Paving the Way to Success: Linking the Strategic Ecosystem of Entrepreneurial Start-Ups with Market Performance

Abstract

Entrepreneurial endeavors are pivotal to development and growth. Therefore, it is critical to recognize and prioritize the need for building new, sustainable businesses that will create and offer products and services aligned with the interests of diverse stakeholders. Considering that the success of early-stage firms is shaped by both internal factors and external conditions, the aim of this study is to examine the prospects for sustainability of entrepreneurial start-ups by assessing how their strategic ecosystem influences market performance, a key metric of their capacity to successfully compete and survive. To identify performance precursors, we surveyed the founders of 108 new ventures regarding a broad range of internal determinants—including strategic factors, human resources, networking with external partners, pre-entry funding choices, and board profiles—while controlling for external influences. Our findings suggest that the performance differentials across these firms can be largely attributed to their human assets, strategic foundation, and board heterogeneity, as reflected in gender diversity in decision boards and the number of founders. Service start-ups and those that have been found in urban centers were also associated with better performance compared to manufacturing new ventures and those located in a country’s periphery. In the opposite direction, funding choices and established partnership-based networks were not related to successful market penetration. Likewise, the impact of start-ups’ industry growth, the rate of technology obsoletion, as well as several other macro-environmental influences show limited impact.

1. Introduction

There is much research suggesting that entrepreneurship matters in sustainable economic development, these days perhaps more than ever [1,2,3]. Entrepreneurial new ventures, in particular, are considered drivers of innovation, creators of new industries, agents of growth, substantial sources of employment, and potential solutions to both social and environmental concerns [4,5]. Echoing their importance, there is a conspicuous body of empirical studies examining the causes of post-entry success (or failure) of entrepreneurial start-ups [6,7]. To synthesize evidence rooted in the empirical front, we conclude that the survival and long-term viability of new ventures is shaped by a constellation of factors that originate both within the organization—such as strategic orientation, resource deployment and configurations, network capabilities and organizational demography—and from external conditions, including market and technology dynamics, institutional quality, governmental support, and competitive pressures.

However, most scholarly investigations are predominately focused on selective performance drivers such as the effects of social [8], financial, and entrepreneurial capital [9], organizational forms and resources [10], the cognitive and emotional abilities of entrepreneurs [11], the impact of industry [12], and local entrepreneurial systems [13]. Although insightful and useful, these studies seem to be fragmented: the narrow lens of investigating specific determinants limits our understanding of the broader context and makes us lose sight of the ‘bigger picture’ of performance antecedents [14]. In other words, it seems that the existing literature lacks an integrated framework that conceptualizes multifaceted antecedents pertaining to start-ups’ performance. This is even more pronounced when assessing the influences of the strategic ecosystem of entrepreneurial ventures, i.e., the interconnected network of resources, capabilities, decisions and partnerships, on market penetration and sustained growth. Providing the co-existence of different activities and planning focus, as well as multiple objectives, structures, and phenomena within the organizational milieu, we believe that integrated and multidiscipline-based approaches are much needed for theory building and empirical testing in the research of the relationship between strategic management orientations and the sustainability of a start-up ecosystem.

The above motivates this research: we draw from resource-based perspectives [15], competency views [16], network tenets [17], financial economics [18], sustainable finance assumptions (e.g., [19,20]), and organization theory in board composition [21] to test the relationships of a wide array of determinants with start-ups’ post-entry success. Specifically, we employ a unique sample of 108 new ventures founded in Greece to conceptualize and validate the associations of human and strategic capital, partnership-based linkages, funding decisions, and board profiles with market-based performance, while accounting for external influences.

Our research offers three main contributions: First, to cite [22] (p. 18) research on start-ups’ sustainability has relied on a narrow range of theoretical lenses, resulting in a limited conceptual diversity of existing studies. Here, we propose and empirically test an integrative, multidisciplinary framework of performance determinants, addressing a critical gap in the extant research. By showing that performance differentials stem from a system of attributes working in concert, we respond to recent, direct calls for a more holistic approach toward the study of the complex and organizationally inseparable nature of new ventures’ performance (e.g., [23,24]). Second, providing that entrepreneurial new ventures are typically resource-constrained [25], we inform entrepreneurial new ventures and performance studies by identifying the key determinants underpinning their market success. To be precise, we show the specific internal and external factors that trigger market performance so as to offer practical insights for new entrepreneurs seeking to build resilient businesses. In so doing, we empirically substantiate that the human capital of both founders and employees, innovative business models, adaptive-to-customers strategies, and board heterogeneity impact significantly on the successful practice of nascent entrepreneurship. We conclude that internal factors are more influential in explaining performance outcomes than external influences. Finally, in entrepreneurial new ventures’ research, much of the focus has been placed on US Silicon Valley-type contexts and different variants as prime examples of regional entrepreneurial activity [26]. However, these are relatively unique environments for new ventures when compared to those operating in most other parts of the world. Here, we provide initial evidence of the start-ups’ performance determinants in an EU peripheral economy (Greece), which are rarely studied in the literature. In Greece, the start-ups created per year have risen to a record number during the last decade, since these firms were conceived as stopgaps to market and state failures in the business and social landscapes [27]. Therefore, our context seems also relevant for similar post-crisis environments.

2. Theoretical Underpinnings of the Study and Research Model

The entry of a new venture into a market and its projected performance is shaped by a constellation of firm-internal and external factors (e.g., [10,14,23,28,29,30]). For instance, to cite [31] (p. 2), “most of start-ups open their business with their own idea and management strategies, but they suffer to reach their knowledge infrastructure into the real business procedure because of lack of information and insufficient financial resource. Therefore, the utilization of external resources and local community support plays a significant role in the initial settlement of start-ups”. In the internal context, elements such as the individual characteristics of founders, founding teams and employees, strategic orientation and business modeling, resource orchestration, dynamic capabilities and marketing intensity, human assets, and organizational features may determine start-ups’ ability to grow and survive. Externally, scholars have found evidence that conditions such as institutional support, technological trends, partnerships, and clusters affect new ventures’ competitiveness and future prospects [32,33]. Our conceptualization is anchored at the firm level and considers external influences, allowing us to contextualize performance precursors mainly rooted within the organizational space.

Human assets: Human capital effects on performance are well recorded, with research highlighting its contribution in multiplying firms’ success prospects [34]. Human capital theory, originally developed by [35,36], indicates that people possess, to varying extents, intellectual capital, i.e., knowledge, experience, and skills that create economic value. To further refine, individuals or groups endowed with higher levels of knowledge stock, expertise, and other competencies tend to achieve greater performance outcomes than those who possess lower levels [37]. In the entrepreneurial new ventures’ research, a human capital perspective has been used to predict a variety of outcomes, such as becoming a nascent entrepreneur, new venture formation, and new venture performance and survival [38]. This research stream concludes that the human assets of the founding teams and/or individual entrepreneurs are determinant for post-entry success and future growth [39,40], safeguarding ventures against competitive rivalry [16]. This is because they allow for the discovery, creation, and exploitation of entrepreneurial opportunities [41,42]. The knowledge, experience, and skills of employees also assist in the accumulation of new knowledge and the creation of advantages for new firms [43,44].

