It’s All About Your Team!
An empirical research on team composition characteristics and team climate factors that influence the innovative performance of teams
Master Thesis MSc. Business Administration Track: Entrepreneurship & Innovation
By Romina Benfatto – 10260226 24th of June 2016
Supervisor Thesis (UvA):
Dhr. dr. Wietze van der Aa Supervisor Thesis XLFamily:
Dhr. Jaspar Roos
‘Successful innovation is not a single breakthrough. It is not a sprint.
It is not an event for the solo runner. Successful innovation is a team
sport, it’s a relay race.’
Statement of Originality
This document is written by me; Romina Benfatto. I declare that the text and work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it. I declare to take full responsibility for the contents of this document. The faculty of Economics and Business is responsible solely for the
supervision of completion of the work, not for the contents.
Acknowledgement
Over the last six months I have had the opportunity to dive deeper into team innovation and the related factors that play a sufficient role in this subject. On the one hand, I did so while reading and analyzing literature on team innovation, and thereby creating and conducting the current body of research on this topic. On the other hand, I learned more about team
innovation through practice by joining several innovation events, talking to different
organizations and seeing teams work in practice. The results of this journey are presented here in this study.
As gaining insights into teams of different organizations is quite challenging, I am grateful for all the help I have gotten to accomplish this research. Therefore, I first of all would like to thank my thesis supervisor Wietze van der Aa for all his support, energy and enthusiasm, but most of all for his valuable insights and input. Furthermore, I would like Jaspar Roos CIO and Innovator at XL family, along with all his colleagues of the XLFamily, for their enthusiasm about this research topic. Thank you for giving me the opportunity to join some of your innovative events, helping me to gather my data and providing me with lots interesting insights and contacts.
Abstract
This study focuses on the influential potential of team composition and team climate on the innovativeness of teams. In this study, the influence of team composition is researched via five individual (personality) diversity characteristics. Using existing constructs of previous literature a questionnaire was designed and completed by 128 individual members of teams, active or involved in the innovation field. The aim was to provide innovation managers a more clear understanding of possible influences of team composition characteristics and team climate factors on teams’ innovative performances. The results indicate that all five
characteristics of individual diversity; neuroticism, extraversion, openness to experience, agreeableness, and conscientiousness are positively related to team innovation. Team climate also proves to be a direct positive influence on team innovation and, in addition, team climate enhances the positive relation between the individual diversity characteristics and team innovation even further. A team-level based analysis shows that agreeableness and
conscientiousness are the most important characteristics for team innovation. As this study indicates that team composition and team climate are indeed related to each other and to the innovativeness of teams, it is advisable for managers of innovation teams to have a closer look into the composition and climate of their team(s). Innovation managers can use the insights of this study to orchestrate their teams based on individual characteristics and optimize the team climate in order to obtain the most innovative team outcomes.
‘’Some of the greatest innovations over the past century have sprung from the creative juices of individuals. But these are exceptions to the rule. The overwhelming majority of successful innovations come not from individuals striving heroically in a shed, but from team efforts orchestrated systematically by enterprises’’ – (‘’Innovating by numbers…’’, 2003, p.3).
1. Introduction
The business environment is changing continuously, underlining the importance for organizations to keep up with these changes by staying innovative (Crossan & Apaydin, 2010). According to West and Farr (1990), innovation can generally be defined as the introduction and application of processes, products or procedures new to the relevant unit of adoption within a group, organization or wider society, and that is intended to benefit the group, individual or wider society. Improvement or renewal of offerings forms a crucial aspect to secure the long-term survival, growth, and profitability of organizations (Jong, 2007). One way for organizations to enhance the ability to innovate is through their employees. Organizations can exploit the ideas generated by employees and use these as a foundation for new or improved products, work, and service processes (De Jong & Den Hartog, 2007).
The pressure from competition and the need for innovation has caused a change in the way work is organized from individual jobs to more team-based work structures. Varies researches underline the importance and effectiveness for organizations to encourage their employees to work in teams (De Dreu, 2002; Hülsheger, Anderson, & Salgado, 2009). More and more organizations create specialized innovation teams whose main focus is to generate and develop new ideas or renewal existing ones. Making use of teams increases the loyalty, creativity, and commitment of employees (Ragazzoni, Baiardi, Zotti, Anderson, & West, 2002). As a consequence, nowadays ideas are proposed and implemented by work teams within an organization (Hülsheger et al., 2009; Messmann & Mulder, 2012). This makes it, now more than ever, important for teams to be and stay innovative. Teams that generate effective innovations are able to adapt to the continuous, complex, and changing environment (De Dreu, 2002).
Teams consist of two or more individuals that work together towards a common goal by sharing their knowledge and expertise. It is important to note that it is the individual member that possesses knowledge and shares this knowledge, not the organization itself (Kogut & Zander, 1992). The innovative outcome of a team is therefore related to the collaboration among its members whose diverse experiences and knowledge will act
synergistically and determine the performance of the team (Kogut & Zander, 1992; De Dreu, 2002; Hülsheger et al., 2009). This knowledge has caused an increase in interest and
importance into the different variables, characteristics, and factors that may affect the innovative performance of teams (De Dreu, 2002; Hülsheger et al., 2009; Somech & Drach-Zahavy, 2013). The configuration of a team is thought to determine the probability of teams to come up with innovative, non-obvious, and valuable inventions (Wutchy, Jones, & Uzzi, 2007).
Looking at the literature, there is no shortage of work done examining the impact of different aspects and factors of team composition. However, the relative importance and amount of variance of these factors are strongly depended on the team situation, and most importantly, the desired outcome (Mathieu, Tannenbaum, Donsbach, & Alliger, 2014). In case of team composition, team diversity is a concept that gained importance in the academic field during the last years. This can be clarified by the fact that today’s business environment is characterized by an increase in globalization and organizations getting involved in
international markets, extending their business across different cultures and countries
(Richard, 2000). However, not only internal processes seem to influence team innovation, but also external processes like team climate are becoming more relevant (Somech & Drach-Zahavy, 2013). Getting a better understanding into the team composition factor team diversity and the external factor team climate is important for innovation managers. This way, they will be one step closer towards the creation of an optimal innovative team based on concrete research, since a solid overview and their relation to the outcome ‘innovativeness of teams’ is lacking (Bantel & Jackson, 1989; Harrison & Klein, 2007; Hülsheger et al., 2009; Somech & Drach-Zahavy, 2013).
The purpose of this study is to contribute to the existing body of research by
investigating the impact of several team-related factors on the innovativeness of teams. By sending out a survey to a large sample of team members this study attempts to identify which of the by theory adduced factors actually have an impact on innovative teams, and so tries to reveal patterns or similarities across teams. Creating a better understanding and a solid
overview of the influence of several team-related factors in relation to team innovation brings managers one step closer towards orchestrating and managing their teams more effectively. It is important to get more knowledge about the configuration of factors and how these lead to the innovative team outcomes. To do so, this research attempts to give clarification to the following central research question: To what extent does team composition and team climate influence the innovativeness of teams in organizations?
