Literature review

Entrepreneurial team composition represents a foundational and increasingly prominent focus within entrepreneurship research, with team diversity emerging as a critical factor in shaping venture dynamics, strategic decision making, and overall outcomes (Das et al., Reference Das, Das and Chattopadhyay2021; Linder et al., Reference Linder, Lechner and Villani2025; Sundermeier & Mahlert, Reference Sundermeier and Mahlert2023). Entrepreneurial team diversity, commonly defined as ‘the distribution of differences among the members of a unit concerning a common attribute’ (Harrison & Klein, Reference Harrison and Klein2007) is increasingly recognized as a key determinant of how ventures navigate complexity, innovate, and adapt in dynamic environments (Das et al., Reference Das, Das and Chattopadhyay2021). Research on entrepreneurial teams has traditionally categorized diversity into two broad categories: surface-level and deep-level diversity. Surface-level diversity refers to observable traits such as age, gender, nationality, education, and professional background (Linder et al., Reference Linder, Lechner and Villani2025; Ma, Xiao, Guo, Tang & Singh, Reference Ma, Xiao, Guo, Tang and Singh2022; Troise, Giovando, Jabeen & Bresciani, Reference Troise, Giovando, Jabeen and Bresciani2024; Wei, Mo, Zhang & Yang, Reference Wei, Mo, Zhang and Yang2024). In contrast, deep-level diversity encompasses less immediately apparent attributes, which become more evident through interactions and communication over time, including individuals’ values, beliefs, attitudes, and opinions (Chen & Liu, Reference Chen and Liu2024; Kollmann, Stöckmann, Meves & Kensbock, Reference Kollmann, Stöckmann, Meves and Kensbock2017; Schoss, Urbig, Brettel & Mauer, Reference Schoss, Urbig, Brettel and Mauer2020; Su, Luo, Lau & de Jong, Reference Su, Luo, Lau and de Jong2024). The study conducted by Zhou and Rosini (Reference Zhou and Rosini2015) further refines this distinction, classifying diversity characteristics into three broad categories: demographic diversity, which includes factors such as age, gender, race, and ethnicity; informational diversity, which encompasses differences in education, professional experience, and expertise; and personality and cognitive diversity, which covers individual personality traits and behavioral patterns.

A growing body of literature has recognized the importance of entrepreneurial teams’ diversity in shaping start-ups’ success (Jin et al., Reference Jin, Madison, Kraiczy, Kellermanns, Crook and Xi2017; Sundermeier & Mahlert, Reference Sundermeier and Mahlert2023). The Upper Echelons Theory offers a powerful lens through which to understand how the characteristics of founding teams shape organizational outcomes (Hambrick & Mason, Reference Hambrick and Mason1984). According to this perspective, strategic choices, innovation trajectories, and overall firm performance are not merely rational processes but are deeply influenced by the psychological, cognitive, and demographic attributes of team members. They interpret information and navigate uncertainty based on their individual experiences, values, and cognitive styles, which inevitably shape collective decisions and venture direction (Finkelstein, Hambrick & Cannella, Reference Finkelstein, Hambrick and Cannella2009; Li, Reference Li2017; Rössig & Schmidt, Reference Rössig and Schmidt2025). Within entrepreneurial teams where decisions are often made collaboratively under high uncertainty, these dynamics are even more pronounced. The Upper Echelons perspective thus provides a strong theoretical foundation for analyzing how diversity across demographic (e.g., age, gender, education), informational (e.g., functional and industry experience), and cognitive (e.g., personality traits, thinking styles, motivation) dimensions can impact team cohesion, communication, and ultimately, start-up success or failure (Berman, Shoham & Teltch, Reference Berman, Shoham and Teltch2024; Boling & Vecchiarini, Reference Boling and Vecchiarini2024; Qian, Zhang & Kuai, Reference Qian, Zhang and Kuai2024; Zhou & Rosini, Reference Zhou and Rosini2015). It has been suggested that diverse teams, with their proposed benefits of increased creativity and innovativeness (Sethi, Smith & Park, Reference Sethi, Smith and Park2002; Sundermeier & Mahlert, Reference Sundermeier and Mahlert2023), are considered particularly well-suited to address the complex, non-routine challenges typical of entrepreneurial ventures. Heterogeneous groups bring a broader range of perceptions, skills, and knowledge, which enhances their ability to process information, adapt to change, and generate innovative solutions (Kakarika, Reference Kakarika2013; Zhou & Rosini, Reference Zhou and Rosini2015). Empirical evidence confirms that team diversity improves cognitive task performance, including creative problem-solving, strategic decision-making, and overall performance (Martins & Sohn, Reference Martins and Sohn2022; Qi et al., Reference Qi, Armstrong, Yang and Li2022). Linder et al. (Reference Linder, Lechner and Villani2025) demonstrate that informational diversity expands the cognitive resources required for exploring and developing creative solutions. Additionally, diverse entrepreneurial teams engage in faster learning and iterative experimentation, key capabilities for early-stage ventures. In the same vein, Wei et al. (Reference Wei, Mo, Zhang and Yang2024) suggested that so-called informational diversity (e.g., differences in educational and professional backgrounds) can act as an initial resource, generating positive stress (challenge stress) that stimulates team involvement and improves overall performance. Ma et al. (Reference Ma, Xiao, Guo, Tang and Singh2022) reinforced this perspective by showing that informational diversity can promote knowledge sharing and strengthen team stability, especially in culturally collectivist-oriented contexts. Su et al. (Reference Su, Luo, Lau and de Jong2024) found that variation in entrepreneurial passion within teams promotes resilience and sustained effort under uncertainty. Also, Kier and McMullen (Reference Kier and McMullen2020) demonstrated a positive relationship between imaginativeness diversity and the quality of new ideas that are generated.

