The Effects of Motivation and Organizational Experience on Innovation Network Centrality

The Effects of Motivation and Organizational Experience

on Innovation Network Centrality

Master Thesis

MSc Strategic Innovation Management

Thomas Beelaerts S1790587 tbeelaerts@gmail.com

First Supervisor: Prof. dr. W.A. Dolfsma Second Supervisor: Prof. F. Noseleit

Word count: 12 041

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Abstract

Sharing knowledge among organizational members is crucial to innovation. Given the existence of motivational barriers in this process, previous literature has investigated the influence of motivation on a range of factors as knowledge sharing intentions (Lin, 2007), innovation network centrality (Aalbers et al., 2013) and interpersonal links (Hansen et al., 2005). However, research combining a number of factors present in every organization is still lacking. This study applies a motivational perspective and focuses on the interaction between intrinsic motivation, extrinsic motivation and organizational experience. We empirically test the effects of these types of motivation on an individual’s number of innovation network contacts and also whether these relationships are strengthened by the organizational experience of the focal actor. Analyzing data from a survey at a large American organization, this study shows that while extrinsic motivation is negatively related to an individual’s number of innovation network contacts, intrinsic motivation has no significant relationship. Additionally, this study raises a number of conclusions and important implications regarding innovative knowledge transfer.

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Index

1.  Introduction  ...  4  

2.  Background  ...  8  

2.1 Networks and Innovation ... 8

2.2 Individual Attributes and Networks ... 9

2.2.1 Motivation ... 11

Intrinsic Motivation ... 12

Extrinsic Motivation ... 13

2.2.2 Organizational Experience ... 15

3.  Research  Methodology  ...  20  

3.1 Data Collection Methods ... 20

3.2 Measures ... 21

Extrinsic and Intrinsic Motivation ... 21

Network centrality ... 22

Organizational Experience ... 23

Control Variables ... 23

3.3 Data Reliability & Validity ... 24

4.  Results  ...  26  

4.1 Descriptive Statistics ... 26

4.2 Correlation and Regression ... 27

Correlation ... 27

Regression ... 28

5.  Discussion,  Implications  &  Conclusion  ...  31  

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1. Introduction

Knowledge is a critical resource for organizations, providing a sustainable competitive advantage in a competitive and dynamic economy (Foss and Pedersen, 2002). However, in order to fully benefit from knowledge as an organizational resource, it is not sufficient for organizations to focus purely on selecting employees who have specific knowledge (Brown and Duguid, 1991). Given the fact that these employees, and therefore their knowledge, are spread throughout the organization it may be unavailable there where it can best be put to use (Cross et al., 2001). As employee knowledge sharing provides opportunities for mutual learning (Huber, 1991), knowledge sharing is a fundamental means through which employees can contribute to knowledge application and, ultimately, to the competitive advantage of the organization (Wang and Noe, 2010). Klein and Prusak (1994) illustrated the importance of using workers’ knowledge, by suggesting that organizations compete with one another on the basis of their intellectual resources. Similar to financial, physical, or human capital, knowledge capital allows organizations to increase the value of their products and services. Additionally, innovation management research has repeatedly acknowledged the positive association between innovation performance and the exchange of knowledge and information (Hansen et al., 2005).

Even though the benefits of knowledge sharing have received considerable attention in literature (e.g. Cabrera and Cabrera, 2005; Wang and Noe, 2010) and many organizations have invested considerable time and money into knowledge management initiatives, there remain important barriers that hinder knowledge sharing. It has been estimated that at least $31,5 billion are lost every year by Fortune 500 companies as a result of failing to share knowledge (Babcock, 2004). This failure of knowledge management initiatives to facilitate knowledge sharing has been attributed to a lack of understanding how the organizational and interpersonal context, as well as individual characteristics, influence knowledge sharing (Voelpel et al., 2005). Therefore, in order to gain more insight into the barriers that hinder knowledge sharing, the context and the individual characteristics of actors that play a role in this process need to be examined further.

5 to cooperate and share knowledge. A better understanding of individual characteristics that affect knowledge sharing can lead to optimization of this process and, ultimately, to the development of competitive advantage and increased performance. Therefore, a lot is to be gained by deepening our knowledge about individual characteristics that affect knowledge sharing.

From a network perspective, knowledge transfer within a firm can be studied through the identification of social relations and their idiosyncratic content and objectives (Emirbayer and Goodwin, 1994). Thus, the structure of an intra-organizational network is the antecedent of innovative knowledge transfer (Borgatti and Halgin, 2011). Accordingly, over the last decade, researchers have increasingly used a social network perspective to study how knowledge is acquired and transferred in organizational knowledge networks (Kilduff and Tsai, 2003; Cross and Borgatti, 2004). However, although an employee’s network position creates the opportunity to engage in knowledge sharing with colleagues, the employee needs adequate motivation to fully exploit this opportunity (Reinholt et al., 2011). Additionally, self-determination theory (Deci and Ryan, 2000) proved that different types of motivation could have different effects on knowledge sharing. Prior studies indicate that knowledge sharing is heavily influenced by both intrinsic motivation- resulting in increased learning and an inclination to participate in voluntary knowledge sharing (Lin, 2007; Osterloh and Frey, 2000) and extrinsic motivation, as monetary incentives significantly impact employee participation (Fenwick and Olsen, 1986). To the extent that knowledge sharing in networks has been discussed in literature, motivation has ranged from being treated as endogenous to network structure (Burt, 1992; Reagans and McEvily, 2003) to being an independent predictor (Gupta and Govindarajan, 2000).

6 While motivation can have a predictive effect regarding knowledge sharing and network contacts, the organizational experience of the actor could also have an important effect on this relationship. In order to interact effectively, a certain level of social legitimacy and competences are needed (Carboni and Ehrlich, 2013). This legitimacy and competences have been named in prior studies (e.g. Borman et al., 1993) as stemming from organizational experience.

Therefore, although intrinsic and extrinsic motivation are identified in previous literature as key determinants of knowledge sharing behavior (e.g. Siemsen et al., 2008), the influence they have on an actor’s network has not been clearly shown. As the main effect of motivation- an inclination to participate - could be key to building and utilizing networks, having a comprehensive understanding of personal motivations could well be crucial to managing the level of innovative knowledge transfer. Furthermore, in order to benefit maximally from knowledge transfer, it is imperative to fully understand the effects of different levels of experience within organizations.

