Running head: KNOWLEDGE SHARING OF INDIVIDUAL EMPLOYEES 1
How to Exploit the Power of Knowledge?
The Role of Organizational Governance Mechanisms in Knowledge Sharing Processes of
Individual Employees
University of Amsterdam
Master’s Thesis
Graduate School of Communication
Master’s program Communication Science
Name: Caroline Jechow
Student number: 11117974
Supervisor: Wim J. L. Elving
Abstract
This thesis empirically investigates the relationship between different governance
mechanisms and knowledge sharing behavior. It aims to find out in which way governance
mechanisms exert their impact on knowledge sharing. The hypotheses are developed based on
the theory of planned behavior, which allows studying the role of attitudes and intentions
towards knowledge sharing within this relationship. The data was collected by means of a
self-completion questionnaire. The sample consists of 100 respondents working in
knowledge-intensive fields. Two major findings are revealed through the analyses. Firstly, the
governance mechanisms tested provoke two types of knowledge sharing behavior, namely
volitional and less volitional. Secondly, for further analyses the concept of knowledge sharing
was split into knowledge donating and knowledge collecting. Interestingly, only knowledge
donating is stimulated by the governance mechanisms tested. The thesis contributes to the
need of studying how individuals can be encouraged to share their knowledge. Thus, it
follows the ideas of the knowledge governance approach, which evaluates organizational
governance mechanisms as valuable tools to stimulate knowledge sharing at an individual
level. The thesis also generates insights for organizations as the findings can support
practitioners in developing an appropriate knowledge sharing strategy.
Introduction
Knowledge is a crucial asset for organizations. It is widely considered to be an
important competitive advantage because it cannot be imitated easily by other organizations
(Teece, 1998). Thus, more and more organizations implement knowledge management
systems where they can aggregate and organize the organization’s knowledge (Rosen, Furst,
& Blackburn, 2007). Still, those knowledge management systems will not produce
competitive advantages if employees do not contribute their knowledge to them (ibid.). Only
KNOWLEDGE SHARING OF INDIVIDUAL EMPLOYEES 3 individual learning (Andrews & Delahaye, 2000; Nidumolu, Subramani & Aldrich, 2001),
knowledge creation (Pemsel & Müller, 2012), productivity increase (Dyer & Nebeoka, 2000)
and performance achievement (Bartol & Srivastava, 2002) can be accomplished. Those
outcomes together will not only accumulate competitive advantage but also determine
sustainable organizational growth (Grant, 1996).
The motivation to share knowledge is not guaranteed in organizations (Foss, Minbaeva,
Pedersen & Reinholt 2009; Osterloh & Frey, 2000). The existence of a phenomenon like
knowledge-sharing hostility, which includes refusing to share one’s knowledge and not using
other’s knowledge, clearly illustrates that (Husted, Michailova, Minbaeva & Pedersen, 2012).
Consequently, an extensive body of literature evolved which aims to find out how knowledge
sharing can be stimulated.
The thesis contributes to the literature by identifying organizational mechanisms that
positively influence knowledge sharing. It follows the idea of the knowledge governance
approach, which assumes that organizational mechanisms can help creating conditions that
encourage employees to share their knowledge (Foss, 2007; Foss, Husted & Michailova,
2010). The study specifically aims to find out the following:
RQ: In which ways do formal and informal governance mechanisms influence
individual knowledge sharing processes?
This question allows identifying how specific cognitive and behavioral processes of
individuals will be influenced by organizational governance mechanisms. Thus, it looks at
how macro-level conditions can influence micro-level conditions and actions (Foss, 2007).
Focusing on formal and informal governance mechanisms is valuable because they inevitably
interact (Foss et al., 2010). Interestingly, the authors (2010) detect that not many studies have
situated in a field within the knowledge governance approach where not much attention has
been paid to so far. Besides theoretical contributions, the results will also have a strong
significance from a practical perspective. For managers aiming to enhance knowledge
processes in their organizations it is crucial to comprehend how to design favorable
micro-level conditions that stimulate knowledge sharing. Results from this study will generate
valuable insights to inform strategic planning and decisions.
The thesis is structured as follows. Firstly, a literature overview is presented elaborating
on important theoretical underpinnings such as the peculiarities of knowledge, the knowledge
governance approach and the theory of planned behavior. Subsequently, the hypotheses
guiding the study are introduced. Next, the employed methodology is explained and the
measurements of the different concepts are specified. This part is followed by the presentation
of the results of the hypotheses testing. The discussion of the most important findings, their
theoretical as well as practical implications, limitations and a conclusion complete the thesis.
Theoretical Framework
Knowledge
Knowledge is a term which became increasingly relevant for the management literature
in the 1990’s. Since then it has considerably affected theory in various fields. It can be
defined as a personal “justified true belief” (Nonaka, 1994, p. 15). Knowledge is nurtured
through dynamic processes in which personal beliefs are continuously developed, adapted and
justified (Nonaka, 1994). This means that knowledge is not static, given to or possessed by
somebody but it evolves through actions (Ribeiro, 2012). It is often distinguished between
tacit and explicit knowledge (Nonaka, 1994). Tacit knowledge is very personal and cannot be
easily articulated or written down (ibid.). It is “tied to the senses, movement skills, physical
KNOWLEDGE SHARING OF INDIVIDUAL EMPLOYEES 5 Explicit knowledge, in contrast, refers to knowledge which can be easily expressed (Nonaka,
1994).
The “knowledge movement” (Foss, 2007) spans many disciplines as for example
economics, management, organization theory, organizational behavior as well as psychology
or sociology (Argote, 2003; Foss et al., 2010). Each discipline is concerned about finding
ways to achieve advantages linked to knowledge. Although, all of them follow different
approaches and research strategies (Sergeeva & Andreeva, 2016), in most of the cases, a
central aspect is the creation and sharing of knowledge (Nonaka, 1994, Foss et al. 2009).
Knowledge Sharing
Knowledge sharing is an important part in the process of knowledge creation (Nonaka,
1994, Pemsel & Müller, 2012). The latter is seen as a macro-level process as it results from
individual knowledge which is shared, then amplified and made available to a broad group of
employees (Nonaka, 1994). The sharing of knowledge obviously precedes knowledge
creation (ibid.). It is defined as the process of transmitting one’s unique knowledge (Pemsel &
Müller, 2012). Knowledge is shared through communication between individuals and is an
interactional process. It includes sending one’s knowledge as well as receiving other person’s
knowledge (Foss et al., 2009). The value of one’s knowledge increases when it is shared,
because it becomes available to a broader group (van den Hooff & de Leeuw van Weenen,
2004). As a consequence it can connect to other people’s knowledge and enable the creation
of new knowledge (ibid.).
