A study of individual and market level factors to estimate key determinants of financial success among solopreneurs

Abstract

This study investigates how personality traits, age, risk propensity, and structural factors influence the long-term financial success of solopreneurs, defined as solo-founded ventures typically characterized by the absence of a dedicated workforce during the early years of operation. Using data from 4,470 solo-founded ventures in the German Institute for Employment Research/Centre for European Economic Research start-up panel, we model sustained profitability over the first 7 years as a binary outcome and test seven hypotheses grounded in trait, decision-making, and resource-based theories. The regression results show that two psychological traits - Openness and Conscientiousness - significantly increase the likelihood of long-term profitability (odds ratio OR = 1.616; OR = 1.279). Preference for low-risk projects (OR = 1.607) and being age ≤ 50 years (OR = 2.126) further enhance success odds. Conversely, general risk attitude, gender, sector affiliation, and research and development involvement show no significant effects. The results challenge assumptions about sectoral or innovation-based advantages and emphasize the significance of psychological variables in solopreneurship. This study contributes to a multidimensional understanding of entrepreneurial success and supports the development of evidence-based support strategies tailored to individual trait-context combinations.

Introduction

While solopreneurship has gained recognition as a distinct form of entrepreneurship, its financial success patterns remain poorly understood. The early years of solopreneurial ventures - particularly the first seven - are marked by high risk of failure. However, empirical research linking individual psychological traits to financial performance during this phase is limited. Most existing studies focus on general self-employment, entrepreneurial intentions, or business survival, but rarely isolate solopreneurs or track long-term profitability. Although personality traits have been extensively investigated in entrepreneurial contexts, their connection to sustained financial success - as opposed to motivation or entry decisions - remains largely unexplored.

This study addresses this gap by focusing on economically active solopreneurs who report revenue and profit/loss over seven consecutive business years. Based on the German Institute for Employment Research/Centre for European Economic Research (IAB/ZEW) start-up panel (IZP) - a longitudinal dataset covering firm- and founder-level dynamics in Germany¹ - we analyze how stable personality traits, particularly the Big Five, interact with moderating factors such as risk propensity, gender, industry sector, and age to predict sustained profitability. Our sample integrates solopreneurs from high-tech, service, and construction sectors, thereby providing a cross-sectoral perspective that remains underrepresented in empirical entrepreneurship research².

By analyzing concrete influencing factors during the first 7 years of operation, this study highlights a critical yet often overlooked phase of entrepreneurial development - a period in which many solopreneurs fail despite favorable personality traits or intentions.

Prior research on entrepreneurial success often employs broad definitions and tends to overlook the early financial trajectories of solopreneurs. Although frameworks such as Trait Activation Theory (TAT), Prospect Theory (PT), and the Resource-Based View (RBV) are frequently referenced, they are rarely operationalized alongside objective financial performance data. This study addresses this gap by examining how personality traits and contextual factors interact through three theoretically distinct lenses:

TAT³ posits that personality traits translate into observable behaviors only when activated by relevant situational cues. These cues may arise at the task, social, or organizational level - for example, in settings involving high autonomy, innovation demands, or performance pressure. In this study, we apply the theory to examine whether, and under which contextual conditions - such as solopreneurs’ engagement in research and development (R&D), levels of autonomy, or industry sector (high-tech [HTS], e.g., IT, engineering, scientific services; versus other sectors [OTS], e.g., construction, retail, personal services; see Table 1)² - these traits become behaviorally and financially relevant.

Table 1.

Overview of analyzed business sectors².

Economic Sector	Description	Example
High-Tech Sectors (HTS)
Cutting-edge technology manufacturing	Sectors with an R&D intensity of over 7%	Production of pharmaceuticals, manufacturing of advanced computer hardware, fabrication of precision instruments for measuring, and navigation technology
High-technology manufacturing	Industries with an R&D intensity between 2.5% and 7%	Manufacture of machinery and engines, automobile production, production of various chemical products
Technology-intensive services	Service providers focusing on R&D in science, engineering, agriculture, and medicine	Specialized research firms, technical planning offices, providers of communication technologies
Software	Sectors specializing in software development or web design	Companies specializing in customized software solutions, digital design agencies
Non-High-Tech Sectors (OTS)
Non-high-tech manufacturing	Manufacturing industries excluding high technology, including food processing, textile, and metalworking	Food manufacturers, textile factories, metalworking plants
Skill-intensive (non-technical or consulting services)	Service providers requiring specialized knowledge but not primarily technology-oriented	Tax consulting and auditing firms, marketing agencies
Other business-oriented services	Service providers predominantly supporting other businesses	Leasing companies, cleaning, and waste disposal services
Consumer-oriented services	Providers of services for end consumers	Restaurants, hair salons, cleaning services
Construction	From construction and civil engineering to specialized crafts	Construction companies, heating contractors, painting companies
Wholesale and retail market (w/o trade agents)	Trading companies maintaining a direct customer relationship	Car dealerships, wholesale and retail stores

PT, developed by Kahneman and Tversky⁴, models individual decision-making under uncertainty. Applied to solopreneurs, it suggests that financial outcomes depend on how individuals perceive and respond to risk - particularly in ambiguous, high-stakes environments such as early startup phases.

The RBV, introduced by Barney⁵, offers a strategic perspective that emphasizes how firm-specific, intangible resources - such as experience, social capital, and tacit knowledge - contribute to sustained competitive advantage. In this study, founder age is treated as a proxy for the accumulation of such resources over time.

Empirical evidence highlights the volatility of financial outcomes among solopreneurs. Metzger⁶ reports high failure rates during the early self-employment stages, and Greiner⁷ identifies the formative years as critical for long-term viability. Individual-level factors - particularly risk tolerance and strategic experience - are central in shaping entrepreneurial decision-making and financial success. Although previous research has explored cultural and institutional determinants - such as national culture, social norms, and value systems⁸ - the combined influence of personality traits, risk perception, founder age, and sector-specific context on financial performance remains empirically underexplored.

To address these gaps, we analyze solopreneurs’ profit trajectories over a 7-year period using longitudinal data from the IAB/ZEW start-up panel². The dependent variable is a binary indicator of sustained profitability. Predictor variables include Big Five personality traits, risk propensity, founder age, and industry classification. The theoretical contribution of this study lies in integrating these individual and contextual factors within three distinct explanatory frameworks to explain long-term financial outcomes.

In line with TAT, we conceptualize context as a triggering mechanism that influences the expression and relevance of personality traits. In our model, contextual conditions include the solopreneur’s industry sector (classified as high-tech or other), engagement in R&D activities, and the broader entrepreneurial environment characterized by uncertainty and resource constraints.

Accordingly, this article is guided by the following three theory-anchored research questions (RQ):

1. RQ1 (TAT): How do personality traits - particularly openness, conscientiousness, extraversion, agreeableness, and emotional stability - affect solopreneurs’ financial success when activated by contextual factors such as industry sector (HTS vs. OTS), R&D engagement, or innovation pressure?

