1 Introduction

Ecosystems (Adner 2017; Tsujimoto et al. 2018), and innovation ecosystems (IE) in particular (Durst and Poutanen 2013; Yaghmaie and Vanhaverbeke, 2019; Talmar et al. 2018; Adner 2017; Aarikka-Stenroos and Ritala 2017), are attracting a rapidly growing attention in both academia and managerial practice (Vargo et al. 2015; Thomas and Autio 2020). Typically for nascent fields, scholars underline the harmful conceptual shortcomings regarding the definition of ecosystems (Granstrand and Holgersson 2020; Klimas and Czakon 2022; Scott et al. 2021; Tsujimoto et al. 2018), blurry conceptualizations (Aarikka-Stenroos and Ritala 2017; Oh et al. 2016), or the deficit of operationalizations (Ritala and Almpanopoulou 2017). Several gaps in the current understanding of ecosystems (Bouncken and Kraus 2021; Thomas and Autio 2020) are widened by rapid changes in the business environment, growing uncertainty (Bouncken and Kraus 2021), and increasing openness to both cooperation and coopetition (Zhang and Watson 2020). Additionally, it is also quite common to find different terms for the same type of ecosystems, the same term for different ones, or no distinction between various types of ecosystems despite fundamental differences (de Gomes et al. 2018; Pattinson et al. 2018).

According to the ecosystem perspective, every type of ecosystem is directly focused on value co-creation (Aarikka-Stenroos and Ritala 2017; Kapoor and Lee 2013; Vargo et al. 2015). However, co-creation is driven by different processes in different types of ecosystems e.g. knowledge co-creation in knowledge ecosystems, innovation co-creation in innovation ecosystems, or co-creation of new business ventures in entrepreneurial ecosystems. In contrast to networks or alliances, joint value creation in ecosystems does not necessarily involve explicit rules of value capture (Bouncken et al. 2020). Importantly, in ecosystems a multi-actor approach is adopted (Adner 2017; Pilinkiene and Maciulis 2014; Bacon and Williams 2021), as opposed to actor-to-actor orientation in value co-creation, transcending the classic dyadic (Vargo and Lusch 2011) linear, sequential approach to joint value creation (Vargo and Lusch 2004). Indeed in ecosystems, the co-creation with customers (Prahalad and Ramaswamy 2004) and even of communities with customers (Prahalad 2004), goes much further by involving co-creation with other external actors, and even entire networks of actors (Vargo and Lusch 2011).

Recent literature points to IE as currently fastest growing thread of research (Dąbrowska et al. 2019; Granstrand and Holgersson 2020; Klimas and Czakon 2022), but it also points out conceptual inconsistencies (Durst and Poutanen 2013; Lin et al. 2018; Thomas and Autio 2020) and shortcomings (Oh et al. 2016; Ritala and Almpanopoulou 2017; Adner 2017). For instance, although the structural approach to IE acknowledges actors as constitutive components, there is no agreement regarding the types of relevant actors. Moreover, the ego-centric view focused mainly on the single most powerful actor dominates (e.g. Beliaeva et al. 2019; Dąbrowska et al. 2019), leaving other actors poorly recognized or characterized. Additionally, even though IE actors are acknowledged to play different roles within IEs (Dedehayir et al. 2016), and to be engaged in IEs activities to various degrees (Granstrand and Holgersson 2020), there is no coherent view on how this engagement can be differentiated e.g. among the focal and peripheral actors. Furthermore, while the literature on IE expresses the need for pooling innovation capabilities across the entire co-innovation process, the bulk of research attention is paid to innovation output, which leaves other phases of the co-innovation process unexplored (Klimas and Czakon 2022). To sum up, our study addresses the above shortcomings and the gaps stemming therefrom.

We aim to identify innovation ecosystems from the perspective of those engaged in innovation processes. Such perceptions are consequential because they have implications for behaviours, including actions aimed at others in the business environment or carried out collectively with others (Czakon and Czernek-Marszałek 2021). We aim to establish the various types of actors (who?), the distinct roles (what?), the different stages (when?), and the diverse engagement in co-innovation processes (how?) as perceived by those involved in innovation ecosystems.