Despite existing research emphasizing the positive effects of team diversity, numerous negative effects can also arise (Yoo, Lee & Lee, Reference Yoo, Lee and Lee2024). Amason, Shrader and Tompson (Reference Amason, Shrader and Tompson2006) found that diversity in age, education, and experience negatively affects the sales of startups operating in highly dynamic environments. Bjørnåli and Aspelund (Reference Bjørnåli and Aspelund2012) and Pinelli, Cappa, Franco, Peruffo and Oriani (Reference Pinelli, Cappa, Franco, Peruffo and Oriani2020) showed that age and educational diversity hinder international growth and funding. Blank and Carmeli (Reference Blank and Carmeli2021) noted that experience diversity reduces investment opportunities. Zhang and Wang (Reference Zhang and Wang2017) linked educational and experience diversity to lower profitability. Differences in personality, passion, and motivation (Boone & Hendriks, Reference Boone and Hendriks2009; De Mol, Cardon, de Jong, Khapova & Elfring, Reference De Mol, Cardon, de Jong, Khapova and Elfring2020; Heckhausen, Reference Heckhausen2013) increase conflict and weaken team cohesion.

Despite these insights, the extant literature fails to identify the impact of entrepreneurial team diversity in the context of venture survival. Existing research focuses disproportionately on short-term performance indicators rather than the existential risk of start-up failure. In addition, much of the existing literature fails to contextualize the effects of team diversity within the dynamic processes of the start-up journey. Focusing primarily on contextual factors such as team cohesion and conflict and environmental conditions, studies overlooked the evolving nature of diversity’s impact at different stages of development. The extant literature assumes that the composition of entrepreneurial teams remains the same as start-up growth. However, this is not a plausible assumption because entrepreneurial teams are defined as ‘open, dynamic systems’ (Sundriyal, Lévesque, Wennberg & Norgren, Reference Sundriyal, Lévesque, Wennberg and Norgren2024). As suggested by several studies, entrepreneurial teams are not fixed entities, but they adapt and evolve alongside the growth and development of their ventures (Patzelt et al., Reference Patzelt, Preller and Breugst2021; Yusubova, Andries & Clarysse, Reference Yusubova, Andries and Clarysse2020). Failing to consider how the impact of diversity evolves as start-ups progress through different stages, such analyses miss critical insights into the changing needs and challenges of entrepreneurial teams. To support this dynamic perspective, research on technology start-ups has shown that teams with strong technical expertise are most effective during the initial stages of idea generation and product development. In contrast, as start-ups transition to commercialization and growth, combining technical and commercial expertise becomes essential for success (Yusubova et al., Reference Yusubova, Andries and Clarysse2020). Similarly, Tzabbar and Margolis (Reference Tzabbar and Margolis2017) demonstrated that the positive effects of founding team human capital, such as educational heterogeneity and diverse experience, are more pronounced in the growth stage compared to the early stage. In the early stages, alignment among team members regarding goals, beliefs, and abilities is particularly critical for creating a cohesive vision (Chen & Kanfer, Reference Chen and Kanfer2006). Additionally, Muñoz-Bullon, Sanchez-Bueno and Vos-Saz (Reference Muñoz-Bullon, Sanchez-Bueno and Vos-Saz2015) found that diversity in industry and start-up experience is vital for moving a venture from the conception to the creation phase. In sum, these studies highlight the importance of tailoring team diversity to the specific requirements of each development stage.

