Non-linear and interaction effects of causation and effectuation on new venture performance: the role of ecosystem support

Entrepreneurial ventures are by definition emerging young firms driven by innovation and growth (Carland et al., 1984). While achieving innovation and growth is undoubtedly challenging for virtually all companies, it might be particularly challenging for entrepreneurial ventures due to their liability of newness (Singh et al., 1986). However, we do see entrepreneurial ventures succeed in the market and frequently outperform their competitors. This leaves business leaders, governments and performance researchers alike asking: How do they do it? In the present paper, we argue that entrepreneurial decision-making logics and ecosystem support play a crucial role in the success of entrepreneurial ventures.

Research has identified two dominant decision-making logics used by entrepreneurs to maneuver the environments of constant uncertainty they face when starting and growing a venture: causation and effectuation. Causation is a prediction-based logic that involves diligent and long-term planning (Sarasvathy, 2001). Effectuation, in contrast, is an experimental way to achieve the best possible outcomes, leveraging existing resources in flexible ways to find innovative and actionable solutions (Sarasvathy, 2001). Prior research has studied the performance effects of causation and effectuation extensively (Cai et al., 2017a,b; Wiltbank et al., 2006; Sarasvathy, 2001; Smolka et al., 2018; Szambelan et al., 2020) to better understand how entrepreneurs overcome obstacles, exploit opportunities and stay innovative (Hmieleski and Baron, 2008). Causation and effectuation were found to have positive impacts on innovation and firm performance, both separately and in combination with each other (Berends et al., 2014; Zhang et al., 2023; Barwinski et al., 2020). Indeed, following the original conceptualization by Sarasvathy (2001), scholars increasingly investigate the joint effects of the two logics (Yu et al., 2018), suggesting that entrepreneurs often employ a hybrid approach (Braun and Sieger, 2020; Kerr and Coviello, 2020; Perry et al., 2012) that represents a type of organizational ambidexterity (Luo and Rui, 2009). Organizational ambidexterity has also been identified in the new venture context (Rui and Lyytinen, 2019), where it takes the form of a contextual separation of exploration and exploitation (Rippa et al., 2019), for example, in the form of causation and effectuation (Mauer et al., 2024). Entrepreneurs who design for organizational ambidexterity increase the resilience and performance of their ventures across different situations or contexts (Alzamora-Ruiz et al., 2021; Braun and Sieger, 2020; Ortega et al., 2017). Moreover, some studies have started exploring non-linear effects of both approaches, suggesting J-shaped or (inverted) U-shaped effects on performance (e.g. Peng et al., 2020).

While entrepreneurs’ decision-making logics are essential for the new venture performance, they do not “occur in isolation” (Cai et al., 2017a,b, p. 1). The effectiveness of causation and effectuation is also subject to the influence of diverse internal and external factors. Internal factors such as specific tasks, business models, developmental stages, or founding teams (Berends et al., 2014; Ruiz-Jiménez et al., 2021; Yu et al., 2018) and external factors such as environmental uncertainty (Karami and Tang, 2021; Peng et al., 2020), networks, or market conditions (Shirokova et al., 2020) influence the effectiveness of causation and effectuation. Entrepreneurs operate in a set of institutions that predefine norms and beliefs in the business environment (Liedong et al., 2020; Bruton et al., 2010; Scott, 2005). Their ability to innovate and consequently secure their survival is highly connected with the knowledge and support of others (Morris et al., 2024; Marcon and Ribeiro, 2021). Indeed, entrepreneurial ventures “do not exist in a vacuum” (Fischer et al., 2022, p. 1). They receive various forms of support, such as funding, mentoring, or networking opportunities from their broader ecosystems (Kollmann et al., 2023; Stam, 2015), which can impact entrepreneurs' behavior (Filippelli et al., 2025). It is thus important to gain a more nuanced understanding not only of how effectual and causal decision-making affect venture performance but also under which external environmental conditions effectuation, causation, or a combination of both are most effective.

While uncertainty has been examined in several prior studies–often as a moderator–on causation and effectuation (e.g. Bing et al., 2020; Chandler et al., 2011; Peng et al., 2020; Yu et al., 2018), the contingent role of ecosystem support has so far been largely overlooked (Kollmann et al., 2023). This is surprising because support from a venture's external environment significantly influences its performance (De Noni et al., 2018 Sperber and Linder, 2019; Shirokova et al., 2020; Welter and Smallbone, 2008) and was recently investigated in related areas like bricolage (Kollmann et al., 2023), a resourceful entrepreneurial behavior that concerns how entrepreneurs make do with the resources at hand (Baker and Nelson, 2005). By investigating how entrepreneurial ecosystem support affects the effectiveness of causation and effectuation, we aim for a more holistic understanding of the nexus of entrepreneurial decision-making and ecosystem support for theory and practice.

