Introduction
The current labor market challenges human resources because employees who have the required skills are difficult to find (Hansen & Hauff, Reference Hansen, Hauff and Busold2019; Jackson, Reference Jackson2017; Priebe, Reference Priebe2023). In response, HR managers increasingly hire talent from groups that have been underrepresented in organizations, resulting in new hires from these groups in the US exceeding majority hires for the first time in 2019 (Long & van Dam, Reference Long and van Dam2019). Consequently, organizations’ teams have become more diverse (Homan, Buengeler, Eckhoff, van Ginkel, & Voelpel, Reference Homan, Buengeler, Eckhoff, van Ginkel and Voelpel2015; Wallace, Reference Wallace2021). Diverse teams vary in terms of age, educational background, ethnic affiliation, disability, or gender (Guillaume, Dawson, Otaye-Ebede, Woods, & West, Reference Guillaume, Dawson, Otaye-Ebede, Woods and West2017; Van Knippenberg & Mell, Reference Van Knippenberg and Mell2016), increasing the amount of unique information and values in teams.
Research identifies manifold positive outcomes of team diversity (Horwitz & Horwitz, Reference Horwitz and Horwitz2007; Joshi & Roh, Reference Joshi and Roh2009; Tasheva & Hillman, Reference Tasheva and Hillman2019). Team diversity enables teams to approach tasks from multiple viewpoints and improves creativity, problem-solving, and decision-making (Chen, Xie, & Zhou, Reference Chen, Xie and Zhou2020; Kim & Song, Reference Kim and Song2021; Smulowitz, Becerra, & Mayo, Reference Smulowitz, Becerra and Mayo2019; Yao, Liu, & He, Reference Yao, Liu and He2022). These findings are grounded in the variety perspective on team diversity, which focuses on how team diversity contributes to the team’s proficiency and ability to process information (Harrison & Klein, Reference Harrison and Klein2007). However, team diversity can also be viewed from a separation perspective, which focuses on how dissimilarity among team members separates them from each other and hinders collaboration (Harrison & Klein, Reference Harrison and Klein2007; Van der Vegt, van de Vliert, & Oosterhof, Reference Van der Vegt, van de Vliert and Oosterhof2003). These two perspectives of within-team diversity as variety or separation also have different implications for outcomes: Team diversity from the variety perspective is associated with positive outcomes, including creativity and high-quality decision-making, while the separation perspective is associated with negative outcomes like low cohesiveness and low task performance (Harrison & Klein, Reference Harrison and Klein2007). Team dissimilarity is a way of conceptualizing team diversity from a separation perspective (Van der Vegt et al., Reference Van der Vegt, van de Vliert and Oosterhof2003) and we use the term to distinguish the variety perspective (i.e., team diversity) from the separation perspective (i.e., team dissimilarity). The majority of research associates team dissimilarity with negative outcomes because perceived dissimilarity among team members leads to categorization processes (Ambady, Bernieri, & Richeson, Reference Ambady, Bernieri and Richeson2000; Hogg & Terry, Reference Hogg and Terry2000; Zellmer-Bruhn, Maloney, Bhappu, & Salvador, Reference Zellmer-Bruhn, Maloney, Bhappu and Salvador2008). Following the social categorization perspective, the categorization leads to splitting teams into subgroups (Hogg, Reference Hogg, Hogg and Tindale2001; Tajfel, Billig, Bundy, & Flament, Reference Tajfel, Billig, Bundy and Flament1971). Those subgroups are distinct from one another and lack intergroup communication and coordination (Brickson, Reference Brickson2000). Thus, teams whose dissimilarity separates team members from one another rather than how diversity can add to the team’s proficiency are associated with reduced problem-solving and decision-making skills (Brickson, Reference Brickson2000).
These negative team outcomes are especially harmful in the top management team (TMT) because, following upper echelons theory (Hambrick & Mason, Reference Hambrick and Mason1984), TMT members’ characteristics, such as the information and values they hold, are determinants of the organization’s strategic orientation, including the scope of innovation activities (Schubert & Tavassoli, Reference Schubert and Tavassoli2020). In addition, TMTs are inherently diverse, as TMT members’ functional backgrounds typically differ, with specialized knowledge rooted in the information they have and unique perspectives that are rooted in their values. Consequently, TMTs are typically characterized by informational and value dissimilarity (Miller, Chiu, Wesley, Vera, & Avery, Reference Miller, Chiu, Wesley, Vera and Avery2022; Narayan, Sidhu, & Volberda, Reference Narayan, Sidhu and Volberda2021). However, research on TMT diversity is largely one-sided. Much of the upper echelons literature takes the variety perspective focusing on how diversity enables the TMT to use a greater amount of information and values (e.g., Belderbos, Lokshin, Boone, & Jacob, Reference Belderbos, Lokshin, Boone and Jacob2022; Mihalache, Jansen, van den Bosch, & Volberda, Reference Mihalache, Jansen, van den Bosch and Volberda2012; Talke, Salomo, & Kock, Reference Talke, Salomo and Kock2011). Literature taking the separation perspective that focuses on how information or value dissimilarity may split the TMT and may cause harm for organizations is lacking (Song, He, & Yan, Reference Song, He and Yan2022). Examining the other side of the coin by taking the separation perspective and investigating TMT dissimilarity is necessary to get a more holistic picture of TMT diversity and its association with innovation capability.
To the best of our knowledge, the present study is the first to fill this research gap by exploring how informational and value dissimilarity between TMT members is associated with innovation capability. The study focuses on innovation capability since it bridges the willingness to pursue an innovation strategy with the successful implementation of innovation activities (Smith, Busi, Ball, & van der Meer, Reference Smith, Busi, Ball and van der Meer2008), that contribute to innovation performance and firm performance (e.g., Samson, Gloet, & Singh, Reference Samson, Gloet and Singh2017; Yeşil, Büyükbeşe, & Koska, Reference Yeşil, Büyükbeşe and Koska2013). In line with recent studies, we distinguish innovation capability into incremental and radical innovation capability (Forés & Camisón, Reference Forés and Camisón2016; Gui, Lei, & Le, Reference Gui, Lei and Le2022; Liu, Reference Liu2021) and conduct an exploratory study with TMT members from various industries in Germany.
The study sheds light on the complex association of the co-occurrence of informational and value dissimilarity with innovation capability. We add to the upper echelons literature by taking a separation perspective on TMT diversity, integrating the separation perspective of team diversity with upper echelons theory in the context of innovation capability. Furthermore, by distinguishing informational dissimilarity from value dissimilarity, we add to the scarce empirical literature on the role of differences in TMT member’s values. The study also adds to the social categorization literature by exploring empirically perceived TMT dissimilarity in the context of innovation capability. For organizations, the study can guide them to create high-performing TMTs to facilitate innovation capability.