2. RQ2 (PT): How does risk propensity moderate the relationship between personality traits and sustained profitability in uncertain entrepreneurial environments?

3. RQ3 (RBV): How does founder age - as a proxy for accumulated entrepreneurial resources - influence long-term financial outcomes among solopreneurs?

Entrepreneurial success is a complex and multidimensional concept that extends beyond traditional financial benchmarks to include subjective goals and long-term business viability⁹. This complexity requires a focused analytical lens that considers both measurable financial outcomes and the personal traits that shape them. Accordingly, this study prioritizes sustained profitability over a 7-year period as a robust and objective indicator - while acknowledging that deeper dimensions of entrepreneurial success may lie beyond financial performance alone.

By explicitly modeling the interaction between individual traits and contextual conditions, this study advances a more granular and evidence-based understanding of financial success in solo-founded ventures. The findings contribute to theoretical refinement and offer practical insights for supporting a growing yet vulnerable segment of the entrepreneurial landscape.

Literature review

This literature review synthesizes current knowledge on (i) financial success and (ii) solopreneurship by integrating prior research on personality traits, contextual influences, and entrepreneurial outcomes. It builds upon the foundations laid in the section “Introduction” and structures the key theoretical dimensions into a coherent analytical framework.

Defining, measuring, and predicting financial success in entrepreneurship

Entrepreneurial success is a multifaceted construct that goes beyond simplistic notions of income or survival. While financial indicators such as revenue growth, profitability, and return on investment remain central benchmarks of success, they do not fully capture its multifaceted nature¹⁰. Profit maximization is often regarded as a core business objective¹¹; however, recent perspectives emphasize broader contributions, including innovation and societal value creation¹². Nevertheless, many entrepreneurs continue to earn incomes comparable to - or below - those of salaried employees¹³. Subjective success indicators such as job satisfaction, autonomy, and personal development (e.g., self-directed learning and goal-oriented behavior) are increasingly regarded as equivalent to financial metrics and are considered in explaining entrepreneurial success¹⁴.

Building on this, we adopt a financial perspective by defining success as sustained profitability over a 7-year period. This aligns with recent efforts to assess entrepreneurial performance based on long-term economic outcomes rather than short-term growth or subjective assessments¹⁵.

Using profitability as the dependent variable provides an objective and comparable measure, enabling a binary classification (profit in all years vs. not), which aligns analytically with the logistic regression model applied in this study.

Among potential predictors of financial success, personality traits - particularly the Big Five - have received increasing attention. While the Big Five framework is extensively used to examine entrepreneurial intentions, motivation, and firm longevity^16,17, its application to financial performance - especially regarding profitability or revenue growth - remains limited Most studies emphasize early-stage behavior or subjective outcomes - such as entrepreneurial intentions or self-reported success - while long-term financial performance remains underexplored^18,19. As McCarthy et al.²⁰ note, personality-performance research in entrepreneurship often relies on broad or composite success constructs, and clear financial endpoints such as profitability or revenue growth are rarely isolated.

Recent studies link openness to entrepreneurial success; conscientiousness also shows relevance in meta-analyses, although single-study results are mixed^18,21. Conscientiousness fosters planning, persistence, and goal orientation - behaviors that support entrepreneurial performance and are commonly associated with profitability¹⁸. Openness facilitates innovation, opportunity recognition, and adaptability to market dynamics¹⁴. Given the temporal stability of the Big Five traits as shown in prior research^22,23, we consider them theoretically suitable and empirically reliable predictors of financial success in solopreneurial contexts.

Our previous research has shown that solopreneurs display consistent trait profiles across sectors, with higher openness and conscientiousness and lower neuroticism than the general population²⁴ Cluster analyses further indicate that these traits align with sector-specific conditions such as innovation pressure, risk environment, and founder autonomy²⁵ - which are consistent with TAT³. These findings support the notion that personality traits gain behavioral relevance when triggered by specific situational triggers, particularly in high-autonomy contexts such as solopreneurship.

Overall, personality-based traits - particularly openness and conscientiousness - can meaningfully explain variance in financial outcomes. However, the link between Big Five traits and sustained profitability remains underexplored. This study addresses that gap using a large, representative sample of solopreneurs tracked over a full 7-year cycle.

The solo entrepreneur concept

A solopreneur, as defined by the IZP, is an individual who establishes and operates a business without employing staff, typically within the first seven years of operation. They operate across various sectors, including high-tech industries, traditional trades, and services².Only around 4% of men and 2% of women transition from solo self-employment to employership within two years, indicating that upward mobility into job creation roles remains rare²⁶.

Data from the 2017 IZP show that only 39% of surveyed solopreneurs had employees; those with staff reported an average of 2.4 full-time equivalents (FTEs). Employment varied by sector − 46% in high-tech/software, 31% in traditional services, and 39% in construction. High-tech and software firms averaged 3.1 FTEs at founding, compared to 2.3 FTEs in service and construction sectors. Across all sectors, 94% of solopreneurs were market-active, with a median revenue of approximately EUR 120,000². These sector-specific findings are based on standardized annual surveys from the IAB/ZEW Start-up Panel, which ensures comparability through consistent questionnaires, harmonized industry coding, and weighting for representativeness¹.

Age

Entrepreneurs span a broad age range. Azoulay et al.²⁷ report that the average age of company founders is 42, with substantial variation across industries - e.g., around 40 in software and closer to 47 in sectors like biotechnology and oil and gas. In Germany, 36% and 23% of founders in 2022 were 25–34 years and 35–44 years old, respectively²⁸, contradicting the common image of the young entrepreneur. While entrepreneurial success is influenced by various factors beyond age Bosma et al.²⁹, Zhao et al.³⁰ show that age correlates positively with financial success and firm size, negatively with growth, and not at all with survival - underscoring its differentiated role across success dimensions³⁰.

According to Azoulay et al.²⁷, e.g. also correlates positively with business survival¹⁰, although Winistörfer³¹ reports no statistically significant relationship between founder age and business survival. The RBV⁵ links sustained success to intangible resources like experience, knowledge, and networks. Zhao et al.³⁰ show that older entrepreneurs leverage these to improve financial outcomes. Consistent with H1 and the RBV, we assume that founder age - serving as a proxy for accumulated resources - positively affects sustained profitability.

