The first hypothesis was confirmed, as job autonomy indeed turned out to be a predictor

of IWB (b = 0.313, p < .01). This suggested that employees who perceive high job autonomy,

are more likely to display IWB. Furthermore, the results pointed out that knowledge donating

behavior predicts IWB (b = 0.323, p < .01), while knowledge collecting behavior does not (b =

-0.119, ns). Hypothesis 4a is therefore partially accepted, as solely one dimension within the

concept of KSB positively affected IWB. It seems that employees who often donate knowledge,

present higher levels of IWB, compared to employees who donate knowledge less frequently.

Employees who often collect knowledge, do not exhibit more IWB than employees who are not

as eager to collect knowledge. With respect to the control variables, the analysis showed that

country and educational level play significant roles in the prediction of knowledge donating

behavior, knowledge collecting behavior and IWB. The HPRA also indicated that female

employees significantly report lower scores on IWB compared to male employees.

The mediating potential of both knowledge donating and knowledge collecting behavior

in the relationship between job autonomy and IWB was examined for different levels of social

media use. The findings showed that social media use does not function as a strengthener in the

relationship between knowledge donating behavior and IWB (b = 0.012, ns), as well as

20

knowledge collecting behavior and IWB (b = 0.036, ns). As a result, hypothesis 6 and the

subsequent assumption for a moderated mediation were rejected (Preacher et al., 2007)

In consideration of the indirect effects, HPRA indicated that knowledge donating

behavior is a significant mediator in the relationship between job autonomy and IWB at low and

high levels of social media use (b = 0.064, CI = 0.015 to 0.144 at -1 SD; b = .059, CI 0.012 to

0.145 at +1 SD). However, the results showed that knowledge collecting behavior does not

mediate the relationship between job autonomy and IWB (b = -0.003 CI = -0.035 to 0.009 at -1

SD; b = 0.000, CI = -0.017 to 0.010 at +1 SD). Notably, the direct effect of job autonomy on

IWB remained present in the parallel mediation model (b = 0.313, p < .01). This suggests that

knowledge donating only partially mediates the relationship between job autonomy and IWB.

As such, hypothesis 4b was partially confirmed. An employee who perceives high job autonomy

exhibits higher levels of IWB, while also displaying IWB, because of their more frequent

knowledge donating behavior at work, compared to employees with less job autonomy.

Table 2

Mediating effect of KSB for different levels of social media use (N=250)

Note. SPSS Bootstrap results PROCESS macro (Hayes, 2013). CI = 95%; Number of bootstrap: 5000; ** = Coefficient is significant at the .01

level (2-tailed); * = Coefficient is significant at the .05 level (2-tailed); KSB = Knowledge sharing behavior; IWB = Innovative work behavior;

se = Standard error; SD = Standard deviation; ; se = Standard error; t = the sample value of the t-test statistic; p = probability; LLCI = Lower

Level Confidence Interval; ULCI = Upper Level Confidence Interval; R2 = Multiple correlation squared (measure of strength of association); F

= Critical value for statistical significance in an F test; N = Total number of cases.

Consequent

Model 1a Model 1b

Knowledge donating

behavior

IWB Knowledge collecting

behavior

IWB

Antecedent coefficient se p coefficient se p coefficient se p coefficient se p

Job autonomy (path a and c’) 0.207 .074 .000 0.313 0.057 .000 0.034 0.082 .680 0.313 0.057 .000

KSB (path b) 0.323 0.048 .000 -0.119 0.098 .225

Social media use 0.032 0.227 .887 0.032 0.227 .887

Interaction term (KSB*Social media

use)

-0.012 0.048 .805 0.036 0.043 .403

Gender 0.136 .091 .137 -0.135 0.068 .049 0.063 0.102 .533 -0.135 0.068 .049

Age 0.005 .004 .273 -0.006 0.003 .054 -0.001 0.005 .822 -0.006 0.003 .054

Country 0.358 .097 .000 0.345 0.085 .000 -0.359 0.108 .001 0.345 0.085 .000

Higher level of education -0.204 .093 .029 0.274 0.070 .000 -0.003 0.104 .975 0.274 0.070 .000

Long tenure 0.065 .110 .555 0.081 .085 .337 -0.197 0.123 .110 0.081 0.085 .337

Mediating role of the KSB in

relationship between job autonomy

and IWB under different levels of

social media use

Bootstrapping for testing

significance of indirect effect

Effect se LLCI ULCI Effect se LLCI ULCI

KSB (Social media use: 1 SD

below)

0.064 0.032 0.015 0.144 -0.003 0.009 -

0.035

0.009

KSB (Social media use: mean) 0.062 0.030 0.016 0.136 -0.001 0.006 -

0.027

0.006

KSB (Social media use 1 SD above) 0.059 0.033 0.012 0.145 0.000 0.006 -

0.017

0.001

R2 = 0.435 R2 = 0.478 R2 = 0.066 R2 = 0.478

F(6, 243) = 9.571** F(11, 238) = 19.835** F(6, 243) = 2.920** F(11, 238) = 19.835**

21

To further understand the relationship between job autonomy (𝑋) and, respectively

knowledge donating (𝑌1) and knowledge collecting behavior (𝑌2), two additional mediation

analyses (Model 4) were performed with, in both cases, the combination of AMO to share

knowledge (𝑀) as mediator (Hayes, 2013). The model was visualized in conceptual form in

Figure 3 and the estimated regression coefficients were depicted in Table 3.

Before AMO to share knowledge was considered within the mediation analyses, it was

required to determine whether synergy exists between the different AMO elements or the linear

model had to be adopted. Unfortunately, factor analysis indicated that the respective AMO scales

did not represent three distinct factors and that this question and, subsequently, hypothesis 2b

could not be addressed. Therefore, this study decided to use the mean of the summation of AMO

to share knowledge items (i.e. the linear model).

Figure 3

Conceptual model visualizing two hypothesized simple mediation models

Note. X = Independent variable; M = Mediator; Y = Dependent variable; AMO = Ability, motivation and opportunity.

Using this combined variable, hypothesis 2a was confirmed, as the combination of AMO

to share knowledge impacted both knowledge donating and knowledge collecting behavior

(respectively, b = 0.776; b = 0.700, p < .01). This implies that an employee who has the ability,

motivation and opportunity to share knowledge, in turn, shares (i.e. donates and collects)

knowledge more frequently. Hypothesis 3a was also confirmed by the data, as job autonomy

significantly forecasted the combination of AMO to share knowledge in both mediation analyses

(b = 0.143; b = 0.138 p < .01). As such, the more job autonomy an employee perceives, the more

combined of ability, motivation and opportunity to share knowledge one will have. Hypothesis

3b was completely rejected, as controlling for the combination of AMO to share knowledge, job

autonomy did not predict knowledge donating (b = 0.112, ns) and knowledge collecting behavior

(β = -0.043, ns). Nevertheless, when disregarding the AMO model, job autonomy appeared to

be a significant predictor of knowledge donating behavior (b = 0.143, p < .01). Remarkably, in

22

line with the results from the correlation matrix, the analyses indicated that within Dutch

organizations knowledge collecting behavior is less common than in organizations in Aruba.

Alternatively, knowledge donating behavior occurs more frequently in Dutch organizations than

in Aruban organizations. With respect to educational level, the findings indicated that employees

with a lower educational level donate knowledge more often than higher educated employees.

The results of the HPRA confirmed the mediating role of AMO to share knowledge in the

relationship between job autonomy and KSB. In specific, the combination of AMO to share

knowledge functioned as mediator in the relationship between job autonomy and knowledge

donating behavior (b = 0.111, CI = 0.034 to 0.198) and job autonomy and knowledge collecting

behavior (b = 0.096, CI = 0.026 to 0.181). For the results indicated that no direct effect of job

autonomy on knowledge donating behavior and knowledge collecting behavior exists when

considering the combination of AMO to share knowledge in the analyses, a full mediation was

found. As such, hypothesis 3c was confirmed. Hence, employees who experience high job

autonomy, in turn have more combined ability, motivation and opportunity to share knowledge

and are consequently more likely to actually donate and collect knowledge.

