
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

71794
10.1038/s41598-024-71794-5
Article
The relationship between entrepreneurial personality patterns linked to risk, innovation and gender across industrial sectors
Hagenauer Wolfgang xhagenau@node.mendelu.cz

1
Zipko Harald T. 2
1 https://ror.org/058aeep47 grid.7112.5 0000 0001 2219 1520 Mendel University Brno, Brno, Czech Republic
2 University of Applied Sciences, Vienna, Austria
6 9 2024
6 9 2024
2024
14 2086412 4 2024
30 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
This study examines the personality patterns of solo founders in both high-tech and non-high-tech sectors during the first seven years of their entrepreneurial journey to emphasize the patterns’ implications during policymaking, investment decisions, and self-assessments. IAB/ZEW startup panel microdata for the sector classification of 4470 solo entrepreneurs in Germany were analyzed to identify Big Five trait patterns influenced by risk propensities, innovation inclination, and gender. The entrepreneurial profiles indicate positive openness, emotional resilience, and sector-specific clusters. Conscientiousness suggests flexibility, and while variations in extraversion and agreeableness exist, negative neuroticism was predominantly found, except for gender-related differences and multidimensional service innovators. Big Five traits provide information about important foundational profile patterns to describe unique solo entrepreneur types influenced by risk, innovation, and gender. Originality and value: Risk propensity characterizes ‘Adaptive Services,’ ‘Dynamic Knowledge Innovators,’ and ‘Strategic Risk Navigators.’ Additionally, ‘Multidimensional Service Innovators’ and ‘Focused Tech Innovators’ signify innovation understanding. The Big Five profiles show openness and emotional stability across sectors, providing crucial insights for effective entrepreneurial support and investment strategies.

Keywords

Big Five
Entrepreneur
Sector analysis
Personality profiles
Subject terms

Human behaviour
Scientific data
issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Individual entrepreneurs are crucial in driving innovation and economic diversity across various industries. These entrepreneurs face unique challenges and opportunities that significantly influence their business trajectories, especially during the critical first seven years of their operations. Understanding the interplay between the Big Five personality traits and individual entrepreneurs’ age, gender, and risk propensity is crucial for identifying the factors shaping their decision-making behavior, willingness to innovate, and engagement in research and development during their ventures’ early stages.

The Big Five personality traits—Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism—are vital in shaping entrepreneurial behavior. For instance, Conscientiousness is characterized as being organized, reliable, and goal-oriented, while Extraversion, associated with assertiveness and energy, promotes organizational skills and networking capabilities1, leading to positive entrepreneurial outcomes including effective stress management and successful network building2–4. Conversely, high Neuroticism, associated with stress susceptibility, and low Openness, indicative of a lack of flexibility and adaptability, can hinder entrepreneurial success1 by increasing stress and anxiety, negatively affecting decision-making and overall business performance5,6. Low Openness can limit adaptability and innovation, crucial for navigating complex business environments3,7.

The Big Five model’s significance in the entrepreneurship context is underscored via research findings that consistently illustrate correlations between these personality dimensions and entrepreneurial tendencies, indicating associations rather than causal relationships. The Big Five traits, often referred to as the OCEAN-model, include:Openness (O): Fosters a spirit of innovation and intellectual curiosity, crucial for identifying business opportunities7–9.

Conscientiousness (C): Shapes determination and structure, closely linked to the intention to establish a business or entrepreneurial commitment10–12.

Extraversion (E): Characterized by sociability and energy, showing a positive correlation with ventures’ success10,13.

Agreeableness (A): Manifested through interpersonal harmony and willingness to cooperate, with varying effects on business success depending on context. Higher values are typical for executives, while a balanced level is often found in entrepreneurs13,14.

Neuroticism (N): Represents emotional reactivity and tends to have an inverse relationship with entrepreneurship. Lower expressions of neuroticism are associated with professional advancement13,15,16.

Davidsson17 argues that, while high-tech entrepreneurs may not fundamentally differ from those in traditional sectors, their behavior exhibits greater situational variability. This suggests that personality traits such as conscientiousness can impact differently depending on the entrepreneurial journey’s stage. Research by Freiberg and Matz18 supports this view, showing that conscientiousness is positively associated with early-stage success, e.g., securing initial funding, but may negatively impact later-stage outcomes, e.g., acquisition or IPO. Zhao et al.19 also emphasize the importance of conscientiousness, generally linking it to positive entrepreneurial success across stages.

These findings highlight the importance of analyzing personality traits in the early years of entrepreneurship. Understanding how traits such as conscientiousness, openness, and extraversion influence entrepreneurial outcomes across different industries is crucial. Big Five personality traits are generally stable throughout an individual's life20,21, although significant life events during company founding and operation could influence them22,23. However, these changes might not significantly impact the overall personality24, although Boyce et al.25 present controversial conclusions based on the same micro-data sample.

The dynamic interactions between founders' entrepreneurial activities and their personality traits remain crucial throughout a company's lifetime. This underlines the importance of focusing on relevant personality traits and their influence on entrepreneurial behavior during business development’s critical early stages.

Common categorizations of entrepreneurs as self-employed individuals or the focus on specific groups like start-ups do not reflect the actual diversity26,27. The term "entrepreneur," originating from French and established by Cantillon28 to describe risk-prone and innovative individuals, contrasts with the German term “Unternehmer,” which encompasses a broader spectrum of economic actors, including owners and managers, with a focus on their ownership and capital functions (Hartmann 2010). The diverse facets of entrepreneurship, from Cantillon's emphasis on coordination ability to Kirzner's29 focus on market inefficiencies, highlight the need for precise terminological distinction—a need reflected in Germany's legal texts, which use different definitions in different contexts: commercial law (HGB § Sect. 1, 1–2. 2017), civil law (BGB § Sect. 14, 1, 2. 2002), and tax law (EStG § 15, 1–4. 2009; EStG § 18, 1–4. 2009). This linguistic and conceptual diversity highlights the need for precise definitions and distinctions in entrepreneurial research.

Gaps in the current research literature

An analysis of partially published and unpublished data indicated that attempting to differentiate entrepreneur profiles across various industries solely based on the Big Five personality dimensions is insufficient to fully capture entrepreneurship’s multifaced aspects30,31. The Big Five model summarizes behavioral tendencies at a high level of abstraction, limiting specificity and explanatory power in the entrepreneurial context32–34. Personality questionnaires capture the self-image of respondents, which can be influenced by situational and contextual factors, leading to discrepancies between self-assessments and external assessments32,35 and show discrepancies between self-assessments and external assessments32,35. Furthermore, these traits do not account for variability influenced by environmental and situational factors33.

