Entrepreneurial ecosystems in the food industry: a systematic literature review through the lens of complex adaptive system theory Open Access

The food industry, encompassing the entire value chain from primary production to consumer-facing businesses, is fundamental in the economy and society. This study focuses on entrepreneurship across multiple sectors of the food industry, including agricultural production, processing, distribution and retail of food products and beverages. Each of these sectors has significant implications for human health (Monteiro et al., 2010), environmental sustainability (Del Borghi et al., 2014; Green et al., 2020) and social welfare (Maertens et al., 2012; Sexton, 2000). A dynamic food industry requires entrepreneurs and innovators able to cope with changing consumer preferences (Aschemann-Witzel et al., 2021), environmental challenges (Larkin et al., 2020) and technological opportunities (Trivelli et al., 2019) across the entire food value chain. Therefore, understanding the factors that enable or constrain entrepreneurship in the various sectors of the food industry is crucial for advancing theoretical knowledge and practical insights into the contemporary mechanisms of development for this industry.

Entrepreneurship is a complex phenomenon that relies not only on entrepreneurs' characteristics but also on the other multiple actors and factors existing in the specific operational context (Sendra-Pons et al., 2022). Regarding this, the entrepreneurial ecosystem (EE) concept provides a useful framework to capture the complexity and diversity of entrepreneurship by focusing on the interactions and interdependencies among elements that influence the entrepreneurial process and outcomes (Stam and van de Ven, 2021). The EE studies have emerged as a prominent research stream in recent years, with contributions from various disciplines, such as economics, geography, management and sociology. These studies have explored EEs considering different contexts, systems and levels of analysis to understand the EEs' diversity in terms of structure and dynamics across various types of settings (Theodoraki and Catanzaro, 2022; Wurth et al., 2022). EE research has also followed policymakers' interest in developing policies that support the creation and growth of competitive and sustainable ventures in specific territories or local systems (O'Connor and Audretsch, 2023; Theodoraki et al., 2022).

To better understand EEs and explain their complexity, Roundy et al. (2018) proposed the complex adaptive system (CAS) theory as a useful theoretical tool for analysing EEs. A CAS is a type of system consisting of many different interacting components that can self-organise, adapt and learn from the environment (Holland, 1992). Analysing the EEs through the CAS theory, Daniel et al. (2022) identified four key dimensions, namely place, people, purpose and process, to investigate the complexity and the adaptability of EEs.

While the number of literature review studies on EEs has increased in recent years (Asmit et al., 2024; Bejjani et al., 2023; Chaudhary et al., 2024; Cloitre et al., 2023; Frimanslund et al., 2023; Wurth et al., 2023), there is still a paucity of studies analysing the food industry from the EE perspective. The existing literature on EEs in the food industry is fragmented and diverse, even though this concept would offer a valuable lens to examine the contemporary dynamics and competitiveness of the food systems.

Therefore, this study aims to address this knowledge gap by providing a comprehensive analysis of the state of the art in entrepreneurship and EE research applied to the food industry. It aligns with the approach proposed by Daniel et al. (2022) and employs the conceptual framework of CAS theory to operationally analyse this specific field of research through a systematic literature review (SLR). Hence, the present study aims to offer a critical and theory-based interpretation of current knowledge in sector-specific EEs. The CAS framework allows this review to analyse studies on food industry EEs comparatively and contribute to systematising research findings so far available, suggesting future research directions in the food industry, and identifying transferable lessons that could enrich the broader understanding of EEs beyond this industry.

EEs have gained significant scholarly attention as interconnected systems of actors, institutions and resources that foster entrepreneurship within a defined geographic area (Cohen, 2006). Over time, researchers have expanded the EE concept, exploring its mechanisms, structural components and impact on entrepreneurial outcomes. Wurth et al. (2023) identified causal links between EE dynamics and firm growth, innovation and economic welfare. Other studies have applied institutional, regional and sector-specific lenses to examine EEs. For instance, Cloitre et al. (2023) analysed their evolution through an institutional perspective, while Asmit et al. (2024) investigated EEs in rural contexts, highlighting unique challenges and opportunities. Scholars have also characterised digital EEs (Bejjani et al., 2023) and examined financial flows within EEs (Frimanslund et al., 2023). More recently, Chaudhary et al. (2024) explored the theoretical foundations of EEs, integrating sustainability and digital transformation into the discussion. Despite these advancements, EE research remains largely fragmented across different domains, with limited focus on industry-specific ecosystems such as the food sector.

The food industry presents a unique case for EE analysis due to its inherently complex structure, which involves multiple stakeholders, regulatory frameworks and interdependent value chains. Unlike other industries, food systems require a balance between public interests, such as food security and sustainability, and private sector objectives, such as competitiveness and innovation (Boesen et al., 2017; Jarosz, 2000; Sonnino and Marsden, 2006). Given this complexity, EE research in the food industry has the potential to generate novel insights into fostering entrepreneurship, supporting innovation and informing policy development. By examining how EEs operate within the food sector, scholars can contribute to understanding the factors that enable or constrain the emergence of innovative and sustainable agrifood ventures. Moreover, from a policymaker's perspective, an EE approach can help design interventions that enhance the competitiveness of territorial food systems while addressing broader societal challenges such as healthy diets, environmental resource management and social welfare.

Given the complexity of EEs, scholars have increasingly turned to CAS theory as a conceptual tool to explain their dynamic and evolving nature (Roundy et al., 2018). The complex systems theory was introduced by Weaver (1948) from the natural sciences and technology field. Complex systems were described as “problems which involve dealing simultaneously with a sizable number of factors which are interrelated into an organic whole” (Weaver, 1948, p. 537). Concurrently, Wiener's (1948) work on cybernetics introduced the concept of feedback loops and self-regulation in systems, which are crucial to understanding complex adaptive behaviours, and Von Bertalanffy's (1950) General Systems Theory provided a framework for understanding the principles common to all complex systems, regardless of their specific nature. Simon (1962) introduced the concept of “bounded rationality” and highlighted how individuals and organisations make decisions within constraints.

After, Buckley (1968) developed the complex systems theory as the “Complex Adaptive Systems” CAS theory to study social systems. This author explained the complexity of sociological development and illustrated system-adaptation ideas to create social value and change. This was also influenced by Ashby and Pierce's (1957) Law of Requisite Variety, which stated that for a system to maintain stability, its control mechanism must have at least as much variety (possible states) as the environment it is trying to manage. Holland (1992) further developed the theory, defining CAS as systems with many interacting individuals that adapt or learn as they interact.