Gender

According to the GEM Global Report (2019), men tend to pursue financial goals and high-growth ambitions, whereas women often balance entrepreneurship with family responsibilities or community-oriented motivations²⁹. Gender-related factors shape entrepreneurial pathways; however, their implications for long-term financial outcomes remain underexplored²⁹. Conversely, women tend to establish solo or family-based ventures, particularly when lacking social or cultural capital - an effect associated with weaker initial performance³². They also operate in less capital-intensive industries and face notable challenges in securing external funding³³. Consequently, they rely heavily on self-financing and informal funding sources such as family loans³². Additionally, the influence of personality traits on entrepreneurial intentions has been shown to vary across cultural and institutional contexts³⁴, which further emphasize the relevance of gender as a context-sensitive factor in entrepreneurial research. These insights highlight gender as a potentially important explanatory variable in understanding solopreneurs’ financial success. Given the inconclusive evidence - and in line with our demographic H2 - we include gender as an exploratory variable in our model, in the absence of consistent theory-driven predictions regarding its effect on financial.

Industry sector

Self-employment comprises a heterogeneous group of individuals who differ in entrepreneurial responsibility, decision-making autonomy, and task complexity²¹. However, much of the literature still focuses on dynamic, innovation-driven start-up environments^20,35, underrepresenting the diversity of business contexts - from high-tech to traditional sectors - and neglecting large parts of the small and medium-sized enterprise (SME) landscape.

To capture these contextual differences, we follow the sector classification used in the IAB/ZEW start-up panel, based on the WZ 2008 standard of the German Federal Statistical Office. We distinguish between HTS, including IT, engineering, and scientific services, and OTS, such as construction, retail, and personal services^2,36. This distinction reflects fundamental differences in innovation requirements, market conditions, and growth potential. According to IAB/ZEW data, solopreneurs are active in both domains, but show distinct patterns in revenue expectations, R&D engagement, and growth orientation. Table 1² provides an overview of the analyzed business sectors. Given that sector-specific financial outcomes among solopreneurs remain underexplored, we test in line with H3 whether founders in high-tech sectors are more likely to achieve sustained profitability than those in OTS.

Innovativeness/R&D

R&D activities are central to fostering innovation in entrepreneurial ventures, enabling the development of novel products, ideas, and practices^37,38. Innovation is extensively recognized as a key driver of entrepreneurial value creation and economic transformation^11,39. Schumpeter and Ahlstrom¹¹. Zhao et al.⁴⁰ show that entrepreneurial awareness, motivation, and cognition each enhance innovation capacity.

SMEs play a critical role in technological advancement, particularly through their flexibility, niche specialization, and radical innovation potential⁴¹. At the firm level, these capacities are linked to organizational performance via innovative competencies⁴². Building on these insights and in line with H4, we include R&D engagement as an empirically motivated explanatory variable. While innovation is extensively regarded as performance-relevant, no established theory directly links R&D activities to financial success in solopreneurial contexts.

Big five traits

The Big Five personality traits provide a stable and multidimensional framework for analyzing entrepreneurial behavior and performance^18,21. Prior research identifies openness and conscientiousness as particularly strong predictors of entrepreneurial success, while traits such as extraversion and emotional stability have been linked to venture survival and adaptation under specific contextual conditions^17,22. To account for these differentiated effects, we apply a comprehensive modeling approach that includes all five traits, allowing for the identification of both primary and secondary predictors of sustained profitability.

This study uses TAT³, as its primary theoretical framework, which explains how personality traits gain behavioral relevance when activated by trait-relevant situational cues. In future research, cybernetic models such as CB5T⁴³ may further enhance explanatory power by conceptualizing personality as an adaptive control system regulating long-term behavior under uncertainty.

In line with this theoretical rationale and H5, we empirically test the association between all five Big Five traits and sustained profitability among solopreneurs (see the section “Psychological hypotheses”).

Risk propensity

Risk propensity reflects an individual’s tendency to act under uncertainty, influenced by both stable personality traits and deliberate decision strategies^44–46. PT⁴ provides a framework for understanding these decisions, highlighting that solopreneurs are more sensitive to potential losses than to equivalent gains. This cognitive asymmetry fosters a risk posture that is neither naïvely risk-seeking nor strictly avoidant but calibrated toward preserving financial stability while enabling entrepreneurial action^37,45. Empirical studies confirm that entrepreneurs show higher risk propensity compared to non-entrepreneurs^45,47. Overconfidence and elevated self-efficacy can further amplify such risk-taking behavior, especially when expectations are not grounded in actual capabilities or market realities^45,47.

However, this trait does not follow a simple linear path to success. Excessive risk-taking increases exposure to failure, while overly cautious behavior limits opportunity exploitation. A growing body of research points to a non-linear, inverse U-shaped relationship between risk propensity and business survival: moderate levels are associated with the highest likelihood of sustaining a venture over time^48,49.

Importantly, risk propensity is conceptually and empirically distinct from the Big Five personality traits. Although traits such as openness and conscientiousness are robust predictors of entrepreneurial outcomes, risk propensity captures variance in decision behavior that these broader dimensions do not fully explain. Accordingly, it is recommended to measure it separately when analyzing entrepreneurial behavior under uncertainty^50,51. Building on these findings, and consistent with PT, we formulate H6: solopreneurs with a balanced risk propensity - neither extreme risk-takers nor highly risk-averse - are more likely to achieve sustained profitability across the early business lifecycle.

Hypotheses: a multi-dimensional model of solopreneurs’ financial success

To analyze the conditions under which solopreneurs achieve sustained profitability, we formulate a structured set of hypotheses that distinguish between demographic characteristics, company-related context variables, and psychological dispositions. This structure reflects our assumption that financial success results from the interaction of individual traits and contextual influences, as captured through logistic regression analysis.

Demographic hypotheses

Demographic characteristics such as age and gender represent personal attributes that may influence entrepreneurial behavior, access to resources, and long-term viability.

H1

Founder age is associated with sustained profitability, reflecting accumulated entrepreneurial experience, industry knowledge, and strategic judgment (RBV⁵).

H2

Gender has an effect on the likelihood of sustained profitability among solopreneurs (empirically motivated).

Company-related hypotheses

Company-related characteristics, such as sector and innovation activity, reflect external structures and strategic decisions that may shape entrepreneurial performance.

H3

Solopreneurs in high-tech sectors are more likely to achieve sustained profitability than those in OTS.

H4

Engagement in R&D activities is positively associated with sustained profitability (Both hypotheses are empirically grounded and based on sectoral and innovation-related performance patterns),

Psychological hypotheses

Psychological traits are understood as internal dispositions that shape entrepreneurial decision-making and behavior under uncertainty.

H5

Among solopreneurs, the Big Five personality traits - openness, conscientiousness, extraversion, agreeableness, and emotional stability - are associated with sustained profitability (TAT³).

H6

Solopreneurs with a pragmatic and balanced risk orientation are more likely to achieve sustained profitability than those with extremely high or low risk propensity (PT⁴) models.

Comprehensive hypothesis−modeling interactions

In addition to single effects, logistic regression modelling enables the simultaneous examination of interdependencies between variables and robust estimators for a prognostic classification of financial success. We therefore derive an overarching hypothesis that reflects the integrated nature of our model:

H7

The financial success of solopreneurs is shaped by the combined influence of demographic, company-related, and psychological factors, including their interaction effects.

