Knowledge sharing in organizations : the interplay between the engineering and the emergent approach

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Knowledge sharing in organizations: The interplay between the

engineering and the emergent approach

Supervisor: Dr. Iina Hellsten

Presented to the

Graduate School of Communications

Master’s Programme Communication Science

in Partial Fulfillment of the Requirements for the Degree of

Master of Science (M.Sc.)

Submitted on June 30th, 2017

Nina Merz

Abstract

Knowledge sharing is an important resource in creating a competitive advantage for an organization. Especially the distinction of the engineering and emergent approach of knowledge sharing seems to be valuable in explaining the relation of influencing factors. However, these two approaches have not been established yet in the literature. Therefore, this study contributes to existing research in two ways. First, starting from the emergent and engineering approach perspective different influence factors were included in the research model to explain knowledge sharing within organizations. Second, two different leadership styles were taken into consideration on the organizational level to examine the power of management regarding the influence on knowledge sharing. A sample of 207 employees from different organizations and various industries was employed to study the interplay between social and organizational factors in their influence on knowledge sharing. Results have shown that besides the positive direct effect of transformational leadership and the negative direct effect of transactional leadership on knowledge sharing, especially the intrinsic motivation as well as reciprocity of the relation between the members of an organization explain knowledge sharing best. This study adds prove to the assumption that in order to manage knowledge sharing efficiently both the engineering and the emergent approach plays a significant role.

Keywords: Leadership, Knowledge Sharing, Knowledge Management, Social Capital, Organizational Culture, ICT Infrastructure

Introduction 3 Introduction

"Leadership is the key to 99 percent of all successful efforts.” - Erskine Bowles, 1998

Knowledge sharing has been increasingly recognized as a valuable resource in modern

organizations (A. Cabrera & Allen, 1999; Nahapiet & Goshal, 1998; Spender & Grant, 1996). It helps to improve decision making processes as well as to reduce redundancies in daily working practice, can increase innovation within a company, help grasping tacit knowledge and reduce on-boarding time of new employees (Burt, 1992; Deloitte, 2017; Scarbrough, 2003). The results of a KPMG1study on knowledge sharing in organizations as well as other research indicate that in order to successfully implement a knowledge sharing culture within an organization not only the individual employee but also the managements’ leadership style play a key role (Becerra-Fernandez & Sabherwal, 2001; KPMG, 2000).Naturally, both scholars and practitioners strive to understand the determinants of knowledge sharing and the management’s role therein. This boils down to the central research question of this study:

How does the management’s leadership style influence knowledge sharing within an organization?

Determinants of knowledge sharing are typically clustered on two levels: individual and

organizational (Ipe, 2003; Lin, 2007). An increasing body of research has devoted itself to studying the underlying determinants of knowledge sharing along with the role of leadership (Carmeli, Gelbard, & Reiter-Palmon, 2013; Gagné, 2009; Lin, 2007; Y. Liu & DeFrank, 2013; Srivastava, Bartol, & Locke, 2006; Van den Hooff & Huysman, 2009). Although recognizing the existence of various determinants on several levels influencing knowledge sharing (Connelly & Kelloway, 2003; Lee & Choi, 2003)2, most researchers concentrate on either the individual or the organizational level (Lin, 2007). Along these levels, the present paper aims to shed light on knowledge sharing by simultaneously analyzing organizational factors – via the engineering approach – and individual factors – through employing the emergent approach. According to this framework, it is assumed that knowledge sharing is dependent on the social relations between the employees and "inherently emergent in nature" (Van den Hooff & Huysman, 2009, p. 1). Furthermore, the engineering approach claims that knowledge sharing can be managed (Van den Hooff & Huysman, 2009). Here, leadership plays an important role in stimulating an environment that fosters the processes of knowledge sharing (Van den Hooff & Huysman, 2009).Thus the emergent approach is referring to the social aspects of knowledge sharing, whereas the engineering

1

KPMG stands for the name of the founders: Klynveld Peat Markwick Goerdeler. It is a global network of professional firms providing Audit, Tax and Advisory services. (https://home.kpmg.com/xx/en/home.html)

2

4 Theoretical Framework approach is referring to the possibilities of the management to influence those social aspects (Van den Hooff & Huysman, 2009). Therefore, in the conceptual framework of this study, leadership as the organizational determinant of interest is modeled as prerequisite to individual determinants within a mediation analysis.

In a cross-organizational survey this study investigates the interplay of the emergent and

engineering approach of knowledge sharing, manifested in the management style and communication as well as the social capital within organizations. The analysis is based on a sample of 207 individuals employed in 15 industries ranging from the banking and finance sector to education and science as well as transportation sector.

With regard to academia, this paper adds value to the existing literature in two ways. First, it contributes to the discussion about the conceptualization of transactional and transformational leadership styles by including both perspectives in the analysis. A comprehensive body of knowledge exists that shows that both concepts can be inherent in a leader rather than distinguished from each other (Bass & Avolio, 1992). However, some researchers still categorize them as two opposing leadership styles. For example, research found a clear distinction between those leadership styles referring to the leader’s communication style (De Vries, Bakker-Pieper, & Oostenveld, 2010). A task-oriented leadership was highly associated with verbal aggressiveness, whereas a more charismatic leader was characterized by an assured, supportive, and non-aggressive communication style (De Vries et al., 2010). Second, the approaches applied in this study are not widely researched yet (Van den Hooff & Huysman, 2009). Thus, the results of this study contribute to the evaluation of those perspectives.