The following section begins by drawing on upper echelons theory to lay out the role of information and values in TMTs. Next, we introduce social categorization in teams, specifically with regard to informational and value dissimilarity in teams and the need to distinguish incremental and radical innovation capability. We derive the research model from this foundation, which we examine using primary data from TMT members. After presenting the findings, we discuss the study’s contributions and limitations and offer directions for future research.
Theoretical premises
Upper echelons theory
Upper echelons theory (Hambrick, Reference Hambrick2007; Hambrick & Mason, Reference Hambrick and Mason1984) postulates that top managers’ characteristics influence their decision-making, thus shaping such organizational outcomes as innovation capability. It also acknowledges that a team of top managers, rather than a single top manager, typically makes strategic decisions (Hambrick, Reference Hambrick, Augier and Teece2018). Since TMTs must make strategic decisions under ambiguous circumstances and face more stimuli than they can process thoroughly (Hambrick, Reference Hambrick, Augier and Teece2018), their decisions are guided by their inherent characteristics instead of purely objective analysis (Hambrick & Mason, Reference Hambrick and Mason1984). Hambrick and Mason (Reference Hambrick and Mason1984) identify two clusters of characteristics that shape TMT member’s decision-making. Individual information about future events, alternatives, and consequences that is grounded in their educational, functional, and professional backgrounds (Hambrick & Mason, Reference Hambrick and Mason1984; Miller et al., Reference Miller, Chiu, Wesley, Vera and Avery2022) and individual values about what they want to achieve with the organization that derive from their attitudes, principles, and motivations (Finkelstein, Hambrick, & Canella, Reference Finkelstein, Hambrick and Canella2009; Li, Liu, & Wan, Reference Li, Liu and Wan2008).
A substantial body of literature examines how diversity in TMT members’ characteristics guides the strategic decisions they make in influencing organizational outcomes (e.g., Cuypers, Patel, Ertug, Li, & Cuypers, Reference Cuypers, Patel, Ertug, Li and Cuypers2022; Homberg & Bui, Reference Homberg and Bui2013; Ponomareva, Uman, Bodolica, & Wennberg, Reference Ponomareva, Uman, Bodolica and Wennberg2022). Building on the propositions of upper echelons theory, scholars argue that TMT members’ diverse information and values combine to the benefit of the organization (Hambrick & Mason, Reference Hambrick and Mason1984; Hemmert, Cho, & Lee, Reference Hemmert, Cho and Lee2024). For example, the literature identifies that variety in TMT members’ individual work experience, education, or functions expands the TMT’s pool of information from which its members can generate more ideas and spot additional opportunities, benefitting innovation (e.g., Belderbos et al., Reference Belderbos, Lokshin, Boone and Jacob2022; Qian, Cao, & Takeuchi, Reference Qian, Cao and Takeuchi2013; Schubert & Tavassoli, Reference Schubert and Tavassoli2020). Therefore, this literature conceptualizes TMT diversity from the variety perspective that benefits innovation.
Social categorization in dissimilar teams
The literature also proposes another perspective on team diversity, the separation perspective (Harrison & Klein, Reference Harrison and Klein2007). It contends that TMT members’ dissimilarity in terms of their information and values separates them from one another, inhibiting their ability to leverage information and values for decision-making (Harrison & Klein, Reference Harrison and Klein2007). Informational dissimilarity refers to perceiving differences in the information other team members hold because of differences in their education, functional, and professional backgrounds (Hobman, Bordia, & Gallois, Reference Hobman, Bordia and Gallois2004; Shemla, Meyer, Greer, & Jehn, Reference Shemla, Meyer, Greer and Jehn2016; Van der Vegt et al., Reference Van der Vegt, van de Vliert and Oosterhof2003). Value dissimilarity refers to perceiving differences in other team members’ perspectives because of differences in their principles that guide their work-related behavior, values, and motivation (Hobman et al., Reference Hobman, Bordia and Gallois2004; Shemla et al., Reference Shemla, Meyer, Greer and Jehn2016).
Based on their perceptions of dissimilarity, team members categorize others into subgroups that have high levels of similarity with and high levels of dissimilarity from themselves (Tajfel & Turner, Reference Tajfel, Turner, Austin and Worchel1986). According to the social categorization perspective (Hogg, Reference Hogg, Hogg and Tindale2001; Tajfel et al., Reference Tajfel, Billig, Bundy and Flament1971), the emergence of such subgroups leads to intergroup bias such that information from and the values of similar team members are integrated into decisions while those from dissimilar team members, i.e. from a different subgroup, are disregarded or devalued (Hogg, Reference Hogg, Hogg and Tindale2001). Consequently, team dissimilarity is associated with negative outcomes like a lack of helping behavior (Liao, Chuang, & Joshi, Reference Liao, Chuang and Joshi2008), empathy on the other team members’ viewpoints (Williams, Parker, & Turner, Reference Williams, Parker and Turner2007), and team turnover (Finkelstein et al., Reference Finkelstein, Hambrick and Canella2009; Jackson et al., Reference Jackson, Brett, Sessa, Cooper, Julin and Peyronnon1991). Specifically, research associates value dissimilarity in teams with such negative outcomes as low cohesiveness, low trust levels, poor communication (DeBode, Fox, & McSweeney, Reference DeBode, Fox and McSweeney2023; Dose & Klimoski, Reference Dose and Klimoski1999), poor team performance, poor team effectiveness (Jehn, Northcraft, & Neale, Reference Jehn, Northcraft and Neale1999), and low team involvement (Hobman et al., Reference Hobman, Bordia and Gallois2004).
Concerning, informational dissimilarity on a team, previous studies show inconsistent results. Pelled, Ledford, and Mohrman (Reference Pelled, Ledford and Mohrman1999)argue that educational dissimilarity is a sign of specialized knowledge that benefits perceived information access and decision-making influence, Adamovic (Reference Adamovic2020)and Chattopadhyay, Finn, and Ashkanasy (Reference Chattopadhyay, Finn and Ashkanasy2010) indicate positive and negative outcomes. Adamovic (Reference Adamovic2020) suggests that it can encourage information exchange and information elaboration but can also reduce team members’ willingness to contribute their information if it is unique to the team. Adamovic (Reference Adamovic2020) argues that this is because team members expect disadvantages upon perceiving informational dissimilarity between the team members. According to Chattopadhyay et al. (Reference Chattopadhyay, Finn and Ashkanasy2010), professional dissimilarity, one type of informational dissimilarity, decreases negative emotions and negative behaviors, such as yelling or making sarcastic comments toward other team members, by high-status team members, but increases these negative behaviors among low-status team members. Finally, Hobman et al. (Reference Hobman, Bordia and Gallois2004) find that informational dissimilarity decreases team involvement because team members overemphasize common information and neglect unique information, which reduces communication within the team. Further, Van der Vegt et al. (Reference Van der Vegt, van de Vliert and Oosterhof2003) find that team members’ categorization processes decrease members’ identification with the team.
