Searching for clarity: the influence of leadership and
top management support on the relation between
social capital and knowledge sharing
Sidney Klinker - 10969616
Corporate Communication Master Thesis Supervisor: Luzia Helfer
Abstract
In the current Knowledge Age, knowledge and the sharing of it has become the most valuable resource of an organization. It is currently assumed that social capital, which includes
concepts such as trust, social interaction, shared norms and values, increases knowledge sharing in an organizational setting. The current study set out to study how transformational leadership and top management knowledge values (and their interaction) influences the relationship between social capital and knowledge sharing. With this, this study maps out the potential influence of management in this process. Through an online survey data was
collected from (young) professionals working in the public and private sector. The data indicates a relationship between structural social capital dimension and knowledge sharing in organizations. No two-way or three-way interactions were found in the current data,
indicating that a possible relationship between transformational leadership and top
management knowledge values does not moderate the relationship between social capital and knowledge sharing. Limitations of the current study are discussed and guidelines for future research are set out.
1 Searching for clarity: the influence of leadership and top management support on the
relation between social capital and knowledge sharing
While transitioning from the Industrial Age towards the Knowledge Age (Nielsen, Du & Kolmos, 2008), it has become clear for both academics and practitioners that knowledge has become the most important organizational resource for organizations trying to gain a competitive advantage in the market (Ndofor & Levitas, 2004; Kogut & Zander, 1992). Knowledge not only allows an organization to manage its daily operations, but at the same time acts as the intellectual capital of an organization. It then becomes possible for
organizations to gain a successful competitive edge if it is able to efficiently create, share and manage knowledge internally (Kogut & Zander, 1992).
In theory this sounds straight forward, however, the processes underlying the creating, sharing, exploiting and especially the managing of knowledge are still relatively unclear (Nonaka, Toyoma & Konno, 2000). Many of the issues that organizations and managers face stem from the very nature of knowledge. Literature recognizes knowledge to consist of both tacit and explicit aspects (Nonaka & Konno, 1998), with studies concerning knowledge sharing and management mostly focusing on either aspect of knowledge. However, most studies focusing on the explicit aspects of knowledge proved to be disappointing and lacking explanatory power of the processes underlying knowledge sharing and management.
For example, studies have shown that the simple storing of data and information in databases does not result in better sharing of knowledge in organization (Schoeneborn, 2013). Other studies have shown that direct managerial influence on organizational knowledge sharing processes are ineffective (Hislop, 2002) and that measures such as formalizing knowledge sharing processes through introducing compulsory team meetings to exchange knowledge had (virtually) no impact on the sharing of knowledge in an organization (Fontaine & Lesser, 2002; Jackson & Williamson, 2011). Other studies conclude that one of the biggest
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complexities in knowledge management stems from extracting tacit knowledge from employees and making it available for sharing and organizational use (Haldin-Herregard, 2000; Lang, 2001).
Studies such as these have caused shifts in how academics view (organizational) knowledge. As a result, knowledge is currently primarily being seen as something which is “personal, subjective, socially determined, primarily tacit, and related to daily practice” (van den Hoof & Huysman, 2009, p. 1). The belief is that knowledge emerges from, and is
primarily determined by, the social dynamic present in a group of people. Their interpersonal and group dynamic often determine how, and if, knowledge is shared (van den Hoof & Huysman, 2009).
Prominently present in relation to the social dynamic and workings of an organization is the concept of social capital, which helps with explaining the interpersonal relations and group dynamic found in an organization (Nahapiet & Ghoshal, 1998). Social capital, consisting of the structural, relational and cognitive dimension is seen as facilitating the internal social infrastructure of an organization (Nahapiet & Ghoshal, 1998; Tsai & Ghoshal, 1998). Studies have shown that increasing the social capital present in an organization has a positive effect on knowledge sharing in organizations (e.g. Chang & Chuang., 2011; Chang, Huang, Chiang, Hsu & Chang, 2012; van den Hoof & Huysman, 2009).
While the influence of social capital on knowledge sharing seems to indicate more clarity concerning the sharing aspect of knowledge, questions surrounding the role of a manager remain. If we follow the social capital literature, it seems that a manager is expected to create and foster an environment that is knowledge friendly and stimulates knowledge sharing (e.g. Dyer & Nobeoka, 2000; Hansen, Nohria & Tierney, 1999). However, virtually no research has been conducted on the potential positive or negative influence of a manager on the relationship between social capital and knowledge sharing. Current research focusing
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on leadership and knowledge sharing focuses more on what style of leadership have a moderating influence on the relationship between knowledge sharing and organizational performance/innovation (Lee, Gillespie, Mann & Wearing, 2010; Mittal & Dhar, 2015; Pauliene, 2012). This study aims to fill this knowledge gap, by both adding to the literature and having practical relevance. Does style of leadership increase/weaken the effect of social capital on knowledge sharing? What style of leadership is best suited for an organization focused on the creation and sharing of knowledge? These are all relevant questions this study will focus on.
As important as it is for a manager to possess the right qualities to manage a knowledge team, it should be emphasized that large, complex organizations often have multiple layers of management, with top management (CEO, board of directors) dictating a certain vision. If a manager does not receive support from top management itself, his management practices to (positively) influence the relation between social capital and
knowledge sharing may all be in vain (Hsu, 2008). Davenport and Prusak (2000) reported that when top management perceives knowledge as a key strategic resource and knowledge
sharing as the foundation for value creation, they will support a range of knowledge
management practices that aim to facilitate knowledge sharing within organizations. But what happens if there is no support from top management, but a knowledge positive manager is in place? If we accept leadership style can have an impact on knowledge sharing, in what way is this impact influenced by support from top management? No research (that the author is aware of) has considered such a relation, which is why it will be considered in this study. The research question this study will thus try to answer is as follows:
In what way does the interaction between leadership style and top management support influence the relation between social capital and knowledge sharing?
4 Theoretical framework
Knowledge sharing
As stated in the introduction, our current knowledge regarding the processes
underlying sharing, creating and managing knowledge is still relatively unclear. This lack of clarity originates in the very nature of knowledge, which is reflected in the definition of knowledge sharing this study uses: "Knowledge sharing is the process where individuals mutually exchange their (tacit and explicit) knowledge and jointly create new knowledge" (Van den Hooff & De Ridder, 2004, p. 118). The issues reside in the two aspects of
knowledge that literature recognizes, the tacit and explicit aspects. Explicit knowledge “can be expressed in words and numbers and shared in the form of data, scientific formulae, specifications, manuals and the like” (Nonaka & Konno, 1998, p. 42). Explicit knowledge is mostly devoid of context, and can readily and easily be shared between individuals (in organizations). In earlier days, and in some cases even modern times, it was thought that sharing and management of knowledge could be done through working with databases and IT, since the explicit knowledge seemed so easily sharable (van den Hooff & Huysman, 2009; Schoeneborn, 2013). Both in research and practice (e.g. Schoeneborn, 2013), this approach proved to be disappointing, since the information accessed and ‘shared’ this way missed context and sense making proved to be difficult. Knowledge proved to be more volatile, and reliant on the context and social community it is embedded in. Knowledge is thus more tacit: “highly personal and hard to formalize, making it difficult to communicate or share with others” (Nonaka & Konno, 1998, p. 42).