Table 3

Mediating effect of the combination of AMO to share knowledge (N=258 and N=263)

Note. SPSS Bootstrap results PROCESS macro (Hayes, 2013). CI = 95%; Number of bootstrap: 5000; ; ** = Coefficient is significant at the .01

level (2-tailed); * = Coefficient is significant at the .05 level (2-tailed); AMO = Ability, motivation and Opportunity; se = Standard error; t = the

sample value of the t-test statistic; p = probability; LLCI = Lower Level Confidence Interval; ULCI = Upper Level Confidence Interval; R2 =

Multiple correlation squared (measure of strength of association); F = Critical value for statistical significance in an F test; N = Total number of

cases.

Consequent

Model 1a Model 1b

Combination of AMO to

share knowledge

Knowledge donating

behavior

Combination of AMO

to share knowledge

Knowledge collecting

behavior

Antecedent coefficient se p coefficient se p coefficient se p coefficient se p

Job autonomy (path a and c’) 0.143 .050 .000 0.112 0.063 .077 0.138 .050 .007 -0.043 0.073 .554

Combination of AMO to share knowledge (path b) 0.776 0.078 .000 0.078 0.010 .000

Gender 0.009 .062 .881 0.101 0.077 .188 0.010 .062 .866 0.077 0.089 .385

Age 0.005 .003 .069 0.000 0.004 .995 0.005 .003 .101 -0.006 0.004 .155

Country 0.047 .067 .477 0.298 0.082 .000 0.038 .066 .567 -0.368 0.095 .000

Higher level of education -0.008 .064 .904 -0.221 0.079 .006 -0.004 .064 .955 0.010 0.091 .911

Long tenure -0.021 .076 .780 0.077 0.093 .412 -0.002 -.002 .976 -0.157 0.107 .144

Mediating role of the combination of AMO to share

knowledge in relationship between job autonomy and

KSB

0.111 0.042 0.096 0.039

Bootstrapping for testing significance of indirect effect

Bias corrected confidence intervals LLCI 0.254 LLCI 0.026

ULCI 0.198 ULCI 0.181

R2 = 0.064 R2 = 0.414 R2 = 0.059 R2 = 0.242

F(6, 252) = 2.887* F(7, 251) = 19.907** F(6, 256) = 2.662* F(7, 255) = 11.609**

23

To test the whole model, two sequential mediation analyses (Model 6) were performed

(Hayes, 2013). For the first analysis, the effect of job autonomy (𝑋) on IWB (𝑌) was considered

via the combination of AMO to share knowledge (𝑀1) and knowledge donating behavior (𝑀2),

while controlling for the interaction effect of social media use (𝑊). The second analysis differed

with the previous in its inclusion of knowledge collecting behavior (𝑀3) as second sequential

mediator. Social media use was included as interaction term and entered as control variable

within the analysis. The model was conceptually exhibited in Figure 4.

Figure 4

Conceptual model visualizing hypothesized moderated sequential mediation model with two

parallel mediators

Note. X = Independent variable; M = Mediator; W = Moderator; Y = Dependent variable; AMO = Ability, motivation and opportunity.

The results of the first analysis showed that job autonomy affects IWB directly (b < 0.292,

CI = 0.190 to 0.408), via the combination of AMO to share knowledge (b < 0.032, CI = 0.007 to

0.082), via knowledge donating behavior (b < 0.030, CI = 0.001 to 0.086) and, consecutively,

via the combination of AMO to share knowledge and knowledge donating behavior (b < 0.026,

CI = 0.005 to 0.064). The results of the second analysis indicated that job autonomy affects IWB

directly (b = 0.293, CI = 0.179 to 0.408) and via the combination of AMO to share knowledge

(b = 0.070, CI = 0.029 to 0.137). In line with previous results, the paths via knowledge collecting

behavior (b = 0.004, CI = -0.028 to 0.001) and the two sequential mediators (b = -0.008, CI = -

0.028 to 0.001) appeared nonsignificant. This indicated that hypothesis 5 is partially accepted,

as just one sequential mediation effect turned out to be significant (i.e. through the combination

of AMO to share knowledge and knowledge donating behavior). Furthermore, corroborating

with the findings of the previous analyses, social media use did not act as significant interaction

term within the sequential mediation analyses (respectively, b = .010, CI = -0.058 to 0.078;

0.057, CI = -0.010 to 0.124). This provided enough evidence for the rejection of hypothesis 7

that stated social media use acts as moderator in the sequential mediation effect between job

24

autonomy and IWB. Remarkably, the results implied that the combination of AMO to share

knowledge not only plays a significant part in the prediction of KSB, but also in forecasting

IWB.

To conclude, a summary of the current study’s hypothesis testing was schematically

depicted in Table 4.

Table 4

Summary of hypothesis testing

Note. H = hypothesis

# Hypothesis Status

H1 Job autonomy enhances innovative work behavior Accepted

H2a The combination of ability, motivation and opportunity to share

knowledge enhances knowledge sharing behavior.

Accepted

H2b Synergy exists between ability, motivation and opportunity to share

knowledge.

Untested

H3a Job autonomy enhances the combination of ability, motivation and

opportunity to share knowledge.

Accepted

H3b Job autonomy enhances knowledge sharing behavior. Rejected

H3c The combination of ability, motivation and opportunity to share

knowledge mediates the relationship between job autonomy and

knowledge sharing behavior.

Accepted

H4a Knowledge sharing behavior enhances innovative work behavior. Partially accepted

H4b Knowledge sharing behavior partially mediates the relationship between

job autonomy and innovative work behavior.

Partially accepted

H5 The combination of ability, motivation and opportunity to share

knowledge and knowledge sharing behavior sequentially mediate the

relationship between job autonomy and innovative work behavior.

Partially accepted

H6 Social media use strengthens the relationship between knowledge

sharing behavior and innovative work behavior.

Rejected

H7 Social media use moderates the strength of the, through the combination

of ability, motivation and opportunity to share knowledge and

knowledge sharing behavior, sequentially mediated relationship between

job autonomy and innovative work behavior, such that the indirect effect

of knowledge sharing behavior is stronger under high levels of social

media than under low levels of social media use.

Rejected

25

Conclusion and discussion

Conclusion

The purpose of this study was to empirically link KSB to workplace innovations. The

study also aimed at examining the role of job autonomy and the combination of AMO to share

knowledge in relation to KSB and IWB. Furthermore, this study focused on the potential

moderating effect of social media use in the relationship between KSB and IWB. A cross-

sectional research design was used to gather data from 292 respondents within 63 organizations

located in Aruba and the Netherlands. By means of this study, additional insights about how and

why job autonomy and KSB affect IWB, were gained. Also, this study functioned as a starting

point for empirical linking social media use to workplace innovations.

The results showed that job autonomy has a positive direct effect on IWB (H1) and

knowledge donating behavior, while it did not impact knowledge collecting behavior (H3b).

Moreover, knowledge donating behavior was found to positively affect IWB, whereas

knowledge collecting behavior did not (H4a). As a result, only knowledge donating behavior

was found to partially mediate the relationship between job autonomy and IWB (H4b).

Furthermore, this study found that the combination of AMO to share knowledge is a strong

predictor of KSB (H2a), but was not able to check for synergy between the three determinants

(H2b). Job autonomy was positively related to the combination of AMO to share knowledge

(H3a). The combination of AMO to share knowledge was found to fully mediate the relationship

between job autonomy and, respectively, knowledge donating and knowledge collecting

behavior (H3c). It was discovered that the combination of AMO to share knowledge and

knowledge donating behavior sequentially mediate the relationship between job autonomy and

IWB (H5). In line with previous results, this did not extent to the sequential mediation analysis

with knowledge collecting behavior as second sequential mediator (H5). Notably, the two

sequential mediation analyses indicated that the combination of AMO to share knowledge

mediates the relationship between job autonomy and IWB. Finally, the analyses indicated that

social media use does not moderate the relationship between KSB and IWB (H6) and no

moderated sequential mediation effect was present (H7). Yet, positive associations of social

media use with knowledge donating behavior and IWB were found.

Discussion

First, the results suggested that job autonomy directly impacts IWB. This finding supports

the solid basis of literature that demonstrated the importance of job autonomy for IWB (e.g.

Unsworth et al., 2005; Ohly et al., 2006; Slåtten & Mehmetoglu, 2011; Hammond et al, 2011;

De Spiegelaere et al., 2014a). In this respect, job autonomy seems to offer employees the required

26

opportunity to generate and implement their ideas at work (Hammond et al., 2011).

