Drivers for SMEs participation in entrepreneurial ecosystems: evidence from health tech ecosystem in Northern Finland

2.1 The EE metaphor

Entrepreneurial behaviors are the outcome of attitudes, desires and opportunities embedded in a certain context in which people work and live (Szerb et al., 2013). Consequently, the entrepreneurial environment impacts the types of SMEs that are created (necessity or opportunistic) and how quickly they grow (Isenberg, 2010). Although biological symbiotic relationship perspectives provide useful insights, Kuckertz (2019) argues that EEs (being complex adaptive systems) exhibit far more distinctive features beyond those natural ecosystems such as forests and other living habitats. However, the vital role played by the natural ecosystems management literature toward theorizing the EE phenomenon cannot be neglected (Hwang and Horowitt, 2012). Building from the natural science perspective, Kuckertz (2019) describes EE as a complex agglomeration of regional entrepreneurial activities and various forms of informal and formal supports to benefit its large economic and societal environment. Thus, EEs are composed of interconnected entrepreneurial actors, entrepreneurial organizations (e.g. firms, venture capitalists, business angels, incubators and banks), institutions (universities, public sector agencies and financial bodies) and entrepreneurial processes that connect, mediate and govern the performance within the local entrepreneurial environment (Mason and Brown, 2014). In this study, we focus on SMEs to be able to concentrate on certain EE participants that are one of the key actors in the EE (Spigel and Harrison, 2018) and this way acquire an in-depth understanding of the micro-level issues still less understood in the EE.

Vibrant EEs, such as Silicon Valley in the USA and Tel Aviv in Israel, have been well-known for their notable contributions to entrepreneurship and economic growth. In addition, Cunningham et al. (2019) pointed out that by fostering competitive advantages and value among firms and sectors, EEs ultimately shape regional innovation outcomes and speed up innovations and innovativeness among the members. Entrepreneurial stakeholders, particularly entrepreneurs, normally have several motives other than economic resources for being members of EEs. Members of EEs also benefit from potential non-economic (e.g. social) motives that emanate from agglomerations and networks of coexistence of both entrepreneurial firms and other supporting actors such as government agents, universities and financial and research institutions. Thus, EEs act as habitats for both nascent, high-growth and innovative SMEs by in-housing critical tangible and intangible resources necessary for entrepreneurial processes (Tabas et al., 2020).

Unlike the old concepts of clusters, industrial districts and regional innovation systems, which place little concern on entrepreneurs and SMEs, the concept of EE lays emphasis on the entrepreneurs' centric view (Kansheba, 2020). This view places entrepreneurs and their related SMEs as the focal actors responsible for initializing and undertaking entrepreneurial processes and activities while other actors provide supportive services to ensure the success of such entrepreneurial activities (Kuckertz, 2019; Nicotra et al., 2018). Cowell et al. (2018) pointed out that entrepreneurial processes and activities emerge in complex systems with multiple integrated actors. The EE phenomenon has been used to expound on the embedded interconnections between entrepreneurs and other stakeholders and how such interactions shape entrepreneurship routines and performance (Bischoff et al., 2018; Theodoraki et al., 2018).

2.2 The drivers for participating in an EE

A systemic entrepreneurship that is self-sustained requires a supportive environment (a vibrant and healthy ecosystem) which necessitates frequent and effective participation and engagement from members of such an ecosystem who think alike (Kuckertz, 2019). However, the extant literature has mainly focused on identifying relevant components, stakeholders and embedded interactions that define a particular EE (Colombo et al., 2019). In this study, we seek to explore an overlooked question of the drivers of SMEs as focal EE actors to engage and actively commit to a certain ecosystem.

Entering or exiting an ecosystem is attributed to the perceived benefits and (or) costs to members of such ecosystems. As EEs are characterized by formal and informal networks, they have the potential to create several opportunities that influence the behavior of their members (Isenberg, 2010). These opportunities include easy access to resources and strengthening member relations and trust, increasing firms' survival due to economies of scale (Theodoraki et al., 2018). SMEs' participation in the ecosystem results in discovering and creating new business opportunities (Nambisan et al., 2018) as they implement open innovations to benefit from the inflow of external and internal knowledge (Gassmann et al., 2010). Moreover, firms gain access to information on markets, customers, distributors and regulations (Banc and Messeghem, 2020). As contended by Kansheba and Wald (2020), firms within EEs enjoy additional benefits beyond their resources and capabilities due to shared risks and resources. In relation to this, Roundy and Bayer (2019) argue that to manage resource dependencies properly in the EE different strategies should be adopted to mitigate risk and obtain resources.