Since recognizing the tacit nature of knowledge, organizations and researchers have focused more on understanding the social nature of knowledge. They have moved towards the idea that the creation and sharing of knowledge is a process of social dynamic between
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& Huysman, 2009). In the same vein, academia now considers organizations to be social communities (Kogut & Zander, 1996), in which interpersonal relationships and group dynamics are an important indicator of the successfulness of creation and sharing of knowledge within an organization (Nahapiet & Ghoshal, 1998; Chow & Chan, 2008). An organization that understands its role as a social community, which fosters and stimulates interpersonal and group relationships, could then expect to perform better than its counterparts who do not pay attention to these aspects. The advantages are clear, an organization with a better social infrastructure will have higher mobility and accessibility of knowledge, which has been linked to increases in organizational performance and innovation (Yu, Hao, Dong & Khalifa, 2013; Cumming, 2004). This could lead to a competitive edge of an organization. In a successful organization then, social dynamics would be a staple in an organizations’ setup to create and distribute knowledge.
Social capital
Prominently featured in the literature concerning knowledge sharing and social dynamics is the concept of social capital. The original concept of social capital focuses on how relational resources impact community social organizations (e.g. Loury, 1977).
Throughout the years the social capital concept has garnered academic attention, with authors focusing on individual aspects of the concept (e.g. Loury, 1977) and applying the concept to a myriad of phenomena, such as economic performance of firms (Baker, 1990) and geographic regions (Putnam, 1993). Recognizing that the then state of the concept was not
unidimensional and that a clear definition was missing, Nahapiet and Ghoshal (1998) theorized about a new and integrated focus of the concept and proposed a new definition. Currently the dominant view concerning social capital, Nahapiet and Ghoshal define social capital as “the sum of the actual and potential resources embedded within, available through,
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and derived from the network of relationships possessed by an individual or social unit” (1998, p. 243).
At the time of their publication, the view that an organization could be considered a social community and that an organizational advantage could be gained through knowledge sharing and innovation was only an emerging idea. As a result, Nahapiet and Ghoshal focus on the influence of social capital on knowledge sharing and innovation in an organizational setting. In this process, social capital is seen as a productive resource, which facilitates the internal functioning and contributes to value creation in an organization, which leads to organizational innovation (Nahapiet & Ghoshal, 1998; Tsai & Ghoshal, 1998). To understand how social capital influences this process, Nahapiet and Ghoshal (1998) proposed an
integrated model of social capital, which consists of three dimensions: structural, relational and cognitive social capital.
The structural dimension of social capital describes how actors are connected in a social dynamic, and how they can leverage these connections to gain an advantage (Nahapiet & Ghoshal, 1998; Tsai & Ghoshal, 1998). Relational social capital refers to assets embedded in these relationships which can influence the behavior of actors (Nahapiet & Ghoshal, 1998; Yu et al., 2013). For example, trust could be developed between actors. The trust between actors will make it easier for both actors to get certain things done for which the others support is needed, compared to a relationship devoid of any trust (Tsai & Ghoshal, 1998). Finally, the cognitive dimension of social capital refers to the extent that actors in a social network construct meaning and share representations through codes, shared language and systems of meaning (Chang & Chuang, 2011; van den Hooff & Huysman, 2009).
Although defined as separate dimensions, Nahapiet and Ghoshal (1998) recognize that the dimensions share features and in some cases are interrelated. This follows logically from the argument that the three dimensions taken together supply motive and reason for
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individuals to act as a collective and actively share knowledge (Yu et al., 2013). Quantitative research by van den Hooff and Huysman (2009) and Tsai and Ghoshal (1998) indeed finds the dimensions to be interrelated, but their findings indicate that the dimensions are valid as separate entities as well and should be analyzed as such.
While all three dimensions have been shown to positively influence knowledge sharing in organizations (e.g. Chang & Chuang, 2011; van den Hooff & Huysman, 2009), studies have indicated differences in strength between the dimensions and the way they influence knowledge sharing. Current studies (e.g. Chiu, Hsu & Wang, 2006; van den Hooff & Huysman, 2009) indicate that the structural dimension has the strongest influence on knowledge sharing, followed by the relational and cognitive dimension. It is argued that frequent social interaction (the manifestation of structural social capital) deepens
relationships, which allows for trust to be build (relational dimension) and in turn common viewpoints to be created (cognitive dimension). This leads to more knowledge sharing and organizational value creation (Chiu et al., 2006; Moran, 2005). Following this argument, the following hypothesis are stated:
Hypothesis 1: Structural social capital has a strong positive influence on knowledge sharing
in an organization.
Hypothesis 2: Relational social capital has a moderate strong positive influence on
knowledge sharing in an organization.
Hypothesis 3: Cognitive social capital has a weak positive influence on knowledge sharing in
an organization.
The influence of leadership styles
With knowledge creation and sharing thus seeming to emerge from social capital and being a primarily tacit asset, the question that has risen in academia is what the function of
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management is during this process. Initially it was speculated that management could directly influence the knowledge sharing/creation process, but studies such as Hislop (2002) and Wu, Du, Li, and Li (2010) have shown direct influence, through measures such as introducing incentives and punishments for the sharing of knowledge to be ineffective. Other studies take an engineering approach to knowledge management (van den Hooff & Huysman, 2009).
The engineering approach to knowledge management recognizes the emergent and social nature of knowledge sharing and creation, and instead of trying to directly influence it, takes a more indirect approach by stimulating the process and creating favorable conditions for the emerging process to happen. This approach aims to facilitate knowledge sharing by maximizing the social capital of an organization, and often focuses on both the organizational structure as well as the culture (Egan & Kim, 2000; Hinds & Pfeffer, 2003; Gold, Malhotra & Segars, 2001). While these studies all propose several measures to be taken by organizations to stimulate social capital and improve knowledge sharing, they seem to gloss over the role the actual manager plays in daily organizational operations. To successfully implement the proposed knowledge management measures, the effect of leadership should not be
underestimated by both organizations and academics. Employees need to be made aware why certain measures have been implemented and if/what changes in attitude are required from them (Politis, 2001).