The Knowledge Governance Approach
The knowledge governance approach (KGA) is predominantly concerned about
starting point for all knowledge processes
1994).
The KGA is a well-recognized
(Pemsel, Wiewiora, Müller, Aubry & Brown, 2014
important strategic resource for organizations (Husted et al., 2012). Foss (2007) assumes an
organization’s management to have a critical impact on knowledge. Accordingly, the KGA
studies how knowledge processes like sharing, integrating and creating, can be coordinated
and improved through organizational governance mechanisms (Foss, 2007;
2014). With this approach, knowledge governance finds itself
organization and knowledge processes” (Foss, 2007, p.
processes “according to which organizations are directed and controlled” (Pemsel et al., 2014,
p. 1414). Applying those mechanisms to knowledge processes is supposed to make them more
effective by overcoming obstacles which hinder them (ibid.). Knowledge governance
mechanisms can be either formal or informal in natur
later on (e.g. Pemsel & Müller, 2012).
Figure 1. Level of analysis. Taken from Foss et al., 2010, p. 460 By means of those mechanisms
employee’s motivation to share
starting point for all knowledge processes: the personal knowledge of an employee
cognized approach within the diverse knowledge movement
, Müller, Aubry & Brown, 2014) and considers knowledge to be an
important strategic resource for organizations (Husted et al., 2012). Foss (2007) assumes an
to have a critical impact on knowledge. Accordingly, the KGA
studies how knowledge processes like sharing, integrating and creating, can be coordinated
and improved through organizational governance mechanisms (Foss, 2007;
pproach, knowledge governance finds itself at the “intersection of
organization and knowledge processes” (Foss, 2007, p. 38). Governance mechanisms are
processes “according to which organizations are directed and controlled” (Pemsel et al., 2014,
Applying those mechanisms to knowledge processes is supposed to make them more
effective by overcoming obstacles which hinder them (ibid.). Knowledge governance
mechanisms can be either formal or informal in nature, as will be described more in
later on (e.g. Pemsel & Müller, 2012).
. Level of analysis. Taken from Foss et al., 2010, p. 460.
By means of those mechanisms, micro-level preconditions are created which enhance an
employee’s motivation to share his/her knowledge (see figure 1, step 1). This motivation is employee (Nonaka,
within the diverse knowledge movement
) and considers knowledge to be an
important strategic resource for organizations (Husted et al., 2012). Foss (2007) assumes an
to have a critical impact on knowledge. Accordingly, the KGA
studies how knowledge processes like sharing, integrating and creating, can be coordinated
and improved through organizational governance mechanisms (Foss, 2007; Pemsel et al.,
at the “intersection of
38). Governance mechanisms are
processes “according to which organizations are directed and controlled” (Pemsel et al., 2014,
Applying those mechanisms to knowledge processes is supposed to make them more
effective by overcoming obstacles which hinder them (ibid.). Knowledge governance
e, as will be described more in-depth
level preconditions are created which enhance an
KNOWLEDGE SHARING OF INDIVIDUAL EMPLOYEES 7 supposed to result in actual knowledge sharing behavior (step 2) which can then be amplified
to a macro-level (Foss, 2007).
Summarizing, the KGA evaluates knowledge sharing as an individual behavior which
can be stimulated through governance mechanisms (Foss, 2007). To effectively study which
governance mechanisms influence knowledge sharing behavior, the theory of planned
behavior (TPB) will be introduced. This theory has empirically proven to predict human
behavior in specific contexts (Ajzen, 1991; Conner & Armitage, 1998; Madden, Scholder
Ellen & Ajzen, 1992). Since both approaches share a common aim – predicting behavior –the
TPB can be seen as an advantageous supplement to the KGA. Both theories will be linked for
the development of the hypotheses.
Theory of Planned Behavior
The theory of planned behavior is an advancement of the theory of reasoned action
(TRA) (Madden et al., 1992). Both assume behavioral intention to be a direct antecedent of
behavioral performance (Madden et al., 1992). Behavioral intention is best defines as a
person’s motivation to execute a certain behavior. It describes the effort a person is planning
and willing to make, to engage in the behavior in question. The performance of this behavior
becomes more likely, the higher a person’s behavioral intentions are (Ajzen, 1991).
Behavioral intention is formed through the attitudes of a person towards a certain behavior
and the subjective norm (Madden et al., 1992). Attitudes are developed through salient beliefs
and information about the specific behavior and its outcome. Subjective norm, on the other
hand, is constituted through beliefs about what coworkers are perceived to think of the
behavior (Conner & Armitage, 1998). In the TPB a third variable is integrated into the model,
perceived behavioral control. This concept describes whether a person perceives a behavior to
be difficult or easy to perform. It depends on, for example, a person’s skills but also given
attitudes and subjective norm, the latter is assumed to also influence the actual behavior
directly (Madden et al., 1992). Integratin
volitional and less easily performable behaviors which were not captured within the TRA
(Conner & Armitage, 1998). Variables external to the model are assumed to exert their
influence on behavior not only t
norm or perceived behavioral control and intentions (Madden et al., 1992).
Figure 2. Path model for the theory of planned behavior.
In the analyses, subjective norm and perceived behavioral control will not be assessed
separately as they overlap to a certain extent with the governance mechanisms
utilized in the hypotheses which are
become clear when those concepts are defined, they all include some nuances of subjective
norm and perceived behavioral control, respectively. By excluding them I will be able to draw
more distinct conclusions in the end.
Knowledge Governance Mechanisms
Governance mechanisms are often divided into formal and informal mechanisms
(Pemsel & Müller, 2012). Formal mechanisms can be incentive and reward systems, the use
of information systems, for exam
design (Husted et al., 2012), or rules and regulations (Grandori, 2001). Informal mechanisms
are also called relational governance mechanisms as they aim
between employees (Pemsel & Müller, 2012). They comprise facets such as organizational attitudes and subjective norm, the latter is assumed to also influence the actual behavior
directly (Madden et al., 1992). Integrating this variable made the model applicable to less
volitional and less easily performable behaviors which were not captured within the TRA
(Conner & Armitage, 1998). Variables external to the model are assumed to exert their
influence on behavior not only through intentions, but in a series through attitudes, subjective orm or perceived behavioral control and intentions (Madden et al., 1992).
