The Main Drivers of Ambidextrous Innovation: A Meta-Analysis on the Team-Level

The Main Drivers of Ambidextrous

Innovation:

A Meta-Analysis on the Team-Level

G.J. (Geert-Jan) Kollenstart

Student number 1771698 Jaltadaheerd 129, 9737HH Groningen, The Netherlands

06 – 13777913

geertjankollenstart@gmail.com

Assessor: dr. J.D. (Hans) van der Bij Co-assessor: dr. C. (Cees) Reezigt

University of Groningen Faculty of Economics and Business

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The Main Drivers of Ambidextrous Innovation:

A Meta-Analysis on the Team-Level

Abstract

Ambidexterity has proven to be a positive contributor to the performance of organisations (Holmqvist, 2004). Ambidexterity is known, in organizational science, as the “ability of a firm to simultaneously explore and exploit” (O’Reilly & Tushman, 2008). This phenomenon has received considerable attention at the organizational level, however, the team level is often neglected. There are studies focussing on the team level contributors to ambidexterity, albeit fragmented and diffused. The aim of this research is draw an integrated picture of what factors lead to ambidexterity on the team level. A meta-analysis, based on the meta-analysis performed by Hülsheger et al. 2009, is conducted to examine the success factors for ambidexterity. A study-pool of 30 original independent studies have been used to find 15 meta-factors which were expected to influence ambidexterity at the team level. The results of this research lead to four variables contributing to ambidexterity at the team level; task interdependence, the availability of resources, autonomy and flexibility. A moderator analysis has been performed for heterogeneous variables, proving that the type of team influences the relation between the meta-factors and focal constructs.

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Inhoudsopgave

Data Collection and Methodology ... 14

Select Studies as Input for the Analysis ... 15

Protocol for Meta-analysis ... 16

Analysis & Results ... 17

Exploration ... 17 Exploitation ... 18 Moderator analysis ... 19 Discussion ... 21 Limitations ... 24 Conclusion ... 25 References ... 26 Appendix ... 33

1. Pool of articles used for the meta-analysis ... 33

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Introduction

The notion of ambidexterity as a concept is widely adopted recently. A broad definition of ambidexterity, as given by Rothaermel & Alexandre (2009), is “an individual’s ability to use both hands with equal ease”. In the organizational science ambidexterity is defined more specifically. O’Reilly & Tushman (2008) stated that organizational ambidexterity is a firm’s ability to simultaneously explore and exploit. Scholars have proven that ambidexterity leads to sustainable competitive advantage (Crossan et al. 1999; Holmqvist, 2004) contributing to the survival of any firm (Barney, 1991). However, research on ambidexterity mainly focuses on the firm level. Little research has been conducted on the team level of ambidexterity, resulting in unclear and ambiguous variables and antecedents.

The team level is useful and interesting as a large portion of innovations are developed at the team level, instead of the firm- or individual level (Brown & Eisenhardt, 1995).

Research on organizational ambidexterity started with the contribution of Duncan (1976). March (1991), thereafter, developed a definition of ambidexterity which has become the leading standard in this field. He stated that ambidexterity is achieved by balancing exploration and exploitation, allowing an organization to be creative and adaptable, while at the same time being traditional and relying on proven methods. Exploration is associated with organic structures, loosely coupled systems, path breaking, creativity, autonomy, chaos, and adaptability. Exploitation, in contrast, is associated with mechanistic structures, tightly coupled systems, path dependence, routines, control and bureaucracy, and stable markets and technologies (Ancona et al. 2001; Brown and Eisenhardt 1998; Lewin et al. 1999). Academics have shown that the organizational designs built to pursue exploration are decentralized and organic (flat structure), while those that are built to pursue exploitation are centralized and mechanistic (hierarchical structure) (Burns and Stalker, 1961; Thompson, 1967; Duncan, 1976; O’Reilly and Tushman, 2008).

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behavioral capacity to simultaneously demonstrate alignment and adaptability across an entire business unit (Gibson & Birkinshaw, 2004). This strategy is also described and recognized as parallel structures (Gibson & Birkinshaw, 2004). With parallel structures, the explorative and exploitative activities take place in the same unit, rather than separated.

Over the past years, ambidexterity on the firm level has been extensively researched and tested. Research, however, on team level, is more scarce and fragmented. This fragmentation leads us to the objective of this study, which is to unite information concerning ambidexterity on the team level. The central question is stated as follows:

“What are the (most important) variables at the team level leading to ambidexterity.”

To answer this question, the present and available literature has to be combined and analysed to draw new insights regarding ambidexterity on the team level.

Due to the fragmented information, the dependent variables are named differently across papers, although covering the same subject. For example, scholars discuss about exploration versus exploitation (Jansen et al, 2009), while others name this radical versus incremental innovation (Andriopoulos & Lewis, 2009). In the remainder of this research, these variables are labelled exploration and exploitation.