Additional theoretical lenses, such as the need for Achievement (nAch), Locus of Control (LoC), Entrepreneurial Self-Efficacy (ESE), and innovativeness alongside the Big Five traits provide additional insights into entrepreneurial characteristics (McClelland36 and Hansemark37; Rotter38; Bandura39–41; Chen et al.,42–46.

These frameworks highlight the importance of traits like risk aversion, self-efficacy, and internal locus of control in predicting entrepreneurial behavior11,33,34,47.

Studies indicate minor differences in the Big Five traits among entrepreneurs, non-founder CEOs, and inventor-employees, suggesting the insufficiency of the Big Five traits alone33. Grant and Langan-Fox48 found that low neuroticism, high extraversion, and high conscientiousness aid stress management in managers, though further investigation is needed for solo entrepreneurs. Marcati et al.45 demonstrated that innovation capacity among SME entrepreneurs is directly linked to the Big Five traits, particularly openness to experience and extraversion, significantly influencing propensity to adopt innovations. Kritikos8 showed that openness to experience correlated positively with risk tolerance and trust, crucial for effective leadership in entrepreneurial settings. Schmitt-Rodermund49 identified high extraversion, high conscientiousness, high openness, low agreeableness, and low neuroticism as significantly influencing entrepreneurial behavior and success. Prior to founding, conscientiousness and openness are drivers and fundamental prerequisites for establishing a business12,50.

Meta-analyses further reveal that differences in entrepreneurial personality traits vary significantly by environment and context, complicating generalized statements about these traits19,51. Therefore, a multidimensional personality framework that includes traits such as self-efficacy, innovativeness, locus of control, and need for achievement is necessary to fully understand and predict entrepreneurial behavior52. This approach acknowledges the heterogeneity of entrepreneurial personalities and the varied influences of these traits across different phases of entrepreneurship, from pre-founding to early-stage development and beyond12,50,53.

Future research should include additional moderating factors to enable a more nuanced distinction of entrepreneurs by sector. This involves variables such as risk propensity, readiness for innovation, and gender, particularly in the initial start-up period up to seven years after founding. These insights guide the current study’s methodological direction, focusing on the diversity within the individual entrepreneur landscape26,27,54,55—from leading high-tech firms, to fundamental service providers such as catering and cleaning businesses, which act entrepreneurially by taking responsibility for their employees and supporting families through their activities.

This study identifies individual entrepreneurs’ personality profiles during the first seven years of their business activity—a critical phase for enterprise development and survival. Previous research highlights the importance of personality traits in influencing entrepreneurial behavior, yet gaps remain in understanding how these traits manifest across different industries20,21. This research explores how these traits, along with age, gender, and risk propensity, influence individual entrepreneurs’ entrepreneurial profiles during the early stages of their ventures.

Entrepreneurs in this study encompass a broad spectrum of activities, ranging from traditional crafts and retail to software development and technology-intensive manufacturing. This definition captures the diversity of entrepreneurial roles, requiring not only organizational skills but also the willingness to take risks to maintain operations and achieve business goals.

Considering the initial phase of founding a company, personality traits’ influence and interactions with personal factors and environmental challenges are critical. This leads to the following research questions:How do the Big Five personality traits influence the risk propensity and decision-making behavior of solo entrepreneurs across different industries within the first seven years of business operations?

How do personality dimensions affect entrepreneurs’ willingness to innovate and engage in research and development during the early business phase?

How do gender differences in the Big Five traits manifest in the entrepreneurial landscape across various industries?

Methodology

Personality profiles in the founding phase

This study develops a comprehensive understanding of the initial phase of solo entrepreneurs in Germany (within the first seven years after founding), focusing on the specific challenges and dynamics of different industry sectors characterizing this critical phase56,57. The examined correlations and patterns are between the Big Five personality traits and factors such as entrepreneurial risk propensity, innovative ability, and gender.

Data collection

The IAB/ZEW Start-up Panel, which includes survey results from 2018 and 201958, served as the foundation for this study’s data collection. Economically active companies refer to those registered on the commercial register, which utilized external financing sources such as debt or trade credit at the time of founding, or actively participated in economic activities through other means, such as significant customer relationships. The data collection was conducted using an IAB/ZEW Panel survey59,60. This IAB/ZEW Panel survey aims to track the development of start-ups over a period of up to seven years, excluding takeovers, microscale start-ups, and side businesses. As presented in Table 1, for an industry-specific study, the classifications of economic sectors from the German Federal Statistical Office61 were used to distinguish between high-tech and non-high-tech sectors59,60. Table 1 Overview of sector types and their descriptions.

Business sector	Description	
Non-High-Tech Sectors (OTS)	
    Non-High-Tech-Manufacturing	Manufacturing industries outside of high technology, including food, textiles, and metal processing	
    Skill-Intensive Services	Service providers requiring specialized knowledge, but not primarily technology-oriented	
    Other Business-Oriented Services	Service providers primarily support other businesses	
    Consumer-Oriented Services	Providers of services to end consumers	
    Construction	From construction and civil engineering to specialized craftsmanship	
    Wholesale and Retail market	Trading companies that maintain direct customer relationships	
High Tech-Sectors (HTS)	
    Cutting-Edge Technology Manufacturing	Industries with an R&D intensity exceeding 7%	
    High Technology-Manufacturing	Sectors with an R&D intensity between 2.5% and 7%	
    Technology-Intensive services	Service providers focusing on R&D in science, engineering, agriculture and medicine	
    Software	Industries specializing in software development or web design	

This categorization enabled the examination of correlations between entrepreneur profiles and industry sectors while differentiating entrepreneur segments within the solo entrepreneur spectrum.

Personality analyses in entrepreneurship—the contribution of the Big Five

In 2018 and 2019, the IAB/ZEW Start-up Panel incorporated the Big Five personality model to gain insight into business founders’ characteristics. As described by Egeln et al.59,60 the IAB/ZEW Start-up Panel questionnaire used a shortened version of the Big Five scales based on 15 dedicated questions from the Socio-Economic Panel by Gerlitz and Schupp62. These modified scales were tested for internal consistency and validity and the empirical evidence supported its use in large-scale surveys. The questions provided reproducible results and could act as reference scores for Germany63.