Path model for the theory of planned behavior. Adapted from Madden et al.
subjective norm and perceived behavioral control will not be assessed
overlap to a certain extent with the governance mechanisms
hypotheses which are: organizational culture, guidelines and job design. As will
become clear when those concepts are defined, they all include some nuances of subjective
norm and perceived behavioral control, respectively. By excluding them I will be able to draw
ct conclusions in the end.
echanisms
Governance mechanisms are often divided into formal and informal mechanisms
(Pemsel & Müller, 2012). Formal mechanisms can be incentive and reward systems, the use
of information systems, for example in form of knowledge databases (Foss, 2007), work
design (Husted et al., 2012), or rules and regulations (Grandori, 2001). Informal mechanisms
are also called relational governance mechanisms as they aim to coordinate
(Pemsel & Müller, 2012). They comprise facets such as organizational attitudes and subjective norm, the latter is assumed to also influence the actual behavior
g this variable made the model applicable to less
volitional and less easily performable behaviors which were not captured within the TRA
(Conner & Armitage, 1998). Variables external to the model are assumed to exert their
hrough intentions, but in a series through attitudes, subjective
et al. (1992)
subjective norm and perceived behavioral control will not be assessed
overlap to a certain extent with the governance mechanisms that will be
and job design. As will
become clear when those concepts are defined, they all include some nuances of subjective
norm and perceived behavioral control, respectively. By excluding them I will be able to draw
Governance mechanisms are often divided into formal and informal mechanisms
(Pemsel & Müller, 2012). Formal mechanisms can be incentive and reward systems, the use
ple in form of knowledge databases (Foss, 2007), work
design (Husted et al., 2012), or rules and regulations (Grandori, 2001). Informal mechanisms
e the interaction
KNOWLEDGE SHARING OF INDIVIDUAL EMPLOYEES 9 culture, management styles, trust or communication flows (Pemsel et al., 2014). Only a few
studies pay attention to the interaction between formal and informal mechanisms yet (Foss et
al., 2010). This thesis contributes to the exploration of this interaction. I focus on work design
and rules and regulations as formal mechanism and organizational culture as an informal
governance mechanism.
Hypotheses Development
As knowledge is shared through interaction between people, the focus is put on
variables within the governance mechanisms, which promote communication between
individuals. Variables representing formal governance mechanisms (job autonomy, formal
feedback, task interdependence, guidelines) and an informal mechanism (organizational
culture) will be introduced in the following.
Job Autonomy
Job autonomy refers to having a certain degree of freedom and independence regarding
the planning and completion of one’s job (Hackman & Oldham, 1975). It is positively related
to the responsibility an employee experiences for his/her job (ibid.). That seems to be
reflected in the finding that people spend significantly more time on contributing knowledge
to a repository when they experience high degrees of autonomy in their job and perceive their
knowledge being valuable than when they have little autonomy (Pee & Chua, 2016).
Additionally, job autonomy significantly influences an employee’s intrinsic motivation to
share knowledge (Foss et al., 2009). The TPB states that behavioral intentions (motivation)
are influenced by attitudes towards a certain behavior (Ajzen, 1991). That is why, job
autonomy is expected to positively affect a person’s attitudes towards knowledge sharing.
Thus, I hypothesize that job autonomy will influence knowledge sharing through a person’s
H1a: Employees with high levels of job autonomy will be more inclined to share their
knowledge than employees with lower levels of job autonomy. This effect will be mediated by
a person’s positive attitudes and behavioral intentions towards knowledge sharing.
Formal Feedback
Another motivator in the spectrum of work design is the degree to which an employee
receives formal feedback on his/her job performance from supervisors (Foss et al., 2009;
Hackman & Oldham, 1975). In particular formal feedback was found to influence sending
knowledge (in contrast to receiving) through extrinsic motivation (Foss et al., 2009). In line
with that, and based on the findings of the TPB, I argue that formal feedback on knowledge
sharing activities will positively influence knowledge sharing behavior through a person’s
attitude and behavioral intentions.
H1b: Employees with high levels of regular, formal feedback on their knowledge
sharing activities will be more inclined to share their knowledge than employees with lower
levels of formal feedback. This effect will be mediated by a person’s positive attitudes and
behavioral intentions towards knowledge sharing.
Task Interdependence
The work design variables introduced so far belong to the variables introduced by the
job characteristics theory (Hackman & Lawler, 1971). However, Foss et al. (2009)
encouraged future research to broaden the scope and also test job characteristics which do not
belong to the ones proposed by Hackman and Lawler’s job characteristics model (1971). Task
interdependence is seen as a useful supplement to the model (Kiggundu, 1983). It has proven
to be important for many theories in the field of organizational behavior (Pearce & Gregersen,
1991). Task interdependence describes the degree to which an employee is required to work
together and cooperate with other employees to perform successfully (Langfred, 2007). Thus,
KNOWLEDGE SHARING OF INDIVIDUAL EMPLOYEES 11 Interdependence was found to be related to internal motivation (Humphrey, Nahrgang &
Morgeson, 2007). As internal motivation/behavioral intentions are crucial for behavioral
performance, I expect high levels of task interdependence to influence knowledge sharing
behavior positively through a person’s attitudes and behavioral intentions towards knowledge
sharing.
H1c: Employees with high levels of task interdependence will be more inclined to share
their knowledge than employees with lower levels of task interdependence. This effect will be
mediated by a person’s positive attitudes and behavioral intentions towards knowledge
sharing.
Guidelines
Guidelines are defined as “a set of rules or instructions that are given by an official
organization telling you how to do something, especially something difficult” (guideline).The
role of guidelines about how and why to share knowledge has not been analyzed in research
so far. However, whether an organization provides sufficient guidance for knowledge sharing
processes or not, seems to be a logical determent of knowledge sharing behavior. That is why
I assume employees that are aware of how and why to share their knowledge to show higher
levels of actual knowledge sharing
H2a: Employees being aware why knowledge sharing is important due to respective
guidelines will be more inclined to share their knowledge than employees not knowing very
well why knowledge sharing is important. This effect will be mediated by a person’s positive
attitudes and behavioral intentions towards knowledge sharing.