As mentioned above, this research tries to find new insights on existing literature and knowledge. The meta-analysis is chosen to integrate the fragmented literature, due to its ability to analyze data by statistical techniques. Meta-analysis applies statistical procedures that are specifically designed to integrate the results of a set of primary empirical studies.

(Song, Podoynitsyna, van der Bij, and Halman, 2008). The results of these primary empirical

studies, however, have proven to be inconsistent, as shown by Hülsheger, Anderson & Santiago (2009). Besides, the procedure for the meta-analysis compensates for quality differences by correcting for different artefacts and sample sizes (Hunter and Schmidt, 1990, 2004), providing for a more reliable and comprehensive picture of the phenomenon. Therefore, the method of analysis used in this study is the meta-analysis.

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Starting point of this research is the meta-analysis performed by Hülsheger, Anderson and Santiago (2009). Although this is the starting point, it is not a one-on-one blueprint. Several choices Hülsheger et al. (2009) made, are not in line with this research. For example Hülsheger et al. (2009) used, alongside the team level, business units and individual innovation. For this research, only the team level is seen as relevant. Secondly, Hülsheger et al. (2009) explores innovation as a whole, rather than separating it into exploration and exploitation. This research tries to find out the differences between exploration and exploitation. Separation creates a more in depth view between differences and uncovers variables leading to ambidexterity. Furthermore, even though their research is about innovation, there is a lack of literature based on pure innovation. For instance from the journal of product innovation management. Finally, Hülsheger et al. (2009) their research encompasses three decades of data from up to 2008. Data consisting literature based on pure innovation and data past 2008 will be incorporated to create a more complete picture. Hülsheger et al. (2009) found that the team process variables; support for innovation, vision, task orientation, and external communication displayed the strongest relationship with creativity and innovation. This research tries to discover whether or not these variables hold for exploration and/or exploitation and possibly reveal other variables affecting ambidexterity on the team level.

In the remainder of this report, the pool of meta-factors will be discussed first, thereafter, the method and protocol for the meta-analysis will be elaborated. Then the analysis and results will be displayed. The results will be discussed subsequently incorporating the limitations and paths for future research.

Literature review

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Ex-ante meta-factors

Team size refers to the number of employees participating in a team. In most researches team

size is defined as a control variable. Here, however, team size is treated as an independent variable. The larger the team, the more difficult for team members to interact with other team members given the dramatic increase of (possible) individual links between team members as team size grows (Schulze & Hoegl, 2006). This argument especially holds for exploitation. The stream of thought on exploration is that more input in the team, through the team size, will increase the creativity of the team through a wide variety of insights (Hülsheger et al. 2009).

Hypothesis 1: Team size positively influences (1a) exploration, (1b) but negatively influences exploitation

Team longevity is described as the time a team is together. Research has indicated that the

longer people work together in teams, the less innovative they become over time. Team members become more homogeneous, and less inclined to be critical and to challenge the status quo (West & Anderson, 1996). This phenomenon is known as groupthink. Groupthink is detrimental for exploratory tasks in the team. Conversely, the longer a team remains in the same composition, the more efficient and effective one works within the team, leaving the whole team to be more efficient and effective. Creating certain processes, procedures and interpersonal knowledge takes time to develop (Yeh, Chu & Lue, 2005). These factors foster the efficiency, which in turn leads to increased exploitation.

Hypothesis 2: Team longevity negatively influences (2a) exploration, (2b) but positively influences exploitation

Job relevant diversity refers to the employee-specific abilities and knowledge bases which

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O’Reilly, 1998). Therefore, teams with high job relevant diversity should perform better on tasks that benefit from multiple perspectives than homogeneously staffed teams. Therefore, job relevant diversity is positively related to exploration. Contrariwise, exploitation requires more homogeneous teams, which promote cohesiveness, commitment, and member satisfaction (Tsui, Egan, & O'Reilly, 1992). This entails that job relevant diversity is expected to be counterproductive in the case of pursuing exploitation.

Hypothesis 3: Job relevant diversity positively influences (3a) exploration, (3b), but negatively influences exploitation

Task interdependence refers to the extent to which team members are dependent on one

another to carry out their tasks and perform effectively (Hülsheger et al. 2009). Exploration is positively influenced by a higher degree of task interdependence. Interdependence between team members enables the exchange of ideas, discussion of divergent viewpoints, and integration and evaluation to create high-quality products or suggest innovative procedures (Hülsheger et al. 2009). Logical reasoning would lead one to believe that this dependency is not suitable for exploitative actions. These actions require quite stable environments in which a team member could work effectively. For the sake of exploitation, no new ideas and viewpoints are needed. Therefore, task interdependence would be counterproductive for the exploitative actions.

Hypothesis 4: Task interdependence positively influences (4a) exploration, (4b) but negatively influences exploitation.

