Reframing the ideal investor through entrepreneurial experience

Investors are widely recognized as vital enablers of the survival, growth, and scalability of technology-based ventures (Singh and Mungila Hillemane, 2023; Cumming et al., 2023). While the provision of capital is fundamental to early venture development, the role of investors often extends far beyond financing. Many contribute directly to a venture's strategic direction and provide operational and managerial expertise (Sannino, 2024; MacMillan et al., 2022; Granz et al., 2021; Croce et al., 2019). In line with this expanded role, prior research has examined the criteria investors use to evaluate entrepreneurial proposals, highlighting factors such as growth potential, business model viability, and market readiness (Berger and Hottenrott, 2021; Block et al., 2021; Sanchez-Ruiz et al., 2021). In contrast, considerably less attention has been devoted to the reciprocal side of this relationship, namely how entrepreneurs evaluate potential investors (Weniger et al., 2023; Granz et al., 2021; Hallen and Pahnke, 2016). This gap is noteworthy given that the investor–entrepreneur relationship is inherently bilateral, shaped by processes of mutual evaluation and negotiation. Entrepreneurs actively interpret, compare, and prioritize investor attributes in light of their goals, values, and prior experiences. Gaining deeper insight into these evaluative judgements is therefore essential both for advancing theoretical perspectives on entrepreneurial finance and for fostering stronger alignment within investor–entrepreneur partnerships (Cohen and Wirtz, 2022; Agrawal et al., 2021).

As research on entrepreneurial finance has advanced, increasing attention has shifted to the entrepreneur's perspective within the investor–entrepreneur relationship (Granz et al., 2021; Huang and Knight, 2017; Zou et al., 2016). This literature highlights that entrepreneurs are not passive recipients of capital but active decision-makers who strategically choose which investors to engage with. Recent studies (e.g. Müller et al., 2023; Cohen and Wirtz, 2022) show that understanding these choices requires more than observing entrepreneurs' actions; it also involves examining the motivations, cognitive processes, and strategic reasoning that shape their evaluations of potential investors. Gonzalez-Uribe and Klingler-Vidra (2025) further demonstrate that entrepreneurs refine their expectations not only through successful funding experiences but also through rejection, which becomes a valuable source of learning. Collectively, those researches has begun to reposition the entrepreneur as an active agent in shaping their relationships with investors.

This emerging perspective challenges the long-standing assumption that the investment process is driven primarily by investors. Instead, recent evidence conceptualizes it as a reciprocal decision-making process in which both parties actively shape outcomes (Cohen and Wirtz, 2022; Agrawal et al., 2021; Hallen and Pahnke, 2016). Industry practice reflects this shift: the Venture Equity Report (2024) indicates that entrepreneurs increasingly prioritize investors who align with their values, act ethically, and support long-term objectives, rather than selecting investors solely on the basis of capital provision. Nevertheless, despite this growing recognition, research has only begun to investigate how entrepreneurs construct and adapt their notions of the “ideal” investor. In particular, little is known about how these perceptions evolve over time or how they are shaped by entrepreneurs' cumulative experiences in building and financing ventures.

In response to this gap, the present study investigates how entrepreneurs form perceptions of the ideal investor, with particular attention to how these views are shaped by accumulated entrepreneurial experience. Rather than treating perceptions as fixed, the study conceptualizes them as dynamic, shaped by repeated fundraising and the challenges of venture development. Prior research suggests that inexperienced entrepreneurs often emphasize symbolic investor attributes such as investment size or a prominent track record (Hallen and Pahnke, 2016; Zou et al., 2016). In contrast, the present study argues that accumulated experience fosters a shift toward more substantive evaluative criteria. With greater experience, entrepreneurs increasingly prioritize attributes such as ethical conduct, strategic alignment, and relational fit, reflecting a deeper recognition of the interpersonal and organizational factors that sustain long-term venture success (Granz et al., 2021; Glücksman, 2020). They also place higher value on investors who respect their autonomy, demonstrating both increased confidence in their own judgement and a stronger belief in their ability to shape governance and strategy independently (Dabić et al., 2023; Hwang et al., 2020).

Drawing on survey data from 195 entrepreneurs in the United States, United Kingdom, France, and Australia, this study makes three contributions. First, it examines how entrepreneurs evaluate and select investors, addressing an underexplored yet increasingly important question in entrepreneurial finance. By shifting attention from investors to entrepreneurs, the study challenges the traditionally investor-centric view and offers a more balanced account of decision-making in venture capital (Granz et al., 2021). Second, it demonstrates how entrepreneurial experience shapes investor preferences (Cohen and Wirtz, 2022; Metrick and Yasuda, 2021). While prior research has contrasted novice and experienced entrepreneurs in areas such as opportunity recognition and risk management (Dabić et al., 2023), this study shows that experience also influences evaluations of investor attributes. Third, by extending the focus beyond financial capital, the study develops a more comprehensive framework for understanding what entrepreneurs seek in investor relationships (Rauwerda and De Graaf, 2021). Recognizing the multidimensional nature of these expectations clarifies how alignment between entrepreneurs and investors can be strengthened, thereby improving venture outcomes.

Securing external investment is a critical yet challenging task for technology-based ventures, particularly when uncertainty is high, and commercial viability remains unproven (Lerner and Nanda, 2020). Entrepreneurs in these contexts often face credibility gaps, lacking the track record or institutional legitimacy typically required by formal financing channels (Garud et al., 2025; Croce et al., 2019). To address these gaps, investors provide more than capital. They contribute strategic guidance, open access to valuable networks, and offer reputational endorsement that helps validate ventures in the eyes of other stakeholders (Granz et al., 2021). These contributions make the quality of the investor–entrepreneur relationship central to understanding entrepreneurial success.

At the same time, this relationship is not always straightforward. Portraying entrepreneurs as passive recipients of support overlooks their active role in shaping and managing interactions with investors. Differences in expectations around control, decision-making, and involvement can create tensions that directly influence venture performance (MacMillan et al., 2022; Zou et al., 2016). Understanding how entrepreneurs assess and respond to investor contributions is therefore critical, particularly as their ventures and experiences develop over time.

