HOW DOES MULTIPLE TEAM MEMBERSHIP
INFLUENCE INDIVIDUAL CREATIVITY: THE ROLE
OF BOUNDARY SPANNING AND ROLE OVERLOAD
Thom E. van den BosS2591227
Master’s Thesis Human Resource Management Faculty of Economics and Business
University of Groningen 14-06-2020 Supervisor: C. Chen
Word count: 6211
ABSTRACT
This research focuses on the concept of multiple team membership (MTM). Since its introduction, MTM has gained increased interest in academic literature. Although we know a lot of the possibilities nowadays, it is unclear what the actual effects on individual creativity are. Based on knowledge acquisition theory, social capital theory, and attention-based theory, I tried to explain the positive relationship between MTM and individual creativity through boundary spanning. Furthermore, I tried to explain how this effect is conditional on role overload. I have not found any results on the proposed relationships through survey data from the field. Although there were no significant results, this study contributes to combining the fields of study on multiple team membership and boundary spanning, offering research avenue in the field of boundary spanning, and helping managers and employees understand information sharing and role overload on the work floor.
INTRODUCTION
The last decade has been enlightening for organizational developments. One of these developments is the existence of multiple team membership, introduced by O’Leary, Mortensen and Woolley (2011). Rapp and Mathieu (2019) underline the growing interest in multiple team membership, whereas individual employees are more frequently involved in more than one team. In addition, being part of multiple teams is a growing trend among global companies, with over 80 percent of managers working in multiple teams (Mortensen & Gardner, 2017). This concept of multiple team memberships requires new insight in the what and how. O’Leary et al. (2011) propose in their research that the number of teams an individual is a member of, might influence learning on the individual level. In their research, Van de Brake, Walter, Rink, Essen and Van der Vegt (2018) state that individuals working in multiple teams rely on social resources, in which multiple team membership fosters individual creativity as it exposes one to new resources (Perry-Smith, 2006). Creativity literature found “that factors that promote the learning of knowledge and skills are associated with creativity” (Hirst, van Knippenberg & Zhou, 2009, p. 289). Furthermore, the study of Hirst et al. (2009) found that team learning is a factor that fosters the individual creativity. The knowledge acquisition theory (Reagans and McEvily, 2003), can be a possible explanatory mechanism for learning through creativity, which in turn explains the relationship between multiple team membership and individual creativity.
Although research interest in the concept of multiple team membership has increased (Wageman, Gardner & Mortensen, 2012), little research has yet been executed on the concept of multiple team membership and its effects on individual members (Rapp & Mathieu, 2019). Research that has been done on the concept of multiple team membership, however, does provide mixed results; some research suggests that multiple team membership on the individual level can have positive results, while other research shows negative results on the individual level (Van de Brake, Walter, Rink, Essens and Van der Vegt, 2019). One of the questions that remain unanswered in the literature is the relationship between multiple team membership and individual creativity. This study particularly focuses on an explanatory mechanism for this relationship.
When working in multiple team membership, an individual has the access to multiple resources. Thus, to increase once individual creativity, one need to integrate new knowledge and insights from different sources. This activity is captured by boundary spanning. Boundary spanning is the “behaviors intended to establish relationships and interactions with external actors that can assist their team in meeting its overall objectives” (Marrone, Tesluk and Carson, 2007, p. 1424). Within boundary spanning, the gathering of external information is an important function, for that individuals in one team receive information from external sources which they process and interpret and might communicate with their fellow team members (Hansen, 1999., in Marrone, Tesluk & Carson, 2007). Based on social capital theory (Bourdieu, 1986; Nahapiet & Ghoshal, 1998), I argue that being part of different teams provides access to external contacts who in turn provide various external information to individual members. This could increase the learning of individual members as they frequently receive external information, distribute and coordinate tasks amongst teams, and acquire resources from other teams. Accordingly, this information received might be used by the individual to foster creativity, since newly received information could lead to new ideas. Thus, I argue that boundary spanning is an explanatory mechanism of the relationship between multiple team membership and individual creativity.