Goal interdependence refers to the extent to which team members’ goals and rewards are

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Subsequent research has proven that cooperative goals, e.g. goal interdependence, promote reflexivity which in turn results in team innovation. (Tjosvold, Tang & West, 2004). Team innovation is defined by Tjosvold in terms of effectiveness of the team, through the eyes of subsequent external users of the teams’ output. This definition can be rephrased to exploitation. Therefore, goal interdependence will contribute to a higher degree of exploitation. In short, both exploration and exploitation benefit from a higher degree of goal interdependence, which indicates that this meta-factor could lead to ambidexterity.

Hypothesis 5: Goal interdependence positively influences (5a) exploration, and (5b) exploitation.

Resources refer to the availability of resources allocated to the team to perform the tasks for

which it is responsible for. Resources are synonymous for organizational slack, as defined by Geiger & Makri (2006); “slack can be considered as resources available to an organization which are in excess of the minimum necessary to produce a given level of organizational output”. Amabile, Conti, Coon, Lazenby & Herron (1996) found that having sufficient resources positively contributed positively to the exploration of the team. In other words, having an overcapacity of resources fosters exploration, as opportunities arise with extra resources. There is no stress or conflict for the resources, which provides for a peaceful environment for exploration to flourish. For exploitation, Voss, Sirdeshmukh & Voss (2008) found that slack resources also contribute positively to exploitation. As described above, internal peace will be improved when resources are available. Due to internal peace and no rivalry, every employee can focus on their task(s), which fosters the exploitative actions performed in the team. Therefore, this factor should, according to the present literature, be one of the factors actually fostering ambidexterity.

Hypothesis 6: The amount of resources available positively influences (6a) exploration, and (6b) exploitation

Ex-post meta-factors

Autonomy refers to the degree of freedom personnel have in day to day operating decisions

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e.g. exploration. Individuals produce more creative work when they perceive themselves to have a choice in how to accomplish the tasks that they are given (e.g., Amabile & Gitomer, 1984). Besides facilitating creativity in solving problems, autonomy enhances team learning, which is needed to be creative in the future. This remark especially holds for uncertain environments (Imai et al., 1985; Thamhain 1990; Moorman and Miner, 1998). For exploitation, Tatikonda & Rosenthal (2000) found that projects having a greater degree of project management autonomy have higher levels of project execution success. This success is measured as the degree to which an innovation can be exploited (Time to market, unit cost & technical performance). Firms should create a clear structure at the project level, and autonomy at a working level within the project. This creates the optimal structure to achieve project execution success. Therefore, autonomy is believed to have a positive influence on exploitation as long as there is a structure functioning as boundary for the team. This results in the assumption that this meta-factor fosters ambidexterity

Hypothesis 7: Autonomy positively influences (7a) exploration, and (7b) exploitation

Managerial support for innovation refers to the degree of support, facilitating actions and

empowerment by the management of the team. Supervisory support encourages creativity by means of fair and supportive evaluation. Under these circumstances, people are less likely to experience the fear of negative criticism that can undermine the intrinsic motivation necessary for creativity (Amabile et al , 1996). Managerial support is a method used to give employees the feeling that they are respected, encouraged and assisted in their work. It creates the right atmosphere for employees to excel in creative performance, therefore positively contributing to exploration. Managerial support for innovation, as a systematic innovation process guided by the manager, leads to a far more effective exploitation of innovations (Sheu & Lee, 2011). Consequently, guidance and a straightforward process result in a higher degree of exploitation. As a side note, the managerial support can provide the structure of boundaries of the firm, needed for autonomy to be effective for exploitation. Assumed is, that managerial support for innovation fosters ambidexterity on the team level.

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Participative safety refers to enabling team members to participate in decision making and

sharing ideas without fear of ridicule or ostracism (Pirola-Merlo & Mann, 2004). Hence, this factor consists of two parts. Namely, decision making and the feeling of being safe within the team. The latter is focused on the team, whereas the former is focused on the management of the team. Direct management involvement eventually decreases as responsibility for the project is devolved to the local level (employees themselves) (Caldwell, 2003). If this safety is high, members of teams are more inclined to come up with new ideas, due to a lack of concern about negative judgment by others (West & Farr, 1990). Logical thinking suggests that participative safety is not as positively influencing exploitation, as it does influence exploration. In exploitative tasks, efficiency and effectiveness is crucial. This is standardized and more straight-forward than the exploration part. Standardized tasks do not require a lot of safety, as they are well defined and repetitive tasks. However, there is no downside regarding participative safety on exploitation.