Building on this premise, the present study examines how entrepreneurs' criteria for selecting investors evolve with experience. Such experience may stem from repeated funding cycles, ongoing interactions with investors, or lessons learnt from both successful and unsuccessful attempts to secure capital. Entrepreneurial learning theory (Jones et al., 2022; Wang and Chugh, 2014) emphasizes that entrepreneurs refine their decision-making through practice, reflection, and feedback, while signalling theory (Connelly et al., 2025) highlights the importance of demonstrating legitimacy to external stakeholders. In early stages, or when entrepreneurs lack experience, establishing legitimacy is paramount, which leads them to prioritize symbolic investor attributes such as reputation and investment size. However, as entrepreneurs gain experience, they learn to interpret signals differently and place less emphasis on symbolic cues. Instead, they give greater weight to substantive factors that sustain long-term success, including trust, ethical conduct, strategic alignment, and respect for autonomy (Falik et al., 2016; Cohen and Wirtz, 2022). Guided by this perspective, the study argues that entrepreneurs' perceptions of the ideal investor are dynamic and evolve with experience, shaping their decisions about partnership formation and the quality of working relationships.

First-time or early-stage entrepreneurs often focus on tangible, performance-based investor attributes, such as an investor's track record or the size of the investment. For those with little fundraising experience, these characteristics serve as symbolic signals of legitimacy and financial security (Hallen and Pahnke, 2016). This reliance on symbolic cues reflects both the need to establish credibility with external stakeholders and entrepreneurs' limited understanding of the relational dynamics that underpin investor–entrepreneur partnerships.

As entrepreneurs accumulate experience in seeking external funding through multiple rounds of fundraising, negotiations, and interactions with diverse investors (Metrick and Yasuda, 2021), they learn that symbolic signals do not necessarily translate into supportive relationships. A larger investment, for example, may increase investor control through formal mechanisms such as board representation, voting rights, and strategic vetoes (Cumming et al., 2023; MacMillan et al., 2022; Zhang et al., 2003). While such mechanisms protect investor interests, they can also generate tensions when they conflict with the entrepreneur's vision or values (Zou et al., 2016). Unlike investors, entrepreneurs typically lack formal mechanisms of control and must instead depend on trust and interpersonal dynamics to sustain effective relationships (Gamez-Djokic et al., 2022; Wohlgemuth et al., 2019). This imbalance places entrepreneurs in a more vulnerable position. When investor priorities diverge from venture goals, the absence of equivalent control mechanisms can limit entrepreneurs' autonomy, strain relationships, and, in some cases, undermine the long-term prospects of the venture.

Consequently, as entrepreneurs gain experience, they begin to evaluate investors on criteria that extend beyond financial capacity. They place less emphasis on symbolic indicators of legitimacy and greater weight on substantive attributes of partnership quality, such as ethical conduct, transparency, fairness, and mutual respect (Croce et al., 2019). This shift marks a transition from a transactional logic of fundraising, where capital and reputation dominate, to a relational logic that prioritizes trust, value congruence, and long-term collaboration (Taggart and Zenor, 2022; Agrawal and Hockerts, 2019). Within this relational perspective, experienced entrepreneurs increasingly value investors who bring industry expertise and contextual knowledge. Such attributes not only strengthen credibility but also demonstrate an investor's ability to provide tailored, actionable guidance that addresses the venture's specific challenges and opportunities (Singh and Mungila Hillemane, 2023; Jones et al., 2022; MacMillan et al., 2022). By selecting investors whose values align with their own and whose expertise complements their capabilities, entrepreneurs reduce the risk of conflict and foster stronger, more collaborative partnerships. Overall, these insights suggest that fundraising experience shapes how entrepreneurs refine their conception of the “ideal” investor. We therefore propose the following hypothesis:

While fundraising experience exposes entrepreneurs to the complexities of investor interactions, experience in venture development offers an equally important influence on how they perceive investors. Entrepreneurs who have founded and managed multiple ventures accumulate practical knowledge that shapes their evaluation of strategic alignment and the long-term value of partnerships (Winkler et al., 2023; Dabić et al., 2023).

Venture development requires entrepreneurs to make repeated strategic decisions, manage crises, and adapt to changing market. These experiences refine their ability to recognize which investor attributes truly add value. Less experienced entrepreneurs often rely on symbolic attributes, such as reputation or track record, as signals of legitimacy (Hallen and Pahnke, 2016). Over time, however, exposure to the challenges of venture creation and growth reveals that symbolic cues alone do not ensure effective support. As a result, entrepreneurs increasingly prioritize substantive attributes, such as industry expertise and contextual knowledge, which indicate an investor's ability to provide tailored guidance and long-term value (Winkler et al., 2023; Rosenbusch et al., 2011). This shift marks a transition from dependence on surface-level signals to a deeper appreciation of investors as strategic partners.

In addition, entrepreneurs who have guided ventures through different stages of development often cultivate distinct leadership styles and a stronger sense of self-efficacy (Winkler et al., 2023; Liang and Chen, 2021). As their confidence grows, they pay greater attention to relational and strategic fit, favouring investors who respect autonomy, share long-term goals, and engage constructively (Amit et al., 2022; Zou et al., 2016). In this context, ethical conduct and mutual respect, alongside substantive expertise, increasingly define their conception of the ideal investor. Drawing on the arguments outlined above, we propose the following hypothesis:

Investors play a multifaceted role in venture development, contributing not only financial capital but also a wide range of value-added resources that can shape the trajectory of growth (MacMillan et al., 2022; Cumming et al., 2023; Huang and Knight, 2017). Beyond their financial commitment, many investors bring prior entrepreneurial or professional expertise, industry knowledge, and extensive networks. These resources make investor involvement more than transactional as they represent opportunities for entrepreneurs to acquire new insights, refine strategic decision-making, and access markets or business partners that might otherwise remain out of reach. In this sense, investors often function as both financiers and developmental partners, whose influence extends into the strategic and learning processes of the venture.

