THE INTERACTIVE EFFECT OF OPENNESS TO EXPERIENCE ON THE RELATIONSHIP BETWEEN AN INDIVIDUAL’S NETWORK DIVERSITY AND
JOB PERFORMANCE
Master thesis University of Groningen Faculty of Economics and Business
Human Resource Management & Organizational Behavior Department
June 2017
2 ABSTRACT
Organizational social networks (i.e., the relationships through which an individual employee is connected with his or her coworkers) have helped individuals to ease
collaboration with others in an increasingly dynamic and complex work environment. In the present study, a network is examined from the point of an individual employee (focal actor) and the relationships he or she has with other employees (alters). Within such networks, individual employees have a preference to engage in relationships with similar others. However, in practice, individuals work and interact with people of different tenure and gender. This study proposes that network diversity (the degree to which the network of an individual is diverse in tenure and gender) has an important impact on an individual’s job performance. Furthermore, it posits that an individual’s openness to experience moderates the relationship between the diversity in his or her social network and his or her job performance. It is expected that for high levels of openness to experience the relationship between network diversity and job performance is positive. In contrast, for low levels of openness to experience the relationship between network diversity and job performance is expected to be negative. Quantitative social network measures were used to analyze data from a big organization in the field of applied science located in the Netherlands. Implications of the study results for future research are discussed.
3 INTRODUCTION
Networks have become increasingly important for employees to help them collaborate effectively in their dynamic work environment by using social relations for work activities. (Kilduff & Tsai, 2003; Murase, Doty, Wax, DeChurch & Contractor, 2012). Moreover, networks are important for individual employees since the social interactions within a network enhance or constrain access to valuable resources (Brass, 1984; Ibarra, 1993). The present study will investigate the network of an individual employee. An individual’s network – also referred to as an ego network – is composed of a focal actor (the individual employee) and a set of component actors (such as colleagues) who are connected to the focal actor through some kind of predefined relation (such advice ties, friendship ties and collaborative ties) (Krackhardt, 1992; Nebus, 2006; Wasserman & Faust, 2008).
Since employees often work together with a variety of colleagues, individual employees have to deal with diversity in their networks. Such diversities may have a distinctive impact on the social interactions of an individual employee. In work-related settings, in particular, network diversity can shape the interactions between individual employees within organizations since interactions are determined by organizational context instead of personal choice (Ibarra, 1993). Previous research has investigated diversity using the relational approach, which puts a focus on differences between groups or individuals (e.g. Harrison, Price & Bell, 1998; Riordan, 2000). However, this study takes a different
perspective by examining differences within an individual’s social network. Here, network diversity is defined as differences in surface-level aspects between coworkers in the network of the focal actor (e.g. Tsui, Porter & Egan, 2002). The aim of the present study is to focus on the diversity of the entire network of an individual and its effects, because it may have
4 In particular, research suggests that there are good reasons to assume that network diversity has a positive impact on performance. For instance, Cross and Cummings (2004) state that diverse ties within a network can contribute to individual performance. They argue that these ties provide opportunities for employees to gather unique information and consider diverse perspectives when completing their work. Research on knowledge sharing indicates that these unique sources of unique knowledge can be more valuable than sources of
knowledge shared by most employees (Granovetter, 1973; Burt, 1992). Moreover, Corsaro, Cantù and Tunisini (2012) highlight the importance of diverse actors within a network for promoting innovative behavior. According to them, differences in knowledge bases, capabilities and competences and perceptions of the actor have a positive influence on
innovative behavior. Especially for knowledge intensive settings, where knowledge exchange is considered to be highly important for effectiveness (Carmeli, Atwater & Levi, 2011), these innovative behaviors and diverse sources of information are important for an individual’s job performance. Therefore, these findings indicate that network diversity in an individual network may have a positive impact on job performance.
Importantly, however, research on work groups and communication suggests that network diversity influences job performance negatively. Based on findings in work groups, comparable to networks, Pelled, Eisenhardt and Xin (1999) suggest that diversity can lead to potential conflict among employees. For example, they propose that diversity may lead to stereotyping and biaseswhich is likely to cause conflict. In addition, Ancona and Caldwell (1992) propose that network diversity may complicate communication between employees. They argue that network diversity may lead to decreased frequency of communication
5 between employees. These findings suggest that network diversity may have a negative influence on the performance of individual employees.