Boundary spanning might be a possible explanation for the effect that the number of teams has on individual creativity. Nevertheless, a condition is that individuals are still able to leverage the potential resources embedded in their multiple team membership, meaning that they still need to be able to carry out boundary spanning activities. Gilboa, Shirom, Fried and Cooper (2008) found that individuals’ attention and perception of information narrows down when several stressors cause overload in the cognitive system. This can cause people to ignore certain performance-related information. Attention-based theory (Ocasio, 1997) explains how an individual's cognition affects the attention. I argue that role overload that an individual might experience has a negative effect on the attention span of an individual and therefore weakens the relationship between multiple team membership and boundary spanning. This can therefore become a condition for boundary spanning behavior, and thus a condition for how individual creativity is affected by multiple team membership.
practical contribution of this research is to understand that MTM may have some boundaries. When these boundaries are exceeded, then an employee might no longer be able to properly fulfil the job.
THEORY
This section will provide an in-depth overview on the different concept presented in the conceptual model (figure 1). Furthermore, this section will explain the theories that are fundamental for the different proposed relationships between the concepts. These explanations will provide hypotheses for the model presented, which will be the fundament of my study.
Figure 1: conceptual model
The relationship between multiple team membership and individual creativity
Multiple team membership is defined as being a member of more than one team at the
same time of work, and therefore working on multiple projects with different people (O’Leary et
al., 2011). Henceforth, the number of teams will refer to MTM, and is used in this study as the main effect of MTM. Working in multiple teams might benefit individual creativity. Individual creativity can be defined as “employee’s generation of novel and useful ideas concerning products, procedures and processes at work” (Amabile, 1988; Oldham & Cummings, 1996, in Hirst et al., 2009; Taggar, 2002; Perry-Smith, 2006).
acquisition of knowledge from other sources, and by this helps process this knowledge (Cross & Cummings, 2004).
I argue that MTM positively influences individual creativity. The knowledge acquisition theory explains the proposed relationship because being part of multiple teams means that one has extended the network, which in turn increases the perspective and thus creativity. Since the number of teams one is in, is associated with the number of sources from which information gathering is possible, an individual can enlarge one's idea through this multiple information sources, and this might in turn lead to a higher individual creativity level. Therefore, the first hypothesis is as follows:
Hypothesis 1: The number of teams one is a member of is positively associated with individual
creativity.
The mediating role of boundary spanning
I further propose that boundary spanning mediates the proposed relationship in hypothesis 1. Boundary spanning itself is defined as the “behaviors intended to establish relationships and interactions with external actors that can assist their team in meeting its overall objectives” (Marrone et al., 2007, p. 1424). The social capital theory can explain why boundary spanning is the mediating factor.
According to social capital theory, social capital is a bundle of networks and relationships offering a resource for social activities, providing members of the network with capital that is owned by and available to all members of the network (Bourdieu, 1986; in Nahapiet & Ghoshal, 1998). Nahapiet and Ghoshal “define social capital as the sum of the actual and potential resources embedded within, available through, and derived from the network of relationships possessed by an individual or social unit” (1998, p. 243).
The social capital theory as presented above is a theory about the possibilities in one’s network to acquire new resources. Looking at this perspective, I argue that this theory is a condition for the knowledge acquisition theory. Thus, when an individual wants to acquire new knowledge, that same individual first needs an extended network with new resources.
membership and individual creativity (which in turn is through knowledge acquisition theory). The extension of one’s network by external information resources causes the boundary spanner to acquire more information. Accordingly, boundary spanning reflects individuals’ behavior of utilizing these social resources and is therefore a mechanism to explain the relationship between multiple team membership and individual creativity. The second hypothesis is as follows:
Hypothesis 2: The number of teams one is a member of is positively associated with his or her
boundary spanning behavior.