We empirically ground both the structural (Granstrand and Holgersson 2020) and the dynamic (Bouncken and Kraus 2021) views on IEs, and develop a framework for mapping the actors, their roles and their degree of engagement in co-innovation processes. Our in-depth study focuses on the Gaming Innovation Ecosystem in Poland, a globally successful example of a knowledge-intensive and highly creative innovation ecosystem. We collected field data over 3 years (between 2015 and 2017) using triangulated data sources including non-participatory observations (5), structured (13) and semi-structured interviews (8 exploratory and 17 member-checks). Following a theory-driven, directed approach (Hsieh and Shannon 2005), we coded our data with deductive codes derived from the academic and grey literature to map the actors, their roles and their engagement in the co-innovation processes in their own perception. Then, we matched the theoretical and empirical patterns (Bouncken et al. 2021) in order to theorize from these rich insights.

Our findings support, extend and alter recent conceptual developments regarding the innovation ecosystem (Granstrand and Holgersson 2020; Klimas and Czakon 2022) using the structural perspective (Adner 2017). We provide a synthesis of the literature and an empirical list of the various actors involved in innovation ecosystems. We single out the various roles played in innovation ecosystems, and the relevant phases of co-innovation processes. We develop an empirically grounded, fine-grained view on distinct roles and the involvement of actors in the five phases of the co-innovation process.

2 Conceptual background

The ecosystems approach emphasizes the strategic relevance of business surroundings in organizations' activities, which help firms in achieving a sustainable competitive advantage (Zhang and Watson 2020). Ecosystems are a way of seeing an organizations’ environment (Sun et al. 2017), or as a useful way of operationalizing this environment (de Gomes et al. 2018). Compared to inter-organizational networks, ecosystems are more boundaryless (Durst and Poutanen 2013), but are not open-ended (Adner 2017). Ecosystems have boundaries within which organizations may thrive, and beyond which their immediate interest is much limited. Therefore, setting out ecosystems’ boundaries is crucially important for conceptual rigor and clarity, but is also meaningful to those involved in their operations (Walrave et al. 2018), because the way an environment is perceived has implications for competitive and collaborative actions (Czakon and Czernek-Marszałek 2021). An ecosystem’s boundaries can be identified using such criteria as: actor (Gawer and Cusumano 2014a), structure (Phillips and Srai 2018), the ecosystem’s value proposition criterion (Walrave et al. 2018), geography (Aarikka-Stenroos and Ritala 2017; Mazzucato and Robinson 2018; Valkokari 2015), technology (Aarikka-Stenroos and Ritala 2017; Autio and Thomas, 2014), and product (Tsujimoto et al. 2018). Depending on the criterion adopted, ecosystems may be defined differently in terms of scope and structure. Therefore, a key challenge for the ecosystems stream of research relates to delineation criteria.

From the output perspective, two types of ecosystems are important due to their impact on technological development, product creation and growth (Bouncken and Kraus 2021): innovation and entrepreneurship ecosystems. Given the scarcity of empirical findings on innovation ecosystems, they still appear as an emerging concept requiring research (Adner 2017; Dąbrowska et al. 2019; Granstrand and Holgersson 2020; Klimas and Czakon 2022; Ritala and Almpanopoulou 2017; Thomas and Autio 2020; Yaghmaie and Vanhaverbeke 2019).