Given the fact that existing network research that includes motivation has provided a less nuanced view of motivation by treating it as a unitary concept (e.g. Reinholt et al., 2011), and empirical studies that treat motivation as a predictor of network position are lacking, this research can deepen our understanding of different types of motivation and their effects on networks. In order to fully benefit from knowledge located within organizations, organizational knowledge sharing must be enhanced (Palazollo et al., 2006), but how this can be accomplished still remains a critical question. Therefore, this study can contribute to

existing literature in a number of ways.

7 Second, we aim to take a step in explaining the interaction between two factors frequently mentioned to be of importance in innovation settings: the motivation and the organizational experience of an actor, thus linking a motivational approach to network theory. Even though both motivation and organizational experience have received frequent attention in literature, studies linking these factors remain scarce.

Third, given the fact that all variables are analyzed with the individual as the unit of analysis, this study will generate proposals for the management and stimulation of motivation at a personal level, as well as a better understanding of the role of organizational experience.

In sum, this research will aim to provide more insight into the importance of types of motivation and their effects on networks. Additionally, we aim to shed light on the possible moderating effect of organizational experience on this relationship. Building on the crucial recognition of employee attitudes affecting innovation processes, we address the following research questions:

1. What is the relationship between an individual’s motivation and the number of innovation network contacts he/she has?

2. What role does organizational experience have with regard to the relationship

between motivation and innovation network contacts?

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2. Background

Knowledge networks emerge as organizational members use flexible and dynamic communication relationships to retrieve and transfer knowledge (Su et al., 2010). Due to the fact that knowledge is crucial to developing competitive advantages, the location and utility of knowledge and the accompanying access to knowledge should be well known within companies. The emphasis on transferring intellectual material among people creates a greater need to identify what individuals know, as well as what drives these individuals to share knowledge and with whom (Palazzolo, 2005).

As a result, organizations are increasingly conceptualized as networks in which organization members or units (teams, departments or business units) represent nodes that are connected by relational ties (Brass et al., 2004). These ties between individuals within networks can facilitate knowledge transfer and, additionally, enhance the quality of information received (Hansen et al., 1999). Different studies show that both the number of direct ties and the personal relationships an actor has with other actors are positively related to the quantity and the perceived utility of knowledge shared (Chiu et al., 2006; Wasko and Faraj, 2005). Therefore, knowledge sharing has been assumed to be an underlying outcome of network position (Obstfeld, 2005), making individuals’ network positions a crucial factor for knowledge management purposes.

9 However, literature concerning individual attributes and their effects on innovation network are far more limited. Only recently have studies begun to consider how individual attributes affect the realization of social capital benefits (e.g. Grant and Berry, 2011). Even though previous studies have underlined the importance of individual traits in knowledge sharing situations, specifically in knowledge networks, they simultaneously call for more research to explore how these characteristics affect social network development and change (Mehra et al., 2001). Discovering more about the factors that enhance sharing of information in networks is thus thought to be vital in order to gain a sustainable competitive advantage through innovation.

2.2 Individual Attributes and Networks

Different studies suggest that individuals are predisposed to certain work attitudes and behaviors (e.g. Judge and Bono, 2001) and indeed the literature concerning the role of individual attributes in knowledge sharing is rich (e.g. Carbrera et al., 2006; Lin, 2007; Wasko and Faraj, 2005). Furthermore, previous research has acknowledged the fact that individual psychological characteristics matter in the context of knowledge sharing in networks (Klein et al., 2004). However, while linking personality and social networks is recognized as important (Kilduff & Tsai, 2003), few studies have actually done so and those that did have adopted a variety of perspectives on the relationship between personality and networks. These perspectives range from treating personality as distinct from social network characteristics and determining which explains more variance (Casciaro, 1998), to perspectives that examine whether aspects of personality correlate with network properties. An example is a study by Burt et al. (1998), which found that a small number of personality items (e.g. independence and extroversion) were significantly correlated with the presence of structural holes.

10 holes, are thus exposed to a greater diversity of information and may develop a higher need for cognition as an outcome (Simmel, 1955).

Existing research acknowledges motivation, opportunity and ability as predictors of knowledge sharing (Hansen and Nohria, 2004), but rarely focuses on the structure of relationships between actors. These relationships are however key to innovative knowledge transfer in networks as they present the opportunity to share. While prior network research has generally assumed that opportunity equals motivation (e.g. Burt, 1992), more recent studies have argued that motivation should be viewed as distinct from opportunity or even as a potential predictor of this opportunity (Adler and Kwon, 2002; Anderson, 2008). Additionally, motivated information processing theory suggests that to take others’ perspectives and benefit from social interaction, employees need to have a desire to do so (Caruso et al., 2006). An often-cited theory is that of motivation as a primary trigger for knowledge transfer (Osterloh and Frey, 2000). Accordingly, a number of studies has investigated the influence of motivation on knowledge sharing, ranging from Szulanski (1996), who investigated motivation as a barrier to knowledge sharing between organizational units, to research on why organizational members would be induced to contribute to public knowledge goods, also known as organizational information commons (Yuan et al., 2005).

Additionally, a frequently mentioned individual characteristic in knowledge sharing contexts is the organizational experience of actors. While extrinsic or intrinsic motivation can cause actors to be incentivized to share knowledge, experience can facilitate this process (Sturman, 2003). An individual comes to understand social knowledge, values and behaviors that play a role within an organization through organizational socialization (Chatman, 1991), which develops as he/she spends more time with an organization. Through this process, employees gain familiarity with an organization’s culture, leading to increased knowledge of the systems and coworkers (Feldman, 1976). In turn, this familiarity could lead to more opportunities and improved competences regarding the sharing of knowledge.

11 prior literature to discern the potential effects of organizational experience on innovative knowledge transfer.

2.2.1 Motivation

Several researchers have highlighted the effect that motivation can have on both knowledge sharing and interpersonal links (Gupta and Govindarajan, 2000; Hansen et al., 2005). A study by Reinholt et al. (2011) showed that actors should possess both a central role in a network and be sufficiently motivated in order to maximize knowledge sharing. Other studies have taken a different approach, implying that a central role in a network could be caused by motivation and, in turn, the centrality of an actor could facilitate knowledge sharing (e.g. Obstfeld, 2005). Accordingly, different studies treat motivation as an antecedent of network centrality, thereby implying an indirect effect of motivation on knowledge sharing (e.g. Tsai, 2001). However, past studies have repeatedly shown that there exist several types of motivation, which could have adverse effects on knowledge sharing (Amabile, 1993; Lin, 2007).