The moderating role of role overload
Role overload can be explained by attention-based theory. This theory suggests that, “at the level of individual cognition, attentional processes focus the energy, effort, and mindfulness of organizational decision-makers on a limited set of elements that enter into consciousness at any given time” (Ocasio, 1997, p. 190). Through this cognitive process, an individual's attention becomes selective and tends to focus on those ideas or objects that are associated with the ideas or objects that are important for the individual at that moment. In return, this focused attention moves away from other ideas or objects that are available at the same time (Kahneman, 1973, in Ocasio, 1997).
I argue, based on role overload that one might experience on the job, that individuals are unable to take action (boundary spanning) at all times despite that social resources (multiple team membership) for taking this action are available at those moments. This means that individuals who experience role overload, will devote their time and effort to the more routine work which is required for realizing the minimum performance standards. As a result, these individuals do not have time to proactively look for external sources and to learn various knowledge from these external sources to develop creativity. Thus, role overload causes individuals to choose where they focus their attention on and may therefore have a negative influence on the relationship between MTM and boundary spanning, which in turn influences the by boundary spanning mediated relationship between MTM and individual creativity. The third and fourth hypothesis are as follows:
Hypothesis 3: The positive relationship between multiple team membership and boundary
spanning is moderated by role overload such that the relationship is weaker when role overload is higher.
Hypothesis 4: The indirect effect of multiple team membership on individual creativity is mediated
by boundary spanning and moderated by role overload for the path from boundary spanning to individual creativity.
METHOD
For this research, I made use of survey data. I approached many organizations and employees that work within multiple teams in order to gather my data. For this, I looked for organizations that work on a project basis, work in clustered teams, or explicitly mention that they are an organization working with MTM. The organizations approached for this survey work in the public sector, commercial sector, financial sector, agricultural and floricultural sector, and fitness industry.
teams (88 participants) that were approached, 19 supervisors (86%) and 56 subordinates (85%) finished and completed the survey.
Participants
Combining both datasets provides a total dataset of 19 supervisor-subordinate relationships, thus meaning that the total dataset exists of 19 supervisors and 50 subordinates (at least two subordinates per supervisor). To ensure that all needed data is available, those participants that lack supervisor data are excluded from the dataset. On the contrary, those teams that do not include all three subordinates, but do include supervisor data are not excluded from the dataset. This is because these individual scores can still be usable for the analyses, and in this case a larger population will significantly increase reliability due to the already small population.
For all participants, 58% is female and 42% is male. The range of their ages is between 21 and 60 years old, with a mean of 36,78 and a standard deviation of 11,45. Thus most participants have an age between 25 and 48 years old. Furthermore, 76,8% of all participants has the Dutch nationality, and 23,2% has a non-Dutch nationality. The educational level of the participants varies in high school or below (11 participants, 15,9%), junior school (19 participants, 27,5%), Bachelor of (applied) science degree (33 participants, 47,8%) and a master’s degree (6 participants, 8,7%).
Measures
Multiple Team membership. I measured MTM with 1 item (Chang, 2014). I did this by
asking each subordinate “In how many teams/on how many projects do you currently work?”.
Boundary spanning. I measured boundary spanning with 9 items (Marrone et al, 2007). I
did this by asking each subordinate on a scale from 1 to 5 (“1 – Never”, “2 – Rarely”, “3 – Occasionally”, “4 – Usually” and “5 – Always”). Example items are “Coordinating activities with other teams”, “Resolving project problems with other groups” and “Proactively seeking advice, information, knowledge, or insights from people outside your team” (α = .802).
Individual creativity. I measured individual creativity with 4 items (Farmer, Tierney &
Role overload. I measured role overload with 4 items (Brown, 2005; Marrone et al., 2007).