In their study of how EEs take form, Thompson et al. (2018) argue that SMEs' actions and decisions within EEs are induced by endogenous influences such as cultural cognition, resources, interactions and exogenous influences such as governmental support. Advocates of EE governance (Kuckertz, 2019; Spigel, 2017) posit that EEs emerge from the actors' intrinsic motivations and self-regulation with few targeted or political triggers (Feld, 2012). Thus, individual views and personal judgments on potential drivers such as access to critical resources and support define and shape actors' decisions within such ecosystems.

Firms in an EE amalgamate their complementary skills, capabilities and resources, which finally encourage innovation and new value creation between themselves. However, participation in an ecosystem differ from one firm to another depending on the heterogeneous interests, motives and expectations of such ecosystems (Nicotra et al., 2018). For instance, while some firms engage in an ecosystem to maximize their economic profits, others look for prestige and social recognition (Cowell et al., 2018). Thus, understanding the diverse drivers that induce firms (and other players) to engage in an ecosystem is crucial and enlightens our theorizing regarding EE functionality. SMEs' participation in an EE is also largely influenced by their experiences with it. Isenberg (2010) argues that as SMEs legitimize themselves and establish social networks by interacting with other members, it becomes easier for them to harness critical entrepreneurial resources offered by such an EE than those not involved (Spigel and Harrison, 2018). Furthermore, EE actors (e.g. SMEs) may become reluctant to exert maximum participation in the ecosystem if the costs associated with it (such as resources and time spent) exceed the perceived benefits derived from it (Bischoff et al., 2018). Thus, in this study, we focus on exploring various drivers for health tech SMEs to engage and commit to an ecosystem. The health tech context was chosen as it represents a novel, societally important and fast-growing business field that has scarcely been researched in the EE literature.

3.1 Data collection

Finland is known as a small giant in medical technology and is home to over 300 companies operating in the health tech sector (Health-tech Finland, 2019). Health tech is the second largest sector in Finland which accounts for more than half of Finland's technology exports, making it a highly important business field with an aggregate turnover of about 2.6 billion euros (Health-tech Finland, 2019). The empirical data for this study was collected from the OuluHealth ecosystem that offers products or services to nearly 3 billion people worldwide.

We used purposive sampling to identify suitable SMEs in the health tech ecosystem to be explored in this study. Purposive sampling helps the researcher identify and select information-rich informants for in-depth examination, which was the primary rationale behind choosing purposeful sampling over random or theoretical sampling techniques. The selection process was based on the following criteria to ensure versatile and comprehensive data. First, SMEs were selected following the definition of the European Commission: “Micro, small and medium-sized enterprises (SMEs) refer to enterprises which employ fewer than 250 persons and which have an annual turnover not exceeding 50 million euros, and/or an annual balance sheet total not exceeding 43 million euro” (EU, 2003/361/EC, 2003). Second, the selected SMEs were from the health tech sector, which includes companies from MedTech and health services. Third, SMEs at different stages of growth were selected following Scott and Bruce's (1987) defined stages of growth in small businesses. The study benefits from selecting SMEs to varying stages of growth because it gives more detailed information about SMEs' motivations to engage in the ecosystem. The first category of SMEs is at the initial stage, in which companies have recently started their business and are searching for internal and external funding to enhance their business. The second category of companies is at the growth stage, where companies from the domestic market are searching for other markets to sell their products and services. Finally, the companies in the expansion stage are developing their business in international markets through various sales channels. The common denominator for all SMEs was their ambitions to grow and develop their business.

Initially, the researchers identified the SMEs from the OuluHealth ecosystem website where the potential interviewees' contact information was found. The top management of the selected companies were chosen to be interviewed because they make the major strategic decisions for companies to engage in the ecosystem and can best reflect on their business development. The researchers contacted 50 SMEs and finally conducted 19 interviews in the same EE. The selection of 19 interviews was based on category saturation (Corbin and Strauss, 2008), meaning that the interviews with representatives of companies continued until they yielded relatively few new insights. Table 1 provides an overview of the respondents.

A semi-structured interview protocol was used to collect data. A total of 17 interviews were conducted face-to-face and two online. The interviews started by asking general questions about the informants and the company background, followed by more detailed questions such as what motivates their company to engage in the ecosystem, why they find it relevant, and how they collaborate/compete with other ecosystem members. The interviews were audio-recorded with permission and later transcribed word-for-word, resulting in over 200 pages of transcriptions. All the interviews were in English and ranged from 47 to 90 minutes. Apart from the primary data, the interview data was triangulated with secondary data (e.g. information from the websites, press releases and literature on these topics) providing a general understanding of the health tech business and the companies' operations.