Sample and statistical analysis

Sample

Sample size did challenge us in scheduling analysis methods used for big data. The IAB/ZEW-survey in 2018 and 2019 included entrepreneurs whose companies were no older than seven years. Only solo founders with a complete Big Five dataset were included in further analyses. Personality data were collected after the company was founded, either in the founding year or in subsequent years. Responses to each Big Five question were provided on a 5-point Likert scale ranging from 1 to 5. The survey included questions on the company’s approach to conducting R&D (‘yes’ ↔ ‘no’), gender (‘female’ ↔ ‘male’), and willingness to take risks.

The information of the latter variable was derived by answering two questions concerning personal risk propensity using a 5-point Likert scale (‘My company’s decisions are…: waiting and cautious [1] ↔ bold and offensive [5]’; ‘My company favors projects with… : low risk [1] ↔ high risk [5]’).

Statistical analysis

All descriptive-statistical analyses of discrete variables were based on the presentation of percentages of relative frequencies. Continuous characteristics were described using the arithmetic mean ± one standard deviation and quartiles. Where applicable, the corresponding 95% confidence intervals were included.

To determine the Big Five dimensions based on the 15 Big Five assignable items of the ZEW questionnaire, exploratory factor analysis (EFA) was conducted using a standard oblimin rotation and a predetermined number of five factors. Both Bartlett’s sphericity and sample adequacy tests with Kaiser, Meyer, and Olkin (KMO) were applied before the EFA to confirm the correct application. Factor loadings with a value of ≥ 0.25 defined relevant dimensions. Confirmatory Factor Analysis (CFA) was performed to reinforce the overall personality trait analyses64. The CFA-relevant cut-off values were based on the tables provided by Hu and Bentler65.

Similar to the approach of McCarthy et al.9 matrices with effect size estimations for each industry sector Pearson’s r correlation coefficient were calculated to estimate the correlation between each Big Five trait and (i) risk propensity variables, (ii) research and development, and (iii) gender, respectively.

A data-mining cluster and associated heatmap analysis was conducted based on previous Pearson’s r coefficient matrices to reveal remarkable visible similarity patterns of the Big Five personality dimensions combined with risk propensity, involvement in research and development, and gender. Dendrograms represented the respective relationship degree between the matrix dimensions. Rectangles depicted in heatmap figures indicated each Pearson’s r-estimate translated to a meaningful color code between −0.2 and 0.2.

The final data-mining results and follow-up discussion were not focused on ‘correlations’; rather, they emphasized visible similarity groups as seen in dendrograms, as well as their clades, and combined pattern clusters within depicted heat plots.

The probability of error for hypothesis tests (e.g., Bartlett’s test) and for specified confidence intervals was set at 5%. All analyses were performed using R software. CFA, heatmap, cluster, and dendrogram analyses were performed using R libraries 'lavaan' and 'ComplexHeatmap'64,66,67.

Results

Sample characteristics

Based on 60,237 records, the final sample consisted of 4470 unique entrepreneurs with a completed Big Five dataset. The founding years ranged from 2011 to 2018. The total number of start-ups varied between 185 and 1025, exhibiting a left-skewed distribution (Fig. 1A).Fig. 1 Summary of sample characteristics based on depicted distributions of discrete and continuous variables, respectively. Error bars represent the 95% Confidence Interval.

The sample included 612 female (13.7%) and 3858 male (86.3%) entrepreneurs (Fig. 1B). Figure 1C shows that nearly two-thirds of the included companies belonged to other industry sectors (2877, 64.4%) and the remainder were high-tech enterprises (1593, 35.6%).

With 3487 (78.2%) compared to 971 (21.8%) companies, the majority stated that they had not conducted any R&D (Fig. 1D). Additionally, the age distribution was slightly right skewed with a mean value of 44.6 ± 11.0 years, a total range of 18 to 100 years (sic!), and a median of 44 (Q1: 36; Q3: 52; Fig. 1E). The evaluation of the companies’ risk decision behavior ‘cautious ↔ offensive’ spectrum showed no notable frequencies (Fig. 1F). Furthermore, the risk propensity for project preferences indicated a notable decline in the preference from ‘low risk ↔ high-risk’ projects (Fig. 1G).

Big Five—factor analysis

Bartlett's test for sphericity (p < 0.001) and the Kaiser–Meyer–Olkin test for sampling adequacy (KMO = 0.710) demonstrated sufficient strength of partial and significant correlations, predominantly owing to the large sample of 4470. Based on Gerlitz and Schupp and Dehne and Schupp62,63, the EFA successfully extracted five dimensions from three dedicated questionnaire items related to each of the Big Five traits. Factor loadings ranging from 0.26 to 0.74 accounted for 37% of the total variance (Fig. 2A).Fig. 2 (A) Explanatory factor analysis of the Big Five dimensions, determined from 15 items. *) Values of four Big Five items were inverted before the subsequent analyses). (B) Presentation of a German start-up entrepreneur based on density distribution patterns of the Big Five traits derived from the IAB/ZEW-startup panel sample.

CFA confirmed the Big Five traits derived from the 15 questions of the IAB/ZEW-panel questionnaire (Comparative Fit Index: 0.859, Root Mean Square Error of Approximation: 0.061, and Standardized Root Mean Square Residual: 0.055). The final distribution of each Big Five trait indicated notable patterns, with neuroticism showing a negatively skewed left-sided distribution. The four remaining traits exhibited a positive skew to the right, with conscientiousness showing a pronounced emphasis (Fig. 2B).

Cluster analysis

Risk propensity—decision behavior

In the context of risk propensity in decision-making, extraversion and openness appear as a distinct cluster according to the heatmap analysis (Fig. 3A), revealing a greater risk propensity. Extraversion was the most pronounced dimension. Conscientiousness and agreeableness were found to be another trait bundle, showing a balanced relation to risk propensity. Neuroticism, characterized by lower scores in this analysis, indicated emotional stability, corresponding to cautious behavior and a tendency to deliberate during decision-making.Fig. 3 Cluster- and heatmap-analysis revealing patterns based on correlations between Big Five dimensions by industry sector: (A) Decision behavior (‘waiting and cautious [1] ↔ bold offensive [5]’), (B) project preference (‘low risk [1] ↔ high risk [5]’), (C) research and development activities (‘no ↔ yes’), and (D) gender (‘male ↔ female’), respectively. Rectangles in heat plots represent Pearson’s r estimates as translated color codes ranging from ‘−0.2 ↔ + 0.2.’