Interestingly, research tends to focus on one dissimilarity characteristic at a time, such as educational background, professional background, or work behavior, although the reality is that team members simultaneously respond to different kinds of dissimilarity. For example, informational and value dissimilarity are characteristic of the specialized knowledge and perspectives in the TMT and are present within the TMT at the same time. This co-occurrence of different types of dissimilarity evokes simultaneous categorization processes (Grigoryan, Cohrs, Boehnke, van de Vijver, & Easterbrook, Reference Grigoryan, Cohrs, Boehnke, van de Vijver and Easterbrook2022). Considering the co-occurrence of different dissimilarities is necessary since categorization into subgroups is possible only when a team deals with a small number of dissimilarity characteristics (Crisp & Hewstone, Reference Crisp and Hewstone2007). Research shows that people cannot process categorization of more than four categories (Halford, Baker, McCredden, & Bain, Reference Halford, Baker, McCredden and Bain2005). If more than four types of dissimilarity co-occur, team members can no longer form clearly distinguishable subgroups (Macrae & Bodenhausen, Reference Macrae and Bodenhausen2001), so categorization processes will result in subgroups that may be dissimilar in one characteristic but similar in another (Chung et al., Reference Chung, Liao, Jackson, Subramony, Colakoglu and Jiang2015). For example, categorization may lead to forming subgroups that differ in values but share similar information. Multiple social categorization theory (Crisp & Hewstone, Reference Crisp and Hewstone2007) postulates that the more dissimilar a team, the less likely it is to form subgroups. As overlaps between the subgroups increase, no harmful categorization of team members occurs, and team members can leverage information and values from one another (Crisp & Hewstone, Reference Crisp and Hewstone2007). Overall, then, the social categorization perspective and multiple social categorization theory diverge: The former predicts that team dissimilarity leads to intergroup biases and negative team outcomes, for example low team effectiveness (Hogg, Reference Hogg, Hogg and Tindale2001; Tajfel et al., Reference Tajfel, Billig, Bundy and Flament1971). The latter predicts positive outcomes for highly dissimilar teams because no harmful categorization occurs, so no intergroup bias arises (Crisp & Hewstone, Reference Crisp and Hewstone2007). This controversy has not yet been solved in TMT diversity research. In fact, research around TMT diversity focuses on the variety perspective and neglects the separation perspective. Therefore, our study seeks to shed light on the role of the separation perspective in TMTs, thus contributing to clearing up the controversy of categorization in TMTs.
Incremental and radical innovation capability
Innovation capability is the ability to apply and integrate resources to generate innovations (Lawson & Samson, Reference Lawson and Samson2001) and refers to the use of resources rather than their availability (Menguc, Auh, & Yannopoulos, Reference Menguc, Auh and Yannopoulos2014; Teece, Pisano, & Shuen, Reference Teece, Pisano and Shuen1997). The innovation literature classifies innovation capability into incremental and radical (e.g., Lei, Ha, & Le, Reference Lei, Ha and Le2019; Slater, Mohr, & Sengupta, Reference Slater, Mohr and Sengupta2014; Wang, Lu, Zhao, Gong, & Li, Reference Wang, Lu, Zhao, Gong and Li2013). Incremental innovation capability is the ability to ‘generate innovations that refine and reinforce existing products and services’ (Subramaniam & Youndt, Reference Subramaniam and Youndt2005, p. 452). That is, to make moderate changes in existing services and products, as the underlying processes and knowledge are reinforced and do not significantly change (Menguc et al., Reference Menguc, Auh and Yannopoulos2014). In contrast, radical innovation capability is the ability to ‘generate innovations that significantly transform existing products and services’ (Subramaniam & Youndt, Reference Subramaniam and Youndt2005, p. 452). That is, the ability to make major changes to existing products, services, and processes or develop new ones by, for example, using breakthrough technology (Menguc et al., Reference Menguc, Auh and Yannopoulos2014). The capability to generate radical innovation requires a substantial change or transformation of prevailing processes and knowledge by, for example, using new resources or developing new capabilities (McGrath, Reference McGrath2001). Research shows that incremental innovation capability is of particular relevance to organizations when improving the consumer experience (Menguc et al., Reference Menguc, Auh and Yannopoulos2014), while radical innovation capability may be necessary to keep up with rapidly changing customer demands and environmental changes (Slater et al., Reference Slater, Mohr and Sengupta2014).
Increasingly, research distinguishes between incremental and radical innovation capability and identifies distinct antecedents for each type (e.g., Forés & Camisón, Reference Forés and Camisón2016; Gui et al., Reference Gui, Lei and Le2022; Han et al., Reference Han, Lyu, Ji, Zhu, Su and Bao2020; Slater et al., Reference Slater, Mohr and Sengupta2014), but most research on the implications of TMT diversity does not differentiate between the two (e.g., Azeem & Baker, Reference Azeem and Baker2020; Quintana-García & Benavides-Velasco, Reference Quintana-García and Benavides-Velasco2016; Song et al., Reference Song, He and Yan2022). Heyden, Sidhu, van den Bosch, and Volberda’s (Heyden, Sidhu, van den Bosch, & Volberda, Reference Heyden, Sidhu, van den Bosch and Volberda2012) study is an exception, as it argues that TMT members’ experience diversity increases the TMT’s pool of knowledge and, therefore, increases the set of feasible ideas for radical innovation. In contrast, little diversity in TMT members’ experience deepens the team’s knowledge but leads to compliance with the status quo and, therefore, to incremental innovation capability. These results suggest that the TMT’s composition in terms of diversity has distinct implications for the team’s type of innovation capability.
Research model
We use an exploratory study design to shed light on the association between TMT members’ informational dissimilarity/value dissimilarity and incremental innovation capability/radical innovation capability. Exploratory research is beneficial if the goal is to extend research by examining relationships when underlying information is scant (Swedberg, Reference Swedberg, Elman, Gerring and Mahoney2020). Although the body of literature concerning TMT diversity and innovation is substantial, it is deficient in several respects.