Human capital assets of both founders and employees can be divided into general—such as knowledge—and specific (or task-related)—such as education and experience—. These categories are interrelated; for instance, specific human capital assets increase performance and the chances of new venture survival [45] by adding significant experiential knowledge inputs to start-ups. According to [46] (p. 607), the most common human capital assets investigated in scholarly investigations exploring start-ups’ performance antecedents is work experience (39.9% of cases) and education (26.6% of cases).

Strategic capital: Mobilizing and combining resources to build new organizations is an undertaking laden with uncertainty and unforeseeable hazards [47]. Thus, accumulation and re-combinations of idiosyncratic intangible resources and capabilities [15] with the purpose of distilling them into a sustainable competitive advantage lays at the core of start-ups’ strategic foundation. Drawing on these resource-based tenets, [48] argue for the prominence of resource orchestration by the founding teams in explaining the performance variations in start-ups. The strategic approach of entrepreneurship also advocates the superiority of rationalized planning for better outcomes. Central to this argument is the notion of proactiveness: by employing a coherent pre-launch strategic analysis, new ventures can gain a priori insights of possible hindrances they are bound to face in their marketspaces and places, enabling, thus, a more promising pre-entry planning [49]. Finally, inherent to start-ups’ success trajectory is their ability to deliver innovative products and/or services that create value. According to [50], innovation-oriented ventures are more likely to gain enhanced performance outcomes in the long run compared to their counterparts scarcely fostering any developments in their offerings. Beyond product and/or service innovation, business modeling is another factor that matters to new ventures. Configuring value proposition with customer needs and behaviors, in particular, is a key concern for new entrepreneurs [51], as it enables better adjustment to market conditions and unveils novel ways of value creation and capture [52].

Networking: Many studies argue that start-ups play a pivotal role in a large fraction of innovations (e.g., [53,54]). However, it is not easy for these ventures to successfully innovate because of their limited business experience and resources. For instance, [55], in his seminal work, proposed that the propensity of start-ups to fail exists because they have not established effective relationships with external agents, lacking also a track record with outside buyers and suppliers. To compensate for these deficiencies, collaborative formations with external organizations are considered an effective solution [56]. Thus, leveraging partnership-based networking opportunities early on is a key goal for nascent firms in order to address their liabilities of newness and smallness and establish connections with their marketplace.

The significance of networking is well-admitted in the entrepreneurship literature [57], yet its empirical investigation with the performance of new ventures has remained relatively unattended. Networks’ contributions consist of, but are not limited to, endowing nascent firms with both tangible (i.e., infrastructure, financial capital) and intangible (i.e., knowledge sharing, intellectual capital, and advisory) resources, critical to their survival and growth [58,59].

Business incubators/accelerators have become a ubiquitous phenomenon in many parts of the world and are viewed as an effective tool for promoting the development and growth of entrepreneurial new ventures [60]. They provide business assistance and compensate efforts of start-ups to assure critical resources and take advantage of their capabilities, enabling a more effective handling of their infancy-stage rigors [61]. As a result, incubated new ventures typically experience enhanced performance in terms of sales revenue and employment growth compared to non-incubated [62]. Establishing links with other firms in the industry is also typical for firms to exploit networking opportunities. New ventures’ relations with existing firms could pave the way for privileged information, technology transfer, and resources, increasing their survival prospects [63]. Network ties of entrepreneurial ventures also include partnerships with research centers to leverage on knowledge economy and trigger innovation. Academic entrepreneurship could also be the onset of entrepreneurial venturing: probing the relationship between network capabilities and performance of university spin-offs, [64] conclude that networking is an important determinant of new ventures success.

Funding sources: Adequate financial capital is fundamental for the survival and growth for every firm. When it comes to new ventures’ formation, ensuring initial strategic funding can stipulate success or failure, which is why funding decisions/alternatives attract considerable interest in the entrepreneurial literature [65]. To a broad extent, relevant research is concerned with deciding an optimal capital structure for nascent firms, since debt and equity hold distinct implications for current performance and future competitiveness [66]. Faced with the reef of securing their growth, start-ups typically choose between internal and external funding in search of an equilibrium point between current liabilities and future prospects.

Among the funding options available, financial bootstrapping is the most commonly considered by entrepreneurial founders, especially when they encounter financial constraints and their access to financial markets is limited [67]. Bootstrapped firms tap mainly into informal means of financing, such as family, friends, and personal finances, attaching greater importance to maintaining proprietorship rather than exploiting growth opportunities through the attraction of external funding [68]. Financial bootstrapping provides a viable alternative to new ventures’ external financing; however, empirical evidence of its relationship with performance produced mixed results [69]. For instance, while several studies hold that a primary cause of failure for new firms is the inability to receive adequate external financing, [70,71] suggest that bootstrapping is related to increased profitability.

External funding sources, along with the decision to consider financial intermediaries into an early-stage venture’s funding base, not only ensure sufficient financing but also shape start-ups’ strategic trajectory. They do so by introducing mechanisms of selection, monitoring, and governance that influence their growth potential [72]. Within the external funding alternatives, bank loans and angel finance appear as compelling methods to fund initial start-up activity, while venture capital (VC) (both domestic and international) seems to be more an option when strategic thresholds have been met to attract larger funds [73].

Bank loans impose financial discipline within new ventures by introducing repayment obligations, detailed financial projections, and performance benchmarks [74]. By adopting [75]’s model of delegated monitoring (a term originally introduced by [76]), we argue that banks may act as quasi-governance agents, leveraging their informational advantage and contractual mechanisms to monitor and influence new ventures’ behavior and strategic trajectory. Financial discipline rooted in credit lines, auditing requirements, and loan covenants can enhance a firm’s operational efficiency, accountability, reputation, and trustworthiness [77]). It also improves strategic decision-making by providing a clear, data-driven, and scenario-planning understanding of resource constraints and opportunities, enabling new entrepreneurs to mitigate risks and navigate complex environments with resilience [78]).

In addition, emerging trends in sustainable finance (e.g., [19,20]) have positioned banks—through the loans they provide to new ventures—as valuable contributors in shaping their sustainable strategic trajectory. They do so by introducing the achievement of environmental and social objectives into their lending practices. These incentives also encourage the financial discipline of start-ups. Green finance via modern mechanisms, such as green credit lines, ESG-linked loans, and sustainability-linked funding, offer favorable terms to ventures that aim to successfully address social and environmental concerns [79]. Early-stage firms that take advantage of these mechanisms gain reputational benefits and trustworthiness. From a strategic ecosystem perspective, these instruments align financial success with environmental and social impact under the triple-bottom-line approach [80]. Thus, sustainable finance mechanisms not only enhance the financial performance of new ventures but can also incentivize sustainable behavior [81]).