Hypothesis 9: Participative safety positively influences (9a) exploration, but does not negatively, nor positively influence (9b) exploitation

Vision refers to a shared commitment to clear objectives (Pirola-merlo & Mann, 2004). It

represents a high order goal and motivation to get the best out of the team. If the vision is well articulated and spread throughout the team, the organizational goals and overall directions will become clear to team members. Team members perceive those as attainable, and feel committed to these goals. Clearly stated goals help team members to channel their efforts; they may give their work meaning, and in turn, they may motivate individuals toward enhancing their innovative performance. (Hülsheger et al, 2009). These goals guide both explorative and exploitative tasks within the team. Albeit, that for explorative tasks a more abstract vision is needed to guide in creative processes. Whereas exploitative tasks require more concrete objectives and goals function as a common thread to achieve a certain level of performance. Under the right circumstances, vision is hypothesized to lead to a higher degree of ambidexterity.

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Internal communication refers to the degree of communication between team members.

Communication is defined as the (task-) specific interaction within a project. According to Hülsheger et al. (2009), communication is a necessary basis for mutual monitoring, back-up behavior, and the provision of feedback. Consequently, communication is vital both for exploration and exploitation. The logic behind this reasoning is that exploration needs the communication of team members to come to creative insights. Exploitation, on the other hand, needs the feedback and monitoring in order to create an environment suitable for ending up with high efficiency and effectiveness, hence exploitation. Internal communication is thus expected to contribute to the ambidexterity on the team level.

Hypothesis 11: Internal communication positively influences (11a) exploration, and (11b) exploitation

External communication refers to the degree of interaction between team members and

non-team members. There are two types of ties a non-team member can have with non-non-team members; strong ties and weak ties. Strong ties represent frequent contacts that almost invariably have affective, often friendly, overtones and may include reciprocal favours. Weak ties represent infrequent contacts that, because they are episodic, do not necessarily have affective content (Nelson, 1989). Strong ties provide more in depth knowledge, whereas weak ties provide more diverse and distant knowledge (Conway & Steward, 2009). Therefore it is logical to conclude that exploration benefits from weak ties, because it can get new insights from the external party. In contrast, exploitation benefits from strong ties, since it deepens the understanding of the team member regarding a particular topic. Thus, if one looks at external communication in terms of strong and weak ties, external communication provides for a higher degree of exploration as well as exploitation. External communication is thus expected to contribute to the ambidexterity on the team level.

Hypothesis 12: External communication positively influences (12a) exploration, and (12b) exploitation

Task conflict refers to the degree of task-specific conflict within a team. Task-specific

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conflict on team creativity, and concluded that task conflict is negatively influencing team creativity. Conversely, according to De Dreu & Weingart (2003), task conflict can increase team members’ tendency to scrutinize task issues and to think more deeply, which can foster learning and the development of new and creative insights, leading the team to become more creative. This illustrates that there is no unequivocal stream of thought regarding task conflict. For this hypothesis the stream of thought of De Dreu & Weingart (2003) is followed, expecting task conflict to have a positive impact on exploration. Using this stream of thought, the expectation regarding exploitation would be that it would suffer under these workings. Here exploitation would be negatively influenced by task conflict as exploitative tasks need quiet and safe environments to be performed.

Hypothesis 13: Task conflict positively influences (13a) exploration, but negatively influences (13b) exploitation

Relationship conflict describes social and emotional conflicts stemming from interpersonal

disagreements (Jehn, 1995). This kind of conflict causes a lot more psychological stress than task conflict does. Psychological stress is always detrimental for the performance of the team, since employees are less motivated and less focused on the task as they are distracted by the conflict. Relationship conflict, therefore, hinders innovativeness of the team as a whole, due to, for example, lower internal communication or not sharing the vision is amongst team members. The distinction between exploration and exploitation here does not matter a lot since, for both, tasks relationship conflict is bad.

Hypothesis 14: Relation conflict negatively influences (14a) exploration, and (14b) exploitation

Flexibility refers to the willingness of team members to try out new procedures and to

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is not necessary for performing exploitative tasks, nor is it detrimental. Therefore, flexibility does not harm nor improves exploitative tasks

Hypothesis 15: Flexibility positively influences (15a) exploration, but does not negatively, nor positively influence (15b) exploitation

The expectation prior to the analysis is that goal interdependence, resources, autonomy, managerial support, vision, internal- and external communications, contribute to the ability of the team to pursue ambidexterity.

Data Collection and Methodology

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sometimes a real distinction, e.g. between exploration/exploitation or creation /implementation, is made in the study. Another possibility is that the construct of innovation stands for exploration, finding new ways, or exploitation, making ways more efficient and effective. This distinction is crucial in identifying ambidexterity at the team level, because it reveals the difference between the two focal constructs. If there is no distinction within innovation, this could lead to a false and distorted picture, which is undesirable. Therefore, the first selection criterion is whether or not this distinction is present. The second selection criterion is the unit of analysis. Present literature focuses primarily on organizational ambidexterity or the individual characteristics of employees (Andriopoulos & Lewis (2009), Jansen, Tempelaar, van den Bosch & Volberda (2008), Taylor & Helfat (2009)). Hülsheger et al. (2009) used individuals, teams and business units as level of analysis. This research is targeted on the team level only, filtering out individual- and business unit level of analysis. The final criterion for the selection of studies is the presence of a correlation matrix. Meta-analysis uses the correlation between dependent and independent variables as core input (Song et. al., 2008). The correlations together, after correcting for differences, draw a picture how a particular variable is related towards the focal construct of exploration and/or exploitation.