Inspired by these developments in CAS theory, Fuller and Moran (2001) applied the concepts of adaptation, evolution, fitness and interdependence to explore business dynamics. Their work demonstrated how these established CAS principles could be used to understand organisational change and development in business contexts. Lissack and Letiche (2002) further applied the CAS approach in the business organisation field, and explained that, in a CAS, the actions happening at one level come from, and simultaneously are affected by, processes and behaviours at various levels, and the outcomes of the entire system. A system's complexity is defined by the presence of many actors, their varying degrees of independence and the many levels of interconnectedness among them (Dentoni et al., 2021). In the business and society context, humans as actors in the system may choose to reorganise themselves individually or collectively (namely, in groups or subsystems like families, friends, businesses or even entire nations) in response to shifting circumstances in the larger system that surrounds and interacts with them (Whiteman et al., 2013). This generates complexity (Lansing, 2003). CAS shows complexity and adaptability, since it can change through experience (Schindehutte and Morris, 2009). The elements keep reacting to each other and the environment, affecting the system and how it responds to disruptions and adjusts to changes (Messier and Puettmann, 2011).

In the case of EEs, the CAS theory was first proposed by Roundy et al. (2018) as a useful theoretical tool for explaining the development of EEs. Roundy et al. (2018) contributed to entrepreneurship studies by refining a definition of EE complexity. They explored the complexity at the level of both the entrepreneur and the enterprise to understand the influences of EE emergence. They identified a set of EE properties (namely, self-organisation, open-but-distinct boundaries, complex component, nonlinear dynamics, adaptability through dynamic interactions and sensitivity to initial conditions) and a set of forces operating in the EE (namely, entrepreneurs' intentionality and adaptive tensions, coherence and injection of resources).

Successively, Daniel et al. (2022) provided an example of how the CAS theory may be used to create an integrative framework for EEs that encompasses the variety of dynamic actors and interdependencies to comprehend EEs in practice. The authors modified some elements typical of CAS and provided a framework for analysing EEs through four elements (the CAS 4P framework): place, people, purpose and process (Daniel et al., 2022). Table 1 explains the CAS 4P framework, and the associated CAS characteristics, proposed by Daniel et al. (2022).

The place dimension captures the system's boundaries and path dependence, which define its scope, identity and historical influences. It focuses on anchoring system components, resources and interactions within specific geographical contexts, including cities, towns, rural areas, regions or nations (Adhikari et al., 2018). The people dimension reflects the complex interdependencies and the adaptive and self-organising nature of the agents in the system, which create synergies, feedback loops and innovation. EEs are based on the essential role of diverse individuals as entrepreneurial agents and supporting contributors within the system (Daniel et al., 2022). The purpose dimension accounts for the nonlinearity and the emergence of the system, which result from the sensitivity to small changes and the creation of new patterns and properties. In EEs, the overarching purpose typically aligns with a region's aim for survival, growth and sustainability, as actors engage in intentional activities that advance their diverse objectives (Audretsch et al., 2021). The process dimension encompasses the structure and the hierarchy representing the scale and the different levels of complexity of the system. It also considers feedback and delays in the system and how information and signals affect the system's behaviour and outcomes. Ongoing adaptations and negotiations position an EE as a continuously evolving system that maintains a state of dynamic equilibrium (Morel and Ramanujam, 1999). As argued by Brown et al. (2023), this approach can effectively contribute to capturing the elements of complexity, adaptability and dynamism of EEs.

According to the objective of the present study, we used the CAS theory as a valuable framework for understanding the interconnected, dynamic and evolving nature of entrepreneurship in the food industry EEs. Indeed, given the food industry's unique characteristics – such as complex supply chains, regulatory constraints and sustainability challenges – the lens of the CAS 4P framework can offer a structured way to examine the sector-specific entrepreneurial processes and the interactions among the various actors, institutions and resources in EEs. Considering the expanding body of research on EEs in the food industry, this systematic literature review is proposed to analyse the extant research and lay the foundation for further exploration of EEs in this specific sector.

The present study conducts a SLR following the PRISMA statement, which offers a transparent and comprehensive account to identify, select and review previous studies (Sarkis-Onofre et al., 2021). It provides researchers with a consistent and replicated method to analyse reviewed studies' purpose, methods and findings (Page et al., 2021). The conceptual objective guiding our review was the CAS 4P framework to explore the dimensions shaping the dynamics of the EE in the food industry and offer a comprehensive analysis of the entrepreneurial specificities of this industry and the systems and environments in which it operates.

Initially, following previous studies, we used “entrepreneur* ecosystem*” as a search term for EEs (Cho et al., 2022; Theodoraki et al., 2022). As secondary search terms, “food” and “beverage” were included. This represents the study's focus on the food industry. As additional search terms, other words that are related to the food industry were defined (e.g. reconfiguration). To establish these terms, an iterative approach was employed, beginning with a non-systematic literature review. Furthermore, search terms assigned by authors in relevant studies were examined. Specifically, we find search terms that indicate the food industry but do not have “food” or “beverage” in their titles, abstracts or keywords (Sauer and Seuring, 2023). Based on our examination, the resulting additional search terms related to the food industry were “wine” and “beer”. Moreover, “wine” as a search term is important because wine has a substantial body of literature dedicated to it (Lam et al., 2020; Weatherbee et al., 2019), with several journals specialised in wine economics, marketing and tourism (Carbone, 2021; Kotur, 2023; Outreville et al., 2022). Additionally, wine is a dynamic and distinctive sub-industry of the food industry with a long history, tradition and territorial identity (Rocchi and Gabbai, 2013). “Beer” as a search term is also important because beer is another sub-industry with a growing body of literature (Aguiar et al., 2022; Kline et al., 2017). Adding wine and beer as single key terms has resulted in 17 articles consistent with our research aims.

Figure 1 presents the detailed research protocol of the SLR, along with the number of articles identified, to ensure high-quality results and enhance comprehension.

To improve the quality of the funnel (Kraus et al., 2020), Scopus and Web of Science databases were utilised. These databases were selected for their extensive coverage across various journals, subjects and disciplines (Singh et al., 2021). Searches were conducted using specific terms applied to titles, abstracts and author-provided keywords, as recommended by previous scholars (Sauer and Seuring, 2023). These search terms were converted into search criteria and adapted for each database accordingly. The funnel was restricted to English-language peer-reviewed scholarly journals dated prior to October 10, 2024. This approach improved the quality of the reviewed material by leveraging the rigorous peer-review process that journal articles undergo before publication (Champenois et al., 2021).