Financial discipline fuels start-ups growth: [82] analyze capital structure decisions of new entrepreneurial ventures, reporting that start-ups rely heavily on external debt in the form of banking loans and credit lines, associating also the higher levels of external debt with start-ups’ faster revenues’ growth. Meanwhile, [83] found that new ventures obtaining bank loans outperform other firms in terms of growth and survival prospects. These findings suggest that bank loans not only fund daily operations [84] but also enforce a financial structure that leads to sustainable growth. In addition, financial discipline mechanisms embedded within relational banking help explain why ventures with similar strategic endowments may exhibit varying performance outcomes, owing to the nature of their banking relationships.

Angel financing is another segment of entrepreneurial finance. Besides capital infusion for early-stage development, they also offer strategic support: angel investors typically offer mentorship, industry connections, and professional experience [85]. In this line, [86] records that angel investors contribute to start-ups’ better performance and subsequent growth by offering value-added services, including governance support and access to entrepreneurial networks. They also offer enhanced credibility: their decision to invest in a new venture reflects confidence in its business model, leadership aspirations, and organizational routines. In so doing, they send a positive signal to the market that the start-up is trustworthy. Collectively, these mechanisms of embedded expertise and signaling, where angel investors act as advisors and validators of potential, points, in general, toward a positive relationship between angel financing and new ventures’ performance (e.g., [87]).

VC is typically conceived as a tool for early-stage companies to finance their operations that otherwise would not be possible [71]. Beyond providing funding that reduces existential risk, scales start-ups’ operations, and enhances strategic alignment and execution, VC firms typically engage in active oversight through board participation, strategic guidance, and performance monitoring [88]. However, extant research delineates an obscure picture of VC finance relative to firm performance, making it hard to draw reproducible conclusions [89]. While, for instance, [90] found no statistically significant relationship between VC and new ventures’ survival, empirical evidence reported in [87] suggests that start-ups conjoining angel and VC are more likely to attract larger funds and stand better chances of success. To further expand on this line, this is due to three main reasons: the rigorous venture capitalists’ pre-investment screening process, their active post-investment monitoring, and the value-adding services they provide throughout new ventures’ development [91,92].

Organizational factors/board demography: Nowadays, there is a renewed academic interest in the various notions and mechanisms of corporate governance and how these can affect organizations (e.g., [93,94]). Past work associating board composition with performance often examined the educational diversity of board members, as well as their age, gender, and ethnicity (e.g., [95,96]). In general, while diversity might result in specific organizational inefficiencies (e.g., [97,98]), there is a broad consensus among scholars that board heterogeneity leads to higher decision quality due to the interaction of multiple perspectives, experiences, and behaviors [99], which, in turn, substantiate in high performance [100]. In entrepreneurial start-ups, where board members are normally identical to the members of the founding team, the locus of interest is shifted toward assessing the prevalence of founding team’s demographics in ventures’ success. In this regard, [101] assess the impact of key decision-makers homogeneity on start-ups’ performance, concluding that an undiversified team composition, in terms of gender and age, is less likely to produce substantial financial benefits for the organization. Adding to this, [102] posit that new ventures with more diversified boards experience increased performance levels, whereas positive effects are lessened, assuming more homogenous board configurations. According to [103], start-ups’ board size is negatively associated with performance variations: in larger boards, decisions are better scrutinized and are consequently less exposed to bias. As of gender diversity, the evidence is mixed: a greater female board representation was found non-significant for new ventures’ performance in a bulk of cases, but significant in others [104]. Finally, board stability allows members to develop working relationships and understand each member’s perspective, which can enhance organizational efficiencies [105].

Research model: Considering that we draw upon an ecosystem perspective and our purpose is to offer a holistic, multidisciplinary view of the impact of a wide range of determinants on start-ups’ performance, we chose to test a model-driven research design over traditional hypotheses’ testing (Figure 1). This strategy allows for synthesizing diverse theoretical domains over a concept of interest [106]. According to [107], in resource-constrained environments, model-driven research designs enable the assessment of several relationships operating simultaneously, allowing us to prioritize determinants based on their relative impact.

3. Materials and Methods

3.1. Sample

We tested our research model on a sample of entrepreneurial start-ups founded in Greece. A study of new ventures in the local context seems appropriate: as stated, successful start-ups contribute to building a viable business ecosystem [108,109], especially in post-crisis environments [110]. They also create and maintain a functional market economy as a means of stimulating competition and job creation [4,5,111]. Since a core purpose underlying the design of our study was to ensure that its results could be generalized (like many other countries, Greece has wide regional and sectoral disparities in its start-up initiatives and ventures’ performance; see, for instance, [54]), our sampling strategy included entrepreneurial new ventures from different industrial settings.

We identify an entrepreneurial start-up as a venture typically in the early stages of its formation. Following the established literature in the field (e.g., [112]), we define these firms as companies, no older than six years, that offer innovative outputs (products and services) and pursue scalable business opportunities in environments of high uncertainty and turbulence. Moreover, while it is now widely acknowledged that entrepreneurial activity can arise across different organizational arrangements; including corporate spin-offs, family firms, and social enterprises, our study follows [113] and focuses only on independent start-ups as the organizational form that contextualizes our research.

Selecting an appropriate sample that could serve as the foundation of our empirical investigation was a challenge, since in the local economy, there is no official, all-encompassing list of early-stage ventures [114]. To address this issue, we drew upon multiple sources: the incubation and acceleration center of Athens University of Economics and Business (ACEin), the records of the Greek General Secretariat of Research and Technology (GSRT), and the ‘Elevate Greece’ database, the official resource for in-depth information about the Greek startup ecosystem. In our sampling strategy, we only included firms that were able to commercialize their output/offering, thus possessing a recordable knowledge about the relationships of a wide array of various internal factors and external influences with performance. In total, we have compiled a sampling frame that included 616 new entrepreneurial ventures. In making our sample compilation, we obtained the contact information of founders/CEOs through the directories of these associations as well as through our academic institution professional network and an extensive search on the internet (e.g., the LinkedIn^® social network).

3.2. Data Collection

Entrepreneurship research emphasizes the importance of identifying key respondents for firm-level data collection. In this study, start-up founders served as our informants for evaluating early-stage ventures’ organizational and strategic issues. The reliability and validity of start-up founders’ perceptions have been repeatedly stressed in the literature, where the views of the respondent entrepreneur typically reflect those of the firm [115,116]. Consequently, in the absence of hard data, subjective assessments provided by founders were held as reliable sources of information.