Materials and methods

Sample and dataset preparation

After formal approval of the data use agreement, the dataset was made available through the accredited ZEW Research Data Centre (ZEW-FDZ) as Scientific Use Files^1,52. The IAB/ZEW start-up panel, jointly administered by the ZEW-Leibniz IAB, provides anonymized microdata from representative random samples of newly founded firms between 2011 and 2018¹.

The panel combines firm- and founder-level indicators in a modular, wave-specific structure and applies harmonized questionnaire design, sector classification, and multilevel weighting to ensure comparability across waves¹. Researchers may access the data either onsite or via secure remote environments such as bwCloud ZEW Research Data Centre (ZEW-FDZ).

The panel is part of Germany’s official research infrastructure (RatSWD) and is recognized as a high-quality data source for empirical entrepreneurship studies. Its methodological transparency, rigorous documentation standards, and long-term empirical relevance have been extensively evaluated in numerous peer-reviewed research¹. The ZEW itself is regarded as one of Germany’s leading economic research institutes, known for combining policy-relevant empirical analysis with institutional expertise at both the national and European levels⁵³.

All analyses were based on eight microdata sources, with dedicated panel-based questionnaires. These questionnaires included a consistent set of questions across survey waves. This ensured that key firm-level information (e.g., annual profit/loss) was collected in in a standardized format. This structure enabled a coherent and comparable dataset covering up to seven consecutive financial years. For the survey years 2017 and 2018, a Big Five inventory was integrated into the questionnaire to assess the personality traits of the participating founders. Following the completion of the data cleansing process - which included steps such as duplicate removal and category name standardization - a final dataset was compiled for all subsequent analyses. This dataset spans eight consecutive business years (see Fig. 1).

Fig. 1.

Flowchart of data acquisition (n: sample size per year) and analysis steps. ^†) Frequencies of foundations in business year⁵⁴. ^b) The Big Five assessment in the IAB/ZEW surveys of 2017 and 2018).

Analytical framework

The final dataset included 4,470 records, including only solopreneurs with a complete information on: (a) demographic variables (age, gender); (b) firm-related characteristics (year of foundation, profit and loss data, industry classification, and indicators of innovative activities); and (c) psychological aspects. Variables are listed in Table 2⁵⁴ show 5isk propensity was assessed through respondents’ self-reported approaches to decision-making and project-level risk evaluation. The Big Five personality traits were measured using a validated short scale⁵⁵, in which respondents rated themselves on a 5-point Likert scale (1 = “does not apply at all” and 5 = “applies completely”) across openness, conscientiousness, extraversion, agreeableness, and neuroticism).

Table 2.

Variable descriptions.

Variable		Description	Measurement Method	Unit of Measurement
Outcome Variable
Financial success		Profit or neither break-even/loss	Self-reported	Yes/No1
Moderating Variables
Age		Age of respondents	Self-reported	Years
Gender		Gender of respondents	Self-reported	Female/Male1
Industry Sector		Type of industry sector	Assigned by IAB/ZEW	HTS/OTS1
Innovativeness		Engagement in R&D activities	Survey question	Yes/No1
PT Openness		Creativity, curiosity, appreciation for art	Self-reported	Likert scale2
PT Conscientiousness		Thoroughness, diligence, efficiency	Self-reported	Likert scale2
PT Extraversion		Sociability, assertiveness, talkativeness	Self-reported	Likert scale2
PT Agreeableness		Compassion, cooperation, kindness	Self-reported	Likert scale2
PT Neuroticism		Emotional instability, stress management	Self-reported	Likert scale2
Risk Propensity 1		Decision behavior: waiting vs. offensive	Survey question	Likert scale2
Risk Propensity 2		Preference for low- vs. high-risk projects	Survey question	Likert scale2

Variables derived from the official questionnaires of the IAB/ZEW Start-up Panel (2017, 2019); HTS: high-technology sectors; OTS: non-high-technology sectors; PT: personality trait. ¹binary categories, ²integer values from 1 to 5.

Statistical methods

Descriptive Statistics: General sample characteristics were obtained using descriptive statistical analyses of discrete variables based on the presentation of absolute and percentages of relative frequencies. Continuous characteristics were described using the arithmetic mean ± one standard deviation and the Q₁, median, and Q₃ quartiles. Where appropriate, the corresponding 95% confidence intervals (CIs) were added.

Exploratory factor analysis (EFA): First, based on the Big five questionnaire results an EFA was performed to obtain an evidence-based and validated personality trait profile for each solopreneur. The EFA was calculated with a standard oblimin rotation and a fixed number of five factors to assign. Both Bartlett’s sphericity test and the Kaiser-Meyer-Olkin (KMO) sample adequacy test were applied before computing the factor analysis to confirm correct application of the EFA. The results of the EFA factor-loading table were used to define a final appropriate cut-off value of ≥ 0.25 to validate the relevant items as a dedicated single Big Five-dimension⁵⁶. These sample-specific Big Five profiles were compared with published representative samples of the German population^57,58. Finally, to validate the EFA results, a confirmatory factor analysis (CFA) was performed to assess the EFA results using the root mean square error of approximation (RMSEA < 0.07) and Tucker-Lewis index (TLI > 0.95) as described by Hooper et al.⁵⁹.

Exploratory data analysis: To address H1-H6 Fisher’s exact test and Mann-Whitney U-test were applied to identify statistically significant differences between financially successful and unsuccessful solopreneurs across key variables, including gender, risk propensity, R&D engagement, industry sector, and personality traits.

Binary Logistic Regression: After the identification of unique solopreneurs with complete records of profit or loss/break-even data across seven consecutive business years, a binary outcome variable - “Financial Success: yes = 1, no = 0” - was added to the dataset to classify solopreneurs into two groups. A correlation matrix analysis with a critical cutoff of 0.9⁶⁰, a Durbin-Watson test and a variance inflation factor analysis were applied to the dataset to reveal any multicollinearity. Finally, a binary logistic regression model was developed to test H7 to estimate the predictive value odds ratios (ORs) of categorical and (pseudo-) metric variables for sustained profitability.

The probability of error for 2-sided hypothesis testing procedures (e.g. Mann-Whitney U-test, Bartlett’s test) and for all specified CIs was set at 5%. All statistical analyses were conducted using R version 4.5.1 (www.r-project.org)⁶¹. CFA was performed using the lavaan library.

Results

This section presents the empirical results in three stages: (1) descriptive statistics and factor structure validation, (2) bivariate group comparisons aligned with H1-H6, and (3) a multivariate model testing the joint effects of all predictors (H7).