To get a clear understanding of the different constructs mentioned in the central research stated above, several sub-questions are set out:
- What is an innovative team? How can it be defined?
- What is team composition? And what characteristics play an important role in relation to team innovativeness?
- What is team climate? And what factors play an important role in relation to team innovativeness?
First, a theoretical overview is written to provide a clear understanding of all the
constructs and their relation to each other. Next, the methodology section is set out to show how the research is conducted and the subsequent outcomes are presented in the results section. Lastly, a discussion with points of improvement and managerial implications are provided and a conclusion of the found results in relation to the existing theory is given.
2. Literature review
2.1 Team innovation. What is team innovation? How can it be defined?
According to West and Wallace (1991), team innovation can be defined as: ‘The intentional introduction and application within a team of ideas, processes, products or procedures new to the team, designed to significantly benefit the individual, the team, the organization, or the wider society’. This demonstrates the fact that the introduction of new ideas to the team is intentional and that they are purposed to create benefits for different stakeholders. In order for a team to be innovative, the members must generate creative ideas and need to critically process those to either discard ideas that seem useless or implement ideas that seem promising (Somech & Drach-Zahavry, 2013). This makes innovative teams increasingly important due to the fact that contemporary organizations clarify that innovation is one of their core competences (Somech & Drach-Zahavry, 2013). Even though the research on organizational teams is growing, literature on the combination of teams and innovation is still quite scarce. It seems that the research did not yet touch upon the importance of
innovation teams for the performance of organizations, despite the fact that some authors showed that innovative teams can indeed facilitate change, benefit existing business, or create new ones (Crossan & Apaydin, 2010).
Now that the potential importance of innovative teams is clarified, it is essential to have a clear understanding of what the process of innovation within teams looks like. Previous research concluded that innovative behavior could be divided up into two different phases: idea generation and idea implementation (Kleysen & Street, 2001). Messmann and Mulder (2012) take this a step further by investigating innovative behavior on a team-level and looking at innovative team behavior as a whole process. The authors state that to improve the development of innovations, it is of great importance to understand the innovative work behavior of employees. Therefore, the authors have created an instrument to study the construct innovation more deeply by adding three more dimensions to the study of Kleysen and Street (2001). Innovative work behavior is divided into five dimensions: opportunity exploration, idea generation, idea promotion, idea realization, and reflection (Messmann & Mulder, 2012). Opportunity exploration refers to employees being attentive to the work environment and keeping up with recent events and developments. Idea generation encompasses employees’ expressing ideas and discussing them, where idea promotion is about winning the support of supervisors and colleagues, and for instance negotiating with key actors about resources and needed permission. Idea realization, or implementation so to say, comprises the development of a practical model or prototype of the innovation, making others familiar with details, and test outcomes for possible undesired effects. Lastly, reflection relates to employees assessing the progress of the innovation and improving strategies for possible upcoming future situations. Innovative teams score high on all of these five
dimensions (Messmann & Mulder, 2012). In order to study and define innovative behavior of team members, the dimensions identified by Messmann and Mulder (2012) are used in this current research.
2.2 Team Composition: Team diversity. What is team composition? And what characteristics play an important role in relation to team innovativeness?
The level of innovativeness, or the innovative outcomes as a result of team effort so to say, differ with great extent between teams. This has to due to with several internal team factors, like team composition, that are either positively or negatively related to the
innovativeness of a team (Hülsheger et al., 2009). The composition of a team can be referred to as the configuration of member attributes in a team, which is thought to have a powerful influence on team processes and outcomes (Somech & Drach-Zahavy, 2013). Somech and Drach-Zahavy (2013) explain that team composition is thought to be related to team outcomes because it affects both the amount of knowledge as skills of team members during the
completion of a team task. Although research shows a wide pallet of different team composition factors, team diversity seems to be a recurring factor in relation to team innovativeness gaining ground in both importance and dominance (West, Hirst, Richter, & Shipton, 2004; Hülsheger et al., 2009; Somech & Drach-Zahavy, 2013). This can be clarified by the fact that today’s business environment is characterized by an increase in globalization and organizations getting involved in international markets, extending their business across different cultures and countries (Richard, 2000). This phenomenon has triggered employees to seek employment outside the ‘traditional borders’ and an increase in culturally diverse
workforces. In addition, organizations have also responded to this phenomenon by hiring a diverse set of people (Richard, 2000). After conducting a large review about several team composition models, Mathieu et al. (2014) illustrate the great importance of getting more clarification on team diversity and their effects. The authors argue that future research should consider investigating which team diversity factors are related to the different team processes and outcomes under what circumstances (Mathieu et al., 2014).
Team diversity refers to the different characteristics of individual team members regarded to a common attribute among those members, like age for instance (Harrison & Klein, 2007). The composition of all these characteristics determines the behavior of an individual within a team. Subsequently, the characteristics of each individual work
synergistically with the characteristics of other team members and therefore influences the outcome of the teamwork (Hülsheger et al., 2009). This makes diversity a concept of great impact for organizations and managers, as it influences the innovative performance of teams (Hülsheger et al., 2009). During the last years of academic innovation research a distinction between different categories of team diversity has been made. Diversity can be categorized in three ways, each overarching other types of prominent team members’ characteristics
(Harrison & Klein, 2007). The first category is called demographic diversity and refers to characteristics as gender, age, and nationality, also known as so-called surface-level characteristics (Harrison & Klein, 2007; Hülsheger et al., 2009). The second category is referred to as cognitive diversity and involves characteristics like expertise, experiences, and perspectives of team members (Miller, Burke, & Glick, 1998). The third diversity category is labeled as individual diversity and covers characteristics that are part of a person’s personality and traits like openness or dedication (Costa & MacCrae, 1992a).
Even though a distinction between the different categories of diversity is present, some categories have received sufficiently more attention compared to others. Up to now, diversity research in the field of innovation mainly focused on demographic and cognitive diversity
(Van Knippenberg & Schippers, 2007; Hülsheger et al., 2009; Somech & Drach-Zahavy, 2013). Research on demographic diversity usually includes characteristics as gender, age, nationality, and education (Auh & Menguc, 2005; Harrison & Klein, 2007; Hülsheger et al., 2009). Even though this subject received a sufficient amount of attention in the academic field, no consensus is found whether or not demographic diversity within teams positively influences team innovation. Some authors state that demographic diversity has a positive influence on the creativity and innovativeness of teams (Bantel & Jackson, 1989; West et al., 2004). Others argue that background diversity in contrast leads to negative influence of creativity and team innovativeness (Bantel & Jackson, 1989; Hülsheger et al., 2009). A deeper analysis into the results shows that the negative influence of demographic diversity is either small and insufficient (Hülsheger, et al., 2009) or not clearly explained (Bantel & Jackson, 1989).