Theoretical Framework Knowledge Sharing

The primary source of knowledge and the transfer of knowledge is attributed to interactions between the individuals in an organization (Argote & Ingram, 2000). Research has shown that factors on different levels within an organization can influence knowledge sharing (Connelly & Kelloway, 2003; Lee & Choi, 2003). On a complex level, recent literature like the work of Van den Hooff and Huysman (2009) distinguishes the two levels of individual determinants and organizational determinants

corresponding to what they call the emergent and the engineering approach of knowledge sharing. Within the emergent approach, on the one hand, the exchange of knowledge is primarily determined by interpersonal relationships that emerge from social capital rather than being facilitated by the management directly (Van den Hooff & Huysman, 2009). According to Bourdieu (1986), social capital is defined as a network of social relations between individuals. Its essence is that the network of relationships comprises a valuable resource to the members of the network as well as the organization in

Theoretical Framework 5 which the network is active (Leana & Van Buren, 1999; Nahapiet & Goshal, 1998; Widén-Wulff & Ginman, 2004). The networks described by social capital are based on norms, trust, understanding, and reciprocity, which make the collaboration of individuals more effectively with regard to achieving shared goals (Nahapiet & Goshal, 1998; Widén-Wulff & Ginman, 2004). Knowledge sharing is found to increase with a higher level of social capital (Collins & Hitt, 2006) and is mainly manifested in the communication among employees. Social capital facilitates the access to knowledge and enhances the motivation to exchange knowledge (Nahapiet & Goshal, 1998). Moreover, several individual factors can be summarized that have been identified as determinants of knowledge sharing, namely personality traits of the employees (e.g., Lin, 2007; Matzler, Renzl, Mueller, Herting, & Mooradian, 2008),

motivation to share knowledge (Osterloh, Frost, & Frey, 2002), beliefs and attitudes towards knowledge sharing (Bock, Zmud, Kim, & Lee, 2005; Kolekofski & Heminger, 2003), and expected rewards (Bartol & Srivastava, 2002). Furthermore, it has been shown that characteristics of a social network influence the transfer of knowledge (Brown & Duguid, 2001; Orlikowski, 2002).

On the other hand, the engineering approach states that efficient knowledge sharing also needs a stimulating environment created through organizational and technological structures (Van den Hooff & Huysman, 2009). Leadership can be interpreted as part of an organizational structure and will be assigned to the engineering approach in this paper. As the authors conclude, both approaches of knowledge sharing support each other and cannot be seen as mutually exclusive (Van den Hooff & Huysman, 2009).

In contrast to other studies that see leadership style as direct enabler of knowledge sharing processes (e.g., Carmeli et al., 2013; Srivastava et al., 2006; Xue, Bradley, & Liang, 2011) , this study will take another approach. Due to the fact that literature on organizational behavior and leadership has shown that the leadership style rather indirectly influence knowledge sharing by directly influence the motivation to engage in certain organizational behavior (Podsakoff, MacKenzie, Moorman, & Fetter, 1990), leadership can also be seen as an upstream factor to the motivation of knowledge sharing that influences the individual determinants of knowledge sharing. Thus, another distinction is made and the conceptual model includes organizational determinants, the individual determinants defined as enablers of knowledge sharing, and the knowledge sharing processes. Thereby, the following conceptual model (see Figure 1) will be examined. The following subchapters will subsequently detail each key concept of the model.

6 Theoretical Framework

Figure 1. Conceptual Model.

The Engineering Approach of Knowledge Sharing

The engineering approach takes a management perspective on knowledge sharing and is focused on the role of infrastructure when it comes to facilitating knowledge sharing (Van den Hooff &

Huysman, 2009). In order to maximize social capital, which is seen as the main catalyst for knowledge sharing due to different characteristics, two infrastructures are distinguished to be influencers for social capital: 1) technical and 2) organizational infrastructure (Gold, Malhotra, & Segars, 2001). The technical structure is limited to the technology used to communicate within an organization.

Information- and communication technology (ICT) that facilitates knowledge sharing by overcoming various barriers like time and spatial constraints (Wasko & Faraj, 2005). The establishment of a knowledge-friendly culture that fosters knowledge and creativity is mainly the goal of organizational infrastructure (Gold et al., 2001; Van den Hooff & Huysman, 2009). Although these infrastructures do not directly affect knowledge sharing, they influence factors of individual motivation on knowledge sharing (Van den Hooff & Huysman, 2009). The infrastructures are the main possibilities for adjustment available to management on all levels of an organization and further important for the purpose of this study.

Organizational infrastructure. The organizational infrastructure includes creating a

knowledge-friendly environment within an organization that values knowledge transfer and knowledge creation (Van den Hooff & Huysman, 2009). Especially, leadership styles were found to have an impact on knowledge sharing. For example, researchers found that supportive leader behavior (e.g. servant leadership, empowering leadership, and transformational leadership) positively influences internal knowledge sharing (Carmeli et al., 2013; Lin, 2007). Song, Park, and Kang (2015) examined the influence of leadership style on knowledge-sharing climate and found a servant leadership style to positively influence knowledge-sharing climate as a mediator for knowledge sharing. Furthermore,

Theoretical Framework 7 empowering leadership was found to be positively related to knowledge sharing as well (Srivastava et al., 2006; Xue et al., 2011). Other researchers specifically focused on the transformational and transactional leadership styles.

Especially with transformational leadership, a positive impact was found on knowledge sharing (Y. Liu & DeFrank, 2013; Li, Shang, Liu, & Xi, 2014; Zhang, Tsui, & Wang, 2011). Four components, also known as the 4 I’s, were extracted to define transformational leadership (Avolio, 1994): 1)

Idealized Influence, 2) Inspirational Motivation, 3) Individualized Consideration, and 4) Intellectual Stimulation. Transformational leaders inspire their employees to achieve exceptional outcomes, change their expectations and perceptions, and motivate them to do more than usually expected (Bass & Riggio, 2006). They are concerned with the needs of their employees and challenge them to be creative as well as to focus on the common good (Bass & Riggio, 2006). Specifically, transformational leadership was found to be positively related with favorable work behaviors (Vigoda-Gadot, 2007) and knowledge management (Bryant, 2003)). Furthermore, Han, Seo, Li, and Yoon (2016) found a positive influence on employees’ motivation for knowledge sharing. Therefore, the following hypotheses are formulated:

H1: Transformational leadership positively influences knowledge sharing.

In contrast to the transformational leader, transactional leadership is mainly based on an exchange of performance and rewards (Avolio, Walumbwa, & Weber, 2009). Employees will be rewarded for meeting expected goals to satisfy their self-interest, but not inspired to go beyond that and serve the common good (Bass, 1990). The performance criteria as well as the rewards that can be expected for meeting them are clearly formulated by the leader (House, Woycke, & Fodor, 1988). In line with this definition, research found a negative effect of transactional leadership on knowledge sharing (Zhang et al., 2011). According to the body of knowledge the following hypotheses are formulated:

H2: Transactional leadership negatively influences knowledge sharing.