Descriptive statistics and factor validity

The initial dataset from the IAB/ZEW start-up panel included 38,287 solopreneurs. Of these, 4,470 cases provided complete responses to the 15-item Big Five trait inventory. Among them, 1,274 also reported complete profit/loss data for all seven business years following their start-up and were included in the final regression analysis (see Fig. 1). Within this analytical sample, 78 solopreneurs (42.8%; 95% CI: 35.56–50.39) achieved sustained profitability.

To validate the measurement model, an exploratory factor analysis (EFA) was conducted on the 15 items. Bartlett’s test of sphericity and the KMO test for sampling adequacy confirm that the data are suitable for factor analysis. EFA revealed five factors, each successfully condensing three of the planned personality questions into an associated trait, with factor loadings ranging from 0.26 to 0.74. CFA confirmed the EFA, with an RMSEA of 0.061 and a TLI of 0.951. Table 3 summarizes the results of the factor analysis.

Table 3.

Exploratory factor analysis of the 15 big Five-related items as part of the IAB/ZEW Start-up panel surveys in 2018 and 2019.

Big Five-related Items: “I am someone who…”	Factor 1	Factor 2	Factor 3	Factor 4	Factor 5
Openness
…is original and brings new ideas.			0.570
…values artistic experiences.			0.430
…has a vivid and good imagination.			0.680
Conscientiousness
…works thoroughly.	0.740
…is rather lazy.*	0.420
…completes tasks effectively and efficiently.	0.600
Extraversion
…is communicative and talkative.		0.680
…can go out on their own and be sociable.		0.630
…is reserved.*		0.490
Agreeableness
…is sometimes a bit rude to others.*					0.490
…can forgive.					0.260
…is considerate and kind toward others.					0.680
Neuroticism
…often worries				0.540
…gets nervous easily.				0.650
…is relaxed and can handle stress well.*				0.450

*Original values of four Big Five items were inverted prior to subsequent analysis. .

Group-level comparisons were then conducted as a preliminary step prior to hypothesis testing.

Bivariate comparisons by hypothesis (H1-H6)

Bivariate results for the independent variables are presented in Table 4. Preliminary group-level comparisons were conducted to assess associations between each hypothesized predictor and sustained profitability. Bivariate analyses included Mann-Whitney U-tests for ordinal/metric variables and Fisher’s exact test for categorical variables. Table 4 summarizes group comparisons between solopreneurs who reported profit in all seven consecutive business years (n = 78) and those who did not (n = 104). The results are presented in line with the hypothesized dimensions.

Table 4.

Summary of sample characteristics and comparison of statistical measures for companies without (“No”) and with (“Yes”) subsequent profit in 7 consecutive business years.

Variable	Sample	Subsequent Profit in 7 Consecutive Business Years		p -Value
		No (n = 104)	Yes (n = 78)
Age (Years) 2)
Mean ± 1 SD	49.3 ± 9.4 (48.0–50.7)	51.0 ± 9.4	47.2 ± 9.0	0.005**
Q2 (Q1-Q3)	50.0 (43.0–56.0)	52.0 (46.0–57.2)	49 (41.0–52.0)
Age, Grouped 1)
25–50	93 (51.1;4 3.6–43.6)	42 (45.2; 34.8–55.8)	51 (65.4; 53.8–75.8)	< 0.001***
≥ 51	89 (48.9;4 1.4–41.4)	62 (9.7; 59.0–79.0)	27 (34.6; 24.2–46.2)
Gender 1)
Female	17 (9.3; 5.5–5.5)	8 (7.7; 3.4–14.6)	9 (11.5; 5.4–20.8)	0.444
Male	165 (90.7; 85.5–85.5)	96 (92.3; 85.4–96.6)	69 (88.5; 79.2–94.6)
Industry Sectors 1)
High technology	94 (51.6; 44.1–44.1)	59 (56.7; 46.7–66.4)	35 (44.9; 33.6–56.6)	0.135
Non-high tech	88 (48.4; 40.9–40.9)	45 (43.3; 33.6–53.3)	43 (55.1; 43.4–66.4)
R&D 1)
No	140 (76.9; 70.1–70.1)	74 (71.2; 61.4–79.6)	66 (84.6; 74.7–91.8)	0.016*
Yes	42 (23.1; 17.2–17.2)	30 (28.8; 20.4–38.6)	12 (15.4; 8.2–25.3)
Big Five Traits (Q2 (Q1-Q3)) 2)
Openness	3.7 (3.3–4.3)	4.0 (3.7–4.3)	3.7 (3.3–4.0)	0.015*
Conscientiousness	4.3 (4.0–4.7)	4.3 (4.0–4.7)	4.3 (4.0–4.7)	0.129
Extraversion	4.0 (3.3–4.3)	4.0 (3.3–4.3)	4.0 (3.3–4.3)	0.608
Agreeableness	4.0 (3.7–4.6)	4.0 (3.6–4.4)	4.0 (3.7–4.6)	0.779
Neuroticism	2.3 (1.7–3.0)	2.3 (1.7–3.0)	2.3 (1.8–3.0)	0.926
Risk Propensity (Q2 (Q1-Q3)) 2)
Decision Behavior Waiting vs. Offensive	3.0 (2.0–5.0)	3.0 (2.0–5.0)	3.0 (1.3–4.0)	0.142
Preference for Low- vs. High-Risk Projects	2.0 (1.0–3.0)	3.0 (1.0–4.0)	2.0 (1.0–3.0)	< 0.001***

Categorical frequencies are absolute values with percentages and 95% confidence intervals within parentheses.

¹⁾ Fisher’s exact test, ²⁾ Mann–Whitney U-test.

H1: Founder age.

Founder age was significantly associated with profitability. Solopreneurs with sustained profit were younger (47.2 ± 9.0 years) than their counterparts (51.0 ± 9.4 years; p = 0.005). When categorized, 65.4% of younger founders (≤ 50 years) reported sustained profitability, versus 34.6% of older founders (≥ 51 years; p < 0.001).

H2: Gender.

No significant gender effect was observed (p = 0.444). Female solopreneurs were underrepresented in both groups (7.7% vs. 11.5%), thereby offering no support for H2.

H3: Industry sector (HTS vs. OTS).

HTS affiliation was not significantly associated with profitability (56.7% vs. 44.9%; p = 0.135). Therefore, H3 is not supported in bivariate terms.

H4: R&D engagement.

A significant inverse relationship was found between R&D engagement and profitability (p = 0.016). Only 15.4% of profitable solopreneurs reported R&D activity, in contrast to 28.8% in the non-profitable group (p = 0.016), which contradicts H4.

H5: Big Five traits.

Among the five traits, only Openness differed significantly between groups (p = 0.015), with profit-makers reporting lower median scores (3.7) than non-profit-makers (4.0). Conscientiousness, Extraversion, Agreeableness, and Emotional Stability showed no group-level differences. Therefore, H5 is only partially supported, and the observed effect of Openness runs counter to expectation.

H6: Risk propensity.