Second, the results showed that job autonomy has a positive influence on knowledge

donating behavior, while it did not affect knowledge collecting behavior. However, when

considering the combination of AMO to share knowledge, both direct relationships turned out to

be nonsignificant. The results showed that the combination of AMO to share knowledge fully

mediates the relationship between job autonomy and both knowledge donating and knowledge

collecting behavior. These results corroborate with evidence indicating that the AMO model

functions a strong meta-theoretical basis for linking organizational determinants to KSB

(Radaelli et al., 2014). Also, the results further substantiated the evidence suggesting that job

autonomy serves as a motivator (Foss et al., 2009), facilitator (Cabrera & Cabrera, 2005; Siemsen

et al., 2008) and perhaps even ability-enhancing determinant for KSB (Gallie et al., 2012).

Third, this study could unfortunately not test for synergy between the AMO elements, as

factor analysis did not indicate a clear three-factor pattern. This methodological issue could have

been caused by the decision to adapt Radaelli et al.’s (2014), not yet highly validated, scales.

Moreover, since the three scales measured AMO to donate and collect knowledge at the same

time, respondents might have perceived the scales as too general and, consequently, were unable

to accurately express their perception. After all, the AMO elements could affect both knowledge

sharing processes in different ways (Reinholt, Peterson & Foss, 2011).

Fourth, even though it was expected that knowledge donating as well as knowledge

collecting behavior would predict IWB and mediate the relationship between job autonomy and

IWB, this study solely found that knowledge donating behavior did so. In innovation literature,

little consensus exists about how KSB affects innovation and, therefore, this study contributes

to this ongoing debate. For instance, evidence from the hospitality sector indicated that both KSB

dimensions impact IWB (Hu, Horng & Sun, 2009; Kim & Lee, 2013). From another perspective,

Lin (2007a) and Liao, Fei and Chen (2007) showed that, although knowledge collection and

donation both affect innovation capability, they do not so to the same degree. Conversely, Yeşil,

Koska and Büyükbeşe (2013) argued that knowledge collecting behavior does not affect

innovation capability at all. A potentially interesting explanation of the results was found within

a study by Mehrabani and Shajari (2012). The scholars suggested that knowledge collecting

behavior indirectly affects innovation capacities through knowledge donating behavior. In line

with their reasoning, employees may instead start with asking their colleagues for knowledge,

then further disseminate the new insights to relevant contacts and subsequently build upon this

newly acquired knowledge to (jointly) come up with and implement new ideas (i.e. present

IWB). Either way, assessing two dimensions of KSB remains debatable, for communication and

27

knowledge sharing between colleagues usually involves a dyadic relationship and requires some

degree of reciprocity (Ipe, 2003, Van den Hoof & De Ridder, 2004). For example, the question

could be posed whether it is even possible that employees exclusively collect knowledge from

colleagues and not donate theirs at all? Or is it realistic to state that employees who donate their

knowledge and, in turn, show IWB, do not ask for the feedback and input from colleagues

whatsoever?

Fifth, regarding the partial mediation of knowledge donating behavior, the suggestion of

the paradoxical (in this case, double) role of job autonomy for innovation processes of Battistelli

et al. (2013), De Spiegelaere et al. (2014a) and Giebels et al. (2016) was verified. Specifically,

the present study showed that job autonomy is vital for IWB in two ways: directly, as factor that

provides the opportunity to do so and indirectly, as impetus for KSB.

Sixth, this study did not empirically verify the moderating effect of social media use on

the relationship between KSB and IWB. This implies that the mediation effect through

knowledge donating behavior did not proceed conditionally. Interestingly, significant positive

associations of social media use with knowledge donating behavior and IWB were found. A

potential explanation for these findings was found in the field of knowledge management.

Studies showed that information communication technology (e.g. social media) have an indirect

or direct effect on either knowledge donating, knowledge collecting behavior or both (Hendriks,

1999; Van den Hooff & De Ridder, 2004; Van den Hooff & De Leeuw van Weenen, 2004; Lin,

2007; Wang & Noe, 2010; Dalkir, 2013, Kettinger et al., 2015). In accordance, it is argued that

social media functions as a tool for KSB and can better be classified as determinant of

opportunity to share knowledge (Levy, 2009). This suggests that KSB instead functions as

mediator in the relationship between social media use and IWB.

Seventh, the findings suggested that the combination of AMO to share knowledge also

plays an important part in the prediction of IWB. An exciting explanation was provided by

Radaelli and colleagues (2014). They argued that ability and opportunity to share knowledge are

proxies for displaying IWB, as the knowledge sharing and innovation process more or less

require the same skills and work situation. Individual IWB does not happen in a social vacuum,

but encompasses knowledge, opinion and experience exchange. Employees with strong

capability to share knowledge will accordingly be more likely to include organizational members

in their workplace innovations (Zhou & Li, 2012). In a similar vein, an open organizational

climate and an adequate workload seem pivotal in providing employees the chance to display

KSB and IWB (Radaelli et al., 2014). However, motivation to share knowledge is not likely to

be a synonym of an employee’s drive to generate and implement ideas, since the array of

28

motivational factors (e.g. objectives, extrinsic motivators, reciprocity expectation) for IWB seem

different from those related to KSB (Radaelli et al., 2014).

Eighth, the results indicated that employees in Aruban organizations exhibit less

knowledge donating behavior and IWB than employees working in the Netherlands, whereas

they relatively collect knowledge more frequently. Explanations for these findings may be found

in the differences in national culture (Hofstede et al., 2010). Hutchings and Mohannak (2007)

explained that employees from Latin American countries share their knowledge less frequently,

as their organizational cultures are characterized by compartmentalization, territorialism and

high power distance. In the same vein, employees in Aruba turned out to show less IWB than

employees from the Netherlands, which could be explained by Aruba’s uncertainty-avoiding

culture that does not encourage experimentation and trial-and-error (Janssen, Van de Vliert &

West, 2004).

Ninth, employees with a lower education donated their knowledge more often than higher

educated employees. The finding contradicts with the existing literature which claims the exact

opposite (Sveiby & Simons, 2002; Wang & Noe, 2010). A possible explanation for this finding

lies in the use of a convenience sample, for no representative sample was drawn from each

occupation, organization and industry; three factors that are proved to impact employees’ KSB

(Ipe, 2003; Riege, 2005; Wang & Noe, 2010).

Tenth, higher educated employees displayed significantly more IWB. The latter finding

contrasts the evidence from a meta-analysis of Hammond and colleagues (2011) which suggests

that educational level is no significant predictor of IWB. The choice for a convenience sample

could also be the explanation of this particular finding.

Limitations and directions for future research

Several methodological and theoretical limitations and subsequent future research

directions are noted. Methodologically speaking, the study’s measurement of AMO to share

knowledge could function as a first methodological limitation. The three scales measuring AMO

to share knowledge simultaneously assessed the respondents’ ability, motivation and ability to

both donate and collect knowledge. As each AMO element does not necessarily play an equally

important role for predicting knowledge donating and knowledge collecting behavior (Reinholt

et al., 2011), the scales assessing AMO to share knowledge might have been too unspecified and

the validity of the scales might have been limited. As a result, future researchers are advised to

create scales that separately asses AMO to donate and AMO to collect knowledge. Second,

another potential flaw may lie in the shortening of the Van den Hooff and De Ridder’s (2004)

knowledge collecting scale. The reliability and validity of scale might have been dropped, as it

29

included only two items (Raubenheimer, 2004). To ensure better psychometric properties, it is

recommended to use the original 4-item knowledge collecting scale of Van den Hooff and De

Ridder (2004). Third, the present study’s positive formulation of all items could have resulted in

unjustifiably high reliability scores and overestimated research findings (Nunnally, 1978; Maes,

Ummelen & Hoeken, 1996). Therefore, researchers are reinforced to phrase a variety of items in

a negative way. Fourth, the choice for a cross-sectional study may also be classified as

methodological constraint, since it was not possible to examine the change in variables over time

and, subsequently, check for causality (Dale & Davies, 1994; Selig & Preacher, 2009).