Technical infrastructure. The technical infrastructure refers to information- and communication technology (ICT) that can support the diffusion of knowledge by facilitating communication (Wasko & Faraj, 2005). Most researchers agree that ICT infrastructure facilitates knowledge sharing and thus supports the knowledge sharing processes within organizations in various ways (Spiegler, 2003). ICT allows for collecting and storing information and makes it accessible to a wider range of individuals independent of time and space (Roberts, 2000; Wasko & Faraj, 2005). Although the simple transmission of information does not result in creation of new knowledge (Bolisani & Scarso, 1999), a good information flow can enhance the process of getting new knowledge at little

8 Theoretical Framework cost (Roberts, 2000). Nowadays, electronic communication can further create a co-presence necessary to transfer more tacit knowledge which is not easy to express verbally. However, the introduction of ICT does not automatically lead to an increase in knowledge sharing. Thus, a lot of discussion exists among researchers about the role of these technologies and about whether providing the infrastructure is enough to enhance knowledge sharing within organizations (Hendricks & Vriens, 1999; Matzler & Mueller, 2011; Roberts, 2000). In contrast to simply providing the infrastructure, encouraging the use of such infrastructure may affect knowledge sharing within organizations positively. As the use of ICT goes beyond the simple infrastructure, ICT use can be interpreted as a bridging element between the engineering and emergent components. ICT use fits into the same level as other enablers. Accordingly, the third hypothesis is formulated as follows:

H3: ICT use positively mediates the relationships between leadership style and knowledge sharing.

The Emergent Approach of Knowledge Sharing

As knowledge sharing is a interactive process of knowledge creation in which meaning is constructed, social aspects of the interaction within an organizations are crucial to the phenomenon (Van den Hooff & Huysman, 2009). Knowledge donation arises from a shared intrinsic motivation as a product of social relations (Van den Hooff & Huysman, 2009). Therefore, the concept of social capital contributes to the explanation of knowledge sharing processes (Van den Hooff & Huysman, 2009). Social capital has been found to influence the evolution of human capital (Coleman, 1988), the

economic performance of organizations (Baker, 1990) and urbanization (e.g., Putnam, 1995). It also has been shown that social capital enhances the development of intellectual capital within organizations (Nahapiet & Goshal, 1998). The construct of social capital consists of three dimensions: the structural, the cognitive, and the relational dimension (Nahapiet & Goshal, 1998). The structural dimension refers to the composition and density of the network and describes the ties among members as well as the patterns of interaction (Nahapiet & Goshal, 1998; Widén-Wulff & Ginman, 2004). The cognitive dimension comprises the content aspect of social capital (Nahapiet & Goshal, 1998; Widén-Wulff & Ginman, 2004). It includes shared languages and shared codes that increases the understanding and an effective communication among members. The affective component of social capital is described by the relational dimension. This dimension describes the relations according to their level of interpersonal trust, identification with other members of the network, and the existence of shared norms.

With regard to knowledge sharing, the structural and cognitive dimensions identify the

opportunities of an individual to share knowledge (E. Cabrera & Cabrera, 2005). First of all, individuals need an established relationship to others with whom they can share their knowledge (structural social

Theoretical Framework 9 capital). Second, members within the network need to understand each other in order to successfully exchange information and knowledge (cognitive social capital). With the time, relationships are

growing and communication is becoming more effective as the number of interactions results in a set of shared codes that lead to better understanding (Granovetter, 1992; Nahapiet & Goshal, 1998). However, several studies found that especially the relational dimension influences knowledge sharing positively (E. Cabrera & Cabrera, 2005; Van den Hooff & Huysman, 2009). Thus, trust in the skills and the behavior of co-workers and the supervisor is primarily seen as motivator for knowledge sharing within an organization (Fukuyama, 1995; Nahapiet & Goshal, 1998; Putnam, 1995). Research has shown that trust positively influences knowledge sharing (Al-Alawi, Al-Marzooqi, & Mohammed, 2007; Hau, Kim, Lee, & Kim, 2013; Staples & Webster, 2008).

Especially, leadership styles with focus on establishing trustful and strong relationships between manager and employees but also between co-workers, like the transformational leadership style, may enhance knowledge sharing. Hence, the following hypotheses are proposed:

H4: Trust of employees in their co-workers and immediate supervisor positively mediates the relation between transformational leadership and knowledge sharing.

H5: Trust of employees in their co-workers and immediate supervisor negatively mediates the relation between transactional leadership and knowledge sharing.

Equally important as mutual trust is the reciprocity of the relationship (E. Cabrera & Cabrera, 2005). Relational social capital as well as the creation of knowledge is based on the interaction of individuals (Chiu, Hsu, & Wang, 2006; Nahapiet & Goshal, 1998; Wasko & Faraj, 2005). Anchored as well in the social exchange theory (Bandura, 1986), the interaction between humans to exchange social as well as material and non-material resources is essential to create knowledge (e.g., Homans, 1961; Nahapiet & Goshal, 1998; Van den Hooff & Huysman, 2009). Research has shown a positive effect of reciprocity, defined as a mutual give-and-take, on knowledge sharing behavior (Lin, 2007).

Reciprocity is described to be a motivator in organizations where knowledge sharing results in participants gaining higher expertise and more recognition (Bartol & Srivastava, 2002). On the one hand, the actions between those individuals are motivated by the expected returns they get from these interaction with others (Blau, 1964; Weiss, 1999). The analysis of costs and benefits is guided by self-interests and based on prior experience with the relationship. Benefits can range from tangible (e.g. promotional prospects, monetary rewards) to intangible (e.g. status, reciprocity). Furthermore, the quality of human interactions is underlying the obligation that favors are returned in the future (Blau, 1964). The intention of sharing knowledge was found to be positively related to reciprocity (Hau et al.,

10 Theoretical Framework 2013). Previous research has shown that there is a positive relationship between reciprocity and the quantity of knowledge sharing (Chang & Chuang, 2011; Chiu et al., 2006).On the other hand, reciprocity can exists independent of self-interest (Gouldner, 1960). If there is a moral norm of reciprocity within an organization, individuals may exchange their knowledge because it seems fair to them doing so (Chiu et al., 2006).