No effect was found for decision behavior (offensive vs. waiting; p = 0.142). Profit-makers showed a stronger preference for lower-risk projects (Median = 2.0 vs. 3.0; p < 0.001), thereby supporting H6.

Multivariate logistic regression results (H7)

To assess joint effects a binary logistic regression model was estimated using the full sample (n = 1,274) by incorporating all predictors from H1-H6.

The model showed adequate fit (Akaike information criterion (AIC) = 1,116.5; Tjur’s pseudo R² = 0.297), high classification accuracy (85.1%; 95% CI: 83.0–87.0), and statistical power of 0.832. After the analysis of coefficients in the correlation matrix with variance inflation factor values below 1.5 and finally a non-significant Durbin-Watson test result (p = 0.434), there was no evidence of multicollinearity. Table 5 summarizes the correlation structure of the independent variables and confirms the absence of multicollinearity among predictors.

Table 5.

Correlation matrix of predictor variables used in binary logistic regression model.

	1.	2.	3.	4.	5.	6.	7.	8.	9.	10.	11.
1. Age	-
2. Gender	−0.008	-
3. Industry sector	−0.083	−0.181	-
4. Research & Development	0.070	0.176	−0.347	-
5. Openness	−0.076	0.059	0.098	−0.135	-
6. Conscientiousness	−0.008	−0.052	−0.046	0.156	−0.063	-
7. Extraversion	−0.028	−0.008	−0.010	−0.019	0.197	0.061	-
8. Agreeableness	−0.007	−0.058	−0.060	0.016	0.129	0.217	0.069	-
9. Neuroticism	0.029	−0.111	0.003	0.029	0.016	0.105	0.152	0.140	-
10. Decision Behavior Waiting vs. Offensive	−0.031	0.046	0.007	−0.112	0.118	0.015	0.100	0.036	0.215	-
11. Preference for Low- vs. High-Risk Projects	−0.046	−0.087	0.031	−0.206	0.009	−0.064	0.030	−0.025	0.181	0.361	-

Table 6 presents the full output of the logistic regression model, including coefficients, odds ratios and 95% confidence intervals. Four predictors significantly increased the likelihood of sustained profitability: Founder age ≤ 50 (OR = 2.126 [1.225–3.690], p = 0.007), Openness (OR = 1.616 [1.033–2.526], p = 0.035), Conscientiousness (OR = 1.279 [1.041–2.114], p = 0.043) and Project-level risk preference (OR = 1.607 [1.283–2.014], p < 0.001). These results confirm aspects of H1, H5, and H6 by highlighting the predictive value of stable personality traits and risk orientation.

Table 6.

Results of binary logistic regression forecast Model.

Variables and Categories	Coefficient	p -Value	Odds Ratio ± 95% CI
Intercept	−0.303	0.766	0.738 [0.100–5.427]
Age, grouped
> 50 †
18–50	0.754	0.007**	2.126 [1.225–3.690]
Gender
Female †
Male	0.122	0.806	1.130 [0.426–2.997]
Industry Sectors
High technology †
Non-high tech	0.247	0.411	1.280 [0.710–2.308]
R&D
No †
Yes	−0.482	0.176	0.618 [0.308–1.240]
Big Five Traits
Openness	0.480	0.035*	1.616 [1.033–2.526]
Conscientiousness	0.246	0.043*	1.279 [1.041–2.114]
Extraversion	−0.218	0.261	0.804 [0.549–1.176]
Agreeableness	−0.161	0.445	0.851 [0.563–1.287]
Neuroticism	−0.218	0.244	0.804 [0.557–1.161]
Risk Propensity
Decision Behavior Waiting vs. Offensive	−0.046	0.664	0.955 [0.777–1.174]
Preference for Low- vs. High-Risk Projects	0.475	< 0.001***	1.607 [1.283–2.014]

Odds ratios with corresponding 95% confidence intervals (CIs) of categorical and continuous factors as an estimate for a continuous annual financial statement with a profit for seven consecutive business years. .

^†Reference category.

In contrast, no significant effects emerged for Extraversion, Agreeableness, or Emotional Stability, nor for the broader structural/contextual variables: gender, HTS affiliation and R&D engagement.

Collectively, these results support H7: sustained profitability among solopreneurs is best explained by an interplay of selected psychological and demographic characteristics. Notably, some predictors (e.g., Conscientiousness) that were not significant in bivariate analyses emerged as significant when modeled jointly; this highlights the value of a multivariate approach.

Discussion

The results show that selected predictors - particularly founder age, openness, conscientiousness, and project-level risk preference - were significantly associated with sustained profitability. However, exploring H1-H6 based on the bivariate analyses in Table 4 would overlook the complex interdependencies among psychological, demographic, and structural factors. These variables often interact or exert influence only under specific contextual conditions. To capture these dynamics, we applied a multivariate binary logistic regression model (H7), which evaluates the combined predictive power of key variables while controlling for their interactions. The following discussion centers on the success factors that proved significant in the integrated model and their contributions to sustained financial success among solopreneurs.

Demographic factors: age and gender

There was a significant difference between the age of founders with versus without sustained financial success (H1; p = 0.005). No significant results were revealed when comparing the profitability of companies founded by female and male solopreneurs (H2; p = 0.444).

Within the multivariate model (H7), founder age significantly predicted sustained profitability. Solopreneurs aged 18–50 were over twice as likely to maintain consistent profits (OR = 2.126; 95% CI: [1.225–3.690]; p = 0.007) than those over 50. This contrasts with earlier studies, such as Kautonen⁶², who emphasized advantages among older founders (50–64) regarding accumulated experience, capital, and education.

The discrepancy highlights the relevance of context and measurement. While prior research often relied on subjective or continuity-based indicators, we define success as sustained financial profitability - a stringent, objective criterion. From an RBV perspective⁵, entrepreneurial outcomes are shaped by intangible assets including domain knowledge, social capital, and strategic judgment.

In Germany, 36% of founders are aged 25–34 and 23% are 35–44²⁸. Peña⁶³ emphasizes that entrepreneurial competencies such as education, experience, and motivation accumulate over time and are positively associated with new venture survival and long-term performance. Azoulay et al.²⁷ similarly found that the most successful ventures are often launched around age 45, with industry-specific experience increasing success rates by up to 85%. From an RBV-informed perspective, the 18–50 age group may strike a favorable balance: sufficient experience and resource access, combined with adaptability and strategic responsiveness. Age influences access to information, financial capital, and risk-related decision strategies - factors that shape entrepreneurial success^29,30. Gender, by contrast, showed no significant association with sustained profitability within the H7 model.

Company-related factors: industry sector and R&D engagement

No significant difference in terms of sustained financial success was found between high-tech and other industries (H3; p = 0.135). However, a significant result was found for solopreneurs with and without research and development activities (H4; p = 0.016).