We initially collected 143 journal articles, which we then examined to make up the appropriate set of studies to be analysed. As the first-level inclusion criteria, 102 articles, after removing duplicates, were read and categorised. Articles not including the EE focus were excluded; for example, those where scholars only mentioned EE in the abstracts, but the specific research focus was absent. A funnel of 63 articles was thereby identified for further analysis. After reading the full articles, 29 articles remained. The exclusion criteria again included the absence of an EE focus. We also added two other relevant articles that we identified through back-and-forth analysis of references of retrieved articles. Finally, based on our search and filtering, we obtained 31 studies for the analysis. The selected 31 studies, including their author (year), title, journal, country, sub-industry, region, method, data source and type of data collection, are summarised in Table A1 in Appendix.

Since the number of selected studies was relatively small, we chose to proceed manually with a content analysis (Cho et al., 2022; Cloitre et al., 2023). Content analysis has been conducted using the four dimensions of the CAS 4P classification by Daniel et al. (2022) to guide our coding analysis. The first author of this paper read and coded the selected 31 articles. The second and third authors checked and assessed the analysis. In case of controversy, the authors together evaluated and discussed the contested analysis until reaching an agreement (Snyder, 2019).

The yearly number of identified articles shows an increasing trend of publications, with a minimum of one article in 2014 and 2015 and a maximum of eight articles in 2023 (Table A1), with the earliest in 2014. Remarkably, more than half of the articles were published after 2019, confirming that the great interest in this research area is relatively recent. This surge can be attributed to Spigel's (2017) study, which provided a clear conceptual framework for EEs, inspiring new analyses of entrepreneurship in relation to ecosystem attributes (Theodoraki et al., 2022). Reviewed articles have been published in 26 different journals, most ranked in the first quartile of the 2023 SCImago Journal Rank indicator (Table A2).

The USA and Italy were the most studied countries, contributing six and three articles, respectively. Notably, a significant portion, 56%, of the countries analysed are classified as developed. Within these studies, the wine sector appeared most frequently in the analysed literature, discussed in seven articles, followed by the general agri-food sector and agri-food tourism, covered in four articles. Methodologically, most of the research relied on a qualitative analysis approach, with 24 articles using semi-structured interviews. In contrast, a mere two articles adopted a quantitative approach, analysing survey data.

This section analyses EEs in the food industry using the CAS 4P framework – place, people, purpose and process – to uncover key dynamics and trends. A comprehensive reviewed studies overview is presented in Table A3.

The place dimension covers an EE's geographical, organisational and regulatory boundaries, along with path dependence and local rules shaping its evolution. These boundaries can be geographical (e.g. regions, landscapes) or institutional (e.g. local organisations, food consortia). Our analysis reveals how these factors influence food entrepreneurs' opportunities and challenges, detailed in the Boundaries and Path Dependence subsections.

Goes beyond simple physical boundaries, there is also a concept of operational closure, as developed by Maturana and Varela (1991) and Luhmann (1991). Operational closure refers to the idea that systems are self-referential and self-generating, maintaining their identity through internal processes rather than direct input from the environment. This means (1) self-referential nature: the ecosystem operates based on its internal logic and processes, rather than being directly controlled by external factors; (2) indirect environmental interaction: while the ecosystem interacts with its environment, this interaction is mediated through the system's internal processes and interpretations; and (3) circular causality: changes in the ecosystem are primarily determined by its internal structure and organisation, with external factors acting as triggers rather than direct causes (Luhmann, 1991; Maturana and Varela, 1991).

The reviewed studies showed that food industry EEs transcend geographic boundaries, interact with their environments indirectly, exhibit dynamic and evolving borders, operate across multiple scales and perspectives, and are often defined by functional rather than spatial criteria.

In the analysis of the EE in Åland Islands (Finland) conducted by Rytkönen et al. (2023), self-referentiality emerged from the unique identity, culture and property protection laws of this EE. The internal processes maintain the system's identity and influence its development, creating a circular causality where the ecosystem's characteristics reinforce its distinctiveness (Rytkönen et al., 2023). The Canavese wine production ecosystem (Italy) interacts with its environment indirectly, through internal processes that interpret and respond to external factors (Moggi et al., 2022). This study showcased an EE that successfully addressed an industrial crisis by fostering sustainability practices, traditional landscapes and local connections (Moggi et al., 2022). Forrest et al. (2023) demonstrated how EE boundaries (in the Greater Phoenix Area of Arizona, USA) can evolve over time through internal active project planning, adapting to new opportunities and community needs. While geographic location remains relevant, Panetti et al. (2023) analysed how the FoodValley EE (in the Netherlands) extends beyond physical boundaries through innovation dynamics and knowledge networks. Magni et al. (2022) revealed how EEs can operate at multiple scales, from individual firms to regional collaborations, each offering different perspectives on the system's functioning. This confirms Marques et al. (2021)'s findings. The example of the Brazilian wine ecosystem structured into regions with consortia that managed and promoted wine production, highlighted functional boundaries defined by collaborative relationships and shared objectives rather than strict geographic limits (Marques et al., 2021).

The analysed studies show that boundaries in EEs in the food industry are not static or solely geographic but are socially constructed, shaped by internal dynamics, cultural values and relational networks. These boundaries' evolving, multi-scalar and functional nature underscores the need to view EEs as adaptive systems whose contours are defined as much by shared practices and identities as by physical space.

The reviewed studies showed that path dependence and local rules include local norms and regulations, which reflect a region's historical choices and legacy systems, shaping entrepreneurial activities and institutional frameworks by reinforcing traditional practices and fostering continuity.

According to Perry and Woolard (2023), the success of a deregulation policy in the craft beer industry in North Carolina derived from a path-dependent behaviour based on previous regulatory environments. Rytkönen et al. (2023) pointed out that agricultural supports and food-related activities (such as campaigns promoting local food consumption) should be tailored following specific local rules to be successful. Path dependence can also be reflected on how local varieties of maize were tied to traditional practices in Mexico, reflecting local agricultural norms that resist industrialisation (Hoogendoorn et al., 2018).

Further, cultural heritage and traditions are crucial in shaping EEs through path dependence. In the Indian wine industry, the tension between globalisation and local traditions influences how winegrowers develop their products, balancing international appeal with local authenticity (Singh and Wagner, 2023).