Together, the literature shows contradicting findings about the benefits of diversity in individual networks. Importantly, however, these studies do not take into account that people can differ in their reactions to the interaction with employees that have similar and dissimilar characteristics. This study will use the personality dimension openness to experience to take into account the effects of an employee’s personality. Next, surface-level aspects of diversity (gender and tenure) will be examined since these characteristics are almost immediately visible and highly salient (Guillaume, Brodbeck & Riketta, 2012). In addition, these salient differences may form a barrier for employees with low levels of openness to experience. Individuals with high levels of openness to experience may be open to individuals that differ from themselves and therefore may profit from these diverse experiences. This study suggests that openness to experience moderates the relationship between an individual’s network diversity and job performance. We expect that for high levels of openness to experience the relationship between network diversity and job performance is positive. In contrast, for low levels of openness to experience, we expect the relationship between network diversity and job performance to be negative.
The purpose of this study is to examine the moderating effect of openness to experience on the relationship between job performance and network diversity of an individual’s network within an organization. Furthermore, it extends existing theory by
6 Last, this study concludes with the discussion of the results, implications and limitations of the conducted research.
Figure 1: conceptual model
THEORETICAL BACKGROUND Network diversity
A network is a finite set or sets of actors and the relations defined between them, and focuses on relational information (Wasserman & Faust, 2008). Specifically, an individual’s network entails the coworkers with whom the focal actor is connected through ties of a specific kind (Kilduff & Tsai, 2003; Wasserman & Faust, 2008). Network researchers have distinguished several types of ties through which individuals can be connected. For instance, advice ties contain information, advice and resources necessary to perform a task and help individuals to profit from experiences and expertise of their coworkers (Umphress, Labianca, Brass, Kass & Scholten, 2003; Balkundi & Harrison, 2006). Friendship ties are based on liking and affection and form sources of social and emotional support, enhance open
7 This has profound implications, since different type of ties may have different benefits for individual employees.
Research suggests that a network can have beneficial effects for the well-being and performance of individual employees. For example, an individual’s network can provide emotional and instrumental support which is important for the well-being of an individual (Agneessens, Waege & Lievens, 2006). Moreover, an individual’s network can contribute to the improvement of individual performance (Cross & Cummings, 2004). This is important for an organization, since better individual performance of employees may eventually contribute to organizational performance. Networks have been studied using different approaches, such as a focus on the strength of the ties between actors (Granovetter, 1973). Other approaches include density (Friedkin, 1981), connectivity (Burt, 1992) and centrality (Freeman, 1982).
Within individual networks, diversity has not received broad attention by researchers. For instance, research has focused on the absence of relations (Burt, 1992) and the strength of relations between individuals (Granovetter, 1973). Whereas diversity is widely discussed within teams (e.g. Van Knippenberg & Schippers, 2007; Horwitz & Horwitz, 2007), it has not been studied at a similar frequency in networks. However, literature has shown that diversity may have an important impact on performance (e.g. Cross et al., 2001; Sosa, 2011) and therefore this study will look at diversity of an individual’s network. The literature makes a distinction between surface-level and deep-level categories (Jackson, May & Whitney, 1995). The present study focuses on surface-level categories since these aspects are almost
8 In contrast, high openness to experience will help individuals to cope with these salient
differences and may ease cooperation.
In line with the relational approach of diversity, network diversity (figure 2) is defined as differences in gender and organizational tenure between an individual employee and those individuals with whom he or she interacts (Riordan, 2000; Tsui, Porter & Egan, 2002; Chattopadhyay, Tluchowska & Lawrence, 2004). It is important to address these specific surface-level categories of diversity since they can reflect and generate individual biases, prejudices and stereotypes (Fiske & Neuberg, 1990). Moreover, individuals use these surface-level categories to assign themselves and others to social classifications involving ascribed patterns of thought, attitudes and behaviors (Fiske, 2000a). Thus, surface-level categories play an important role in defining relationships and interactions between individuals.