Exposure to diverse investors across funding contexts allows entrepreneurs to discern which forms of support truly advance venture development and which add limited value (Granz et al., 2021). Over time, this experience sharpens their ability to distinguish between symbolic contributions that provide only surface-level legitimacy and substantive contributions that deliver enduring strategic benefits (Amit et al., 2022; Zhang, 2019). With accumulated experience, entrepreneurs increasingly prioritize substantive support, such as strategic guidance, long-term growth planning, and access to networks. These forms of input draw on investors' expertise while preserving entrepreneurial autonomy, enabling founders to navigate uncertainty without losing control of daily operations. By contrast, symbolic or operational involvement is often seen as less useful, since entrepreneurial teams are typically better equipped to make context-specific decisions quickly and effectively (Amit et al., 2022). Excessive investor participation in routine matters may even generate inefficiency or conflict with the entrepreneur's vision. Based on this reasoning, we propose the following hypothesis:

In the early stages, inexperienced entrepreneurs often welcome investor engagement in routine or operational activities, interpreting it as a reassuring signal of commitment. Symbolic forms of involvement, such as offering advice on staffing, marketing execution, or other day-to-day tasks, help them cope with uncertainty by providing legitimacy and visible signs of investor support (Mousa and Gallagher, 2024; Zou et al., 2016). From a signalling perspective, these gestures allow inexperienced entrepreneurs to demonstrate credibility to external stakeholders, even if the contributions themselves do little to strengthen venture performance. Yet while these signals may reduce anxiety and bolster legitimacy in the short term, they seldom contribute meaningfully to growth and can eventually create inefficiencies or even misalignment with the entrepreneur's goals.

With accumulated experience in founding, managing, and scaling ventures, entrepreneurs begin to see these limitations more clearly. Through repeated involvement in strategic decision-making, governance, and problem-solving, they develop a deeper understanding of what drives long-term success (Amit et al., 2022; Croce et al., 2019). This experiential learning enhances their confidence in managing uncertainty (Haneberg, 2021; Lattacher and Wdowiak, 2020) and encourages them to be more selective about when and how investors should be involved. At this stage, operational involvement is no longer seen as a positive signal but rather as unnecessary or even counterproductive.

Instead, experienced entrepreneurs come to value substantive forms of support that align with strategic needs. Inputs related to market positioning, scaling strategies, exit planning, or crisis management are regarded as complementary to their expertise and as contributions that enhance the venture's long-term potential without undermining autonomy (Zhang, 2019; Greenberg and Mollick, 2017). Importantly, in signalling terms, prioritizing substantive investor contributions allows entrepreneurs to convey a different message to stakeholders: rather than seeking legitimacy through association with investors' symbolic gestures, they signal maturity, strategic competence, and the capacity to build sustainable partnerships. In this way, their choice of substantive support reflects not only their learning but also a deliberate effort to project credibility and resilience in the eyes of markets, partners, and future investors. On this basis, we propose the following hypotheses:

Data for this study were collected from entrepreneurs affiliated with investor–entrepreneur networks in the United States, United Kingdom, France, and Australia. Access to this targeted group of venture founders was facilitated by one of the researchers, who was an active member of the network. Initial participants were recruited through direct outreach to network contacts, and additional respondents were identified using snowball sampling. This method is widely accepted in entrepreneurship research, as it enables access to founders who are otherwise difficult to reach and has been applied effectively in studies where representative sampling is not feasible (Davidsson and Honig, 2003; Jack, 2005). Moreover, although the entrepreneurs were based in different countries, they operated within broadly similar institutional and cultural contexts. All of these countries have mature venture ecosystems in which private investment plays a central role in new venture development, making them well-suited and comparable settings for examining entrepreneurs' perceptions of investors.

Data collection was conducted through an online questionnaire administered in six waves between December 2021 and April 2024. In total, 230 responses were received. Ten responses were excluded because the entrepreneurs had only recently launched their ventures and had not yet engaged in meaningful funding activity or investor interactions. Since the study focuses on how entrepreneurial experience shapes the evaluation of investors, it was important to include only respondents who had progressed beyond the initial venture formation phase. This exclusion criterion is consistent with entrepreneurial learning theory, which emphasizes that experience-based learning and evaluative judgement emerge through repeated exposure to challenges such as seeking external investment and managing investor relationships. An additional 25 responses were removed due to substantial missing data in key variables. The final dataset consists of 195 complete responses, yielding an effective response rate of 85% after accounting for incomplete and ineligible cases (to be updated with recruitment data). While 195 may appear modest for a multi-country study, this size is consistent with prior entrepreneurship and entrepreneurial finance research, which often relies on hard-to-reach populations such as entrepreneurs with varying experience in funding and venture development. Comparable sample sizes have been used in several studies, such as those by Hsu (2004), Zacharakis and Shepherd (2001), and Brush et al. (2018) in a cross-national context. These precedents demonstrate that modest samples are both common and acceptable in this field, particularly given the well-recognized challenges of accessing entrepreneurs and investors involved in funding processes.

To ensure the content validity of the survey instrument, a multi-stage validation process was undertaken. An initial version of the questionnaire was developed based on a review of relevant literature, then refined through expert feedback and pilot testing. Revisions were incorporated following cognitive debriefing interviews and feedback from a group of practicing entrepreneurs, resulting in the finalized instrument used for data collection.

Among the respondents, 46% are between 20 and 30 years, 28% between 31 and 40 years and the rest are above 40 years old. 62% identified as male and 38% as female. In terms of the sector, it covers various sector including healthcare manufacturing, ICT (including service).To assess potential response bias, both parametric and non-parametric tests were conducted comparing early and late respondents, as well as respondents and non-respondents, on key observable characteristics including industry, age, and entrepreneurial experience. No statistically significant differences were observed at the 0.05 level, suggesting that non-response bias is unlikely to threaten the representativeness of the sample.

Figure 1 presents the conceptual model of the study. The first set of hypotheses examines how entrepreneurial experience influences the criteria entrepreneurs use when selecting investors, while the second set explores how experience shapes their expectations regarding the type of support or assistance sought from investors.