Researchers are strongly advised to adopt a longitudinal research design, as Maxwell and Cole

(2007) and Ployhart and Vandenberg (2010) stress the particular importance of this research

design for studies that investigate indirect effects (for example, this study). Fifth, the current

study’s convenience sampling method might have wrongfully influenced the data, as

generalizability of the findings may have been decreased (Highhouse & Gillespie, 2009). To

overcome this potential sampling error, scholars are advised to use a (stratified) random sample

(Rossi, Wright & Anderson, 2013). Sixth, the results may have been distorted, because the

country in which the sampled organizations are located in, played a vital role in predicting KSB

and IWB. To ensure higher cross-cultural and cross-national generalizability, scholars could

repeat this study in different cultural and national. For this, scholars are encouraged to pay

particular attention to construct validity of the scales (Schaffer & Riordan, 2003; Tsui, Nifadkar

& Ou, 2007) and discover patterns in organizational behavior across different cultural

dimensions (Tsui et al., 2007; Hofstede et al., 2010)

Firstly, in terms of theoretical constrains and research directions, Cabrera and colleagues

(2006) reasoned that job autonomy fosters KSB through idea generation. It is argued that an

autonomous job stimulates employees to generate ideas that, in turn, could potentially be shared

with colleagues. This implies that the present study’s hypothesized causal direction between

KSB and IWB may in fact be reversed. Hence, researchers are advised to address the causality

between job autonomy, KSB and IWB. A longitudinal research design would therefore also be

advisable from a theoretical point of view (Anderson et al., 2010). Secondly, since Holman,

Totterdell, Axtell, Stride, Port, Svensson and Zibarras (2012) discovered that job autonomy is of

less importance for idea implementation than for idea generation, the present study’s disregard

for the different phases may have resulted in flawed findings. Scholars are encouraged to

separately investigate the phases of IWB to uncover the relative importance of antecedents

(Hammond et al., 2011; Anderson et al., 2014; De Spiegelaere et al., 2014b). Thirdly, in line

with the present study, it is recommended to differentiate between the two KSB dimensions,

30

since little agreement exists about the role of knowledge donating and knowledge collecting

behavior in relation to innovation. Controversially, challenging this study’s factor analysis

results and the existing research tradition (e.g. Van den Hooff & De Ridder, 2004; Hsu et al.,

2007; Cavaliere et al., 2015), it is also advised to examine whether it is even possible to separate

knowledge collecting from knowledge donating behavior. Fourthly, an obvious limitation lies in

the impossibility to assess the hypothesized synergy between the AMO variables due to

unfortunate factor analysis results. As such, scholars are recommended to further study the

potential interaction effects between ability, motivation and opportunity to donate and collect

knowledge (Siemsen et al., 2008; Tuuli, 2012). Fifthly, the present study did not theoretically

hypothesize the positive effect of the combination of AMO to share knowledge on IWB, whereas

a strong linkage between the two variables was empirically detected. Post-hoc literature review

indicated that there is a significant overlap in terms of required skills and facilitators for KSB

and IWB (Radaelli et al., 2014). Scholars are encouraged to further research the intersection

between AMO to share knowledge and AMO to innovate. Sixthly, the conceptualization of social

media use might have been too narrow or even inappropriate. Up until now, most organizations

still perceive social media as Facebook and WhatsApp as constraints for value creation

(Tredinnick, 2006; Vaast & Kaganer, 2013), as compared to more accepted applications such as

email or intranet (Lantz, 2003; Ramsay & Renaud, 2012). Scholars are encouraged to further

uncover the potential of social media for workplace innovations (Anderson et al., 2014). This

could be done by examining the potential direct effect of social media as knowledge management

tool (i.e. opportunity to share knowledge factor) on KSB or IWB. Otherwise, the social media

use scale could be optimized by means of broadening the scope by differentiating between, for

example, company-owned (e.g. VPN and databases) and public social media (e.g. LinkedIn),

between one-on-one virtual communication (e.g. private WhatsApp chat) and mass virtual

communication (e.g. Facebook group), and between strong ties (e.g. direct colleagues) and weak

ties (e.g. an old college professor).

Recommendations for practitioners

Within the present study’s limitations, several recommendations for practitioners are

posed. Considering the pivotal importance of job autonomy for KSB and workplace innovations,

it is recommended for practitioners in the fields of HR, knowledge and innovation management

to collaborate and create synergy (Cabrera & Cabrera, 2005; Lin & Kuo, 2007; Chen & Huang,

2009). The results of this study implied that this suggested strategy alignment should be

considered within a broader business sense, as an employee’s combination of AMO to share

knowledge seems to be a good forecaster of his or her AMO to present IWB. Hence, in line with

31

Radaelli and colleagues (2014), it is argued that knowledge and innovation managers should

adopt a shared set of company-level interventions to foster KSB and workplace innovations.

Examples are setting up cross-functional teams, fostering cultures of caring (i.e. trust and

cooperation), building supportive climates, creating intrinsically motivating jobs, offering

knowledge education and guaranteeing adequate workloads (Cabrera & Cabrera, 2005; Wang &

Noe, 2014; Radaelli et al., 2014). In accordance with, for example, Janssen et al. (2004),

Michailova and Hutchings (2006), Hutchings and Mohannak (2007) and Hislop (2013), the

present study’s research findings indicated that the extent to which employees engage in KSB

and IWB differs from culture to culture, and perhaps even from nation to nation. As a result, the

findings provided knowledge and innovation managers working in an international context a

clear stimulus to pay attention to national and cultural differences in creating corporate strategies.

In the same vein, practitioners are urged to consider educational level of their workforce in

designing strategy. Furthermore, considering the positive association between social media and

both KSB and IWB and existing evidence that suggests a relationship between social media and

innovation (Bradley & McDonald, 2011; Aral et al., 2013), practitioners are encouraged to give

social media a chance.

All in all, this study demonstrated that for employees to share knowledge, they need to

have the AMO to do so. In turn, the degree to which employees share their own knowledge with

colleagues proved to have a beneficial effect on their capacity to generate and implement ideas.

Within the process of sharing knowledge and innovating, the positive influence of job autonomy

appeared to be clear-cut. Although less well-defined, also social media might play a part in

fostering a workforce that effectively shares knowledge, ideates and innovates.

32

References

Adler, M. A. (1993). Gender differences in job autonomy: The consequences of occupational

segregation and authority position. Sociological Quarterly, 449-465.

Akhavan, P., Hosseini, S. M., Abbasi, M., & Manteghi, M. (2015). Knowledge-sharing determi-

nants, behaviors, and innovative work behaviors: An integrated theoretical view and

empirical examination. Aslib Journal of Information Management, 67(5), 562-591.

Anderson, N., Potočnik, K., & Zhou, J. (2014). Innovation and creativity in organizations a

state-of-the-science review, prospective commentary, and guiding framework. Journal

of Management, 40(5), 1297-1333.

Aral, S., Dellarocas, C., & Godes, D. (2013). Introduction to the special issue-social media and

business transformation: A framework for research. Information Systems Research,

24(1), 3-13.

Aulawi, H., Sudirman, I., Suryadi, K., & Govindaraju, R. (2009). KSB, antecedent and their

impact on the individual innovation capability. Journal of Applied Sciences Research,

5(12), 2238-2246.

Baer, M. (2012). Putting creativity to work: The implementation of creative ideas in

organizations. Academy of Management Journal, 55(5), 1102-1119.

Barrick, M. R., Mount, M. K., & Li, N. (2013). The theory of purposeful work behavior: The

role of personality, higher-order goals, and job characteristics. Academy of Management

Review, 38(1), 132-153.

Battistelli, A., Montani, F., & Odoardi, C. (2013). The impact of feedback from job and task

autonomy in the relationship between dispositional resistance to change and innovative

work behaviour. European Journal of Work and Organizational Psychology, 22(1), 26-

41.

Bell, B. S., & Kozlowski, W. J. (2002). Goal orientation and ability: interactive effects on self-

efficacy, performance, and knowledge. Journal of Applied Psychology, 87(3), 497.

Bender, A. R., Naveh-Benjamin, M., & Raz, N. (2010). Associative deficit in recognition

memory in a lifespan sample of healthy adults. Psychology and Aging, 25(4), 940.

Blanchard, O. (2011). Social media ROI: Managing and measuring social media efforts in your

organization. New York City: Pearson Education.