To address this critical gap, the present study adopts a dynamic lens to investigate the underexplored dark side of entrepreneurial team diversity. Unlike prior research that focuses predominantly on successful ventures, this study uniquely examines failed start-ups across distinct developmental stages, aiming to identify which diversity characteristics may undermine team effectiveness and compromise venture survival. In doing so, it offers a nuanced, stage-contingent understanding of entrepreneurial team diversity, contributing to a more realistic and actionable framework for team design and management in entrepreneurship.

Data collection

The data for this study, derived from secondary sources, were collected through the Failory database, a platform dedicated to sharing detailed stories and reflections on business failures. Failory offers a rich repository dedicated to startup founders and teams, offering over 200 interviews with failed and thriving startups, over 100 blog articles on various aspects of building and managing startups, and analyses of why over 200 startups and company projects have failed. The website also features a collection of tools and resources to assist entrepreneurs in their ventures. Failory was chosen over other databases (e.g., Crunchbase or CB insights) because it provides deeper insight into businesses’ challenges rather than just financial outcomes. While platforms like Crunchbase or CB Insights focus mainly on financial information like funding rounds, valuations, and market performance, Failory captures the real reasons behind business failures through experiences and lessons learned. This makes it especially valuable for understanding what went wrong, how, and why, offering perspectives beyond metrics. Moreover, Failory database is particularly relevant because it organizes failure interviews by reconstructing them across three distinct development stages: idea development, product conception and development, and launch. This structured approach allowed us to analyze failures consistently across different phases of the startup journey, aligning with the research question and providing a clearer understanding of challenges at each stage. The data were collected following a rigorous multi-stage selection process to ensure the data collected was highly relevant and aligned with the research question. This approach followed three systematic stages, carefully extracting information from the dataset to maintain the focus on the scope of the study:

1. 1) The Failory database includes over 200 interviews featuring start-ups at various stages. At this preliminary stage, only cases that explicitly documented experiences of failure were selected. Success stories and start-ups that were still operational were excluded, ensuring the focus remained solely on understanding the factors contributing to failure.

2. 2) Since the study aims to examine the impact of team diversity, only start-ups with a team-based structure were included. Start-ups led by a single founder were excluded to maintain a consistent analytical focus on team dynamics rather than individual behavior. This step ensured that the dataset was directly relevant to the study’s exploration of challenges related to entrepreneurial team composition.

3. 3) In the final stage, the authors carefully reviewed each narrative to identify cases where the failure of the start-up was linked to team characteristics and governance dynamics. This step was essential to isolate cases where team-related factors played a critical role in the outcome, ensuring that the study’s findings would be precisely targeted and meaningful.

This selection process reduced the final sample to 10 interviews, each with detailed and in-depth information, allowing for a retrospective analysis of the team’s development stages and diversity characteristics. Specifically, the final sample comprises innovative startups that failed at different developmental stages (See Table 1). This variation allows for a comprehensive assessment of how entrepreneurial team diversity influenced survival at distinct stages.