H2b: Employees being aware how to share their knowledge due to respective guidelines
will be more inclined to share their knowledge than employees not knowing very well how to
share their knowledge. This effect will be mediated by a person’s positive attitudes and
Organizational Culture
Informal and formal knowledge governance mechanisms presumably interact regarding
their influence on knowledge sharing (Foss et al., 2010). This will be studied analyzing to
what extent a knowledge sharing friendly organizational culture influences the relationship
between work design and knowledge sharing. An organizational culture describes a
company’s values, underlying assumptions and norms (Schein, 1985). It has the potential to
shape organizational member’s behavior (Zheng, Yang & McLean, 2010). Furthermore, it
strongly impacts on the way employees make sense of their daily tasks and, consequently,
might also shape their knowledge sharing behavior (ibid.). The same authors (2010) found
that organizational culture is strongly related to the effectiveness of knowledge management,
which includes knowledge sharing. In particular, sharing tacit knowledge is strongly enhanced
in companies where a clan culture dominates (Suppiah & Sandhu, 2011). A clan culture can
be understood as a knowledge sharing friendly culture as it is characterized by attributes such
as teamwork, open communication and values like collaboration, trust and support (Hartnell,
Ou & Kinicki, 2011). To observe the interaction effect of informal and formal governance
mechanism, I will look at how a knowledge sharing friendly organizational culture influences
the relationship between work design (formal mechanisms) and knowledge sharing.
According to the finding of Suppiah and Sandhu (2011) this relationship will be more
pronounced when people work in companies, which value knowledge sharing and
cross-departmental communication.
H3a: Employees with high levels of job autonomy will be more inclined to share their
knowledge than employees with low levels of job autonomy. This effect will be even more
pronounced for employees working in an organization, which encourages knowledge sharing.
H3b: Employees with high levels of regular, formal feedback will be more inclined to
KNOWLEDGE SHARING OF INDIVIDUAL EMPLOYEES
even more pronounced for employees working in an organization
knowledge sharing.
H3c: Employees with high levels of
their knowledge than employees with low levels of
even more pronounced for employees working in an organization
knowledge sharing.
Figure 3 illustrates the hypotheses graphically.
Figure 3. Theoretical model.
This section will give a detailed overview about the
information about the procedures of
measurements employed in the
Procedures and sample
To investigate the hypothesized
survey design was conducted.
OF INDIVIDUAL EMPLOYEES
even more pronounced for employees working in an organization, which encourages
: Employees with high levels of task interdependence will be more inclined to
their knowledge than employees with low levels of task interdependence. This
even more pronounced for employees working in an organization, which encourages
he hypotheses graphically.
Methodology
his section will give a detailed overview about the conducted study
information about the procedures of the research as a whole, the sample of
the questionnaire.
the hypothesized relationships properly, quantitative research using a
The concepts specified in the hypotheses were
13 which encourages
be more inclined to share
task interdependence. This effect will be
which encourages
conducted study providing
research as a whole, the sample of the survey and the
quantitative research using a
means of a self-completion questionnaire. The questionnaire contained mainly items that were
used and validated in previous research on the specific topic. However, it was pre-tested with
five experts on comprehensibility which resulted in minor changes regarding the phrasing of
certain items. Where necessary, explications were added. The questionnaire was distributed
via a link to colleagues, friends and family, of whom many are working in knowledge
intensive organizations. Those were explicitly asked to share the questionnaire with their
colleagues and acquaintances working in relevant organizations.
The questionnaire was completed by 100 persons (56% female). The majority is
German (60%) and Dutch (10%). The average age of the participants is 30 years (SD = 8,73).
Due to the low average age it is not surprising that 35% of the participants work as interns or
working students. However, as these two groups are especially dependent on other
employee’s knowledge and, in turn, can share their knowledge from education, I consider
them as respondents providing valuable input. Regular workers amount to 53% of the
respondents. The remaining 12% are either temporary workers or self-employed people
working for organizations. The education level of the participants is exceptionally high, 92%
have obtained a bachelor’s degree or higher.
Measures
Knowledge Sharing: The 14 items to measure knowledge sharing behavior were taken
from van den Hooff and de Leeuw van Weenen (2004). The respondents were asked to
indicate how often they perform the behaviors stated (1 = never, 5 = every time). Items the
respondents had to evaluate were for example “when I’ve learned something new, I tell my
colleagues in my department about it” or “colleagues within my department tell me what their
skills are, when I ask them about it”. Van den Hooff and de Leeuw van Weenen (2004)
distinguished between knowledge donating and collecting, instead of sending and receiving
KNOWLEDGE SHARING OF INDIVIDUAL EMPLOYEES 15 so to and knowledge donating refers to asking colleagues for specific knowledge. Scoring
high on this variable means that knowledge is shared very often.
Job Autonomy: This concept was measured with three items taken from Foss et al.
(2009). Participants were asked, for example, to what extent they “have the opportunity for
independent initiative” (1 = not at all, 5 = to a large extent). Scoring high on this variable
means that people have a high degree of job autonomy.
Feedback: This concept was assessed using two items from Foss et al. (2009). The
original scale consisted of three items. One item was deleted as the pre-test has shown that it
was not exhaustively distinguishable from another item from the scale. Respondents had to
evaluate to what extent they receive “formal acknowledgment” and “feedback from my
superior on my job performance” (1 = not at all, 5 = to a large extent). Scoring high on this
variable means that people receive a high degree of formal feedback from their superiors.
Task Interdependence: Respondents had to evaluate how much they agreed with the
presented statements. From the original 11 items from Pearce and Gregersen (1991) three
were excluded because they did not form a reliable factor in previous research (ibid.). The
remaining eight items stated for example “I work closely with others in doing my work” or “I
rarely have to obtain information from others to complete my work” (1 = strongly disagree, 5
= strongly agree). After reverse coding items that were negatively phrased, scoring high on
this variable means that people are very dependent on others doing their job.