Following a request to participate in the survey, we sent our questionnaire to all 616 firms. Within one month of the first mailing, we received completed responses from 28 ventures. We followed the initial mailings with second, third, and fourth rounds of reminders, along with questionnaires to non-respondents. The second round of mailings yielded 44, the third 37, while the fourth round yielded 11 responses. Accordingly, we had a total of 120 completed questionnaires. Some of these questionnaires had missing information on key variables. We removed these from the final analysis, resulting in a final sample of 108 completed questionnaires. This represents a response rate of 17.5 per cent, which is considered acceptable in similar survey-based studies, and taking also into consideration that our respondents were top executives [117]. In the entrepreneurial field, in particular, [118] recorded that a third of studies have response rates under 25%, suffering significantly from lower response rates than other areas of management [119].

We formally tested for response bias following the procedure suggested by [120]. This test includes comparing responses received in the early and late rounds. The t-tests revealed no significant difference between early and late respondents. Similarly, we found no significant differences between responding and non-responding firms with respect to corporate characteristics such as size, sector, and years of operation. Among our 108 responding start-ups, the vast majority (70.3%) are located in the capital (Athens), while the remaining 29.7% were founded in the periphery of the country. Our sample covered six industry sectors: technology and applications (62.9% of sample), healthcare (4.6%), financial services (1.8%), tourism (10.1%), agri-food (14.9%), and light manufacturing (5.7%). Average annual corporate sales ranged from EUR 34.789 to EUR 875.000 (mean 167.015; std. dev. = 141.751), and the average number of corporate employees ranged from 2 to 25 (mean 8.27; std. dev. = 4.71).

3.3. Survey Instrument

Our survey instrument had subjective measures for organizational demographics—the factors accounting for start-ups’ success in their respective marketspace and some industry/macro controls (market growth rate of the industry, rate of technology obsoletion, quality of the local entrepreneurial system, and aggregate R&D spending)—and objective measures for firm performance and other firm-level control variables (size, location, and sector of activity). The developed questionnaire used attitudinal scaled questions and covered various characteristics that were deemed important from the literature in relation to our research objectives (e.g., background details of the start-up, details about the founders’ background, employment history). To avoid response patterns, some questions were reverse-coded. To build trust with our respondents, establish consistency, and encourage participation, our questionnaire was accompanied by a covering letter that aimed to assure participants of full anonymity and confidentiality of individual survey responses.

We administered the survey instrument via the Google forms survey tool in both Greek and English. Web surveys have become increasingly popular in academic research, as manifested in the growing numbers of web survey methodologies [121]. Overall, internet-based surveys minimize time lags during distribution rounds and usually provide more responses compared to mail surveys [122]. The design of our survey instrument was in complete accordance with the ethical guidelines and research policy of our academic institution.

3.4. Variables

Dependent variable: Organizational performance has been [123] and continues to be [124] one of the most commonly studied output variables in entrepreneurial research. Accordingly, the performance of new ventures serves as our dependent variable. No commonly accepted set of performance criteria or methods by which new ventures should be evaluated exist [125]. Frequently used performance metrics include sales indicators, employment and asset growth, and profitability indices [29]. Each variable has its own strengths and weaknesses [10]. Here, we used a market-based performance metric, the volume of start-up net sales (in euros), as our performance measure. The specific variable was selected, since it has been suggested that revenues derived from sales are often indicative of technical quality, market acceptance, and overall new venture success [126]. Furthermore, sales are highly correlated with firms’ ability to leverage efficiency advantages [123]. In parallel, widely employed financial performance indicators (such as return on assets, equity, and/or investments) seem inappropriate here because, in the early stages of a venture’s development, these firms typically earn little (if any) profits, invest heavily, and thus burn capital [127].

Explanatory variables: Human capital, strategic constructs, partnership-based networks, funding choices, and organizational (board profile/demography) factors are the core explanatory variables in our models. Drawing mainly from [46,128], we have developed our construct for human capital assets with six items, aiming at capturing the work experience, knowledge, and skills of the ventures’ employees and founders. Strategic factors are mainly rooted in [129]’s work on strategic entrepreneurship and [50]’s work on the importance of resource orchestration in start-ups. These are captured with nine items, trying to frame strategic renewal, sustained regeneration, domain redefinition, business modeling and the importance of firm resources and capabilities on performance. They include start-ups’ business model architecture, resource decisions, dynamic capabilities, and elements of strategy decision-making. Networking reflects the expectations, attitudes, and tendencies of start-ups to create and maintain network dynamics. An extended network represents start-ups’ capabilities in terms of resource acquisition. Partnership-based networks included the liaisons of start-ups with research centers, academia, incumbents/accelerators, and industry at large (four items) [48,130,131]. The finance of the venture included both internal (bootstrapping) and external (venture and seed capital, business angels, and bank loan) sources (six items). All our multi-item scales, together with the source of items, are operationalized and reported in

Table 1. Results of confirmatory factor analysis.

Multi-Item Constructs and Items	Standardized Factor Loadings	Cronbach a	Composite Reliabilty-CR	Average Variance Extracted-AVE
Human capital factors (measured on discrete, seven-points Likert scales: scale value 7 = very important; scale value 1 = not at all important)
Sources: [46,116,128]
Please evaluate the importance of the following statements for the performance of the venture:
Previous, other-industry, working experience, knowledge and skills of the founding team	0.8112	0.814	0.752	0.544
Previous, same-industry, working experience, knowledge and skills of the founding team	0.6874
Entrepreneurial experience, such as past start-up experience or prior business ownership, of the founding team a	0.4874
Previous, same-industry working experience, knowledge and skills of employees	0.6641
Previous, other-industry working experience, knowledge and skills of employees a	0.4965
Highly educated and specialized founding team	0.5215
Strategic factors (measured on discrete, seven-points Likert scales: scale value 7 = very important; scale value 1 = not at all important)
Sources: [49,50,52,132]
Please evaluate the importance of the following statements for the performance of the venture:
A coherent pre-launch strategic planning analysis to identify a market arena that others have not recognized or actively sought to exploit a,b	0.4702	0.795	0.741	0.529
Development of an innovative business model	0.7791
Configuration of the value proposition with close alignment to customer needs and behaviors	0.7085
Focus on the provision of differentiated products/services in the market	0.7305
Innovative marketing positioning strategies a,b	0.4812
Advanced technological capabilities	0.688
The deviation from industry incumbents by fundamentally altering the way we compete a	0.4516
The deviation from industry incumbents in the creation of new products and services b	0.6885
Resource orchestration decisions for value creation	0.5088
Networking (partnership-based linkages) factors (measured on discrete, seven-points Likert scales: scale value 7 = very important; scale value 1 = not at all important)
Sources: [48,130,131]
Please evaluate the importance of the following statements for the performance of the venture:
Networking with research centers	0.7114	0.673	0.632	0.423
Networking with academia	0.6482
Networking with business incubators/accelerators	0.5812
Networking with other firms in the industry a	0.4788
Finance factors (measured on discrete, seven-points Likert scales: scale value 7 = very important; scale value 1 = not at all important)
Sources: [20,68,71,86]
Please evaluate the importance of the following statements for the performance of the venture:
Bootstrapping	0.8226	0.781	0.732	0.516
Domestic venture capital	0.7584
International venture capital	0.6921
Seed capital a	0.4596
Business angels a	0.4178
Commercial banks	0.5513

Notes: n = 108; CFI: 0.947; NFI: 0.902; RMSEA: 0.049. ^a item dropped in the following empirical analysis due to low factor loading. ^b reverse-coded item in the initial version of the questionnaire.