First, most upper echelons research uses objective proxies to measure the diversity of information and values. The proxy for diversity in values relies on donations to political parties (Narayan et al., Reference Narayan, Sidhu and Volberda2021), whereas studies often measure informational diversity using demographic proxies like educational background, education level, functional specialization, and experiences across industries (e.g., Azeem & Baker, Reference Azeem and Baker2020; Chemmanur, Kong, Krishnan, & Yu, Reference Chemmanur, Kong, Krishnan and Yu2019; Chung, Cho, & Kang, Reference Chung, Cho and Kang2018; Li & Huang, Reference Li and Huang2019; Li, Liu, Lin, & Ma, Reference Li, Liu, Lin and Ma2016; Qian et al., Reference Qian, Cao and Takeuchi2013; Wu & Park, Reference Wu and Park2019; Zhong, Song, & Chen, Reference Zhong, Song and Chen2021). However, Miller et al. (Reference Miller, Chiu, Wesley, Vera and Avery2022) claim that these demographic proxies perform poorly, which questions the predictions derived from them and the underlying mechanisms they help to identify. Consequently, the authors call for a more direct assessment of TMT characteristics and TMT diversity. A perception measure ensures that salient differences are measured, which may provide a more suitable proxy because team members act on their perceptions rather than reality (Lawrence, Reference Lawrence1997) and may not see all objective differences as salient (Randel, Reference Randel2002). However, few studies assess TMT diversity in terms of TMT members’ perceptions (Alexiev, Jansen, van den Bosch, & Volberda, Reference Alexiev, Jansen, van den Bosch and Volberda2010; Mehrabi, Coviello, & Ranaweera, Reference Mehrabi, Coviello and Ranaweera2021; Mihalache et al., Reference Mihalache, Jansen, van den Bosch and Volberda2012).
A second respect in which the literature concerning TMT diversity and innovation is deficient lies in distinguishing between informational diversity and value diversity in the TMT. Some studies (Alexiev et al., Reference Alexiev, Jansen, van den Bosch and Volberda2010; Mehrabi et al., Reference Mehrabi, Coviello and Ranaweera2021; Mihalache et al., Reference Mihalache, Jansen, van den Bosch and Volberda2012) use a direct measure from Campion, Medsker, and Higgs (Reference Campion, Medsker and Higgs1993), which asks TMT members about the TMT’s characteristics, but these studies focus on informational characteristics and do not address values. In fact, most studies rely on proxies that reflect informational differences like educational background, functional background, or expertise (e.g., Chung et al., Reference Chung, Cho and Kang2018; Li & Huang, Reference Li and Huang2019; Talke et al., Reference Talke, Salomo and Kock2011) and disregard diversity in values (Narayan et al., Reference Narayan, Sidhu and Volberda2021). Differentiating the two types of dissimilarity would allow for a more holistic assessment of the implications of TMT diversity, as values shape team processes (Chrobot-Mason, Ruderman, Weber, & Ernst, Reference Chrobot-Mason, Ruderman, Weber and Ernst2009).
Third, upper echelons research focuses on the variety perspective of team diversity, which conceptualizes team diversity as a process in which diversity in information and values are additive (Harrison & Klein, Reference Harrison and Klein2007), thus accelerating the generation of feasible ideas that benefit innovation (e.g., Belderbos et al., Reference Belderbos, Lokshin, Boone and Jacob2022; Boone, Lokshin, Guenter, & Belderbos, Reference Boone, Lokshin, Guenter and Belderbos2019; Mehrabi et al., Reference Mehrabi, Coviello and Ranaweera2021). However, team diversity also contributes to forming homogeneous subgroups that distinguish groups from each other (Hogg, Reference Hogg, Hogg and Tindale2001; Tajfel et al., Reference Tajfel, Billig, Bundy and Flament1971), separating team members and hindering them from working together effectively (Harrison & Klein, Reference Harrison and Klein2007). Research that examines TMT diversity from a separation perspective, that is, how diversity in information and values separate TMT members from each other, is lacking (Song et al., Reference Song, He and Yan2022). Therefore, whether the implications of the social categorization perspective (Hogg, Reference Hogg, Hogg and Tindale2001; Tajfel et al., Reference Tajfel, Billig, Bundy and Flament1971) or multiple social categorization theory (Crisp & Hewstone, Reference Crisp and Hewstone2007) apply in the context of informational and value dissimilarity between TMT members remains unexplored, as are whether and to what extent TMTs suffer from categorization processes and their detrimental effects on innovation capability.
Finally, scarce upper echelons research distinguishes between incremental and radical innovation capability. While the types of innovation capability suggest different strategic orientations (McDermott & O’Connor, Reference McDermott and O’Connor2002; Un, Reference Un2010), both types are suitable if organizations are to innovate (Gui et al., Reference Gui, Lei and Le2022; Sheng & Chien, Reference Sheng and Chien2016). In addition, the antecedents of the types of innovation capability are distinct (e.g., Forés & Camisón, Reference Forés and Camisón2016; Gui et al., Reference Gui, Lei and Le2022; Slater et al., Reference Slater, Mohr and Sengupta2014). Therefore, the ability to derive implications requires distinguishing between incremental and radical innovation capability, but few studies address how TMT diversity is associated with the types of innovation capability.
Taken together, the lack of literature that addresses the association between TMT diversity and incremental innovation capability/radical innovation capability and that takes the separation perspective in addressing informational and value dissimilarity results in scant information on the expected direction and magnitude of associations. Therefore, an exploratory study design in an initial examination of how the co-occurrence of the two types of dissimilarity are associated with incremental and radical innovation capability is required.
Figure 1 shows the research model of this study.
Figure 1. Research model.
Method
Data collection and sample
We use a quantitative approach and collect data through an online survey. We survey TMT members from organizations that have more than 250 full-time employees across industries in Germany. Table 1 provides details about the sample composition. A key advantage of using a survey is that it enables the respondents’ perceptions of their team members’ dissimilarity to be assessed. Respondents were recruited via an online panel provider. To remedy potential issues in data quality, we conducted multiple checks. We eliminated respondents who completed the survey too quickly, deleted surveys with inconsistencies in answers (e.g., indicating that more women were in the TMT than TMT members overall), and checked the respondents’ IP addresses to ensure that only unique IP addresses were included in the final sample. These procedures resulted in a final data set of 91 observations.
Table 1. Sample composition
N = 91.
Measurement of constructs and variables
Weuse established scales to measure the latent variables of our research model and, since we surveyed top managers from German organizations, we translated the scales into German following Brislin’s (Reference Brislin1970) back-translation procedure to ensure equivalence between original and translated scale such that a third person conducted the back-translation. Unless stated otherwise, we measure all items on a seven-point Likert-type scale ranging from 1 = strongly disagree to 7 = strongly agree. Table 2 shows all items used in the study.
Table 2. Measurement model
a Item dropped due to low item reliability.
Incremental innovation capability and radical innovation capability, endogenous variables
We measure the endogenous variables with the scales developed by Subramaniam and Youndt (Reference Subramaniam and Youndt2005). Respondents are asked to describe their organizations’ innovations in the last 5 years compared to their competitors. Sample items are ‘Innovations that reinforce your prevailing product/service lines’. for incremental innovation capability and ‘Innovations that make your prevailing product/service lines obsolete’. for radical innovation capability.
Informational dissimilarity and value dissimilarity, exogenous variables
We opt to measure perceived dissimilarity since it has a more substantial effect than objective dissimilarity does (Turban & Jones, Reference Turban and Jones1988), and team members may not see all objective differences as salient (Randel, Reference Randel2002). A perception measure assesses whether the respondents perceive dissimilarities between themselves and other TMT members and ensures that salient differences are captured, which is essential for categorization processes (Ambady et al., Reference Ambady, Bernieri and Richeson2000; Zellmer-Bruhn et al., Reference Zellmer-Bruhn, Maloney, Bhappu and Salvador2008).