Cognitive diversity is often referred to as functional heterogeneity, which involves the diversity in the roles of team members (Somech, 2006; Somech & Drach-Zahavy, 2013). This means that teams consist of people originating from different functions and disciplines who have a pertinent expertise in a proposed course of action (Somech & Drach-Zahavy, 2013). Previous research indicates that functional heterogeneity positively influences team creativity and innovation implementation in several ways (Hülsheger et al., (2009); Somech & Drach-Zahavy, 2013). Hülsheger et al. (2009) investigated job-relevant diversity as cognitive diversity factor and argue that constructing a team of people with different organizational roles, who possess a wide array of knowledge, skills, and expertise, helps that team solving complex tasks like the development of new ideas and procedures. Cognitive diversity broadens the perspectives of team members because of the incorporation of diverse kinds of information and therefore facilitates the generation of new ideas and approaches (West, 2002; Somech & Drach-Zahavy, 2013). It overcomes the so-called phenomenon ‘groupthink’ and thereby helps in the process of decision-making (Singh & Fleming, 2010).
As sufficient research into demographic- and cognitive diversity is conducted, this leaves us with the last category, namely individual diversity. Why did this category receive less attention compared to the others? One explanation for the fact that less is known about individual diversity in relation to innovation is that this dimension is less easily captured by the existing typologies. Therefore it received less attention even though it may be equally relevant in order to understand innovativeness of teams (Costa & MacCrae, 1992a; Van Knippenberg & Schippers, 2007). For this reason, this study emphasizes individual diversity
and focuses on specific personality characteristics of team members, as most sufficient research related to innovation is lacking in this area.
Team diversity – Individual. As stated above, individual diversity covers
characteristics that are part of a person’s personality and traits such as openness or dedication (Costa & MacCrae, 1992a). Academics agree that individual diversity influences the
performance of a team, but the results are inconclusive whether this relationship is positive or negative (Hülsheger et al., 2009). On the one hand, researchers argue that diversity has a positive impact on the performance of a team because each individual brings unique attributes to the table (Cox & Blake, 1991). A heterogeneous team could help to overcome challenges that the team is facing more easily, avoid failure more, and enhance the chance of creating a breakthrough invention (Richard, 2000; Singh & Fleming, 2010).
On the other hand, there are also some academics emphasizing that individual diversity has some disadvantages as a result (Horwitz & Horwitz, 2007). High level of individual diversity in teams can for instance delay the decision-making process as cultural barriers and different opinions can be an obstacle to reach consensus (Horwitz & Horwitz, 2007). Homogeneous teams often have more common perspectives and references, making it easier for them to work together and communicate with each other and thereby creating common ground faster (Horwitz & Horwitz, 2007). This also seems to be the ruling thought when comparing the conclusions of the little amount of other studies that investigated individual diversity and team performance. Harrison, Price, Gavin, and Florey (2002) found for instance that diversity in personality and traits among team members is negatively related to team integration. Authors agree that diversity in personality of team members possibly leads to the perception of members feeling that others are different from the ‘self’ and
therefore results in the rise of a social categorization processes (Van Knippenberg et al., 2004; Van Knippenberg & Schippers, 2007). Taken this into account, no conclusive answer can be given to the question whether or not individual diversity has a positive or negative influence on team innovation. Therefore, it is first needed to dive deeper into the characteristics that are related to individual diversity and team performance in order to get more clarification on how individual diversity possibly influences team innovativeness and therewith the performance of the whole organization. This leaves the question: which personality characteristics are of great relevance for team members to possess or not to possess in order to improve their
As personality seems to be an important predictor of creative behavior in social psychology (Egan, 2005) a growing number of studies have started to link diversity in team members’ personalities, often conceptualized via the five-factor model of personality, to team performance and sometimes even innovativeness (Costa & MacCrae, 1992a; Van
Knippenberg, De Dreu, Homan, 2004; Van Knippenberg & Schippers, 2007). However, so far these studies show very inconsistent outcomes in regard to personality diversity and team performance. A clear overview of how each characteristic specifically affects team
innovativeness as an outcome in the business field is lacking. Therefore further research to understand this relationship and specifically the relation with team innovativeness in organizations seems in order (Costa & MacCrae, 1992a; Van Knippenberg & Schippers, 2007). Looking at the five-factor model of personality, five prominent characteristics have been developed and proven to be dominant in terms of job performance (Costa & MacCrae, 1992a; John & Srivastava, 1999). The presence or absence of these personality characteristics is positively related to the performance of individuals at work (Barrick & Mount, 2005). The factors are successively neuroticism, extraversion, openness to experience, agreeableness and conscientiousness. The meaning of each personality characteristic and their relation with team innovativeness will be further elaborated upon below.
Neuroticism. Costa and MacCrae (1992a) have summarized evidence and reviewed
several approaches of assessment of the characteristics in the literature. The authors state that the factor neuroticism is related to the level of emotional distress that a person experiences. Persons who have a high score on emotional stability are generally relaxed, calm, and even-tempered. These individuals can handle situations that are stressful without feeling or getting upset (Costa & MacCrae, 1992a). In contrast, people that score low on emotional stability, or high on neuroticism so to say, experience emotions like fear, sadness, anger, and
embarrassment (Costa & MacCrae, 1992a). Patterson (2002) provided an extensive theoretical overview of person-level predictors of innovation at work. In the section ‘innovation and personality’ Patterson (2002) refers to the five-factor model and presents per characteristic all results that have been found in literature up until then. Here, aspects that could determine the relationship between neuroticism and innovation are discussed extensively. It is found that there are some similarities between the characteristics of neuroticism and the factors that determine innovative behavior, like impulsiveness (Feist, 1999; Patterson, 2002). However, Patterson (2002) concludes that there appear to be some inconsistencies within the literature, as some studies did not find any association between neuroticism and creative thinking, a factor closely related to idea generation and therefore possibly innovation (King, Walker, &
Broyles, 1996). Patterson (2002) argues that the unexpected positive relationship between neuroticism and creativity found by some authors might be explainable by the fact that these studies were conducted with scientists and artists (Feist, 1999; Patterson, 2002). Feist (1999) for example, concludes with great conviction that artists appear to be more anxious,
impulsive, and emotionally labile than the group of scientists that were part of the same study. However, no evidence is presented that the same results will maintain for a sample of
innovators working at a big commercial company. The positive association between neuroticism and innovation could therefore be differential depending on the domain of interest and sample. As the results of previous research seem to be inconclusive and dependent on the situation, it cannot be assumed with certainty that a team member of an innovation team containing high levels of neuroticism has a positive or negative influence on the innovative outcome of that team. Therefore, the following research question is drawn.
RQ1: Is the characteristic neuroticism positively or negatively related to team innovativeness?