Guidelines: As guidelines were not tested before in empirical research, I designed the
items myself. I used four items to measure whether specific guidelines about knowledge
sharing exist and whether employees know where to find them. Respondents were asked to
evaluate items such as “my company provides sufficient material (documents, guidelines etc.)
about how to share knowledge” or “I know exactly where to look for material, which explains
reverse coding one item that was negatively phrased, scoring high on this variable means that
employees perceive that their company provides sufficient material about how and why to
share knowledge and that those materials are findable. I expected to find two factors – one
comprising the items about why to share knowledge, the second one including the items
regarding how to share knowledge. This will affect the testing of hypotheses 2a and 2b.
Organizational Culture: Whether an organization features a culture that enhances
knowledge sharing was measured with three items taken from Husted et al. (2012). The
respondents were asked to appraise items such as “values and attitudes of my company
support knowledge sharing” as well as “in my company people cooperate across departments”
(1 = strongly disagree, 5 = strongly agree). A high score on this variable reveals whether
respondents perceive their organization to provide a culture that fosters interaction between
employees.
Knowledge Sharing Intentions: A respondent’s intention to share knowledge was
assessed with five items by Bock, Zmud, Kim and Lee (2005). As the items from the
knowledge sharing scale, these items also allude to explicit and tacit knowledge. Example
items are: “I always intend to provide my manuals, methodologies and models for members of
my organization” and “I intend to share my experience or know-how from work with
organizational members more frequently in the future” (1 = very unlikely, 5 = very likely). A
high intention to share knowledge is revealed by a high score on this variable.
Attitudes towards Knowledge Sharing: The five items measuring attitudes towards
knowledge sharing were also taken from Bock et al. (2005). Respondents were asked to assess
their agreement with statements such as “my knowledge sharing with other organizational
members is harmful” or “my knowledge sharing with other organizational members is
valuable” (1 = strongly disagree, 5 = strongly agree). After reverse coding the items that were
negatively framed, a high score on this variable means that the respondent has positive
KNOWLEDGE SHARING OF INDIVIDUAL EMPLOYEES 17 Demographics: Demographic information of the respondents were assessed to include
them into the analyses as control variable. Items asked for example for the respondents’ age,
educational level or type of employment contract.
Table 1 gives an overview about the means, standard deviations, reliabilities and
correlations of all variables.
Table 1
Means, standard deviations, Cronbach’s Alpha and correlations
M SD 1 2 3 4 5 6 7 8 1. Knowledge Sharing 3.79 . 56 .88 2. Job Autonomy 3.74 .82 .37*** .81 3. Feedback 3.26 1.00 .32*** .33** .74 4. Task Interdependence 3.76 .67 .26** .12 -.05 .81 5. Guidelines 2.92 .98 .25* .18 .24* .09 .81 6. Organizational Culture 3.53 .93 .53*** .29* .42*** .28** .51*** .84 7. Attitudes 3.92 .61 .43*** .36*** .18 .40*** .07 .36*** .76 8. Intentions 3.78 .76 .44*** .36*** .28** .35*** .25* .46*** .55*** .81 Note. N = 100.
In bold: value for Cronbach’s Alpha of respective scale * p < .05 / ** p < .01 / *** p < .001
Results
To generate more accurate results for the effects in question, every analysis was
statistically controlled for demographic information including age (in years), gender (female
vs. male), educational level (high vs. low), type of working contract (regular employee vs.
other), number of working hours and number of employees in the department of the
respondent. Although, they are not correlated with the dependent variable, it seems reasonable
to argue that these influence a person’s knowledge sharing behavior. Figures 4 to 7 illustrate
the path models of the serial multiple mediation models applied to test hypotheses 1 (a, b, c)
Hypotheses Testing
For each mediation analysis a path model
the mediation. Estimates of the direct and indirect effects
Hypothesis 1a assumed that a person’s positive attitudes and behavioral intentions towards
knowledge sharing mediate the positive effect of job autonomy on knowledge sharing. As
illustrated in figure 4 all path coefficien
Figure 4. Path model displaying unstandardized path coefficients for model 1a. Notes. * p < .05 / ** p < .01 / *** p < .001
Correspondingly, the indirect effect linking job autonomy to knowledge sharing via
attitudes and intentions is also significant
employee has in his/her job, the more favorable are his/her attitudes towards kn
sharing. These positive attitudes result in higher knowledge sharing intentions
lead to a stronger knowledge sharing behavior. However, job autonomy also significant
influences knowledge sharing behavior directly,
positive way. The mediation found is partial. Nevertheless, hypothesis 1a is supported.
Figure 5. Path model displaying unstandardized path coefficients for model 1b. Notes. * p < .05 / ** p < .01 / *** p < .001
For each mediation analysis a path model provides the path coefficients for each
stimates of the direct and indirect effects can be found in a separate table.
Hypothesis 1a assumed that a person’s positive attitudes and behavioral intentions towards
knowledge sharing mediate the positive effect of job autonomy on knowledge sharing. As all path coefficients are positive and significant.
Path model displaying unstandardized path coefficients for model 1a. * p < .05 / ** p < .01 / *** p < .001
Correspondingly, the indirect effect linking job autonomy to knowledge sharing via
also significant (see table 2). That means, the more autonomy an
job, the more favorable are his/her attitudes towards kn
sharing. These positive attitudes result in higher knowledge sharing intentions
lead to a stronger knowledge sharing behavior. However, job autonomy also significant
ledge sharing behavior directly, independently of attitudes and
he mediation found is partial. Nevertheless, hypothesis 1a is supported.
Path model displaying unstandardized path coefficients for model 1b. .01 / *** p < .001
the path coefficients for each step of
can be found in a separate table.
Hypothesis 1a assumed that a person’s positive attitudes and behavioral intentions towards
knowledge sharing mediate the positive effect of job autonomy on knowledge sharing. As
Correspondingly, the indirect effect linking job autonomy to knowledge sharing via
That means, the more autonomy an
job, the more favorable are his/her attitudes towards knowledge
sharing. These positive attitudes result in higher knowledge sharing intentions that, in turn,
lead to a stronger knowledge sharing behavior. However, job autonomy also significantly
attitudes and intentions, in a
he mediation found is partial. Nevertheless, hypothesis 1a is supported.
KNOWLEDGE SHARING OF INDIVIDUAL EMPLOYEES 19 Hypothesis 1b assumed formal feedback, through attitudes and intentions, to have a
positive influence on knowledge sharing behavior. This hypothesis cannot be supported
because mediation does not occur. Receiving formal feedback does not significantly influence
neither a person’s attitudes about knowledge sharing nor a person’s intentions to share
knowledge (see figure 5). Consequently, none of the indirect effects is significant (see table
2). However, as can be seen in table 2, formal feedback influences knowledge sharing
behavior directly. Employees receiving higher levels of formal feedback seem to be more
inclined to share their knowledge.