In the multivariate H7 model, neither sector affiliation nor R&D engagement showed a significant association with sustained profitability. Solopreneurs in high-technology sectors (HTS) did not differ in financial outcomes from those in OTS (OR = 1.280; 95% CI: [0.710–2.308]; p = 0.411), and R&D engagement lacked predictive value (OR = 0.618; 95% CI: [0.308–1.240]; p = 0.176). Previous research links entrepreneurial behavior to innovation potential and firm growth, especially in early-stage ventures^11,39,40, yet these effects may be muted in solo contexts. Solopreneurs often face resource constraints that limit their ability to convert R&D inputs or sectoral positioning into financial returns. Sector affiliation rarely yields strategic advantage without supporting factors like team scale, product-market fit, or executional capacity. As highlighted by McCarthy et al.²⁰ and Barreira⁶⁴, internal capabilities are crucial for leveraging external advantages.

Binary indicators for HTS and R&D may overlook the intensity, timing, or integration of innovation efforts. From an RBV perspective, they signal potential rather than guaranteed outcomes. In solopreneurship, structural variables appear less predictive than psychological or behavioral traits.

Psychological factors: personality traits and risk propensity

Of the Big Five personality traits, only Openness showed a significant difference between financially successful and unsuccessful solopreneurs (H5; p = 0.015).

In the integrated H7 model, three psychological variables showed significant associations with sustained profitability: Openness, Conscientiousness, and a context-specific risk preference. The Big Five traits - Extraversion, Agreeableness, and Emotional Stability, as well as general risk attitude - did not show significant effects.

Openness fosters opportunity recognition and adaptability, while Conscientiousness supports structure, efficiency, and consistent execution. Both traits showed significant positive effects in the logistic regression: Openness (OR = 1.616; 95% CI: [1.033–2.526]; p = 0.035) and Conscientiousness (OR = 1.279; 95% CI: [1.041–2.114]; p = 0.043), consistent with prior findings^16,20,35. These effects remained robust in the multivariate model, controlling for other traits and contextual variables.

In the solopreneur setting - characterized by full responsibility, external demands, and the absence of team support - personality becomes a central behavioral driver. Openness enables learning and innovation; Conscientiousness ensures goal-directed behavior and task completion. This aligns with TAT, which posits that traits gain relevance through situational triggers. The Cybernetic Big Five Theory (CB5T)⁴³ complements this view by framing traits as parameters in a self-regulating system: Openness reflects cognitive flexibility (Plasticity), Conscientiousness supports goal maintenance and behavioral stability. Together, they enable self-regulation in uncertain environments.

These mechanisms support adaptive self-regulation and help solopreneurs overcome barriers and seize opportunities - especially in individualistic cultures where innovation is valued⁸.

Increased Conscientiousness also contributes to long-term financial performance. Antoncic et al.^16,35. link it to efficiency and organization - key entrepreneurial factors. Other studies, including Leutner et al.⁵¹, similarly confirm its predictive value. Konon and Kritikos⁶⁵ note that entrepreneurs generally exhibit higher Conscientiousness than salaried employees, reflecting a distinct personality profile. Hagenauer and Zipko²⁴ confirm that solopreneurs consistently score higher in Openness and Conscientiousness than the general population and show sector-specific trait variations - demonstrating how contextual demands shape trait expression, consistent with TAT³. Their model emphasizes how factors like uncertainty and innovation pressure influence personality expression and success. Educational attainment may further enhance the application of these traits. Cheng and Smyth⁶⁶ note that education improves the use of socially and professionally beneficial traits, whereas Hagenauer and Zipko²⁵ highlight interactions between risk propensity, innovation, and personality. These findings reinforce the need for multidimensional analyses to understand solopreneurs’ financial success. Future research could explore whether traits like Extraversion or Emotional Stability interact with contextual variables and influence entrepreneurial outcomes in more nuanced ways. Our operationalization of sustained profitability captures financial success during the first seven years and aligns with established metrics in entrepreneurial research. Overall, the results confirm the explanatory relevance of Openness and Conscientiousness within the broader H7 framework, while other personality traits were not supported in this model.

There was no significant difference in risk-taking behaviour in relation to decision behavior waiting vs. offensive (H6; p = 0.142). However, a significant difference was found in the assessment of preference for low- vs. high-risk projects between financially successful and unsuccessful solopreneurs (H6; p < 0.001).

Risk propensity also plays a selective role in the multivariate approach of H7: While general risk attitude did not predict profitability (OR = 0.955; 95% CI: [0.777–1.174]; p = 0.664), a preference for low-risk projects significantly increased the odds of sustained profit (OR = 1.607; 95% CI: [1.283–2.014]; p < 0.001). This aligns with findings linking entrepreneurial success to innovation strategies grounded in opportunity assessment and balanced risk-taking⁶⁷. Balanced risk-taking - avoiding both overconfidence and excessive caution - supports both short-term profitability and long-term survival^45–49. These findings reinforce PT⁴, which explains how perceived losses and gains influence decision-making under uncertainty. In this study, risk becomes financially relevant only when contextualized - e.g., through project-level choices. The lack of effect for general risk attitudes suggests that abstract risk measures may be less predictive without situational framing, consistent with PT’s reference-point logic. This is particularly relevant in entrepreneurship, where decisions are made under uncertainty, time pressure, and emotionally weighted trade-offs⁶⁸.

Methodologically, effects were estimated within a multivariate logistic regression model controlling for personality, demographic, and structural factors, confirming the robustness of associations. Future research could examine whether divergences between abstract and behavior-specific risk constructs reflect differences in framing competence, risk awareness, or entrepreneurial experience - especially in solo-founded ventures.

Theoretical integration: a unified interpretation framework

Figure 2 presents an integrated interpretation model that visualizes how the explanatory frameworks used in this study - TAT, PT, and the RBV - interact with psychological, demographic, and contextual predictors of financial success. This model highlights the explanatory role of trait-context constellations rather than isolated main effects.

Fig. 2.

Integrated Theoretical Model with Empirical Results and Theoretical Sources. The Model was defined with sustained profitability as dependent variable and a set of independent variables used as interacting factors associated with profitability.

Conclusion

This study demonstrates that solopreneurs’ financial success during the first seven years is shaped by a combination of personality traits, age, and risk propensity. The integration of PT, TAT and the RBV provides a comprehensive framework for understanding these dynamics.

Strengths and limitations

A key strength of this study is its focus on the first seven years of solopreneurship, a period critical to businesses’ survival and success, which distinguishes it from those that do not differentiate among companies or company segments. Leveraging a large sample size, our approach allows for a deeper analysis of the unique challenges and success factors of this group of entrepreneurs. Furthermore, monetary profit as a central success criterion provides an objective and easily understandable basis for assessing entrepreneurial success. This study’s methodological rigor is supported by its use of data from the IZP and the implementation of robust EFA. The logistic regression, with a statistical power of 0.832, supports the validity and reliability of the results. The consideration of various influencing factors - including the Big Five personality traits (openness and consciousness), age, and risk propensity - facilitated a differentiated and thorough analysis of the determinants of entrepreneurial success. Integrating PT, TAT, and the RBV offers a multi-dimensional perspective, highlighting how risk propensity, personality traits, and resource accumulation drive financial success.