Current knowledge management research has focuses mostly on a possible moderating effect of leadership styles on the relation between knowledge sharing and organizational outcomes (e.g. Mittal & Dhar, 2015; Ravangard, Karimi, Farhadi, Sajjadnia & Shokrpur, 2016). These studies pay no attention to the idea of leadership having an effect on the relation between social capital and knowledge sharing. However, studies by (e.g.) Cohen and Prusak (2001) have argued that a transformational style of leadership can potentially strengthen the effect social capital has on the motivation and ability of knowledge sharing in an organization,
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which in turn could have a positive effect on organizational outcomes. This makes it an avenue of research which is both academic and practically relevant to study. The argument from Cohen and Prusak is not a surprising one, since the transformational style of leadership is more human-oriented (de Vries, Bakker-Pieper & Oostenveld, 2009). Transformational leadership has proven to be effective in changing the values, attitudes and motivation required for transitioning to a more knowledge friendly organization (Bass, 1985; Hammer, Ommen, Röttger & Pfaff, 2012). Contrastingly, transactional leadership, a task oriented style of
leadership and often seen as the counterpart to transformational leadership, has been shown to no effect on several knowledge management dimensions (Politis, 2001) or even have
detrimental consequences for knowledge sharing motivation (Gagne, 2009).
Other studies indicate that the effect of social capital can be strengthened effectively in an everyday work setting, and that the transformational leadership style is particularly
effective at this (de Vries, Roe & Taillieu, 2002; de Vries, van den Hooff & de Ridder, 2006). Managers applying transformational leadership principles can change the organizational culture and climate, inspire and motivate employees, transform attitudes and values from employees, stimulates group cohesiveness, stimulate trust and respect between employees and increase job performance from employees (Bass, 1985; Bass, 1999; Podsakoff, MacKenzie, Moorman & Fetter, 1990; Judge, Piccolo & Ilies, 2004; Bass, Avolio, Jung & Berson, 2003). Managers employing a transformational leadership style accomplish this through showing behavior which is consistent with what is required of other employees, carrying out a clear vision which is easy to follow for employees, stimulating employees intellectually, providing individual support, having high performance expectations of employees and stimulating collaboration between employees (Podsakoff et al., 1990).
Following the literature it can then be assumed that transformational leadership will have a positive moderation effect on the relation between social capital and knowledge
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sharing. Integrating transformational leadership into the relation between social capital and knowledge sharing will thus further our understanding of knowledge management and serves practical relevance for organizations. This leads to the following hypothesis:
Hypothesis 4: Transformational leadership will positively influence the relationship between
relational social capital and knowledge sharing
Hypothesis 5: Transformational leadership will positively influence the relationship between
structural social capital and knowledge sharing
Hypothesis 6: Transformational leadership will positively influence the relationship between
cognitive social capital and knowledge sharing
Top management knowledge values
As stated before, the engineering approach to knowledge sharing recognizes that direct management influence on knowledge sharing processes through formalization and
systemization of knowledge sharing processes is usually not very effective (Hislop, 2002; Wu et al., 2010; Fontaine & Lesser, 2002; Jackson & Williamson, 2011). Organizations thus aim to create favorable conditions for knowledge sharing processes to emerge. As argued in the previous section, appointing transformational managers potentially increases the effect of social capital on knowledge sharing in an organization.
It is however too simple to think of a transformational manager as the sole actor in the equation. Large corporations are often complex organizations which include multiple layers of management, ranging from lower levels to a top management layer which often includes a CEO and a board of directors. It is important to realize that the values a manager holds towards knowledge sharing and management processes are not always one and the same with the way top management views the same processes. Studies show that the failure of creating and fostering favorable conditions for knowledge sharing processes in an organization can
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often be attributed to inadequate support from top management (e.g. Chua & Lam, 2005; Singh & Kant, 2008; Akhavan, Jafari & Fathian, 2005). Concurrently, studies have shown that if top management sees knowledge as a source of competitive power, knowledge sharing practices have a stronger effect on knowledge sharing in organizations (Hsu, 2008; Bartlett & Ghoshal, 2002). Many of these knowledge sharing practices, such as developing the correct social environments (Alavi, Kayworth & Leidner, 2005/2006) seem to be aimed at increasing the effect of social capital on knowledge sharing. Studies by Davenport and Prusak (2000) and Hsu (2006) state that if top management sees value in the creation and sharing of knowledge, the then following knowledge sharing practices will lead to more knowledge sharing in an organization. While it follows logically from this that an interaction can take place between transformational management and top management support, literature around this subject is severely lacking. In the authors opinion it is very likely that an interaction between the two will have an effect on the relation between social capital and knowledge sharing in an organization.
To expand further on this point a closer look needs to be taken at the relation between (transformational) leadership and organizational culture. Top management knowledge values not only translate in knowledge sharing practices, but according to studies it is also a
precursor of organizational culture, and changes therein (Hsu, 2008; Lee & Choi, 2003). Denison (1996) states that organizational culture is “the deep structure of organizations, which is rooted in the values, beliefs and assumptions held by organizational members” (p. 654). The current dominant paradigm about leaders and culture, the functionalist school (Schein, 1985), states that leaders are the instigators of cultural change in an organization. The (new) values, beliefs and assumptions top management then hold towards knowledge in their organization will possibly initiate a cultural change in their organization. Transformational leadership then enters the equation as the most potent style of leadership to push these
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changes through (Sarros, Cooper & Santora, 2008; Bass, 1999). Through creating a vision, motivating employees to perform beyond expectations and stimulating them intellectually, a transformational manager, backed by the values from top management regarding knowledge sharing, can increase the effect of the already present social capital on knowledge sharing in an organization.
In the current Knowledge Age I expect top management to hold positive values regarding knowledge sharing, which results in a positive interaction with transformational leadership. I thus argue here that a transformational leader will influence the relation between social capital and knowledge sharing in an even stronger way when he is supported by the values top management hold towards knowledge sharing. To this end I state the following hypothesis:
Hypothesis 7: There exists a positive 3-way interaction between structural social capital, top
management knowledge values and transformational leadership, and knowledge sharing.
Hypothesis 8: There exists a positive 3-way interaction between relational social capital, top
management knowledge values and transformational leadership, and knowledge sharing.
Hypothesis 9: There exists a positive 3-way interaction between cognitive social capital, top
management knowledge values and transformational leadership, and knowledge sharing.
These hypothesis lead to a proposed conceptual model (see Figure 1), which will be tested here through a quantitative study. The following sections discuss this process and the results.
13 Figure 1. Conceptual theory model of the current study.
Method
To test the conceptual model in practice, an online survey method was adopted. This section describes the item measures, data collection process and participants used in this study.
Measures
Knowledge sharing. The scale used to measure knowledge sharing in participants
originates in research from de Vries et al. (2006), concerning knowledge sharing and communication styles. The original scale was used. The scale consisted of eight items, an example item (see Appendix A for all items) being: “I like to be kept fully informed of what my colleagues know”. The items were measured on a 5-point Likert scale which ranges from 1 (Strongly disagree) to 5 (Strongly agree), consistent with the original research. Although a
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principal component analysis revealed that knowledge sharing was not a unidimensional scale (see Appendix B), a reliability analysis of the construct indicated a high reliability of the construct (α = .74). This indicates that the scale has high internal consistency, after which it was decided to use the complete original scale. A mean index was then created (M = 4.01, SD = .49).