Select Studies as Input for the Analysis

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grouped together and labelled as one meta-factor, e.g. team size, autonomy, support for innovation etc. In the end, 15 meta-factors were identified, which represent the combination of variables abstracted from the final study-pool.

Protocol for Meta-analysis

The protocol used for the meta-analysis is designed by Hunter and Schmidt (1990). Again, there are two possible approaches. The first draws on Pearson correlations between the meta-factor and the focal construct. The second draws on the regression coefficient between the meta-factor and the dependent variable. Since the third selection criterion of the studies concerned the availability of the correlation matrix, the Pearson correlations is the approach followed in this study. All of the required input is available in every study selected. Moreover,, this approach is strongly encouraged by Hunter and Schmidt, as correlations between two variables are independent of other variables in the model (Song et al., 2008). Another important consideration for the Pearson correlation-approach is the use of random effects models instead of fixed effects models (Hunter and Schmidt, 2004). The difference lies in the fact that fixed effect models assume that the same true correlation value between meta-factor and dependent variable underlies all studies in the meta-analysis, while random effects models allow for the possibility that population parameters vary from study to study (Song et al.,2008). Assuming that differences exist between the final sample study-pool, the random effect models are most suitable to create higher levels of validity and reliability. Once these choices have been made, the second step is to correct for sample size differences, and measurement errors.

First, to correct sampling error, the sample correlation was weighted by sample size (Hunter and Schmidt, 1990, 2004). The formula for the weighted average of correlations corrected for sample size is

𝑟

� =

∑

𝑁

𝑟

𝑖=1

∑

𝑁

where Ni stands for the sample size of the primary study i, and roi for the observed correlation

of the primary study i.

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and the square root of the reliability of performance. The formula for real population correlation is

𝜌 =

𝑟

𝑟

������� ∙ �𝑅�������𝑦𝑦

where Ā is the compound reliability correction factor; √Rxx is the average of the square roots

of reliabilities of independent variables composing a given meta-factor; and √Ryy is the

average of the square roots of reliabilities of dependent variables composing a given meta-factor.

After these corrections, the next step is to analyse whether or not the meta-factors are success factors in the pursuit of ambidexterity within the team. The analysis has two possible outcomes; homogeneous and heterogeneous meta-factors. This is tested on the basis of the variance-test. Total variance is the sum of the real variance of the population correlation, variance due to artefacts and variance due to sampling error. The meta-factor is assumed to be homogeneous if the real variance is no more than 35 percent of the total variance. It is required to be homogeneous, and significant in order to be a success factor. An overview of the variance-calculations can be found in Appendix 2.

Significance, thereafter, is measured by the confidence interval. The confidence interval is not explaining the variability of individual correlations, but it informs us about the accuracy of the mean corrected correlation and can be used as a significance test (Hülsheger et al. 2009). If the confidence interval does not include zero, the mean corrected correlation is considered to be significant.

For heterogeneous meta-factors a moderator analysis was conducted. Basically, the composition of the meta-factors is altered in order to create different output, preferably homogeneous meta-factors. This alteration consists of splitting up certain meta-factors into variables regarding research teams and operating teams within the different meta-factors, and run the meta-analysis again. The analysis will show whether or not this moderator analysis was fruitful or not.

Analysis & Results

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There are two hypotheses which could not be tested, goal interdependence (H5a) and external communication (H12a), due to a lack of available variables in the literature-pool. Table 1 indicates that the meta-factors task interdependence, resources, autonomy, participative safety, relation conflict and flexibility all have a real variance as the percentage of total variance which is lower than 35%. These meta-factors show to be homogeneous. To be a success factor, these homogeneous results also need to be significant. This is measured by the confidence interval. According to the determination of significance through the confidence interval, task interdependence, resources, autonomy, participative safety, relation conflict and flexibility turn out to be significant. Therefore hypotheses H4a, H6a, H7a , H9a, H14a and H15a are all supported

a Real variance higher than 35% means that the meta-factor has moderators. b See Table 3 for suggested moderators*.

Exploitation

Second, according to Table 2, team size, team longevity, task interdependence, goal interdependence, resources, autonomy, managerial support for innovation, internal communication, external communication and flexibility display homogeneous results.

Table 1 Exploration

Hypothesis Meta-factor Total N K ρ 95% confidence interval Real variance as percentage of total variance (%)a Moderators b Ex-ante establishment

1a Team size 412 5 0,30 0,08; 0,45 45 Yes*

2a Team longevity 460 6 -0,03 -0,42; 0,37 76 Yes*

3a 4a 6a Ex-Post establishment

Job relevant diversity Task interdependence Resources 591 51 343 7 3 4 0,09 0,37 0,43 -,60; 0,75 0,27;0,52 91 0 33 Yes 7a Autonomy 93 2 0,39 0

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As shown by the confidence interval, all results, apart from team longevity, prove to be significant, since the confidence interval only shows an interval containing zero for team longevity.