Innovation ecosystems are sets of “interdependent actors who combine specialized yet complementary resources and/or capabilities in seeking to (a) co-create and deliver an overarching value proposition to end-users, and (b) appropriate gains received in the process” (Walrave et al. 2018: 104). If actors are the constitutive component of any innovation ecosystem, defining an IE implies listing its participants. Surprisingly, the bulk of the literature takes an ego-centric approach, and the perspective of single actors, with either their direct or indirect innovation co-creative relationships with the firms responsible for the launch of innovation. Recent literature reviews clearly show that the pool of empirical works is limited, and innovation ecosystems “are characterized in most cases by a one-on-one relationship between the focal firm and its partner. Inter-organizational relations involving multiple partners are rather rare” (Yaghmaie and Vanhaverbeke 2019: 3). Such focus is increasingly seen as an impediment to the development of ecosystems understanding (Arora et al. 2019; Jucevičius and Grumadaitė 2014).

While the ego-centric perspective remains the most popular even in recent studies (Dąbrowska et al. 2019), scholars recognize the need to take a broader perspective (Klimas and Czakon 2022). Indeed, as pointed out by Granstrand and Holgersson (2020), innovation ecosystems can operate around one focal organization such as a hub firm, innovation orchestrator or anchor firm (Beliaeva et al. 2019; Dąbrowska et al. 2019), but also as eco-centric or operating around a few innovation leaders. In the same vein, Sun et al. (2019) indicate that innovation processes can be created and coordinated by one actor in a top-down approach, or emerge in a self-coordinated bottom-up way. Therefore, seeing IEs as “organized around a focal firm or a platform” (Autio and Tomas 2014: 3) seems to be too narrow, thus hampering conceptual developments (Arora et al. 2019; Jucevičius and Grumadaitė 2014). Few notable studies approach innovation ecosystems as intentionally orchestrated and collectively coordinated by several actors (Holgersson et al. 2018; Song 2016). Therefore, innovation ecosystems are more accurately defined as a “cooperation environment surrounding the innovation activities of its co-evolving actors, organized across co-innovation processes, and resulting in co-creation of new value delivered through innovation” (Klimas and Czakon 2022: 6). Hence, IE conceptually differ from innovation alliances, which are focused on value co-creation through co-innovation processes. IEs may be focused on innovation-related value creation (Bouncken et al. 2020) through collaborative innovation or open innovation, but not necessarily co-innovation, in other words through open models of innovation (less complex and less open) other than co-innovation (Lee et al. 2012).

Due to the deficit of empirical investigation on eco-centric innovation ecosystems, our study focuses on the Gaming Innovation Ecosystem (GIE) operating around the video game industry (VGI), but not organized around a single focal video game developer (VGD).

2.1 Gaming innovation ecosystems

GIEs are non-egocentric innovation ecosystems operating around many actors, none of which is a leader, focal firm or ecosystem orchestrator. Examining them takes the entire gaming innovation ecosystem as the unit of analysis (Ritala and Almpanopoulou 2017). GIEs are complex, multidisciplinary, dynamic structures comprised of co-evolving actors cooperating under co-innovation processes to release innovative video games, thus providing gamers and players with a co-created value proposition. Following Mercan and Göktaş, GIEs “consist of economic agents and economic relations as well as the non-economic parts such as technology, institutions, sociological interactions, and the culture: (2016: 102) related to games and gaming.

In terms of the institutional framework developed by Granstrand and Holgersson (2020), a GIE covers a set of various and evolving actors (video game developers, gamers, game distributors, publishers, etc.), their activities (video game development processes, sale, promotional events, market research, etc.), artifacts (various video games, their improvements, patches, etc.), and the institutions and relationships, including complementary and substitute relationships (inter-organizational, interpersonal, inter-community, etc.), that are important for the innovative performance of an actor or a population of actors (Banks and Potts 2010)

2.2 GIE actors

This study focuses on the actors, processes and resources involved in generating intended ecosystem outputs, that is co-innovations (Lee et al. 2012). Additionally, the outputs are seen as a feature of innovation ecosystems that distinguish one from another (Aarikka-Stenroos and Ritala 2017; de Gomes et al. 2018; Ritala and Almpanopoulou 2017; Valkokari 2015).