The first dependent variable in this study reflects the criteria entrepreneurs use to evaluate the attributes of an ideal investor. Respondents were asked to rate the importance of a series of investor attributes on a scale from 1 to 100, with higher values indicating greater perceived importance. A comprehensive list of the evaluated attributes is provided in Table 1.

To explore patterns in how entrepreneurs evaluate investor attributes, this study employed k-means clustering rather than factor analysis. While factor analysis is commonly used to reduce dimensionality and identify latent constructs based on patterns of covariance among variables, it is primarily suited to uncovering underlying structures or theoretical dimensions (Hair et al., 1998; DeVellis, 2017). In contrast, the goal of this analysis was to identify distinct groups of entrepreneurs who exhibit similar evaluation patterns across multiple investor attributes. K-means clustering was selected because it allows for the classification of respondents into empirically distinct profiles based on their ratings of investor characteristics. This approach is particularly appropriate when the objective is typological rather than dimensional (Hair et al., 1998; Ketchen and Shook, 1996). Through k-means clustering, the analysis was able to reveal meaningful subgroups of entrepreneurs. Moreover, k-means clustering offers a practical advantage in this context: it facilitates further analysis by generating a categorical variable that captures membership in preference-based profiles. This categorical outcome enabled a clear examination of how entrepreneurial experience variables predict membership in these distinct clusters.

The analysis revealed three distinct preference-based clusters among entrepreneurs (Table 2). The first cluster, labelled “Track Record and Reputation” (n = 102), represented the largest group in the sample. Entrepreneurs in this group assigned consistently high importance to most investor attributes, with a particular emphasis on the investor's reputation, track record, and investment value. Interestingly, this group scored relatively low on ethical behaviour, suggesting a decision-making approach primarily guided by performance-based considerations rather than normative or relational factors. The internal consistency of this cluster was strong (Cronbach's α = 0.71), reflecting a coherent underlying logic in their preferences.

The second cluster, labelled “Ethics and Relational Qualities” (n = 33), was characterized by a strong preference for investor attributes such as ethical conduct. In contrast to the first group, these entrepreneurs placed significantly less emphasis on financial metrics, including valuation proposals and investment track records. They also valued alignment and perceived similarity with investors, reflecting a stronger focus on trust, shared values, and relational fit. Although this profile demonstrated moderate internal reliability (Cronbach's α = 0.60), it clearly represented a distinct evaluative orientation that prioritizes ethical alignment over transactional performance.

The third cluster, referred to as “Balanced Consideration” (n = 60), reflected a more integrative evaluative approach. Entrepreneurs in this group prioritized investors with relevant industry knowledge and the capacity to provide strategic value. While ethical behaviour was moderately important, attributes such as investor prestige and valuation received comparatively less emphasis. This pattern suggests a logic of strategic alignment, in which entrepreneurs seek partners who understand their business context and can contribute meaningfully beyond capital. The internal reliability of this cluster was acceptable (Cronbach's α = 0.68).

As a robustness check, we compared the k-means solution with Ward's hierarchical clustering. The two methods produced highly consistent results, confirming the stability of the three-cluster typology, as shown in  Appendix 1.

The second dependent variable captures the types of value-added support that entrepreneurs seek from investors beyond financial capital. To assess this, respondents were asked to rate the importance of 16 distinct forms of investor value-added contributions using a scale from 1 (not important at all) to 100 (extremely important). A complete list of the value-added support is presented in Table 3.

To examine the underlying structure of entrepreneurs' expectations regarding investor support, we conducted an exploratory factor analysis (EFA) (Fabrigar et al., 1999; Hair et al., 1998). Given the relatively long list of survey items and the potential for conceptual overlap among them, EFA was appropriate for reducing the data into a smaller number of interpretable dimensions that reflect distinct patterns in how entrepreneurs evaluate investor support. Unlike clustering techniques such as k-means, which group individuals into mutually exclusive categories, EFA is designed to uncover continuous latent constructs that can co-exist within individual decision frameworks. This approach aligns with our theoretical goal of capturing the multidimensional nature of investor support preferences rather than segmenting entrepreneurs into fixed types.

Prior to analysis, we assessed the suitability of the data using standard diagnostics. The correlation matrix indicated adequate inter-item associations, and both the Kaiser-Meyer-Olkin (KMO) measure and Bartlett's test of sphericity confirmed the appropriateness of the dataset for factor analysis. Using principal component extraction and maximum likelihood estimation, we tested models with four to six factors, as shown in Table 4. Although the six-factor solution demonstrated the best statistical fit (χ²(39) = 31.92, p = 0.782; AIC = 195.68; BIC = 460.79), it produced a Heywood case, indicating a problematic solution with inadmissible variance estimates. In contrast, the five-factor model offered a good fit (χ²(50) = 49.48, p = 0.494; AIC = 192.02; BIC = 421.13), avoided estimation issues, and provided stronger theoretical clarity than the four-factor alternative (χ²(62) = 81.38, p = 0.050). The five retained factors capture distinct dimensions of investor support expectations and accounted for 97.3% of the total variance. All items demonstrated strong primary loadings (≥0.60) and minimal cross-loadings. Internal consistency was satisfactory, with Cronbach's alpha values ranging from 0.71 to 0.83.

The first factor, Network and Governance Support, captures the investor's value added support in networking and governance. High loadings were observed for items such as providing contacts with key customers and prospects (loading = 0.825), adding board members with an operating background (0.791), financial background (0.701), serve a board member (0.772) and recruiting new managers (0.785). These findings suggest that investors in this category primarily support ventures by enhancing their external network capital and internal human capital.

The second factor, Strategic Development Support, reflects the investor's engagement in shaping strategic direction. It is characterized by strong associations with supporting the development of new products or services (loading = 0.843), developing strategies from scratch (0.815), and evaluating existing strategies (0.870). These high values indicate that investors contribute to high-level decision-making and strategic alignment.