The present study builds on prior work by Peng et al. (2020) and Yu et al. (2018). In more detail, we consider both the individual non-linear effects of causation and effectuation (Peng et al., 2020) as well as their combined interaction effects on entrepreneurial venture performance under different environmental conditions (Yu et al., 2018). In more detail, we analyze a large cross-country sample of 861 European entrepreneurial ventures, using hierarchical regression analysis to identify the curvilinear and interaction effects of causation and effectuation on entrepreneurial venture performance. Using ecosystem support as a moderator, we split our sample into two groups, investigating ventures with low ecosystem support versus high ecosystem support. Our results show that causation has a significantly positive J-shaped performance effect. Moreover, causation and effectuation have a statistically significant positive interaction effect on performance. Both of these effects are amplified for entrepreneurial ventures with low ecosystem support. For effectuation alone, however, we find no statistically significant effect on venture performance.

Our results contribute to entrepreneurship and the broader field of innovation research as well as related practice in multiple ways. First, we add to the current discussion on non-linear (Peng et al., 2020) and interaction effects of causation and effectuation (Yu et al., 2018) and thereby also advance the literature around organizational ambidexterity in entrepreneurial ventures (Ling et al., 2022; Smolka et al., 2018). Second, we introduce a novel perspective by integrating causation and effectuation with entrepreneurial ecosystem research, thus offering a more holistic view of entrepreneurial decision-making in light of ventures’ environments. In so doing, we also advance entrepreneurial ecosystem research with empirical evidence about how entrepreneurs deal with different levels of ecosystem support. Third, our findings allow for extrapolation to innovation processes that are taking place outside the new venture setting, such as in corporates. Building on our contributions, we discuss practical implications for policymakers, entrepreneurs and other ecosystem actors who seek guidance on how support mechanisms affect entrepreneurial decision-making logics to enhance new venture success.

Academic research has seen a constant increase in interest in explaining new venture performance (Donbesuur et al., 2020). In particular, the market success of a venture and its position relative to competitors are key determinants of its competitive advantage (Anwar, 2018). Although all entrepreneurial ventures are–by definition–growth-oriented, some ventures grow faster than others or can even achieve exponential growth, which scholars have labeled as hyperscaling (Coviello et al., 2024). Extant research on causation and effectuation has established that both decision-making logics can have positive effects on venture performance (Zhang et al., 2023), and recent studies further suggest that these effects might be non-linear (Peng et al., 2020).

Causation was found to enhance firm performance for both experienced and novice entrepreneurs (Ruiz-Jiménez et al., 2021). With its structured and planning-oriented focus on clear goals and measurable returns, causation benefits new ventures that seek to increase their legitimacy (Smolka et al., 2018). A causal logic can be particularly helpful in complex high-tech industries and when new ventures try to secure funding, for example, from banks or venture capitalists (Reymen et al., 2016), as careful planning helps the firm to articulate its strengths and weaknesses to avoid certain risks, thus benefitting the venture’s overall performance (Smolka et al., 2018). Thus, causation can reduce entrepreneurs' uncertainties in what to do next and overcome a status quo bias (McMullen and Shepherd, 2006). Furthermore, we suggest that entrepreneurial ventures benefit over-proportionately from an intensive application of causation principles, as opposed to a half-hearted or no application of causation. Hence, we hypothesize a positive and curvilinear relationship between causation and venture performance.

In contrast, effectuation is often found to increase performance by putting the limited resources of entrepreneurial ventures to the best possible use (Ruiz-Jiménez et al., 2021). It is argued that effectuation is a valuable mechanism to cope with uncertainty (Xu et al., 2023) and can positively influence opportunity exploration (Cai et al., 2017a,b; Smolka et al., 2018). Moving beyond a linear line of thought, Peng et al. (2020) hypothesized and found a J-shaped curvilinear relationship between effectuation and new venture performance. They argue that effectuation and its core principles–affordable loss, bird-in-hand, co-creation partnerships and leveraging contingencies (Sarasvathy, 2001)–can improve and even boost performance because its experimental style specifically suits new ventures, which have no historical data to predict the future. Following this logic, we hypothesize a positive and curvilinear relationship between effectuation and venture performance.