Accordingly, reciprocity was found to be positively related to both transformational and

transactional leadership style (Liangding, Jiwen, Chaoping, Rongjun, & Yongxia, 2007). Employees of a transformational leader perceive higher support and thus are more likely to give something back independent of the expected rewards (Erhart, 2004; Song et al., 2015; Walumbwa, Hartnell, & Adegoke, 2010). Furthermore, if the cost of sharing knowledge equals the benefits, employees are more likely to share their knowledge as this meets their self-interest (Bartol & Srivastava, 2002). A transactional leader enhances behavior guided by self-interest and rewards in turn of performance (Avolio et al., 2009; Pillai, Schriesheim, & Williams, 1999). Hence, transactional leadership also may also positively influence reciprocity. Therefore, the mediation of the relation between leadership style and knowledge sharing through reciprocity is expected to be positive with both transformational and transactional leadership style. Accordingly, the following hypotheses are formulated:

H6: Reciprocity of the relation between co-workers positively mediates the influence of transformational leadership on knowledge sharing.

H7: Reciprocity of the relation between co-workers positively mediates the influence of transactional leadership on knowledge sharing.

Individual Motivation and Knowledge Sharing

In addition to the motivation derived from relational specification, research also focused on individual factors that enhance knowledge sharing within organization (e.g., Lin, 2007; Matzler & Mueller, 2011; Moran & Goshal, 1996; Quinn, Anderson, & Finkelstein, 1996). Especially, the influence of an individuals’ enjoyment to help others and their self-efficacy were striking in existing research (Ipe, 2003; Lin, 2007; Wasko & Faraj, 2000).

The enjoyment of helping others which derives from altruism was found to positively influence knowledge sharing behavior (Lin, 2007; Wasko & Faraj, 2005). In the organizational context, altruism can be defined as behavior that helps co-workers to complete relevant tasks or problems without being explicitly rewarded (Organ, 1988). The motivation for sharing knowledge may come from an intrinsic desire to help others (Davenport & Prusak, 1998) because solving problems is challenging or

Method 11 others is a characteristic inherent to a person, it is expected that the mediation effect of this construct on knowledge sharing is independent from the leadership style. Therefore, the following hypotheses are proposed:

H8: Both transformational and transactional leadership styles positively influence knowledge sharing mediated by the employees’ enjoyment to help others.

The second individual factor found to influence knowledge sharing is self-efficacy. According to Bandura (1977), self-efficacy can be defined as self-assessed confidence in the own abilities required to achieve certain levels of performance. In the context of knowledge sharing, self-efficacy is mainly attributed to the perceived value of the knowledge when solving a specific problem (Luthans, 2003). Research confirmed a positive influence of self-efficacy on knowledge sharing (Bock & Kim, 2002; Hsu, Ju, Yen, & Chang, 2007; Wasko & Faraj, 2005). Insights from the leadership literature have shown a positive influence of transformational leadership on self-efficacy (Walumbwa & Hartnell, 2011) and further found self-efficacy to be a significant mediator between transformational leadership and employee outcomes in the health sector (Nielsen, Yarker, Randall, & Munir, 2009; Salanova, Lorente, Chambel, & Martínez, 2011), in the public sector (Pillai & Williams, 2004), and in the private sector (Gong, Huang, & Farh, 2009; J. Liu, Siu, & Shi, 2012; Walumbwa & Hartnell, 2011; Walumbwa, Mayer, Wang, Workman, & Christensen, 2011). Accordingly, the following hypotheses are formulated:

H9: Self-efficacy positively mediates the relation between transformational leadership and knowledge sharing.

H10: Self-efficacy negatively mediates the relation between transactional leadership and knowledge sharing.

Method Sample and data collection

In order to analyze the proposed hypotheses, a quantitative online-survey was created with Qualtrics. As the survey was not limited to certain countries and the language of the survey was English, a draft questionnaire was pretested online by five persons of different nationality, gender and educational background to ensure clarity and a comprehensive wording of the questions as well as to avoid misunderstandings especially for non-native speaker. According to the feedback of the volunteers, minor changes were made before the final questionnaire was sent out. Formulations that can be easily misunderstood have then been changed to clarify the purpose of the question. For example, the single question about work experience was separated into two questions asking about working experience in total and working experience at the current employer.

12 Method The link to the final online questionnaire was sent to a convenient sample of 110 people via private e-mail. A reminder was sent to those persons that had not responded to the first message after one week. Further, a snowball sampling was applied with the participants by asking them to share the link with colleagues or friends. Through this strategy, the survey reached 370 people from which 250 answered the questionnaire. All participants are full- or part-time employees in various organizations of a wide range of industries in different countries. Participants completed the survey between May 2nd and May 16th, 2017. The response rate of 67.57% was quite high, which can be explained by the personalized message every participant got as well as with the reminder e-mail after one week. 60.4% of the respondents were female and the average working experience in years was 7 (SD = 8.05). A total of 17.2% of the participants held a management position. Most of the respondents work in the

following sectors: Consultancy (16.9%), communication (14%), banking and finance (11.1%), education and science (9.7%), and health care (7.2%).

Measurements

Scales used to measure the relevant constructs in this study were mostly adapted and tested from previous studies about knowledge sharing and leadership. All constructs were measured with different items using a five-point Likert scale (ranging from 1= strongly disagree to 5= strongly agree). A list with the items of all scales is provided in the Appendix A. The reliability of each scale was measured with Cronbach’s alpha (α range .72 − .95). For all scales, an explorative factor analysis was applied to confirm the dimensions of the underlying latent constructs using the criteria eigenvalue greater than 1 and varimax rotation. The analysis confirmed the expected uni-dimensionality of the independent variables and the mediators as well as the two dimensions of knowledge sharing. For each underlying construct, an additive index was used in the analysis. Table 1 gives an overview about the means and standard deviations, bivariate correlations, and alpha coefficients of the main variables in scope.