The third factor, Growth and Exit Support, includes investor actions that prepare the firm for scaling or a successful exit. This factor loads highly on items such as assisting in further acquisitions (loading = 0.846), navigating short-term crises (0.868), and facilitating a high-value exit (0.599). The pattern suggests an active role in helping entrepreneurs to manage important events and crisis.

The fourth factor, Operational and Financial Monitoring, represents a more day-to-day operational role. Items such as monitoring operating performance (0.713) and monitoring financial performance (0.633) indicate a hands-on approach to manage and increase efficiency.

The fifth factor, labelled Applied Managerial Support, captures investor contributions aimed at enhancing internal processes. This dimension is defined by moderate loadings on items such as evaluating marketing plans (0.856) and providing financing (0.589. Although the loadings for this factor were somewhat lower than others, the items suggest a pragmatic orientation that emphasizes investor support aimed at improving operational effectiveness and providing financial resources.

Composite scores were constructed for each factor and used in subsequent hypothesis testing. As a robustness check, we estimated a partial least squares structural equation model (PLS-SEM) using SEMinR (Hair et al., 1998). The measurement model exhibited satisfactory reliability and validity, and the results closely mirrored those from the exploratory factor analysis, confirming the stability of the factor structure. Results are reported in  Appendix 2.

This study conceptualizes experience into two main categories: experience in fundraising campaigns and experience in new venture development.

The first variable, Diversity of Investor Engagement, captures the breadth of entrepreneurs' exposure to different investor categories. Respondents indicated whether they had previously worked with angel investors, venture capital firms, growth equity providers, private equity firms, or other funding sources. Each affirmative response was coded as one point, yielding a cumulative score ranging from one to five. This variable serves as a proxy for entrepreneurs' familiarity with diverse investment models and the varying expectations associated with different types of investors.

The second variable, Years of Fundraising Experience, captures the number of years entrepreneurs have actively engaged in fundraising activities. This variable serves as a proxy for the duration and continuity of exposure to investor interactions, negotiations, and funding processes.

The third variable, total external capital raised, reflects the cumulative funding an entrepreneur has secured across all ventures. This was coded categorically into three tiers: “1” for funding between $0 and $1 million, “2” for $1 million to $10 million, and “3” for over $10 million. Higher values on this scale are interpreted as evidence of deeper experience navigating investor relationships, managing expectations, and coordinating funding rounds.

The fourth variable, Funding Success Rate, reflects the entrepreneur's self-perceived effectiveness in obtaining external investment. While an objective success rate could theoretically be derived by calculating the proportion of successful funding attempts relative to total campaigns initiated, such precision is difficult to achieve in practice. Fundraising activities often involve several stages, informal interactions, and uncertain outcomes. Entrepreneurs may also pursue multiple funding avenues simultaneously or have difficulty recalling failed attempts across a lengthy career. To address these limitations, respondents were asked to rate their perceived success in securing funding on a scale from 0 to 100. This self-reported measure was subsequently calibrated and standardized for use in the analysis.

The second category of experience relates to new venture development and is captured by two variables reflecting the scope and context of entrepreneurial activity. The fifth variable, Industry-Specific Serial Entrepreneurship, measures the number of ventures the entrepreneur has previously founded within the same or a closely related industry. This variable reflects depth of sector-specific knowledge, which may shape entrepreneurs' assessments of investors' strategic relevance and industry fit. The sixth variable, Cross-Industry Serial Entrepreneurship, captures the number of ventures founded in different industries from the entrepreneur's current venture. This measure reflects the breadth of entrepreneurial experience across diverse contexts and may inform more adaptable or generalized expectations regarding investor involvement.

In addition to the main independent variables, demographic and contextual controls were included to account for potential confounding effects. Specifically, gender and age were controlled to help isolate the influence of entrepreneurial experience on the dependent variables and to enhance the robustness of the analysis. To address the risk of common method bias, we conducted Harman's single-factor test. The results showed that no single factor accounted for a majority of the variance, indicating that common method bias was not a major concern. We tested the first set of hypotheses on investor attributes using multinomial logistic regression, appropriate for categorical outcomes from k-means clusters. The second set, on the importance of investor value-added support, was analyzed using OLS regression, with robustness checks conducted via PLS-SEM, as reported in  Appendix 3.

Correlation coefficients among the key independent variables, cluster memberships, and the five extracted factors representing entrepreneurs' expectations of investor involvement are reported in the  Appendix 4. The results reveal several associations. Years of Fundraising Experience show a positive correlation with expectations for Network and Governance Support (r = 0.55) and Operational and Financial Monitoring (r = 0.63), suggesting that entrepreneurs with more experience in securing funding place greater value on investor contributions in these areas. Diversity of Investor Engagement is strongly associated with Strategic Development Support (r = 0.67), indicating that broader exposure to different types of investors heightens entrepreneurs' expectations for strategic input. Conversely, Cross-Industry Serial Entrepreneurship is negatively correlated with Network and Governance Support (r = −0.24), implying that entrepreneurs with more diverse sectoral backgrounds may place less emphasis on investor involvement in networking and governance. Overall, the correlation matrix offers preliminary support for the idea that entrepreneurial experience influences how entrepreneurs evaluate the value-added contributions of investors.

To examine how entrepreneurial experience predicts investor preference profiles, we estimated a multinomial logistic regression model, using Profile A (Track Record and Reputation) as the reference category, as shown in Table 5. The model was statistically significant (χ²(14) = 138.80, p < 0.001), indicating that the independent variables reliably differentiate among the three investor preference groups. The model explained approximately 35.5% of the variance in investor profile selection (pseudo R² = 0.3551) and represented a significant improvement over the null model (log likelihood = −126.04).

Compared to the Track Record and Reputation group, entrepreneurs classified under Ethics and Relational Qualities (Profile B) were significantly more likely to have accumulated more years of fundraising experience (β = 0.268, p < 0.001) and to have engaged with a broader range of investor types (Diversity of Investor Engagement: β = 1.520, p < 0.001). They also reported higher levels of total capital raised (Total Capital Raised: β = 1.402, p = 0.004) and had greater exposure to different sector through their carer as a serial entrepreneurs (Cross industry serial entrepreneurship: β = 0.455, p = 0.030). The effect of Funding Success Rate approached significance (β = 0.026, p = 0.081), suggesting a weak positive association. Neither gender nor Industry-Specific Serial Entrepreneurship was a significant predictor for this profile.