I did this by asking the subordinates on a scale from 1 to 5 (“1 – Strongly disagree”, “2 – somewhat disagree”, “3 – Neither disagree nor agree”, “4 – somewhat agree” and “5 – Strongly agree”). Example items are “I have too much work for one person to do” and “I have to take on too many roles and responsibilities at work” (α = .893).
Control variables
Creative Requirement. I measured creative requirement with 3 items (Unsworth, Wall &
Carter, 2005). I did this by asking the subordinates on a scale from 1 to 5 (“1 – Strongly disagree”, “2 – somewhat disagree”, “3 – Neither disagree nor agree”, “4 – somewhat agree” and “5 – Strongly agree”). Example items are “My job requires me to have ideas about changing work goals and objectives” and “My job requires me to have ideas about work procedures” (α = .754).
Source Expertise. I measured source expertise with 3 items. I did this by asking the
subordinates on a scale from 1 to 5 (“1 – Strongly disagree”, “2 – somewhat disagree”, “3 – Neither disagree nor agree”, “4 – somewhat agree” and “5 – Strongly agree”). Example items are “are capable” and “are knowledgeable” (α = .761).
Age. I measured age with 1 item. I did this by asking each participant “What is your age?
(in years)”.
Organizational Tenure. I measured organizational tenure with 1 item. I did this by asking
each participant “How long have you been working in this organization? (in months)”.
Operationalization
In order to test my hypothesis, I executed different analyses using SPSS. First, some descriptive analyses and a Pearson Correlation Table were executed in order to test reliability and control variables. The control variables influencing either boundary spanning, or individual creativity are used in the further analyses.
To test hypothesis 1, 2 and 3 I used a multilevel regression table. This table shows whether there were any correlations on the condition role overload for both boundary spanning and individual creativity.
RESULTS
In order to get a better understanding of the used data, I gained insight in the descriptive statistics such as the number of participants, means, standard deviations, minimum and maximum scores, and correlations amongst all predictors and the control variables age and gender. Table 2 shows the descriptive statistics whereas table three shows a Pearson Correlation analysis. Furthermore, a multilevel regression model is used to test hypotheses 1,2 and 3 whereas a model 7 PROCESS analysis (Hayes, 2013) is used to test hypothesis 4.
Descriptive statistics
Following a simple linear regression analysis, the normal distribution (figure 1) suggests a skewed distribution to the right for individual creativity.
Figure 2: Normal distribution of Individual Creativity Figure 3: Normal P-P plot of Individual Creativity
The normal distribution of the dependent variable individual creativity shows a skewness to the right with a mean of 3.53 and a standard deviation of 1.00. The same can be said about the normal distribution of the mediator boundary spanning and the conditional moderator role overload. However, the normal distribution of MTM differs from this, having an expected skewness to the left with mean 2.40 and standard deviation 1.69, meaning that a large amount of the population works in approximately four or less teams.
= -.333, p < .05). Furthermore, boundary spanning is positively linked with creative requirement ( = .502, p < .01) and source expertise (r = .285, p < .05).
Table 1: Descriptive statistics
N Mean SD Minimum Maximum 1. MTM 50 2.40 1.69 1 10 2. Boundary Spanning 50 3.19 .68 1.78 4.78 3. Individual Creativity 50 3.53 1.00 1.25 5.00 4. Role Overload 50 2.84 1.18 1.00 5.00 5. Creative Requirement 50 4.31 .73 1.00 5.00 6. Source expertise 50 4.33 .67 2.33 5.00 7. Age 69 36.78 11.45 21 60 8. Organizational Tenure 69 109.64 122.08 2 476 Note: *p <.05, **p <.01
Table 2: Pearson Correlation Analysis
Multilevel regression analysis
The multilevel regression analysis is used to test the moderation effect of role overload. Thus, it will test hypotheses 1, 2 and 3. This analysis is done in two forms: one with boundary spanning as the dependent variable of the model, and one with individual creativity as the dependent variable.