Entrepreneurial ventures strive for rapid growth and scalable business models, often through the development of innovative, technology-based products (Kollmann et al., 2016). Unlike established firms, entrepreneurs in new ventures do not have a customer base or the means to outperform their markets through economies of scale. Consequently, they frequently turn to business model innovation as a key competitive advantage (Anwar, 2018). However, innovating is a continuous and resource-consuming process (Buccieri et al., 2020), demanding that entrepreneurs put available resources to the best possible use. This requirement leaves entrepreneurs between wanting to thoroughly plan their next steps, while at the same time making the most of sudden challenges and unexpected opportunities. As suggested by Yu et al. (2018), this dilemma is inherently anchored in the research around organizational ambidexterity and literature on causation and effectuation (Lavie and Rosenkopf, 2006; Tushman and O'Reilly, 1996).

Looking at the organizational ambidexterity literature teaches us about how firms apply exploitation and exploration strategies simultaneously to exploit existing knowledge for existing business and explore new knowledge for new business (Luo and Rui, 2009; Anzenbacher and Wagner, 2020; García-Hurtado et al., 2022; He and Wong, 2004; Hwang et al., 2023; Tian et al., 2021; Rosing and Zacher, 2017). While initial research argued that organizational ambidexterity is detrimental to firm performance because of the battle for resources (March, 1991), more recent publications agree that exploitation and exploration are complementary (Wei et al., 2014). A recent study on grand challenges in the pandemic context further proved the importance of organizational ambidexterity through the simultaneous implementation of exploration and exploitation to achieve innovation and performance in a situation of crisis (Christofi et al., 2024).

In the venture context, the idea of ambidexterity seems equally fitting, as ventures face limited resources and legitimacy, requiring entrepreneurs to fluidly switch between or blend approaches that allow them to develop a venture from states of higher uncertainty (connected with the idea of exploratory innovation) to states of higher predictability (connected to the idea of exploitation). At the same time, ambidexterity faces small and emerging structures, very much unlike respective structures in established firms, which renders ambidexterity in ventures less structural and more cognitive and behavioral, as well as temporally distributed (Rippa et al., 2019; Mauer et al., 2024). In this regard, causation and effectuation represent cognitive styles and related behaviors that map onto the ambidexterity dimensions of exploration and exploitation.

Following the ambidexterity view by Yu et al. (2018), and opposed to the previously often used but oversimplifying separate investigations of causation and effectuation, we argue that the two contrasting decision-making logics of causation and effectuation can complement each other and can therefore coexist and assert a positive effect in the same venture at the same time. Indeed, empirical studies (e.g. Brettel et al., 2012; Luo and Rui, 2009; Reymen et al., 2016; Rui and Lyytinen, 2019; Vanderstraeten et al., 2020) have shown that both logics in combination can increase innovativeness for companies with high-technology products, a key characteristic for many new entrepreneurial ventures. These findings further support the idea that the coexistence of causation and effectuation increases entrepreneurial venture performance. Given these findings, it is crucial to move beyond the myopic either-or approach and investigate the interaction between causation and effectuation. Therefore, we hypothesize that ventures that are able to combine the effectual ability to flexibly adapt (exploration as the natural venture mode) with the benefits of structured causational planning (exploitation as an aspired development state) will likely display higher performance. Put formally.

The external environment plays a crucial role in shaping venture growth and success (Arend et al., 2015; Jones et al., 2018; Silva and Grützmann, 2022). Prior research highlights that contextual factors such as rapid change, environmental uncertainty and resource scarcity significantly affect new ventures' performance (Anwar et al., 2022; Laskovaia et al., 2019; Osiyevskyy et al., 2023; Subrahmanya, 2017; Tan and Peng, 2003). While entrepreneurship often focuses on how ventures navigate these challenges from within, a complementary source of support can come from getting embedded in a supportive entrepreneurial ecosystem (Kollmann et al., 2023). Several studies have demonstrated that external support mechanisms, such as comprehensive development programs, can enhance entrepreneurial outcomes (Saxena and Siddharth, 2021; Yam et al., 2011), but only a few studies have systematically examined the moderating effect of external support on the effects of decision-making strategies (Sperber and Linder, 2019). Yet, research indicates that the presence or absence of various ecosystem actors such as government agencies, business associations, educational institutions and intermediaries can significantly shape entrepreneurial behavior and, ultimately, affect venture performance (Kollmann et al., 2023; Sperber and Linder, 2019).