Social capital. As stated before, the construct of social capital consists of three
separate sub constructs: structural, relational and cognitive social capital. The scale for structural social capital is based on a study by van den Hooff and Huysman (2009),
concerning social capital and knowledge sharing. No items were changed before using it in the current study. The scale consists of seven items, for example (see Appendix A for full list): “When a customer client has a question, I know which colleague or department will be able to help”. A 5-point Likert scale was used, consistent with previous studies, which ranges from 1 (Strongly disagree) to 5 (Strongly agree). A principle component analysis indicated the scale loaded on two factors (see Appendix B). The whole construct however proved (highly) reliable (α = .75), indicating sufficient internal consistency to continue using the complete scale. Lastly, a mean index was constructed (M = 3.86, SD = .55).
The relational social capital scale is based on a study by van den Hooff and Huysman (2009). No items were changed for the current study. The scale consists of five items, such as (see Appendix A for full list): “When I tell someone what I know, I can count on it that he or she will tell me what he or she knows”. Consistent with other research and the current study, a 5-point Likert scale was used, ranging from 1 (Strongly disagree) to 5 (Strongly agree). A principal component analysis indicated that the scale is unidimensional (see Appendix B). Analysis of the reliability indicates that the construct is (highly) reliable (α = .88), after which a mean index was computed (M = 3.75, SD = .91).
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The scale for the cognitive social capital construct was created by van den Hooff and Huysman (2009) in conjunction with the scale for relational social capital. The original scale was used in the current study. The scale consisted of four items, an example being (for a full list, see Appendix A): “Sometimes I do not understand my colleagues when they tell me something about their work”. A principal component analysis indicated that the scale was not unidimensional (see Appendix B). Unexpectedly the reliability of the construct proved to be insufficient (α = .40), revealing a low internal consistency of the scale. Upon taking a closer look, the item “Sometimes I have difficulty formulating what I know in such a way that my colleagues can understand” seems to measure systems of meaning among participants, while the other three items measure shared representations and interpretations among participants. With the item removed from the scale, the reliability of the construct improves (α = .48), however it is still under the generally accepted lower bound of .6. The decision was made to include cognitive social capital in the data analysis, but it will be treated as an exploratory factor. A mean index was thus also computed for the three remaining items (M = 3.31, SD = .79).
Transformational leadership. The scale for transformational leadership used in the
current study is derived from a study by Carless, Wearing and Mann (2000). The scale contains seven items, an example of one of the used items is (others are found in the Appendix A): “[my supervisor] encourages thinking about problems in new ways and questions assumptions”. Consistent with the scales measures social capital and knowledge sharing, the scale used a 5-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree). A principal component analysis shows transformational leadership to be a
unidimensional scale (see Appendix B). Analysis indicated a high reliability for the construct (α = .92), after which a mean index was computed (M = 3.71, SD = .87).
16 Top management knowledge values. The scale for the final variable, top
management knowledge values, was derived from a study by Hsu (2008). The scale is composed of six items, such as (see Appendix A for full listing) “Top management regards firm-specific knowledge as a source of competitive advantage”. Again a 5-point Likert scale was used, ranging from 1 (strongly disagree) to 5 (strongly agree). A principal component analysis indicates top management knowledge values is a unidimensional scale (see Appendix B). Reliability of the construct was high (α = .87), after which a mean index was constructed (M = 3.54, SD = .82).
Control variables. On the basis of theory, several measurements were included in the
survey to be used as control variables when conducting data analysis. Most prominently, literature agrees that gender can impact knowledge sharing, either as a direct predictor or as a moderator (e.g. Miller & Karakowsky, 2005; Connelly & Kelloway, 2001). Differences can also exist between the public, private and non-profit sectors, due to differences in
competitiveness and organizational culture (e.g. Cong & Pandya, 2003; Boyne, 2002). For this reason, work sector was included as a control variable (and recoded as a dummy for data analysis use). Although less prominent, literature points to both age and work experience as possibly influencing motivation and ability to share knowledge in an organizational setting (e.g. De Long & Fahey, 2000; Sveiby & Simons, 2002; Michailova & Husted, 2003). Both (continuous) variables were thus included as control variables in future analysis.
Procedure
For this study, an online survey was created. Participants who expressed an interest in participating in the study received an URL which directed them to the online survey.
Participants were welcomed with an introduction, which explained the purpose of the study, affiliation of the author to the University of Amsterdam and guaranteed anonymity of the participant’s (personal) information in case of their participation. This was followed up by an
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item concerning the participant giving an informed consent of participating in the study. When a participant agreed to participate in the study, the actual study was presented.
Questions were then presented, starting with the general demographic items, followed by the research specific items. These items were presented per variable, with a participant only being able to advance to the next set of items if all previous items were completed. Most
participants finished the survey in 5 – 10 minutes. The author received no feedback from participants about something unusual concerning the study. Participants were given no incentive to participate in the study. All survey data was collected between April and May of 2016.
Participants
The participants for the study were recruited through a convenience sampling method. This method was chosen due to pragmatic reasoning. The author has close proximity and easy access to a student and young professional sample because of his background, and other sampling methods were not viable due to time constraints.
A total of 152 participants participated in the constructed online survey. 50
participants were removed from the initial sample, either for not completing the survey or for failing a build in control item. The control item asked participants whether they were salaried employees in an organization, a freelancer, or their own boss. In case the participant selected either the freelance or the own boss option, the survey ended at that question. This check was built in for the purpose of increasing the integrity of the study. Freelancers and own bosses often do not work in organizational environments which include top management or even direct supervisors. They are thus not fit for the study and were excluded from the final sample. Seven respondents were removed from the survey this way.
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The final sample thus contained 102 participants, all salaried employees in an organization. The sample consisted of 56.9% (n = 58) male participants and 43.1% (n = 44) female participants. The mean age of all participants was 28.82 years old (SD = 7.95), with the youngest participant being 20 years old and the oldest participant being 59 years old. On average the education level of all participants was high, with 59.8% (n = 61) having finished a research degree and 30.4% (n = 31) having finished a professional degree. A majority of the participants, 57.4% (n = 58) work in the private sector or work in the public sector, 37.6% (n = 38). Finally, the participants on average had 5.53 years of work experience (SD = 7.09), ranging from participants having less than one year of work experience to participants having 30 years of work experience.
Results
The research question stated at the start of this study was: in what way does the interaction between leadership style and top management support influence the relation between social capital and knowledge sharing? Positive relations between all variables were expected, as stated in the different hypothesis. To test the conceptual model stated in the theory section, a multiple regression analysis was conducted. A multiple regression analysis allows us to predict a linear relationship between the independent/moderation variables and the dependent variable.