Moreover, path dependence is also evident in how EEs adapt to external pressures, including market demand and environmental challenges. Yaslak et al. (2023) highlighted that the shift from a rural to an industrial and service economy in Turkey has influenced the agricultural sector's structure and entrepreneurial patterns to become more digitalised. Manyise and Dentoni (2021) illustrated that agricultural commodity exchange in Malawi, facilitating trade under varying market conditions and climatic stresses, mitigated farmers' vulnerabilities.

The initial conditions, such as founding industries or pivotal economic policies, set a trajectory that EEs tend to follow. This is seen in the artisan food movement in Åland, which was shaped by the region's response to geopolitical events and local economic crises (Rytkönen et al., 2023). The development of the Brazilian wine ecosystem reflected a path-dependent process influenced by initial investments in specific regions and the evolving support infrastructure (Gruba et al., 2022; Marques et al., 2021). Similarly, McKague et al. (2021) discussed the impact of initial entrepreneurial activities and subsequent scaling efforts in the case of the development of a network of agricultural shops in Africa.

Finally, the products or services the entrepreneurs offer depict high place specificity. In the analysed studies, food products frequently embody the traits of a specific location, particularly noticeable in tangible consumer goods that require local resources for their creation (e.g. wine) (Adhikari et al., 2018; Díaz-Correa and López-Navarro, 2018; Dressler, 2022).

The analysed studies show that path dependence and local rules contribute to shaping the trajectory of EEs in the food industry by embedding historical, institutional and cultural constraints into present-day entrepreneurial practices. These mechanisms foster continuity, resilience and place-specific identity while structuring how ecosystems adapt to change and integrate new opportunities.

The people dimension focuses on the human interactions driving entrepreneurship. It includes complex interdependencies (collaborative networks and stakeholder engagements) and adaptive, self-organising behaviours (responses to market changes) (Daniel et al., 2022). The main findings of this review reveal that successful food industry EEs rely on strong networks for resource and knowledge sharing, as well as flexible, adaptive structures that encourage innovation and collective problem-solving.

Collaborative networks emerged to include complex interdependencies. Badia et al. (2024) focused on a collaborative project that included agri-food businesses that created an industrial tourism network and displayed how the network was activated based on existing ties and trust among the businesses and the common interest in promoting the local area. Yaslak et al. (2023) showed how the entrepreneurs interacted with various actors to exchange knowledge, develop ideas, obtain financial support and source products to manage an e-commerce platform for rural entrepreneurs. Craft breweries, despite competition, prioritised cooperation to enhance the sector's overall success (Mars, 2020). Similarly, Dubé et al. (2020) discussed how social enterprises leverage information communication technologies to connect markets and actors across the food chain.

Complex interdependencies can also constitute multi-stakeholder engagement. Salvado et al. (2023) described a stakeholder–entrepreneur value-cocreation pyramid consisting of four levels (functional benefits, emotional benefits, life-changing behaviour and social impact attitude) and its ability to increase stakeholders' satisfaction in a wine route in Portugal. A collaboration platform of farmers, entrepreneurs and academics drives sustainable urban agriculture in South Africa according to Malan (2020). Díaz-Correa and López-Navarro (2018) illustrated a proactive engagement of local stakeholders where smallholders, through collaborative efforts with universities and local businesses, enhanced their education in viticulture and market position. The initiative supported fair pricing, technical support and integrated cultural and educational activities, strengthening local ties and resource utilisation.

Further, local communities play a significant role in fostering EEs, often providing support through incentives such as tax benefits, grants and resource sharing, which are crucial for business establishment and growth (Perry and Woolard, 2023).

Effective EEs are marked by resource and knowledge sharing, which drives innovation and efficiency. Similarly, Adhikari et al. (2018) discussed how agricultural networks supported young entrepreneurs in developing competencies and adapting to changing agricultural conditions in South Australia. Finally, the founder of a reputed restaurant created a local and global ecosystem based on open innovation and the cooperation of local suppliers and global culinary figures facilitated knowledge exchange and collective innovation (Chesbrough et al., 2014).

The reviewed studies show that complex interdependencies in EEs in the food industry are not merely functional arrangements but are embedded in place-specific social, cultural and institutional dynamics. Ecosystems rely on dense trust-based relationships and multi-stakeholder engagements. Such interdependencies enable value co-creation and knowledge exchange and reinforce local embeddedness, allowing EEs to evolve while maintaining coherence with their territorial identity.

Adaptive and self-organising features within EEs in the food industry refer to the capacity of actors to adjust their behaviours, reconfigure relationships and innovate in response to evolving market and environmental conditions. Based on the following reviewed studies, rather than being centrally managed, these systems respond dynamically through local initiatives and emergent coordination among stakeholders.

Ossowska et al. (2023) reflected on a self-organising behaviour that extended beyond the food festival EE to adapt strategies and offerings based on interactions at the event and broader market trends. Further, EEs often innovate through stakeholder cooperation, where different groups create or improve new solutions. Small producers are thriving through direct-to-consumer sales channels like farmers' markets and community-supported agricultural models, adapting to a market that increasingly values local and sustainably sourced products (MacFall et al., 2015). This shift demonstrated an adaptive response to consumer demand and market conditions, moving away from traditional, large-scale agricultural practices.

The characteristics of adaptive and self-organising can also constitute innovation through stakeholder cooperation. A formal partnership of academic, public and civic sector actors collaboratively established an accelerator platform to advance the sustainable food economy in the Phoenix area (Forrest et al., 2023).

The sharing of resources and collective problem-solving are prevalent in EEs of the food industry, helping to overcome challenges that might be too large for individual entities. This is evident in the collaborative efforts in the Guarapuava craft beer producers' association (Gruba et al., 2022), where local government and businesses worked together to formalise and support the craft beer sector.

Lastly, agents within EEs often engage in decentralised decision-making, allowing for more flexible and responsive actions at the local level. This is seen in how the food artisans in Åland managed their operations and marketing strategies without central control, relying instead on local networks and shared interests. The established connections between producers and consumers were facilitated through farmers' markets and “Reko circles,” pop-up sales organised through Facebook (Rytkönen et al., 2023). Further, the partnership between farmers and seed companies in Malawi provides limited credit for maize inputs, which showed a decentralised approach to problem-solving that adapted to local financial constraints (Hoogendoorn et al., 2018).

These cases expose that adaptiveness and self-organisation in EEs in the food industry are not spontaneous traits but are cultivated through people-based experimentation and the reconfiguration of actor relationships. A capacity emerges from actors' ability to collaboratively reshape roles, rules and routines in response to shifting constraints and opportunities.