In order to perform this analysis, I ran a mixed model linear regression in SPSS. Step 1 includes an empty model with the dependent variable individual creativity. Step 2 includes adding the standardized control variables that correlate with individual creativity, in this case age and organizational tenure. Step 3 includes adding the standardized predictor variables multiple team membership as the independent variable and role overload as the moderator. Step 4 includes adding the interaction term of the standardized predictor variables multiple team membership and role overload. For individual creativity (table 4) as the dependent variable, there is significant variance on both the individual level (p < .01) and team level (p < .05). On the contrary, no significance is found in the effect of multiple team membership on individual creativity (B = .000, t = .007, p = .995), role overload on individual creativity (B = .081, t = .781, p = .441). Thus, hypothesis 1 is rejected.
In order to perform this analysis, I ran a mixed model linear regression in SPSS. Step 1 includes an empty model with the dependent variable boundary spanning. Step 2 includes adding the standardized control variables that correlate with boundary spanning, in this case creative requirement and source expertise. Step 3 includes adding the standardized predictor variables multiple team membership as the independent variable and role overload as the moderator. Step 4 includes adding the combined effect of the standardized predictor variables multiple team membership x role overload. For boundary spanning (table 3) there is significant variance on the individual level (p < .01), however there is no significant variance on the team level in any step (p
> .05). Moreover, there is no significance found in the effects of multiple team membership on
boundary spanning (B = .059, t = .652, p = .518) role overload on boundary spanning (B = -.052,
t = -.552, p = .584). Thus, hypothesis 2 is rejected. However, there is a significant effect of the
-Table 3: multilevel regression analysis for boundary spanning
Boundary Spanning
Predictors Step 1 Step 2 Step 3 Step 4
Creative Requirement .306 (3,627)** .303 (3,342)** .316 (3,423)**
Source Expertise .143 (1,667) .142 (1,610) .151 (1,690) Multiple Team Membership .059 (.652) .095 (.907) Role Overload -.052 (-.552) -.079 (-.754) MTM x Role Overload -.095 (-.781) Individual Level Variance .000 .000 .001 .001 Team Level Variance .142 .640 .485 .542 -2 Restricted Log Likelihood 101,877 94,079 99,661 101,447
Note: *p <.05, **p <.01
Note: *p <.05, **p <.01
Table 4: multilevel regression analysis for Individual Creativity
Individual Creativity
Predictors Step 1 Step 2 Step 3 Step 4
Moderated mediation analysis
In this part I have conducted a moderated mediation analysis based on the PROCESS model 7 (Hayes, 2013) in SPSS. Based on the Pearson Correlation table as presented above, I use creative requirement, source expertise, age and organizational tenure as my control variables for this analysis since those have a significant correlation with the outcome variable and the mediator. Running the analysis, I looked at three aspects: the direct and indirect effect of the mediator, the conditional effect of the moderator, and the total conditional mediated effect. The model is used with a 95% confidence interval. With this analysis, I can either confirm or reject hypothesis 4.
Hypothesis 4 states that the proposed relationship is conditional on the level of role overload that one experiences. Thus, to see whether the mediated relationship is conditional I conducted the same PROCESS model 7 (Hayes, 2013) analysis in SPSS, using a mean centered variable for role overload (M = 2,835
Based on the mediation analysis, there is no direct of MTM on individual creativity (B = -.128, t = -1.536, p = .132), as well as that there is no indirect effect of MTM on individual creativity through boundary spanning (B = .005, LLCL = -.297, ULCL = .040.