The first three hypothesis expected to find a positive influence of all three dimensions of social capital on knowledge, with the structural dimension having the strongest effect, followed by the relational and lastly the cognitive dimension. A multiple regression analysis was conducted to test these hypothesis. The multiple regression model with knowledge sharing as dependent variable, and structural social capital, cognitive social capital, relational social capital, gender, private and public sector, age and work experience entered as
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problem, with a VIF ranging from 1.07 to 5.69 across variables. The regression model can thus be used to predict organizational knowledge sharing. The strength of the model is weak however, with just 16.4% (R2 = .16) of variance in knowledge sharing being predicted by structural social capital. The regression model shows that structural social capital is a
significant, but weak predictor of knowledge sharing in organizations (b* = .17, t = 3.52, p = .001, 95% CI [0.07, 0.27]). For each extra point on the structural social capital it is predicted that knowledge sharing increases by .17. See table 1 for regression coefficients and t-values for the independent variables entered in the model. The data supports hypothesis 1, finding the strongest effect for the structural social capital dimension. A full comparison between the three social capital dimensions is hard however, since the relational and cognitive dimension are no significant predictors in this model. Hypothesis 2 and 3 are thus not supported by the data.
Table 1. Summary of Multiple Regression Analysis of Factors Related to Knowledge Sharing
Predictor Variable b* B SE B t 95% CI
LL UL
Structural social capital .17* .35 .05 3.52 .07 .27 Relational social capital .01 .03 .05 .24 -.09 .12 Cognitive social capital .07 .14 .05 1.24 -.04 .18 Gender .03 .03 .10 .29 -.17 .24 Private sector .10 .10 .23 .43 -.35 .55 Public sector .03 .03 .23 .13 -.42 .48 Age -.01 -.09 .01 -.39 -.03 .02 Work experience .00 .06 .02 .26 -.03 .04
Note. N = 101; b* = unstandardized beta coefficient; B = standardized beta coefficient; SE B = standard error; t
= t-test statistic; CI = confidence interval; LL = lower limit; UL = upper limit. * p < .05.
To test hypothesis 4, 5 and 6, which expected to find a positive moderation effect of transformational leadership on the relation between social capital and knowledge sharing, interaction effects were added to the regression models. The second model included knowledge sharing as dependent variable, and structural social capital, cognitive social capital, relational social capital, transformational leadership, top management knowledge,
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two-way interactions between the main variables, gender, private and public sector, age and work experience as independent variables. The tested model was not significant (F(17,83) = 1.45, p = .136), which indicates that model can’t be used to predict the dependent variable, knowledge sharing. No interactions between the variables are thus found, which means hypothesis 4, 5 and 6 are not supported by the current data.
To test hypothesis 7, 8 and 9, which expected to find a positive three-way interaction between (the dimensions of) social capital, transformational leadership, top management knowledge values and knowledge sharing, three-way interactions were added to a last multiple regression model. The third regression model included knowledge sharing was entered as dependent variable, and structural social capital, cognitive social capital, relational social capital, transformational leadership, top management knowledge, two-way interactions and three-way interactions between main variables, gender, private and public sector, age and work experience as independent variables. The regression model was not significant (F(20,80) = 1.36, p = .166), indicating it can’t be used to explain variance and predict knowledge
sharing. No three-way interactions were thus found between the independent variables, indicating no support for hypothesis 7, 8 and 9.
Discussion
This study set out to analyze the influence of the interaction between transformational leadership and top management knowledge values on the relationship between social capital and organizational knowledge sharing. Previous qualitative and quantitative studies indicated that positive relations between most variables would be found, which was argued in the theoretical framework. Through an online survey, with a convenient sample of participants, the conceptual model (found in figure 1) was analyzed through a regression model. Results of the analysis only partially supported the formulated expectations: only one regression model
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was significant, and the data only supports hypothesis 1. All other hypothesis had to be rejected, since no main effects or interaction effects were found between the variables.
Most unexpected are the results concerning the main effect of social capital. Where other studies have found clear effect of all three dimensions of social capital on knowledge sharing (van den Hooff & Huysman, 2009; Chang & Chuang, 2011), the data of the current study only points towards the structural dimension predicting knowledge sharing in
organizations. While it was expected that the structural dimension would be the strongest predictor, failing to reproduce significant main effects for the other two dimensions is surprising. Since the effect of structural social capital in the present study is weak, the data could indicate that structural social capital is a precursor to the other two dimensions of social capital. This interpretation of the data is strengthened by the notion present in social capital literature (e.g. Chiu et al., 2006; van den Hooff & Huysman, 2009) that the manifestation of the structural dimensions, frequent social interaction, is a precursor for relational and cognitive social capital to develop in organizations. Organizations looking to potentially increase the effect of the relational and cognitive social capital dimensions would be wise to prioritize the structural dimension and stimulate employees to interact with each other frequently in order to see an increase in, for example, mutual trust (relational dimension) and shared systems of meaning (cognitive dimension) between employees.. To stimulate social interaction, several measures can be taken by organizations. Stimulating openness and a willingness to share knowledge (van den Hooff & Huysman, 2009) and creating an organization which has low levels of inter-organizational hierarchy (Chow & Chan, 2008; Granovtetter, 1992) are all measures which can stimulate social interaction. These measures could thus improve the structural social capital of an organization, and in turn possibly increase the relational and cognitive social capital dimension of an organization.
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When taking into account the weak effect of structural social capital as a predictor of knowledge sharing, it is less surprising that both relational and cognitive social capital do not directly predict knowledge sharing. It is still however, surprising, keeping in mind previous studies who did find main effects of both dimensions. When taking a closer look at the relational dimension of social capital, a possible alternate explanation can be found in the contents of the dimension. According to Nahapiet and Ghoshal (1998), the relational dimension consists of trust, norms, obligations and social identification. Nahapiet and Ghoshal especially pay attention to the concept of trust, and theorize that it is an important predictor for relational social capital. An initial analysis by Thai and Ghoshal (1998), which operationalized relational social capital solely through ‘trustworthiness’, seems to confirm this. However, more recent research (Chiu et al., 2006), which also takes into account the norms, obligations and social identification find that trust is not a predictor for knowledge sharing in organizations. Their reasoning for this is that with frequent social interaction between employees, employees seem to not need interpersonal trust to freely share
knowledge. Although their research focused on virtual communities, this could explain the lack of result here as well. The reason for this is that when we inspect the scale used in the current study for relational social capital, the creators (van den Hooff & Huysman, 2009) clearly followed the relational social capital concept from Nahapiet and Ghoshal, and created a scale mainly around the concept of trust. The lack of a main effect of relational social capital in this study might then be explained by the operationalization of the concept.
The lack of prediction power of the cognitive social capital dimension has a mostly methodological explanation. Firstly, the lack of reliability and validity of the scale will have played a role when entering it into a regression model. With a reliability of .48, the current scale is not suited to be used in any sort of conclusive prediction model of knowledge sharing. While in initial research by van den Hooff & Huysman (2009) the scale reliability was
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acceptable they stated that future research might revisit the scale for further improvement. Future studies could potentially seek to create and validate a new scale regarding the cognitive social capital dimension.