All together, team size, task interdependence, goal interdependence, resources, autonomy, managerial support for innovation, internal communication, external communication and flexibility are success factors. This provides support for hypotheses H1B, H4B, H5B, H6B, H7B, H8B, H11B, H12B, and H15B

a Real variance higher than 35% means that the meta-factor has moderators. bSee Table 4 for suggested moderators* .

Moderator analysis

In the moderator analysis, the focus rests on altering the grouping of variables, in order to create an outcome which is homogeneous. For exploration, team size, managerial support for innovation and team longevity were grouped differently. Furthermore, for exploitation, job relevant diversity and participative safety were re-examined. The approach of altering the grouping of the variables was to find out whether the variables in their original studies

Table 2 Exploitation

Hypothesis Meta-factor Total N K ρ 95% confidence interval Real variance as % of total variance a Moderators b Ex-ante establishment 1b Team size 971 10 0,22 0,16; 0,41 31 2b Team longevity 559 6 0,06 -0,01; 0,12 10 3b 4b 5b 6b Ex-post establishment

Job relevant diversity Task interdependence Goal interdependence Resources 410 51 69 264 7 3 3 3 0,16 0,20 0,15 0,26 -0,20; 0,50 67 0 0 0 Yes* 7b Autonomy 288 4 0,25 0,17; 0,31 11

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examined research teams or operating teams. For most meta-factors it was possible to make this distinction, except for managerial support for innovation (exploration). This distinction revealed other outcomes than the first meta-analysis.

Table 3 Moderator Analysis Exploration

Metafactor Separation Total N K ρ 95% confidence interval Real variance as % of total variance Success- factor?

Team Size Operating team Research team 77 335 2 3 -0,02 0,35 X 0,15; 0,48 0 44 Yes No Managerial support for Innovation Service firm Production firm 130 278 3 4 0,54 0,38 0,21; 0,72 X 53 0 No Yes Team longevity Operating team Research team 338 122 3 3 0,10 -0,38 -0,08; 0,27 X 50 0 No Yes

K= number of focal articles

Table 4 Moderator Analysis Exploitation

K= number of focal articles

The moderator analysis illustrates that an increase in team size for operating teams pursuing exploration has a homogeneous significant negative effect (ρ = -0,015). Managerial support for innovation seems to be a significant homogeneous contributor to exploration in production firms, this leads more to a relation which was expected of the analysis. Team longevity results to be a homogeneous significant negative predictor for exploration within research teams. The moderator analysis provides also insight in the relation between job relevant diversity and exploitation. It shows that job relevant diversity is a homogeneous significant contributor to exploitation for research

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teams, while job relevant diversity is homogeneous and significantly hinders exploitation. In the end, the moderator analysis shows that differences exist between production and research teams regarding exploration and exploitation. Therefore, the type of team has to be considered in the relationship between the meta-factors and the focal constructs.

Discussion

Table 5 displays an overview of the key findings of this meta-analytic summary. Several points stand out. First, there are four variables(*), task interdependence, resources, autonomy and flexibility, which can be considered enabling ambidexterity, since they show

homogeneous significant outcomes for both exploration (HA’s) and exploitation (HB’s). Being homogeneous, significant, and showing a positive ρ-value, indicates that

implementing each one of the four variables into the team will lead to a higher ability of behaving ambidextrous. Looking at these variables individually, none of them contradicts, nor is in conflict with one another. Therefore, these four variables could be implemented together, in order to achieve a higher degree of ambidexterity. However, this study does not consider the interconnection of the variables. A stream of further research could investigate the interplay between the four variables. Theoretically, these would not conflict with each other, but could it be that they enhance each other? And if they do, what would be the best combination of variables to produce ambidexterity within the team? Another possibility would be that variables, which turned out to be heterogeneous, influence other variables, which appear homogeneous which will lead to the ambidexterity after all. Research has proven a particular interplay between task- and goal interdependence (De Dreu & West, 2001; De Dreu, 2002), as well as the interplay between managerial support for innovation and autonomy (Tatikonda & Rosenthal, 2000 ; Sheu & Lee, 2011). Therefore, the interplay between variables could be a very interesting starting point for future research.

Second, there are three meta-variables, goal interdependence, external communication and relationship conflict, of which there were not enough variables captured to conduct the analysis for either exploration or exploitation. Further research for these meta-factors is desired. Because for all of these meta-factors, the variable which has been measured, turned out to be homogeneous and significant. Therefore, the missing variables, if they turn out to be homogeneous and significant as well, the meta-factors are proven to contribute to the

22 Table 5 Key Findings

Meta-factor is inferred homogeneous if Varreal < 35%-Vartotal..