The studies that have attempted to link motivation to networks have primarily focused on intrinsic and extrinsic motivation, utilizing a variety of indicators of motivation as cohesion (Kadsuhin, 2002) and altruism (Hung et al., 2011). Other studies empirically tested whether intrinsic motivation (Teigland and Wasko, 2009), extrinsic motivation, or both (Aalbers et al., 2013) had significant effects on the networks of the focal actors. In line with both these studies and self-determination theory which states that different types of motivation can have adverse effects on promoting behavior (Deci and Ryan, 2000), we propose different effects for extrinsic motivation and intrinsic motivation on an actor’s network position. Extrinsic motivation focuses on the goal-driven reasons, i.e. rewards or benefits earned when performing an activity (Deci and Ryan, 1985), while intrinsic motivation indicates the pleasure and inherent satisfaction derived from a specific activity (Lin, 2007). While intrinsic motivation has been found to be critical to cooperation both within and beyond organizational boundaries, extrinsic motivation has a significant influence on productiveness (Osterloh and Frey, 2000).

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Intrinsic Motivation

In Social Exchange Theory (SET) it is proposed that all human behavior is aimed at benefit maximization and cost minimization (Hung et al., 2011). SET underlines the existence of relatively long-term relationships in contrast to one-time exchanges (Molm, 1997). For this study, this distinction is of vital importance, as innovation network contacts are often enduring relationships over time. As intrinsic motivation is an important determinant of individual behavior, it is key to the development of social relations (Deci and Ryan, 1987).

In the context of Social Exchange Theory, the most important form of intrinsic motivation is altruism. Altruism is based on the intrinsic enjoyment of helping others (Kankanhalli et al., 2005). During social exchanges, social and individual costs and benefits influence the contribution of knowledge. Costs include the loss of knowledge power and the codification effort, while the benefits include knowledge self-efficacy and enjoyment in helping others (Kankanhalli et al., 2005). The conceptualization of these two factors is key, as both knowledge self-efficacy and enjoyment in helping others have empirically been linked to intrinsic motivation (Lin, 2007). Therefore, intrinsic motivation is thought to be of paramount importance to social exchanges. Accordingly, Moch (1980) showed that highly intrinsically motivated individuals were socially better integrated than less motivated individuals.

In line with SET, it is expected that individuals who are intrinsically motivated will be better connected in knowledge exchange networks, as the benefits derived from this exchange outweigh the costs. This notion is based on several arguments, the first of which is that these individuals experience a high level of enjoyment when sharing knowledge (Constant et al., 1996) and will therefore share more. The second argument is in line with Social Capital Theory, which underlines that social capital could promote knowledge sharing among partners because they possess common values, facilitating them to build mutual trust (Wasko and Faraj, 2005). This social capital is built upon the development of social relations, of which the intrinsic motivation of individuals is an antecedent.

13 to share knowledge with colleagues (Wasko and Faraj, 2005; Kankanhalli et al., 2005).

Additionally, altruism is highlighted as an important predictor of both social relationships and knowledge sharing (Kankanhalli et al., 2005). Therefore, knowledge contributors who

experience enjoyment from helping others and who believe that they can contribute to organizational performance by sharing their knowledge may be more favorably oriented towards knowledge sharing and therefore more inclined to share knowledge (Lin, 2007).

In sum, a high level of intrinsic motivation stimulates social relations and the sharing of tacit knowledge (Amabile, 1994). Both these effects of intrinsic motivation are key to innovation networks, as the centrality and input of individuals is key to innovative knowledge transfer. Accordingly, the contribution of one's tacit knowledge in organizational contexts is determined for a large part by an actor’s intrinsic motivation (Aalbers et al., 2013). Therefore the effectiveness of knowledge sharing, and indirectly the effectiveness of innovation, hinges for a large part on the intrinsic motivation of the individuals who are engaged in this process (Osterloh and Frey, 2000). For all these reasons, we argue that intrinsic motivation has a beneficial effect on the number of network contacts in innovation networks.

H1: The number of contacts an individual has in the innovation network is positively influenced by their intrinsic motivation.

Extrinsic Motivation

From the perspective of Economic Exchange Theory (EET), each person’s behavior is influenced by rational self-interest (Hung et al., 2011). The difference between Social Exchange Theory and Economic Exchange Theory is that under the former there is no clear obligation to receive future benefits (Kankanhalli et al., 2005). Therefore, SET takes a more intrinsically motivated approach while EET has more utility in explaining the effect of extrinsic motivation on knowledge sharing (Molm, 1997). Given the fact that extrinsic motivation is a construct that pertains whether an activity is done in order to attain some separable outcome (Ryan and Deci, 2000), in EET an actor will share knowledge if he/she feels that the obtained rewards outweigh the costs (Constant et al., 1994). The fundamental goals of highly extrinsically motivated actors are to receive organizational rewards or reciprocal benefits (Vallerand, 2000).

14 benefit could be the monetary reward for knowledge sharing (Beer and Nohria, 2000) while other extrinsic benefits would be reputation feedback that can lead to active participation (Donath, 1999) and reciprocity, the expectation that an actor’s sharing will be reciprocated, thereby ensuring ongoing sharing (Wasko and Faraj, 2005). When actors believe they can obtain reciprocal benefits or rewards by sharing their knowledge, they are more likely to view knowledge sharing favorably (Lin, 2007). However, if these individuals perceive the potential benefits of knowledge sharing to be disappointing or overly distant in time, they will have lower knowledge sharing intentions (Kollock, 1999).

Second, a significant finding by Bock and Kim (2002) showed that extrinsic motivation could negatively affect the attitude towards knowledge sharing when extrinsic rewards were expected. Also, the accumulation of extrinsic rewards can lead to increasingly less information sharing given the fact that the perceived instrumental value of sharing drops (Lepper and Henderlong, 2000). Therefore, if extrinsic rewards are administered in a manner in which these become less satisfying, they can negatively affect knowledge transfer. Furthermore, literature repeatedly underlines the fact that extrinsic motivation can have negative effects which are very much dependent on both the circumstances and the actors. As stated by Amabile (1993, p. 188): ‘Individuals are extrinsically motivated when they engage in the work in order to obtain some goal that is apart from the work itself’. This clearly sets extrinsic motivation apart from the work itself, so work will often have an instrumental value when extrinsically motivated. Therefore, we propose that if individuals are highly extrinsically motivated, they will view knowledge sharing as a means-to-an-end, resulting in less long-term network contacts. While maintaining diverse direct relations holds various benefits to the actor (Aalbers et al., 2013), the costs of maintaining these relationships can outweigh the benefits (Buechel and Buskens, 2012). This is especially the case in innovation networks, as innovative knowledge transfer is based on a process which is characterized by uncertainty and barriers (Whelan and Carcary, 2011). Therefore, highly extrinsically motivated individuals could effectively cease to invest in these relationships, as the expected rewards from sharing knowledge are uncertain.