Detailed values of the factor loadings, eigenvalues, and explained variance are displayed in Appendix A. Knowledge sharing. According to Van den Hooff and Van Weenen (2004), knowledge sharing is composed of two dimensions, knowledge donating and knowledge collecting. Knowledge donating is defined as the willingness to pass knowledge on to colleagues. It was measured with a three-item scale, an example item is: "When I have learned something new, I tell my colleagues about it." (Van den Hooff & Van Weenen, 2004). The reliability of the knowledge donating scale was α = .81. Knowledge collecting was measured with four items and assesses behaviors connected to the transmission of skills and expertise (Van den Hooff & Van Weenen, 2004). An example item is: "I share my skills when they ask for it." The scale indicated a high reliability of α =.90.

Method 13 transformational leadership(Carless, Wearing, & Mann, 2000). The scale showed a good reliability in previous studies and is developed to measure a leader’s behavior from the employee perspective by asking for example, "My immediate supervisor gives encouragement and recognition to staff." With this sample the reliability of the scale was α = .92.

Transactional leadership. In order to measure transactional leadership, six items assessing the transactional dimension were derived from the Multi Leadership Questionnaire (Form 6S) (Bass & Avolio, 1992) which has been widely used to evaluate a leader’s performance. A sample item was "My immediate supervisor calls attention to what I can get for my accomplishments". The reliability of the scale was satisfying with α =.72.

Enjoyment in helping others. A four-item scale derived from Wasko and Faraj (2000) was used to measure enjoyment of helping others. The items asked about the pleasure an employee perceives when sharing his knowledge with others, for example "it feels good to help someone sharing my knowledge". The scale indicated a high reliability with α = .95.

Self-efficacy. Measured with a scale developed by Spreitzer (1995), self-efficacy assesses the self-perception of an employee about their capability of sharing relevant knowledge with colleagues. An example item is: "I am confident in my ability to provide knowledge that others in my company consider valuable". The scale had an acceptable reliability of α = .57.

Trust. Measured with the three-items trust scale from (Wrightsman, 1991) which also has been used in other studies relevant to this context (e.g., Van den Hooff & Huysman, 2009), this scale assesses Trustin colleagues and the supervisor as well as includes trust in the value of the skills of others. A sample item is: "I completely trust the skills of my colleagues and/or my supervisor." With α = .87, the scale indicated high reliability.

Reciprocity. The scale evaluating the reciprocity of the relationships between employees and their colleagues or supervisor was again derived from Wasko and Faraj (2000). Reciprocity was measured with two items including the sample item "I know that my colleagues and/or my supervisor will help me, so it is fair to help them through sharing my knowledge." The reliability of the scale was α = .87.

ICT. Evaluated with four items, ICT use is related to the availability and usability of the technology implemented to facilitate knowledge sharing (Lin, 2007). For example, participants were asked to rate this statement: "My company uses technology that allows employees to share knowledge with other persons inside the organization." The reliability of the scale was satisfying (α = .75).

Control variables. As recommended by several other studies (e.g., Lin, 2007), the model controls for gender, education, working experience, size of organization, as well as department, and

14 Results management position. A dummy variable was created of gender (0 = male; 1 = female) and

management position (0 = no; 1= yes)c. Working experience, size of organization and size of

department was measured as a continuous variable. The educational level was recoded into a categorical variable dividing the group into lower and higher education.

Results

Before testing the hypothesized mediation model, a preliminary correlation analysis on the primary variables was conducted to study the nature of their interrelationship. Table 1 depicts pair-wise correlations of our variables in scope of the analysis.

First, correlations of the independent and dependent variables were considered. Results showed that knowledge donating positively correlated with both transformational (r = .32, p < .01) and transactional leadership style (r = .20, p < .01). Knowledge collecting was found to correlate with transformational leadership (r = .17, p < .05).

Second, and turning to the dependent variable, knowledge donating positively correlated with the mediators enjoyment of helping others (r = .54, p < .01), self-efficacy (r = .46, p < .01), trust (r = .53, p < .01), reciprocity (r = .57, p < .01), and ICT use (r = .32, p < .01). Knowledge collecting showed a positive correlation with the mediators enjoyment of helping others (r = .73, p < .01), self-efficacy (r = .49, p < .01), trust (r = .62, p < .01), reciprocity (r = .73, p < .01), and ICT use (r = .16, p < .05).

Concerning correlations between mediators, positive correlations between the ICT use and enjoyment of helping others (r = .17, p < .05), self-efficacy (r = .18, p < .05), trust (r = .26, p < .01), and reciprocity (r = .26, p < .01) were found. Furthermore, the analysis showed correlations between enjoyment of helping others and self-efficacy (r = .58, p < .01), trust (r = .56, p < .01), and reciprocity (r = .63, p < .01) as well as between self-efficacy and trust (r = .36, p < .01), and self-efficacy and

reciprocity (r = .42, p < .01). Last, trust showed a positive correlation with reciprocity (r = .84, p < .01). Finally, some significant correlations were found concerning the control variables, with both independent and dependent variable as well as mediators which confirms the necessity to include them in the analysis (see Table 1). All correlations satisfactorily validated the distinctiveness of the latent constructs in the model and gave first indications about possible mediation effects (Baron & Kenny, 1986).

Results 15 T able 1 Bivariate Corr elations and Reliability of the Main Constructs. M (SD) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 1. T ransformational Leadership 3.79 (0.83) .915 2. T ransactional Leadership 3.54 (0.68) .69** .723 3. Kno wledge Collecting 4.49 (0.76) .16* .10 .807 4. Kno wledge Donating 3.88 (0.86) .32** .20** .59** .901 5. T rust 4.11 (0.84) .57 .49** .62** .53** .868 6. Reciprocity 4.32 (0.83) .40** .36** .73** .57** .84** .869 7. Self-Ef ficac y 4.10 (0.77) .16** .12 .49** .46** .36** .42** .729 8. Enjo yment of Helping Others 4.49 (0.79) .19** .16** .73** .54** .56** .63** .58** .949 9. ICT use 3.40 (0.91) .27** .26** .16** .32** .26** .26** .18* .17* .749 10. Management Position 0.17 (0.38) -.08 -.04 -.20** -.13 -.24 -.19** .04 -.07 -.08 11. W orking Experience 7.06 (8.50) -.22** -.13 -.13 -.05 -.24 -19** .06 -.02 -.06 .27** 12. Size of Or g anization 5 (2) -.02 -.05 .04 -.06 -.08 -.03 .14* .10 .08 -.00 .02 13. Size of Department 2.32 (1.16) .05 -.01 -.05 .05 -.03 -.02 .04 .07 .13 .03 .07 .52** 14. Education 0.96 (0.20) .15* .08 .06 .02 .16* .15* .07 .06 .03 -.03 -.45** .01 -.00 15. Gender 1 (0.49) .04 .07 -.01 -.05 .00 .01 -.30** -.08 -.02 -.20** .27** -.13 -.19** .02 Note . N =207. V alues on the diagonal and bold represent reliabilities (α ). * p < .05, ** p < .01