Figure 2: low network diversity (left) versus high network diversity (right), with the focal actor (1) as the central actor in his or her network
Negative consequences of network diversity
9 themselves, which is referred to as homophily (e.g. Boucher, 2015), facilitates communication between individuals, reduces the likelihood of interpersonal conflict and causes the behavior of similar individuals to be more predictable (McPherson, Smith-Lovin & Cook, 2001; Kossinets & Watts, 2009; Rivera, Soderstrom & Uzzi, 2010). As a result, this may improve the likelihood of efficient collaboration within an individual’s network. Moreover, important concepts as trust and solidarity are easier to establish within a network characterized by low diversity compared to a network characterized by high diversity (Banks & Carley, 1996; Mollica, Gary & Trevino, 2003). Thus, in a less diverse network individuals will have a tendency to frequently interact with other employees and to establish trust in their relationships with other employees.
These frequent interactions and trust-based relationships have positive consequences for employees. According to Borgatti, Jones and Everett (1998), this eases the exchange of information between employees. They argue that less diverse networks may improve
communication and transmission of tacit skills by the reduction of ambiguity in the process of information exchange. In addition, low network diversity can lead to more positive attitudes and decreased turnover (Williams & O’Reilly, 1998). Furthermore, Tajfel & Turner (1986) state that a less diverse network can lead to perceptions of a supportive working environment and less discrimination. These are important conditions to take into consideration when
predicting performance. For example, workers who are similar on demographic characteristics feel more at ease engaging in work tasks, which ultimately can lead to more positive work outcomes (Pelled et al., 1999; Guillaume, Brodbeck & Riketta, 2012).
A network diverse in surface-level aspects may have a negative influence on
10 attachment (Konrad, Winter & Gutek, 1992; Pelled & Xin; 1997). Thus, interaction with coworkers diverse in surface-level aspects can have a negative influence on individual employees. Moreover, Pelled (1996) suggests that surface-level diversity may increase emotional conflict and therefore may have a negative influence on effectivity.
Positive consequences of network diversity
However, previous research also suggests that high levels of network diversity within an individual network may influence performance positively. For instance, research on knowledge sharing (e.g. Cummings, 2004) suggests that diverse contacts in a network can contribute to the performance of individual employees by more efficient problem solving and the allocation of responsibilities. Furthermore, Joshi and Jackson (2003) argue that a high level of diversity may provide cognitive resources which can contribute to improving performance. Research shows that communication with dissimilar individuals can help resolve complex situations (Krackhardt & Stern, 1988). In addition, the importance of surface-level diversity has been highlighted by Jackson, Joshi and Erhardt (2003). They suggest these aspects of diversity are specifically effective in solving complex and non-routine problems.
11 Interaction with coworkers that are different on surface-level aspects may have benefits for individual employees. For example, research has demonstrated that these surface-level differences can contribute to innovation and complex problem solving by diversified sources of information, knowledge and perspectives (Van Knippenberg & Schippers, 2007; Tyran & Gibson, 2008). Likewise, Kearney and Gebert (2009) argue that individuals with surface-level differences contribute to a higher variance in task-relevant perspectives and problem solving capabilities. Moreover, Kunze, Boehm & Bruch (2011) argue that surface-level diversity creates a broader range of non-redundant task-relevant knowledge, skills and abilities, since individuals that differ in these aspects have obtained a diverse range of work, life or
organizational experiences. These non-redundant knowledge, skills and abilities may help individuals perform better making use of their network.
Thus, network diversity has been found to potentially have beneficial consequences for individual employees. This implies that network diversity has a distinct influence on individual performance, however this influence may depend on different situational contexts. Therefore, this study aims to explain the influence of network diversity on individual
performance through the moderating effect of openness to experience.
The moderating effect of openness to experience
12 less risk averse and more willing to consider options that differ from their own (George & Zhou, 2001; Lauriola & Levin, 2001). In addition, individuals who are more open to new experiences are able to deliver novel solutions to problems and creative ideas that challenge the status quo (McCrae, 1987). In contrast, individuals who score low on openness to experience have a preference to adopt familiar ways of doing things to reduce uncertainty about the soundness of their decisions (George & Zhou, 2001).