Bradley, A. J., & McDonald, M. P. (2011). The social organization: How to use social media to

tap the collective genius of your customers and employees. Boston: Harvard Business

Press.

Burrus, D. (2010). Social networks in the workplace: the risk and opportunity of Business 2.0.

33

Strategy & Leadership, 38(4), 50-53.

Boudreau, J., Hopp, W., McClain, J.O., Thomas, L.J., 2003. On the interface between operations

and human resources management. Manufacturing & Service Operations Management,

5(4), 179– 203.

Cabrera, E., & Cabrera, A. (2005). Fostering knowledge sharing through people management

practices. The International Journal of Human Resource Management, 16(5), 720–735.

Cabrera, A., Collins, W. C., & Salgado, J. F. (2006). Determinants of individual engagement in

knowledge sharing. The International Journal of Human Resource Management, 17(2),

245-264.

Cavaliere, V., Lombardi, S., & Giustiniano, L. (2015). Knowledge sharing in knowledge-

intensive manufacturing firms. An empirical study of its enablers. Journal of Knowledge

Management, 19(6), 1124-1145.

Centraal Bureau voor de Statistiek. (2015, February 15). Werkzame beroepsbevolking;

vergrijzing per bedrijfstak SBI 2008 [webmagazine statistics]. Retrieved April 26, 2016

from:

http://statline.cbs.nl/StatWeb/publication/?VW=T&DM=SLNL&PA=80832NED&LA

Centraal Bureau voor Statistiek (2016a, February 11). Arbeidsdeelname; kerncijfers. Retrieved

[webmagazine statistics]. April 26, 2016 from: http://statline.cbs.nl/Statweb/public

tion/?DM=SLNL&PA=82309ned&D1=2&D2=12&D3=0&D4=0&D5=l&STB=G1,G3,

G4,G2,T&VW=T.

Centraal Bureau voor de Statistiek. (2016b, February 11). Bevolking; hoogst behaald

onderwijsniveau; geslacht, leeftijd, herkomst [webmagazine statistics]. Retrieved April

26, 2016 from:

http://statline.cbs.nl/Statweb/publication/?DM=SLNL&PA=82275NED&D1=0&D2=0

&D3=0&D4=0-1,4-5&D5=0,4-5,8,12-13&D6=60

63&HDR=T,G1,G3,G5&STB=G2,G4&VW=T .

Central Bureau of Statistics. (2010). Fifth Population and Housing Census. Oranjestad:

Nederlands Interdisciplinair Demografisch Instituut (NIDI).

Chen, C. J., & Huang, J. W. (2009). Strategic human resource practices and innovation perfor-

mance— The mediating role of knowledge management capacity. Journal of business

research, 62(1), 104-114.

Chiu, C. M., Hsu, M. H., & Wang, E. T. (2006). Understanding knowledge sharing in virtual

communities: An integration of social capital and social cognitive theories. Decision

Support Systems, 42(3), 1872-1888.

34

Church, K., & de Oliveira, R. (2013). What's up with whatsapp?: comparing mobile instant

messaging behaviors with traditional SMS. In Proceedings of the 15th international

conference on Human-computer interaction with mobile devices and services. ACM.

Cohn, M., Emrich, S. M., & Moscovitch, M. (2008). Age-related deficits in associative memory:

the influence of impaired strategic retrieval. Psychology and Aging, 23(1), 93.

Correa, T., Hinsley, A. W., & de Zuniga, H. G. (2010). Who interacts on the Web?: The

intersection of users’ personality and social media use. Computers in Human Behavior,

26(2), 247-253.

Cramer, D. (2004). Advanced Quantitative Data Analysis. In A. Bryman (Ed.), Understanding

Social Research. Berkshire: Open University Press.

Cummings, L.L., Schwab, D.P. (1973). Performance in Organizations. Scott, Foresman and

Company, Glenview, IL.

Dale, A., & Davies, R. B. (Eds.). (1994). Analyzing social and political change: a casebook of

methods. Sage.

Dalkir, K. (2013). Knowledge management in theory and practice. London: Routledge.

De Jong, J., & den Hartog, D. (2010). Measuring innovative work behaviour. Creativity and

Innovation Management, 19(1), 23-36.

De Spiegelaere, S., Van Gyes, G., & Van Hootegem, G. (2014b). Innovatief Werkgedrag als

concept: definiëring en oriëntering. Gedrag en Organisatie, 27(2), 139-156.

De Spiegelaere, S., Van Gyes, G., De Witte, H., Niesen, W., & Van Hootegem, G. (2014a). On

the relation of job insecurity, job autonomy, innovative work behaviour and the mediating

effect of work engagement. Creativity and Innovation Management, 23(3), 318-330.

Dorenbosch, L., Van Engen, M. L. V., & Verhagen, M. (2005). On‐the‐job innovation: the

impact of job design and human resource management through production ownership.

Creativity and Innovation Management, 14(2), 129-141.

EUWIN. (2012). Dortmund/Brussels Position Paper: Workplace Innovation as Social

Innovation [WWW document]. URL http://ec.europa.eu/

enterprise/policies/innovation/files/dortmundbrussels-position-paper-

workplaceinnovation_en.pdf [accessed on 10 April 2016].

Evers, A., Van Vliet-Mulder, J.C., Groot, C.J. (2000). Documentatie van Tests en Testresearch

in Nederland. Assen: Van Gorcum & Comp. B.V.

Foss, N. J., Minbaeva, D. B., Pedersen, T., & Reinholt, M. (2009). Encouraging knowledge

sharing among employees: How job design matters. Human Resource

Management, 48(6), 871-893.

35

Gachago, D., & Ivala, E. (2015). Web 2.0 and social media: Essential tools for contemporary

teaching and learning. Moving beyond the hype: A contextualised view of learning with

technology in higher education, 19.

Gagné, M. (2009). A model of knowledge-sharing motivation. Human Resource Management,

48(4), 571.

Gallie, D., Zhou, Y., Felstead, A., & Green, F. (2012). Teamwork, skill development and

employee welfare. British Journal of Industrial Relations, 50(1), 23-46.

Giebels, E., de Reuver, R. S., Rispens, S., & Ufkes, E. G. (2016). The Critical Roles of Task

Conflict and Job Autonomy in the Relationship Between Proactive Personalities and

Innovative Employee Behavior. The Journal of Applied Behavioral Science, 1-22.

Gong, Y., Cheung, S. Y., Wang, M., & Huang, J. C. (2012). Unfolding the proactive process for

creativity integration of the employee proactivity, information exchange, and

psychological safety perspectives. Journal of Management, 38(5), 1611-1633.

Hackman, J. R., & Lawler, E. E., III (1971). Employee reactions to job characteristics. Journal

of Applied Psychology Monograph, 55, 259–286.

Hammond, M. M., Neff, N. L., Farr, J. L., Schwall, A. R., & Zhao, X. (2011). Predictors of

individual-level innovation at work: A meta-analysis. Psychology of Aesthetics,

Creativity, and the Arts, 5(1), 90.

Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process analysis:

A regression-based approach. New York: Guilford Press.

Hayes, A. F. (2015). An index and test of linear moderated mediation. Multivariate Behavioral

Research, 50(1), 1-22.

He, W., & Wei, K. K. (2009). What drives continued knowledge sharing? An investigation of

knowledge-contribution and-seeking beliefs. Decision Support Systems, 46(4), 826838.

Hendriks, P. (1999). Why share knowledge? The influence of ICT on the motivation for

knowledge sharing. Knowledge and process management, 6(2), 91.

Highhouse, S., & Gillespie, J. Z. (2009). Do samples really matter that much. Statistical and

methodological myths and urban legends: Doctrine, verity and fable in the

organizational and social sciences, 247-265.

Higón, D. A. (2012). The impact of ICT on innovation activities: Evidence for UK SMEs.

International Small Business Journal, 30(6), 684-699.Science, 332(6025), 60-65.

Hislop, D. (2013). Knowledge management in organizations: A critical introduction. Oxford

University Press.

Hofstede, G., Hofstede, G.J., & Minkov, M. (2010). Cultures and Organizations: Software of

36

the Mind. Revised and Expanded 3rd Edition. New York: McGraw-Hill.

Holman, D., Totterdell, P., Axtell, C., Stride, C., Port, R., Svensson, R., & Zibarras, L. (2012).