Table 5: Regression results for mediation
B (SE) t p A-path .148 (.220) .194 .847 B-path .281 (.243) 1,153 .255 Direct effect -.128 (.083) -1,536 .132 Indirect effect .005 (.022) LLCL = -.297, ULCL = .040 Creative Requirement -.022 (.224) -.010 .921 Source Expertise -.056 (.223) -.252 .802 Age -.013 (.017) -.759 .452 Organizational tenure -.001 (.002) -.925 .360
for the conditional effect of role overload show that there is no significant effect on the low (B = .027 (-1,545), 95% conf. interval: -.062, .229), medium (B = .008 (.165), 95% conf. interval: -.032, .078), and high (B = -.002 (1,165), 95% conf. interval: -.084, .045) level of role overload. Thus hypothesis 4 is not confirmed and therefore rejected.
Table 6: Conditional indirect effects of MTM through boundary spanning at values of role overload
Individual creativity
Role overload B (M) Confidence level of 95% for
confidence interval Low .027 (-1,545) (-.062, .229) Medium .008 (.165) (-.032, .078) High -.002 (1,165) (-.084, .045)
The results of this analysis lead to the conclusion of the hypotheses as presented in table 7
Table 7: conclusions of the hypotheses
Hypotheses Results
Hypothesis 1: The number of teams one is a member of is positively associated with individual creativity.
Rejected
Hypothesis 2: The number of teams one is a member of is positively associated with his or her boundary spanning behavior
Rejected
Hypothesis 3: The positive relationship between multiple team membership and boundary spanning is moderated by role overload such that the relationship is weaker when role overload is higher
Rejected
Hypothesis 4: the indirect effect of multiple team membership on individual creativity is mediated by boundary spanning and moderated by role overload for the path from boundary spanning to individual creativity.
DISCUSSION
In this section, I will provide an overview on my research and the different contributions it has on both the theoretical and practical level. Furthermore, I will critically reflect on my research and provide ideas for future research. This section is followed by an overall conclusion of my research.
In my research, I have found no relationships between those that I have proposed. There is no significant relationship between multiple team membership and individual creativity, meaning that being part of multiple teams does not directly have a positive association with individual creativity. Furthermore, this direct relationship does not necessarily happen when an individual demonstrates boundary spanning behavior, meaning that boundary spanning is not an explanatory mechanism for the positive relationship between multiple team membership and individual creativity. It also seems that role overload has neither a negative effect on the relationship between multiple team membership and boundary spanning, nor on the relationship between multiple team membership and individual creativity. Nonetheless, role overload does have a negative effect when it is high, thus it could become negative in other relationships when role overload increases.
Theoretical contributions
First, my research contributes to the already existing literature on multiple team membership. My contribution is that I combined the boundary spanning and multiple team membership research, proposing a mechanism for understanding how multiple team membership leads to individual creativity. Since multiple team membership is a rather new concept, only limited researchers moved their attention to this concept (Rapp & Mathieu, 2019). Nevertheless, the potential field in understanding multiple team membership that O’Leary et al. (2011) introduced is rather brought, as Van de Brake et al. (2019) showed by noticing that multiple team membership can have both positive and negative effects on an organization. Thus, this study is only a small example of the possibilities that research can still touch within the concept of multiple team membership.
relationship, the idea of incorporation social and sociological theories could open new opportunities in the research on multiple team membership and boundary spanning.
Another contribution of this research is that there seems to be no explanatory mechanism or any correlation between variables. Although O’Leary et al. (2011) expected that multiple team membership fosters individual creativity, my research highlights that this relationship is not as straightforward as assumed. Furthermore, while this direct relationship is not confirmed, it is neither explained by boundary spanning. Thus, the introduced concept of multiple team membership and its expected effect on individual creativity is not clear yet. This study is one of a starting point for more research on how multiple team membership leads to individual creativity.
A third contribution to existing literature is the consideration of role overload. In my study, I investigated the effect of role overload on the relationship between multiple team membership and individual creativity. Although there is no certain negative effect of role overload found, my research suggest that a high level of role overload leads to a negative effect on the other variables. This contributes to the existing literature on multiple team membership. My findings provide a possible explanation of why being a member of too many teams could lead to little effects of multiple team membership on individual creativity, as argued by previous research (e.g. O’Leary et al., 2011).