Figure 6. Path model displaying unstandardized path coefficients for model 1c.
Notes. * p < .05 / ** p < .01 / *** p < .001
Hypothesis 1c suggested that task interdependence would positively influence
knowledge sharing behavior through attitudes and intentions towards this behavior. As
depicted in figure 6, all coefficients describing the relevant path ways for the hypothesized
mediation effect are significant and, as expected, positive. Consequently, an indirect effect in
the way hypothesized was also found (see table 2). This indicates that people having high
levels of task interdependence in their job, have more favorable attitudes towards knowledge
sharing that result in higher knowledge sharing intentions and eventually lead to stronger
knowledge sharing behavior. There is no significant direct effect from task interdependence
on knowledge sharing behavior. Attitudes and intention fully mediate the relationship
between task interdependence and knowledge sharing behavior. As a result, hypothesis 1c can
Table 2
Mediation analyses: Estimates of effects for models testing hypotheses 1a, 1b, 1c Model 1a Job Autonomy Model 1b Feedback Model 1c Task Interdependence
Estimate LL - UL Estimate LL – UL Estimate LL - UL Total .34*** .18, .49 .22** .09, .35 .17 -.02, .37 Direct (XY) .20* .04, .36 .16** .05, .28 -.03 -.22, .17 Total indirect .14 .03, .38 .06 -.02, .19 .20 .06, .46 Indirect 1 .05 -.01, 19 .02 -.01, .09 .10 .02, .28 Indirect 2 .05 .01, 16 .01 -.01, .07 .07 .01, .24 Indirect 3 .04 .004, 14 .03 -.004, .10 .03 -.02, .14 Note. N = 84. Dependent variable = knowledge sharing behavior.
X = independent variable of the corresponding model, mentioned in table below model number. Unstandardized regression coefficients are displayed.
Abbreviations: LL –UL = lower and upper limit of a 95% confidence interval. * p < .05 / ** p < .01 / *** p < .001 (not applicable to indirect effects)
Indirect 1: X attitudes knowledge sharing
Indirect 2: X attitudes intentions knowledge sharing Indirect 3: X intentions knowledge sharing
Figure 7 displays the path coefficients for each step of the mediation analysis conducted
to test hypothesis two. Hypotheses 2a and 2b assumed that guidelines about (a) why and (b)
how to share knowledge would positively influence knowledge sharing behavior through
attitudes and intentions. I could not test these hypotheses the way I intended to because the
four items used for measuring the two concepts did not form two separate, reliable factors but
just one. In order to not miss insights about the role of guidelines in the knowledge sharing
process, I decided to bundle the hypotheses into one, now assuming that guidelines
concerning knowledge sharing would positively influence knowledge sharing behavior
through attitudes and intentions.
Figure 7. Path model displaying unstandardized path coefficients for model 2.
KNOWLEDGE SHARING OF INDIVIDUAL EMPLOYEES 21 Table 3
Mediation analysis: Estimates of effects for the model testing hypothesis 2 (originally 2a & b)
Model 2 guidelines Estimate LL - UL Total .17* .03, .12 Direct .13* .002, .25 Total indirect .04 -.02, .16 Indirect 1 .01 -.01, .07 Indirect 2 .01 -.01, .06 Indirect 3 .02 -.01, .10
Note. N = 84. Dependent variable = knowledge sharing behavior. Unstandardized regression coefficients are displayed.
Abbreviations: LL –UL = lower and upper limit of a 95% confidence interval. * p < .05 / ** p < .01 / *** p < .001 (not applicable to indirect effects)
Indirect 1: Guidelines attitudes knowledge sharing
Indirect 2: Guidelines attitudes intentions knowledge sharing Indirect 3: Guidelines intentions knowledge sharing
Testing the newly created hypothesis revealed that it has to be rejected. According to
the results, guidelines do not significantly influence an employee’s attitudes regarding
knowledge sharing. Guidelines neither influenced an employee’s intentions to share
knowledge. Hence, as can be seen in table 3, all estimates for the indirect effects are
insignificant as their bootstrap confidence intervals contain zero. However, guidelines seem to
play a role in knowledge sharing processes. The direct effect from guidelines on knowledge
sharing behavior is significant which means that employees knowing why knowledge sharing
is important and how to do so are more inclined to share their knowledge.
Hypothesis 3a, 3b and 3c shifted the focus towards the interaction of formal and
informal governance mechanisms. Therefore, moderation analyses were conducted (see figure
Figure 8. Path model for moderation analyses used to test hypotheses 3a, 3b, 3c.
Hypothesis 3a assumed that organizational culture would interact with job autonomy
and consequently moderate the effect from job autonomy on knowledge sharing behavior. As
shown in table 4, the model used to test this hypothesis fits the data well. Together with the
control variables, job autonomy, organizational culture and their interaction explain 45% of
the variance in an employee’s knowledge sharing behavior. Although job autonomy and
organizational culture independently predict knowledge sharing significantly, they do not
interact. Consequently, hypothesis 3a has to be rejected as the effect of job autonomy on
knowledge sharing is not more pronounced in organizations whose culture is enhancing
knowledge sharing.
Hypothesis 3b assumed that organizational culture would moderate the relationship
between feedback and knowledge sharing in a way that employees getting regular formal
feedback and work in organizations supporting knowledge sharing, would share knowledge to
a greater extent. As illustrated in table 4 the model fits the data well and explains 37% of the
variance in an employee’s knowledge sharing behavior. In this model only organizational
culture predicts knowledge sharing behavior significantly. The interaction between feedback
and organizational culture as well as feedback itself do not significantly predict an employee’s
knowledge sharing behavior. The latter is surprising as model 1b indeed revealed a significant
direct effect from feedback on knowledge sharing behavior. Nevertheless, hypothesis 3b has
to be rejected as organizational culture does not moderate the relationship between feedback
KNOWLEDGE SHARING OF INDIVIDUAL EMPLOYEES 23 Hypothesis 3c assumed organizational culture to moderate the effect between task
interdependence and knowledge sharing behavior. Employees who experience a high degree
of task interdependence and work in an organization fostering knowledge sharing are
supposed to engage more into knowledge sharing. Also model 3c fits the data well, explaining
35% of the variance in an employee’s knowledge sharing behavior. As in the models before,
the interaction effect between the two governance mechanisms on knowledge sharing is not
significant. Hypothesis 3c consequently has to be rejected. In this model only organizational
culture significantly predicts knowledge sharing in a positive way. That means that people are
more inclined to share their knowledge when they work in organizations supporting
knowledge sharing.