Extraversion. Extraversion is often associated with the concept sociability, as
extraverts are thought to be talkative, active, and assertive. A person that scores high on extraversion likes to be around people in large gatherings and in groups (Costa & MacCrae, 1992a). They are energetic and upbeat, but also optimistic, adventurous, and often cheerful. Therefore, extraverts prefer situations that can be described as exciting (Costa & MacCrae, 1992a). Gelade (2002) compared different group samples with each other on the way they scored for the five characteristics. The results show that the groups of innovators score higher on extraversion (Cohen’s d = 0,36) than the groups of scientists (Cohen’s d = 0,03) and artists (Cohen’s d = 0,15). Gelade (2002) therefore states that there is a link between extraversion and innovation, as innovative people score higher on extraversion than people that are adoptive. Based on the characteristics of extraversion described by Costa and MacCrae
(1992a), King et al. (1996) argued that extraversion was positively connected to creativity (r = 0,26, p < 0,05), which is often a precursor of innovation. As innovation is often described as the ‘enjoyment of creating new domains’ this refers to persons that are active and adventurous (Costa & MacCrae, 1992a; King et al., 1996). Extraverts like to work in groups, which makes it more likely for those people to be innovative in teams. After studying a large body of literature, Patterson (2002) also convincingly concluded that more generally extraversion has been shown to be a valid predictor for occupations where interpersonal factors are likely to be
important for effective job performance (e.g. sales, managers). This suggests that extraversion is likely to be important in managerial and professional occupations where innovation is an important part of the job role, particularly in the implementation phase of the innovation process. It is therefore expected that team members with a high score on extraversion will be more innovative.
H1a: The characteristic extraversion is positively related to team innovativeness
Openness to Experience. Openness to experience is associated with terms like
intelligence, curiosity, imaginativeness, and broad-minded. A person that is open to
experience prefers variety and independence of judgment (Costa & MacCrae, 1992a). They are open and curious to new suggestions and ideas, and do not judge situations immediately. In contrast to people that are closed to experience, open people are highly interested in the feelings and emotions of others (Costa & MacCrae, 1992a). People closed to experience like to stick to their usual way of doing things and do not like changes (Costa & MacCrae, 1992a). In the right setting, people that are open to experience are often unconventional, in terms that they dare to question authority and are eager to adopt new ideas. Not surprisingly, King et al. (1996) found that the characteristics of open people are closely related to creative behavior, even more than the other four of the five-factor model (r = 0,47, p < 0,01). Patterson (2002) also concludes that this dimension is most closely associated with innovation (Feist, 1999), and argues that openness enhances the intrinsic motivation of an individual towards novelty and therefore works in a multiplicative way to produce creativity and innovation (King et al., 1996; Patterson, 2002). Patterson (2002) concludes this result by stating that the openness to experience is likely to be the most important characteristic to predict the propensity for innovation. Via summation of evidence and review of several approaches, the same conclusion was drawn by Costa and MacCrae (1992a) who argue that there is a positive relation between openness to experiences and innovation. As open people are interested in the thoughts of others, it can be assumed a team member that is open to experience things results to be more innovative than a team member that is not.
H1b: The characteristic openness to experience is positively related to team innovation.
Agreeableness. Agreeableness is associated with characteristics like tolerant, caring,
agreeableness are often perceived as sympathetic and pleased to help others. They expect that people treat them the same way as they do. In contrast, individuals that score low on
agreeableness are associated with characteristics as argumentative, headstrong, and outspoken (Costa & MacCrae, 1992a). Similarly, innovative individuals are often described as
argumentative, challenging, and non-conforming (Patterson, 2002). These people prefer to ‘compete instead of cooperate’, and are less compassionate and compliant than people that score high on agreeableness (Costa & MacCrae, 1992a). In line with the study of Costa and MacCrae (1992a), Patterson (2002) argues convincingly that a large variety of studies have demonstrated that there is a negative association between the characteristic agreeableness and creativity and innovation (Feist, 1999; King et al., 1996). King et al. (1996) show for instance that there is a negative relation found between agreeableness and creativity (r = -0,23, p < 0,05). More specifically, Patterson (2002) concludes that low agreeableness may be more influential in the implementation phase of the innovation process, since this phase involves social processes. Taking all these findings into account, some rather contradictive findings result from this literature analysis. On the one hand, it is estimated that high agreeable people perform better in teams, as they are willing to cooperate (Costa & MacCrae, 1992a; King et al., 2006). A high level of agreeableness leads to conformity among people (King et al., 2006). On the other hand, however, when agreeableness is specifically linked to innovation, it is not obvious that this characteristic leads to more creative and innovative behavior within teams (King et al., 1996; Patterson, 2002). Although King et al. (1996) suggest agreeableness leads to conformity; innovation rather requires action, autonomy, and independence of thought. It seems that even though agreeableness may be good for team solidarity, it does not automatically cause teams to be more innovative. Since no conclusive assumption can be made based on current research findings, the following research question is drafted:
RQ2: Is the characteristic agreeableness positively or negatively related to team innovativeness?
Conscientiousness. Conscientiousness is defined as people being careful, orderly,
responsible, persevering, punctual, and methodical (Costa & MacCrae, 1992a). A substantial majority of literature in the area of conscientiousness, creativity, and innovation has
demonstrated that a lack of conscientiousness is associated with innovation (King et al., 1996; Feist, 1999; Patterson, 2000). Arguments to support this association are found in the though that people who are conscientious deal with others by being compliant. These individuals do
not challenge authority, avoid arguments, like rules and ‘do not make waves’ (Hogan & Ones, 1997). Hogan and Ones (1997) have conducted an extensive meta-analysis on the five
characteristics of individual diversity. The authors conclude that individuals that score high on conscientiousness are more likely to comply with the organizational rules and norms, and are more resistant to changes at work. These characteristics of conscientiousness are not stimulating innovation, as innovative people are trying to find new ways to do things and this way sometimes even deliberately try to break with existing norms (Patterson, 2002).
However, Patterson (2002) indicates that there is still a lot of diversity found in literature, as the conceptualization of conscientiousness is very broad. Patterson (2002) elaborates that conscientiousness is for instance conceptualized as orderly, methodical, and dutifulness, but also as achievement striving and competence. The problem for innovation research and conscientiousness is that on the one hand innovation is positively related to achievement striving (Costa & MacCrae, 1992a), but on the other hand negatively associated with dutiful and methodical (Hogan & Ones, 1997). Applying all this information on the current subject of study team innovation, it is difficult to create one solid assumption. No evidence is provided or found that could explain how these results could possibly relate to team innovation. It is therefore expected that:
RQ3: Is the characteristic conscientiousness positively or negatively related to team innovativeness?