16 Results Mediation Analysis

The purpose of this study is to test the influence of transformational and transactional leadership on knowledge sharing through a variety of mediators. As leadership style and knowledge sharing are measured with two dimensions each, the dependent (DV) and independent variables (IV) were varied and four variants of the theoretical model estimated in total:

Variant 1: Transformational leadership as IV, knowledge collecting as DV Variant 2: Transformational leadership as IV, knowledge donating as DV Variant 3: Transactional leadership as IV, knowledge collecting as DV Variant 4: Transactional leadership as IV, knowledge donating as DV

According to the conceptual model introduced in the theoretical framework, in each of the above-mentioned variants, trust, reciprocity, enjoyment of helping others, self-efficacy, and ICT use were deployed as mediators. All estimated variants controlled for a person’s gender, educational level, working experience, and whether they work in a management position as well as the size of the organization and the department a participant works in.

The mediation analysis of the theoretical model was tested by using model number 4 within PROCESS macro for SPSS (Hayes, 2013). Bias corrected standard errors based on 5,000 bootstrap samples drawn were employed. Figure 2 gives an overview of the regression coefficients tested in the model and a summary of all direct, total and indirect effects is given in Tables 2 and 3.

Leadership style. Both the transformational and the transactional leadership style showed an influence on the motivational factors. The effect of a transformational leadership style positively influenced trust (b=.55, t=9.41 CI 95% [.44, .67], p=.000), reciprocity (b=.39, t=5.87, CI 95% [.26, .52], p=.000), enjoyment of helping others (b=.18, t=2.70, CI 95% [.05, .32], p=.008), self-efficacy (b=.19, t=2.97, CI 95% [.06, .31], p=.003), and the use of ICT (b=.31, t=4.03, CI 95% [.16, .46], p=.000). Also the transactional leadership style influenced trust (b=.57, t=7.72, CI 95% [.43, .72], p=.000), reciprocity (b=.42, t=5.27, CI 95% [.27, .58], p=.000), enjoyment of helping others (b=.19, t=2.33, CI 95% [.03, .35], p=.021), self-efficacy (b=.18, t=2.37, CI 95% [.03, .33], p=.019), and ICT use (b=.36, t=3.90, CI 95% [.18, .54], p=.000). Contradicting Hypothesis 1, Transformational leadership style negatively influenced knowledge collecting (b=-.13, t=-2.68, CI 95% [-.22, -.03], p=.008), but did not influence knowledge donating. As expected, transactional leadership negatively influenced knowledge collecting (b=-.19, t=-3.49, CI 95% [-.29, -.08], p=.001), no influence was found on knowledge donating.

Results 17

Figure 2. Overview of Regression Coefficients of the Mediation Model. c = coefficient for knowledge collecting, d = coefficients for knowledge donating. *p < .05, ** p< .01

Knowledge donating. Most of the social and individual factors were found to directly influence knowledge donating. Regardless of which leadership style was included in the estimated model a positive effect on knowledge donating was found from enjoyment of helping others (transformational leadership: b=.22, t=2.61 t=2.61, CI 95% [.05, .39], p=.010; transactional leadership: b=.20, t=2.30, CI 95% [.03, .37], p=.023), self-efficacy (transformational leadership: b=.25, t=3.18, CI 95% [.09, .40], p=.002; transactional leadership: b=.26, t=3.32, CI 95% [.11, .42], p=.001), reciprocity

18 Results (transformational leadership: b=.25, t=2.27, CI 95% [.03, .47], p=.025; transactional leadership: b=.22, t=1.90, CI 95% [.00, .44], p=.048), and ICT use (transformational leadership: b=.14, t=2.69, CI 95% [.04, .25], p=.008; transactional leadership: b=.16, t=3.08, CI 95% [.06, .27], p=.002). However, trust did not influence knowledge donating.

Knowledge collecting. When including the transformational leadership style, the estimated model showed a positive effect from enjoyment of helping others (b=.37, t=6.44, CI 95% [.26, .48], p=.000) and reciprocity (b=.37, t=5.00, CI 95% [.22,.52], p=.000) on knowledge collecting. The same motivational factors also affected knowledge collecting positively when transactional leadership was included in the model (enjoyment of helping others: b=.37, t=6.51, CI 95% [.26, .48], p=.000; reciprocity: b=.38, t=5.20, CI 95% [.24, .52], p=.000). In contrast to the expectations, ICT use had a negative effect on knowledge collecting, though not significant.

Table 2

Direct and Total Effects on Knowledge Sharing

Direct effects Total effects

R2 _{B SE t p 95%CI R}2 _{B SE t p 95%CI} LL UL LL UL Knowledge Donating Transformational Leadership 0.47 0.10 0.07 1.39 0.165 -0.04 0.24 0.14 0.35 0.07 4.84 0.000 0.21 0.49 Transactional Leadership 0.46 -0.07 0.08 -0.83 0.408 -0.22 0.09 0.07 0.25 0.09 2.86 0.005 0.08 0.43 Knowledge Collecting Transformational Leadership 0.68 -0.13 0.05 -2.68 0.008 -0.22 -0.03 0.08 0.15 0.06 2.26 0.025 0.02 0.27 Transactional Leadership 0.69 -0.19 0.05 -3.49 0.000 -0.29 0.08 0.06 0.11 0.08 1.38 0.169 -0.05 0.26

Note. N=207; CI = confidence interval; LL = lower limit; UL = upper limit.