We measure the exogenous variables using an adapted version of Hobman et al.’s (Reference Hobman, Bordia and Gallois2004) perceived dissimilarity scale. To avoid a priming effect in the perception of dissimilarity, respondents first answered the items about their perceptions of the dissimilarity among TMT members before providing information about its objective composition.
Control variables
To address the possibility of alternative explanations for the organization’s innovation capability, we include controls on the organizational, team, and individual levels. At the organizational level, we include the organization’s size, industry, and scope of internationalization. We use the number of full-time employees to measure organization size (Kianto, Sáenz, & Aramburu, Reference Kianto, Sáenz and Aramburu2017). Larger organizations may have access to more resources, which influences innovation capability (Camisón-Zornoza, Lapiedra-Alcamí, Segarra-Ciprés, & Boronat-Navarro, Reference Camisón-Zornoza, Lapiedra-Alcamí, Segarra-Ciprés and Boronat-Navarro2004). Respondents indicated the organization’s industry using a provided list. The organization’s industry imposes constraints on organizations, which affects their innovativeness (Acar, Tarakci, & van Knippenberg, Reference Acar, Tarakci and van Knippenberg2019). To measure the scope of internationalization, we ask respondents to indicate the number of countries the organization operates in (Zahra & George, Reference Zahra, George, Hitt, Ireland, Camp and Sexton2017). The wider scope of international activities increases the organization’s incentive to innovate and access to resources, stimulates its ability to adapt to various needs, and specifically fosters radical innovation capability (Freixanet & Rialp, Reference Freixanet and Rialp2022).
The controls we include at the team level are the TMT’s size, objective TMT educational diversity, and objective TMT gender diversity. We measure TMT size as the number of TMT members, as larger TMTs can find it difficult to leverage the potential of individual team members (Bass, Reference Bass2019). We measure objective TMT educational diversity using a binary variable that equals ‘1’ if at least one of the TMT members differs from the other in educational background, such as in their major area of study, and ‘0’ if all TMT members have the same educational background or their backgrounds are unknown. Objective TMT’s educational diversity is positively associated with the likelihood that it will engage in innovation activities because it increases its ability to identify problems (Schubert & Tavassoli, Reference Schubert and Tavassoli2020). Following previous studies (e.g., Hemmert et al., Reference Hemmert, Cho and Lee2024; Quintana-García & Benavides-Velasco, Reference Quintana-García and Benavides-Velasco2016), we also include objective TMT gender diversity, measured using the Blau Index (Blau, Reference Blau1977). We ask respondents to indicate the number of women in the TMT and derive the number of male TMT members from the size of the TMT. TMT gender diversity is associated with broader perspectives, and studies identify a positive association between TMT gender diversity and innovation (Dezsö & Ross, Reference Dezsö and Ross2012; Hemmert et al., Reference Hemmert, Cho and Lee2024; Miller & Del Carmen Triana, Reference Miller and Del Carmen Triana2009).
The individual-level control we use is functional background, measured as the functional area with which TMT members are associated. Functional background influences the involvement in decisions (Bunderson, Reference Bunderson2003) and can, thus, affect the strategic decision to pursue innovation activities.
Common method bias
Since we collected the variables of interest from a single source, common method bias could be an issue. To address this problem, we ensured that the variables were captured in separate sections of the survey (Podsakoff, MacKenzie, & Podsakoff, Reference Podsakoff, MacKenzie and Podsakoff2003). We assessed common method bias with the marker variable approach (Lindell & Whitney, Reference Lindell and Whitney2001), using ‘having littered the environment’ as the marker variable, which is not theoretically related to the research model. The correlations do not substantially change after the marker variable’s effect is parceled out. Therefore, the results of neither the marker variable test nor Harman’s single factor test indicate an issue with common method bias.
Key informant bias
To mitigate the potential for key informant bias associated with the self-reporting of incremental and radical innovation capability, we comply with Homburg, Klarmann, Reimann, and Schilke’s (Reference Homburg, Klarmann, Reimann and Schilke2012) suggestion to survey informants who hold high hierarchical positions, as those in higher positions tend to provide more accurate evaluations, than those in lower positions. We also asked the participants to rate their competence in answering the questions and eliminated four observations that indicated low competence. Then we follow Homburg and Wielgos (Reference Homburg and Wielgos2022) in rerunning the analysis by dropping seven observations that indicate moderate competence. The results did not change after dropping these observations, indicating that the participants who identified as moderately confident did not influence the results.
Analysis and findings
We employ partial least squares structural equation modeling (PLS-SEM) for the data analysis. PLS-SEM is preferable to covariance-based structural-equation modeling with small sample sizes and few indicators (Mehmetoglu & Venturini, Reference Mehmetoglu and Venturini2021). To estimate the required sample size, we follow the recommendations of Hair, Gult, Ringle and Sarstedt (Reference Hair, Gult, Ringle and Sarstedt2021), indicating a minimum sample size of 69, considering the minimum path coefficient and a power of 80% in relation to a significance level of 5%. Moreover, choosing PLS-SEM rather than multiple linear regression analysis allows the strength of associations to be compared (Hair et al., Reference Hair, Hult, Ringle, Sarstedt, Danks and Ray2021). We test the research model using PLS-SEM 10,000 bootstrapping to determine the significance of the correlations.
Reliability and validity of measures
Reflective measurement model
The measurement model contains four reflective measurement models: Informational dissimilarity, value dissimilarity, incremental innovation capability, and radical innovation capability. We assess the reliability and validity of the measures following the recommendation of Mehmetoglu and Venturini (Reference Mehmetoglu and Venturini2021). Table 2 shows the details about these assessments.
In assessing radical innovation capability, we found that one indicator loaded below the suggested threshold value of 0.708 (Hair, Risher, Sarstedt, & Ringle, Reference Hair, Risher, Sarstedt and Ringle2019) (see Table 2), and the AVE for the latent variable increased after we excluded it, so we eliminated it from the model. We also eliminated one indicator of the latent variable informational dissimilarity because of substantial cross-loading. In the final model, all standardized loadings are above the suggested threshold of 0.708. The AVE values exceed the recommended threshold level of 0.5. Dillon-Goldstein’s rho (D.G. rho), Rho (a) (ρA), and Cronbach’s α values are above the suggested level of 0.7 as well (Bagozzi, Yi, & Phillips, Reference Bagozzi, Yi and Phillips1991; Mehmetoglu & Venturini, Reference Mehmetoglu and Venturini2021) (see Table 2). These results indicate the model’s reliability and convergent validity.