Institutional frameworks and industry norms also exert an influence on entrepreneurs by shaping expectations and behavior (Lin and Yeh, 2024). For example, Teng et al. (2020) show that governmental and institutional actors, including universities and business associations, serve as critical sources of support (see also Stam, 2015; Singer et al., 2015). However, the structure and intensity of such support vary across contexts, and scholars have emphasized the ambiguity inherent in entrepreneurial ecosystems (Chaudhary et al., 2023). Generally speaking, institutional theory posits that ventures perform better when they align with prevailing norms and expectations (DiMaggio and Powell, 1983). In particular, perceived institutional and normative pressures within ecosystems can either reinforce existing strategies or lead to strategic change (Filippelli et al., 2025), depending on how entrepreneurs interpret and react to these pressures. In this regard, the entrepreneurial ecosystem acts as a contextual moderator, capable of amplifying or attenuating the effects of entrepreneurial decision-making logics (Lee et al., 2024).

Building on this view, we argue that the effect of decision-making styles and behaviors by entrepreneurs on their ventures' performance will depend on their perceptions of support from ecosystem institutions and intermediaries, such as governmental agencies, incubators, accelerators, business associations and the education system (Kollmann et al., 2023; Singer et al., 2015).

In low-support ecosystems, formal and informal institutions will not be particularly tailored to the realities of entrepreneurial ventures. The business landscape will be less prepared for and tailored to the needs of new and growing companies and will be much more primed by the logic of existing and stable businesses. In this context, the ecosystem will primarily require adherence to established business strategies, such as structured planning, goal orientation and accountability–the hallmarks of causation logic. Causation approaches match the low-support ecosystem expectations for structure and prediction, as they make ventures appear more legitimate to resource providers, partners and customers, thus improving access to capital, talent and markets. Against this background, understanding and patience for the realities of entrepreneurial ventures will be low, reducing the potential for failed approaches with ecosystem stakeholders. Hence, we expect that in low-support contexts, entrepreneurs who focus on a causal logic, engaging in structured and goal-oriented planning, are more likely to align with institutional expectations of the ecosystem (Hubner et al., 2022). This will increase the effect of the decision-making logic on performance.

In line with our argument for hybrid or ambidextrous decision-making, the expectation that ecosystem embeddedness helps entrepreneurs with creating the necessary ecosystem around their ventures does not hold for low-support contexts as much as it may in ecosystems where interaction is predefined through respective structures and processes (high support). In ecosystems with low support, ventures will not necessarily face institutional voids–gaps in capital access, regulatory support, education and professional services, as discussed in institutional theory on emerging markets (Khanna and Palepu, 2010). However, access to relevant resources will still depend much more on the entrepreneurs' individual networking and ecosystem-building activities. Effectuation, especially its mean-based principle in combination with its crazy-quilt principle, enables entrepreneurs to build networks of self-selected stakeholders to fill structural gaps in the ecosystem–for instance, finding mentors, informal investors or other partners who co-develop solutions.

Effectuation is, in essence, an approach that lowers uncertainty by allowing for directly available means-based action and interaction targeted at knitting together the proverbial patchwork quilt of self-selected stakeholder commitments. In that regard, effectuation can be described as a prescription for building your own supportive micro-ecosystem around your venture. We therefore expect effectuation to have a substitutional effect in situations where pre-existing ecosystem support is weak. We therefore hypothesize.

Finally, and in consequence of the reasoning above, we expect high-support ecosystems to be more welcoming and patient with entrepreneurial ventures that have not fully adopted the causation approach. At the same time, in high-support contexts, the need for effectuation will be lower as the institutional set-up will support getting embedded into the ecosystem. In contrast, entrepreneurial ventures in low-support ecosystems will benefit from the combination of causation and effectuation. Causation will equip ventures with sufficient resilience to move through a causation-focused ecosystem, while effectuation will allow the venture to explore the ecosystem and build its own network of self-selected stakeholders to reduce the uncertainty connected to the venture development process.

In sum, we hypothesize that the interaction effect of causation and effectuation on venture performance is contingent upon the level of ecosystem support. In particular, we expect these effects to be more pronounced in environments with low ecosystem support, where entrepreneurs must compensate for structural voids, and less pronounced in highly supportive environments that reduce the need for hybrid or adaptive strategies. Hence, our final hypothesis is.

Following the extant literature in the field of causation and effectuation research (e.g. Bing et al., 2020; Sarasvathy, 2008), we tested our hypotheses using survey data. Our study uses a subsample drawn from the European Startup Monitor (ESM) 2018 dataset (Steigertahl and Mauer, 2018). The ESM is a large-scale survey of the European entrepreneurial ecosystem that is funded and supported by the European Commission's Department for Internal Market, Industry, Entrepreneurship, and Small and Medium-sized Enterprises. Because objective data on entrepreneurial ventures is often unavailable, and many internal variables require considering the founders’ or leaders’ cognition and their awareness and evaluation of business opportunities (Alvarez and Busenitz, 2001; Lyon et al., 2000), the ESM is often used (e.g. Hora et al., 2018; Kraus et al., 2017) to generate unique insights into European entrepreneurial ventures and the entrepreneurial ecosystem. For example, Kollmann et al. (2023) used ESM data to investigate how entrepreneurial bricolage relates to ventures’ innovativeness and internationalization and how governmental entrepreneurship support programs moderate these relationships.