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H2: The number of contacts an individual has in the innovation network is negatively influenced by their extrinsic motivation.

2.2.2 Organizational Experience

Often considered solely as a control variable, organizational experience is rarely included in explicit theorizing (Sturman, 2003). However, different studies have delved into temporal attributes that affect both performance and knowledge sharing (e.g. Borman et al., 1993; Carboni and Ehrlich, 2013). Organizational experience may be related to job performance through increased knowledge and competences (Borman et al., 1993). The meaning of organizational experience, frequently referred to as tenure, in this context is simply the amount of time an individual has been employed at an organization. While a higher level of organizational experience could be argued to have a direct, positive effect on the number of relationships an actor has, this is specifically not the case concerning innovative networks, as the knowledge exchanged is very specific. In a recent study, Aalbers et al. (2013, p. 631) empirically examined this relationship, stating: ‘Surprisingly, having enjoyed a long tenure at a company does not lead an employee to have more inter-unit ties’. However, even if experience does not directly influence the number of contacts an individual has within innovative networks, it is still a crucial factor regarding knowledge transfer.

16 In the context of innovative knowledge transfer, new product and process knowledge usually resides in the minds of managers and knowledge workers responsible for such innovations (Drazin & Rao, 2002). In this context, the potential value of higher levels of organizational experience can be observed. Knowledge is often tacit and non-codifiable and develops and expands as actors spend more time in specific jobs and industries (Smith et al., 2005). Therefore, organizations with managers and knowledge workers with more extensive work experience in an industry will tend to have greater expertise and thus more knowledge to exchange (Lord & Maher, 1990). As this knowledge is accumulated throughout an organization, it provides opportunities for valuable knowledge transfer.

While organizational experience may not appear directly related to knowledge sharing, an actor builds individual and organizational knowledge through the acquisition of organizational experience (Nonaka, 1994). If however, individuals do not possess relevant knowledge, they can be expected to share less knowledge and therefore have fewer contacts in an innovation network. Even if highly motivated, actors who lack abilities and/or relevant knowledge will fail to effectively share knowledge within organizations (Reinholt et al., 2011). Accordingly, literature provides different reasons that greater organizational experience could provide individuals with the necessary knowledge and abilities to benefit their level of knowledge sharing.

17 Second, Casciaro and Lobo (2008) found that overlapping knowledge, gained from greater experience, increases the likelihood that employees will turn to each other for work-related exchanges and advice. While this may seem directly linked to the number of contacts any given person has, their study indicates that in fact these individuals’ tendency to share increases. As the primary driver of knowledge sharing is motivation (Osterloh and Frey, 2000), this tendency to turn to other actors will in fact contribute to motivated knowledge sharing. Also, an individual’s experience could have a large effect on the degree to which peers value his/her information (Carboni and Ehrlich, 2013), leading to an increase in acceptance of knowledge when individuals have greater experience. In turn, an increase in acceptance can benefit the focal actor, as sharing his/her knowledge is more easy given the fact that other employees will accept this contribution. Therefore, if an actor is motivated, greater organizational experience could facilitate the sharing of knowledge as the acceptance by his/her peers of knowledge increases.

Third, individuals with a greater organizational experience often have a history of working with different people. They are more likely to have a shared understanding of what knowledge is located where, increasing their ability to transfer knowledge effectively (Carboni and Ehrlich, 2013). Also, a study by Ibarra and Andrews (1993) raised the point that tenure may provide individuals with intra-team legitimacy as well as a deeper understanding of how to communicate both within the organization and within the team. Members with greater tenure may be seen as more likely to embody the ideals of the team than members of lesser tenure because they have had more time to observe, accept, and adopt predominant norms and values (Chao et al., 1994). Through this shared understanding and higher legitimacy, these individuals will be better able to identify people that have relevant knowledge and subsequently, will better be able to contact them successfully (Sturman, 2003). Provided that actors are sufficiently intrinsically motivated, which will render them more likely to share knowledge (Lin, 2007), higher levels of shared understanding and legitimacy will enable these individuals to share knowledge more effectively than individuals with lower tenure.

18 shared interpretations and meanings within a collective.’ Cognitive capital develops as an individual interacts over time with others, learning the skills, knowledge, specialized discourse and norms of the practice, and is thus closely linked to organizational experience. As an individual’s organizational experience grows, most likely so does his/her cognitive capital and thereby the ability to share knowledge. Following the ability-opportunity-motivation theory, this increased ability will benefit knowledge sharing.

In sum, organizational experience can have a beneficial effect on the accumulation of knowledge (Carboni and Ehrlich, 2013), tendency to share (Casciaro and Lobo, 2008), individuals’ shared understanding and legitimacy (Ibarra and Andrews, 1993) and cognitive capital (Wasko and Faraj, 2005). For all of these reasons, greater tenure may make individuals better equipped to handle their knowledge and may increase their competences with regard to the exchange of knowledge. Accordingly, we argue that greater tenure may strengthen the effect of intrinsic motivation on network contacts, as individuals become more proficient in exchanging knowledge in social contexts.

H3a: A higher level of organizational experience strengthens the effect of intrinsic motivation on the number of contacts in an individual’s innovation network.

As opposed to intrinsically motivated individuals to whom knowledge sharing is mainly altruistically motivated, extrinsically motivated people will tend to engage in knowledge transfer as a way to receive rewards and benefits (Lin, 2007). Individuals that have greater tenure at organizations may possess the relevant competences, legitimacy and knowledge required, but if extrinsically motivated, knowledge sharing continues to have only an instrumental value. Literature hints at a couple of reasons why greater organizational experience could strengthen the negative effect of extrinsic motivation on an individuals network centrality.

19 al., 2005). Accordingly, over time it will be increasingly clear to individuals within organizations which actors will share without reciprocal expectations. As sharing knowledge with extrinsically motivated individuals entails reciprocal costs, and the fact that with greater tenure individuals identify the actors with whom these costs will be incurred, extrinsically motivated individuals could thus be less involved in knowledge sharing. While this would not directly affect their contacts regarding innovative knowledge transfer, as it results from extrinsic motivation, it could strengthen the negative effect of extrinsic motivation on network centrality. Accordingly, greater organizational experience could make it more challenging for the individuals to connect to peers given that organizational members will link up more with peers perceived as intrinsically motivated.