The purpose dimension in EEs focuses on the goals and driving forces guiding ecosystem activities. It includes nonlinearity (small changes leading to significant outcomes) and emergence (new business models responding to an evolving market) (Roundy et al., 2018). Our findings indicate that EEs in the food industry undergo dynamic transformations from small innovations, with new sustainable models emerging to meet societal and environmental challenges.

The interconnected elements of EEs result in nonlinear dynamics and feedback loops, which happen when an activity feeds back on itself either directly or through intervening processes (Cilliers, 2002).

Nonlinearity in EEs in the food industry is characterised by the ecosystem's sensitivity to small changes, which can lead to large, often unpredictable effects. In the food industry EEs, small changes can act as catalysts for significant transformations. For example, the introduction of craft breweries in economically depressed areas revitalised these regions and spurred complementary entrepreneurial activities, transforming the local economic landscape in unpredictable ways (Perry and Woolard, 2023). EEs in the food industry demonstrated a high degree of dynamicity to changes in market demands or environmental conditions. Small-scale innovations and knowledge sharing within niche networks can transform business practices and ecosystem structures (Gruba et al., 2022; Magni et al., 2022). This illustrates how minor modifications or introductions of new ideas can catalyse broader systemic shifts.

Dynamic responses to market and environmental shifts can also characterise nonlinearity in EEs in the food industry. EEs can adapt dynamically to new technologies and funding landscapes (Hrustek et al., 2022; Ludwig et al., 2022). These adjustments often lead to the emergence of new market opportunities and adaptation strategies that significantly impact the food industry's resilience and sustainability.

Further, innovations within the food industry EEs can scale rapidly or influence broader changes across the industry, as seen in Panetti et al. (2023), where efforts to promote sustainable food production practices and healthy eating environments swiftly influenced regional and even global markets. Shared knowledge and common goals among ecosystem actors can enhance individual business capacities and scale these benefits across the ecosystem (Moggi et al., 2022; Reis et al., 2022). This spreading of innovation is crucial for developing a thriving EE, as new practices and technologies are widely adopted, leading to cumulative growth and development.

Emergence is “the process by which patterns or global-level structures arise from interactive local-level processes” (Lichtenstein, 2011). In the case of EEs in the food industry, this can be the development of new business models, the rise of sustainability initiatives, cultural and environmental awareness, and digital innovation adoption.

Emergent properties in EEs often include the development of new business models that integrate various sectors or respond to specific market needs. The emergence of a regional “food bowl” concept that aligned various goals to build a sustainable and connected agribusiness community (Daniel et al., 2022). Distribution of micro-entrepreneurial strategies and food hubs respectively introduced new operational models that addressed local needs and fostered community resilience, demonstrating adaptive and innovative business practices in response to market demands and social challenges (Dubé et al., 2020; MacFall et al., 2015).

EEs often give rise to sustainability initiatives that address ecological concerns while promoting economic growth. For example, the urban food forest enterprise in Arizona studied by Wiek and Albrecht (2022), originated as a sustainable enterprise that would provide jobs and livelihood opportunities in an economically marginalised urban area while pursuing social and environmental goals, such as providing healthy food and a cooler microclimate. The cultivated meat EE in Singapore showed how the EE was facilitated by easy access to funding and talent, and how it contributed to the emergence of newcomers and new products in the cultivated meat supply chain (Reis et al., 2022).

Further, emergent EE properties include cultural and environmental awareness. Indian winegrowers integrated sustainability-oriented practices and the vision of wine as a cultural product into their operations, emerging as leaders in sustainable wine tourism (Singh and Wagner, 2023). Cultural values and environmental considerations were integrated into business practices, showing how the food industry increasingly aligned with broader societal values such as community well-being and sustainable resource use (Chesbrough et al., 2014; Díaz-Correa and López-Navarro, 2018).

Lastly, the wine industry 4.0 faced challenges like a lack of digital skills and resistance to change (Dressler, 2022). By fostering a network of interconnected actors, the EE enabled knowledge sharing, resource pooling and the development of tailored solutions that addressed the specific needs of the wine industry. The introduction of information communication technologies to bridge gaps in food availability and affordability, as discussed by Dubé et al. (2020), along with efforts to enhance agricultural outputs through technological interventions, as noted by Hoogendoorn et al. (2018), signifies the pivotal role of technology in transforming EEs in the food industry.

These studies suggest that emergence in EEs in the food industry is often not accidental but seeded through intentional experimentation and cross-sector alignment that repurposes local assets towards sustainability and competitiveness goals. The emergent properties of cultural entrepreneurship, sustainability transitions or digital integration demonstrate that the EEs can evolve beyond their original scope driven by the collective capacities of local actors.

The process dimension in EEs covers how ecosystems operate and evolve. It includes hierarchy (multi-layered stakeholder interactions), feedback and delays (responses to changes) (Daniel et al., 2022; Stam and van de Ven, 2021). Our findings suggested that these interactions and adaptive structures shape food industry EEs, while immediate and delayed feedback are crucial for strategic decisions and long-term sustainability.

Simon's (1962) concept of hierarchy in CAS emphasises how the systems are often composed of interrelated subsystems, which themselves contain smaller subsystems, forming a hierarchical structure. This hierarchical nature allows for faster evolution and adaptation, increased stability and decomposability for analysis and understanding. In the context of EEs in the food industry, we can observe hierarchies in layered stakeholder engagement, multi-level functions and impact, and nested innovation and adaptation.

A multi-tiered structure of stakeholder interaction in EEs is depicted by engagements that ranged from local artisanal collaborations to broader socioeconomic partnerships (Gruba et al., 2022; Hrustek et al., 2022). This tiered involvement helped foster diverse contributions and shared responsibilities, which are crucial for the evolution of the ecosystem.

Hierarchy in EEs also often involves multi-level functions and impact. Different functions, such as capacity building, consulting and networking, evolved within the ecosystem, highlighting the various levels of organisational involvement and expertise contributing to sustainable food economy transformations (Forrest et al., 2023). EEs in the food industry also operated across various business maturity levels, from local start-ups grappling with regulatory frameworks to mature companies influencing framework conditions for innovation (Ludwig et al., 2022; Reis et al., 2022). This showed how impacts and functions were distributed across different business maturity levels, affecting stakeholders differently.

Lastly, hierarchy in EEs often involves nested innovation and adaptation, too. For example, there is a presence of different innovation fields and strategic directions, like circular agri-food models (Panetti et al., 2023). In the food industry, innovation was not isolated but instead nested within the ecosystem, driven by interconnected activities across various actors and levels (Dressler and Paunovic, 2020; Wiek and Albrecht, 2022). This interconnectedness enables ecosystems to adapt dynamically to new challenges and opportunities, reflecting a hierarchy where changes at one level influence others.