Based on these findings, this study proposes that openness to experience is the most suitable personality trait in the context of network diversity. Openness to experience may help individual employees to reduce the salience of surface-level differences, since individuals who score high on openness to experience have a preference for variety. (Costa & McCrae, 1992). Importantly, individuals that possess the characteristics of high openness to experience should be able to enhance their ability to profit from diverse perspectives and information diversity related to network diversity. For instance, as Ekehammer and Akrami (2003) show, openness to experience is related to beliefs and attitudes toward diversity. The study of Anderson (2008) demonstrates that need for cognition – conceptually related to openness to experience – has a significant influence to obtain the benefits of informational differences within a social network. Homan et al. (2008) have shown the importance of openness to experience in diverse organizational settings. Their findings demonstrate that openness to experience can lead to better performance when diversity was high. This illustrates that high openness to experience leads to a positive relationship between network diversity and job performance.
13 information does not occur frequently. In addition, Flynn (2005) shows that individuals with high levels of openness to experience have more positive attitudes toward deviating members than individuals who score low on openness to experience. As a result, it is expected that individuals who score low on openness to experience may not benefit from diverse and broad sources of information and are less willing to listen to individuals that differ from themselves. Consequently, this suggests that low openness to experience directs the relationship between network diversity and job performance to be negative.
Hypothesis 1: Openness to experience moderates the relationship between network diversity and job performance, such that this relationship is positive when openness to experience is higher, and negative when openness to experience is lower.
METHOD
This study tested the proposed hypothesis using gathered data from a big organization in the field of applied science in the Netherlands. The data was collected in 2014, from one organizational location that hosted three departments. Within the organization work was structured in research projects. Moreover, work was highly collaborative and knowledge-intensive. As such, employees were well educated and worked intensively together with coworkers from other departments. Employees worked in multiple teams and projects at the same time, which means that their networks played an important role to effectively perform in their job. Therefore, it offered an ideal setting to examine the effect of diversity within an individual’s network.
14 of detailed demographic information to determine network diversity. A total of 107
employees were approached to participate in this research. The final sample included 74 employees who agreed to participate. More than half of the employees were men (54,5%), with an average age of 42.15 years (s.d. = 9.24). The average respondent had worked in the organization for 11 years (s.d. = 7.61).
Measures
Network diversity. This study examined the extent to which an individual employee collaborated with his or her coworkers. To obtain information about the network of an individual, network-related questions were given to individual employees (cf. Marsden (1990). All respondents received an alphabetical list of the 107 employees at the organization, and respondents were asked to mark each individual with whom they had exchanged
information about work-related topics during the past six months.
The diversity of an individual’s network was measured using Blau’s index (1977). This index was calculated as follows: 1 - ∑pi2 where pi is the proportion of individuals within
a network who are of the demographic group i. For example, if i is male, then pi is the
15 Openness to experience. The moderation variable openness to experience was
measured by using a Dutch translated version of the NEO-Five Factor Inventory (Costa & McCrae, 1992). This scale contained 11 items that were answered on a 5-point Likert-scale from 1 (very inaccurate) to 5 (very accurate). Sample items included: “I consider myself as someone who has a very vivid imagination”, “I prefer to carry out tasks that involve routine work” (reverse coded) and “I consider myself to be an inventive person”. The items were averaged to calculate an overall score (α = .78).
Individual Performance. This study measured performance using both an assessment for standard and innovative performance. Since a knowledge-intensive organization was examined, innovative performance was important to take into consideration as well.
Standard performance was measured by using a Dutch translated version (Van Yperen
& De Jong, 1997) of the scale for job performance used by Podsakoff and MacKenzie (1989). Supervisors evaluated their employees on efficiency, quality and how well they performed based on a scale from 1 to 7. Sample items included: “This worker fulfills all responsibilities required by his/her job” and “This worker meets all the formal performance requirements of the job”.