Second, as employees have greater tenure at an organization, they will tend to be more familiar with the reward systems. Literature has highlighted the fact that extrinsic rewards can negatively affect extrinsically motivated individuals if these rewards are not designed properly (Amabile 1997; Bock and Kim, 2002). Regarding rewards that are designed to be non-contingent, (i.e. that do not depend on task engagement, task completion or task performance) perceptions may differ between employees and management regarding appropriate extrinsic rewards. This difference in perception will be more pronounced as employees have greater tenure at an organization (Meyer, 1975). As organizational tenure increases, individuals expand sense-making activities to include job-related aspects of the organizational context (e.g. pay and benefits, working conditions, company policies) (Wagner et al., 1987). Therefore the conditions under which rewards are granted are increasingly known. Accordingly, extrinsically motivated individuals with greater tenure may only contribute to knowledge sharing minimally, to the level that they receive rewards.

In sum, when applied in the context of this study, the fact that knowledge sharing has an instrumental value to extrinsically motivated individuals, combined with greater organizational experience could strengthen the negative effect of extrinsic motivation on innovation network centrality.

H3b: A higher level of organizational experience strengthens the effect of extrinsic motivation on the number of contacts in an individual’s innovation network.

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FIGURE 1 Theoretical Model

3. Research Methodology

All data analyzed were collected in one firm, implying that the design of the study facilitated a constant of external factors that could have influenced intra-organizational knowledge sharing (Reinholt et al., 2011). Given the fact that the aim of this study was to analyze individual employees’ motivation, their tenure and network centrality, the goal was to involve as many individual respondents involved in knowledge sharing as possible. This design offered advantages over surveys that are designed to target a large number of firms but only a handful of respondents per organization as it gives a more in depth view of the focal organization and its innovative network.

21 networks. The data gathered was based on a socio-centric network, which implies that the actors of the network are known or easily determined. This is due to the fact that a socio-centric network study usually focuses on closed networks and entails that the boundaries of the network were a priori defined, in accordance with organizational members. The socio-centric approach is often seen as the gold standard because of its ability to gather data for the entire network (Chung et al., 2005). Additionally, this type of network analysis represents the saturation sample of interests and, more importantly, the analysis allows for the results to be generalized to the population (Chung et al., 2005).

In total there were 112 respondents, of which 20 were not part of the department of interest, eight respondents had left the company before completion of this study and one individual was on long-term leave. Therefore, we the sample consisted of 83 individuals, a response rate of 74%.

3.2 Measures

Extrinsic and Intrinsic Motivation

Items used to operationalize the constructs measuring intrinsic and extrinsic motivation were adapted from the Work Preference Inventory (WPI) of Amabile et al. (1994). The WPI is specifically designed to determine individual differences in intrinsic and extrinsic motivational orientations (Aalbers et al., 2013). The conceivers of this inventory noted that it might be particularly useful in integrating concepts of motivational orientation into more general personality theories (Amabile et al., 1994). We used 30 propositions that were designed to measure both intrinsic and extrinsic motivation, which are all included in the Appendix (Figure 2 B). These propositions were measured on 4-point scales. Both extrinsic motivation and intrinsic motivation consisted of 15 items, and additionally, each of the primary scales included meaningful sub-factors that could serve as more fine-grained breakdowns of the elements of intrinsic and extrinsic motivation i.e., secondary scales (Amabile et al., 1994).

Examples of the items measuring intrinsic motivation include ‘I enjoy trying to solve

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motivated by the money I earn’. The Cronbach’s alpha and descriptive statistics of all scales

are included in the next section. Additionally, the complete scales can be found in the appendix.

Network centrality

In order to properly analyze the effects of motivation and tenure on an actor’s position in an innovation network, we built on prior studies that delve into innovation networks (Aalbers, et al., 2013; Cross and Prusak, 2002; Rodan, 2010). Building on the network perspective, we opted for a dependent variable that is an innovation network indicator. This variable has been used in the studies mentioned to give an overview of the knowledge transfer network in which individuals were embedded. In line with these studies, we used degree centrality, which is the number of direct contacts an employee is connected to (Reinholt et al., 2011) as our measure of innovation network centrality. Given the fact that the number of contacts is generally negatively correlated with network constraint, an often used measure of structural holes (Burt, 1992) and positively correlated with betweenness centrality (Mehra et al., 2001), this measure is a good indicator of network positions in large, open networks (Reinholt et al., 2011). Additionally, past literature has noted that organizational charts are poor indicators of interpersonal relations (Krackhart and Hanson, 1993), which makes degree centrality a more potent measure for studying knowledge transfers in organizations. Also, different authors have argued that degree centrality is the most suitable measure to capture an individual’s visibility in a network and his/her communication activities (e.g. Tsai, 2001).

The centrality measure used was a self-reported measure of the contacts that were most useful to the actor with regard to formulating new ideas. In this way, the question is about the transfer of new or complex knowledge that was specifically not perceived as related to the ongoing business of the organization (Aalbers, et al., 2013). Employees were asked:

“Some contacts are particularly useful in helping you to be creative in your job, such as helping you to generate new ideas. Who are the key people that help you the most to formulate new ideas?”. The respondents were asked to indicate the contacts, and sort them

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Organizational Experience

Building on prior literature, which makes a distinction between job experience and organizational experience, we focus on organizational experience, as organizational

experience suggests an accumulation of work- related information that is conceptually distinct from job experience (Sturman, 2003). Furthermore, accurate specification of the context of experience (i.e. job versus organizational level) is important because experiences gained in different contexts could have unique effects (Tesluk & Jacobs, 1998). Organizational experience was measured by letting the respondents indicate which category of tenure they were in. The categories ranged from less than 3 years of tenure to more than 10 years of tenure. High values indicate longer organizational tenure. In total there were 5 categories, which were all converted to dummy variables in order to allow for statistical analysis.

Control Variables

We included two control variables. Given the fact that data was collected in two different locations that were geographically separated, we controlled for the effects that this geographic difference could make. In situations that geographic distance is large, problems concerning

communication and coordination can hamper knowledge transfer.In cross-border knowledge

transfers, the level of communication infrastructure and the quality of information flows account for important effects of geographic distance (Ghemawat, 2001).

24 The second control variable was based upon the fact that numerous studies have raised the importance of seniority within organizations for the network centrality of actors (e.g. Stvilia et al., 2011). Given the fact that seniority levels within a team can impact both intra-group interactions and individual status within the team itself, we opted to control whether actors held a senior role or not. Seniority of individuals is conceptualized in terms of the hierarchical status of an actor within an organization (Stvilia et al., 2011). A study by Cohen and Zhou (1991) of 224 research and development teams in 29 corporations indicated that seniority is positively related to an individual’s status within a team but negatively related to the level of interaction the senior team member has with other team members. This negative effect on interaction with other employees could have effects on the network of that individual. Accordingly, we controlled for the effects of seniority by including a dummy

variable in which 0 = non-supervisory role and 1 = supervisory role.