GIE actors can be characterized as: (1) operating in a hyper-dynamic, technologically advanced and knowledge-intensive industry (Xu et al. 2018); (2) running a business in a fast-developing emerging industry that tightly links technology, art and business (Schmalz et al. 2014; Xu et al. 2018); (3) struggling with above-average uncertainty due to unilinear and unpredictable innovation development (Russell and Smorodinskaya 2018; Schmalz et al. 2014); (4) facing increasing and progressive digitalization (Oh et al. 2016), and (5) operating under high innovation and competitive pressure (Koch and Bierbamer 2016; Oh et al. 2016; Russell and Smorodinskaya 2018). Such environmental characteristics favour the creation of innovation ecosystems and encourage active participation in its activities.

An individual GIE consists of formal and informal organizations, individuals and communities of individuals interested, and engaged in co-innovation processes aimed at the development and release of new games. This multilateral structure is both complex and loose at the same time. On the one hand, the innovation ecosystem involves various types of actors targeting different main goals (e.g. developers vs gamers) with differing interest in the co-creation of new value (e.g. developers vs university). Moreover, these actors are connected through a set of dyadic, triadic and multi-actor relationships that take the form of innovation co-creation relationships, i.e. a type of co-creation relationship (Vargo 2009) aimed at the mutual realization of the innovation process, and thus exploited during the co-innovation process (Klimas 2019).

On the other hand, the innovation ecosystem is loose as the relationships among and between actors are usually exploited ad hoc and periodically in an informal way through social connections. Indeed, the development of video games runs cumulatively and it is hard to plan everything a priori, thus new product development processes are not only seen as risky, but uncertain as well (Schmalz et al. 2014). Additionally, the video game industry reflects typical features of the creative industry, including extraordinary uncertainty (Lingo and Tepper 2014), a high level of independent creativity (Tschang 2005), and favouring social relationships. “Video game developers can be understood as a unique social group called an occupational community” (Weststar 2015: 1238) strongly embedded in social relationships within the industry. Social relationships among developers are a significant source of value co-creation (Tschang 2007; Zackariasson and Wilson 2010), but social relationships with communities of users (Burger-Helmchen and Cohendet 2011; Parmentier and Mangematin 2014), communities of gamers (Marchand and Hennig-Thurau 2013), or communities of modders and hackers (Poor 2013)Footnote 1 are also important for co-innovation.

2.3 GIE co-innovation processes

Under the “ecosystem of business, where individuals, organizations, governments, and economies are all networked and interdependent, we need a new innovation model” (Lee et al. 2012: 818). This most open form of innovation is co-innovation (Lee et al. 2012), i.e. a process in which external partners such as firms, institutes, NGOs and customers are involved in the innovation effort through the sharing of knowledge, other resources, costs and benefits to create unique customer value (van Blokland et al. 2008). Co-innovation takes the form of interactive development (Bossink 2002), and therefore is a co-evolving process (Royer and Bijman 2009) based on ongoing convergence, collaboration and co-creation among actors (Lee et al. 2012). There is a gap in the ecosystems literature relative to the identification of actors involvement in subsequent stages of the co-innovation process. Some conceptual suggestions are available, but lack empirical examination. For instance, Autio and Thomas (2014) point to the discovery, development, deployment and delivery of new products or services. Klimas (2019) posits that co-innovation is a cooperative process covering: co-discovery, co-development, co-deployment and co-delivery of new products or services. Other relevant stages such as implementation (Ernst et al. 2020); adoption (Baregheh et al. 2009), post-launch (Hoyer et al. 2010), adjustments and diversification (Geissdoerfer et al. 2016) can be found in the new product development stream of literature. By integrating these propositions, we expand the 4-stage framework for the co-innovation process developed by Klimas (2019) by adding a process focused on innovation co-dissemination, as suggested in innovation management literature (Table III in Baregheh et al. (2009). Therefore, we see the process of co-innovation as consisting of five subsequent stages (Fig. 1): co-discovery i.e., ideation and concept design; co-development i.e., prototyping and production; co-deployment i.e., product implementation; co-delivery i.e., marketization and commercialization; and co-dissemination i.e., late promotion, adjustments and re-configurations.