After the initial regression model was run to analyze the effect of social capital on knowledge sharing, a second and third regression model were run. The remaining hypothesis were tested by adding transformational leadership, top management knowledge values, two-way and three-two-way interaction to the regression models. Both the model including two-two-way and the model including three-way interaction effects between variables proved to be insignificant, showing no support for the interaction hypothesis and thus being unusable for predicting knowledge sharing in organizations. Explanations could point towards a sample which is too small, resulting in low statistical power of a model (Button et al., 2013).
Variables correlating highly could also results in lower statistical power of a regression model Frost, 2013). Alternate interpretations of the results could also come from existing theories and studies. In the present study I argued a two-way and three-way interaction between social capital, transformational leadership and top management knowledge values. As the data shows, this conceptual model does not hold up, for both two-way and the three-way interaction. A plausible alternate explanation is that transformational leadership, top
management knowledge values, and the interaction between transformational leadership and top management knowledge values does not moderate social capital and knowledge sharing, but is/are a precursor of social capital. Another possibility is that (structural) social capital mediates between transformational leadership, top management knowledge values and knowledge sharing.
Literature also indicates support that this could be a plausible alternate explanation. As stated in theoretical framework, current research on transformational leadership has
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transformational leadership as a moderator still has its merit, it can also be argued that it has a greater influence on the social capital dimensions present in an organization.
Transformational leadership is human-oriented, and is about creating trust between employees, promoting group cohesiveness and creating a shared norms and vision for employees (Hammer et al., 2012; Carless et al., 2000). All of these measures are reflected in either one of the social capital dimensions. Pfaff et al. (2004) and Srivastava, Bartol and Locke (2006) argue that transformational leadership also influences organizational culture, which has been shown to be a (weak) precursor of all three dimensions of social capital (van den Hooff & Huysman, 2009). Top management knowledge values then enter the equation, as they have been shown to be a precursor to changes in organizational culture (Hsu, 2008; Alavi et al., 2005-2006). The values top management hold (towards knowledge) can determine organizational culture, according to the functionalist school (Schein, 1985; Collins & Porras, 1996), which is underlined by other (quantitative) research (e.g. Alavi et al., 2005-2006; Hsu, 2008). Management literature has often linked organizational culture and transformational leadership together (Xenikou & Simosi, 2006; Denison, 1990; Denison & Mishra, 1995). It is then a plausible concept that transformational leadership and top management knowledge values will interact to influence social capital in an organization.
To test these alternate explanations, additional statistical testing was conducted. Firstly, a direct effect of both transformational leadership and top management knowledge values was tested through a multiple regression model. Since previously structural social capital was the sole significant social capital dimension, only this dimension was used in regression analysis. The model with structural social as dependent variable, and
transformational leadership, top management knowledge values, two-way interaction between the main variables, gender, private and public sector, age and work experience entered as independent variables was significant (F(8,92) = 3.00, p = .005). Collinearity indicated no
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problem, with a VIF ranging from 1.07 to 5.94 across variables. The regression model can thus be used to predict structural social capital. The strength of the model is however only moderate, with only 20.7% (R2 = .21) of the variance in structural social capital being explained by the model. The regression analysis indicates that transformational management (b* = .18, t = 3.33, p = .001) and top management knowledge values (b* = .12, t = 2.24, p = .028) are both a weak predictor of structural social capital (see table 2 in Appendix C for regression coefficients from all variables). This indicates support for the alternate explanation that transformational management and top management knowledge values are a precursor for structural social capital in an organization.
Secondly, mediation was tested for structural social capital on the relation between top management knowledge values, transformational leadership and knowledge sharing, using the Baron and Kenny method (1986). Since both variables have a direct effect on structural social capital, a mediation effect is plausible. To test the c path for both variables, two regression models were tested. The first model used knowledge sharing as dependent variable, and transformational leadership, gender, private and public sector, age and work experience entered as independent variables. The model was not significant (F(6,94) = .72, p = .639), indication no direct effect, and implying no mediation occurred between the variables. The second model used knowledge sharing as dependent variable, and top management
knowledge values, gender, private and public sector, age and work experience entered as independent variables. The regression model was also not significant (F(6,94) = .89, p = .505), indicating no significant c path, and implying no mediation of top management knowledge values being present.
To summarize, this study found structural social capital to be a weak predictor of knowledge sharing in organizations. No support for other hypothesis was found, for which several plausible explanations can be found. Several of these plausible explanations are
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supported by additional data analysis, while other alternate explanations give us some guidelines for future research, which will be discussed next, as will limitations of the current study.
Apart from the issues surrounding the validity of the cognitive social capital scale, the current study suffers from some other (methodological) limitations, which need to be
addressed. First, the convenient sample used is not fully generalizable over the whole population. Due to the convenient sampling method, mostly young professionals with low amounts of work experience completed the online survey. It is a real possibility that the more senior workforce has different ideas or a different mentality towards sharing knowledge in organizations. This study also lacks information of what branches these people were
employed in. It can be expected that people in rigid, hierarchical organizations perceive this study in a very different light compared to people employed in a start-up. To note here as well is the very high educational level of the final sample. Less than 10% of the sample did not at least finish a professional degree. While it can be expected that organizations where
knowledge is the main resource employ higher educated people, this will not always be the case. Especially in the Netherlands, at least 55% of the population between 30-34 years old did not graduate from a professional (or higher) degree (Achterberg, 2014). This could color the present findings. It is also unclear what the origins of the participants is. Due to the author originating from the Netherlands, it can be expected most participants are Dutch as well. The current study thus does not account for other countries, which could possibly have an effect on the findings. External validity of the current study is thus not optimal.
Secondly, the sample size of the study was on the low side. Almost a third of the participants was excluded from the final sample, either for not completing the survey or failing the control item. The final sample size might have influenced the power of some of the regression models used. Studies mostly agree a model lacking power often improves the most
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from enlarging the used sample (Kramer & Theimann, 1987), something the present study lacked time for.
Lastly, a survey always suffers from subjectivity of the participant. It is impossible to determine whether participants fully understood all the questions, answered truthfully or are aware of the full context of the situation. People’s own interpretation of the questions or socially desirable answers will always haunt the validity of a study using the survey instrument.
Future research will be smart to address some, or most, of these limitations if authors decide to go further down this path or research. While this study suffers from some
shortcomings, it provides an interesting starting point for future research into the relation between transformational leadership, top management knowledge values, social capital and knowledge sharing. Although the present study did not find the results it was hoping to find, a revised conceptual model which keeps in mind the present findings and interpretations could potentially find more clear data about the intricacies and interaction of these variables. While we know a lot already about the way organizations work in the current Knowledge Age, the world around us keeps on changing and providing us with new academic challenges and ways to gain a competitive edge as an organization.