HTS Technology-intensive and OTS Skill-intensive services, along with HTS Software and HTS High Technology-manufacturing, formed a cluster showing high values in openness (O++) and extraversion (E++), while uniformly exhibiting low values in neuroticism (N++). Conscientiousness (C−) and agreeableness (A−) tended to be lower throughout the cluster, with the HTS High Technology-manufacturing sector revealing slightly higher agreeableness values (A+). Another overarching cluster was formed by OTS Wholesale and retail market, Consumer-oriented services, Non-high-tech Manufacturing, and Other Business-oriented services. Their decision-making behavior was characterized by high values in openness (O++) and extraversion (E++). Additionally, consistently low to moderately low values were observed for neuroticism (N−), with a neutral to positive rating for conscientiousness (C0/+) and a neutral to negative rating for agreeableness (A0/−).

Risk propensity—project preference

When exploring risk propensity and project choices, the analysis (Fig. 3B) revealed a polarization between two dominant trait clusters: the dimensions of openness and extraversion showed a moderate to high-risk propensity, while the traits of conscientiousness and agreeableness tended toward very low-risk behavior. Furthermore, neuroticism was associated with the low-risk spectrum, except in the case of HTS Cutting-edge Technology Manufacturing, which displayed a more balanced risk propensity. Notably, HTS Software exhibited a pronounced risk propensity concerning openness, while the other dimensions aligned with the subsequent cluster. The HTS Technology-intensive services, OTS Skill-intensive services, and OTS Other Business-oriented services cluster persistently exhibited high openness (O++). All three industries were further characterized by increased extraversion (E+) as well as a very low level of neuroticism (N−−). Additionally, they exhibited low levels of conscientiousness (C−) and agreeableness (A−). Contrastingly, OTS Construction showed a clear risk propensity associated with extraversion (E++) and conscientiousness (C+).

Despite the different frequency distributions regarding individual questions, as depicted in Fig. 1F and G of the IAB/ZEW Start-up panel, extraversion and openness exhibited distinct similarities, indicating a higher risk orientation. Conscientiousness and agreeableness exhibited different tendencies depending on the industry. Neuroticism tended to be associated with a lower willingness to take risks, except in the HTS cutting-edge technology manufacturing sector.

Research and development

Upon examining R&D activities, openness emerged as a consistently stable and individual trait (Fig. 3C). This was particularly evident in the HTS Technology-intensive services and OTS Non-high-tech manufacturing, which, along with HTS Software and OTS Skill-Intensive (non-technical or consulting) services, exhibited a clear R&D orientation. HTS Cutting-edge technology manufacturing, as a standalone sector, exhibited a balanced inclination towards R&D.

Agreeableness, conscientiousness, and neuroticism formed a visible right-sided clustered pattern trio exhibiting a moderate decline to a weak association with R&D. Extraversion appeared divided: an R&D-affiliated segment, represented by OTS Construction, OTS Consumer-oriented services and HTS Cutting-edge technology manufacturing, was observed; however, a segment with a neutral research orientation was also observed.

Additionally, an industry cluster was formed, including OTS Non-high-tech manufacturing, HTS Software, HTS High-technology manufacturing and OTS Other Business-oriented services. This group predominantly exhibited high openness (O++) and positive extraversion (E+)—aside from OTS Non-high-tech manufacturing, which exhibited negative extraversion (E−). Furthermore, this group also showed consistently low levels of neuroticism (N−), while conscientiousness tended to be negative, and agreeableness ranged from moderate to positive. Another cluster was formed, comprising OTS Consumer-oriented services, HTS Technology-intensive services, OTS Skill-intensive services, OTS Construction, and OTS Wholesale and retail market. This cluster showed above-average openness to R&D (O++) and positive extraversion (E+/+), except for OTS Wholesale and retail market, which scored with neutral extraversion (E0).

The remaining traits varied depending on the industry. Conscientiousness exhibited volatility from positive to slightly negative, whereas agreeableness ranged from neutral to negative, and neuroticism ranged from negative to slightly positive.

Gender

The examination of the Big Five personality profiles of solo entrepreneurs across various business sectors, considering gender distribution as a potential influence factor, allowed for the identification of distinct behavioral patterns.

Figure 3D presents the connection between gender, the Big Five, and the industry sectors. It became apparent that some industries, such as construction, were found to have predominantly male traits while others exhibit predominantly female traits. Across industries, neuroticism was the predominant trait with the highest number of female attributes.

Agreeableness was dominant in specific industries, particularly in OTS Skill-Intensive (non-technical or consulting) services, with nearly 100% female proportion. Similarly, extraversion had a remarkably high expression in HTS Cutting-edge technology manufacturing. Female-dominant traits were noticeable concerning conscientiousness in OTS Skill-Intensive (non-technical or consulting) services, HTS Cutting-edge technology manufacturing, and HTS High-technology manufacturing. However, OTS Construction and HTS Technology-intensive services were male-dominated in all dimensions. Furthermore, agreeableness, extraversion, and conscientiousness formed an inter-industry cluster combination, while openness and neuroticism formed a distinctive cluster.

Discussion

Entrepreneurship research is characterized by a plethora of definitional approaches and theoretical perspectives, ranging from the early works of Cantillon28 through Knight68 and Schumpeter69 to the modern interpretations of Mises70 and Kirzner29. This diversity reflects the dynamic and multifaceted nature of entrepreneurship; however, it also raises questions regarding the clarity and coherence of the research field. Particularly, heterogeneous industries and the challenges in selecting adequately differentiated comparison populations pose methodological challenges for researchers71–73.

Despite these obstacles, Metzger74 underscored the initial business years’ critical importance. Similarly, previous studies also refereed to different business phases, as emphasized by Greiner’s model57 of organizational development, which highlighted the importance of adaptability and targeted management in various development phases.

At the heart of the present study is the assumption that personality traits exhibit fundamental consistency, which can be modulated to a limited extent by external factors such as age, gender, or significant life events75–77. Additionally, this study’s findings recognized the potential for significant life events, such as founding a business, to impact personality traits. This dynamic interaction between entrepreneurial activities and personality traits underlined the importance of considering both directions of influence during the analysis22,23.

The relative stability of these traits, as evidenced by Specht, Luhmann, and Geiser78, Bloeser et al.79, Stemmler et al.80, Soto and John81 and Lytras et al.77 provided a solid foundation for the analysis of entrepreneurial personalities.