. Our constructs were captured with seven-point Likert-type questions, where the scale item (7) represented a strong prevalence of the statement, whereas the scale value (1) represented the opposite case. Finally, providing the importance of founding demographics upon the performance of start-ups [95], organizational demography measured the number of founding team members, their educational specialization (a binary variable; where 1 = a degree in entrepreneurial/managerial studies; 0 = a technical/engineering degree), board stability (in months), the number of board meetings per year, and female representation in the start-up board (percentage).

Control variables: To avoid extraneous variation, we applied a number of industry/macro and firm-level controls that could also influence the performance of new ventures. More specifically, we adopted [133], and controlled for (i) start-up characteristics (firm size and location) and (ii) industry and macro-environmental effects. We measured firm size in absolute terms as the total number of the venture’s employees (including both full-time and part-timers). Location was captured with a dummy variable (1 = the start-up is founded at the capital; 0 = the start-up is founded in the periphery of the country). We also controlled for industry effects by the use of a dummy for sector of activity (1 = services, 0 = production/manufacturing). Other market controls were also assessed with seven-point Likert-type scales and included evaluations of the market growth rate of the industry and the rate of technology obsoletion. Finally, the quality of the local entrepreneurial system and gross domestic spending on R&D, macro-environmental factors that may impact on start-ups’ performance [31], were also included in our analysis.

3.5. Assessing Validity and Reliability

To assess the content and face validity of our scale items, we adopted the procedure described in [134]. Specifically, we first consulted two experts in the entrepreneurship field: based on their recommendations, we revised our scale items by incorporating their suggestions. Then, we pilot-tested our scales in two phases: in phase 1, ten (10) postgraduate students expected to hold, in the very-near future, a degree in entrepreneurship evaluated the items for clarity and relevance, leading to further refinements. In phase 2, five (5) start-ups’ employees assessed the revised scales. While most coefficient alphas (75%) for the proposed scales in both pilot tests were above the cut-off point of 0.70, as a final refinement, we were offered additional feedback from two scholars in the entrepreneurship area, which led to further improvements.

To assess the reliability and validity of our measures, we followed the two-step approach described in [135]. First, as an initial diagnostic check for reliability, we calculated the coefficient alphas and the average intra- and inter-construct correlations. The alpha values for human capital (0.81), strategic (0.79), networking (0.67), and financial (0.78) factors were all greater than, or very close to, the recommended 0.70 threshold [136]. The average intra-construct correlations were significantly higher than the inter-construct correlations. More importantly, all intra-construct correlations were statistically significant (p < 0.01), while many inter-construct correlations were not, providing preliminary support for both the internal consistency and discriminant validity of our constructs [134].

Next, we conducted a confirmatory factor analysis (CFA) to check for structural validity [137], using a factor loading > 0.5 to remove the constructs’ weak indicators, and to (i) test whether the data supported the four-factor proposed model and (ii) assess whether the proposed factor structure was different from alternate solutions (see Table 1). Most of the standardized factor loadings were greater than the recommended cut-off point of 0.5 [137], and the percentage variance explained by these factors were 21.4, 14.8, 9.6, and 12.9, respectively. Moreover, the distribution of standardized residuals was symmetrical around zero and contained no large residuals. The composite reliability (CR) indices ranged between 0.63 and 0.75. Although the internal consistency is usually measured with Cronbach’s alpha, this criterion is sensitive to the number of items on the scale, so the CR became an alternate with greater adequacy [138]. Finally, the variance extracted by each factor (AVE) ranged between 0.42 and 0.54. It should be noted that convergent validity remains acceptable when CR exceeds 0.6, even in cases where AVE falls below the 0.5 threshold (Fornell and Larcker, 1981) [139]. However, we also conducted PLS analysis, calculating square roots of AVE for each construct and construct correlations. Constructs demonstrate adequate discriminant validity when AVE square roots exceed inter-construct correlations [139]. Analysis confirms this condition. We also assessed discriminant validity via the heterotrait–monotrait (HTMT) ratio [140] and the more recent CI_CFA method [141]. All HTMT values were below the suggested cut-off points (0.90 or 1.0) [142] (p. 70), and each pair of latent correlations was below 1.0, with significant χ² statistic (p < 0.05) [141], indicating adequate support for discriminant validity.

Various model fit indexes derived from the analysis are shown in the lower portion of Table 1. While the χ² to degrees-of-freedom ratio was below the suggested cut-off point of 3.0 [143], providing that χ² statistic is highly sensitive to sample size [144], other, more powerful, fit indexes—such as the comparative fit index (CFI), normed fit index (NFI), and root mean square error of approximation (RMSEA)—were computed. Their values met the threshold requirements (CFI and NFI > 0.90; RMSEA < 0.1) suggested by psychometric researchers. Taken together, these results further demonstrate acceptable discriminant and convergent validity of our constructs.

We performed a series of statistical tests to alleviate common-method concerns. We conducted multiple confirmatory factor analyses (CFAs) to assess the validity of our proposed measurement model against alternative plausible measurement structures using various fit statistics (chi-square; RMSEA, CFI, TLI). The chi-square values and fit indices demonstrated that our hypothesized model provided significantly superior fit to the data compared to a single-factor model. These findings offer robust evidence for the distinctness of our key constructs examined in this research and indicate that common method bias does not compromise the proposed relationships among constructs [145]. Additionally, when we tested a CFA model (measurement model) that connected each indicator to one unified construct (representing potential common method variance) instead of distinct factors, the model fit deteriorated substantially. Consequently, we determined that common method variance was not a significant concern in this study.

4. Findings and Discussion

Table 2. Correlations, means, and standard deviations.