Mediation effects. As proposed in the hypotheses, a mediated effect of the leadership style on knowledge sharing through several enablers is expected. According to Hayes (2013), confidence intervals that do not contain zero indicate a significant indirect effect. Overall, the analysis showed a complete mediation of this relation, when transformational leadership and knowledge donating were included in the analysis, and a partial mediation when transactional leadership and knowledge donating were included. For knowledge collecting different effects were found. Whereas the model including transformational leadership showed a partial mediation, no mediation effect was found when transactional leadership was included in the estimated model.

The normal theory test for specific indirect effects showed that there was a positive mediation of ICT use between knowledge donating and transformational leadership (transformational leadership: b = .04, Boot SE = .02, 95% bc CI [.01, .10]), while no mediation effect was found with knowledge

collecting. ICT use positively mediated the relation between transformational leadership and knowledge donating (b = .06, Boot SE = .03, 95% bc CI [.02, .12]). Hypothesis 3 is partially accepted for the mediated relationship between transformational leadership and knowledge donating, and has to be

Results 19 rejected for all other predicted relations.Trust did not mediate the effect of leadership on knowledge sharing. Consequently, Hypotheses 4 and 5 are rejected.

Accepting Hypothesis 6, the relationship of transformational leadership and knowledge sharing was positively mediated through reciprocity on both knowledge donating (b = .10, Boot SE = .05, 95% bc CI [.01, .20]) and knowledge collecting (b = .14, Boot SE = .04, 95% bc CI [.08, .25]). The

relationship of transactional leadership and knowledge sharing was also positively mediated through reciprocity only on the knowledge collecting dimension (b = .09, Boot SE = .06, 95% bc CI [.00, .23]). Thus, Hypothesis 7 is partly accepted.

Supporting Hypothesis 8, a positive mediation between both leadership styles and knowledge sharing was found for enjoyment of helping others (Knowledge collecting: transformational leadership: b= .04, Boot SE = .02, 95% bc CI [.01, .10]; transactional leadership: b = .04, Boot SE = .03, 95% bc CI [.00, .12]; knowledge donating: transformational leadership: b = .04, Boot SE = .02, 95% bc CI [.01, .11]; transactional leadership: b = .07, Boot SE = .04, 95% bc CI [.01, .16] ).

Self-efficacy was found to positively mediate the relation between transformational leadership style and knowledge donating (b = .05, Boot SE = .02, 95% bc CI [.01, .11]), whereas no mediation was found with knowledge collecting. Thus, Hypothesis 9 is partially accepted.

The relationship between transactional leadership and knowledge donating was found to be mediated, although in the opposite direction of the prediction (b = .05, Boot SE = .03, 95% bc CI [.01, .11]). No mediation was found on knowledge collecting, implying that Hypothesis 10 is rejected.

With regard to the covariates, the analysis showed that with an increasing size of the organization employees donate less knowledge, while employees working in a management position in general are less likely to collect knowledge.

In summary, Hypotheses 2, 3, 7, 8, and 9 can only be partly accepted for the knowledge

collecting dimension of knowledge sharing. Hypothesis 6 can be fully accepted and Hypotheses 1, 4, 5, and 10 have to be rejected.

20 Discussion Table 3

Indirect Effects on Knowledge Sharing

Transformational Leadership Transactional Leadership B BootSE Boot 95% CI B BootSE Boot 95%CI

LL UL LL UL Knowledge Donating Trust 0.02 0.07 -0.13 0.15 0.08 0.07 -0.05 0.24 Reciprocity 0.10 0.05 0.01 0.20 0.09 0.06 0.00 0.23 Self-Efficacy 0.05 0.02 0.01 0.11 0.05 0.03 0.01 0.11 EHO 0.04 0.02 0.01 0.11 0.04 0.03 0.00 0.12 ICT use 0.04 0.02 0.01 0.10 0.06 0.03 0.02 0.12 Knowledge Collecting Trust 0.05 0.05 -0.05 0.16 0.05 0.05 -0.05 0.16 Reciprocity 0.14 0.04 0.08 0.25 0.16 0.06 0.07 0.03 Self-Efficacy 0.02 0.01 0.00 0.05 0.02 0.01 0.00 0.05 EHO 0.07 0.03 0.02 0.14 0.07 0.04 0.01 0.16 ICT use -0.01 0.01 -0.03 0.02 0.00 0.02 -0.03 0.03 Note. N=207; CI = confidence interval; LL = lower limit; UL = upper limit. Bootstrap sample size 5,000

Discussion

This study provides interesting insights from both theoretical and practical perspective. According to the proposed hypotheses, the direct effects of transformational leadership on knowledge sharing were found to be positive, whereas the direct effects of transactional leadership on knowledge sharing were found to be negative. The mediation analysis provides a strong support for the hypothesized

relationships concerning leadership style. One social (reciprocity) and three individual determinants (ICT use, self-efficacy, and enjoyment of helping others) positively mediate the relation between transformational leadership and knowledge donating, whereas one social (reciprocity) and one individual determinant (enjoyment of helping others) positively mediate the influence on knowledge collecting. In contrast to the effects predicted, trust never showed an indirect effect between leadership and knowledge sharing. Thus, the results support the predicted mediated effect of leadership as engineering element on knowledge sharing. However, all significant mediation effects found for transactional leadership were positive instead of negative. Furthermore, leadership positively influence all enablers of knowledge sharing, supporting the assumption that cultural infrastructure enhances the individual motivation to share knowledge (Van den Hooff & Huysman, 2009). In the following, a detailed discussion of the findings, insights for practitioners, limitations of this study, and

recommendations for future research are presented. Discussion of the findings

From a research perspective, both theoretical approaches, the emergent and the engineering, seem to have value in explaining organizational knowledge sharing. The significant direct and indirect effects of leadership on knowledge sharing confirmed that engineering knowledge management can stimulate

Discussion 21 social capital and influence knowledge sharing. On the one hand, leadership positively influences the social determinants of knowledge sharing processes. On the other hand, the direct effect of leadership on knowledge sharing decreases when including the mediators in the analysis. This finding reinforces the assumption that there is an interplay between engineering and emergent approaches of knowledge sharing. This is especially valid for the knowledge donating dimension of knowledge sharing. Both transformational and transactional leaders seem to stimulate a favorable environment for knowledge donating. Transformational leaders will create such an environment by formulating a clear vision and supporting their employees. Transactional leaders reward their employees when goals are met, in turn, employees will be more likely to engage in knowledge donating when it is necessary to achieve set goals.