The insights of this study open new avenues for applying proven concepts from recruitment, such as Job-Fit and Self-Assessment approaches32, to the context of entrepreneurship and cultivating tailored tools for assessing and developing entrepreneurial competences.

Is it possible to classify entrepreneurs (specifically solo founders) in their critical initial founding phase based on quantified variables such as the Big Five personality dimensions and sector-specific experience? The significance of the Big Five model as a method of characterizing entrepreneurial personalities has been considered by Caliendo et al.3 and Cuesta et al.82. Despite the comprehensive analysis of personality profiles using the Big Five model, preliminary and unpublished data revealed that identifying individual, meaningful profiles by industry based solely on these personality dimensions is insufficient.

The Big Five model summarizes behavioral tendencies at a high level of abstraction, limiting its specificity and explanatory power in the entrepreneurial context32–34. Personality questionnaires based on the Big Five model predominantly capture the self-image of respondents, influenced by situational and contextual factors, while discrepancies between self-assessments and external assessments further undermine the reliability of these measures32,35. Previous studies indicate that the Big Five dimensions alone are inadequate for distinguishing entrepreneur profiles across industries, because they do not account for variability influenced by environmental and situational factors33. This makes it difficult to provide generalized statements when personality traits among entrepreneurs vary according to environment and situational context19,83. Therefore, factors such as risk aversion, self-efficacy, and internal locus of control are crucial for understanding and predicting entrepreneurial behavior11,33,34,47.

Minor differences in the Big Five traits within the groups of entrepreneurs, non-founder CEOs and inventor-employees suggest that these traits alone are insufficient for differentiation33. Consequently, researchers are developing a multidimensional personality framework that includes traits such as self-efficacy, innovativeness, locus of control, and need for achievement52.

As such, this expands the research scope to encompass additional moderating factors, including risk propensity, and readiness for innovation in the form of R&D and gender, to enable a more differentiated consideration of entrepreneurs depending on the sector in which they are active. This expansion aimed to achieve a more nuanced understanding of the dynamics of entrepreneurial behavior and improve the precision of the typology of entrepreneur personalities.

Risk propensity is considered a characteristic trait of entrepreneurship43,84,85 and plays a fundamental role in entrepreneurs’ motivation and actions, as emphasized by Shaver and Scott44 and Estay, Durrieu, and Akhter86. Shaver and Scott44 observed a tendency toward higher-risk behavior among entrepreneurs, and Xu and Ruef87 underscored the importance of motivations beyond the material, showing stronger risk propensity in investment decisions and emphasizing autonomy and identity fulfilment.

Risk propensity—decision behavior

In the decision-making behavior analysis, individuals with pronounced extraversion and openness tended to be associated with bolder decisions, similar to the findings of Schlaegel et al.88. These traits are associated with entrepreneurial success, as noted by Baluku et al.13 and Kritikos8, and with a strong inclination towards self-employment, as indicated by Caliendo et al.3.

High openness promotes a propensity for innovation and opportunity recognition, which is crucial for success in dynamic industries7,9,10. Conversely, conscientiousness and agreeableness align with a more balanced risk propensity. Conscientious individuals tend to prefer structured decisions, which can be advantageous in high-precision fields.

Previous studies indicated that conscientiousness is associated with the intention to establish an enterprise11,12. Antoncic et al.10 correlated conscientiousness with the type of entrepreneurship characterized by efficiency and organization, while Konon and Kritikos16 found higher levels of conscientiousness among entrepreneurs and a lower expression of agreeableness and neuroticism.

Agreeableness can contribute to minimizing risks and conflicts, although it tends to be lower among entrepreneurs than among managers13,14. Neuroticism is characterized by cautious and risk-averse decision-making, indicating lower risk propensity. Low neuroticism, which correlates with professional success, can be advantageous in industries where risk minimization and emotional stability are important10,13,15,16. This study’s analysis emphasized the importance of understanding the associations between personality traits and entrepreneurial risk behavior, and how the combinations of these traits in various industries assist in identifying characteristic personality profiles.

Adaptive Services (O++, C0/+, E+, A0, N−−)

The Adaptive Services cluster, comprising OTS Wholesale and retail market, Consumer-oriented services, Non-high-tech Manufacturing, and Other Business-oriented services, is characterized as industries with a strong service orientation and customer proximity. This cluster thrives through risk-aware decision-making and the ability to dynamically adjust to customer needs.

Characterized by openness and extraversion and supported by a stable emotional condition (low neuroticism), these industries effectively manage risks and offer a wide range of flexible, customer-oriented services and products.

Dynamic Knowledge Innovators (O++, C−, E++, A−, N−−)

The cluster Dynamic Knowledge Innovators cluster, comprising HTS Technology-intensive services, OTS Skill-intensive services, HTS Software and HTS High-technology manufacturing, is characterized by its innovation strength and agility in technology-intensive sectors, as well as by high R&D intensity and a strategic focus on innovation enthusiasm, shaping its decision-making behavior.

A continuous pursuit of technological excellence is encouraged by pronounced extraversion, which supports communication and cooperation. The openness to flexibility and competitive management fosters a robust and resilient corporate culture. These characteristics enable Dynamic Knowledge Innovators to effectively respond to technological and market changes, making them a crucial success factor in an innovation-driven economic environment.

Risk propensity—project preference

Openness and extraversion are associated with a moderate to high risk-taking propensity, which is typical of entrepreneurs engaging in new and uncertain projects, indicating a proclivity for innovation. Contrastingly, conscientiousness and agreeableness are associated with a preference for low-risk projects, suggesting a prudent, risk-minimizing approach, which can be beneficial in sectors requiring precision. Neuroticism generally indicated a low risk profile, except HTS Cutting-edge Technology Manufacturing, which exhibited a more balanced risk propensity. Noteworthily, HTS Software exhibited a pronounced inclination toward openness, which is associated with a necessity of risk-propensity in technology-driven industries, fostering innovation and competitive advantage.

Strategic Risk Navigators (O++, C−, E+, A−, N−−)

High openness and strong extraversion characterize Strategic Risk Navigators, which include HTS Technology-intensive services, OTS Skill-intensive services, and OTS Other Business-oriented services. Such navigators are associated with remarkably low neuroticism, reflecting a pronounced tolerance for risk and proactive market orientation.

These companies, from research institutions to leasing companies, respond quickly and efficiently to market dynamics through their desire for innovation and flexibility.