Table 4
Moderation analyses: Interaction of formal and informal governance mechanisms
Note. N=84. Dependent variable = knowledge sharing behavior.
X = independent variable of the corresponding model, mentioned in table below model number. Unstandardized regression coefficients are displayed.
Abbreviations: LL –UL = lower and upper limit of a 95% confidence interval. * p < .05 / ** p < .01 / *** p < .001
Additional Analyses
The knowledge sharing scale combined two concepts, knowledge donating and
collecting. Both are active processes, sharing knowledge of one’s own accord and consulting
for other’s knowledge, but they distinguish regarding their motivation (van den Hooff & de
Leeuw van Weenen, 2004). Consequently, it seems reasonable to test whether results persist
when the hypotheses are tested with knowledge donating (Cronbach’s α = .92) and collecting
(Cronbach’s α = .85) as separate dependent variables. The composition of the models did not Model 3a Job Autonomy Model 3b Feedback Model 3c Task Interdependence
Estimate LL-UL Estimate LL-UL Estimate LL-UL
Constant .54 -1.01,2.09 1.60* .36, 2.83 1.68 -.27, 3.62 X .57** .19, .95 .32 -.03, .67 .22 -.27, .71 Organ. culture .74** .33, 1.15 .61** .26, .95 .62* .08, 1.17 Interaction -.11 -.21, .00 -.08 -.18, .03 -.06 -.20, .08 Adj. R² .45 .37 .35 F 8.60*** 6.48*** 5.90***
change, except for the dependent variables. With the exception of job autonomy, no
significant effects were found for the relationship between work design variables, guidelines,
and knowledge collecting. Job autonomy directly influenced knowledge collection. The
effects found in the original analyses persisted when employing knowledge donating as a
dependent variable. Another analysis also tested whether organizational culture exerts an
influence on knowledge sharing, donating and collecting. The relationship between culture
and knowledge sharing was partially mediated by attitudes and intentions (direct effect = .31, t
= 4.88, p < .001, 95% CI [.181, .432], indirect effect = .02, 95% CI [001, .078]. When testing
the model for knowledge donating only a direct effect remained (effect = .05, 95% CI [337,
.668]. No significant effect was found when knowledge collecting served as the dependent
variable. Tables and path models with coefficients of the additional analyses can be found in
Appendix B.
Discussion
The analyses revealed two important findings. Firstly, only knowledge donating –
sharing knowledge without being asked to do so – is stimulated by the tested governance
mechanisms. Knowledge collecting on the other hand – asking for knowledge and sharing
knowledge after being asked respectively –seems to be independent of the tested governance
mechanisms. Secondly, the tested governance mechanisms apparently stimulate two forms of
knowledge donating. One form of knowledge donating that happens voluntarily and another
one that results from a certain kind of pressure.
Behaviors triggered by positive attitudes and intentions are, following the
argumentation of the TPB, voluntary because this chain of variables reflects that they follow
the donator’s own will (Madden et al., 1992). Job autonomy and task interdependence, hence,
stimulate volitional knowledge donating as they influenced knowledge donating through this
KNOWLEDGE SHARING OF INDIVIDUAL EMPLOYEES 25 clearly supported by previous research. Job autonomy and task interdependence are both
linked to intrinsic motivation (Foss et al., 2009, Humphrey et al., 2007). Intrinsic motivation
makes people engage in behaviors which they enjoy and which are meaningful to them (Foss
et al., 2009). As employees whose jobs involve for example high levels of task
interdependence are dependent on knowledge from others, donating their knowledge in return
can be considered meaningful to them and thus, explain the effects found.
Formal feedback and guidelines, in contrast, did not influence knowledge donating
through the chain of attitudes and intentions. The direct effect suggests that behavior is not
only performed due to the person’s own will (Madden et al., 1992). This view is supported by
the fact that feedback in previous studies was found to have a positive influence on external
motivation (Foss et al., 2009). External motivation for a behavior does not result from the
behavior itself. It is inherent in another outcome which can be achieved through engaging in
the behavior in question (ibid.). In this case it seems that employees rather donate their
knowledge to receive positive feedback from their superiors and not because they feel positive
about it.
Theoretical Implications
The study contributes to the body of literature by revealing the distinction of volitional
and less volitional knowledge donating. Two governance mechanisms stimulate this type of
knowledge donating in a direct way. Direct relationships are generally influenced to some
extent by further mediator and/or moderator variables (Hayes, 2013). Future research should
focus on variables that influence those direct effects. Respective findings would generate a
better understanding of why behaviors such as knowledge donating are carried out although,
to some extent, opposing one’s own will. Considering past behavior to predict current
knowledge donating behavior might be a good direction as it has proven to be a successful
motivation suggests that knowledge is (sometimes) donated to achieve another outcome.
Future research might also explore this notion to find out more underlying mechanisms. Is
knowledge really donated to achieve another outcome? Is this alternative outcome serving the
organization or the employee? Findings in this area are necessary for organizations to know
how to evaluate non-volitional knowledge donating. Besides that, this finding reflects two
opposing views in research. Studies within the stream of knowledge management or
organizational learning assume knowledge sharing to happen volitionally (Pemsel et al.,
2014). The KGA opposes this assumption presuming that employees share their knowledge
not necessarily following their personal conviction but after making a rational decision
reflecting possible benefits and disadvantages (ibid). Obviously both positions are true to a
certain extent.
Apart from that, it also seems important to analyze whether the quality and quantity of
knowledge shared differs when positive attitudes are involved or not. Findings in this area
would help organizations to decide whether one form of knowledge donating should be more
strongly fostered or whether both equally help building a sustainable knowledge exchange.
Linked to that, attention should be paid to possible differences regarding knowledge donating
inside or across departments as well as horizontal and vertical sharing. Presumably, crossing a
departmental or hierarchical boundary costs more effort than not crossing it.