We assess discriminant validity using the Fornell-Larcker criterion and the heterotrait monotrait ratio of correlations (HTMT). All AVE values of the model’s latent variables are larger than the squared correlations, indicating discriminant validity (Fornell & Larcker, Reference Fornell and Larcker1981) (see Table 3). We find that all values of the HTMT are below the threshold value of 0.9 for conceptually similar constructs (Hair et al., Reference Hair, Risher, Sarstedt and Ringle2019). We also calculate the HTMT2 since the HTMT tends to be upward biased for correlated latent variables (Henseler, Reference Henseler2021) and find that the HTMT2 is also below the threshold value of 0.9, indicating discriminant validity.
Table 3. Discriminant validity
The table indicates the squared interfactor correlations. AVEs are in the diagonal in bold. Number 5 to 11 are measured through a single indicator, and therefore average variance extracted (AVE) cannot be computed.
Formative measurement model
Following Hair et al.’s (Reference Hair, Gult, Ringle and Sarstedt2021) recommendations, we include the categorical control variables industry and functional background as a series of binary variables. We assess the formative measurement models by considering the weights of the formative measures and the variance inflation factor (VIF) (Mehmetoglu & Venturini, Reference Mehmetoglu and Venturini2021). Some of the measures’ item weights are not significant, but, following Mehmetoglu and Venturini’s (Reference Mehmetoglu and Venturini2021) recommendation, we keep them in the respective formative measure since each item represents an essential part of the respective construct. We examine possible multicollinearity among the items by calculating the VIF for both constructs. The VIF values of both formative measurement models are below 2.5, so they suggest no issues of multicollinearity (Mehmetoglu & Venturini, Reference Mehmetoglu and Venturini2021). Therefore, overall, the measurement model provides evidence of reliability and validity, and we continue with the evaluation of the structural model in the next section.
Findings from the exploratory study
We assess multicollinearity of the structural model by calculating the VIF. All values are below 2.5, indicating no multicollinearity issues (Hair et al., Reference Hair, Risher, Sarstedt and Ringle2019). The R 2 for incremental innovation capability is 44.0 percent and that for radical innovation capability is 42.1 percent. Table 4 presents the structural model that includes the direct associations. The results suggest a positive association between informational dissimilarity and incremental innovation capability (Informational dissimilarity → incremental innovation capability; β = .586, p < .001), so TMT members who perceive their team members as highly dissimilar from them in terms of information report higher incremental innovation capability than TMT members who perceive their team members as more homogeneous in terms of information. We also find that perception in terms of dissimilarity in values is negatively associated with incremental innovation capability (Value dissimilarity → incremental innovation capability; β = − .273, p < .05). Therefore, whereas TMT members who perceive their team members as dissimilar in terms of information associate that dissimilarity with higher incremental innovation capability, they associate perceiving their fellow TMT members as dissimilar in values is associated with lower incremental innovation capability.
Table 4. Structural model – Direct associations
Fit statistics for the structural model: R 2 for incremental innovation capability: 44.0%,.
R 2 for radical innovation capability: 42.1%.
The findings show that informational dissimilarity is not associated with radical innovation capability (Informational dissimilarity → radical innovation capability; β = − .137, p > .1), but value dissimilarity has a positive and significant association with radical innovation capability (Value dissimilarity → radical innovation capability; β = .600, p < .001). In short, the TMT members in the sample who perceive a high level of value dissimilarity also report a high level of radical innovation capability.
The findings show that informational dissimilarity is not associated with radical innovation capability (Informational dissimilarity → radical innovation capability; β = − .137, p > .1), but value dissimilarity has a positive and significant association with radical innovation capability (Value dissimilarity → radical innovation capability; β = .600, p < .001). In short, the TMT members in the sample who perceive a high level of value dissimilarity also report a high level of radical innovation capability.
We follow Chin, Marcolin, and Newsted’s (Chin, Marcolin, & Newsted, Reference Chin, Marcolin and Newsted2003) two-stage approach to test for interaction effects of informational and value dissimilarity. The first stage estimates the latent variables and builds the main effects, as reported in Table 4. The second stage uses the latent variable scores from the first stage to build the interactive model (Mehmetoglu & Venturini, Reference Mehmetoglu and Venturini2021). One disadvantage of the two-stage approach is the possibility of multicollinearity issues, so we calculate the VIF to assess multicollinearity in the interactive model. All values are below 2.5, indicating no multicollinearity issues (Hair et al., Reference Hair, Risher, Sarstedt and Ringle2019). The R 2 value for incremental innovation capability is 40.6 percent for incremental innovation capability and that for radical innovation capability is 47.6 percent. Table 5 shows the structural model with the interaction effects included.
Table 5. Structural model – Interactive associations
Fit statistics for the structural model: R 2 for incremental innovation capability: 40.6%,.
R 2 for radical innovation capability: 47.6%.
† < p < .05; ** p < .01; *** p < .001.
The findings indicate that the interaction between informational dissimilarity and value dissimilarity is not associated with incremental innovation capability (Informational dissimilarity × value dissimilarity → incremental innovation capability; β = − .025, p > .1). The findings also show no significant interaction between informational dissimilarity and value dissimilarity on radical innovation capability (Informational dissimilarity × value dissimilarity → radical innovation capability; β = .167, p> .1). Hence, the associations between informational dissimilarity and incremental and radical innovation are not contingent on the level of value dissimilarity, and the associations between value dissimilarity and incremental and radical innovation are not contingent on the level of informational dissimilarity.
Sensitivity analysis
To determine whether the reported findings are robust to alternative explanations, we follow Hair et al.’s (Reference Hair, Risher, Sarstedt and Ringle2019) recommendation. To check for unobserved heterogeneity, we employ a response-based unit segmentation in the model that explores the direct associations.Footnote 1 The algorithm uses the residuals of the PLS-SEM analysis to segment the data and analyzes it for each segment. If unobserved heterogeneity is present, the segmented model’s results differ from those of the overall model (Mehmetoglu & Venturini, Reference Mehmetoglu and Venturini2021). We use the group quality index and the permutation test to assess the results of the response-based unit segmentation, and these tests show no indication of unobserved heterogeneity.
To check for nonlinear associations, we first run Ramsey’s (Reference Ramsey1969) regression equation specification test and find no indication of nonlinear associations. Then we follow Svensson et al. (Reference Svensson, Ferro, Høgevold, Padin, Carlos Sosa Varela and Sarstedt2018) in creating a quadratic term of the exogenous variables like an interaction and rerun the model to check for a U-shaped association. The results show a significant and positive quadratic term of value dissimilarity in association with incremental innovation capability (Value dissimilarity × value dissimilarity → incremental innovation capability; β = .324, p < .05). A significant and positive coefficient of the quadratic term is indicative of a U-shaped association (Haans & Pieters, Reference Haans and Pieters2016), so we check whether the turning point lies within the data by setting the partial derivative of the model equation to 0 and solving for it (Obadia & Robson, Reference Obadia and Robson2021). The calculated turning point is 0.200 and is within the data range, indicating a U-shaped association. However, running further analyses by splitting the sample at the estimated turning point or specifying the model such that only a quadratic term of one exogenous variable is included provides no indication of a U-shaped association between value dissimilarity and incremental innovation capability. Therefore, we conclude that value dissimilarity and incremental innovation capability are negatively associated.