3.2 Data analysis

In analyzing the data, we followed the three-step process method proposed by Gioia et al. (2013) to ensure “qualitative rigor” in conducting and presenting our research. The approach relies on the notion of knowledgeable actors who actively construct their reality and can explain their thoughts, intentions and actions (Gioia et al., 2013). Initially, the research team read through all the raw data (e.g. interview transcripts) several times to get familiar with the data and selected interesting phrases and passages. In the second round, common words and phrases were coded from the data, which led to the generation of first-order codes. Second-order themes emerged as the researchers further grouped the first-order codes around a collection of categories that allowed data to be viewed at a higher level of abstraction. Several themes were generated, and similar themes were combined, which generated seven second-order themes. Finally, the similarities and differences between the second-order themes were investigated to determine the aggregate dimensions. The resulting data structure is illustrated in Figure 1.

4.1 Social drivers

We start by discussing the social drivers that refer to different socially oriented factors that induced and motivated the studied SMEs to engage and participate in the EE (see Figure 1). Social aspects are associated with networks with a range of actors who participate in EEs and the social drivers here represent the social links between various actors such as SMEs, departments, groups, or individuals. Based on our data, the ecosystem provides SMEs with networking and collaboration opportunities and communication and knowledge sharing with other SMEs that are identified as key social drivers and will be next discussed in detail.

4.2 Resource drivers

Secondly, resource drivers refer to those resource benefits the SMEs can gain from participating in the EE (see Figure 1). These include actual access to resources, both tangible (such as product validation, skilled employees and financial resources) and intangible (such as information or tacit knowledge). Furthermore, resource drivers include formal and informal support received from the EE and access to markets, both national and international. These three types of drivers are described in the following.

4.3 Cognitive drivers

In the context of entrepreneurship, the cognitive aspects refer to how firms attempt to make sense of the complex world around them, i.e. how they think, reason, decide and use information (Baron, 1998). In the health tech ecosystem, they can be seen to refer for example to shared beliefs, values, attitudes, missions and perceptions. Based on our data, the development of shared goals and common values can aid SMEs in developing their business with the help of the ecosystem around them (see Figure 1). Besides their individual goals, the ecosystem members thus need to work together to achieve their goals. This means that SMEs need to change their attitude from individually centric to ecosystem centric views to be able to realize the full potential of the ecosystem.

5.1 Theoretical contributions

Firstly, we enhance the theorization and a better understanding of EE functionality by exploring the largely under-researched phenomenon, namely drivers for SME participation in EEs. Some studies have touched on the topic and recognized some advantageous features of EEs in general (see, e.g. Cowell et al., 2018; Kuratko et al., 2017) but our study extends and deepens the existing knowledge by systematically examining and identifying comprehensive and explicit typology of drivers related to EEs that can be widely applied in future studies and practice.

Secondly, research on EEs has been dominated mainly by conceptual studies (see, e.g. Isenberg, 2010; Stam, 2015) and therefore, recent studies (e.g. De Brito and Leitão, 2021; Kansheba and Wald, 2020; Spigel, 2017) have called for more empirical research on the phenomenon. Specifically, research in the context of the health tech sector is scarce or even non-existent in the previous research, although it represents a growingly important business field both in Finland and globally. This study fills these gaps and provides empirically grounded results of what drives SMEs in the health tech sector to participate in EEs. In relation to this, our findings support the previous EE literature suggesting that political triggers do not seem to play a significant role in influencing the companies' decisions to engage in an EE (e.g. Feld, 2012; Spigel, 2017). Instead, social drivers (related to aspects such as networking and knowledge sharing), the possibility to exchange diverse resources and cognitive factors such as shared goals and common values seem to play a more significant role in motivating companies to engage and participate in the EE. Although the resource-related factors have been recognized at some level in previous research (e.g. Kansheba and Wald, 2020; Roundy and Bayer, 2019), our study found that the social and cognitive drivers also play an important role in motivating SMEs to engage in the EE, specifically in the health tech context. This brings forward that the non-concrete and non-economic factors often ignored or underestimated in the extant research still might have a massive role in the SMEs' decision making in relation to EEs.

Thirdly, previous EE research has mainly focused on the macro- or meso-levels (see, e.g. Cunningham et al., 2019; Malecki, 2018; Wurth et al., 2021) and there is a lack of research focusing on how focal actors (entrepreneurs and SMEs) behave and shape EEs on a micro-level. In relation to this, De Brito and Leitão (2021) argue that micro-perspectives can provide a richer understanding of EE dynamics. More specifically, focusing on the micro-level helps to understand better the evolution of actors within EEs and their relationship in forming entrepreneurship in a community (Wurth et al., 2021). In line with this, our study suggests that looking at the ecosystem from a micro-level perspective is very useful as it provides a more detailed and accurate picture of the EE and its dynamics, for instance in relation to its formation, development and success factors.