Literature
Akhavan, P., Jafari, M., & Fathian, M. (2005). Exploring the failure factors of implementing knowledge management system in the organizations. Journal of knowledge management practice, 6, 1-8.
Alavi, M., Kayworth, T. R., & Leidner, D. E. (2005). An empirical examination of the influence of organizational culture on knowledge management practices. Journal of management information systems, 22(3), 191-224.
28
Baker, W. E. (1990). Market networks and corporate behavior. American journal of sociology 96(3), 589-625.
Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173-1182.
Bartlett, C. A., & Ghoshal, S. (2002). Building competitive advantage through people. MIT Sloan management review, 43(2), 34-41.
Bass, B., M. (1985). Leadership and Performance Beyond Expectations. New York: Free Press.
Bass, B. M. (1999). Two decades of research and development in transformational leadership.
European journal of work and organizational psychology, 8(1), 9-32.
Bass, B. M., Avolio, B. J., Jung, D. I., & Berson, Y. (2003). Predicting unit performance by assessing transformational and transactional leadership. Journal of applied psychology, 88(2), 207-218.
Bierings, H. (2012). Onderwijsniveau bevolking gestegen. Retrieved from
https://www.cbs.nl/nl-nl/nieuws/2013/40/onderwijsniveau-bevolking-gestegen
Boyne, G. A. (2002). Public and private management: what’s the difference?. Journal of
management studies, 39(1), 97-122.
Brown, J. S., & Duguid, P. (2001). Knowledge and organization: A social-practice perspective. Organization science, 12(2), 198-213.
Button, K. S., Ioannidis, J. P., Mokrysz, C., Nosek, B. A., Flint, J., Robinson, E. S., & Munafò, M. R. (2013). Power failure: why small sample size undermines the reliability of neuroscience. Nature Reviews Neuroscience,14(5), 365-376.
Carless, S. A., Wearing, A. J., & Mann, L. (2000). A short measure of transformational leadership. Journal of Business and Psychology, 14(3), 389-405.
29
Chang, H. H., & Chuang, S. S. (2011). Social capital and individual motivations on knowledge sharing: Participant involvement as a moderator. Information & management,
48(1), 9-18.
Chang, C. W., Huang, H. C., Chiang, C. Y., Hsu, C. P., & Chang, C. C. (2012). Social capital and knowledge sharing: effects on patient safety. Journal of advanced nursing, 68(8), 1793-1803.
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.
Chow, W. S., & Chan, L. S. (2008). Social network, social trust and shared goals in organizational knowledge sharing. Information & Management, 45(7), 458-465. Chua, A., & Lam, W. (2005). Why KM projects fail: a multi-case analysis. Journal of knowledge management, 9(3), 6-17.
Cohen, D., & Prusak, L. (2001). In good company: How social capital makes organizations work. Harvard Business Press.
Collins, J. C., & Porras, J. I. (1996). Building your company's vision. Harvard business review, 74(5), 65.
Connelly, C. E., & Kevin Kelloway, E. (2003). Predictors of employees' perceptions of knowledge sharing cultures. Leadership & Organization Development Journal, 24(5), 294-301.
Cong, X., & Pandya, K. V. (2003). Issues of knowledge management in the public sector.
Electronic Journal of Knowledge Management, 1(2), 25-33.
Cumming, J. N. (2004). Work groups, structural diversity, and knowledge sharing in a global organization. Management Science, 50(3), 352–364.
Davenport, T. H., & Prusak, L. (2000). Working knowledge: How organizations know what they know. Boston, MA: Harvard Business School Press.
30
De Long, D., W., & Fahey, L. (2000). Diagnosing cultural barriers to knowledge management. The Academy of management executive, 14(4), 113-127.
Denison, D.R. (1990). Corporate Culture and Organizational Effectiveness. New York, NY: Wiley.
Denison, D. R. (1996). What is the difference between organizational culture and
organizational climate? A native's point of view on a decade of paradigm wars. Academy of
management review, 21(3), 619-654.
Denison, D. R., & Mishra, A. K. (1995). Toward a theory of organizational culture and effectiveness. Organization science, 6(2), 204-223.
De Vries, R. E., Bakker-Pieper, A., & Oostenveld, W. (2009). Leadership= communication? The relations of leaders’ communication styles with leadership styles, knowledge sharing and leadership outcomes. Journal of business and psychology, 25(3), 367-380.
de Vries, R. E., Roe, R. A., & Taillieu, T. C. (2002). Need for leadership as a moderator of the relationships between leadership and individual outcomes. The Leadership Quarterly, 13(2), 121-137.
De Vries, R. E., Van den Hooff, B., & de Ridder, J. A. (2006). Explaining knowledge sharing the role of team communication styles, job satisfaction, and performance beliefs.
Communication research, 33(2), 115-135.
Dyer, J. H., & Nobeoka, K. (2000). Creating and managing a high-performance knowledge-sharing network: the Toyota case. Strategic management journal, 21(3), 345-367.
Egan, M., & Kim, J. (2000). Knowledge-sharing at the World Bank. Knowledge Management
Review 3(3), 24–27.
Fontaine, M., & Lesser, E. (2002). Challenges in managing organizational knowledge. IBM Institute for Knowledge Based Organizations, Tech. Rep.
Frost, J. (2013). What Are the Effects of Multicollinearity and When Can I Ignore Them? Retrieved from http://blog.minitab.com/blog/adventures-in-statistics/what-are-the-effects-of-multicollinearity-and-when-can-i-ignore-them
31
Gagné, M. (2009). A model of knowledge-sharing motivation. Human Resource Management, 48(4), 571-589.
Gold, A. H., Malhotra, A., Segars, A.H. (2001). Knowledge management: An organizational capabilities perspective. Journal of management information systems, 18(1), 185-214.
Granovtetter, M., S. (1992). Problems of explanation in economic sociology. In: N. Nohria, & R. Eccles (Eds.), Networks and Organizations: Structure, Form, and Action, in Problems of
Explanation in Economic Sociology (pp. 25–26). Boston: Harvard Business School Press.
Haldin-Herrgard, T. (2000). Difficulties in diffusion of tacit knowledge in organizations.
Journal of Intellectual capital, 1(4), 357-365.
Hammer, A., Ommen, O., Röttger, J., & Pfaff, H. (2012). The Relationship Between
Transformational Leadership and Social Capital in Hospitals—A Survey of Medical Directors of All German Hospitals. Journal of Public Health Management and Practice, 18(2), 175-180.
Hansen, M. T., Nohria, N., & Tierney, T. (1999). What’s your strategy for managing
knowledge?. The Knowledge Management Yearbook 2000–2001.
Hinds, P. J., & Pfeffer, J. (2003). Why organizations don’t “know what they know”: Cognitive and motivational factors affecting the transfer of expertise. In Ackerman, M., Pipek, V., & Wulf, V. (Eds.), Sharing expertise: Beyond knowledge management (pp. 3-26). London: The MIT Press.