	Variables	Mean	Std. Dev.	min	max	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17
(1)	Performance	167,015	141,751	34,789	875,000	1.0000
(2)	Members of the founding team	3.220	1.400	1	6	0.2600	1.0000
(3)	Educational specialization	0.385	0.179	0	1	−0.4782	0.2778	1.0000
(4)	Board stability	23.263	10.962	2	48	0.3728	−0.3720	−0.0707	1.0000
(5)	Meetings	7.342	6.743	0	12	−0.1153	0.1950	−0.1904	−0.0110	1.0000
(6)	Female representation	0.331	0.384	0	1.00	0.5069	0.0441	0.0498	−0.2598	0.0462	1.0000
(7)	Human	4.026	1.230	3	7	0.2321	0.3428	0.1284	0.1751	−0.0747	−0.2176	1.0000
(8)	Strategic	5.340	0.875	2	7	0.1953	−0.0907	0.1051	0.0431	0.0847	0.1146	0.3292	1.0000
(9)	Networking (Partnership-based linkages)	4.842	1.105	1	7	0.1218	0.0149	0.0959	0.0802	0.0921	−0.2287	0.2801	0.2978	1.0000
(10)	Financial	4.738	1.329	1	7	−0.1156	0.0823	0.0135	0.0482	−0.0691	−0.3454	0.2485	0.2106	0.2945	1.0000
(11)	Market growth rate of the industry	3.254	1.247	2	6	−0.2588	−0.0657	−0.1199	−0.1255	−0.2654	−0.1453	−0.1203	−0.0987	−0.1120	−0.1725	1.0000
(12)	Rate of technology obsoletion	2.661	1.025	2	6	−0.2544	−0.2556	−0.1255	−0.2955	−0.1286	−0.2415	0.1723	−0.1289	−0.3515	−0.2846	0.3768	1.0000
(13)	Quality of the local entrepreneurial system	5.846	0.818	3	7	0.1552	−0.1537	−0.1407	0.0391	0.0933	−0.0206	0.0234	0.2936	0.3210	0.0303	0.4522	0.5221	1.0000
(14)	Gross domestic spending in R&D	3.655	1.254	1	6	0.1818	0.1540	0.2058	0.2811	0.0921	0.2273	−0.1954	0.2547	0.3224	−0.1548	−0.2598	−0.3397	0.2569	1.0000
(15)	Size	8.276	4.717	2	25	0.1525	0.0774	−0.0197	0.0557	0.0151	0.0133	0.4903	0.1784	0.2907	−0.1474	0.1213	−0.0254	0.2267	0.3540	1.0000
(16)	Location	0.703	0.400	0	1	0.0239	0.0981	0.0209	0.0988	0.0118	−0.0238	0.0632	0.2547	0.4248	−0.0602	0.4638	−0.1466	0.1579	0.6147	0.2034	1.0000
(17)	Sector	0.794	0.379	0	1	0.1588	0.5231	0.2526	−0.0783	−0.0227	−0.0504	0.1235	−0.1835	0.0358	0.0523	−0.1213	−0.2549	−0.3664	0.5543	−0.0558	−0.1286	1.0000

Note: Correlations above 0.105 are significant at the 0.05 level.

reports descriptive statistics and correlations. We can see that the means and standard deviations were reasonable. Such normal dispersal provides an initial legitimization to continue analyzing the data. Correlation coefficients are within the conventional limits. In particular, no correlations are above the 0.65 threshold, suggesting that estimations are not being rendered less precise by multicollinearity [146]. Furthermore, the values of all variance inflation factors (VIFs) are well below the recommended level of 10 (mean VIF = 1.89), which further suggests that the likelihood of multicollinearity is minimal [147]. Our performance indicator was significantly related to all our explanatory constructs, showing the strongest correlation with board demographics, as well as, in particular, the educational specialization of the founders (r = −0.478, p < 0.01) and the extent of female representation in the venture’s decision-making (r = 0.506, p < 0.01).

Our research focus was to assess a range of firm-internal factors accounting for the success of entrepreneurial start-ups, controlling for external influences. To test our hypothesized effects, ordinary least squares (OLS) modeling techniques were used throughout. In the entrepreneurship literature, OLS regressions are a widely employed empirical strategy to assess the relationships between firm-level contingencies and output indicators, including start-ups’ performance (e.g., [28,48,148]).

Table 3. OLS regression estimations.

Regression with Performance Outcome (Volume of Sales a) as the Dependent Variable
	Model 1	Model 2
Constant	4.127 *** (0.285) b	1.635 *** (0.258)
Members of founding team		0.315 ** (0.104)
Educational specialization c		0.279 (0.182)
Board stability d		0.088 (0.125)
Meetings e		−0.092 (0.074)
Female representation f		0.236 ** (0.081)
Human		1.127 *** (0.354)
Strategic		0.896 ** (0.427)
Networking (partnership-based linkages)		1.258 (0.907)
Financial		−1.152 (0.824)
Market growth rate of the industry	−1.893 (0.955)	−1.756 (1.005)
Rate of technology obsoletion	−1.024 (0.734)	−1.137 (0.829)
Quality of the local entrepreneurial system	0.647 (0.502)	0.512 (0.480)
Gross domestic spending in R&D	0.554 (0.839)	0.627 (0.788)
Size	0.495 (0.109)	0.352 (0.209)
Location g	0.774 * (386)	0.652 ** (249)
Sector h	0.641 ** (0.239)	0.727 ** (0.306)
Regression statistics
n	108	108
F-stat	3.17 *	8.79 ***
R square	0.119	0.551
Adjusted R square	0.086	0.449

Notes: *** significant at the 0.01 level, ** significant at the 0.05 level, * significant at the 0.1 level. ^a in ln form; ^b figure in () is the std. error; ^c 1 = entrepreneurial/managerial, 0 = technical/engineering degree; ^d in months; ^e per year; ^f percentage; ^g 1 = Athens, 0 = otherwise; ^h 1 = services, 0 = manufacturing.

presents the results of our empirical specifications.

We first estimated a baseline model (model 1) that reported only the results of our controls on start-ups’ sale volumes. The direct influences of our core explanatory variables are introduced in model 2. We have checked criterion validity by examining R coefficients. In these regressions, R², the coefficient of determination, represents the proportion of variance in the dependent variable accounted for by the independent variables [149]. Here, a considerable increase has been reported in our models’ R² values; from 0.119 to 0.551 (similarly, adjusted R² values—which report the percentage of variation explained by only the independent variables that actually affect the dependent variable—shifted from 0.086 to 0.449), suggesting that our understanding of new entrepreneurial ventures’ performance increases considerably when our core independent constructs are included in the analysis.

Among our controls, only location and start-ups’ sector of activity were statistically significant (β = 0.774 and β = 0.641; at the 0.10 and 0.05 level of significance, respectively). We provide some explanations for these findings, which seem reasonable. By extending the economic geography literature [150,151], we suggest that entrepreneurial new ventures are more productive, innovative, and, thus, more profitable when clustered within a location that captures the benefits of agglomeration externalities associated with access to a diversified and specialized talent pool and capital sources, close to distinctive market segments, qualitative institutional infrastructure, and a matured localized competition system. To put it differently, the concentration of economic activity in specific spaces fulfilling the above characteristics generates positive spillovers for entrepreneurial activity [152,153]. Greece is a country with large and persistent spatial inter-, and intra-regional inequalities [154]. While an overwhelming portion of economic activity—including employment growth and knowledge creation—is concentrated at the capital, the regional economic development of the country is far less developed. Therefore, in Athens, agglomeration effects and knowledge spillovers are evident, arising within clusters related by technology, skills, shared infrastructure, demand, and other linkages. Agglomeration economies lower the cost of starting a business, enhance opportunities for innovations, and enable better access to a more diverse range of inputs and complementary assets [155,156,157]. The co-location of companies, customers, suppliers, and other institutions also increases the perception of innovation opportunities while amplifying the pressure to innovate [158]. Since entrepreneurs are essential agents of innovation, a strong cluster environment fosters entrepreneurial activity [153]. Thus, locating entrepreneurial activity in the capital of the country seems a central element in the puzzle of maximizing start-up sales and performance.