Discussion
This study is a first attempt to use perceived dissimilarity in the TMT to explore TMT diversity from a separation perspective instead of the more common variety perspective (Harrison & Klein, Reference Harrison and Klein2007; Shemla et al., Reference Shemla, Meyer, Greer and Jehn2016). The study differentiates between two TMT characteristics: Informational dissimilarity, which refers to perceiving differences in the information other team members hold because of differences in their education, functional, and professional backgrounds (Hobman et al., Reference Hobman, Bordia and Gallois2004; Shemla et al., Reference Shemla, Meyer, Greer and Jehn2016; Van der Vegt et al., Reference Van der Vegt, van de Vliert and Oosterhof2003), and value dissimilarity, that is perceiving differences in other team members’ perspectives because of differences in their principles that guide their work-related behavior, values, and motivation (Hobman et al., Reference Hobman, Bordia and Gallois2004; Shemla et al., Reference Shemla, Meyer, Greer and Jehn2016). In doing so, we offer a more holistic examination of the associations between two types of TMT members’ dissimilarity and two types of innovation capability, incremental and radical.
The study’s findings indicate that informational dissimilarity is positively associated with incremental innovation capability and value dissimilarity is negatively associated with incremental innovation capability. We also find a positive association between value dissimilarity and radical innovation capability. However, the association between informational dissimilarity and radical innovation capability and the associations between the interactions of the two types of dissimilarity with the two types of innovation capability are nonsignificant.
The positive association between informational dissimilarity and incremental innovation capability likely occurs because, in contrast to the social categorization perspective, the perception of informational dissimilarity between TMT members evokes no harmful categorization of the TMT members into subgroups, indicating support for the multiple social categorization theory (Crisp & Hewstone, Reference Crisp and Hewstone2007). It appears that informational dissimilarity between the TMT members increases information processing, as the TMT members draw on other members’ dissimilar information (Adamovic, Reference Adamovic2020) to examine issues in depth and build on each other’s information to identify new alternatives, thus increasing incremental innovation capability (Forsman, Reference Forsman2009).
Studies that adopt a variety perspective show that information diversity increases radical innovation capability, as TMT members draw on a broader pool of information to identify a larger number of feasible alternatives (Heyden et al., Reference Heyden, Sidhu, van den Bosch and Volberda2012; Mehrabi et al., Reference Mehrabi, Coviello and Ranaweera2021). However, the results of this study suggest that informational dissimilarity is not associated with radical innovation capability. One possible explanation for this result is grounded in the social categorization perspective (Hogg, Reference Hogg, Hogg and Tindale2001; Tajfel et al., Reference Tajfel, Billig, Bundy and Flament1971) that informational dissimilarity leads to a harmful categorization of TMT members, impeding TMT members’ use of information such that it has no impact on radical innovation capability. Another explanation is based on the characteristics that benefit radical innovation capability in that radical innovation capability arises in particular from considering multiple perspectives of an issue instead of an in-depth analysis of information (Forsman, Reference Forsman2009). Thus, informational dissimilarity appears insufficient to stimulate the substantive transformation and change that characterizes radical innovation capability (Subramaniam & Youndt, Reference Subramaniam and Youndt2005). Informational dissimilarity focuses on the information that is in the TMT, rather than the multiple perspectives that characterize value dissimilarity (Hobman et al., Reference Hobman, Bordia and Gallois2004). Consequently, while informational dissimilarity appears to stimulate an in-depth analysis of existing information (Adamovic, Reference Adamovic2020) such that it benefits incremental innovation capability, it has no association with radical innovation capability.
The findings indicate that value dissimilarity is positively associated with radical innovation capability but is negatively associated with incremental innovation capability. The positive association of value dissimilarity and radical innovation capability suggests that TMT members can utilize the different perspectives contained in value dissimilarity. Thus, the finding indicates support for multiple social categorization theory (Crisp & Hewstone, Reference Crisp and Hewstone2007), as no harmful categorization that opposes the use of the dissimilar values in the TMT seems to take place. Value dissimilarity may be associated with differing perspectives on the scope and direction of innovation activities, so it appears to broaden perspectives and inspire visionary ideas that require significant change to realize (Bourne & Jenkins, Reference Bourne and Jenkins2013; Lencioni, Reference Lencioni2002; Slater et al., Reference Slater, Mohr and Sengupta2014), that is, radical innovation capability.
The negative association between value dissimilarity and incremental innovation capability suggests several possible explanations. First, the finding suggests that TMT members categorize harmfully based on their perceived dissimilarity in values, impeding use of the dissimilar values and supporting the social categorization perspective (Hogg, Reference Hogg, Hogg and Tindale2001; Tajfel et al., Reference Tajfel, Billig, Bundy and Flament1971). However, the positive association between value dissimilarity and radical innovation capability suggests that TMT members do not categorize TMT members harmfully based on value dissimilarity, so it seems likely that value dissimilarity between TMT members is not associated with the in-depth information analysis that is necessary for incremental innovation capability (Forsman, Reference Forsman2009). Instead, value dissimilarity enables the TMT to generate ideas and alternatives that go past the incremental, shifting the focus from in-depth analysis of information and thus leading to a negative association of value dissimilarity and incremental innovation capability.
The results also indicate no association between the interaction of informational and value dissimilarity with incremental/radical innovation capability. Multiple social categorization theory implies a positive association here, as the dissimilarities reinforce each other such that no harmful categorization occurs and TMT members can build on their dissimilar information and values (Crisp & Hewstone, Reference Crisp and Hewstone2007). In contrast, the social categorization perspective suggests a negative interaction, as categorization occurs and intergroup biases arise with the higher level of perceived dissimilarity, impeding the use of both information and values (Hogg, Reference Hogg, Hogg and Tindale2001; Tajfel et al., Reference Tajfel, Billig, Bundy and Flament1971). Hence, these findings are inconclusive regarding the predictions of multiple social categorization theory and the social categorization perspective. Still, the nonsignificant interaction suggests that informational and value dissimilarity stimulates the use of different paths to using information and perspectives that occur independently.
Overall, the study’s results suggest that the source of dissimilarity determines its association with the type of innovation capability. Informational dissimilarity appears to enable the TMT to use information to innovate incrementally but is insufficient for generating radical innovation capability. Value dissimilarity appears to provide other perspectives that promote transformation and substantive change and benefit radical innovation capability but contains characteristics that are detrimental to incremental innovation capability. The findings emphasize distinguishing the two types of dissimilarity, as each has a different way of using information and values for the respective type of innovation capability. Thus, perceptions of dissimilarity do not appear to evoke harmful categorization processes, suggesting support for multiple social categorization theory and that dissimilarity can also have positive implications.