Fig. 1
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Source: own work using (Baregheh et al. 2009; Ernst et al. 2010; Hoyer et al. 2010; Klimas 2019; Krafft and Singh 2010; Song et al. 1998)

The stages of the co-innovation process

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2.4 GIE outputs

GIEs main outputs are games, which are the medium of value created, co-created, appropriated and delivered to customers. Video games are typically developed as projects deployed across the innovation process, so game releases can be seen as an innovation output. Video game designing leads to innovation output resulting from “creative processes such as insight or inspiration, or from the form of creativity that ‘blends’ disparate concepts together in novel ways by adapting, adding or combining them” (Tschang and Szczypula 2006: 470). Further on, bringing a game from concept to market” always takes the form of a project in terms of project management (Schmalz et al. 2014). Video game developers (VGDs) are project-based organizations (Legault and Weststar 2015). These project-based and innovation-oriented characteristics provide favourable conditions for the creation and development of a successful innovation ecosystem (Pombo-Juárez et al. 2017; Thomas and Autio 2020).

Video games can be characterized as: (1) integrated and highly modular products (Adner 2006); (2) complex and technologically advanced products (Dattee et al. 2018), (3) products based on several interdependent and complementary technologies (Holgersson et al. 2018), (4) products usually targeting new global demands and adopting new technological solutions (Ferasso et al. 2018), and (5) products with the potential to draw benefits from co-creation or co-development, also through relationships with communities of users (Autio and Thomas 2014; Russell and Smorodinskaya 2018; Niemand et al., 2021). These characteristics are important because they favour the creation of innovation ecosystems and encourage a wide range of actors to actively participate in co-innovation processes.

3 Research design

Our study aims to understand the innovation ecosystem from the perspective of those engaged in innovative processes. In line with managerial perception literature (Czakon and Czernek-Marszałek 2021), we rely on qualitative methods adequate for exploring the “what”, “how”, “when”, “who” and “where” research questions (Gioia et al. 2013) where theory is unavailable, scant or nascent (Graebner et al. 2012). Accordingly, we address the following research questions: who creates IE, what are the distinct roles of the actors, how do these actors engage in the roles and stages, and when/where are these roles performed in terms of the stages of co-innovation processes.

3.1 Empirical setting

Prior innovation ecosystem studies have focused on various industries: design in Great Britain (Sunley et al. 2008), telecommunications in Germany (Rohrbeck et al. 2009), healthcare in the US (Kapoor and Lee, 2013), biofuel in the US (Weil et al. 2014), high-tech manufacturing in China (Wu et al. 2018; Xu et al. 2018), aerospace in the US (Mazzucato and Robinson 2018), and the global jewellery industry (Dąbrowska et al. 2019). We follow calls to investigate industries other than high-tech manufacturing (Kapoor and Furr 2015). We selected the video game industry as it is technologically advanced, knowledge-intensive and at the same time highly creative. It remains surprisingly underexplored in management research (Burger-Helmchen and Cohendet 2011; Mazzucato and Robinson 2018) including ecosystems research (Feijoó 2012; Inoue and Nagayama 2011; Klimas 2019).

We purposefully selected the Polish Gaming Innovation Ecosystem as a globally successful example of a knowledge-intensive and highly creative innovation ecosystem (according to reports by the Entertainment Software Association, Euromonitor International, NewZoo, the Polish Gamers Observatory and Statista). The main criterion used to select this GIE was the intensity of innovation, so the studied context provides a rich example for the study, but is not an extreme case (Suri 2011). The GIE is concentrated around the formal video game industry operating in Poland and covers the entire value (co-) creation processes, including various actors engaged across these processes such as video game developers, publishers, NGOs, gamers, players and different types of gaming communities. Also, video games, video game developers and the video game industry are quite novel industrial contexts in management research, including research on innovation ecosystems (Inoue and Tsujimoto 2017; Ozalp et al. 2018). Finally, prior studies on GIE were run in other national contexts (Inoue and Nagayama 2011), in GIE operating around global corporations (Inoue and Tsujimoto 2017; Ozalp et al. 2018), or were focused on mobile IE only (Feijoó 2012).