Job design and the employee innovation process: The mediating role of learning

strategies. Journal of Business and Psychology, 27(2), 177-191.

Hsu, M. H., Ju, T. L., Yen, C. H., & Chang, C. M. (2007). KSB in virtual communities: The

relationship between trust, self-efficacy, and outcome expectations. International

Journal of Human-computer Studies, 65(2), 153-169.

Hu, M. L. M., Horng, J. S., & Sun, Y. H. C. (2009). Hospitality teams: Knowledge sharing and

service innovation performance. Tourism Management, 30(1), 41-50.

Hutchings, K., & Mohannak, K. (Eds.). (2007). Knowledge Management in Developing

Economies: A Cross-Cultural and Institutional Approach. Cheltenham: Edward Elgar

Publishing.

Ipe, M. (2003). Knowledge sharing in organizations: A conceptual framework. Human Resource

Development Review, 2(4), 337-359.

Janssen, O., Schoonebeek, G., & Van Looy, B. (1997). Cognities van empowerment als de

schakel tussen delegerend leiderschap en innovatief gedrag van werknemers. Gedrag en

Organisatie, 4, 175-194.

Janssen, O., Van de Vliert, E., & West, M. (2004). The bright and dark sides of individual and

group innovation: A special issue introduction. Journal of Organizational Behavior,

25(2), 129-145.

Jarvenpaa, S. L., & Staples, D. S. (2000). The use of collaborative electronic media for

information sharing: an exploratory study of determinants. The Journal of Strategic

Information Systems, 9(2), 129-154.

Kalleberg, A. L., & Van Buren, M. E. (1996). Is bigger better? Explaining the relationship

between organization size and job rewards. American Sociological Review, 47-66.

Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and

opportunities of Social Media. Business horizons, 53(1), 59-68.

Kettinger, W. J., Li, Y., Davis, J. M., & Kettinger, L. (2015). The roles of psychological climate,

information management capabilities, and IT support on knowledge-sharing: an MOA

perspective. European Journal of Information Systems, 24(1), 59-75.

Kijkuit, B., & Van den Ende, J. (2007). The Organizational Life of an Idea: Integrating Social

Network, Creativity and Decision‐Making Perspectives*. Journal of Management

Studies, 44(6), 863-882.

Kim, T. T., & Lee, G. (2013). Hospitality employee knowledge-sharing behaviors in the

37

relationship between goal orientations and service innovative behavior. International

Journal of Hospitality Management, 34, 324-337.

Lammers, J., Stoker, J. I., Rink, F., & Galinsky, A. D. (2016). To Have Control Over or to Be

Free From Others? The Desire for Power Reflects a Need for Autonomy. Personality and

Social Psychology Bulletin, 42(4), 498-512.

Lantz, A. (2003). Does the use of e-mail change over time?. International Journal of Human-

Computer Interaction, 15(3), 419-431.

Lawson, B., & Samson, D. (2001). Developing innovation capability in organisations: a dynamic

capabilities approach. International journal of innovation management, 5(03), 377-400.

Lee Endres, M., Endres, S. P., Chowdhury, S. K., & Alam, I. (2007). Tacit knowledge sharing,

self-efficacy theory, and application to the Open Source community. Journal of

Knowledge Management, 11(3), 92-103.

Leinonen, P., & Bluemink, J. (2008). The distributed team members' explanations of knowledge

they assume to be shared. Journal of Workplace Learning, 20(1), 38-53.

Levy, M. (2009). WEB 2.0 implications on knowledge management. Journal of Knowledge

Management, 13(1), 120-134.

Lin, C. P. (2008). Clarifying the relationship between organizational citizenship behaviors,

gender, and knowledge sharing in workplace organizations in Taiwan. Journal of

Business and Psychology, 22(3), 241-250.

Lin, H. F. (2007a). Knowledge sharing and firm innovation capability: an empirical study.

International Journal of manpower, 28(3/4), 315-332.

Lin, H. F. (2007b). Effects of extrinsic and intrinsic motivation on employee knowledge sharing

intentions. Journal of information science.

Lin, T. C., & Huang, C. C. (2008). Understanding knowledge management system usage

antecedents: An integration of social cognitive theory and task technology

fit. Information & Management, 45(6), 410-417.

Lin, C. Y., & Kuo, T. H. (2007). The mediate effect of learning and knowledge on organizational

performance. Industrial Management & Data Systems, 107(7), 1066-1083.

Liao, S. H., Fei, W. C., & Chen, C. C. (2007). Knowledge sharing, absorptive capacity, and

innovation capability: an empirical study of Taiwan's knowledge-intensive industries.

Journal of Information Science, 33(3), 340-359.

MacInnis, D.J., Moorman, C., & Jaworski, B.J. (1991). Enhancing and Measuring Consumers’

Motivation, Opportunity, and Ability to Process Brand Information from Ads. Journal of

Marketing, 55, 32–53.

38

Majchrzak, A., Faraj, S., Kane, G. C., & Azad, B. (2013). The contradictory influence of social

media affordances on online communal knowledge sharing. Journal of Computer‐

Mediated Communication, 19(1), 38-55.

Maxwell, S. E., & Cole, D. A. (2007). Bias in cross-sectional analyses of longitudinal mediation.

Psychological methods, 12(1), 23.

Mehrabani, S. E., & Shajari, M. (2012). Knowledge management and innovation capacity.

Journal of Management Research, 4(2), 164.

Mergel, I. (2013). A framework for interpreting social media interactions in the public sector.

Government Information Quarterly, 30(4), 327-334.

Michailova, S., & Hutchings, K. (2006). National cultural influences on knowledge sharing: A

comparison of China and Russia. Journal of Management Studies, 43(3), 383-405.

Mura, M., Lettieri, E., Radaelli, G., & Spiller, N. (2013). Promoting professionals' innovative

behaviour through knowledge sharing: the moderating role of social capital. Journal of

Knowledge Management, 17(4), 527-544.

Mwanza-Simwami, D. (2016). Fostering Collaborative Learning with Mobile Web 2.0 in Semi-

Formal Settings. International Journal of Mobile and Blended Learning (IJMBL), 8(1),

34-50.

Ngai, E. W., Tao, S. S., & Moon, K. K. (2015). Social media research: Theories, constructs, and

conceptual frameworks. International Journal of Information Management, 35(1), 33-

44.

Nijstad, B. A., & Stroebe, W. (2006). How the group affects the mind: A cognitive model of idea

generation in groups. Personality and Social Psychology Review, 10(3), 186-213.

Nunnally, J. C. (1978). Psychometric theory (2nd ed.). New York, NY: McGraw-Hil

Ohly, S., Sonnentag, S., & Pluntke, F. (2006). Routinization, work characteristics and their

relationships with creative and proactive behaviors. Journal of Organizational Behavior,

27(3), 257-279.

Okazaki, S., & Taylor, C. R. (2013). Social media and international advertising: theoretical

challenges and future directions. International marketing review, 30(1), 56-71.

Parker, S. K., Williams, H. M., & Turner, N. (2006). Modeling the antecedents of proactive

behavior at work. Journal of Applied Psychology, 91(3), 636.

Paroutis, S., & Al Saleh, A. (2009). Determinants of knowledge sharing using Web 2.0 technolo-

gies. Journal of Knowledge Management, 13(4), 52-63.

Paulus, P. B., & Brown, V. R. (2007). Toward more creative and innovative group idea

39

generation: a cognitive‐social‐motivational perspective of brainstorming. Social and

Personality Psychology Compass, 1(1), 248-265.

Ployhart, R. E., & Vandenberg, R. J. (2010). Longitudinal research: The theory, design, and

analysis of change. Journal of Management, 36(1), 94-120.

Preacher, K. J., Rucker, D. D., & Hayes, A. F. (2007). Addressing moderated mediation

hypotheses: Theory, methods, and prescriptions. Multivariate behavioral research,

42(1), 185-227.

Radaelli, G., Lettieri, E., Mura, M., & Spiller, N. (2014). Knowledge Sharing and Innovative

Work Behaviour in Healthcare: A Micro‐Level Investigation of Direct and Indirect

Effects. Creativity and Innovation Management, 23(4), 400-414.

Ramamoorthy, N., Flood, P., Slattery, T., & Sardessai, R. (2005). Determinants of innovative

work behaviour: Development and test of an integrated model. Creativity and Innovation

Management, 14(2), 142-150.