Table 1.

Sample description

Subsequently, a data triangulation strategy was employed to enhance credibility and mitigate potential biases from self-reported failures. This involved cross-verifying Failory interviews with additionally publicly available secondary sources, including:

1. 1) Websites or personal blogs (e.g., Medium, Startup Graveyard series, and Indie Hackers blog) where founders and team members shared additional perspectives on their experiences.

2. 2) Podcasts or team member interviews published on platforms other than Failory provide alternative interviews and deeper insights into their decision-making processes and failure reflections.

3. 3) Online articles, social media posts, and other news items related to the history of the analyzed start-ups (e.g., LinkedIn, Twitter, TechCrunch, and Business Insider), which offered external validation and broader industry perspectives on each startup’s trajectory.

By systematically cross-referencing these different sources, we could distinguish between perceived and actual causes of failure. This triangulation approach allowed for a more nuanced and credible exploration of the role of team diversity in startup survival. Rather than relying on a single retrospective account, the study constructed a multi-layered understanding of failure, ensuring that its conclusions were not just reflections of individual memory but a broader synthesis of entrepreneurial realities.

Data analysis

The study employed an inductive qualitative analysis guided by Gioia’s methodology (Gioia et al., Reference Gioia, Corley and Hamilton2013) to map crucial diversity characteristics. The analytical process was designed to be iterative, rigorous, and methodologically structured, ensuring both depth and clarity in interpretation (Ratten, Reference Ratten2024; Valenza, Zoppelletto & Fazio, Reference Valenza, Zoppelletto and Fazio2024). The coding process was supported by NVivo 12, a widely recognized Computer-Assisted Qualitative Data Analysis Software (CAQDAS), increasingly adopted in inductive and grounded theory-based studies to ensure rigor, transparency, and analytical coherence (Hess & Lebraty, Reference Hess and Lebraty2025; Maher, Hadfield, Hutchings & De Eyto, Reference Maher, Hadfield, Hutchings and De Eyto2018; Nowell, Norris, White & Moules, Reference Nowell, Norris, White and Moules2017; Woods, Paulus, Atkins & Macklin, Reference Woods, Paulus, Atkins and Macklin2016). The use of this software contributed to enhancing trustworthiness and transparency of the research process. As a recognized qualitative research tool, it facilitated systematic data organization, structured coding, and interpretative consistency (Woods et al., Reference Woods, Paulus, Atkins and Macklin2016). This software allowed for efficient management of a large volume of qualitative data, ensuring that coding remained structured, replicable, and analytically transparent. Thus, rather than serving merely as a technical tool, its use enabled the researchers to systematically conduct open coding, facilitate the emergence of first- and second-order themes, and engage in iterative comparative analysis across multiple narrative cases central to the Gioia methodology approach. In addition, NVivo supported the maintenance of a comprehensive audit trail, thereby reinforcing the trustworthiness and reproducibility of interpretive decisions (Paulus & Lester, Reference Paulus and Lester2024).

The coding process followed Gioia’s three-stage approach, ensuring a structured and methodical transition from raw data to theoretical insights (Perotti, Dhir, Ferraris & Kliestik, Reference Perotti, Dhir, Ferraris and Kliestik2024). The first stage, first-order coding, required deep engagement with the data. Researchers conducted multiple close readings of data transcripts, allowing key concepts to emerge organically without imposing preconceived theoretical assumptions. This stage focused on capturing participants’ perspectives in their own words, ensuring that first-order codes remained informant-centric and reflective of their lived experiences. The authors independently coded the data to strengthen interpretative reliability before coming together to compare results. Any discrepancies were openly discussed, refined, and reconciled, ensuring that the emerging codes maintained coherence and consistency.