3.2 Data collection

To capture perceptions stable over time, we collected data over 3 years (from 2015 to 2017) in three waves of interviews (38) and non-participatory observations (5) with various actors involved in the GIE. We aimed to capture mutual perceptions and diverse views of the same focal GIE, including the view of the researchers through their observations. The general framework of the gaming innovation ecosystem was developed by reviewing prior systematic literature reviews (Czakon et al. 2019; Kraus et al. 2020).

To ensure the reliability and validity of our study (Humble 2009), five types of triangulation were used (Guion et al. 2011): data, investigator, theory, methodological, and context. Data was triangulated from two sources of data sources: secondary i.e., academic and grey literature including industry reports, industry and company websites, online forums and portals for game developers, gamers, game reviewers, etc. (Appendix 1); and primary, that is field research using structured interviews, two rounds of semi-structured interviews and non-participatory observations (Appendix 2). Investigator triangulation consisted in involving two researchers in the analytical process separately, then establishing convergent findings and discussing differences. Theory triangulation involved several theoretical views on co-innovation processes i.e., new product development, innovation ecosystems, and co-innovations. By mobilizing multiple analysis methods, i.e., deductive coding, thematic analysis, and flexible pattern matching we ensured methodological triangulation. Finally, by collecting data in different contextual settings: online and on-site in firms’ headquarters, in neutral locations such as restaurants, and during trade fairs we ensured contextual triangulation. We reached out to individuals representing different industrial perspectives as key informants i.e., video game developers, publishers, a global consulting company and gaming media. Moreover, we run non-participatory observations during trade fairs (Game Industry Conference during Game Arena), B2B events (Digital Dragons and Mastering the Game), and game jams (organized by Digital Dragons Academy).

The research process (Fig. 2) consisted of a literature review (Appendix 1) and the empirical study. To collect data, we designed a long-term process starting in 2015 and finishing in 2017. This process covered two phases: one focused more on exploration and the second more on findings validation through member checks (Appendix 2).

Fig. 2
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Data collection process

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Phase 1 involved three data collection techniques (Appendix 2): 13 structured interviews with VGDs attending Digital Dragons 2015 (May 2015); 8 semi-structured, direct interviews with open questions covering sources of firm innovativeness and a wide range of co-creation relationships used to build, leverage or defend firm innovativeness and create new games (October 2015-March 2016); 5 non-participatory observations (May 2015-November 2016) focused on observation of mutual connections of VGDs and relationships with other VGI members, as well as practices in project management, innovation management, inter-organizational cooperation and strategic management.

Phase 2 consisted of member checks in order to validate the accuracy of participant’s subjectivity representation. This stage aimed to confront initial findings, eliminate inaccuracies, and identify missing evidence for the considered features, types, actors and their roles in the gaming innovation ecosystem from business practice. We collected data through 17 semi-structured, direct interviews with video game developers, between May 2016 and January 2017 (Appendix 2). We used open questions referring to identified co-creation relationships with different actors inside VGI, outside VGI and within gaming communities; key partners considered in business models. Due to important market differentiation, the interviewees represented different types of video game developers: PC, console, mobile game developers, as well as those operating using premium and fermium (face-to-face) monetization models (Klimas 2017). One firm, due to its extraordinary significance in terms of its capabilities in the implementation of different forms of radical innovations i.e. breakthrough, disruptive and game-changing—participated twice in interviews, in the exploration and clarification phases.