The hierarchy of EEs in the food industry reflects a complex but structured interplay of actors, functions and innovations across multiple levels. EEs in the food industry exhibit layered stakeholder participation, differentiated functional roles and nested innovation pathways.

Feedback occurs when system components (individuals or organisations in EEs) have basic, and reactive impacts on other elements, either positively or negatively, making the system dynamic (Sterman, 2001). In the following studies, EEs in the food industry included immediate and long-term feedback, positive and negative feedback loops, delayed responses, systemic barriers and opportunities, as well as cultural and social feedback.

Immediate feedback often arises from direct interactions within the ecosystem (Badia et al., 2024). Conversely, long-term feedback can result from strategic shifts or policy changes, such as those in Forrest et al. (2023), where capacity building and consulting functions evolved over time, reflecting gradual knowledge accumulation and expertise development.

Positive feedback loops can reinforce successful strategies, where growth in the craft brewing industry fed back into local economies, further stimulating regional development (Perry and Woolard, 2023). Positive feedback loops can be inferred from community networks and mutual support among breweries enhanced the ecosystem's vibrancy (Mars, 2020). Negative feedback occurred when barriers or failures led to reassessment and strategy shifts (Rytkönen et al., 2023). A study on the EE of packaged food and beverage ventures in Finland showed how COVID-19 restrictive measures led to immediate and delayed effects on the ventures' performance and survival (Björklund et al., 2020). The ventures responded to the negative feedback by engaging in business model experimentation, such as pivoting, adapting or preserving, and receiving government assistance. Negative feedback was also seen in the challenges faced by industries like beekeeping in Hunedoara, where regulatory and market limitations stifle growth (Nicula and Spânu, 2020).

Delays in feedback can significantly affect outcomes. For example, the EU's decoupling reform took ten years to implement due to cultural and economic differences in the EE studied by Rytkönen et al. (2023), showing how delayed policy effects can hinder immediate local development efforts. The feedback from systemic conditions such as market access, regulatory environments and labour supply critically influences the strategic decisions within ecosystems. Delays in payments in consignment models affected cash flows for farmers, financial sustainability and operational efficiency (MacFall et al., 2015).

Systemic barriers like underdeveloped seed value chains and infrastructure deficiencies can hinder progress, while strategic innovations and community-based solutions can provide significant opportunities for growth (Chesbrough et al., 2014; Hoogendoorn et al., 2018).

Informal institutions and social norms provide feedback that shapes business practices and entrepreneurial activities. Cultural norms and social influences were crucial in shaping the EE through community resilience, identity and collective efforts towards sustainable practices (Adhikari et al., 2018; Díaz-Correa and López-Navarro, 2018).

These cases highlight how feedback processes of EEs in the food industry are shaped by both formal structures and informal practices, with timing and context determining their influence.

This study provided a comprehensive analysis of the state of the art in EE research applied to the food industry through the lens of CAS theory. This approach contributed to advancing knowledge of EEs and their characteristics and dynamics in a specific sectoral context by using the CAS 4P framework.

We observed that the studies related to EEs in the food industry are relatively new because the first study on this topic was published in 2014. However, the interest has grown in recent years, particularly after 2019. The USA and Italy emerged as prominent countries of focus, particularly in the sub-sectors of wine production and agri-food tourism. Most studies employed qualitative methods of analysis.

In this review, applying the CAS 4P framework highlighted several key characteristics of the analysed food industry EEs (Table 2).

Understanding food entrepreneurial ecosystems through the lens of CAS offers a framework to interpret how ecosystem components interact dynamically. Rather than being the result of linear planning or centralised control, these ecosystems evolve through feedback loops, co-evolution and adaptive responses among interdependent actors. This perspective foregrounds the embeddedness of food entrepreneurship within specific cultural, ecological and social contexts, where even small changes – such as new partnerships or regulatory adjustments – can generate cascading effects. Consequently, CAS provides a useful lens to understand the nonlinearity and emergent dynamics characterising the goals, values and transformations observed across EEs in the food industry.

Place emerged as a crucial dimension of EEs in the food industry. In the analysed studies, geographical boundaries represented the main complex resource that identified the specific natural assets and social capital that leveraged the existence of EE and the distinctiveness of supplied food products or services (Forrest et al., 2023; Magni et al., 2022; Marques et al., 2021; Moggi et al., 2022; Rytkönen et al., 2023). Moreover, these boundaries were pivotal in representing the milieu where collaboration between food businesses, their institutions and other local stakeholders took place to develop the EE and its components. An additional aspect of this dimension was linked to the local rules and the cultural values shared by the different actors in the ecosystem that characterised the history and traditionality of the local production, with path-dependent effects (Adhikari et al., 2018; Díaz-Correa and López-Navarro, 2018; Dressler and Paunovic, 2020). This has been underscored as a relevant factor of recognisability and reputation of the food products in the market, but it also affects the opportunities for business innovation and the tension with traditionality. Therefore, the influence of boundaries in generating strong local ties and the reliance on local resources make food system entrepreneurship highly context-dependent compared to other sectors like digital and tech entrepreneurship (Bejjani et al., 2023; Micol et al., 2024). If a CAS assumes relatively fluid adaptation, economic viability in EEs in the food industry is often constrained by institutional path dependencies (e.g. financing limitations, family ownership) (Marques et al., 2021). Thus, we can refine CAS theory by incorporating capital structure inertia, the tendency of entrepreneurs to maintain their existing capital structure (the mix of debt and equity) rather than actively adjusting it in response to changing market conditions or new opportunities (Welch, 2004).

The dimension of people referred to social relationships in EEs of the food industry that generated interdependence between the actors, supported the sense of community and allowed the analysed systems to adapt and self-organise. This review highlighted the role of collaborative networks between food businesses to create value for all actors operating along the food supply chain (Mars, 2020; Perry and Woolard, 2023; Salvado et al., 2023). Moreover, promoting a multi-stakeholder system amplified the potential of value creation from the food industry to other sectors and public institutions (Slater et al., 2024). A relevant actor of EEs in the food industry has been found in the local community. Several reviewed studies noted the need to engage the local community through product quality, sustainability actions, resource sharing and knowledge exchange. Collaborations and interdependencies have been emphasised as the fundamental conditions for responding adaptively and self-organising to market and environmental changes (Forrest et al., 2023; Gruba et al., 2022; Hoogendoorn et al., 2018; MacFall et al., 2015; Ossowska et al., 2023; Rytkönen et al., 2023). Further, CAS values decentralised interaction, but cooperation in EEs in the food industry often hinges on historical trust relations, formal consortia or regulatory mandates (Marques et al., 2021). This can support the concept of structured coopetition, where interactions are not completely random but also shaped by governance (Theodoraki and Messeghem, 2020; Zhang and Li, 2025).