During this phase, diversity-related attributes were identified based on how team members described internal challenges in their narratives. For example, statement such as ‘My business co-founder and I were extremely opposite characters’ or ‘The CTO focused on tech, the CEO favored commercial simplicity’or ‘Seniors valued stability; juniors pushed innovation’ were initially coded under informant-centric categories respectively: ‘divergent characters between co-founders’; ‘divergent priorities from different background’; ‘Young member prefer to innovate while senior resit to change.’ Moving to the second stage, second-order coding, researchers transitioned from descriptive first-order concepts to a more abstract level of interpretation. At this stage, the researchers moved beyond the informants’ language and began identifying patterns and relationships between first-order codes, clustering them into higher-level themes that revealed deeper conceptual structures. Rather than relying solely on frequency, these second-order themes were developed based on their relevance to the research question and their potential to contribute to theoretical understanding. To ensure analytical precision, the research team applied Patton’s (Reference Patton1990) dual judgment criteria, rigorously evaluating each theme’s internal coherence, ensuring logical consistency within categories and its external distinctiveness, and ensuring themes remained conceptually distinct. This process was highly iterative, involving continuous dialogue and refinement within the research team to strengthen conceptual clarity and theoretical contribution. For example, the first order codes ‘different idea about what was a priority,’ ‘favouring complex solutions vs simplicity,’ and ‘disagreement on which features prioritize,’ each derived from informant quotes, together were clustered into a second order theme called ‘difference in ideas.’ In the final stage, second-order themes were synthesized into aggregate dimensions, forming a structured framework that facilitated theoretical development (Magnani & Gioia, Reference Magnani and Gioia2023). This process ensured that emergent insights were integrated into a coherent conceptual model. For example, the second order codes ‘difference in passion,’ ‘difference in personality,’ ‘difference in ideas’ and ‘difference in vision’ that together were grouped under a second-order theme, where finally synthesized into an aggregate dimension “personality and cognitive diversity.

The final data structure, presented in Figure 1, follows Gioia et al. (Reference Gioia, Corley and Hamilton2013) systematic approach, illustrating how raw qualitative data was transformed into a higher-level theoretical insight.

Figure 1.

Data structure.

Once the key entrepreneurial team diversity characteristics had been coded using Gioia’s methodology, the authors employed a post-mortem analysis approach (Ahonen & Savolainen, Reference Ahonen and Savolainen2010) to contextualize the negative effects of these dimensions within each start-up’s developmental trajectory (Dingsøyr, Reference Dingsøyr2005; Schieg, Reference Schieg2007). This approach allowed for a nuanced understanding of how specific diversity factors contributed to failure by embedding them within the temporal evolution of each firm. Specifically, to ensure analytical consistency and transparency, the authors analyzed in depth each case by considering two key elements: (1) the specific diversity characteristics that emerged from the coding process, (2) the specific stage of the start-up’s development where these characteristics produced concrete effects, contributing to the start-up’s failure.

To do so, the authors relied on detailed firm histories to construct comprehensive timelines, systematically tracing which entrepreneurial team diversity attributes were crucial across the ideation, product development, and launch phases. The authors ensured a structured and longitudinal approach to understanding how diversity factors influenced firm trajectories by meticulously documenting key events, decisions, and team dynamics (Dingsøyr, Reference Dingsøyr2005; Dobusch et al., Reference Dobusch, Köster, Schäfer and Seckler2022; Schieg, Reference Schieg2007).

To ensure methodological rigor, the analysis followed a structured and iterative process. The research team meticulously documented key events, strategic decisions, and team interactions for each case, facilitating a consistent phase-by-phase comparison. Through repeated readings and case-level synthesis, diversity dimensions identified during coding were aligned with failure events, allowing for pattern recognition and causal inference (Langley, Reference Langley1999). Throughout this process, the authors engaged in iterative and reflexive discussions to critically examine their interpretations, ensuring consistency, theoretical depth, and methodological rigor. Discrepancies in coding, case interpretation, and narrative reconstruction were addressed through a structured, multi-step validation protocol. This involved independent analysis of each firm’s history, followed by collaborative review sessions to reconcile divergent perspectives and achieve analytical alignment (Ralph, Birks & Chapman, Reference Ralph, Birks and Chapman2015). To further enhance reliability and reduce interpretive bias, the authors triangulated internal interpretations with external secondary sources, subjecting firm interviews to multiple rounds of cross-case comparison and validation (Nowell et al., Reference Nowell, Norris, White and Moules2017). The refinement process included systematically re-reading firm histories and failure accounts to uncover overlooked temporal patterns, reassess causal linkages, and contextualize entrepreneurial team diversity dynamics within broader organizational trajectories. This multi-layered and iterative approach facilitated a deeper understanding of the mechanisms by which specific diversity characteristics contributed to team dysfunctions and consequently to start-up failure. In doing so, the applied processes reinforced the internal validity of the study and supported a robust and transparent synthesis of results (Gioia et al., Reference Gioia, Corley and Hamilton2013; Patton, McKegg & Wehipeihana, Reference Patton, McKegg and Wehipeihana2015).