3.3 Data coding and analysis

This study adopts a flexible pattern matching approach (Bouncken et al. 2021). We adhere to the postulates of logical consistency (literature-driven), subjective interpretation (emerging), and adequacy (hybrid involving deductive and inductive codes). Hence, we combine theoretical patterns and rigorously identified empirical patterns in a pattern matching exercise. Consistent with our research aim, we “validate or (if possible) extend conceptually a theoretical framework” because “existing theory (…) is incomplete and would benefit from further description” (Hsieh and Shannon 2005: 1281). Therefore this study focuses on expanding the transparency and granularity (Humble 2009) of existing views on GIE actors’ roles (Dedehayir et al. 2016; Spelmeyer and Lingens 2018) from the perspective of their engagement in the co-innovation process (Fig. 1). At a more general level, we strive for a “balance between rigid standardization and complete anarchy” (Bouncken et al. 2021: 252) by deploying a pattern matching framework, which operates as an interaction between the deductive and inductive approaches.

Initial coding is theory-driven (i.e., the contexts of new product development, innovation ecosystems and co-innovations in a given industry context). We then use directed content analysis (Fereday and Muir-Cochrane, 2006) as an analytical technique (Hsieh and Shannon 2005) to finally define the list of codes. To code and analyse our data, we followed the conventional process in qualitative research consisting of data reduction, data display and data verification (Miles and Huberman 1994). To address our research questions, we have structured the codes into three categories: actor, role, and co-innovation process.

Regarding the actors, we started with a comprehensive list derived from the literature. Regarding the roles played by actors, we used the four categories identified in prior literature and matched them with the roles reported by our interviewees. Regarding the co-innovation processes, we departed from the literature, which structures these into three, four, or five sub-processes, and found in our data that a more fine-grained list is used by our informants. In order to reinforce the trustworthiness of our coding (Miles and Huberman 1994), we first used data triangulation by confronting the primary data sources with the existing secondary data sources. Next, we validated our findings through member checks with our informants in the second wave of interviews. Thirdly, we triangulated the codes and related findings between the two researchers.

4 Findings

An accurate understanding of innovation ecosystems requires the actors, their respective roles and the processes carried out by those actors to be identified (Dedehayir et al. 2016; Galateanu and Avasilcai 2016; Pattinson et al. 2018; Sun et al. 2019). Accordingly, our findings are structured around actors, roles and co-innovation processes as reported and validated by our informants.

4.1 Who: the actors involved in gaming innovation ecosystems

The actors of IE are firms and other organizations, customers and their communities interlinked by the interest in increasing the innovativeness of products, industry, region, or sector of the economy (Pilinkiene and Maciulis 2014; Aarikka-Stenroos and Ritala 2017). By actor we mean an individual or collective entity directly involved in the co-innovation process (Carayannis and Campbell 2009; Aarikka-Stenroos and Ritala 2017), in value co-creation and value capture (de Gomes et al. 2018; Schroth et al. 2018), but also in fertilizing and accelerating innovation processes (Sun et al. 2019). Generally, actors of innovation ecosystems can be stratified by levels of analysis into four categories: networks of organizations, organizations, individuals, and communities.

Firstly, the literature suggests that innovation ecosystems can be seen as a meta-organizations (Russell and Smorodinskaya 2018; Valkokari 2015) consisting of multi- or meta-organizational (Gulati et al. 2012) actors such as networks, clusters, franchising networks, strategic alliances, etc. However, in our data this type of actor was not identified as no contributors represented the multi-organizational form. Our respondents do not indicate this category, which may be attributed either to its usefulness in GIE involvement or to the somehow abstract construct that networks are.