3.3 Data Reliability & Validity

To ensure content validity, previously validated measurements were used (Amabile et al., 1994). The adequacy of the scale for intrinsic and extrinsic motivation was determined by examining construct validity and internal consistency (Hulland, 1999). In order to assess construct validity, principal component analysis with Varimax rotation was performed on the multi-item scales measuring motivation. According to the following criteria we retained measures for each construct: (1) each measure must have a loading greater than 0.4; (2) no measure must have a loading greater than 0.4 to more than one factor; (3) each measure must load into the correct factor (Song et al., 2011). Additionally, Eigenvalues should be 1 or more.

25 After retaining 9 items for both intrinsic and extrinsic motivation, these were assessed for internal consistency using Cronbach’s alpha. The coefficients were 0.84 α for intrinsic motivation and 0.79 α for extrinsic motivation, both of which exceed the recommended value of 0.70 (Nunnally, 1978), indicating a high level of internal consistency for our variables. We calculated both the mean and the sum of the items of both scales, in order to see which explained more variance, and retained these for further analysis.

Second, in order to be certain that multiple regression analysis was possible, as well as considerations regarding the representativeness of the sample for the population, the data was tested for normality through use of a Shapiro-Wilks test. The data for our dependent variable, Network Degree, was however rejected as normally distributed, at a p-value of 0.00. Additionally, the statistic for Kurtosis was -1.526, indicating a Platykurtic distribution, and the statistic for Skewness was 0.419.

Given the skewed distribution, and indications of non-normal distribution, we scanned for outliers, using the outlier labeling technique as advised by Tukey (1977). Under this method, outliers are identified when they fall outside of a custom range. Using this formula (Upper limit = Q3 + (2.2 * (Q3 - Q1)); Lower limit = Q1 - (2.2 * (Q3 - Q1)), we identified two outliers. These outliers were two respondents, who reported 34 and 54 network contacts respectively. Due to the small sample size, we preferred to winsorize these outliers instead of simply trimming these respondents from the dataset. This technique is also favored due to the

TABLE 1

Factor Loadings from Factor Analysis

!! Intrinsic Motivation Extrinsic Motivation

M26 .81 -.03 M13 .76 -.17 M11 .76 .08 M3 .71 -.01 M27 .59 .30 M30 .59 .13 M8 .54 .19 M5 .51 .17 M20 .49 .21 M21 -.06 .73 M24 .31 .69 M18 .14 .67 M29 .17 .65 M2 -.29 .59 M6 .08 .59 M4 .19 .53 M25 .12 .49 M15 .04 .44

The meaning of each question in the first column is shown in the Appendix. Bold numbers indicate that the measures loaded to the factor.

26 fact that these scores consist of less than 5% of the sample, are legitimate outliers and not based upon entry errors (Duan, 1997).

Additionally, some difficulties concerning tenure were signaled. The categories 3-5 years, 5-8 and 8-10 years covered 9.8, 14.6, and 4.9% of the respondents respectively. This may lead to insignificant results when entered into regression analyses, given that these categories do not reach the generally accepted minimum of 15% of the total respondents. However, given the hypotheses, which predict a stronger effect as tenure grows greater, we were able to divide the data and utilize two categories, both of which encompassed enough of the sample population and compare these. A dummy variable was created for employees with relatively low tenure (0-5 years) and relatively high tenure (5 and more years) and these two variables were used for further analysis.

4. Results

Of the respondents, 65% were non-supervisory employees as compared to 35% who held supervisory roles. Additionally, 74.4 % of the respondents was located in location X while 25.6% was located in location Y. The average network size (degree) of these employees’ networks was 4.59 people with a standard deviation of 2.98. The mean of intrinsic motivation was 3.29 (on a 4-point scale), with a standard deviation of 0.46. The mean of extrinsic motivation was 2.60 (on a 4-point scale), with a standard deviation of 0.56. Using Ucinet 6 we made a graphic representation of the innovation network, which is depicted in Figure 1 B in the Appendix.

27

4.2 Correlation and Regression

Table 2 represents the means, standard deviations and correlations between the variables included in the study. The Pearson (lower diagonal) and Spearman (upper diagonal) correlation coefficients describe the underlying relationship between the variables. Some interesting correlations appear to exist between the variables, illustrated by Table 2 in which they are shown to be relevant at either the p < .05 or the p <.01 levels.

Correlation

First, a negative correlation exists between an individual’s work location and his/her network degree (r = -.30, p < .01). This indicates that employees working at location Y generally tend to have a larger number of network contacts. The second striking correlation is the positive correlation between an individual’s role and his/her network degree (r = .29, p < .05), which indicates that in general; employees in supervisory roles have a higher network degree. Also, extrinsic motivation is negatively correlated with role (r = -.36, p < .01), therefore as extrinsic motivation decreases so do supervisory roles. Finally, extrinsic motivation is positively correlated to intrinsic motivation (r = .34, p < .01), indicating that as one type of motivation rises, so does the other.

The focal point of the research however, the correlation between intrinsic motivation and network degree does not show a relevant correlation (r = -.12, p = n.s.). Extrinsic motivation however is significantly negatively correlated with network degree (r = -.34, p < .01). Regarding these negative correlations, it is a first indication of an effect extrinsic motivation could have. As extrinsic motivation decreases, it appears that network degree and supervisory roles decrease as well.

TABLE 2

Means, Standard Deviations, and Correlations between study variables

Variable Mean SD 1 2 3 4 5 6 1. Location 0.26 0.44 - .01 -.35** .10 .16 -.16 2. Role 0.36 0.48 .01 - .31** -.12 -.34** .43** 3. Network Degree 4.59 2.98 -.30** .29* - -.12 -.31** .31** 4. Intrinsic Motivation 3.29 0.46 .13 -.11 -.12 - .29* .09 5. Extrinsic Motivation 2.60 0.56 .18 -.36** -.34** .34** - -.13 6. Tenure > 5 0.44 0.50 -.16 .43** .29* .07 -.16 - N = 72; † p < 0.10, * p < 0.05, ** p < 0.01.

28 Finally, organizational tenure is positively correlated with both location (r = .43, p = .01) and the number of network contacts (r = .29, p = .05). This indicates that location Y includes individuals with greater tenure, and individuals with greater tenure will tend to have more innovation network contacts.