Implications for theory
This study extends upper echelons theory in two ways: First, it integrates the separation perspective of within-team diversity into upper echelons theory for the context of incremental and radical innovation capability. We apply perceived dissimilarity as a measure of separation to examine how the perception of dissimilarity from other TMT members is associated with the two types of innovation capability. We employ informational dissimilarity as a proxy for perceived information differences from various origins between TMT members and we use value dissimilarity to assess the perceived differences in perspectives. In doing so, we respond to calls from Narayan et al. (Reference Narayan, Sidhu and Volberda2021) and Miller et al. (Reference Miller, Chiu, Wesley, Vera and Avery2022) to use direct measures of TMT characteristics to shed light on the black box of the implications of TMT diversity.
Second, the study extends upper echelons theory by examining value dissimilarity between TMT members empirically in the context of incremental and radical innovation capability while controlling for informational dissimilarity. In doing so, we address the dearth of empirical studies about diversity in values in TMTs. While work values are determinants of team processes (Jehn et al., Reference Jehn, Northcraft and Neale1999; Pinder, Reference Pinder2008; Ros, Schwartz, & Surkiss, Reference Ros, Schwartz and Surkiss1999), most studies rely on measures of TMT information diversity (e.g., Chemmanur, Gupta, & Simonyan, Reference Chemmanur, Gupta and Simonyan2022; Song et al., Reference Song, He and Yan2022; Zhong et al., Reference Zhong, Song and Chen2021). Narayan et al. (Reference Narayan, Sidhu and Volberda2021) is the only study of which we are aware that examines the implications of value diversity in TMTs. By distinguishing between informational and value dissimilarity, the study adds a more holistic assessment to recent literature that examines nuanced relationships between TMT diversity and organizational outcomes (Díaz-Fernández, González-Rodríguez, & Simonetti, Reference Díaz-Fernández, González-Rodríguez and Simonetti2020; Narayan et al., Reference Narayan, Sidhu and Volberda2021). The findings highlight the importance of such an assessment to generate predictions about the outcomes of diverse TMTs and contribute to identifying the antecedents of incremental and radical innovation capability.
Finally, the study extends the multiple social categorization theory (Crisp & Hewstone, Reference Crisp and Hewstone2007) to the context of TMT diversity. Taken together, the findings provide initial support for predicting that, in highly dissimilar TMTs, no harmful categorization occurs. Thus, it appears that TMT members can leverage dissimilar information and values.
Limitations and avenues for further research
The study is a first attempt to explore the association between dissimilarity among TMT members and innovation capability, so it has several limitations that provide avenues for future research. First, notwithstanding the rigor of our work, we encourage future research to enlarge the sample to increase the generalizability of our findings. Moreover, the study is cross-sectional, as one TMT member per organization is sampled at a single point in time. While we find no indication of common method bias, future studies could use a longitudinal study design of the complete TMT to examine how changes in the TMT composition are reflected in perceptions of dissimilarity and contribute to changes in innovation capability. Such a study could provide further insights into the implications of TMT diversity from a separation perspective.
Second, the study focuses on the direct associations between TMT members’ informational and value dissimilarity with incremental and radical innovation capability since it is the first to distinguish between informational and value dissimilarity between TMT members. As the findings indicate both positive and negative associations, depending on the type of dissimilarity and the type of innovation capability, future studies could examine contingency factors and intervening variables. For example, diversity research identifies an inclusive climate as a contingency for leveraging the potential of diversity within the team (Nishii, Reference Nishii2013) or cooperative norms as an intervening variable (Chatman & Flynn, Reference Chatman and Flynn2001). Furthermore, management levels below the TMT are also involved in innovation activities (Schubert & Tavassoli, Reference Schubert and Tavassoli2020). Dissimilarity in hierarchical levels may affect innovation capability, so future studies could examine the interrelationships in types of dissimilarity between hierarchical levels. Examining these contingencies and intervening variables may provide insights into the boundary conditions of how dissimilarity between TMT members contributes to innovation capability and also advance multiple social categorization theory.
Finally, the study’s sample is based on organizations that are headquartered in Germany. Future studies could examine whether the cultural context influences perceptions of dissimilarity. As perception differs among national cultures (Tang, Chen, van Knippenberg, & Yu, Reference Tang, Chen, van Knippenberg and Yu2020), TMT members with a strong collectivist cultural background are more likely to be aware of dissimilarities between team members because fitting in a group is more important in collectivist cultures than in individualist cultures (Hofstede & Hofstede, Reference Hofstede and Hofstede2004). Hence, examining various cultural contexts may extend upper echelons theory as well as multiple social categorization theory further and may also contribute to the generalizability of the study results. Moreover, we encourage future research to consider further outcome variables of TMT dissimilarity, for example, employee satisfaction, or examine interaction effects of perceived and objective dissimilarity.
Managerial implications
The study suggests that organizations’ innovation capabilities can benefit from dissimilarity between TMT members in terms of information and values. However, organizations need to pay attention to the type of innovation capability that is desired. Findings suggest that informational dissimilarity fosters a profound analysis of information that is positively associated with incremental innovation capability, whereas value dissimilarity harms it. Instead, when TMT members perceive dissimilarity in values, it may broaden their perspective, supporting radical innovation capability. Therefore, the kind of dissimilarity among TMT members that may be cultivated should depend on the kind of innovation capability the organization wants to pursue, and organizations should consider how informational and value dissimilarity in the TMT will change when new TMT members are appointed. Increasing value dissimilarity among TMT members while pursuing an incremental innovation strategy is likely to be harmful, although doing so would benefit a radical innovation strategy. Research suggests that the perception of dissimilarities can be increased by priming for specific identities of TMT members so that specific parts of dissimilarity become salient (Forehand, Deshpandé, & Reed, Reference Forehand, Deshpandé and Reed2002). Consequently, priming for informational or value characteristics may increase the perception of dissimilarity between the TMT members and may increase the respective type of innovation capability.
Sarah-Alena Rössig is a post-doctoral research scholar of International Management at Otto von Guericke University Magdeburg, Germany. She received her doctoral degree from Otto von Guericke University Magdeburg. Her current research focuses on diversity in organizations. She has presented her research at international conferences, including the Annual Meeting of the Academy of Management and the Annual Meeting of the Academy of International Business.
Susanne Schmidt is Professor of International Management at Otto von Guericke University Magdeburg, Germany. She received her doctoral degree from the European University Viadrina, Germany. Her research focuses on strategic diversity management, internationalization strategies, and innovation-oriented corporate management. She has published her work in academic journals such as the Journal of Product Innovation Management, Journal of Management, Research Policy, and Employee Relations.
Open access