Data collection for the ESM 2018 took place from February to May 2018. The online survey was distributed by a broad partner network of 20 national and international business associations, three major startup-event organizers and 31 other stakeholders, such as entrepreneurship centers, technology media outlets and the SME Envoys Network, which is a group of high-ranking governmental officials in charge of SME policy in Europe that is administered by the European Commission.

In line with Carland et al.'s (1984) definition of entrepreneurial ventures and the ESM selection criteria, the entrepreneurial ventures in the sample were (1) younger than ten years, (2) sought market and/or employee growth and/or (3) had innovative technologies and/or business models. To ensure good comparability of the firms in our sample, we further only considered ventures in the startup and growth stage according to the ESM categories: these ventures had completed a marketable product and first revenues and users (startup stage, 58.1% of our sample) or already strong sales growth and/or user growth (growth stage, 41.9% of our sample) ^[1]. After excluding missing values or invalid cases, our final sample comprised 861 entrepreneurial ventures ^[2]. The final sample consisted of ventures from all across Europe, covering 28 EU member states (Austria, Belgium, Bulgaria, Croatia, Cyprus, the Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, the Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, Ireland) and 7 non-EU states (Iceland, Macedonia, Moldova, Norway, Switzerland, England. and Ukraine). The sample was well-distributed across these countries, with no country exceeding 10% of the firms. The countries with the highest shares were Germany (9.9%), Italy (9.5%), Switzerland (8.1%), the United Kingdom (7.9%), Ireland (7.2%) and Spain (5.5%). The average firm was 3.5 years old (SD = 2.0) and had 2.0 founders (SD = 1.0) and 12.3 employees (SD = 19.1). Most firms in the sample operated in digital or high-tech industries, such as software as a service (20.3%); IT/software development (19.3%); biomedicine, nanomedicine and medical technology (7.9%); industrial technology, production and hardware (6.7%); or finance technology/fintech (5.6%).

To measure causation and effectuation, we used the ten-item scale developed by Alsos et al. (2014), which is well-suited for the context of entrepreneurial ventures (e.g. Alsos et al., 2016). This scale measures five key principles of causation and five key principles of effectuation, thereby capturing the “essence of effectuation and causation” (Alsos et al., 2014, p. 1). Sample items include “We use the long-term goal that we have set as the starting point and strive to acquire the resources that we need in order to achieve this goal” for causation or “We developed the business based on the resources that we had available, without any clear vision of what the business will become in the end” for effectuation. We measured responses on a five-item Likert scale anchored by 1 = completely disagree and 5 = completely agree. The Cronbach's alpha was 0.759 for causation and 0.720 for effectuation. Following prior research, we measured causation and effectuation as the mean of the summed scores (Alsos et al., 2016). We used these scores to build the two squared terms (Peng et al., 2020; Wu et al., 2020) as well as the interaction term of causation and effectuation (Yu et al., 2018).

Because objective performance measures are typically unavailable for unlisted entrepreneurial ventures (Yu et al., 2018), we used the Wang et al. (2012) four-item scale to measure the ventures’ market performance. Items include questions such as “We have entered new markets more quickly than our competitors” or “Our market share has exceeded that of our competitors”. Such subjective assessments are found to be significantly related to objective measures (Dess and Robinson, 1984). The respondents were asked to indicate their ventures’ performance compared to that of their major competitors. We measured responses on a five-item Likert scale anchored by 1 = completely disagree and 5 = completely agree. The Cronbach's alpha for market performance was 0.795.

Four items measured the respondents’ perceptions of their firms’ ecosystem support, that is, the support of the national government, business associations, the educational system and accelerators/incubators. Our measure is similar to the Global Entrepreneurship Monitor’s ecosystem support measure (e.g. Singer et al., 2015). The five-item Likert scale was anchored by 1 = no support at all and 5 = perfect support and included items like “How would you rate the support of the national government” or “How would you rate the support by accelerators/incubators”. The Cronbach's alpha for this scale was 0.785.