For example, statements such as ‘we had completely different ideas about what we were building’ and ‘we spent more time arguing than executing’ were linked to cognitive and personality diversity, and were mapped to the ideation stage, where the absence of a shared mental model and persistent interpersonal tension led to strategic misalignment and decision paralysis. In this early phase, where clarity of vision and rapid convergence on goals are essential, such divergences significantly undermined team cohesion and slowed the development of a unified business concept.

A quote like ‘our engineering team pushed for complexity while the business side needed speed’ reflected deep informational diversity, especially in terms of functional experience and priorities. This was associated with the product development stage, where the misalignment between technical and commercial logics compromised the team’s ability to make timely trade-offs between product features and market readiness. Finally, quotes such as ‘I chose partners who were unfit to help run the business and were not passionate about the vision of the company’ and ‘The older team members prioritized stability and risk avoidance, while the younger ones wanted to push boundaries and innovate’ that respectively reflect personality and demographic diversity, they were linked to the launch stage, where the differen level of passion and divergent generational perspectives gave rise to emotional disengagement, delayed decision-making, and affective conflict. These internal misalignments ultimately undermined the team’s ability to execute under pressure, sustain momentum, and respond effectively to external demands during market entry.

Findings

Discussions

The study highlights the importance of contextualizing the effects of entrepreneurial team diversity within the different developmental stages of start-ups (Muñoz-Bullon et al., Reference Muñoz-Bullon, Sanchez-Bueno and Vos-Saz2015; Patzelt et al., Reference Patzelt, Preller and Breugst2021; Tzabbar & Margolis, Reference Tzabbar and Margolis2017). The results reveal that the impact of diversity is highly dynamic, varying in nature across different phases of start-up development, thereby challenging the conventional static perspective found in much of the existing literature (Amason et al., Reference Amason, Shrader and Tompson2006; Bjørnåli & Aspelund, Reference Bjørnåli and Aspelund2012; De Mol et al., Reference De Mol, Cardon, de Jong, Khapova and Elfring2020; Pinelli et al., Reference Pinelli, Cappa, Franco, Peruffo and Oriani2020).

Unlike previous studies, which predominantly highlight the benefits of diversity in team composition (Sundermeier & Mahlert, Reference Sundermeier and Mahlert2023; Zhou & Rosini, Reference Zhou and Rosini2015), this study introduces a nuanced, stage-specific perspective that critically examines its potentially negative effects on start-up survival.

This study advances the existing literature by proposing a conceptual framework (see Fig. 2) that integrates two crucial dimensions: team diversity attributes and the distinct phases of a start-up’s lifecycle, enhancing the understanding of how team diversity characteristics (demographic, informational, and personality) influence start-up survival.

Figure 2.

Proposed conceptual framework for the negative effects of entrepreneurial team diversity on venture survival in the different stages of development.

The findings shed light on the ‘dark side’ of team diversity, demonstrating that while heterogeneous teams may bring valuable knowledge and expertise, the misalignment of perspectives, skills, and motivations can pose significant risks, particularly at a critical juncture of the venture life cycle.