‘’Innovation has to be considered a complex interaction of person and situation’’ - (Woodman, Sawyer, & Griffin, 1993)
2.3 Team Climate. What is team climate? And how does team climate influence team innovation?
As argued in the previous section about individual diversity, personal characteristics are thought to have an influence on the innovativeness of a team. Personal characteristics are, however, not the only factors that seem to play an important role regarding team innovation. A difference can be made between so-called input factors, like individual diversity, and processes/output factors, like team climate (Hülsheger et al., 2009). During the last years, researchers have stated that besides personal attributes of team members, team and organizational context are also potential factors to influence team innovation (West & Wallace, 1991; Choi, Anderson, & Veillette, 2009; Somech & Drach-Zahavy, 2013). This
insight caused innovation researchers to adopt an interactional approach in which team composition variables and context variables are investigated simultaneously (Choi et al., 2009; Somech & Drach-Zahavy, 2013). It emphasizes that, to fully understand how to
improve team innovation, knowledge is needed about the contribution of both team members’ characteristics as well as the team climate in which the innovation team operates (Choi et al., 2009). This concept of team climate refers to the value and norms within a team and whether or not those are pro-innovation (Somech & Drach-Zahavy, 2013). To get a deeper
understanding of team climate, it is important to understand on what drivers team climate is build and how these relate to team innovation. During the last years, one model about climate factors and group innovation has specifically dominated the research area (West & Wallace, 1991; West & Anderson, 1996; Ragazzoni et al., 2002; Hülsheger et al., 2009). This model consists of four factors and explains that the innovative performance of teams is related to the factors: vision, participative safety, support for innovation, and task orientation (Ragazonni et al., 2002; Somech & Drach-Zahavy, 2013).
Vision. The first factor vision is ‘an idea of a valued outcome, which represents a
higher order goal and motivating force at work’ (West, 1990, p.310). Vision assesses the extent to which team members have a common understanding of objectives and to the extent that they show high commitment to the common goal of the team (Hülsheger et al., 2009), also referred to as ‘clarity and commitment to objectives’ (West & Anderson, 1996). A high vision means that the common goal is clear to team members and that these goals are
perceived as attainable. In this case, team members feel highly committed to accomplish the goals (Hülsheger et al., 2009; Somech & Drach-Zahavy, 2013). A clear set of objectives gives meaning to the work of team members, helps them to focus on their efforts, and it motivates the team members to enhance their innovative performance (Hülsheger et al., 2009). This is in line with the goal-setting theory, which states that persons with clear defined objectives are more likely to develop new methods of working to reach a goal, because of the focus and direction their efforts have. It is expected that vision has an effect on team innovation, since teams with a high vision are more likely to implement generated new and creative ideas than teams that have an abstract and vague vision. Unclear goals make it difficult to develop practical steps for a team to be innovative, and so the following prospect is made:
Participative safety. The second factor is participative safety, which consists of two
components. One refers to participation in decision-making and the second to so-called intra-team safety, which means that there consists a nonthreatening psychological atmosphere in a team with mutual support and replete with trust (Somech & Drach-Zahavy, 2013). Simply put, participative safety can be referred to as an environment where all team members feel to propose new suggestions, ideas, and solutions to problems in a non-judgmental climate, whether these ideas do or do not fit with the dominant or ruling thought (West, 1990).
Participative safety therefore differs from characteristics as agreeableness and consciousness, as this construct is purely focused on the extent to which participants feel that they can express themselves, and therefore has nothing to do with how their personality characteristics are shaped. It is argued that participative safety has a positive influence on team innovation; a climate in which team members feel safe to speak up and take risks is expected to positively influence the actual implementation of new ideas (Somech & Drach-Zahavy, 2013). A high participative safety leads to more innovative ideas in teams than low participative safety, as team members in the latter case may feel that they are blamed when things go wrong.
H3b: Teams with high participative safety are more likely to be innovative than teams with a low participative safety.
Support for innovation. The third factor, support for innovation, is defined as ‘the
expectation, approval, and practical support of attempts to introduce new and improved ways of doing things in the work environment’ (West, 1990, p.315). The support for innovations within organizations and teams differs to the extent that is communicated to personnel via documents, policy statement, or word-of-mouth (Somech & Drach-Zahavy, 2013). Next to communication, the way that it is enacted upon also refers to support for innovation and varies quite a lot. A climate where active promotion of innovative behavior is supported, such as providing sufficient time to produce and develop new ideas or availability of training, leads to more innovative outcomes (Somech & Drach-Zahavy, 2013). Besides the more practical resources, this factor is more focused on the value that team members experience for the projects they undertake. Support for innovation enhances the willingness to dedicate time, share resources, and cooperate to implement creative ideas (Somech & Drach-Zahavy, 2013). Therefore, it is expected that support for innovation has a positive relation with team
implement new ideas. They dare to take more risks, because failures are tolerated more (Hülsheger et al., 2009).
H3c: Teams with high support for innovation are more likely to be innovative than teams with a low support for innovation.
Task orientation. The last and fourth factor is task orientation, which refers to a
sharing concern among team members for achieving the highest achievable or good
performance (West, 1990; Somech & Drach-Zahavy, 2013). A high level of task orientation results in a higher willingness of team member to work hard, and so it doesn't have an influence on innovation alone, but also reflects to an even more general concern of
performance (Somech & Drach-Zahavy, 2013). This leads to the improvement of the quality of ideas and decisions, because all contrasting opinions and considerations are taken into account (Hülsheger et al., 2009). A high level of task orientation is expected to positively influence team innovation, as team members are more likely to overcome obstacles and transform new ideas into viable products and processes (Somech & Drach-Zahavy, 2013). If task orientation, however, is lacking, innovative ideas are less likely to be transformed into innovative outcomes (Somech & Drach-Zahavy, 2013).
H3d: Teams with high task orientation are more likely to be innovative than teams with a low task orientation.
Teams that strive to be innovative are not only required to generate and develop new ideas but also to make sure that team members implement those creative ideas (Somech & Drach-Zahavy, 2013). Somech and Drach-Zahavy (2013) show that an innovative team climate enhances team creativity extensively (t = 8.94, p < 0,01). It seems that the climate or atmosphere in an organization is a strong facilitator of actual positive outcomes (Ragazzino et al., 2002). In addition, for the actual implementation of innovative ideas, and thus team innovation, team climate plays a crucial role (West & Wallace, 1991; Somech & Drach-Zahavy, 2013). It is therefore expected that:
H2a: There is a positive relationship between individual diversity characteristics and team innovation and this relationship is moderated by team climate, so that this relationship is weaker for lower values of team climate and higher for higher values of team climate.
Based on the findings in literature, team climate is also expected to influence team innovation directly (Somech & Drach-Zahavy, 2013).
H2b: A pro-innovative team climate is positively related to team innovation.
A complete overview of the discussed research questions and estimations is displayed in the conceptual model (Figure 1).
Figure 1 Conceptual model.
3. Method
3.1 SampleThe individuals that took part in the survey had to fit to some restrictions that were determined beforehand. Even though the participants did not have to be part of a specifically called ‘innovation team’, they had to be working in a team that was somehow active in the field of innovation. In order to gather relevant insights, the data is collected in cooperation with the XLFamily. XLFamily is an international incubator and accelerator with stakes in many different initiatives, from impact startups to new innovation tooling (including
Team Climate v Vision v Participative safety v Support for innovation v Task orientation
Team composition – Individual diversity v Emotional stability v Extraversion v Openness v Agreeableness v Conscientiousness Team innovation v Idea generation v Idea promotion v Idea implementation
initiatives like www.chiefhumorofficer.com and www.ventur.es). It has around 90 employees in locations like the Netherlands, Romania and USA.