Ramsay, J., & Renaud, K. (2012). Using insights from email users to inform organisational email

management policy. Behaviour & Information Technology, 31(6), 587-603.

Raubenheimer, J. (2004). An item selection procedure to maximise scale reliability and validity.

SA Journal of Industrial Psychology, 30(4).

Ravenscroft, A., Schmidt, A., Cook, J., & Bradley, C. (2012). Designing social media for

informal learning and knowledge maturing in the digital workplace. Journal of Computer

Assisted Learning, 28(3), 235-249.

Reinholt, M. I. A., Pedersen, T., & Foss, N. J. (2011). Why a central network position isn't

enough: The role of motivation and ability for knowledge sharing in employee networks.

Academy of Management Journal, 54(6), 1277-1297.

Riege, A. (2005). Three-dozen knowledge-sharing barriers managers must consider. Journal of

knowledge management, 9(3), 18-35.

Rossi, P. H., Wright, J. D., & Anderson, A. B. (Eds.). (2013). Handbook of survey research.

Academic Press.

Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and

new directions. Contemporary educational psychology, 25(1), 54-67.

Schaffer, B. S., & Riordan, C. M. (2003). A review of cross-cultural methodologies for

organizational research: A best-practices approach. Organizational Research Methods,

6(2), 169-215.

Selig, J. P., & Preacher, K. J. (2009). Mediation models for longitudinal data in developmental

research. Research in Human Development, 6(2-3), 144-164.

40

Slåtten, T., & Mehmetoglu, M. (2011). Antecedents and effects of engaged frontline employees:

A study from the hospitality industry. Managing Service Quality: An International

Journal, 21(1), 88-107.

Siemsen, E., Roth, A. V., & Balasubramanian, S. (2008). How motivation, opportunity, and

ability drive knowledge sharing: The constraining-factor model. Journal of Operations

Management, 26(3), 426-445.

Sorohan, E. G. (1993). We do; therefore, we learn. Training & Development, 47(10), 47-54.

Sveiby, K. E., & Simons, R. (2002). Collaborative climate and effectiveness of knowledge work-

an empirical study. Journal of Knowledge Management, 6(5), 420-433.

Tredinnick, L. (2006). Web 2.0 and Business A pointer to the intranets of the future?. Business

information review, 23(4), 228-234.

Tsui, A. S., Nifadkar, S. S., & Ou, A. Y. (2007). Cross-national, cross-cultural organizational

behavior research: Advances, gaps, and recommendations. Journal of management,

33(3), 426-478.

Tuuli, M. M. (2012). Competing models of how motivation, opportunity, and ability drive job

performance in project teams.

Tuten, T. L., & Solomon, M. R. (2014). Social media marketing. Sage.

Unsworth, K. L., Wall, T. D., & Carter, A. (2005). Creative Requirement A Neglected Construct

in the Study of Employee Creativity?. Group & Organization Management, 30(5), 541-

560.

Vaast, E., & Kaganer, E. (2013). Social media affordances and governance in the workplace: An

examination of organizational policies. Journal of Computer‐Mediated Communication,

19(1), 78-101.

Van den Hooff, B., & De Ridder, J. A. (2004). Knowledge sharing in context: the influence of

organizational commitment, communication climate and CMC use on knowledge

sharing. Journal of Knowledge Management, 8(6), 117-130.

Van den Hooff, B., & De Leeuw van Weenen, F. (2004). Committed to share: commitment and

CMC use as antecedents of knowledge sharing. Knowledge and Process Management,

11(1), 13-24.

Van Veldhoven, M. J. P. M., Prins, J., Van der Laken, P. A., & Dijkstra, L. (2015). QEEW2.0:

42 short scales for survey research on work, well-being and performance.

Wang, S., & Noe, R. A. (2010). Knowledge sharing: A review and directions for future research.

Human Resource Management Review, 20(2), 115-131.

Wang, Z., & Wang, N. (2012). Knowledge sharing, innovation and firm performance. Expert

41

systems with applications, 39(10), 8899-8908.

Watson, S., & Hewett, K. (2006). A Multi‐Theoretical Model of Knowledge Transfer in

Organizations: Determinants of Knowledge Contribution and Knowledge Reuse*.

Journal of management studies, 43(2), 141-173.

Yan, Y., Davison, R. M., & Mo, C. (2013). Employee creativity formation: The roles of

knowledge seeking, knowledge contributing and flow experience in Web 2.0 virtual

communities. Computers in Human Behavior, 29(5), 1923-1932.

Yeşil, S., Koska, A., & Büyükbeşe, T. (2013). Knowledge sharing process, innovation capability

and innovation performance: an empirical study. Procedia-Social and Behavioral

Sciences, 75, 217-225.

Zhou, K. Z., & Li, C. B. (2012). How knowledge affects radical innovation: Knowledge base,

market knowledge acquisition, and internal knowledge sharing. Strategic Management

Journal, 33(9), 1090-1102.

42

Appendix A

Demographic characteristics of the sample

Table A1

Characteristic N Frequencies Percentage

Gender 282 Female 147 52.1%

Male 135 47.9%

Age 275 Mean 40.03

SD 12.55

Education 287 Elementary 0 0%

Secondary 50 17.4%

Middle 93 32.4%

Higher 102 35.5%

University 42 14.6%

Tenure 292 0-1 years 32.0 11.0%

1-5 years 37.0 37.0%

5-10 years 20.5 20.5%

10-20 years 17.5 17.5%

20> years 14.0 14.0%

Nationality 292 Aruban 143 51.0%

Dutch 149 49.0%

Note. N = Total number of cases; SD = Standard deviation.

43

Appendix B

Scales used including scale Cronbach’s α, Cronbach’s a if-item-deleted and factor loadings

Dim. English scale Dutch scale Factor loadings Cronbach’s a

and a if-item-

deleted

IWB Innovative work behavior Innovatief werkgedrag 5.816 .921

IWB1 Gen. I pay attention to issues that are

not part of my daily work

Ik let op kwesties die geen onderdeel

vormen van mijn dagelijks werk.

.534 .922

IWB2 Gen. I wonder how things can be

improved

Ik vraag me af hoe zaken op het werk

verbeterd kunnen worden.

.649 .917

IWB3 Gen. I search out new working

methods, techniques or

instruments

Ik zoek naar nieuwe werkmethoden, -

technieken of -instrumenten.

.707 .914

IWB4 Gen. I generate original solutions for

problems

Ik ontwikkel creatieve oplossingen

voor problemen.

.668 .916

IWB5 Gen. I find new approaches to execute

tasks

Ik vind nieuwe manieren om taken uit

te voeren.

.697 .914

IWB6 Impl. I make important organizational

members enthusiastic for

innovative ideas

Ik maak invloedrijke personen in mijn

organisatie enthousiast over

innovatieve ideeën.

.808 .908

IWB7 Impl. I attempt to convince people to

support an innovative idea

Ik probeer mensen ervan te

overtuigen innovatieve ideeën te

ondersteunen.

.823 .907

IWB8 Impl. I systematically introduce

innovative ideas into work

practices

Ik introduceer systematisch

innovatieve ideeën in werkmethodes.

.820 .908

IWB9 Impl. I contribute to the implementation

of new ideas

Ik draag bij aan de realisering van

nieuwe ideeën.

.815 .908

IWB10 Impl. I put effort in the development of

new things

Ik span me in voor de ontwikkeling

van nieuwe dingen op het werk.

.792 .909

KSB Knowledge sharing behavior Kennis delen 3.356 1.512 .826

KSB1 Donat. When I’ve learned something

new, I told people in my

organization about it.

Wanneer ik iets nieuws leerde,

vertelde ik personen in mijn

organisatie erover.

.751 -117 .781

KSB2 Donat. When I’ve learned something

new, I saw to it that people in my

organization can learn it as well.

Wanneer ik iets nieuws leerde, zag ik

erop toe dat personen in mijn

organisatie het eveneens kond...

.796 -234 .783

KSB3 Donat. I shared my skills with people in

my organization.

Ik deelde mijn vaardigheden met

personen in mijn organisatie.

.889 -.202 .765

KSB4 Donat. I shared my knowledge with

people in my organization.