We included several control variables as done in prior causation and effectuation research (Yu et al., 2018; Peng et al., 2020). On the firm level, we controlled for firm age (years since a firm’s foundation) and firm size (natural logarithm of the number of employees) (e.g. Galloway et al., 2017; Yu et al., 2018). On the team level, we controlled for the founding teams’ characteristics, including size (number of founders) and the share of female founders (e.g. Boone et al., 2019). Moreover, we included two dummy variables to control for cooperation with other SMEs and with Fortune 500 firms (e.g. Kollmann et al., 2021). Similarly, we used a dummy variable to control for whether a venture had received venture capital (e.g. Vanacker et al., 2013). Finally, we controlled for uncertainty, which can influence a firm's inclination to use effectuation or causation behavior (Yu et al., 2018). We used the Chandler et al. (2011) four-item uncertainty scale, with items such as “When making decisions, it is very difficult to identify and evaluate the different alternatives”, or “The knowledge of how to react to changes in the external environment is hard to come by”, which showed good consistency with an alpha of 0.731.

Following established methodological recommendations, we used both procedural ex ante and statistical ex-post techniques (e.g. Keith, 2014; Podsakoff et al., 2003) to ensure the reliability and validity of our measurements and to control for common method variance. Ex ante, we reduced the potential for issues that could arise from the cross-sectional nature of our data by using different types of scales and spreading items for the study's variables throughout the questionnaire (Chang et al., 2010), thereby separating the dependent and independent variables (Krishnan et al., 2006). We also assured all respondents that their answers would remain anonymous (Podsakoff et al., 2003) and considered answers by both founders and C-level executives to reduce the potential for confirmation bias.

Ex-post, we assessed relevant statistical criteria to confirm our measurements’ reliability and validity. All latent constructs’ Cronbach’s alpha values exceeded the recommended threshold of 0.70, confirming good internal reliability (Cronbach, 1951; Nunnally, 1978). To validate our measurement model and assess construct validity, we followed a two-step procedure, combining exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), as recommended by methodological literature (Widaman and Helm, 2023; Worthington and Whittaker, 2006). Although we employed previously validated measurement scales (e.g. Alsos et al., 2014), they were originally developed in more context-specific settings (e.g. Norwegian ventures) and thus may not exhibit the same psychometric properties in a more diverse, pan-European sample. EFA allowed us to examine the underlying factor structure without imposing theoretical constraints, which is particularly relevant when adapting scales to new empirical contexts. Following this, we applied CFA to confirm the factor structure and ensure theoretical alignment. This sequential validation process enhances both clarity and replicability in empirical entrepreneurship and innovation research.

The EFA yielded loadings that ranged from 0.71 to 0.75 for causation, from 0.69 to 0.74 for effectuation, from 0.67 to 0.75 for ecosystem support, from 0.62 to 0.82 for performance and from 0.69 to 0.80 for uncertainty (KMO = 0.77, p < 0.001), confirming our factor structure. The values for composite reliability (CR) and average variance extracted (AVE) were also satisfactory, with all CR values above the recommended threshold of 0.5 and all AVE values above 0.7 (Fornell and Larcker, 1981; Hair et al., 2019). In addition, we conducted a CFA: The six-factor model demonstrated a good model fit (χ² = 554.48, χ²/df = 2.786, p < 0.001; comparative fit index = 0.919; root mean square error of approximation = 0.046; standardized root mean square = 0.043) (e.g. Hair et al., 2003). Following Podsakoff et al. (2012), we also applied the common latent method factor technique, as used in previous entrepreneurship studies (e.g. Hughes et al., 2014). We conducted a separate CFA that loaded all items onto one single factor and compared it with our six-factor model, finding an inferior model fit (χ² = 2995.08, χ²/df = 14.331, p < 0.001; comparative fit index = 0.363; root mean square error of approximation = 0.125; standardized root mean square = 0.125).

Finally, we conducted tests of the hypothesized non-linear and interaction effects, which are generally less prone to common method variance, as respondents typically do not anticipate such complex relationships and do not adjust their answers accordingly (Aiken et al., 1991; Harrison et al., 1996). These tests confirmed the robustness of our approach, indicating that common method variance is not a serious threat in the present study.

Table 1 shows the descriptive statistics, including the bivariate correlations among the study variables for our full sample of 861 ventures. We found a statistically significant negative correlation between causation and effectuation (r = −0.260, p < 0.001) and that causation was positively correlated with performance (r = 0.274, p < 0.001), as was its squared term (r = 0.274, p < 0.001) as well. Effectuation was negatively correlated with performance (r = −0.155, p < 0.05), as was the squared effectuation term (r = −0.145, p < 0.001). The interaction term of causation and effectuation was positive but not statistically significantly correlated with performance (r = 0.034, p = 0.161). Only effectuation (r = 0.068, p < 0.05) and its squared term (r = 0.063, p < 0.05) had statistically significant and positive correlations with ecosystem support.