As far as the sector of activity is concerned, evidence reported in Table 3 suggests that start-ups’ manufacturing/production has little demand in the domestic market compared to the offerings of service industries. According to scholars (e.g., [159,160]), an increase in manufacturing firms’ sale volumes mainly depends upon factors such as a large amount of capital investment, sophisticated production systems, advanced marketing capabilities and efforts, heavy advertising expenditures, and considerable R&D investments. Typically conceived as possessing limited resources, manufacturing start-ups seem to be disadvantageously positioned on these elements compared with incumbents in the industry, which, furthermore, are able to capture scale economies driving down production costs and exploit consumers’ brand loyalty, which facilitates them not only to expand but also retain their customer base. Therefore, manufacturing start-ups face pressures in increasing their sales margins. In the opposite direction, service start-ups have fewer barriers-to-entry to overcome and little-to-no startup overhead costs, and it is easier for them to globalize and offer more diversified revenue models, rendering the expansion of their sales’ volume as a more anticipated scenario. Finally, the relationship between services’ growth and overall economic growth has become stronger in the past two decades, as services’ average contribution to GDP and value added of national developed economies has increased. The economy of Greece has improved its performance; it is plausible to assume that service start-ups sell more compared to their manufacturing counterparts.

Turning our attention to our individual research assumptions, the results seem to support the human capital [12,161] and the strategic [129] perspectives of entrepreneurship. The positive, statistically significant relationship between human assets and performance (β = 1.127; significant at the 0.01 level) seems to validate these perspectives recorded in the entrepreneurship literature, positing that the knowledge, skills, on-the-job training, and experience of enterprising individuals and founding teams provide the foundation for ventures’ growth and survival [46], since they allow for the discovery and exploitation of entrepreneurial opportunities [41,42]. Within the same lines, imprinting theory [55] suggests that founders’ and employees’ assets (such as their experience and knowledge) are among the most important factors that affect start-ups’ performance and future evolution [162,163]. The importance of the founding team’s human capital on the performance of their venture offers empirical data to support an argument presented by many entrepreneurship scholars, positing that when lacking significant experiential knowledge, start-ups typically build on their founders’ knowledge, experience, and skills, which become the main sources for their knowledge base and performance trajectories [164,165]. Interestingly, despite that human and financial capital are both considered core determinants of start-ups’ success (e.g., [166,167]), the regression coefficient for finances was insignificant (β = −1.152, p > 0.1). Thus, on the basis of our sample, the finance decisions do not have a direct effect on start-ups’ performance.

The positive, statistically significant relationship between our strategic construct and start-ups’ performance (β = 0.896; significant at the 0.05 level) highlights the importance for these ventures to effectually conceptualize their business environment, provide a compelling value proposition, and make effective resource allocations. During the early years of their creation, start-ups are typically confronted with uncertainty, a surge for novelty, low market acceptance, and high costs [109]. In this scenario, these ventures typically promote the search for competitive advantages by being involved simultaneously in opportunity-seeking and advantage-seeking behaviors [168] to pursue the marketplace’s promise for innovations [169]. Throughout this process, it seems that business models and resource combinations may spur the differentiation aspects of new entrepreneurial ventures from industry competitors in ways valued by the market. Our strategy items suggest that the development of an innovative business model that configures the value proposition of start-ups with close alignment to customer needs and behaviors and focus on the provision of differentiated products/services in the market seem to be a determining strategic prerequisite for high-performing entrepreneurial new ventures. This finding accords with [30,132], positing that novelty-centered designs lead to higher firm performance. In parallel, in line with [170], a fit between a firm’s resource investments and its leveraging strategy seems important for augmenting the value proposition offered to customers. Therefore, it can be concluded that the strategic assets of start-ups have positive performance implications. Despite some authors arguing that the strategic choices of start-ups may be influenced by the partnership-based networks they have established, since networks may impact product/service lines and ways of conducting business [48,171], our regression coefficient for the impact of networking with performance was insignificant (β = 1.258, p > 0.1)

Among our constructs for organizational demography, the number of venture founders and the greater female representation on start-ups’ boards are positively and statistically significantly related with performance (β = 0.315, significant at the 0.05 level; and β = 0.236, significant at the 0.05 level, respectively). The founding team size might have a positive impact on performance because the variety of human capital assets (skills, knowledge, and experience) in the decision-making team increases with it [172], leading to better organizational outcomes [40]. As far as female percentage in the participation of the founding team is concerned, advocates of greater female representation on decision boards (e.g., [173]) posit that if a board comprises heterogeneous decision-makers, diversity leverages performance outcomes and success. Male and female entrepreneurs are significantly different in several cognitive characteristics, personality traits, and social skills. Here, we suggest that their co-existence in the ventures’ board has positive impacts on firm performance. This finding may provide partial empirical support to socialist–feminist theory [174]: females have different attitudes and values and, consequently, adopt a different approach to business compared to their male counterparts. Thus, we may conclude that board diversity might, to some extent, explain the performance gaps in start-ups.

Robustness tests and endogeneity: We performed several supplementary analyses to examine the robustness of the findings. A major concern was the possibility of endogeneity, since recent research posits performance as an important contextual variable that may influence our firm-level constructs (e.g., financial structure and network ability). To alleviate this concern, we introduced a time lag between our independent and dependent variables by asking our respondents to evaluate the funding choices and networking of their ventures at, or very near, the point of founding. By measuring these independent variables in year t-x and sales’ performance in year t (i.e., the independent variables are measured in a time-lagged fashion), we can significantly lessen endogeneity issues stemming from simultaneity effects. In addition, to further mitigate endogeneity issues between our performance metric and the independent regressors employed in our OLS estimates, we adopted the instrumental variable-free approach developed by [175]. We chose this approach because we were unable to find suitable instruments [176] needed for instrumental variable techniques like two-stage least squares (2SLS) given the data available to us. The Park and Gupta technique addresses endogenous variables through joint estimation using Gaussian copulas, and their empirical work demonstrates that this method produces results very similar to 2SLS. We performed our analysis by employing the copulaCorrection function of the REndo package, which implements this method in R. Our main findings remained consistent and robust. Finally, the fact that we included important control variables and industry dummies in our analysis can further ease the concern on endogeneity bias [177].