Fourthly, our findings support the study by Nicotra et al. (2018) in that the interests and motives of actors are heterogeneous, which in turn influences their participation in an ecosystem. Our study emphasizes the individual and heterogeneous nature of motivational drivers of SMEs and suggests that different stakeholders emphasize different reasons for their engagement in the EE and these reasons may change in time as both the SMEs and the EE evolve. This implies that there is a link between the drivers and the context. For instance, based on our empirical study, the firm size and stage of the business growth affect the ambition the companies have and what they expect from the EE. For example, for the small and young SMEs, the expertise, reputation and formal and informal support of the more established SMEs are critically important drivers to engage and participate in the EE. On the other hand, more established SMEs can utilize their social networks to find new partners, gain access to skilled human resources or collaborate in R&D. However, what needs to be noted is that these are highly context-related issues depending on various changing factors in the EE and therefore, presenting any specific propositions related to the relationship between the certain drivers and the context is not possible based on our data. Instead, our study provides an in-depth understanding of why the individual actors engage in the ecosystem and further how they seek growth with the aid of the ecosystem. This is an important addition to the previous research as it aids in comprehending the functioning and dynamics of the EE also at a more general level.

Finally, related to the identified drivers, our findings support the study by Roundy and Bayer (2019) that calls upon the need to integrate the resource dependency perspectives in the existing EE framework. We found that SMEs are often driven to engage and actively participate in their ecosystems because of resource constraints. EEs are banks of resources that SMEs can leverage for their growth. Access to resources is also related to the speed of innovation as it can help to shorten the journey to the markets and improve the product to market fit. Hence EEs in the health sector can benefit the whole economy as they improve innovation potential. However, some fail to tap this potential due to a lack of required skills on how to harness available resources. Our study thus sheds light on the resource dependency and management of SMEs within and beyond existing EEs.

5.2 Managerial implications

The present study provides insights for entrepreneurs and managers of SMEs in terms of utilizing the knowledge of different types of drivers for participating in the health tech ecosystem. First, considering social drivers, it is evident that active networking and partnering with various actors (such as potential customers, key opinion leaders and competitors) within an ecosystem can prolong SMEs' survival rates by shaping their routines and operational decisions on what, how and for whom to produce. Actively participating in an EE can also open opportunities and help to find new international business partners, which can be highly valuable especially for SMEs at the initial stage of their business growth. In addition, visibility and access to information provide opportunities from the perspective of economies of scale where particularly SMEs can enjoy synergies of belonging to the EE.

Gaining increased attention in the eyes of different stakeholders both in national and global markets can be very important to SMEs as well as utilizing the other actors' know-how, information flows and open communication. Thus, entrepreneurs and managers need to be aware of and pay careful attention to these socially oriented factors, as they can be critical drivers for them to engage and participate in an EE. If they are ignored, the firm may remain outside the ecosystem where its chances of survival alone can be poor. Furthermore, when managers recognize these social factors and their importance, it becomes easier to grasp the opportunities related to them when those arise in the EE. One important aspect here is to choose the right partners at the right time to be able to reach the set business goals.

Secondly, resource drivers are another important reason for health tech SMEs to participate in an EE. Access to different types of tangible and intangible resources, support in diverse forms, and access to new local and international markets are examples of notable drivers. SMEs in vibrant ecosystems can easily navigate the valley of liability of newness from the support obtained from established actors and their resources. Established firms can help new SMEs to extend their market niches by integrating them into their networks. Therefore, health tech SMEs managers need to recognize their most critical resources and plan how to utilize those EE resources as effectively and efficiently as possible.

Thirdly, ecosystems are fertile soils for SMEs where cognitive drivers are highlighted. Companies can work, co-create value, collaborate and join projects together and help each other to accomplish common goals. Co-creation in an EE often leads to sharing new business ideas and increasing innovativeness, which further creates inspiration and business opportunities for companies participating in the EE. To utilize these cognitive factors, managers need to be open to collaboration and ready to reciprocally share their expertise and resources with other actors in the EE.

5.3 Limitations and future research

This study has some limitations that may also inspire future research. First, our empirical data are collected from health technology SMEs in a local health tech ecosystem in Northern Finland. Although this context (health technology) is relevant and interesting due to the many business opportunities and growth possibilities it offers for SMEs in domestic and international markets (see Tabas and Komulainen, 2020), it also has some limitations. This kind of specific business sector has unique characteristics, such as the sensitive nature of the business and strict regulative control, and therefore, our results cannot be generalized as such to other types of business contexts. However, we believe this study provides a good starting point for understanding the drivers for SME's participation in EEs. For generalization purposes, future research could study other industries and different contexts to enrich our insights. In addition, future research could further explore the drivers of different actors and use quantitative methods to study the causal connections between different concepts.

Moreover, this study is cross-sectional and presented a snapshot of the studied ecosystem. We call for longitudinal studies to understand the dynamics of the studied phenomenon, as EEs form, evolve and develop over time. Therefore, a longitudinal study exploring the same companies in the EE for several years and perhaps having more representatives from each of them, could deepen the understanding of the dynamics of drivers.