Regression

This research took innovation network degree as the dependent variable and included intrinsic motivation, extrinsic motivation and tenure as independent variables. We used hierarchical moderated regression models to investigate the different hypotheses. Given the fact that the variables included in the interaction terms (intrinsic motivation, extrinsic motivation and tenure) included different scales, in line with Hofmann and Gavin (1998), we mean-centered the relevant variables before standardizing and multiplying them together to yield the interaction terms (Friedrich, 1982).

Additionally, with respect to potential multicollinearity issues, Table 2 shows moderate correlation coefficients for all variables. However, as an additional check, we investigated the variance inflation factors (VIF). VIF analyses confirm our view that multicollinearity is not an issue for these variables, as the highest VIF was 2.61. All factors are far below the widely accepted boundary of 10 (Chatterjee and Price, 1991) as well as below the most conservative boundary of 3.

29 First, regarding the control variables, both role (b = .30, p < .01) and location (b = -.31, p < .01) were found to have significant relationships with network degree in Model 1 and 2, as illustrated by Table 3. While role had a positive interaction, location had a negative interaction with network degree. In model 3 however, as extrinsic motivation was added, role became marginally significant (b = .23, p < 0.1), and the coefficient of location decreased just a fraction, while maintaining significance. (b = -.27, p < .05). Interestingly, when tenure was entered into the regression analysis in model 4, the significance of role dropped, which could indicate that the effect of organizational experience is stronger than whether the individual is in a supervisory or non-supervisory role.

Following the results of the analysis, hypothesis 1, which predicted a positive effect of intrinsic motivation on network degree, was rejected (b = -.05, p = n.s.). Intrinsic motivation did not have a significant relationship with the dependent variable in any of the steps of the regression. However, even though not significant, the results regarding intrinsic motivation are interesting. While the relationship with network degree is (b = -.05, p = n.s.) in model 2, the relationship declines almost to complete insignificance (b = .01, p = .92) upon adding extrinsic motivation (model 3). Furthermore, as the interaction term with organizational experience is added, the coefficient turns positive (b = .13) and the p-value decreases drastically (p = .42). This could indicate that there 1) extrinsic motivation has a stronger effect than intrinsic motivation, and 2) there exists some kind of relationship between intrinsic motivation and organizational experience.

30 Hypothesis 3a and 3b predicted that organizational experience would have a moderating effect on the relationship between motivation and network contacts. These hypotheses were however rejected as non-significant for both intrinsic motivation (b = -.20, p = n.s.) and extrinsic motivation (b = -.03, p = n.s.). Additionally, though non-significant, the interaction term for organizational experience and intrinsic motivation displays negative coefficients, going against our initial hypothesis (illustrated by Figures 3 B&C, Appendix). The p-values for this interaction (p = .21; p = .27) do not show such high values as for the interaction between organizational experience and extrinsic motivation (p = .88).

Given the fact that location appeared to yield relatively consistent coefficients, we

opted to remove this variable from the regression analysis and enter a different variable1,

which could potentially be of interest. Table 3C (Appendix) depicts this analysis, which we will further consider in the discussion section.

1  _{As an additional robustness test, we ran a regression analysis with Perceived Organizational Support as control}

variable. This variable was based on 4 statements and had a Cronbach’s alpha of 0.93.  

TABLE 3

Results of Hierarchical Regression Analyses

Network Degree

Steps and Variables 1 2 3 4 5 6

1. Role .30** .30** .23† .16 .18 .17 P-value (.01) (.01) (.06) (.22) (.17) (.21) Location -.31** -.29** -.27* -.24* -.21† -.21† P-value (.01) (.01) (.02) (.04) (.07) (.08) 2. Intrinsic Motivation -.05 .01 -.01 .13 .12 P-value (.66) (.92) (.93) (.42) (.46) 3. Extrinsic Motivation -.21† -.21† -.20 -.18 P-value (.09) (.09) (.11) (.25)

4. Organizational Tenure > 5 years .15 .15 .15

P-value (.22) (.22) (.22)

5. Tenure X Intrinsic Motivation -.20 -.19

P-value (.21) (.27)

6. Tenure X Extrinsic Motivation -.03

P-value (.88)

F 7.67 5.11 4.69 4.10 3.71 3.13

ΔR² .18** .02 .04† .02 .02 .00

Adjusted R² .16** .15** .17** .18** .19** .17** All coefficients reported are standardized. N = 72; † p < 0.10, * p < 0.05, ** p < 0.01.

31

5. Discussion, Implications & Conclusion

Do intrinsic and extrinsic motivation increase or decrease the number of innovation network contacts an individual has? As organizations attempt to improve innovation policies in order to substantially benefit innovative knowledge transfer within organizations, this debate is crucial for designing reward systems and organizational procedures. Although knowledge sharing is valuable, it is difficult to ensure, as innovative knowledge can be difficult to communicate (Hu and Randel, 2014) and individuals can lack motivation to share. In an attempt to contribute to literature concerning these phenomena, we reexamined the effects of intrinsic and extrinsic motivation on innovation networks. Our empirical findings provide no support for any effect of intrinsic motivation, in support of Aalbers et al. (2013). These findings run contrary to prior research that suggests that social relationships are closely linked to high levels of intrinsic motivations and tacit knowledge transfer (Osterloh and Frey, 2000).

The absence of significant findings concerning intrinsic motivation could be explained by the fact that in the context of innovative knowledge transfer, which is by default not perceived as related to the ongoing business of the organization (Aalbers et al., 2013), all actors involved were highly intrinsically motivated. In this study, this would entail that intrinsic motivation as we measured it, incorporating enjoyment and challenge, would not be an important predictor of the number of network contacts, as participation in the focal network is inherent to intrinsic motivation. Additionally, as our dependent variable was a quantitative rather than a qualitative measure, and intrinsic motivation is an indicator of social integration (Moch, 1980) rather than simply the number of contacts, it is clear why intrinsic motivation would have no significant relationship. Accordingly, it could be argued that as intrinsic motivation is characterized by altruism and enjoyment in helping others, intrinsically motivated individuals could focus on a number of innovation contacts and strive to maximize these relationships instead of accumulating a large range of contacts.

32 contexts (e.g. Amabile, 1996), it seems that regarding innovation networks, extrinsic motivation carries more weight.

An explanation of the negative relationship could be that extrinsically motivated individuals perceive the costs of maintaining diverse contacts higher than the potential benefits (Reagans and McEvily, 2003). Also, individuals could choose not to share information when it is not rewarded (Osterloh and Frey, 2000), in accordance with Economic Exchange Theory. These behavioral dynamics, potentially underlying extrinsic motivation, bring into question the value of providing extrinsic rewards as encouragement for sharing knowledge, a common practice across organizations.