Nevertheless, the study has a few limitations. First, the focus on data from Germany and solopreneurs may limit the generalizability of the findings to other countries or types of entrepreneurs. Second, the variability in the understanding and implementation of R&D activities could affect the results’ comparability and consistency. Finally, IZP’s shortened Big Five questionnaire may not provide a highly accurate representation of these personality traits. A more extensive questionnaire could have provided more precise scale estimates with smaller CIs, although these potential effects may be mitigated by the large sample size. These limitations are mitigated by the large sample: 4,470 personality cases and 1,274 model observations ensure high statistical power and stable estimates. Simulation studies show that large-N designs can compensate for reduced scale reliability by narrowing CIs and improving statistical precision. Monte Carlo analyses confirm that power losses due to low reliability are recoverable through increased sample sizes⁶⁹. Likewise, short Big Five instruments such as the BFI-10 retain ~ 70% of full-scale variance and 85% of test-retest reliability, with preserved structural and discriminant validity - even in German panel contexts⁷⁰. These findings support the use of abbreviated trait inventories in large-scale research settings such as ours. We therefore consider the shortened IZP instrument statistically sound and methodologically justified.

Beyond reverse causality: traits as activated dispositions

Key life events, such as founding and running a company, can influence personality traits and potentially introduce reverse causality into trait-outcome relationships^71,72. Approximately 25% of the population experiences extremely stressful events⁷³, and macro events like the Great Recession have shown measurable but moderate effects on personality⁷⁴. However, such influences tend to affect only selected traits and occur gradually over time⁷³.

TAT³ offers a robust framework for interpreting the interplay between entrepreneurial experience and personality expression. It posits that traits such as Openness or Conscientiousness manifest in behaviorally when triggered by trait-relevant situational cues. The entrepreneurial context - characterized by uncertainty, autonomy, and responsibility - provides such activation conditions.

Empirical findings support this view: Kammeyer-Mueller and Wanberg⁷⁵ find that proactive personality predicted adjustment only after initial role adaptation. This supports the notion that dispositional effects emerge through contextual activation rather than immediate trait expression.

In our study, trait data were collected during active operations (2018–2019), within the firms’ first 7 years by capturing personality traits during the dynamic and activating phase of business development rather than before or long after founding.

Additionally, CB5T⁴³ conceptualizes traits as internal parameters within goal-regulating feedback loops. Traits do not cause behavior directly or result from outcomes, but shape how individuals pursue goals and respond to feedback. Collectively, these theoretical and empirical insights suggest that the personality constructs measured in our study represent stable but contextually activated dispositions, not reactive outcomes.

Building on this, CB5T offers a regulatory perspective by viewing traits including Openness and Conscientiousness as parameters within a perception-action control loop. Rather than static descriptors, traits regulate feedback processing, strategy adaptation, and sustained goal pursuit - which reflect internal regulation rather than reactive behavior.

Future research recommendations

Future research should include cross-country analyses to enhance the generalizability of findings and examine cultural and economic differences. Further exploration of PT within entrepreneurial research could elucidate how risk propensity evolves and interacts with financial success under varying economic conditions. Additionally, deeper analysis of TAT may reveal how personality traits adapt to industry-specific pressures or R&D demands, and research on the RBV could clarify how solopreneurs across age groups leverage resources to sustain ventures. Deeper analyses of specific innovation activities and their direct impact on financial success are necessary to offer targeted insights and practical implications. Furthermore, an extensive Big Five questionnaire could improve trait assessments. Longitudinal studies tracking changes over time and external shocks (e.g., recessions) would add depth. Finally, sectoral balance is essential to capturing SME landscape.

Practical implications

The findings of this study offer practical implications for solopreneurs, their environment, advisors, investors, and policy makers. They show that sustained financial success in solopreneurship results from the interaction of personality traits, experience, and decision-making - activated by external conditions, in line with TAT.

First, stable traits - particularly openness and conscientiousness - consistently correlate with profitability. While not easily changed, they can guide self-assessment and improve founder-context alignment. Diagnostic tools may enhance the fit between personal profiles and support measures.

Second, balanced risk-taking - not general risk tolerance - drives success. Under PT, outcomes depend on how losses and gains are weighed. Support programs should prioritize decision quality over risk appetite.

Third, solopreneurs aged 18–50 tend to sustain profitability - not because of age itself, but because of the accumulation of intangible resources including experience, networks, and market insight. From an RBV perspective, these constitute valuable, hard-to-replicate assets. Accordingly, support and funding should prioritize these substantive foundations over mere startup enthusiasm.

Fourth, the first 7 years of self-employment emerge as a decisive phase. Survival through this period reflects the founder’s ability to manage core business functions - liquidity, client acquisition, and operational execution. This phase demands entrepreneurial validation, not self-expression - support programs should offer practical, context-aware guidance.

Fifth, solopreneurship entails full responsibility - not only for the founder but for their environment. With no shared decision-making, every outcome is personal. These pressures and expectations act as activating triggers for personality traits, thereby emphasizing the need for honest, realistic founder education that addresses the unique dynamics of solopreneurship.

Finally, while gender, sector, and R&D engagement were not significant predictors of profitability, they continue to shape access to resources and opportunities. Their inclusion in support systems remains relevant - not for ideological reasons, but because of their practical relevance.

Author contributions

W.H.: Conceptualization, Resources, Methodology and Writing - Original Draft.H.T.Z.: Methodology, formal Analysis, Data Curation, Writing - Review & Editing and Visualization. All Tables & Figures.All authors reviewed the manuscript.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Data availability

The data used in this analysis (“scientific user files”) are derived from the IAB/ZEW Start-up Panel. Access to these data is restricted, and they are the property of the IAB/ZEW. Data are available with the permission of ZEW-FDZ (Leibniz-Zentrum für Europäische Wirtschaftsforschung - Forschungsdatenzentrum). More detailed information on how to apply for a scientific user file can be found here: kooperationen.zew.de/en/zew-fdz/home. For data access inquiries, please contact ZEW-FDZ at: fdz@zew.de [1].Restrictions apply to the availability of these data, which were used under license for this study. In addition, the corresponding author (Wolfgang Hagenauer, xhagenau@node.mendelu.cz) is available to assist with the data access process and may provide further information upon reasonable request.

Declarations

Competing interests

The authors declare no competing interests.

Generative AI in scientific writing

During the preparation of this work, the authors used ChatGPT, an AI language model developed by OpenAI, to assist in improving readability and language. After using this tool, the authors reviewed and edited the content as needed and takes full responsibility for the content of the publication.