Innovative performance was measured using three items based on the scale of Scott
and Bruce (1994) for individual innovative behavior in the workplace. These items referred to idea generation, idea promotion and idea realization. Supervisors rated their employees to what extent they performed these innovative behaviors on a scale from 1 (“never”) to 7 (“always”). Sample items included: “This employee generates creative ideas” and “This employee searches out new technologies, processes, techniques and/or product ideas”.
Control variables. To reduce the possibility that other variables that are likely to affect performance would influence the relations in this study, several control variables were
16 in their jobs (Ng & Feldman, 2008). Similarly, employees who have been employed in an organization for a longer time period were found to perform better (Ng & Feldman, 2010). Next, research indicates that supervisor ratings may discriminate female workers in evaluating their performance (e.g. Pazy, 1986). Therefore, age (in years), gender (0 = “female”, 1 = “male”) and organizational tenure (in years) were included as control variables in the analysis. Furthermore, since a bigger network may influence performance network size was used as a control variable. Next, autonomy was considered as a control variable since research has shown that autonomy is positively related to job performance (Saragih, 2011). Sample items included: “My job is such that I am able to decide how to organize my work” and “I am able to decide how to get my work done”. Last, dummy’s for department were used as a control variable since different departments have different specializations which may influence performance.
RESULTS Descriptive Statistics.
At first, descriptive statistics between the studied variables were determined. As presented in Table 1, results show a significant correlation between gender and tenure (r = .44, p < .01). Furthermore, openness to experience (r = .43, p < .01), network tenure diversity (r = .27, p < .01) and network gender diversity (r = .38, p < .01) significantly correlate with network size. Next, network gender diversity significantly correlates with network tenure diversity (r = .52, p < .01).
Hypothesis testing.
17 interaction effect for openness to experience and network and tenure diversity was included in the regression analysis.
First, the results of the analysis conducted on standard performance are reported in Table 2 and 3. The first model includes the control variables. Model 2 includes the independent variables, for both gender and tenure diversity separately. The full regression model is tested in model 3, including the interaction effect. The results show that there is a small negative effect for both tenure (B = -.10; p > .10) and gender diversity (B = -.03; p > .10), but that these effect are not significant. Thus, no significant interaction effect on standard performance was found.
Next, table 4 and 5 show the results of the analysis performed for innovative performance. Positive estimates for the interaction effect of openness to experience were found for both gender and tenure diversity. However, these were not significant. Therefore, the results suggest that individuals that have high levels of openness to experience do not perform better when they work in a network that has diversity in gender (B = .25; p > .10) or tenure (B = .20; p > .10). Hence, the hypothesis was not supported.
Additional analysis.
In addition, to test if other personality traits could have an interaction effect on the studied outcome variable, other Big Five personality traits were used in an additional analysis. The results of this additional analysis were not reported in the tables. Again, we found no significant results for agreeableness (gender diversity: B = .08, p > .10; tenure diversity: B = .12, p > .10), conscientiousness (gender diversity: B = .13, p > .10; tenure diversity: B = .12, p > .10), extraversion (gender diversity: B = -.13, p > .10; tenure diversity: B = -.09, p > .10) and neuroticism (gender diversity: B = -.19, p > .10; tenure diversity: B = -.23, p > .10).
18 Although the literature suggests that network diversity may have both positive and
negative consequences for individual employees, there has been conducted little empirical research on this topic. This study used the personality trait openness to experience to find a better understanding of the implications of a diverse individual network for individual performance. We found no confirmation for the claim that network diversity has a positive influence on individual job performance, as research on knowledge sharing suggested (e.g. Homan et al., 2007). Also, no evidence was found for the negative effect of network diversity on performance, as research on homophily argued (Rivera et al., 2010). Moreover, our results do not indicate that individuals with higher levels of openness to experience perform better when collaborating in a diverse network.
Despite the non-significant results, this study contributes to existing theory. The results we found are in sharp contrast with previous research which focused on the
implications of diversity on teams. Within this context, research has found both significant negative (e.g. Joshi & Roh, 2009) and positive (e.g. Homan et al., 2007) effects of diversity on performance. Moreover, Van Knippenberg et al. (2004) have also contributed to this perspective, highlighting the positive and negative effects of diversity within teams as well. Thus, literature shows that diversity has a distinct impact on team performance.