Their low conscientiousness and agreeableness indicate a tendency to question conventional methods and bypass rigid structures, making them predisposed actors for innovative and risky projects.

Innovativeness—research and development

R&D activities encompass basic research, applied research, and development, which are crucial for expanding existing knowledge and its application in new contexts89. The IAB/ZEW start-up panel captured R&D by asking whether companies conducted their own R&D work. R&D was defined as systematic, creative work aimed at expanding existing knowledge and using it for the development of new applications. In entrepreneurship, innovation capacity includes the development and implementation of novel creative solutions in the form of new, modified and expanded products, ideas, and practices perceived as novel by the target audience. This novelty is characterized by recency, originality, and difference from the existing ideas86,90.

Schumpeter46,69 and Ahlstrom91 underscored the importance of innovation in entrepreneurship, while Zhao et al.92 found that entrepreneurial awareness, motivation and insight are strongly correlated with innovation. Acs and Audretsch93, and Man et al.94 emphasized the growing significance of SMEs and their contribution to innovation and technological development.

The innovativeness of solo entrepreneurs was captured through the direct measurement of R&D activities, as recorded by the IAB/ZEW start-up panel survey question asking whether respondents undertook R&D work on their own or on behalf of third parties. This ‘yes/no’ measure served as a quantifiable indicator of entrepreneurs' commitment to innovation. Despite its simplicity, this approach provides an initial approximation of the respondents' innovative behavior. In this study, the integration of R&D activities into the Big Five personality traits reflect Kirton’s differentiation95 between adaptive and innovative cognitive styles. This distinction was particularly pertinent given that it encompasses not only general openness to new ideas in terms of ‘General Innovativeness’ (GI), but also a specific willingness to embrace innovations in certain areas as ‘Specific Innovativeness’ (SI). Marcati, Guido, and Peluso45 described the necessity of differentiating between GI (general openness to newness and creativity) and SI (the inclination to early adoption of innovations in specific domains).

Kirton’s95 perspective is reflected in the association between the Big Five and R&D, emphasizing the complexity of innovative behavior and the importance of a nuanced view of innovativeness. This study provides insights into the psychological drivers behind entrepreneurs' innovativeness, illustrating how the Big Five personality traits relate to engaging in R&D activities and serve as an indicator of the general ability to drive and respond to changes in the market or environment.

The Big Five personality dimensions exhibit a strong prevalence of openness (O++) among solo entrepreneurs concerning R&D activities. This inclination toward innovation and creative processes was particularly evident in companies with high R&D intensity, such as HTS Technology-intensive services, OTS Non-high-tech manufacturing and HTS Software. Similarly, OTS Skill-intensive services, OTS Other Business-oriented services and OTS Consumer-oriented services exhibited a high openness (O++), albeit with a slightly lower orientation toward R&D than the previously mentioned sectors.

This demonstrated the openness to new ideas among solo founders in these sectors, which contrasts with works by Koellinger96, Marcati, Guido, and Peluso45 and Peljko and Antončič Auer97 that described a moderate openness and pragmatic approach to innovation as being characteristic of entrepreneurs and their activities.

A recent study by Nguyen et al.98 highlighted a significant correlation between conscientiousness and entrepreneurial innovativeness. In this study, the dimensions of conscientiousness, agreeableness and neuroticism showed varying R&D intensities across sectors. OTS Consumer-oriented and skill-intensive services stood out with higher conscientiousness (C+), while other sectors exhibited moderate to very low values (C0/−−). Regarding agreeableness, HTS High Technology-manufacturing and OTS Non-high-tech Manufacturing differed from the general trend with higher values. Regarding neuroticism, the OTS Wholesale and retail market sector was notable for its positive neuroticism (N+).

These patterns suggested risk-conscious management and underscored the importance of balanced risk management coupled with a pursuit of innovation. Hyytinen et al. drew attention to the crucial role of innovativeness in the success of start-ups and noted the associated risks in the early stages of entrepreneurship.

Extraversion manifests in two distinct expressions, which are associated with the inclination toward R&D activities in various sectors. Sectors such as OTS Construction, HTS Cutting-edge technology manufacturing, OTS Consumer-oriented services and OTS Other Business-oriented services, which exhibit high extraversion (E+/++), not only show a distinct affinity for R&D, but also reflect an open and communicative corporate culture. This attitude is associated with adaptive-innovative leadership, which is essential for modern business success. Verma and Mehta99 confirmed this observation, arguing that transformational, open, and charismatic leadership styles, associated with high extraversion, significantly enhance a company’s innovation capability and adaptability.

Conversely, while some sectors exhibit a neutral stance on R&D, OTS Non-high-tech manufacturing (E−) stands out owing to its high openness to experience (O++), suggesting an unexpected commitment to innovation despite its lower extraversion.

This challenges the traditional view that high extraversion is a prerequisite for innovation and underlines the innovation potential in sectors not typically associated with intensive research activities. Volksbank Wien AG100 made a point of that generational values such as teamwork and innovation, often associated with open and extraverted personalities, are vital components of corporate culture and influence entrepreneurial success across various sectors, including those with traditionally neutral attitudes toward R&D.

Multidimensional Service Innovators (O++, C−/+, E0/++, A−−/0, N−−/+)

The Multidimensional Service Innovators cluster includes OTS Consumer-oriented services, HTS Technology-intensive services, OTS Skill-intensive services, OTS Construction and OTS Wholesale and retail market. They share a pronounced inclination toward innovation and customer orientation, particularly in R&D-intensive service sectors.

Their research affinity, manifested in openness (O++) and supported by neutral to strongly positive extraversion (E0/++), prioritized the importance of cooperation and networking in market adaptation and business growth. Flexible conscientiousness and a diversity in the agreeableness spectrum reflect varied approaches in customer relationship management.

The range of neuroticism displays the variety of corporate cultures and their adaptability in facing challenges.

This cluster signifies business models that evolved to focus on internal optimization and proactive market responses. These models exhibit advanced sophistication concerning systematized processes, incorporation of innovative strategies, and the capacity for continuous improvement and growth that are often observed in the later stages of the start-up phase.

The range within the extraversion, agreeableness and neuroticism traits of this cluster might be attributed to the ambiguity in responses to the question on R&D. Resolving the variability in E, A, and N would likely entail clarifying the distinction between basic research and further development and the application of existing knowledge associated with the Big Five.