Relative to Profile A, entrepreneurs aligned with the Balanced Consideration group (Profile C) were significantly more likely to be female (β = 1.313, p = 0.026) and to report more years of fundraising experience (β = 0.152, p = 0.007). Although Diversity of Investor Engagement was not statistically significant for this group (β = −0.339, p = 0.141), both Total Capital Raised (β = 0.684, p = 0.016) and Funding Success Rate (β = 0.024, p = 0.003) were positively associated. Additionally, cross-industry founding experience (Cross-Industry Serial Entrepreneurship) increased the likelihood of belonging to this profile (β = 0.266, p = 0.021). As with Profile B, Industry-Specific Serial Entrepreneurship was not a significant predictor at conventional levels (p = 0.078).

The findings suggest that entrepreneurs who emphasize ethical and relational criteria in evaluating investors tend to possess greater fundraising experience, broader engagement with formal investors, and deeper exposure to diverse funding environments. In contrast, those who adopt a more balanced evaluative approach are more likely to achieve higher success rates, draw on broader entrepreneurial backgrounds, and exhibit a stronger gender effect. Importantly, these patterns were supported by robustness checks using heteroskedasticity-robust standard errors and bootstrapping, which confirmed the stability of the main results and increased confidence that the identified relationships reflect meaningful drivers of entrepreneurs' perceptions of investors, as reported in  Appendix 5.

To examine how entrepreneurial experience and venture characteristics predict preferences for different forms of investor value-added support, we conducted a series of ordinary least squares (OLS) regressions using five factor scores as dependent variables, as reported in Table 6.

Network and Governance Support: This model was statistically significant (F (9,185) = 10.96, p < 0.001, R² = 0.35). Years of Fundraising Experience was a significant positive predictor (β = 0.19, p < 0.001), indicating that entrepreneurs with more extensive fundraising experience are more likely to value investor contributions to governance and network access. Both Industry-Specific Serial Entrepreneurship (β = 0.09, p < 0.01) and Cross-Industry Serial Entrepreneurship (β = 0.13, p < 0.001) were also positively associated with this factor, suggesting that experienced entrepreneurs perceive governance support as beneficial.

Strategic Development Support: This model showed the strongest explanatory power (F (9,185) = 27.22, p < 0.001, R² = 0.57). Diversity of Investor Engagement emerged as the strongest predictor (β = 0.79, p < 0.001), emphasizing that entrepreneurs with broader investor exposure place high value on strategic input. Cross-Industry Serial Entrepreneurship (β = 0.10, p < 0.001) and Industry-Specific Serial Entrepreneurship (β = 0.08, p < 0.01) were also positively associated. In addition, Years of Fundraising Experience was positively related (β = −0.13, p < 0.005), suggesting that highly experienced entrepreneurs appreciate strategic advice.

Growth and Exit Support: This model was also statistically significant (F (9,185) = 15.81, p < 0.001, R² = 0.43). Years of Fundraising Experience (β = 0.12, p < 0.001) and Cross-Industry Serial Entrepreneurship (β = 0.08, p < 0.05) both predicted a greater preference for investor involvement in growth scaling and exit strategy. This pattern suggests that experienced entrepreneurs recognize the value of investor guidance in high-stakes transition phases.

Operational and Financial Monitoring: The model showed a modest yet statistically significant fit (F (9,185) = 3.03, p = 0.002, R² = 0.13). Both Years of Fundraising Experience (β = −0.10, p < 0.01) and Cross-Industry Serial Entrepreneurship (β = −0.14, p < 0.001) were negatively associated with this dimension. These findings imply that experienced entrepreneurs, especially those with diverse sector backgrounds, may resist investor oversight in day-to-day operations and financial monitoring.

Applied Managerial Support: Although this model approached marginal significance (F (9,185) = 1.72, p = 0.088, R² = 0.08), Cross-Industry Serial Entrepreneurship had a significant negative relationship (β = −0.14, p < 0.001), indicating reduced preference for investors' tactical involvement in operational matters such as marketing evaluation and routine managerial support. Other predictors were not statistically significant in this model.

Overall, the regression results underscore that entrepreneurial experience plays a key role in shaping expectations of investor contributions. More experienced entrepreneurs place stronger emphasis on high-level strategic input and governance engagement, while de-emphasizing operational oversight. To further validate these findings, we also conducted a PLS-SEM analysis. The results, which are presented in the  Appendix 3, not only mirror the regression outcomes but also provide additional support for the theoretical framework by confirming the relationships between constructs. Taken together, the findings demonstrate the reliability of the conclusions across analytical approaches.

The findings support the first hypotheses, showing that entrepreneurial experience plays a critical role in how entrepreneurs evaluate and select investors. Results indicate that years of fundraising experience, total capital raised, and, to a lesser extent, perceived funding success rate are associated with investor preferences. Entrepreneurs with greater experience tend to prioritize investors known for ethical behaviour, strategic alignment, and relational compatibility, rather than those chosen primarily for symbolic markers of legitimacy such as financial prestige or past investment success. This pattern echoes prior work suggesting that entrepreneurs look beyond immediate financial considerations and increasingly emphasize the substantive value investors can contribute to long-term venture growth, alongside ethics and interpersonal trust as foundations of sustainable founder–investor relationships (Drover et al., 2014; Schwienbacher, 2013). The evidence thus suggests that experienced entrepreneurs deliberately select investors who bring complementary expertise and aligned visions, not simply those who offer symbolic prestige.