The data for this research is obtained in two different ways. First of all, innovative team members are reached via the medium LinkedIn where four different promotional self-created blogs about this study were published to promote participation (Appendix C). This medium was chosen since lots of innovative professionals worldwide are easily reached this way. In the blogs, the relevance of this study was briefly described. At the end of the blogs a link towards the online survey was provided, so that interested individuals could participate in the study. Besides posting blogs, innovators were also directly contacted to take part in the research. These individuals were all member of a special ‘innovation groups’ on LinkedIn, like Innovation Management Group and Innovatie 2.0 – Community of Talents, which made it possible to filter out relevant participants for this study. Another way to gather data was via several companies that were interested to get a better understanding of team innovation. These organizations let their team members participate in this study, which adds more accuracy to the research. Added together seven different teams from two different organizations
contributed to this research. More information of the organizations can be found in Appendix A (Table 1).
In total 147 participants took part in the survey of which 19 are removed from the sample as they dropped out, did not complete the survey or did not fit the prior established requirements (like being part of a team). The statistical analysis is performed on the data of the remaining sample of 128 participants. This sample consisted of 76 men and 52 females with an average age of 40 years old (M = 40,66, SD = 11,05). The most common number of members that one team consists is between five (40,6%) and ten (18,8%).
3.2 Procedure
The data of this research is collected via a primary data collection method. An extensive survey is designed and spread among a wide variety of professionals working at different companies located in the Netherlands and beyond. Participants could choose if they would like to fill in an English or Dutch version of the survey. Furthermore, the survey holds items referring to team composition, team climate, and team innovativeness. Before the survey was spread among the participants, a short pre-test was conducted with a small sample (N = 10) to make sure that the questions were understood correctly and the survey did not take up more than 10 minutes. Each participant had to fill in the survey independently, also in cases where teams as a whole received an invitation for the survey. At the end of the survey,
participants were able to leave their e-mail address to eventually receive a rapport of the results. A complete overview of the survey in both English and Dutch can be found in Appendix B.
It can be argued that the research method survey might be to limiting to answer the quite big questions and hypotheses compiled in this study. However, as this research needed to gain lot of personal information of team members in a short amount of time, like several personality characteristics, an online survey services to be a sufficient method. This way, it was possible to measure the personality characteristics via 44 statements. If another method, like interviews for instance, would have been chosen for this subject of study, it would have been almost impossible to capture enough information of different team members, as the interviews would probably be too long. Thereby, conducting somewhat ‘eleven’ interviews among two teams would in this case not provide enough information to draw conclusions with certainty. In addition, a final advantage that an online survey has over interviews is that the participant is completely anonymous, which gives the participant the feeling that he or she can answer honestly even if the answer is not socially desirable.
3.3 Measurement
The questionnaire entails three different components: team composition, team climate and team innovation. Statements that intend to measure these elements are based on existing scales. A short description of every construct with an example statement is given below. Even though all statements are purely originating out of previous literature, a reliability check and factor analysis are conducted for each construct. This is important since a reliability checks enables to examine the consistency of measurements and explanatory factor analysis examines whether or not all the items belong to one factor. In order for the items to form a reliable scale Cronbach’s Alpha should be α = 0.70 or higher. In case a lower value than this standard is found a check will be performed in order to see if deleting other items leads to reliable scale. The criteria for the factor analysis are based on Field (2009) who indicates that (1) Kaiser-Meyer-Olkin (KMO) must be greater than 0,6 and (2) Barlett’s test of sphericity must be significant. The principal component analysis and promax rotation are used for the EFAs.
In line with the criteria, the Kaiser-Meyer-Olkin measure verified the sampling adequacy for the analysis for every construct. As expected, in each case only one component was found with an eigenvalue over Kaiser’s criterion of 1. At the same time and in agreement with Kaiser’s criterion, examination of the screeplot also showed only one ‘dot’ above the
‘knik’. It can be concluded that all items of each construct belong to one factor, and therefore measure what they are supposed to measure. The results for all constructs can be found in Table 3.
Dependent variable: Team Innovation. The statements to measure team innovation are originating from the research of Messmann and Mulder (2012). These authors stated that the process of innovative work behavior in teams consists of 5 dimensions. However, since the focus of this research is not about studying a process but about measuring innovative behavior of teams, not all five dimensions will be taken upon in the questionnaire. Kleysen and Street (2001) argue that the first significantly important step towards actual active innovative behavior is identified as idea generation among individuals and ends with the phase idea implementation. Therefore, participants are asked questions regarding the three dimensions: idea generation, idea promotion, and idea implementation. Breaking up the innovative behavior of teams into three different phases allows this research to investigate in more detail what team composition and team climate factors are of influence (or not) in which behavioral phase. It provides a transparent and clear view of how team innovation is
established.
Idea generation. An existing and reliable scale (α = 0,85) is used to measure idea
generation (Messmann & Mulder, 2012) - 7-point scale (1=not at all to 7=completely). An example item that measures the construct idea generation is: I (or my team) express new ideas. As expected, the reliability check based on the data set of this research also shows that the statements form a reliable scale (α = 0,81, M = 5,53, SD = 0,84). The Kaiser-Meyer-Olkin measure verified the sampling adequacy for the analysis, KMO = 0,739 with Barlett’s test of sphericity χ² (15) = 365,950, p < 0,001.
Idea promotion. An existing and reliable scale (α = 0,83) is used to measure idea
promotion (Messmann & Mulder, 2012) - 7-point scale (1=not at all to 7=completely). An example item that measures the construct idea promotion is: I (or my team) have been promoting new ideas to the supervisor in order to gain his/her active support. A reliability check based on the data set of this research also ensures that the items together form a reliable scale (α = 0,90, M = 5,51, SD = 1,05). The Kaiser-Meyer-Olkin measure verified the sampling adequacy for the analysis, KMO = 0,807 with Barlett’s test of sphericity χ² (10) = 511,561, p < 0,001.
Idea implementation. An existing and reliable scale (α = 0,78) is used to measure idea
An example item that measures the construct idea promotion is: I (or my team) have introduced colleagues to the application of a developed solution. As was the case in the previous dimensions, a reliability check based on the data set of this research also shows that the statements of idea implementation form a reliable scale (α = 0,77, M =5,07, SD =1,09). Again, the Kaiser-Meyer-Olkin measure verified the sampling adequacy for the analysis, KMO = 0,609 with Barlett’s test of sphericity χ² (3) = 152,555, p < 0,001.
Independent variable: Individual diversity. To measure the five characteristics of the five-factor model, an existing inventory is used from previous literature. The 44-item Big Five Factors inventory measures an individual on the Big Five Factors (dimensions) of personality (Costa & MacCrae, 1992b) and is a recreated chart from John and Srivastava (1999) (Table 2) – 5-point scale (1=Strongly Disagree to 5=Strongly Agree). There are some counter-indicative items present for every characteristic. These items are all recoded before they were applied to the reliability check and factor analysis.