19 these findings on diversity in an individual network. In doing so, it tried to contribute to both existing theory and give an implication for practice (such as how to manage diversity). Since diversity is a fact within individual networks, it will remain a factor that deserves further attention.
Furthermore, our findings do not confirm the claim that individuals may experience diversity in their networks differently. At the same time, Homan et al. (2008) have shown that individuals within teams encounter diversity in various ways. According to them, diverse teams that score high on openness to experience perform better than teams that score low on openness to experience. Similarly, Kearney et al. (2009) found that teams benefit from diversity when their need for cognition (conceptually similar to openness to experience) was high. Thus, when individuals have to collaborate with diverse coworkers personality may still be an important aspect. Specifically, the aspects of openness to experience are related to collaborating in a diverse work environment. However, the results of the present study do not seem to confirm this. A possible explanation may be that the surface-level aspects (age and organizational tenure) that were examined in this study were not salient enough for individual employees.
Limitations and future research
This study has several limitations. For example, the sample size of this study is relatively small. 74 employees participated in the research. Moreover, the data was collected in one organization in the Netherlands. Therefore, the results of this study are merely
20 other contexts (such as less knowledge-intensive organizations). This would also increase the sample size and thus reliability.
In addition, the data did not include objective performance measures of employees but supervisor-rated performance measures. Supervisor-rated performance measures are
subjective evaluations of employee performance and therefore they are likely to represent some form of bias. For instance, the leniency bias may provide higher performance ratings to employees (Bretz, Milkovich, & Read, 1992). Future research should consider using a direct assessment of work outcomes to examine whether network diversity is related with objective measures of performance.
21 important influence within social relationships as well. For example, Liao, Chuang and Joshi (2008) have given an insight in combining both personality and deep-level diversity and its influence on work outcomes, using non-visible characteristics to determine diversity.
Moreover, deep-level diversity may be interesting to consider since it includes differences on psychological variables (attitudes), personality factors, values and abilities (Bell, 2007). These underlying differences between employees are hard to see beforehand. However, these
differences may emerge when individual employees interact with their coworkers. In these collaborations, deep-level diversity may have an important influence on performance. Therefore, future research could consider examine deep-level diversity as well.
Furthermore, this study only examined relationships where employees have
collaborated with their coworkers. However, these specific ties are merely one of the many specific ties that exist between employees. For instance, Lincoln and Miller (1979) have examined friendship ties within an organization. It can be imagined that friendship ties are stronger than collaborative ties, specifically since employees can influence with whom they engage in friendship ties whereas this is not the case for collaborative ties (Schulte, Cohen & Klein, 2012). In particular, these ties provide employees social support and a sense of
belonging (Podolny & Baron, 1997). Moreover, friendship ties compromise non-work related communication, such as empathy and consideration (Ibarra, 1993). This shows that employees may use different types of ties for different communication and that these different types may have different benefits for individual employees. Thus, examining these friendship ties may give another perspective to individual network research.
Conclusion
22 non-significant findings of this research, we think that this study has established foundations for new empirical research.