Contrary to the expected relations, both leadership styles seem to positively influence the emergence of social capital of knowledge donating. Furthermore, the positive mediated relationship between transactional leadership and knowledge donating supports the arguments of some researcher that the leadership style employed in this study cannot be seen as mutually exclusive (e.g., Bass & Riggio, 2006; Burns, 1978). Whereas traditionally the leadership styles were distinguished from each other, some researchers argue that they are more the opposite ends of one continuum (Burns, 1978). A leader cannot behave exclusively transactional nor transformational, a leader’s behavior is rather affected by the situation (Fiedler, 1967; Singer & Singer, 1990). According to Bass (1990), transformational leadership builds on a transactional leadership style as both are goal-oriented but transformational leaders encourage employees to act beyond self-interest. In conclusion, both leadership styles may be inherent to one leader and therefore positively influence the determinants of knowledge sharing and in turn knowledge sharing itself.

In contrast to previous research, trust as a crucial element of social capital (Fukuyama, 1995; Nahapiet & Goshal, 1998; Van den Hooff & Huysman, 2009) and being highly related to knowledge sharing (Chang & Chuang, 2011; Hsu et al., 2007; Staples & Webster, 2008), was not found to mediate the relationship between leadership and knowledge sharing, while an influence of leadership was found. This non-existing mediation may be the result of a sample with rather low average working experience. Trust is influenced by the social context. It is related to beliefs about the most likely behavior of others and in turn built over time (Fukuyama, 1995; Roberts, 2000). Employees with less years of working experience at their current employer, may not have established such relationships of trust with their co-workers yet. Especially, employees that worked in a management position showed lower levels of trust, reciprocity, and knowledge collecting. This is not surprising, as the relation between a manager and his staff often is hierarchical. Managers are advisors to their staff, they provide information and

22 Discussion give orders. Their role is rather defined as knowledge provider than knowledge collector; thus, they do not consult their staff for advice.

Reciprocity as the second element of relational social capital was found to be highly related with knowledge sharing, though. Employees that perceive high fairness among co-workers and expect to be rewarded for sharing knowledge are more likely to do so.

However, while research found that both trust and reciprocity as part of relational social capital positively influences knowledge sharing, it is surprising that only reciprocity showed an positive influence on knowledge sharing. An explanation for this finding may be found in the concept of trust. Trust is a dynamic construct, fragile and continuously evolving in nature (Lewicki & Bunker, 1996). Therefore, the influence of trust may change depending on the stage of the relationship. Additionally, scholars suggest that trust is established through previous interactions. Hence, it may build on an accumulation of preceded reciprocity which makes reciprocity a condition for establishing trustful relationships (Hsu et al., 2007; Rousseau, Sitkin, Burt, & Camerer, 1998). This explanation is supported by the high correlation between trust and reciprocity found in this study which indicates an overlap of both constructs.

Organizational structure also influences knowledge sharing as with the increasing size of the organization knowledge sharing decreases. While this is counterintuitive at first sight because of better possibilities to enhance a social network, anonymity may increase as well. The relations might be more superficial since it is impossible to interact with every single person in a larger company.

Another interesting result was found with ICT use that negatively influences knowledge collecting. A reason for this finding may be that some knowledge is easier shared face-to-face; especially the transfer of tacit knowledge is more reliable through personal conversation (Roberts, 2000).

Finally, some practical implications can be derived from the results of this study. First,

establishing structures that facilitate social interactions and possibilities to exchange information and ideas is important to promote knowledge sharing. As reciprocity plays a crucial role in enhancing knowledge sharing, changes in the organizational structure could be employed to promote exchange between employees. For example, diversity concerning the academical background within teams may foster the exchange between co-workers. Furthermore, the management should communicate a knowledge-friendly environment by specifying a clear vision and clear values related to knowledge. Lastly, management should encourage their employees to exchange knowledge in order to develop and establish organizational structures to foster this exchange (e.g., weekly wrap-up meetings within departments).

Conclusion 23 Limitations and directions for future research

Four recommendations are made for future research to overcome the limitations of the present study. First, a randomized sample should be deployed. As this study used a convenience sample, a generalization of the results is not possible. Second, this study focused on the linkage between the engineering and emergent approach of knowledge sharing. However, not all determinants of those approaches were considered in this study. Gold et al. (2001) proposed that the structure of an organization also plays an important role in facilitating knowledge sharing. Future research could include factors such as size of the team, structure of the team, or design of the working space as

moderators. Third, communication plays a crucial role in both the exchange of knowledge as well as the establishment of a knowledge sharing culture. This study focused on a meta-level of communication through assuming the existence of social capital through established social networks within an organization. Van den Hooff and Van Weenen (2004) proposed that communication climate is another determinant of knowledge sharing. In accordance with the results of De Vries et al. (2010) that linked leadership styles and communication, leadership communication styles could be included in future research to enhance the understanding of the role of communication in context of knowledge sharing. Fourth, the study focused on intra-organizational knowledge sharing. However, organizations are not caught in a bubble but live from interaction with their environment. Employees collect knowledge not only from inside the organization but their knowledge is influenced by impressions from outside the organization as well. In order to leverage resources, new strategies emphasizing knowledge sharing beyond organizational boundaries are increasingly developed (Loebbecke, van Fenema, & Powell, 2016). Future research could include the influence of knowledge coming from collaborating

organizations and other stakeholders that can provide valuable knowledge for an organization to stay competitive.

Conclusion

Regarding the research question, this study found that leadership style influences knowledge sharing. However, a distinction has to be made between leadership styles. While transformational leadership positively influence knowledge sharing, transactional leadership was found to have a negative influence. Those direct effects decrease when including several mediators in the model of analysis, proposing that there is an interplay between engineering and emergent factors of knowledge sharing management. Especially, enjoyment of helping others, reciprocity and self-efficacy were confirmed to mediate the relationship between leadership and knowledge sharing. By recognizing that knowledge sharing is a product of emergent and engineering processes, it can be managed more effectively by establishing social capital and create organizational infrastructures that support a knowledge-friendly environment.

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