Table 2
Extraversion. An example items for extraversion is: I see myself as someone who is
talkative. Results of the reliability check indicate that the items form a reliable scale for extraversion (α = 0,88, M = 3,95, SD =0,77). Results of the factor analysis show that the Kaiser-Meyer-Olkin measure verified the sampling adequacy for the analysis, KMO = 0,796 with Barlett’s test of sphericity χ² (28) = 550,287, p < 0,001.
Agreeableness. An example items for agreeableness is: I see myself as someone who is
helpful and unselfish with others. Results of the reliability check indicate that the items form a reliable scale for agreeableness (α = 0,71, M = 4,16, SD =0,53). Results of the factor analysis show that the Kaiser-Meyer-Olkin measure verified the sampling adequacy for the analysis, KMO = 0,632 with Barlett’s test of sphericity χ² (36) = 325,870, p < 0,001.
Conscientiousness. An example items for conscientiousness is: I see myself as
someone who does a thorough job. Results of the reliability check indicate that the items form a reliable scale for conscientiousness (α = 0,75, M = 3,80, SD = 0,58). Results of the factor analysis show that the Kaiser-Meyer-Olkin measure verified the sampling adequacy for the analysis, KMO = 0,624 with Barlett’s test of sphericity χ² (36) = 375,995, p < 0,001.
Neuroticism. An example items for neuroticism is: I see myself as someone who can
be tense. Results of the reliability check indicate that the items form a reliable scale for neuroticism (α = 0,85, M = 2,25, SD = 0,75). Results of the factor analysis show that the Kaiser-Meyer-Olkin measure verified the sampling adequacy for the analysis KMO = 0,626 with Barlett’s test of sphericity χ² (28) = 639,109, p < 0,001.
Openness to experience. An example items for openness to experience is: I see myself
as someone who is curious about many different things. Results of the reliability check indicate that the items form a reliable scale for openness (α = 0,70, M = 4,06, SD = 0,44). Results of the factor analysis show that the Kaiser-Meyer-Olkin measure verified the sampling adequacy for the analysis, KMO = 0,760 with Barlett’s test of sphericity χ² (45) = 572,480, p < 0,001.
Moderating variable - Team climate. Team climate is measured via four factors in the questionnaire. All statements for each factor are based on existing literature. These four factors together determine the specific climate factors for innovation within teams (Anderson & West, 1998).
Vision. Vision is measured on a 7-points scale (1=not at all to 7=completely)
(Burningham & West, 1995; Anderson & West, 1998). An example item that measures the construct vision is: How clear are you about what your team’s objectives are?. The reliability
check shows that the items form a reliable scale (α = 0,93, M = 5,48, SD = 1,08). Results of the factor analysis show that the Kaiser-Meyer-Olkin measure verified the sampling adequacy for the analysis, KMO = 0,830 with Barlett’s test of sphericity χ² (28) = 1023,254, p < 0,001.
Participative safety. Participative safety is measured on a 5-points scale in literature
(Anderson & West, 1998). However, to make further analysis easier to perform and interpret, in this study the statements are measured on a 7-point scale (1=strongly disagree to
7=strongly agree). An example item that measures the construct participative safety is: We share information generally in the team rather than keeping it to ourselves. The reliability check shows that the items form a reliable scale (α = 0,93, M = 5,74, SD = 1,06). Results of the factor analysis show that the Kaiser-Meyer-Olkin measure verified the sampling adequacy for the analysis, KMO = 0,782 with Barlett’s test of sphericity χ² (36) = 1051,343, p < 0,001.
Task orientation. Task orientation is measured on a 7-points scale (1= to a very little
extent to 7= to a very great extent) (Burningham & West, 1995). An example item that measures the construct task orientation is: Do members of the team build on each other’s ideas in order to achieve the best possible outcome?. The reliability check shows that the items form a reliable scale (α = 0,86, M = 5,18, SD = 1,11). Results of the factor analysis show that the Kaiser-Meyer-Olkin measure verified the sampling adequacy for the analysis, KMO = 0,733 with Barlett’s test of sphericity χ² (21) = 533,320, p < 0,001.
Support for innovation. Support for innovation is measured on a 5-points scale in
literature (Anderson & West, 1998). However, to make further analysis easier to perform and interpret, this study measures the statements on a 7-point scale (1= strongly disagree to 7=strongly agree). An example item that measures the construct support for innovation is: People in this team are always searching for fresh, new ways of looking at the problems. The reliability check shows that the items form a reliable scale (α = 0,93, M = 5,17, SD = 1,17). Results of the factor analysis show that the Kaiser-Meyer-Olkin measure verified the sampling adequacy for the analysis, KMO = 0,792 with Barlett’s test of sphericity χ² (21) = 865,483, p < 0,001.
Table 3
Explanatory Factor Analysis.
Constructs KMO Barlett’s test of sphericity (χ²) Cronbach’s Alpha (α) Explaining factors Team Innovation Idea generation 0,739 p < 0,001 0,81 1 (73,91%) Idea promotion 0,807 p < 0,001 0,90 1 (75,10%) Idea implementation 0,609 p < 0,001 0,77 1 (70,00%) Individual diversity Extraversion 0,796 p < 0,001 0,88 1 (69,09%) Agreeableness 0,632 p < 0,001 0,71 1 (64,02%) Conscientiousness 0,624 p < 0,001 0,75 1 (65,81%) Neuroticism 0,626 p < 0,001 0,85 1 (68,09%) Openness 0,760 p < 0,001 0,70 1 (68,98%) Team climate Vision 0,830 p < 0,001 0,93 1 (82,14%) Participative safety 0,782 p < 0,001 0,93 1 (64,79%) Task orientation 0,733 p < 0,001 0,86 1 (73,71%)
Support for innovation 0,792 p < 0,001 0,93 1 (71,65%)
3.4 Control measures
Since the individuals that participated in this survey differ a lot from each other in terms of age, gender, and team size, it is possible that these aspects have an influence on team innovativeness. Therefore, it is necessary to control for these variables during further analyses in order to determine if there could be alternative explanation for the results. The relation between age, gender, and innovation is researched extensively in previous studies and seems to be of great relevance for understanding innovation processes (Lindberg & Schiffbaenker, 2013). Besides these demographic variables it is also important to control for team size. The number of members in one team seems to vary extensively, as four is the lowest and 70 the highest indicated number. As it is stated that the performance of a team depends on the team size, there must be a sufficient number of members in one team to perform a team task most effectively (West & Anderson, 1996).
4. Results
4.1 Descriptive StatisticsMissing values. First a frequency test is conducted for all the statements regarding team innovation, team diversity, and team climate. This way it is examined if there are any errors or missing values present in the data. Neither errors nor missing values were found, which means that there is no need to exclude missing values and that all answers can be used for further analysis.