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APPENDIX TABLE 1
Descriptive Statistics and Correlations
Variable Mean s.d. 1 2 3 4 5 6 7 8 9 10 1. Innovative performance .00 .84 2. Age 42.15 9.24 -.11 3. Gender .53 .50 .10 .45*** 4. Tenure 11.68 7.61 -.13 .79*** .44*** 5. Network size 20.45 9.38 .19 -.09 .32*** -.01 6. Hours 35.97 4.53 .10 -.06 .34*** .07 .14 7. Autonomy 4.39 .56 -.14 .13 -.14 .11 .05 -.06 8. Openness to Experience 3.86 .54 .08 .11 .17 .09 .43*** .17 .12
9. Network diversity tenure .72 .06 .02 -.15 -.16 -.16 .38*** -.07 -.08 .08
TABLE 2
Results of Regression Analysis Predicting Performance Based on Network Tenure Diversity and Openness to Experience
Network tenure diversity
Model 1 Model 2 Model 3
B(SE) B(SE) B(SE)
Control variables Age .01(.02) .01(.02) .01(.02) Gender -.28(.24) -.34(.25) -.36(.26) Tenure -.01(.02) -.01(.02) .00(.02) Network size .01(.01) .02(.01) .02(.01) Hours -.01(.02) -.02(.02) -.01(.02) Autonomy -.15(.17) -.18(.17) -.18(.18) Department A -1.39(.25)*** -1.42(.26)*** -1.39(.26)*** Department B -.80(.22)** -.84(.23)*** -.85(.23)** Independent variables
Network tenure diversity -.09(.11) .27(.72)
Openness to Experience -.03(.10) -.04(.10) Interaction effect Network diversity tenure*Openness to Experience -.01(.19) R2 .39 .39 .40 Adjusted R2 .31 .29 .29 F-value 4.89 3.91 3.53
33 TABLE 3
Results of Regression Analysis Predicting Performance Based on Network Gender Diversity and Openness to Experience
Network gender diversity
Model 1 Model 2 Model 3
B(SE) B(SE) B(SE)
Control variables Age .01(.02) .01(.02) .01(.02) Gender -.28(.24) -.26(.24) -.26(.24) Tenure -.01(.02) -.01(.02) -.01(.02) Network size .01(.01) .01(.01) .01(.01) Hours -.01(.02) -.01(.02) -.01(.02) Autonomy -.15(.17) -.15(.17) -.15(.17) Department A -1.39(.25)*** -1.36(.26)*** -1.35(.26)*** Department B -.80(.22)** -.74(.23)** -.74(.24)** Independent variables
Network gender diversity .08(.10) .18(.80)
Openness to Experience -.01(.10) -.02(.10) Interaction effect Network gender diversity*Openness to Experience -.03(.21) R2 .39 .39 .39 Adjusted R2 .31 .29 .28 F-value 4.89 3.90 3.48
34 TABLE 4
Results of Regression Analysis Predicting Innovative Performance Based on Network Tenure Diversity and Openness to Experience
Network tenure diversity
Model 1 Model 2 Model 3
B(SE) B(SE) B(SE)
Control variables Age .01(.02) .01(.02) .01(.02) Gender .13(.26) .09(.28) .15(.28) Tenure -.02(.02) -.02(.02) -.03(.02) Network size .01(.01) .01(.01) .01(.01) Hours .00(-.04) -.01(.03) -.01(.03) Autonomy -.10(.19) -.12(.20) -.13(.20) Department A -.48(.27)*** -.53(.28)*** -.57(.29)*** Department B -.05(.25) -.01(.26) -.09(.25) Independent variables
Network tenure diversity -.08(.12) -.84(.79)
Openness to Experience .05(.11) .07(.12) Interaction effect Network tenure diversity*Openness to Experience .20(.21) R2 .14 .14 .16 Adjusted R2 .03 .01 .01 F-value 1.26 1.06 1.05
35 TABLE 5
Results of Regression Analysis Predicting Innovative Performance Based on Network Gender Diversity and Openness to Experience
Network gender diversity
Model 1 Model 2 Model 3
B(SE) B(SE) B(SE)
Control variables Age .01(.02) .00(.02) .00(.02) Gender .13(.26) .17(.26) .16(.27) Tenure -.02(.02) -.02(.02) -.02(.02) Network size .01(.01) .01(.01) .01(.01) Hours .00(-.03) -.01(.03) -.01(.03) Autonomy -.10(.19) -.11(.19) -.13(.19) Department A -.48(.27)*** -.53(.28)*** -.55(.28)*** Department B -.05(.25) -.05(.26) -.04(.26) Independent variables
Network gender diversity -.02(.12) -.98(0.89)
Openness to Experience .07(.11) .08(0.12) Interaction effect Network gender diversity*Openness to Experience .25(0.23) R2 .14 .14 .15 Adjusted R2 .03 .00 .00 F-value 1.26 1.01 1.03