A further dimension of analysis was purpose, which focused on goals and forces guiding the ecosystem. Nonlinearity in EE development emerged as a crucial feature. Some studies found that small changes – such as niche innovations or policy shifts – can trigger large-scale transformations across the ecosystem (Gruba et al., 2022; Hrustek et al., 2022; Ludwig et al., 2022; Magni et al., 2022; Moggi et al., 2022; Panetti et al., 2023; Perry and Woolard, 2023; Reis et al., 2022). This aligns with Spigel (2017) analysis, which emphasises the unpredictable nature of interactions between ecosystem actors. Additionally, emergence – the spontaneous creation of new business models and innovations – was a recurrent theme in the analysed literature, reflecting the adaptive nature of EEs in the food industry (Hoogendoorn et al., 2018; Ludwig et al., 2022; Reis et al., 2022). This is consistent with CAS theory, where emergent properties are considered a hallmark of complex systems (Daniel et al., 2022). While CAS emphasises emergent properties and nonlinear innovation, innovative markets of EEs in the food industry sometimes require structured orchestration (e.g. consortia, protected designations or policy incentives) (Marques et al., 2021). Thus, it also supports the concept of guided emergence where market novelty is not spontaneous but institutionally enabled.

Lastly, the dimension of process highlighted the role of hierarchy as well as feedback and delays in the food EEs (Gruba et al., 2022; Panetti et al., 2023; Wiek and Albrecht, 2022). Similar to studies on EEs in other types of industry, these hierarchical structures underscored the multi-level nature of entrepreneurship and the importance of layered engagement among actors (Theodoraki, 2024). Another key finding was the role of feedback and delays in shaping entrepreneurial dynamics within the food industry. Both immediate feedback (e.g. market response) and delayed feedback (e.g. policy reform implementation) affected ecosystem performance significantly (Björklund et al., 2020; MacFall et al., 2015; Mars, 2020; Nicula and Spânu, 2020; Rytkönen et al., 2023). This aligns with existing literature, highlighting how feedback loops influence the evolution of EEs (Frimanslund and Nath, 2024). A key theoretical contribution lies in extending CAS theory by illustrating how hierarchy, as well as feedback and delays in EEs in the food industry, are not only multi-level and dynamic but also institutionally anchored and temporally structured. Unlike fast-moving sectors where feedback is rapid and hierarchy more fluid, the food industry sometimes has delayed feedback loops (e.g. regulatory cycles, certification processes) and rigid hierarchical structures (e.g. consortia, appellation bodies) which shape entrepreneurial trajectories over longer time horizons.

The CAS framework allows this study to advance the understanding of food entrepreneurship in EEs by identifying key distinctive aspects.

The first aspect to consider is that entrepreneurship in the food industry operating within ecosystemic contexts is involved with both physical spaces, characterised by the presence of specific resources and social constructions, defined by internal dynamics, cultural value identification and relational networks within the EE, and between the EE and its environment. This highlights the need to shed more light on the development of food entrepreneurship in adaptive systems shaped by geographical identification and shared practices. While path dependence and local rules embed historical, institutional and cultural constraints within entrepreneurial practices, exogenous pressures (such as climate change, food market volatility and economic turbulences) compel entrepreneurs in food EEs to seek new strategies to adapt to change and mitigate risk. Our analysis highlights the need to deepen understanding of mechanisms that foster entrepreneurial continuity and resilience. It also accounts for sector-specific tensions, including the simultaneous consideration of the environmental, social and economic dimensions of sustainability, and the trade-off between resistance to change and the integration of new business opportunities.

Moreover, the emergence of EEs in the food industry involves the reconfiguration of relationships among actors. Therefore, food entrepreneurship in EEs needs to rely on multi-stakeholder engagement and dense trust-based relationships that enable the value co-creation and knowledge exchange in coherence with the territorial identity. In ecosystem contexts, the sector-specific tension between local embeddedness and scalability can be reshaped through collaborations between food entrepreneurs and other entrepreneurs or actors, creating alternative opportunities for growth through new place-based sources of business differentiation that involve different stakeholders and industries.

Additionally, entrepreneurship in the food EEs is exposed to nonlinear patterns of evolution, leading to new and sometimes unexpected paths of adaptation and growth. At the same time, the emergence of EEs is seeded through collective capacities and cross-sector alignment between entrepreneurs and other local actors. This allows food businesses operating in ecosystem contexts to better manage the tensions generated by growth constraints, which are often due to small-scale, family-based resources and locally sourced production factors. It also enables them to take advantage of the emergent properties of EEs, such as cultural entrepreneurial development, sustainability aims and digital integration, which our analysis has highlighted.

Finally, the EEs' structure in the food industry reflects a complex interplay of actors, functions and innovations across multiple levels. Therefore, it is necessary to examine the role of food entrepreneurs in EEs and their innovation pathways more closely. This is particularly needed in the food ecosystem context, where formal structures and informal practices shape entrepreneurship and its evolution.

Methodologically, our review confirmed a lack of quantitative application for EE analysis in the food industry. As suggested by Leendertse et al. (2022) and Stam and van de Ven (2021), there is still a need for more quantitative-based analyses to better capture the systemic nature of EEs in the food industry. Further, while Daniel et al. (2022) have already suggested a specific method for analysing EEs in the food industry with a meta-methodological approach derived from the CAS theory, agent-based computational modelling can also be used. Finding the important ecosystem components (prior works on EEs could be used for this step), defining the relationships between components by condensing interactions to their essential features, and then tracking how the EE model evolves over numerous simulated iterations are the crucial steps in applying such methods to model the behaviours of EEs (Fischer et al., 2024; Napoli et al., 2025; Roundy et al., 2018). For example, one model could simulate how wine entrepreneurs adopt new wine types, with adoption influenced by peer behaviour, access to financing from banks and regional policy incentives, discovering how innovation diffuses nonlinearly through feedback loops and network effects. Another case is modelling resilience strategies under external shocks, such as geopolitical crises, climate stress or market disruption, where agents explore alternative market channels (e.g. food or wine tourism, digital exports) and learn adaptively.