Check for normal distribution. A descriptive analysis is conducted to examine whether or not the items of team innovation, team diversity, and team climate are normally distributed. As the rule of thumb is that you can speak of normal distribution when skewness and kurtosis are close to 0, between -1 and 1 so to say, it can be concluded that team
innovation is not completely normally distributed. Even though the skewness of all items is close to 0, four of the fourteen items have a kurtosis higher than -1. This means that we can speak of a normal distribution that is flatter (platykurtic) for these four items. The same is the case for individual diversity, which also has a kurtosis score of more than 1 for six of the 44 items. However, this time the distribution for these items is more pointy (leptokurtic) than normal. Lastly, team climate has five out of 31 items that have a substantial positive skewness with a score of higher than 1. However, in case of a reasonably large sample of participants, the skewness will not make a substantial difference and the risk of kurtosis is reduced (Tabachnick & Fidell, 2001). As there were more than 100 participants in this survey, the results of skewness and kurtosis do not have a great impact on the results of this research and further analyses can be performed.
Computing Scale Means and Correlations. The final preliminary step is to perform a correlation matrix. New variables as a function of existing items were created for hypothesis testing. The mean and standard deviation of all items were calculated. The table of correlation coefficients, called correlation matrix, provides information for all of the combinations of variables, like descriptive statistics and correlations among all research variables at the individual level based on the Pearson Correlation test. The results show that there is a high positive relationship between individual diversity and team innovation (r = 0,53, p < 0,01). Team climate is positively related to both team innovation and individual diversity. There is a high positive relationship between team climate and team innovation (r = 0,82, p < 0,01), and between team climate and individual diversity (r = 0,49, p < 0,01) (Table 4).
Table 4
Means, Standard Deviations, Correlations. Means, Standard Deviations, Correlations
Variable M SD 1 2 3 4 5 6 1. Gender 1,41 0,49 - 2. Age 40,66 11,05 -0,21* - 3. Team size 11,38 14,33 0,00 -0,18* - 4. Team innovation 5,43 0,86 0,00 -0,15 -0,13 (0,85) 5. Team diversity 3,68 0,27 0,27** -0,31** -0,25** 0,53** (0,72) 6. Team climate 5,41 0,98 0,02 -0,11 -0,10 0,82** 0,49** (0,87)
** Correlation is significant at the 0.01 level (2-tailed) * Correlation is significant at the 0.05 level (2-tailed)
A closer look into the correlations of the control variables shows that no correlation is found between one of the three control variables and team innovation or team climate. By contrast, individual diversity does show a positive correlation with gender (r = 0,27, p < 0,01), and a negative relation with both age (r = -0,31, p < 0,01) and team size (r = -0,25, p < 0,01). There are also some rather small but significant correlations found between the control variables themselves. The results show that age has a negative relation with gender (r = -0,21, p < 0,05), and team size has a negative relation with age (r = -0,18, p < 0,05).
4.2 Hypothesis testing
Direct effect of individual diversity. A multiple hierarchical regression analysis is used to examine the relationship between the independent variable individual diversity and team innovation. The independent variables included in the model are extraversion,
agreeableness, conscientiousness, neuroticism, and openness to experience. The dependent variable is team innovation. As the five selected predictors of individual diversity are based on academic literature, a hierarchical regression model with the so-called ‘enter’ method is chosen. The variables that need to be controlled for are gender, age, and team size. It is important to make sure that the control variables do not explain the entire possible association between individual diversity and team innovation. Therefore, these variables are added into the regression model first. This way, the shared variability with individual diversity is
credited to the control variables. In this case it can be stated that any observed effect of individual diversity is ‘independent’ from the effect of the three control variables. The five individual diversity characteristics are added in model 2 of the same regression analysis.
Table 5
Hierarchical Regression Model of Individual Diversity.
R
R²
R²
Change
B
SE β
t
Step 1 0,22 0,05 Gender -0,07 0,16 -0,04 -0,41 Age -0,01 0,01 -0,19* -2,03 Team size -0,01 0,01 -0,17 -1,85 Step 2 0,64 0,41*** 0,36 Gender -0,18 0,14 -0,10 -1,29 Age -0,01 0,01 -0,10 -1,18 Team size -0,02 0,01 -0,31*** -4,05 Extraversion 0,34 0,09 0,31*** 3,65 Agreeableness 0,26 0,14 0,17* 1,98 Conscientiousness 0,23 0,11 0,16* 2,01 Neuroticism 0,35 0,10 0,30*** 3,50 Openness to experience 0,75 0,16 0,38*** 4,80 Statistical significance: *p <.05; **p <.01; ***p <.001A hierarchical multiple regression was performed to investigate the ability of the individual diversity characteristics extraversion, agreeableness, conscientiousness, openness to
experience, and neuroticism to predict for team innovation, after controlling for gender, age, and team size. Table 5 shows all relevant results of the analysis. After the first step of the hierarchical multiple regression, three predictors were entered: gender, age, and team size. This model was statistically not significant (F(3, 124) = 2,12, p = 0,10) and explained 4,9% of variance in team innovation. After entry of extraversion, agreeableness, conscientiousness, neuroticism, and openness to experience at Step 2, the total variance explained by the model as a whole was 41,3% (F(8, 119) = 10,47, p < 0,001). The introduction of extraversion, agreeableness, conscientiousness, neuroticism, and openness to experience explained
additionally 36,4% of variance in team innovation, after controlling for gender, age, and team size (R2 Change = 0,36, F(5, 119) = 14,78, p < 0,001). In the final model six out of the eight predictor variables were statistically significant, with openness to experience containing a higher Beta value (β = 0,38, p < 0,001) than extraversion (β = 0,31, p < 0,001), team size (β = -0.31, p < 0,001), neuroticism (β = 0,30, p < 0,001), agreeableness (β = 0,17, p < 0,05), and conscientiousness (β = 0,16, p < 0,05). Team size is the only predictor that has a significant negative effect on team innovation. As the beta’s indicate to what degree each variable affects team innovation if the effects of all other variables are held constant, it can be concluded that all five characteristics of individual diversity have a significant positive relation with team innovation. Based on these results it can be concluded that hypothesis 1a, 1b are supported. Therefore, it can be concluded that extraversion and openness to experience are indeed positively related to team innovation. The results also provide a clear answer to the three research questions (RQ1, 2, 3) that were established for the other three characteristics. Based on the results it can be concluded that neuroticism, agreeableness and conscientiousness are all positively related to team innovation. However, it is important to note that the level of analysis in this research is done on an individual level, which makes it important to
understand that these results only indicate that the characteristics have a positive association with team innovation. How the characteristics should be shaped across the team will be more elaborated upon in the section ‘4.5 extra analysis with teams as level of analysis’ at the end of the result section.
Direct effect of team climate. First, the expected direct effect of team climate on team innovation is examined via a multiple hierarchical regression analysis. The independent variables included in the model are vision, participative safety, task orientation, and support for innovation. The dependent variable is team innovation. Again, as the four selected predictors of team climate are based on academic literature, a hierarchical regression model with the so-called ‘enter’ method is chosen. The variables that need to be controlled for are gender, age, and team size and are added into the regression model first. In this case any observed effect of team climate is ‘independent’ from the effect of the three control variables. The four individual diversity characteristics are added in model 2 of the same regression analysis together with the comprehensive variable team climate.