The results further highlight the role of perceived success in shaping investor preferences. Entrepreneurs who viewed themselves as highly successful in fundraising were more likely to fall within the Balanced Consideration group, which reflects an integrative approach in which financial, strategic, and ethical dimensions are evaluated together. Such entrepreneurs appear able to recognize that the “ideal investor” is not defined by a single criterion but by the combination of substantive contributions (such as expertise and guidance) and symbolic signals (such as reputation and capital access) that together strengthen the partnership. Sturm et al. (2025) similarly demonstrate that while novices often privilege symbolic cues like valuation or reputation, greater experience leads to stronger emphasis on substantive qualities such as trust, empathy, fairness, and cultural fit. This learning dynamic reinforces the interpretation that entrepreneurial experience enables founders to reinterpret signals of investor quality, shifting attention from symbolic reassurance toward substantive value.

When considering venture development experience in the second hypothesis, a more differentiated picture emerges. Industry-specific serial entrepreneurship did not significantly affect preferences, but founding ventures across different sectors was associated with a preference for investors with ethical and balanced profiles. Broader venture experience appears to deepen appreciation of relational and substantive aspects of investor engagement, echoing prior findings on the advantages of cross-sector exposure for strategic decision-making (Hellmann and Puri, 2002; Gompers and Lerner, 1998).

Overall, these findings demonstrate that entrepreneurial experience gradually redefines the “ideal investor.” While symbolic attributes such as reputation and track record may dominate early decision-making, repeated exposure to fundraising processes and venture development shifts attention toward substantive signals of ethical integrity, cultural alignment, and strategic partnership. This perspective also helps explain why female entrepreneurs in our study were more likely to emphasize relational quality and values-based alignment, consistent with prior research highlighting values-driven and collaborative approaches in entrepreneurship (Gupta et al., 2009).

The next hypotheses proposed that as entrepreneurs accumulate experience, they become more selective in the types of investors support they value. Specifically, they were expected to favour substantive strategic contributions while showing less interest in symbolic or operational involvement. The findings support this view, indicating a clear shift in expectations as entrepreneurial experience deepens.

Experience gained through repeated interaction with external funding environments appears central to this change. Entrepreneurs with longer fundraising histories increasingly value support that complements their strategic goals, such as guidance on scaling, governance, or market positioning, while distancing themselves from involvement that risks constraining managerial autonomy. Those exposed to a broader variety of investor types also develop a more refined understanding of the investor's role in collaboration, viewing meaningful contributions as those that provide context-specific insight, alignment, and value-added input without micromanagement. Interestingly, prior fundraising success, whether measured by total capital raised or success rate, was not significantly related to expectations of investor involvement. This suggests that it is not success per se but the diversity and richness of experience that informs how entrepreneurs perceive investor value.

Venture development experience reveals a similar pattern. Entrepreneurs who have built businesses across multiple sectors place greater emphasis on strategic and governance-oriented support while resisting operational oversight. This finding echo evidence that founders, although initially sensitive to symbolic concerns such as equity dilution, increasingly prioritize substantive contributions, such as networks, industry expertise, and knowledge, once they gain more exposure to different investor types and contexts. Through such experience, entrepreneurs encounter both productive and conflictual investor approaches, which strengthens their preference for relationships that preserve autonomy while contributing meaningfully to long-term direction. Schwienbacher (2013) likewise demonstrates that investor choices have enduring implications: specialist investors often provide more substantive value-added input, while generalists offer broader but less tailored involvement. Together, these insights highlight how substantive support becomes more valuable in venture growth, particularly when entrepreneurs already possess strong operational competence and no longer view routine oversight as beneficial.

In summary, these findings support a value-driven model of investor engagement. Entrepreneurial experience not only shapes how investors are evaluated but also clarifies the appropriate boundaries of their involvement. With greater experience, entrepreneurs redefine investor value, seeking substantive contributions that enhance strategic outcomes while avoiding symbolic or operational interference in areas where internal capability is strong. For investors, this underscores the need to demonstrate strategic relevance, communicate a collaborative mindset, and respect the entrepreneur's leadership role in guiding the venture.

This study offers new insight into how entrepreneurial experience shapes perceptions of the “ideal” investor. As experience accumulates, entrepreneurs demonstrate a fundamental shift in evaluative criteria, placing greater emphasis on substantive attributes and contributions while attaching comparatively less weight to symbolic ones.

These findings extend the investor–entrepreneur literature by shifting the analytical lens from investors to entrepreneurs, showing that entrepreneurs actively select and evaluate their investors rather than passively receiving capital. By highlighting the salience of substantive attributes and value-added support, the study enriches theory on the multidimensional nature of investor relationships. It conceptualizes investor choice not as a transactional decision based on symbolic signals, but as a long-term strategic process rooted in alignment, collaboration, and sustained value creation. This perspective helps explain variation in investor–entrepreneur dynamics and provides a foundation for future research on how alignment influences venture performance and sustainability.

The study also carries several practical implications. For investors, demonstrating ethical integrity, maintaining transparency, and building a credible track record of constructive collaboration can serve as important differentiators in an increasingly competitive environment. For institutional actors and venture capital firms, this requires investing in relationship-building practices, signalling long-term commitment, and offering domain-specific guidance that entrepreneurs recognize as substantively valuable. For entrepreneurs, particularly those at earlier stages of venture development, the results suggest the importance of adopting a more comprehensive perspective when evaluating potential investors. Beyond financial capacity, attention should be paid to ethical conduct, knowledge of the target market, and alignment in strategic objectives. Accelerators, incubators, and entrepreneurship education initiatives can play a critical role in supporting this process by equipping entrepreneurs with tools and frameworks for assessing investor suitability in a more structured and informed way. Such practices can foster stronger partnerships between entrepreneurs and investors, ultimately leading to more productive and enduring collaboration.

Despite its contributions, this study has several limitations. First, measuring entrepreneurial experience remains challenging. While we use proxies such as years of fundraising, number of ventures, and diversity of investor engagement, these indicators may not fully capture the complexity of entrepreneurial learning. Future research could incorporate additional dimensions such as the nature of previous ventures, learning from failure, or the influence of entrepreneurial teams. Second, although the sample includes participants from multiple countries, it is largely drawn from developed economies with well-established venture capital markets. As such, caution should be taken when generalizing the findings to emerging or less mature investment ecosystems, where funding norms and investor behaviour may differ. Comparative studies across diverse institutional contexts would offer valuable extensions. Finally, this study does not distinguish systematically between different types of investors. While we account for diversity of investor engagement, future work could explore how experiences with specific investor types such as venture capitalists or angel investors shape entrepreneurs' expectations and preferences. These investor categories differ in involvement, expectations, and relational styles, which may influence how entrepreneurs assess their value.