Based on our comprehensive analysis of the 4Ps in the food EE studies, we have identified four key main implications from analysing the EEs in the food industry (Table 3). These implications directly address the gaps and patterns observed in our review, providing a roadmap for advancing our understanding and development of EEs in the food industry.

Firstly, our analysis revealed a strong reliance of food entrepreneurs on the availability and characteristics of natural and social assets. Therefore, to the extent that the EE operates to build a local-specific identity, food entrepreneurs can take advantage of the intertwining between individual brand reputation and territorial reputation. The role of EE is to amplify this synergy beyond the food system to the other sectors. However, it also exposes vulnerabilities to resource scarcity and external shocks. While Badia et al. (2024) and Rytkönen et al. (2023) put emphasis on place and regional development, there is still a limited understanding of how inter-sectoral linkages influence resilience in territorially-bound ecosystems. Therefore, future studies should explore food systems from a multi-sectoral perspective, examining how food production can drive regional development trajectories. On this basis, practitioners, especially public decision-makers, should put more effort into promoting policies reinforcing multi-stakeholder collaboration. This could reduce the issues related to food entrepreneurs' vulnerability, both of an environmental and economic nature. Also, the limitation in terms of the scalability of the food system is attenuated by the multi-sectoral perspective of the ecosystem approach.

Secondly, the review highlighted the intrinsic link between food systems and local resource quality. Some reviewed studies have analysed this aspect, but more effort should be made in prospecting future scenarios, considering the use of environmental resources and the enhancement of community well-being (Forrest et al., 2023; Moggi et al., 2022; Wiek and Albrecht, 2022). Additionally, policymakers should position EEs as a tool to foster attention to environmental and social sustainability by the economic durability goals of local businesses.

Thirdly, our analysis underscored the food industry's heightened focus on ecological and social concerns rather than other industries, implying context dependence. There is insufficient exploration of how regional specialisation specifically relates to the food industry. For instance, despite agricultural entrepreneurship being one of the most crucial components of the food system and a key economic sector in many regions, it remains underemphasised in the EE studies (Kansheba and Wald, 2020; Wadichar et al., 2024).

Moreover, researchers should investigate business models that leverage the context-specificities (local culture and policies) as a source of competitive advantage in enhancing EE outcomes. Our review suggests furthering the research of the cultural intensity of a region, such as the presence of rooted food production traditions, or culturally embedded culinary practices, along with entrepreneurs' creativity, intersectoral collaborations, local community engagement and public policies, as critical yet underexplored layers of the EE. In this regards, foodie towns and destinations (Joassart-Marcelli and Bosco, 2024), UNESCO gastronomy cities (Park et al., 2023; Pearson and Pearson, 2017) or food halls (Perry et al., 2025) illustrate how food culture and community cohesion serve as the foundations of EEs that contribute to economic and social growth by fostering tourist attraction, cultural development, urban revitalisation and the creation of new market opportunities, among other outcomes. Practitioners and policymakers should recognise that actions that valorise intersectoral collaboration and collective reputation can amplify food EE outcomes, particularly in territories where agri-food distinctiveness and geographical identification are key competitive assets.

Finally, we identified the implications of innovation diffusion. Our analysis highlighted that innovation in the food EEs tended to spread through niche networks and sectoral collaboration. While some studies have already explored social networks, there is still limited empirical evidence on how network ties and social capital of the entrepreneurs influence innovation spillovers of EEs in the food industry (Perry and Woolard, 2023; Salvado et al., 2023). Therefore, future research should explore the mechanisms of innovation diffusion in all layers of food EEs and not only in the food supply chain. Empirical research could expand to examine the role of public–private partnerships in this regard and the knowledge sharing to grow in terms of social capital. Entrepreneurs' attitudes towards innovation, start-up founders' backgrounds, team dynamics and features of successful and unsuccessful ventures are further aspects that are worth exploring.

This literature review showed that the CAS theory was a useful theoretical tool for explaining the dimensions studied for EEs in the food industry. While analysing existing studies, this literature review provided future research and practical implications based on the reviewed articles. Therefore, the 4P framework could also be considered for studying specific EEs in the food industry.

The findings of this review demonstrated the unique characteristics of EEs in the food industry. Specifically, this study contributed to a deeper understanding of how nonlinearity, hierarchy, feedback loops and emergence shape the evolution of the analysed EEs. This highlighted the relevance of supporting small-scale innovations since they can lead to significant transformations. Also, multi-level support systems should be established to address the needs of various stakeholders, implement feedback mechanisms to monitor strategy and policy impact, and make timely adjustments.

This review also refines CAS theory by showing how CAS theory manifests differently in EEs in the food industry due to institutional and structural characteristics. Economic viability is shaped by capital structure inertia and path-dependent financing norms, limiting adaptive reconfiguration. Cooperation emerges not through decentralised spontaneity but via historically rooted, regulated and trust-based structures, supporting the notion of structured coopetition. Similarly, innovation often results from guided emergence, enabled by orchestrated policy and institutional support rather than purely organic processes. Finally, hierarchy and feedback in EEs in the food industry are revealed to be deeply institutionalised and temporally extended, challenging assumptions of fluid responsiveness and underscoring the need for a sector-sensitive CAS perspective.

Additionally, our review pointed out the need for a further sector's focus on sustainability, cultural values, local resource dependence and innovation since they have been emphasised by analysed studies as the primary sources of competitive advantage for food entrepreneurs.

This literature review has a few limitations worth noting. Firstly, this review only focused on peer-reviewed journal articles written in English, which may exclude relevant studies in different types of sources in terms of types of publications or languages. Secondly, our qualitative analysis is based solely on external secondary data, which means we interpret pre-existing journal articles.

Finally, this study not only addressed a methodological gap but also offered a framework for policymakers and entrepreneurs. New insights for policymakers mainly stemmed from understanding EE boundaries and geographical features for more inclusive policies, recognising the importance of path dependence and local rules allowing for tailored policies, and facilitating collaborative networks among stakeholders for innovation. Moreover, this study highlighted the relevance for entrepreneurs of integrating cultural elements into their offerings and actively participating in community support; building strategic alliances and joining EE collaborations can strengthen their network, while adopting agile practices, diversifying products and monitoring sustainable and technological trends can enhance adaptability to dynamic and volatile markets.

The authors would like to extend our profound gratitude to Dr Patrick J. Murphy (Editor-in-chief), Dr Johan Kask (Associate Editor) and anonymous reviewers for providing valuable feedback during the development of this research. The authors would like to thank the Federazione Veneta delle Banche di Credito Cooperativo for supporting this research.

The supplementary material for this article can be found online.