The study contributes to entrepreneurial team literature by extending prior research (Tzabbar & Margolis, Reference Tzabbar and Margolis2017; Yusubova et al., Reference Yusubova, Andries and Clarysse2020), emphasizing the pre-launch phase, which has been largely overlooked. The early stages – idea conception, product development, and initial launch – are particularly susceptible to the disruptive effects of team diversity.

The findings reveal that personality diversity is particularly disruptive in the idea development stage, where foundational strategic decisions are relevant. While cognitive diversity can foster creative solutions (Kakarika, Reference Kakarika2013), it also introduces friction, misalignment, and decision paralysis (Harrison & Klein, Reference Harrison and Klein2007). In this vein, the analysis suggests that deep ideological and cognitive misalignments among co-founders hinder consensus, leading to stagnation and delaying or even derailing venture progress. Moreover, the present study extends the study of Boone and Hendriks (Reference Boone and Hendriks2009) by providing empirical evidence that personality-based separation exacerbates emotional conflicts, particularly in the earliest phases of a venture’s lifecycle, where strategic cohesion is fundamental. Personality differences influence not only decision-making but also the ability of the team to sustain a shared vision, resulting in fragmentation and inefficiency. These findings highlight the critical challenge of personality diversity in early-stage entrepreneurial teams, emphasizing the need for strategies to mitigate its negative impact while preserving the benefits of diverse perspectives.

As ventures transition from ideation to product development, the combined influence of personality and informational diversity becomes critical in determining failure. While diverse expertise enhances problem-solving (Kakarika, Reference Kakarika2013; Zhang & Wang, Reference Zhang and Wang2017), it also introduces coordination challenges that can lead to strategic misalignment and inefficiencies (Bjørnåli & Aspelund, Reference Bjørnåli and Aspelund2012). The findings reveal that the interaction between personality diversity (conflicting strategic visions) and informational diversity (differing technical or commercial expertise) creates tensions that can slow or derail product development. One of the primary challenges in this phase is the misalignment between technical and commercial priorities. Although prior research suggests that heterogeneous expertise enhances adaptability and innovation (Sundermeier & Mahlert, Reference Sundermeier and Mahlert2023), the study highlights its downside concerning communication barriers and decision-making inefficiencies (Pinelli et al., Reference Pinelli, Cappa, Franco, Peruffo and Oriani2020). Diversity in educational background and professional expertise presents a significant challenge during this phase concerning tensions between technical and commercial team members, which were particularly disruptive, reflecting broader coordination challenges in high-novelty firms (Blank & Carmeli, Reference Blank and Carmeli2021). Conflicts between the engineering and marketing teams over product complexity versus market readiness underscore the difficulties of aligning functional priorities. The findings emphasize that informational diversity, combined with personality diversity, exacerbates the risk of fragmentation and slowed decision-making. While diverse problem-solving approaches can drive innovation, they also heighten the likelihood of strategic misalignment and execution delays. This reinforces the idea that the benefits of informational diversity are contingent on effective communication mechanisms and shared vision alignment.

Furthermore, the results suggest that personality diversity becomes even more critical in the launch phase, where differences in passion and commitment among team members are critical for venture survival. While passion is a key driver of entrepreneurial resilience (De Mol et al., Reference De Mol, Cardon, de Jong, Khapova and Elfring2020), its uneven distribution within a team leads to internal conflicts. This supports the argument that misaligned motivation and achievement orientation among entrepreneurs reduce team cohesion (Heckhausen, Reference Heckhausen2013). Passion diversity is a key destabilizing factor, intensifying team tensions at critical transition points. Moreover, in the launch phase, personality diversity interacts with demographic diversity, particularly age diversity, to create additional challenges. Older team members prioritized stability and risk aversion, while younger entrepreneurs sought innovation and speed, creating tensions that delayed key strategic decisions. This finding resonates with previous research suggesting that age similarity facilitates smoother collaboration in international partnerships (Bjørnåli & Aspelund, Reference Bjørnåli and Aspelund2012) and enhances decision-making efficiency (Amason et al., Reference Amason, Shrader and Tompson2006). The study extends these insights by demonstrating how age diversity hinders team consensus in high-pressure environments like start-up launches.