To test our hypotheses, we used hierarchical regression and analyzed our sample in three ways. First, we conducted the regression analyses for the full sample of 861 ventures. Then we applied Gargiulo and Benassi's (2000) grouping strategy, which Yu et al. (2018) also used in the context of causation and effectuation. Splitting our sample into two groups based on the mean of ecosystem support resulted in a group of 451 ventures in Group 1 (low ecosystem support, below or equal to the mean) and 410 ventures in Group 2 (high ecosystem support, above the mean).

Results for the full sample are shown in Table 2 in Models 1–4, while results for Group 1 (low ecosystem support) are shown in Table 3 in Models 1a–4a, and results for Group 2 (high ecosystem support are shown in Table 4 in Models 1b–4b. In our hierarchical regressions, we added the controls and predictor variables step by step. The baseline models–1, 1a and 1b–included all control variables. The second step included the linear causation and effectuation terms (Models 2, 2a and 2b), and the third step added the interaction term of causation and effectuation (Models 3, 3a and 3b). Finally, the squared terms for both causation and effectuation were added in Models 4, 4a and 4b. The tables show a significant change in R² (p < 0.05) for all steps, except for the interaction and squared terms under high ecosystem support (Models 3b and 4b).

Hypothesis 1 proposed a curvilinear (J-shaped) effect of causation on venture performance, and Hypothesis 4 proposed that this effect is a) stronger in an environment with low entrepreneurial ecosystem support and b) weaker in an environment with high entrepreneurial ecosystem support. Our results confirm a statistically significant positive effect of the squared causation term for the full sample (Model 4: β = 0.12, p < 0.05) and that this positive effect is stronger in the sample of firms with low ecosystem support (Model 4a: β = 0.20, p < 0.01). However, in the sample of firms with high ecosystem support, the effect is lower but not statistically significant (Model 4b: β = 0.02, p = 0.75). These results confirm H1 and H4.

Hypothesis 2 theorized a curvilinear (J-shaped) relationship between effectuation and venture performance, and Hypothesis 5 proposed that this effect is a) stronger in an environment with low entrepreneurial ecosystem support and b) weaker in an environment with high entrepreneurial ecosystem support. This relationship is positive but not statistically significant in any of our analyses of the full sample (Model 4: β = 0.03, p = 0.39), the sample of firms with low ecosystem support (Model 4a: β = 0.01, p = 0.79), or the sample of firms with high ecosystem support (Model 4b: β = 0.04, p = 0.43), so we cannot confirm H2 or H5.

Finally, Hypothesis 3 stated that causation and effectuation have a positive interaction effect on entrepreneurial ventures' performance, and Hypothesis 6 added that this effect is a) stronger in an environment with low entrepreneurial ecosystem support and b) weaker in an environment with high entrepreneurial ecosystem support. We found a positive and significant effect for the full sample (Model 3: β = 0.09, p < 0.05) and a larger positive and significant effect for the sample of firms with low ecosystem support (Model 3a: β = 0.14, p < 0.05). However, while the effect for firms with high ecosystem support was lower, it was non-significant (Model 3 b: β = 0.05, p = 0.43). Therefore, H3 and H6 can be confirmed.

Our research contributes to the entrepreneurship literature around causation and effectuation, as well as to related themes from innovation literature such as ambidexterity, always in conjunction with our specific focus on ecosystem support. First, our findings of a positive J-shaped effect of causation and a joint interaction effect of causation and effectuation on venture performance advance recent work by Alzamora-Ruiz et al. (2021) and Peng et al. (2020) on the separate and interaction effects of causation and effectuation. In this way, our findings add to the literature stream of ambidexterity in the new venture context (Ling et al., 2022; Smolka et al., 2018) and in an international environment (Evers and Andersson, 2021) by showing that it is particularly the positive interaction effect of combined causal and effectual decision-making in entrepreneurial ventures that drives venture performance. More specifically, our evidence suggests that effectuation by itself does not drive performance, while causation does, but that the combination of both can give new ventures an additional edge over the causation approach alone.

Second, by differentiating the appropriate combination of causation and effectuation based on the ventures' level of ecosystem support, we provide a more nuanced picture and confirm recent work by Galkina et al. (2021), who found that the relationship between causation and effectuation is not free of tensions and dependent on external factors. By empirically investigating external support, a contextual factor of the entrepreneur's decision-making environment, we contribute to the work of Shirokova et al. (2020) and Sperber and Linder (2019) and add to the work of Yu et al. (2018). We show that the level of ecosystem support is important for the effectiveness of entrepreneurs' decision-making logic. While the application of both causation and effectuation was appropriate for firms with low ecosystem support, this interaction effect vanished for firms in environments with high ecosystem support. Therefore, entrepreneurs can take effectuation and its exploratory nature as an appropriate decision-making tool, especially during the ecosystem-building phase, to somewhat balance out the shortcomings of environments with low ecosystem support.