Significance is inferred if confidence intervals do not include zero Variable Hyp. Hypothesized direction of relationship Mean overall corrected Correlation Homogeneous variable Significance of ρ Ex ante

Team size 1A + 0,30 No Yes

1B -/- 0,22 No Yes

Team longevity 2A -/- -0,03 No No

2B + 0,06 Yes No

Job relevant diversity 3A + 0,09 No No

3B -/- 0,16 No No

Task interdependence* 4A + 0,37 Yes Yes

4B -/- 0,20 Yes Yes

Goal interdependence 5A + X X X

5B + 0,15 Yes Yes

Resources* 6A + 0,43 Yes Yes

6B + 0,26 Yes Yes

Ex post

Autonomy* 7A + 0,39 Yes Yes

7B + 0,25 Yes Yes

Managerial support for innovation 8A 8B + + 0,34 0,32 No Yes Yes Yes

Participative safety 9A + 0,40 Yes Yes

9B Indifferent 0,27 No No

Vision 10A + 0,33 No No

10B + 0,22 No Yes

Internal communication 11A + 0,26 No No

11B + 0,26 Yes Yes

External communication 12A + X X X

12B + 0,31 Yes Yes

Task conflict 13A + -0,15 No No

13B -/- 0,21 No No

Relation conflict 14A

14B -/- -/- -0,39 X Yes X Yes X

Flexibility* 15A + 0,64 Yes Yes

Moderating influences

15B Indifferent 0,65

Type of team

23

Hülsheger et al. (2009) found that goal interdependence and external communication are positive contributors to innovation in the team. This could be considered as an indication that these meta-factors could be contributing to ambidexterity. But since Hülsheger et al. (2009) did not differentiate between exploration and exploitation, future research should provide evidence whether or not they actually contribute to ambidexterity. Third, the ρ-values of the variables show all higher values for exploration than they do for exploitation, except for flexibility. It means that exploration benefits more from these variables than exploitation does. As stated in the introduction, the aim of this research was to explore what factors contribute to ambidexterity. The degree of influence does not matter for this research. However, implications for this pattern could be that focus has to be on exploration, since there is the most benefit to gain. Or research has to focus more on variables affecting exploitation more thoroughly. Although the degree of influence does not matter for this research, the results of this analysis provide input for future research.

24

Furthermore, the result of this study enables a comparison between ambidexterity at the organizational level and at the team level. Previous research regarding ambidexterity on the organizational level focused on the difference between structural and contextual ambidexterity. Ambidexterity takes place either separated, or parallel (Raisch & Birkinshaw, 2008; Gibson & Birkinshaw, 2004). Given the fact that the variables found here contribute to both exploration and exploitation, ambidexterity on the team level could be labelled parallel. Where the organisation has the ability to allocate particular tasks to particular teams (separation), a team has no such prerogative and should pursue both (parallel). Research on this topic suggest there is a “central tension”, namely static versus dynamic perspectives on ambidexterity (Raisch, Birkinshaw, Probst & Tushman, 2009). This tension is in line with the separation and parallelization question. They state that modern contingency theory shows that alignment is a dynamic process rather than a question of static configurations. Thereby arguing that ambidexterity is a dynamic process, rather than a separated process. In the end, one could say that, in order to create ambidexterity on the organizational level, the organization has to chose a dynamic, e.g. parallel structure. Implications for this structure on the team level are that teams need to perform parallel as well. Therefore, the results of this study are very useful, since the suggested variables enable parallel processes within the team. Limitations

As with most research, this research has its limitations. First, this research only takes into

consideration the correlation between meta-factors and focal construct

25

Conclusion

26

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Appendix

1. Pool of articles used for the meta-analysis

Author(s) (Year) Title

Abbey, A., & Dickson, J. W. (1983) R&D work climate and innovation in semiconductors

Agrell, A., & Gustafson, R. (1994) The Team Climate Inventory (TCI) and group innovation: A psychometric test on a Swedish sample of work groups

Amabile, T. M., Conti, R., Coon, H., Lazenby, J., & Herron, M. (1996)

Assessing the work environment for creativity

Ancona, D. G., & Caldwell, D. F. (1992b) Demography and design: Predictors of new product team performance Axtell, C. M., Holman, D. J., Unsworth, K. L.,

Wall, T. D., Waterson, P. E., & Harrington, E. (2000)

Shopfloor innovation: Facilitating the suggestion and implementation of ideas

Axtell, C. M., Holman, D. J., & Wall, T. D. (2006) Promoting innovation: A change study

Cady, S. H., & Valentine, J. (1999) Team innovation and perceptions of consideration: What difference does diversity make? Chen, M.-H. (2006) Understanding the benefits and detriments of conflict on team creativity process De Dreu, C. K. W. (2002) Team innovation and team effectiveness: The importance of minority dissent and

reflexivity

De Dreu, C. K. W., & West, M. A. (2001) Minority dissent and team innovation: The importance of participation in decision making