Last but not least, different informal governance mechanisms, like communication
structures, should be added to the theoretical model to understand how knowledge is spread
within the organization (Pemsel & Müller, 2012). Due to the different kinds of effects found
(indirect vs. direct) it seems reasonable to also test the interaction between formal
mechanisms. Results for those questions are necessary for organizations to gain more insight
KNOWLEDGE SHARING OF INDIVIDUAL EMPLOYEES 27
Practical Implications
Organizations can gain some valuable insights from this study. Looking at them from an
internal communications perspective, the following governance mechanisms deserve to be
highlighted. Firstly, supervisors should provide their employees with specific feedback on
their knowledge sharing activities. Team leaders can leverage the positive effect of feedback
sessions when taking the function of a role model. In this respect they can communicate
expectations and norms regarding knowledge sharing activities more compellingly (Rosen et
al., 2007). Moreover, guidelines about knowledge sharing should be published. Although they
are not necessarily obligatory, they indicate that knowledge sharing is a desired behavior.
Those governance mechanisms might lead to short and long-term effects. In the short-term,
knowledge sharing with the aim to receive positive feedback or perform compliantly to the
guidelines happens mainly as a result of a certain kind of pressure. However, in the long-term,
an employee might even develop positive attitudes towards knowledge sharing and thus
internalize this behavior because of realizing those efforts help other colleagues.
Secondly, achieving the desired long-term effect certainly has to be supported by the
organization. Attitudes are formed through salient beliefs about the outcome of the behavior
in question (Conner & Armitage, 1998). A sustainable strategic approach might focus on
influencing those individual beliefs to stimulate positive attitudes regarding knowledge
sharing among the employees. Establishing an organizational culture which fosters an easy
exchange between employees within and across departments and hierarchy levels as well as
trust seems to be an important measure in order to achieve that goal (Suppiah & Sandhu,
2011). Such a culture is furthermore necessary for achieving meso- and macro-level outcomes
such as knowledge creation. The “spiral of organizational knowledge creation”, which
describes the process of transforming individual knowledge into organizational knowledge,
cannot be initiated if communication cannot flow easily through the organization’s levels
Altering organizational culture and introducing feedback mechanism result in at least a
minor change process, depending on the organization’s status quo. To manage that change
processes successfully, strategic internal communication has proven to be crucial (Tkalac
Verčič, Verčič & Sriramesh, 2012). As a result, it appears recommendable for organizations
to integrate and align the achievement of knowledge goals and respective measures with
strategic internal communication plans.
Limitations
The results of this study have to be interpreted keeping some limitations in mind.
Firstly, the sample size is quite small (N = 100). Secondly, the sample includes respondents
from diverse industry types, positions and contract types. These facts make the obtained
results difficult to generalize. Thirdly, some of the items lack specificity. The items measuring
the concept of knowledge sharing guidelines, for example, are self-constructed and only form
one factor. The results consequently give a very broad idea about how those guidelines may
influence knowledge sharing. Results should be further verified and extended. Scores of the
items measuring formal feedback regarding knowledge sharing activities indicate that
respondents have related it to general formal feedback regarding overall job performance.
Lastly, a self-completion questionnaire was employed. As people get easily tired when filling
those questionnaires in, I had to avoid asking open questions, which could have given some
insights about knowledge sharing tools used within the organizations, for example. As a
consequence, the results of the study have to be evaluated carefully. They only can help
organizations’ managements to develop a first idea of how governance mechanisms can be
employed to stimulate knowledge sharing among employees.
Conclusion
The objective of this study was to find out in which ways formal and informal
KNOWLEDGE SHARING OF INDIVIDUAL EMPLOYEES 29 provide an answer to that question I tested whether a set of formal governance mechanisms
(job autonomy, formal feedback, task interdependence and guidelines) would influence
individual knowledge sharing through attitudes and intentions as proposed by the theory of
planned behavior. Additionally, a possible interaction between those formal governance
mechanisms (except guidelines) with an informal governance mechanism, namely
organizational culture, was tested.
Employing the theoretical model underlying the theory of planned behavior to test the
hypotheses revealed valuable insights into mechanisms underlying knowledge sharing
processes. The study demonstrated that knowledge sharing can be either a volitional or a
non-volitional behavior and that the tested governance mechanisms are capable to encourage both
types of knowledge sharing. Governance mechanisms that provide employees with a certain
latitude to decide with whom, how and when to carry out job tasks seem to create favorable
conditions, which foster knowledge sharing as they make it a necessary and appreciated
behavior to perform well. Other governance mechanisms rather exert a certain kind of
pressure on employees that leads to engagement in knowledge sharing. These are valuable
insights for HR practitioners as it shows that governance mechanisms need to be strategically
chosen and combined depending on organizational circumstances.
The study did not detect whether informal and formal governance mechanisms interact.
Nevertheless, it amplified the existing body of literature through further explaining in which
ways different knowledge processes can be stimulated to successfully exploit the power of
knowledge.
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Appendix A – Questionnaire
Knowledge sharing
Please indicate the frequency of performing the following behaviors by ticking the corresponding box.
Scale
- All items will be measured on the following 5 – point scale 1. Never
2. Rarely
3. Sometimes/occasionally 4. Almost every time 5. Every time
While answering the questions on this page, please keep in mind:
With “colleagues from your department” we mean your colleagues you work closest with on a daily basis.
With “colleagues outside of your department” we refer to any colleagues outside of your department. With them you do not work together on a daily basis. You do not necessarily know them.
The phrases “telling colleagues about” and “knowledge sharing" include providing and receiving knowledge via informal and formal channels. Please, consider all possible channels when selecting your answers.
Informal channels might be (spontaneous) face to face conversations, or a chat via e.g. lync, skype, skype for work, etc. Information shared via informal channels is usually only
accessible for one person/ a few people.
Formal channels include e.g. internal communication platforms (Yammer, SharePoint, Workplace etc.), internal databases, formal documents/forms or the like. Information shared via formal channels is generally available for a bigger audience or even the whole company.
1. When I have learned something new, I tell my colleagues in my department about it. 2. When colleagues within my department learned something new, they tell me about it. 3. Knowledge sharing with department members is considered a normal thing in my company. 4. When I have learned something new, I tell my colleagues outside of my department about it.
5. When colleagues outside of my department learned something new, they tell me about it. 6. Knowledge sharing with my colleagues outside of my department is considered a normal thing.