Ik deelde mijn kennis met personen in

mijn organisatie.

.868 -.183 .770

44

KSB5 Collect. People in my organization told me

what they knew, when I asked

them about it.

Personen in mijn organisatie vertelden

wat ze wisten wanneer ik hen daar om

vroeg.

.444 .757 .837

KSB6 Collect. People in my organization told me

what they knew, when I asked

them about it.

Personen in mijn organisatie deelden

hun vaardigheden met mij wanneer ik

hen daar om vroeg.

.384 .719 .839

AMO Motivation to share knowledge Motivatie om kennis te delen 3.655 1.418 .828

ABIL1 I fit depended only on me, I

would exhaustively share

knowledge.

Als het aan mij ligt, zou ik kennis

uitvoerig delen.

.472 -.153 .823

ABIL2 I am fully capable of sharing

knowledge in written (e.g. mail)

or spoken (e.g. during meetings)

form.

Ik ben volledig bekwaam kennis te

delen in geschreven (bijv. via de mail)

en gesproken (bijv. in meetings)

vorm.

.424 -.166 .828

ABIL3 I believe I am fully capable of

sharing knowledge at any time.

Ik acht mij volledig in staat om op elk

moment kennis te delen.

.599 -.246 .809

MOT1 I intend to frequently share

knowledge

Ik streef ernaar om vaak kennis te

delen.

.656 -.255 .807

MOT2 I will always share knowledge. Ik zal kennis altijd delen. .661 -.219 .808

MOT3 I will always try to share

knowledge in the most efficient

way possible.

Ik zal mijzelf inspannen om altijd

kennis te delen op de meest efficiënte

wijze.

.769 -.223 .796

OPP1 I can devote enough time to KSB Ik kan genoeg tijd besteden aan het

delen van kennis.

.569 .184 .805

OPP2 The climate in my organization

allows me to share knowledge

easily.

Het werkklimaat in mijn organisatie

stelt me in staat om kennis eenvoudig

te delen.

.604 .751 .817

OPP3 The climate in my organization

facilitates informal meetings

where I can share knowledge.

Het werkklimaat in mijn organisatie

ondersteunt informele bijeenkomsten

waar ik kennis kan delen.

.493 .437 .796

JA Job autonomy Baanautonomie 2.549 .808

JA1 Do you have freedom in carrying

out your work activities?

Heeft u vrijheid bij het uitvoeren van

uw werkzaamheden?

.661 .781

JA2 Can you decide how your work is

executed on your own?

Kunt u zelf bepalen hoe u uw werk

uitvoert?

.813 .726

JA3 Can you personally decide how

much time you need for a specific

activity?

Kunt u zelf bepalen hoeveel tijd u aan

een bepaalde activiteit besteedt?

.743 .745

JA4 Can you organize your work

yourself?

Kunt u uw werk zelf indelen? .657 .782

45

Note. Dim. = Dimension; Gen. = Idea generation; Impl = Idea implementation; Donat. = Knowledge donating;

Collect. = Knowledge collecting.

SMU Social media use for innovations Sociale media gebruik voor

innovaties

6.004 .943

I used social media to… Ik heb sociale media gebruikt om…

SMU1 Gen. … come up with ideas and

improvements.

.... ideeën en verbeteringen te

bedenken.

.141 .972

SMU2 Gen. … search for feedback on ideas. ... feedback te krijgen op mijn ideeën. .891 .930

SMU3 Gen. … talk about ways to find new

solutions to problems.

.... te discussiëren over nieuwe

oplossingen voor problemen.

.868 .931

SMU4 Gen. … see how work aspects could be

improved

... te zien hoe aspecten in het werk

kunnen worden verbeterd.

.890 .929

SMU5 Impl. … energize people about ideas. .... mensen enthousiast te maken voor

mijn ideeën.

.919 .928

SMU6 Impl. ... mobilize support for ideas and

solutions.

... steun te verwerven voor mijn

ideeën en oplossingen.

.936 .927

SMU7 Impl. … put ideas into action. ... mijn ideeën op gang te brengen. .944 .926

SMU8 Impl. … realize ideas. ... mijn ideeën te realiseren. .930 .927

46

Appendix C

Original questionnaire

A. Work situation

To begin with we would like to ask you a couple of questions about your current work

situation.

1. What is the name of your organization?

2. How long have you been with the present

organization?

O 0-1years

O 1-5 years

O 5-10 years

O 10-20 years

O 20> years

To what extent do you display each of the described behaviors?

1= Never 2= Rarely 3=Sometimes 4=Often 5=Always

B. Innovative Work Behavior 1 2 3 4 5

1. I pay attention to issues that are not part of my daily work O O O O O

2. I wonder how things can be improved O O O O O

3. I search for new working methods, techniques or

instruments O O O O O

4. I generate original solutions for problems O O O O O

5. I find new approaches to execute tasks O O O O O

6. I make important organizational members enthusiastic for

innovative ideas O O O O O

7. I attempt to convince people to support innovative idea O O O O O

8. I systematically introduce innovative ideas into work

practices O O O O O

9. I contribute to the implementation of new ideas O O O O O

10. I put effort in the development of new things O O O O O

47

How often do you relate with each of the statements?

1= Never 2= Sometimes 3= Often 4= Always

C. Job Autonomy 1 2 3 4

1. Do you have freedom in carrying out your work activities? O O O O 2. Can you decide how your work is executed on your own? O O O O 3. Can you personally decide how much time you need for a

specific activity? O O O O

4. Can you organize your work yourself? O O O O

The following statements concern the sharing of knowledge with your colleagues at work. Please,

note that knowledge sharing includes two activities: 1) Sharing your own knowledge and skills with your

colleagues, and 2) asking your colleagues for their knowledge and skills.

To what extent do you agree or disagree with each of the following statements?

1=Strongly Disagree 2=Disagree 3=Neutral 4=Agree 5=Strongly Agree

D. The role of sharing knowledge at work 1 2 3 4 5

1. I intend to frequently share knowledge. O O O O O 2. I will always share knowledge. O O O O O 3. I will always try to share knowledge in the most efficient

way possible. O O O O O

4. I can devote enough time to sharing knowledge. O O O O O 5. The climate in my organization allows me to share

knowledge easily. O O O O O

6. The climate in my organization facilitates informal

meetings where I can share knowledge. O O O O O

7. If it depended only on me, I would exhaustively share

knowledge. O O O O O

8. I am fully capable of sharing knowledge in written (e.g.

mail) or spoken (e.g. during meetings) form. O O O O O

9. I believe I am fully capable of sharing knowledge at any

time. O O O O O

48

In the last six months, how often did you relate to the following statements?

1=Never 2=Rarely 3=Sometimes 4=Often 5=Always

E. Knowledge sharing 1 2 3 4 5

1. When I’ve learned something new, I told people in my

organization about it. O O O O O

2. When I’ve learned something new, I saw to it that people

in my organization can learn it as well. O O O O O

3. I shared my skills with people in my organization. O O O O O

4. I shared my knowledge with people in my organization. O O O O O

5. People in my organization told me what they knew, when I

asked them about it. O O O O O

6. People in my organization shared their skills with me,

when I asked them to. O O O O O

The following statements concern the use of social media for sharing knowledge with

your contacts about ideas and solutions at work.

There are two things to keep in mind: First, examples of social media applications are Facebook,

Twitter, LinkedIn, WhatsApp, Viber, Telegram and Facebook Messenger. Second, contacts can

vary from current colleagues in your organization to people who have the same educational or

professional background outside the organization you are currently employed in.

In the last six months, how often did you relate to the following statements?

1=Never 2=Rarely 3=Sometimes 4=Often

F. Social media use 1 2 3 4

I used social media to…

1. … come up with work-related ideas and improvements. O O O O 2. … search for feedback on work-related ideas O O O O 3. … talk about ways to find new solutions to work-related

problems. O O O O

49

- Thank you for completing the questionnaire! -

4. … see how work aspects could be improved. O O O O 5. … energize people about work-related ideas. O O O O 6. … put work-related ideas into action. O O O O

7. … realize work-related ideas. O O O O

8. … mobilize support for work-related ideas and solutions. O O O O

G. Demographic What is your age? _______Years

What is your gender? O Male O Female

What is your highest education achieved?

O Primary school

O Secondary school

O MBO

O HBO

O University