Additional robustness checks were also conducted to ensure the validity of our findings. The results were validated using the untransformed dependent variable with bootstrap resampling of 1, 10, and 50 K samples. All bootstrap confidence intervals for the statistically significant parameter estimates maintained consistent signs without crossing zero (confidence interval bounds at 2.5–97.5% did not include zero), confirming that the significant parameter estimates from the OLS model retained their statistical significance under nonparametric testing conditions. Furthermore, we assessed Cook’s distance to verify the absence of multivariate outliers. Following [178], observations with Cook’s distance values >1 were considered outliers. Our analysis revealed that all calculated Cook’s distance values remained below 1, indicating no multivariate outliers were present in the dataset. Furthermore, by adopting [179]’s recommendation suggesting that an organization’s performance should be evaluated in accordance with the specific objectives set, we asked new ventures’ founders to report subjectively the degree to which the sales of their ventures had been successfully achieved by making early and end-year evaluations. This created an ordered seven-point scale-dependent variable. An ordered probit regression was run, and its results were consistent with those shown in Table 3, albeit with common method bias. Finally, we performed an additional robustness check by running the same regressions of start-ups’ performance with two sub-samples split based on the mean firm age (ages 1–3 and 4–6). We found similar results across the two samples (albeit inferior in significance).

5. Conclusions and Limitations

There is broad consensus on the significant role that entrepreneurial start-ups play in advancing the static and dynamic efficiency imperatives of a country’s socio-economic context. However, these firms are typically associated with high failure rates, exceeding commonly reported industry figures [180]. New ventures are especially vulnerable in their post-entry period [181]; therefore, understanding their performance precursors provide valuable insights into their support needs [182]. Our study found that variability in their performance was better “accounted for” when firm-internal factors (knowledge-based inputs and strategies, business models, and governing boards) were included in the specifications; external influences seemed secondary in significance. Therefore, it seems that, in the early stages of a venture, liabilities of smallness and newness necessitate clear strategies, organizational diversity, and a strong human foundation. These internal qualities largely determine start-ups’ ability to exploit ownership advantages, seize opportunities, and respond to challenges. External conditions may influence performance; however, they act more as context setters rather than core determinants of start-ups’ sustainability: a new venture grounded in solid internal strengths and agility is better equipped to navigate even adverse external settings.

Our findings suggest that start-ups’ market success can be largely attributed to the magnitude of their human assets and strategic capital. Thus, we extend human capital perspectives on new entrepreneurial ventures, arguing that individuals with higher levels of human capital operate more successful start-ups. Specifically, founders’ and employees’ experience, knowledge, and skills are fundamental elements of firm growth, since these assets have a direct positive effect on its market success. The argument that firms run by individuals with greater human capital outperform others is hardly new in the literature. However, our research highlights the synergistic effects of a combination of generic (knowledge and skills) and specific (experience) human capital elements of both founders and employees.

Similarly, strategic capital elements—such as the importance of a high-adaptation strategy and business models designed to respond to market conditions—are also associated with new ventures’ market growth. These results complement studies at the intersection of strategy and entrepreneurship (e.g., [183,184]), providing insights for theory extensions. More specifically, while our findings are in line with the arguments of compelling strategy-based perspectives—such as the resource-based view (e.g., [15])—prior analyses have paid less attention to the effects of these resources as a key source of start-ups’ potential strategic advantage. Here, we extend these arguments to entrepreneurial new ventures by positing that resource decisions guide start-ups to increase their market performance in dynamic market environments. According to [185] (p. 1842), start-up companies offer a fertile environment where new resource combinations are likely to emerge; therefore, by learning how these companies orchestrate successfully, their resources can enrich the relevance of strategy theories. In parallel, we reinforce the strategy perspective of business models (e.g., [186]). A business model articulates the logic that demonstrates how a business creates and delivers value to customers. Here, we record that innovative, customer-oriented business models enhance new ventures’ organizational outcomes. In so doing, we contribute to an ongoing discussion about the relationship between the evolution of dynamic business model elements with start-ups’ growth (e.g., [52]).

Finally, in the organizational context, heterogeneity of skills among the members of the founding team is considered a success factor for nascent entrepreneurship. Decision boards, holding diverse knowledge inputs, cognitive characteristics, and personality traits, are expected to predict environmental changes and the appropriateness of actions to take more accurately. This finding is in line with a plethora of scholarly investigations (e.g., [187,188]) arguing that the diversity of skills among the members of founding teams has been considered one of the factors shaping new venture success. In our case, heterogeneity is reflected in greater female representation in decisions and an extended number or founding members participating in the decision-making. The authors of [189] confirm that heterogeneous teams, in terms of expertise and experiences, result in higher performance than homogeneous teams.

Empirically, our findings may be of interest to founders of start-ups and new venture practitioners, in the sense that since these ventures are typically resource-constrained, they may prioritize the relative importance of distinct categories of input elements on their performance levels. New entrepreneurs might value insights from a fully specified model of venture performance that facilitates their shifting priorities as they plan for the success of their firm. Also, the strategic impetus of the new entrepreneurial ventures and the effects of positive human assets indicate that there are synergistic relationships between human and strategic capital and performance for start-ups. Thus, we point toward the importance of a combined strategy of attracting valuable human resources for achieving superior performance. We also recommend that new ventures should focus on novelty when they design their business models to reap the benefits of service/product differentiations. Finally, since we found a strong case for gender diversity on boards, female representation should be encouraged and promoted. Overall, installing a heterogenous board of directors with more independent board members accounts for better performance.

Limitations: Several limitations of the study necessitate caution when interpreting its results and might provide directions for future research. First, our sample size and the ventures’ location (an EU peripheral economy with chronic structural economic pathogenies) may hinder the generalizability of our findings. Another important limitation derives from our output variable: start-ups’ performance was captured with a market-based indicator. Although the performance literature suggests that financial ratio indicators are particularly suitable in research on large, well-established firms [190] and may not be considered as particularly appropriate here—since they could exaggerate relations of interest and confound the interpretation of findings [191]—we acknowledge that the relationship between start-ups’ internal conditions and their performance should accommodate time-based information and, in this direction, the use of ratio financial indicators could lead to more powerful models. In a similar vein, longitudinal, process-focused designs would provide complementary information to our study. Next, stemming from our limited sample size, some variables that might be significant predictors of post-entry start-up performance (e.g., entrepreneurial orientation, initiatives, and founders’ personality traits and cognitions) were omitted from the study. Likewise, to keep the survey’s length manageable, some variables related to external influences—such as the influence of the EU’s environmental policies and regulatory requirements, along with the country’s technological trajectory that could provide new ventures with knowledge and access to innovative environmental products and processes—were not included in our analysis, while others were measured with less precision than would be possible with a longer questionnaire. These considerations represent promising avenues for future research within the context of start-up sustainability, particularly in relation to the interplay between internal and external factors on performance. Finally, as our survey secured responses only from new ventures’ founders, the results may suffer from attribution bias and ego-defensive misrepresentations. To limit this risk, adopting [192], we did not ask respondents to rank the factors that had the greatest impact on their start-up’s performance.