However, while previous studies have often used extrinsic and intrinsic motivation as unitary variables, as have we, it seems that there should be more focus on the sub-factors that make up these types of motivation. While the construct intrinsic motivation in our study was based on a combination of challenge and enjoyment, as validated in literature, extrinsic motivation consisted almost solely of what Amabile et al. (1994, p. 955) defined as ‘outward’. This sub-factor, the notion that individuals are oriented towards the recognition and dictates of others, differs significantly from frequently used items based on organizational rewards or reciprocal benefits (e.g. Lin, 2007). Accordingly, the interaction we found between extrinsic motivation and network degree should be considered in that light. A possibility could be that the organization researched differed significantly with regard to either the reward systems in place or the people employed and their motivational aspects. However, as an additional analysis, we used the scales for intrinsic and extrinsic motivation according to Amabile et al. (1994) (Table 3B, Appendix) and these results also show a significant interaction between extrinsic motivation and innovation network degree. This could lend further support to the existence of a significant interaction between extrinsic motivation and innovation network centrality.

33 seek to interact with opinion makers or dominant actors within an innovation network. This would have important ramifications both from a theoretical perspective as for managerial implications, as we will discuss in the next section.

As an additional test, we ran a regression analysis and removed location in favor of perceived organizational support. As shown by Table 3C (Appendix), this added an interesting dimension, as this inclusion further strengthened the negative relationship between extrinsic motivation and the number of innovation network contacts. In line with these results, it could be that as individuals perceive greater organizational support, when extrinsically motivated, they feel they can be more selective in their interaction with their peers. Therefore they may base their choice of contacts on the potential benefits and feel they will not be punished for less interaction with different people.

While Teigland and Wasko (2009) found that the most consistent predictor of centrality in an MNC’s advice network was tenure in the organization, we found no significant direct relationship between the number of innovation network contacts and tenure, which is in line with previous studies regarding innovation networks (e.g. Aalbers et al., 2013). Therefore, the organizational experience of an individual is not an indicator of innovation network centrality, possibly because these networks encompass specific knowledge that is not accumulated through greater tenure only. However, this does indicate that there exist important differences between types of intraorganizational networks, and as organizations aim to optimize knowledge sharing within networks, these differences should be well known.

34 did not utilize longitudinal data, it could be that in the focal organization, there was relatively little accumulation of competences or skills, leading to no significant differences between individuals with greater organizational experience and lesser experience.

Finally, we found that both the location and the role of individuals matter in innovation network contexts. Even though prior literature has claimed that employees in supervisory roles could have less interaction within diverse networks (Cohen and Zhou, 1991), it seems that individuals in supervisory roles actually tend to have more innovation network contacts. Furthermore, the fact that our results show significant differences in the number of network contacts between locations suggests that more attention ought to be paid to geographical and hierarchical factors when attempting to maximize innovative knowledge transfer.

5.2 Implications

Managerial Implications

We suggest a number of implications for managers concerning the management of innovative knowledge transfer. First, given the negative relationship between extrinsic motivation and innovation network degree, organizations should refrain from emphasizing rewards as primary mechanism to incentivize knowledge sharing, as these could actually harm knowledge transfer. While these types of rewards can be useful in securing temporary compliance (Lin, 2007), they are probably not adequate for use in the context of innovation networks, as these relations could rely upon more long-term relationships and interaction. As the construct of extrinsic motivation in our sample was mainly based upon outward (Amabile et al., 1994), it could be important for firms to acknowledge the effects that the recognition and dictates of others have on the behavior of individuals. In this line of reasoning, it would be important to know whom the key people are, what they dictate and how to gain their recognition.

35

Theoretical Implications

While previous studies have empirically tested the effects of motivation on innovation networks (e.g. Aalbers et al., 2013; Kadsuhin, 2002), our results differ from their findings. Whereas intrinsic motivation has repeatedly been shown to have no significant effect on centrality in innovation networks, we found a significant negative relationship between extrinsic motivation and the number of innovation network contacts. As there exist different types of extrinsic motivation (Amabile et al., 1994; Lin, 2007), and our results were based on one of these types in particular, it is important to distinguish between these types and no longer view extrinsic motivation as a unitary variable. Accordingly, more research is needed concerning the types and exact effects of these types of extrinsic motivation.

Additionally, the interaction between organizational experience and motivation ought to be investigated further. Even though we found no significant effects of organizational experience on an individual’s network degree or as a moderator, organizational experience itself is a longitudinal variable. In our analysis we examined the differences between individuals with high and low tenure, while a more significant line of study would be to investigate the influence of tenure on actors as they work in an organization for a longer time. Therefore, to examine the potential influence of organizational experience more completely it could be necessary to monitor changes in the motivation and network degree of individuals over a time span of possibly 5 years or more.

5.3 Limitations

The findings of this research should be considered in light of several limitations. The first limitation is with regard to the instrumentation used, as having employees enter their own motivation could have affected their behavior, thus reducing internal validity. However, given the fact that this information was relatively easy to grasp and straightforward, we believe this should not be a problem. Furthermore, limitations concerning the relatively small sample size (N = 72) and the specific industry of the focal firm must be mentioned, which could both affect the generalizability of this study.

36 thought to lead to other information timing and control benefits (Burt, 1992). Furthermore, structural holes theory distinguishes between redundancy by cohesion and redundancy by structural equivalence (Burt, 1992). Network degree only takes into account the first form of redundancy, and therefore measuring redundancy by structural equivalence cannot be done using the network degree of focal actors (Anderson, 2008).

Finally, the possibility of reverse causality is a limitation to this study. Following the regression analysis, we cannot say with certainty whether extrinsic motivation affects network position or vice versa. As hinted at by prior literature, social networks that people have could shape their personalities (Burkhardt, 1994), implying that the fact that actors have a lower network degree may cause lower levels of extrinsic motivation. This may also be the case for the location an individual works at and his/her role.

5.4 Conclusion

This study shows that there are factors, possibly playing a large role in innovation contexts, which have either been misinterpreted or not yet fully researched. In an attempt to contribute to literature regarding innovative knowledge transfer, we combined a motivational approach with the inclusion of organizational experience. Based on prior literature, we acknowledged the diverse effects intrinsic and extrinsic motivation could have on an individual’s innovation network degree. Additionally, in order to gain a more complete picture of these potential effects, we empirically investigated the interaction between motivation and organizational experience. This corresponds with the call for more research regarding individual characteristics (Kaše et al., 2009) and other potentially important factors concerning innovation networks (Aalbers et al., 2013).

37 needed concerning these factors, especially regarding extrinsic motivation and the notion of ‘Outward’ (Amabile et al., 1994).