Hislop, D. (2002). Mission impossible? Communicating and sharing knowledge via information technology. Journal of Information Technology, 17(3), 165-177.
Hsu, I.-C. (2006). Enhancing employee tendencies to share knowledge – case studies of nine companies in Taiwan. International Journal of Information Management, 26(4), 326–338.
Hsu, I. C. (2008). Knowledge sharing practices as a facilitating factor for improving
organizational performance through human capital: A preliminary test. Expert Systems with
32
Jackson, M.H., & Williamson, J. (2011). Challenges of implementing systems for knowledge management: Static systems and dynamic practices, in McPhee, R. & Canary H. (Eds.),
Communication and organizational knowledge: Contemporary issues for theory and practice
(pp. 53-68). New York: Routledge.
Judge, T. A., Piccolo, R. F., & Ilies, R. (2004). The forgotten ones? The validity of
consideration and initiating structure in leadership research. Journal of applied psychology,
89(1), 36-51.
Kogut, B., & Zander, U. (1992). Knowledge of the firm, combinative capabilities, and the replication of technology. Organization science, 3(3), 383-397.
Kogut, B., & Zander, U. (1996). What firms do? Coordination, identity, and learning. Organization science, 7(5), 502-518.
Kraemer, H. C., and Thiemann, S. (1987). How many subjects? Statistical power analysis in
research. Newbury Park: Sage.
Lang, J., C. (2001). Managerial concerns in knowledge management. Journal of knowledge
management, 5(1), 43-59.
Lee, H., & Choi, B. (2003). Knowledge management enablers, processes, and organizational performance: an integrative view and empirical examination. Journal of Management
Information Systems, 20(1), 179–228.
Lee, P., Gillespie, N., Mann, L., & Wearing, A. (2010). Leadership and trust: Their effect on knowledge sharing and team performance. Management learning 41(4), 473-491.
Loury, G. (1977). A dynamic theory of racial income differences. In P. A. Wallace & A. M. LaMonde (Eds.), Women, minorities, and employment dis- crimination (pp. 86-153).
Massachusetts: D.C. Heath and Company.
Michailova, S., & Husted, K. (2003). Knowledge-sharing hostility in Russian firms.
33
Miller, D. L., & Karakowsky, L. (2005). Gender influences as an impediment to knowledge sharing: when men and women fail to seek peer feedback. The Journal of psychology, 139(2), 101-118.
Mittal, S., & Dhar, R. L. (2015). Transformational leadership and employee creativity: mediating role of creative self-efficacy and moderating role of knowledge sharing.
Management Decision, 53(5), 894-910.
Moran, P. (2005). Structural vs. relational embeddedness: Social capital and managerial performance. Strategic management journal, 26(12), 1129-1151.
Nahapiet, J., & Ghoshal, S. (1998). Social capital, intellectual capital, and the organizational advantage. Academy of management review, 23(2), 242-266.
Ndofor, H. A., & Levitas, E. (2004). Signaling the strategic value of knowledge. Journal of Management, 30(5), 685-702.
Nielsen, J. D., Du, X., & Kolmos, A. (2008). A Knowledge Building approach for learning engineering: a case study of GENSO (student satellite) project. In SEFI Annual Conference:
Quality Assessment Employability and Innovation.
Nonaka, I., & Konno, N. (1998). The concept of" ba": Building a foundation for knowledge creation. California management review, 40(3), 40-54.
Nonaka, I., Toyama, R., & Konno, N. (2000). SECI, Ba and leadership: a unified model of dynamic knowledge creation. Long range planning, 33(1), 5-34.
Paulienė, R. (2012). Transforming leadership styles and knowledge sharing in a multicultural context. Business, Management and Education, 10(1), 91-109.
Pfaff, H., Badura, B., Pühlhofer, F., & Siewerts, D. (2005). Das Sozialkapital der
Krankenhäuser—wie es gemessen und gestärkt werden kann. In Badura, B., Schellschmidt, H., & Vetter, C. (Eds.), Fehlzeiten-Report 2004 (pp. 81-109). Heidelberg: Springer Berlin. Podsakoff, P. M., MacKenzie, S. B., Moorman, R. H., & Fetter, R. (1990). Transformational leader behaviors and their effects on followers' trust in leader, satisfaction, and organizational citizenship behaviors. The leadership quarterly,1(2), 107-142.
34
Politis, J.D. (2001). The relationship of various leadership styles to knowledge management.
Leadership & Organization Development Journal, 22(8), 354 – 364.
Putnam, R. D. (1993). The prosperous community. The american prospect, 4(13), 35-42. Ravangard, R., Karimi, S., Farhadi, P., Sajjadnia, Z., & Shokrpour, N. (2016). The Effects of Transformational Leadership and Mediating Factors on the Organizational Success Using Structural Equation Modeling: A Case Study. The Health Care Manager, 35(1), 58-71. Sarros, J. C., Cooper, B. K., & Santora, J. C. (2008). Building a climate for innovation through transformational leadership and organizational culture. Journal of Leadership & Organizational Studies, 15(2), 145-158.
Schein, E. H. (1985). Organizational culture and leadership. San Francisco: John Wiley & Son.
Schoeneborn, D. (2013). The pervasive power of PowerPoint: How a genre of professional communication permeates organizational communication. Organization Studies, 34(12), 1777-1801.
Singh, M. D., & Kant, R. (2008). Knowledge management barriers: An interpretive structural modeling approach. International Journal of Management Science and Engineering
Management, 3(2), 141-150.
Srivastava, A., Bartol, K. M., & Locke, E. A. (2006). Empowering leadership in management teams: Effects on knowledge sharing, efficacy, and performance. Academy of management
journal, 49(6), pp. 1239-1251.
Sveiby, K. E., & Simons, R. (2002). Collaborative climate and effectiveness of knowledge work-an empirical study. Journal of knowledge Management, 6(5), 420-433.
Tsai, W., & Ghoshal, S. (1998). Social capital and value creation: The role of intrafirm networks. Academy of management Journal, 41(4), 464-476.
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
35
Van den Hooff, B., & Huysman, M. (2009). Managing knowledge sharing: Emergent and engineering approaches. Information & Management, 46(1), 1-8.
Wu, J., Du, H., Li, X., & Li, P. (2010). Creating and Delivering a Successful Knowledge Management Strategy. In M. Russ (Ed.), Knowledge Management Strategies for Business Development (pp. 261-276). Hershey, PA: Business Science Reference.
Xenikou, A., & Simosi, M. (2006). Organizational culture and transformational leadership as predictors of business unit performance. Journal of managerial psychology, 21(6), 566-579. Yu, Y., Hao, J. X., Dong, X. Y., & Khalifa, M. (2013). A multilevel model for effects of social capital and knowledge sharing in knowledge-intensive work teams. International Journal of Information Management, 33(5), 780-790.