Third, we also see implications for the broader field of innovation management. In that regard, our results further inform the previous findings in the corporate context by Brettel et al. (2012), Berends et al. (2014), Roach et al. (2016), Szambelan and Jiang (2020) and Szambelan et al. (2020), who studied the effects of effectuation logic on innovation in different forms and found relevance of effectuation in different nuances. While our study focuses on new ventures as innovation-driven entities, we can extrapolate our findings to innovation activity in more established organizations. Corporate employees may also face different ecosystem support environments. In contrast to new ventures, however, these may be both internally and externally. Our study directly suggests that external ecosystem support also plays a role with regard to the innovation ecosystem that a firm is embedded in. Furthermore, internal innovation ecosystem setups may differ to the point that corporate employees interested in innovation activity may consider combinations of causation and effectuation if they perceive internal ecosystem support to be rather low.

Methodologically, we contribute to the broader entrepreneurship literature by offering one of the few studies in the new venture context that investigates a cross-national sample of entrepreneurial ventures. In doing so, we can confirm the generalizability of the relationships under study for a broad range of 35 European countries. Moreover, we are among the few studies that applied the harmonized causation and effectuation measurement scale developed by Alsos et al. (2014, 2016) with the goal of enhancing the comparability of findings about causation and effectuation across different studies.

Entrepreneurial ventures face a variety of challenges in dynamic and uncertain environments. Our findings reveal that combining pre-defined, goal-oriented strategies (i.e. causal logic) with adaptive, opportunity-driven behaviors (i.e. effectual logic) can enhance venture performance, particularly in ecosystems with limited institutional support. This indicates that ambidextrous decision-making is a critical capability in low-support environments, whereas in highly supportive ecosystems, a focus on causal logic primarily enhances venture performance. These insights have important implications for different actors within entrepreneurial ecosystems.

For entrepreneurs, the key implication is that decision-making should be context-sensitive. In low-support ecosystems, entrepreneurs are more likely to succeed by combining structured planning with exploratory behavior. Effectuation allows them to address uncertainty, act flexibly and capitalize on unexpected opportunities. At the same time, causal logic helps articulate strategic goals, attract partners and maintain consistency. Ventures in highly developed ecosystems, by contrast, can benefit more from planning-oriented strategies, as these environments offer predictable structures, accessible funding and support infrastructures that reward clarity and precision.

Our findings further offer concrete implications for key entrepreneurial ecosystem actors, such as policymakers and ecosystem designers or venture capital investors, who can use our results to implement adaptive support mechanisms that reflect the varying decision-making needs of entrepreneurs across contexts. Instead of one-size-fits-all programs, flexible and context-sensitive support policies and financial support milestones can help entrepreneurs combine planning (causation) with experimentation (effectuation), depending on the current venture and market conditions. These agile support programs should provide entrepreneurs with the flexibility to pivot and encourage opportunity-driven and strategic logic shifts over time. Accelerators and incubators may apply these insights by developing training and mentoring programs that explicitly cultivate ambidextrous capabilities, for example, by tracking founders’ ability to articulate both structured plans and adaptive responses across program stages. Investors and corporate partners can use ecosystem context as a diagnostic lens when evaluating early-stage ventures, interpreting flexible or non-linear strategies not as risk factors but as signs of cognitive adaptability, especially in areas with low support. Underlining the importance of tailoring support to local needs, especially in rural or peripheral ecosystems, where institutional voids make decision-making ambidexterity a key factor for long-term entrepreneurial success.

Our study has several limitations that provide avenues for further research. First, the countries in our sample are not equally represented and are limited to Europe. Further research could increase the geographical scope of the sample and ensure that each country in the sample is equally represented. Using random sampling (e.g. as suggested by McLeod and Bellhouse, 1983), as we did for one country with a disproportionately high number of firms, could be a way for further research to address this issue. Although we used a large sample, the number of new ventures in each country is unknown, making it impossible to assume representativeness for our results. Finally, our study is cross-sectional, and we did not investigate the ventures over time. Although we addressed this issue with various empirical checks, future research could replicate our study using a longitudinal design to improve the robustness of our results.

The authors gratefully acknowledge the Editor-in-Chief, Professor Vincenzo Corvello, the Associate Editor, Professor Dominik Kanbach, and the anonymous reviewers for their constructive and developmental feedback during the publication process of this manuscript.