Focused Tech Innovators (O++, C−, E−/+, A0/+, N−)

The Focused Tech Innovators cluster, encompassing OTS Non-high-tech manufacturing, HTS Software, HTS High-technology manufacturing and OTS Other Business-oriented services, is distinguished by its high openness to new experiences, and extraversion and agreeableness ranging from neutral to slightly positive, suggesting adaptable relational skills.

A tendency toward lower conscientiousness implies flexibility in business processes, while low neuroticism indicates stress resistance. These characteristics enable Focused Tech Innovators to swiftly adapt to market changes and foster a cooperative work environment.

McCarthy et al.9 closely associated such traits with the success of start-ups in their early stages, illustrated through various entrepreneurial personas: the organized and confident Fighter, the composed Operator with organizational skills and extraversion, and the adventurous and open-minded Leader. Engineers are characterized by creativity and intellectual openness, while developers display similar, albeit more extraverted, tendencies. The Accomplisher is proactive, goal-oriented, and technology-focused. Low neuroticism (N−) supports stress resilience and effectiveness under pressure for Focused Tech Innovators. Howard and Howard1 and Baluku et al.13 confirmed this.

Low conscientiousness (C−) promotes flexibility in project and process design, facilitating rapid adaptation to market changes. Moreover, variations in agreeableness support a cooperative work atmosphere.

Therefore, this cluster combines technological excellence with customer-oriented flexibility, enabling rapid response to market dynamics and driving innovation.

Gender

The exploration of gender dynamics within entrepreneurship confirmed how distinct personality traits and occupational stress impact men and women differently. In sectors such as HTS Software and OTS Consumer-oriented services, neuroticism suggests a greater vulnerability to work-related stress among women, which, according to Alstete5 and Rau et al.6, is associated with increased health risks such as hypertension and depression. Furthermore, Röhl101 observed a gender gap in entrepreneurship across the EU, with women being particularly active in OTS Skill-intensive services, indicating a preference for harmonious work environments and better work–life balance102–104.

Gender-Related Traits (O−−/++, C−/++, E−−/++, A−/++, N+/++)

HTS Cutting-edge technology manufacturing sectors, characterized by high agreeableness (A+) and strong extraversion (E++), suggest a culture of collaboration and networking, indicating women’s inclination toward quality and structure in tech-intensive settings, drawing attention to their conscientiousness (C++). Conversely, OTS Construction reflects male dominance in technical and physically demanding fields, illustrating the persistence of traditional gender roles.

Challenges for women, particularly regarding assertiveness, have been emphasized by research from Ames and Flynn105, indicating that women in such environments face specific barriers owing to traditional role expectations, which hinder their integration and acceptance. Additionally, lower extraversion and openness suggest more reserved social interactions and a reduced openness to new experiences. While personality differences between woman and men do not significantly impact motivation, independence, performance and satisfaction, previous studies indicated that women may exhibit a more cautious approach to risk-propensity than men10,106.

These findings underscored distinct gender dynamics within various sectors, illustrating a predominance of men in fields such as HTS Technology-intensive services and OTS Construction. Conversely, sectors such as OTS Skill-intensive services and Consumer-oriented services, which have a notably higher female presence, show diverse patterns.

Birley107 described the higher entry costs for women, accentuating the critical role of supportive factors, including integration into families, role models, self-confidence, education, networks, and advisory services102,108–110, in facilitating women’s entrepreneurship. Furthermore, the significant challenges in accessing capital restrict their opportunities for initiating and developing businesses, because financiers often demand higher collateral from female entrepreneurs104,111–113.

Overall, the heatmap analysis indicated remarkable diversity in the personality profiles of entrepreneurs across various sectors. Dissimilar to the conventional portrayal of women as a homogeneous group with uniform challenges and strengths, the range of the Big Five values emphasized individual differences and a broad spectrum of entrepreneurial abilities. Both male- and female-dominated sectors revealed complex patterns that defy a one-sided gender dynamic. These findings suggest looking beyond oversimplified stereotypes and recognizing that success in the business world is not monolithic but varied and multifaceted.

Key findings

This study aimed to determine the personality profiles of founders in various sectors and understand how these profiles influence risk-propensity, decision-making, and innovativeness. The following findings were derived based on the three research questions:

How do the Big Five personality traits influence the risk-propensity and decision-making behavior of solo entrepreneurs across different industries within the first seven years of business operations?

Extraversion and openness were found to be associated with higher risk inclination in decision-making and project preference under risk aspects. Contrastingly, conscientiousness and agreeableness showed a more balanced to very low risk inclination depending on the context. Neuroticism was generally associated with lower risk inclination, except in the HTS Cutting-edge Technology Manufacturing sector. These findings illustrated how personality traits influence the risk propensity of solo entrepreneurs and shape their decisions regarding high-risk projects.

How do personality dimensions affect entrepreneurs’ willingness to innovate and engage in R&D during the early business phase?

The identified sector clusters illustrated the diversity in willingness to innovate and commitment to research and development, shaped by different combinations of the Big Five personality dimensions. Entrepreneurial profiles, as presented in Fig. 4, consistently showed strong openness and emotional stability, indicated by negative neuroticism scores. Low conscientiousness prioritized flexibility, except in the Multidimensional Service Innovators and Adaptive Services sectors, which displayed a wide variance. Extraversion ranged from active-energetic to introverted; the latter was specifically found among Focused Tech Innovators, while agreeableness extended from critical-self-determined to moderately positive. Neuroticism was generally low; however Multidimensional Service Innovators covered a broad spectrum from resilience to sensitivity. These results demonstrated how personality dimensions differently influenced willingness to innovate and commitment to R&D.Fig. 4 Entrepreneurial profile schemata, derived from cluster and heatmap-analysis, Big Five traits and four associated factors.

How do gender differences in the Big Five traits manifest in the entrepreneurial landscape across various industries?

The gender-related traits presented in Fig. 4 illustrate the remarkable diversity in the personality profiles (O−−/++, C−/++, E−−/++, A−/++, N+/++) of entrepreneurs across various industries. Instead of portraying women as a homogeneous group with uniform challenges and strengths, the spectrum of Big Five values revealed individual differences.

Certain personality traits, such as heightened neuroticism, indicated increased sensitivity to stress and suggested emotional fluctuations, particularly among women. This nuanced picture clearly supports the need to overcome and acknowledge stereotypical perceptions, recognizing that the characteristics of solo entrepreneurs in the critical first seven years are multifaceted and nuanced. This variance, which was particularly pronounced in innovativeness, corresponds with earlier discussions regarding the ambiguity in R&D questions.