To assess the robustness of the k-means clustering solution, we compared it with Ward's hierarchical clustering. Table A1 shows the results, which indicate a high degree of convergence between the two methods (χ² = 277.95, p < 0.001; Cramér's V = 0.84). Each k-means cluster corresponded closely to a single Ward cluster, with alignment rates ranging from 76% to 98%. The high level of agreement between methods confirms the robustness of the three-cluster solution, suggesting that it captures a stable and interpretable structure in the data.

We assessed the reflective measurement model using partial least squares structural equation modelling (PLS-SEM) implemented in SEMinR (Hair et al., 1998). The model was estimated with Mode A weights, and construct reliability and convergent validity were evaluated using Cronbach's alpha (α), composite reliability (ρC), Dijkstra–Henseler's rhoA, and average variance extracted (AVE). Recommended thresholds are α, ρC, ρA > 0.70 and AVE >0.50.

As shown in Table A2, the constructs Strategic Development Support (α = 0.892, ρC = 0.932, AVE = 0.821, ρA = 0.914) and Network and Governance Support (α = 0.859, ρC = 0.906, AVE = 0.708, ρA = 0.860) demonstrated excellent reliability and convergent validity. Growth and Exit Support also achieved acceptable levels (α = 0.700, ρC = 0.813, AVE = 0.523, ρA = 0.710), meeting minimum thresholds though internal consistency was modest. By contrast, Operational and Financial Monitoring (α = 0.523, ρC = 0.687, AVE = 0.709, ρA = 0.609) and Applied Managerial Support (α = 0.553, ρC = 0.686, AVE = 0.726, ρA = 0.730) showed adequate AVE but fell slightly below reliability benchmarks, suggesting these constructs should be interpreted with caution. Overall, the PLS-SEM findings were consistent with the exploratory factor analysis, supporting the stability of the factor structure.

The structural model was specified to capture both the sequential relationships among the five capability constructs and the influence of exogenous predictors. Five reflective latent constructs were modelled, namely Strategy, Network, Growth, Operational, and Managerial, each measured by their respective indicators. The internal paths followed a staged logic: Strategy was modelled as influencing Network, which in turn predicted Growth; Growth was specified as an antecedent of Operational, which subsequently predicted Managerial capability. This specification reflects the premise that strategic choices shape networking activities, networks facilitate growth, growth supports operational expansion, and operational development enables stronger managerial capacity.

In addition, several observed variables were included as single-item composites to account for demographic and experiential factors shaping entrepreneurial capabilities. These comprised Gender, Years of fundraising experience, Diversity of investor engagement, Total external capital raised, Funding success rate, Industry-specific serial entrepreneurship, Cross-industry serial entrepreneurship, and Cluster membership (clus_1). Each exogenous variable was modelled as a direct antecedent of all five capability constructs, allowing for assessment of their relative influence across domains. This combined specification provided a comprehensive view of both the processual linkages among capabilities and the direct effects of contextual and experiential factors. The model demonstrated substantial explanatory power for several endogenous constructs. As shown in Table A3, the variance explained was highest for Growth (R² = 0.654), Network (R² = 0.627), and Strategy (R² = 0.573), with moderate explanatory power for Operational (R² = 0.352) and Managerial (R² = 0.336).

Bootstrapped path estimates confirmed a set of robust relationships (Table A4). Years of fundraising experience exerted strong positive effects on Network (β = 0.714, p < 0.001), Strategy (β = 0.720, p < 0.001), and Growth (β = 0.749, p < 0.001), highlighting the central role of accumulated entrepreneurial experience. Diversity of investor engagement had a strong positive effect on Strategy (β = 0.881, p < 0.001) but a weaker, marginally significant negative effect on Operational capabilities (β = −0.119, p < 0.05). Industry-specific serial entrepreneurship enhanced both Network (β = 0.149, p < 0.01) and Strategy (β = 0.205, p < 0.01), while cross-industry experience showed a mixed pattern: positive effects on Network, Strategy, and Growth (all p < 0.001), but adverse effects on Operational and Managerial support (β = −0.540, p < 0.001). Cluster membership (_clus_1) also negatively influenced Strategy (β = −0.118, p < 0.05), suggesting heterogeneity in strategic orientations across groups.

Other predictors, including gender, total capital raised, and funding success rate, did not yield significant effects. Given the relatively lower reliability of the Operational and Managerial constructs, findings related to these dimensions should be interpreted with caution.

The robustness analyses confirm that the main results are consistent with the primary models (Table A6). Applying heteroskedasticity-robust standard errors showed that several predictors remained significant, including years of fundraising experience, diversity of investor engagement, cross-industry serial entrepreneurship, and, in some cases, gender. Bootstrapping with 500 replications further supported the stability of these effects, particularly years of fundraising experience and cross-industry serial entrepreneurship, which retained significance with confidence intervals excluding zero. The effects of total external capital raised, funding success rate, and industry-specific serial entrepreneurship also remained significant, though their confidence intervals were somewhat wider, indicating that while they contribute meaningfully, they may be relatively more sensitive to sample variation.

The multinomial logit model also demonstrated strong predictive validity (Table A7). The overall classification accuracy was 66.2%, substantially higher than the 33% expected by chance. Profile C was predicted with the highest accuracy (78.7%), followed by Profile B (68.6%) and Profile A (68.0%). A Pearson chi-square test (χ²(4) = 137.42, p < 0.001) confirmed a strong association between predicted and observed classifications, supporting the reliability of the model.

Overall, these analyses show that the multinomial logistic regression results are both robust to alternative specifications and predictively meaningful. Importantly, the robustness checks confirm that the main findings are stable across different specifications, lending further confidence to the validity of the identified investor preference profiles.