Practical contributions
This study finds its practical contribution in the understanding for managers working with multiple team membership: understanding the dynamics of working in multiple teams unfolds the possibilities it may have for the information gathering and information sharing processes on the work floor. My research provides a potential explanation of why and how multiple team membership benefits creativity. Accordingly, managers can improve the effectiveness of multiple team membership by reminding those employees who work in multiple teams that it is important for them to leverage their connections with external parties by, for instance, gathering new information from those external parties and incorporating those external new knowledge into their work.
that their employees do not feel too stressful to be able to really benefit from multiple team membership. Especially those with experience in multiple team membership could learn from this as they may experience role overload faster or more often compared to individuals that work in a single team, recognizing the negative consequences for others as well.
Limitations and future research
The first limitation of this research is that there are no significant results found in any of the proposed relationships. One reason for this could be that the model I propose does not exist of causality, in which one construct leads to another. Burke Jarvis, MacKenzie and Podsakoff (2003) create a distinction between the principal factor model and the composite latent variable model. They show that a conceptual model can show causality and lead to one component, or that a conceptual model can consist of constructs that all have relationships with the other constructs. For my model it might mean that the proposed constructs have no linear relation, but are all related to one another, or that the constructs have another linear relationship with another than I proposed. Another reason for not finding any significant results is that this is a cross sectional study, meaning that there cannot be concluded any causality because the study is only measuring these constructs on a certain moment in time. Especially this moment in time is rather unlucky since the coronavirus influences organizations all around the globe in the execution of their operations. In addition to this, Lindell and Whitney (2001) show in their study that cross section studies can lead to common method variance, suggesting a model and method to minimalize this. For future research, I suggest to critically look at the model and the relationships between each construct, and most of all to measure the constructs over a longer time period to ensure that the required information is measured more stable over time.
The third limitation of this study can be found in intergroup bias (Hewstone, Rubin & Willis, 2002). For my study this intergroup bias influences the results even more due to the small sample size, considering that this study is executed among teams (which is social groups). These biases could, for instance, exist among the status between individual team members, the supervisors rating their subordinates with high scores to make their team look better, or individual team members that provide social desirable answers to critical questions because they are afraid that their supervisor will find out when they provide a negative rating. For future research, it is therefore recommended to limit the group biases as much as possible. This could be by executing the same research on an individual level only, not on multilevel.
This study is also limited in the use of the concept multiple team membership. In my study, I chose to look at the number of teams that participants are a member of. Although this is suggested by research as a proper measurement for multiple team membership (Chang, 2014), I have not considered the variety of teams one is a member of. This difference in the measurement of teams could influence the results. When an individual participates in multiple similar teams, it may lead to this individual executing their tasks in the same way without much need for creativity. Thus, for future research on the relationship between multiple team membership and individual creativity I suggest looking at the variety in teams as well as the number of teams one is a member of.
In my research I have found a relationship between creative requirement and boundary spanning. This may suggest that the need for creativity motivates people to engage in boundary spanning behavior. This finding provides an interesting avenue for future research in understanding the need for one to engage in boundary spanning behavior. Future research could test whether the relationship between multiple team membership and boundary spanning can be explained by creative requirement.
CONCLUSION
Explanations for this lack of confirmation can be found in the number of participants, intergroup biases, model building and concept usage issues that may have had an influence on the results of this study. For future researchers it will therefore be important to keep in mind that these influences may have a larger impact on this study than expected. In addition, this study found an interesting relationship between creative requirement and boundary spanning that provides avenue for future research.
Nevertheless, this study contributes to the combined fields of academic literature on multiple team membership and boundary spanning, investigating mechanisms that could explain the relationships between these fields. Also, managers and individuals can learn to understand the possible effects of information sharing and role overload on the work floor.
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