Sector-specific insights and need for comprehensive analysis

This study illustrated that the Big Five personality dimensions, particularly when considering factors such as risk tolerance, innovation strength, and gender-specific aspects, can provide valuable insights into the basic behavioral patterns of solo entrepreneurs. However, these dimensions reach their limits in capturing the complex requirements faced by solo entrepreneurs in the initial phase across a broad spectrum of sectors, from consumer services to advanced technology production. To adequately reflect the profound differences among entrepreneurs, the analysis should be expanded to include additional mediators such as cognitive styles, motivation, experience, and sector-specific characteristics. Therefore, a more comprehensive analytical approach is required—one that captures and considers the unique attributes and motivational factors of innovative entrepreneurial personalities.

Future research directions

Based on the findings, future research should aim to:Expand the analytical models to include cognitive styles, motivation, and sector-specific characteristics.

Examine the interplay between these additional factors and the Big Five personality traits to provide deeper insights into entrepreneurial behavior.

Conduct sector-specific studies to better understand the unique requirements and challenges of different industries, particularly in high-tech manufacturing.

Strengths and limitations of the study

The current results concerning the sector-specific personality traits of start-up company founders during their initial phase expand and strengthen the possibilities for successful and anticipatory development of individual companies, and this is also true of the identification of cross-sector clusters (Dynamic Knowledge Innovators, Adaptive Services, Strategic Risk Navigators, Focused Tech Innovators, and Multidimensional Service Innovators).

However, the disproportionate emphasis on certain sectors does not accurately reflect the actual distribution in small and SMEs. Moreover, the question on innovation capability revealed significant variability regarding the understanding and implementation of research and development. At first glance, it may be reasonable to assume that the shortened versions of the Big Five questionnaire of the IAB/ZEW start-up panel may not provide a highly accurate representation of the Big Five traits.

Collecting data to establish the Big Five traits with a larger question set may have provided scale estimates with smaller confidence intervals. However, these possible effects were mitigated owing to the large sample.

Significant life events, such as founding and managing a company, may influence personality traits, which could have led to reverse causality in this study. Additionally, this study’s data collection occurred after the establishment of businesses and focused on the first seven years of entrepreneurial activity. Except for the founding year, the IAB/ZEW-start-up panel did not contain any questions that covered significant life events, such as unemployment, marriage or widowhood22,23. Extremely stressful events occur in approximately 25% of the population76, implying that roughly a quarter of all participants in this study’s sample may have experienced such events. According to Löckenhoff et al.76, these events can lead to significant changes in certain personality traits. Consequently, changes in personality traits due to entrepreneurial activities may have occurred in this study’s dataset. However, they do not generally affect all five main factors of the Big Five, and the average effects are moderate.

Periodic effects, such as the Great Recession, significantly impact personality traits75. However, previous studies found that personality profiles are generally stable over time, despite variability in specific subgroups and age groups78,79. Additionally, the BFI-2 inventory also described the need to control for individual differences81.

Conclusion

This study provides valuable insights into the relationship between the Big Five personality traits and the diverse range of ventures launched by solo entrepreneurs across various sectors, particularly during the critical early stages of their businesses.Regarding risk-propensity and decision-making behavior, the combination of extraversion and openness exhibited a higher risk inclination in decision-making processes, while conscientiousness and agreeableness exhibited a more balanced to low risk inclination depending on the context. Neuroticism was generally associated with lower risk inclination, except in the HTS Cutting-edge Technology Manufacturing sector.

Regarding willingness to innovate and commitment to R&D, the analysis of cross-sector clusters flagged up the diversity in willingness to innovate and commitment to R&D. Entrepreneurial profiles consistently showed strong openness and emotional stability, while variations in conscientiousness and extraversion indicated differentiated approaches to innovation.

Regarding gender differences, the Big Five traits revealed individual differences, particularly depicting pronounced neuroticism among women, indicating increased sensitivity to stress. These findings emphasize the need to overcome stereotypical perceptions and recognize the multifaceted realities of entrepreneurship.

The results of this study have significant implications for various stakeholders:

For Solo Entrepreneurs: The study’s findings can help solo entrepreneurs better understand their strengths and weaknesses and prepare for the challenges of self-employment. This is particularly important during the early stages, when many businesses fail. A deeper understanding of one’s personality can improve the prospects for sustainable business development.

For Policymakers: The insights can help develop targeted support measures and programs tailored to solo entrepreneurs’ specific personality traits and needs. This can enhance the effectiveness of policy interventions and promote innovation across different sectors.

For Investors: A better understanding of the personality profiles of founders can help investors align their decisions with the risk preferences and innovation potentials of entrepreneurs. This can lead to more informed investment decisions, covering both high-tech and traditional sectors.

Overall, this study demonstrated that personality traits play a crucial role in shaping entrepreneurial behavior, particularly during the vulnerable early stages of a business. Furthermore, future studies should consider additional factors such as cognitive styles, motivation, and sector-specific characteristics to further deepen understanding and provide more precise support for entrepreneurs. These insights offer a valuable foundation for developing more effective strategies and programs that cater to the diverse needs and potentials of solo entrepreneurs, accounting for the broad spectrum of industries from high-tech start-ups to traditional SMEs.

Acknowledgements

The authors would like to thank Sarah S. Willson for constructive criticism and proofreading the manuscript.

Author contributions

W.H.: Conceptualization, Resources, Methodology and Writing—Original Draft. H.T.Z.: Methodology, Formal analysis, Data Curation, Writing—Review & Editing and Visualization.

Data availability

The data that support the findings of this study are based on 'Scientific Use Files' related to the IAB/ZEW Start-up Panel. Data are available with the permission of ZEW-FDZ (Leibniz-Zentrum für Europäische Wirtschaftsforschung—Forschungsdatenzentrum; kooperationen.zew.de/en/zew-fdz/home). Restrictions apply to the availability of these data, which were used under license for this study.

Competing interests

The authors declare no competing interests.

Ethical approval and/or consent

The study does not use and/or refer to experimental animals/humans/participants and assigned tissues/material,/transplantation and/or vulnerable groups. The submitted manuscript refers to a 'non-experimental study. All results presented and discussed were analyzed with anonymized data taken from so called 'scientific use files' from the IAB/ZEW Start-up Panel including micro data from computer-assisted telephone interviews (https://kooperationen.zew.de/en/zew-fdz/home). Therefore, it was not necessary to obtain any informed consent.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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