Drach-Zahavy, A., & Somech, A. (2001) Understanding team innovation: The role of team processes and structures. Fay, D., Borrill, C. S., Amir, Z., Haward, R., &

West, M. A. (2006)

Getting the most out of multidisciplinary teams: A multi-sample study of team innovation in health care

Hoegl, M., & Gemuenden, H. G. (2001) Teamwork quality and the success of innovative projects: A theoretical concept and empirical evidence

Keller, R. T. (2001) Cross-functional project groups in research and new product development: Diversity, communications, job stress, and outcomes

Kratzer, J., Leenders, R. T. A. J., & Van Engelen, J. M. L. (2004)

Stimulating the potential: Creative performance and communication in innovation teams Kratzer, J., Leenders, R. T. A. J., & Van Engelen, J.

M. L. (2006)

Team polarity and creative performance in innovation teams

Kurtzberg, T. R., & Mueller, J. S. (2005) The influence of daily conflict on perceptions of creativity: A longitudinal study Pearce, C. L., & Ensley, M. D. (2004) A reciprocal and longitudinal investigation of the innovation process: The central role of

shared vision in product and process innovation teams (PPITs)

Pirola-Merlo, A., & Mann, L. (2004) The relationship between individual creativity and team creativity: Aggregating across people and time

Politis, J. D. (2005) Dispersed leadership predictor of the work environment for creativity and productivity Rickards, T., Chen, M. H., & Moger, S. (2001) Development of a self-report instrument for exploring team factor, leadership and

performance relationships

Schulze, A., & Hoegl, M. (2006) Knowledge Creation in New Product Development Projects

Sethi, R., Smith, D. C., & Park, C. W. (2001) Cross-functional product development teams, creativity, and the innovativeness of new consumer products

Somech, A. (2006) The effects of leadership style and team process on performance and innovation in functionally heterogeneous teams

Tiwana, A., & McLean, E. R. (2005) Expertise integration and creativity in information systems development Tsai, W. P., & Ghoshal, S. (1998) Social capital and value creation: The role of intrafirm networks Vera, D., & Crossan, M. (2005) Improvisation and innovative performance in teams

West, M. A., & Anderson, N. R. (1996) Innovation in top management teams West, M. A., Borrill, C. S., Dawson, J. F.,

Brodbeck, F. C., Shapiro, D. A., & Haward, B. (2003)

Leadership clarity and team innovation inhealth care

Liu, L., & Leitner, D., (2012). Simultaneous Pursuit of Innovation and Efficiency in Complex Engineering Projects—A Study of the Antecedents and Impacts of Ambidexterity in Project Teams.

Visser, De, M.,Weerd-Nederhof, De, P., Faems, D., Song, M., Looy, Van, B., & Visscher, K., (2010).

34

2. Formulas for variance calculation

Vartotal = Varreal + Varartif + Vars.e.

where

Vartotal =total variance of observed correlations from primary studies;

Varreal = real variance of the population correlation;

Varartif = variance due to artifacts (reliabilities);

Vars.e = variance due to sampling error.

Varreal =Vartotal - Varartif - Vars.e.

95% confidence interval of the real population correlation is 1.96�𝑉𝑎𝑟_{𝑟𝑒𝑎𝑙} Metafactor is heterogeneous (moderated) if 𝑉𝑎𝑟_{𝑟𝑒𝑎𝑙} > 35% ∙ 𝑉𝑎𝑟_{𝑡𝑜𝑡𝑎𝑙} 𝑣𝑎𝑟𝑡𝑜𝑡𝑎𝑙 = ∑ [𝑁𝑖�𝑟𝑜𝑜𝑖− 𝑟�����𝑜𝑜 2 ] 𝑛 𝑖=1 ∑𝑛𝑖=1𝑁𝑖 where

rooi = observed correlation of the primary study i;

𝑟𝑜𝑜

����= weighted average of the observed correlations of the primary studies, so that 𝑟𝑜𝑜 ���� =∑𝑛𝑖=1𝑁𝑖𝑟𝑜𝑜𝑖 ∑𝑛𝑖=1𝑁𝑖 𝑣𝑎𝑟𝑎𝑟𝑡𝑖𝑓 = 𝜌2 ∙ 𝐴̅2∙ 𝑉 = 𝑟�𝑎2∙ 𝑉 = 𝑟�𝑎2∙ �𝑉𝑎𝑟(�𝑅𝑥𝑥) �𝑅𝑥𝑥 + 𝑉𝑎𝑟(�𝑅𝑦𝑦) �𝑅𝑦𝑦 � 𝑣𝑎𝑟𝑠.𝑒.= �1 − 𝑅�����𝑜𝑜2� 2 𝑁� − 1 where