Secondly, different types of organizational actors including firms, public institutions and NGOs were identified within the GIE, such as: firms producing the final products, competitors, suppliers and complementors. Interestingly, producers of substitutes were not indicated as actors in the GIE. Besides firms, public organizations were also listed, including the government (Ministry of Culture and National Heritage), public institutions (Cracow Technology Park), research institutes (National Centre for Research and Development) and universities (Silesia University). Interestingly, regulators were not indicated by our informants as actors in the GIE. However, not-for-profit organizational actors such as industry regulators (SPIDOR, which informally coordinates the regulatory initiatives imposed by the government and triggers bottom-up ones) and NGOs (Polish Indie Games Foundation or Polish Game Association) are identified as actors in the GIE.Footnote 2

Thirdly, innovation ecosystems are pictured as distinctive because they frame the engagement of social actors (Aarikka-Stenroos and Ritala 2017), both individuals (Autio and Thomas 2014) and communities (Valkokari 2015). Indeed, in contrast to other types of ecosystems, innovation ecosystems cover both sides of the market, namely demand (communities of gamers and individuals such as modders or testers) and supply, and thus cover complete value chains (Stadler and Chauvet 2018: 113). Such a broad scope of actors favours drawing benefits from both co-innovation pushed by the market (producer-led innovation) and pulled by customers (user-led innovation, customer-led innovation, market-led innovation). Furthermore, it becomes possible to provide a new value proposition based on more complex solutions linking both of the above (Russell and Smorodinskaya 2018).

Relevant individual actors typically are: consumers (players and gamers diversified in terms of interest in getting involved in the process of co-innovation), non-user clients (not identified in our case), politicians (the director of the Department of State Patronage in the Ministry of Culture and National Heritage), and investors (not directly identified as really engaged in our case). Our data shows that the list of previously identified individuals is incomplete. We found additional types of individuals that contribute to co-innovation processes implemented within the GIE: modders, hackers and testers involved at the co-development stage of the co-innovation process, thus directly influencing the newly created value. Furthermore, we found influencers to be important individual actors (YouTubers, bloggers, game reviewers, etc.) involved in co-delivery and co-dissemination stages of the co-innovation process by video game developers (Sect. 5.3.).

Fourthly, communities manifesting an interest in engagement in the co-innovation process were also identified by our informants. These collective actors are seen either as communities of interest or communities of practice. Even though it is difficult to draw a clear demarcation line between these two, communities of interest seem to be more focused on improving and tailoring the co-innovation process to the needs of the participants being targeted by the newly created value, such as communities of gamers, amateur testers and modders considered to be a significant source of fan-based value or even determining a game’s success. In turn, communities of practice focus on the business perspective, on value creation and value appropriation. Our informants identified such collective actors as: communities of developers, indies, professional testers, scriptwriters and 2D and 3D graphic artists. As suggested by Koch and Bierbamer (2016), these two types of communities can be differentiated through the type of work they carry out for the producer of the final products. Communities of practice are therefore seen as communities of specialists directly working for a producer (here for video game developers), while communities of interest are considered to be communities of users exploited informally, thus working indirectly for a producer.

To conclude, the actors that make up innovation ecosystems are diverse. Their different types (Table 1), importance and specific form seems to depend on the co-created products underlying the value proposition offered and delivered to the market, i.e. the product defining the innovation ecosystem’s boundaries (Tsujimoto et al. 2018).

Table 1 Actors in innovation ecosystems
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4.2 What: the roles played in gaming innovation ecosystems

Our informants indicate that actors within the GIE play various roles in the ecosystem (Dedehayir et al. 2016; Galateanu and Avasilcai 2016; Pattinson et al. 2018; Spelmeyer and Lingens 2018; Sun et al. 2019). We identify four intra-ecosystem roles (Dedehayir et al. 2016): leadership, direct value creation, value support and encouraging entrepreneurship. Nevertheless, not all types of actors play these roles. As shown by Su et al. 2018, the roles should be considered across different areas of IE operations, and thus assigned to specific types of actors. Regarding the GIE, game developers might play all of these roles, however gaming communities or individual gamers and players are not interested in, or capable of taking on the role of leadership or encouraging entrepreneurship (Table 2).

Table 2 Roles of actors undertaken within the innovation ecosystem
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In contrast to firm-centric innovation ecosystems, our GIE is collectively orchestrated by many actors. Hence, the leading role is undertaken collectively by video game developers rather than by one single game development studio. This supports recent findings that the leadership role does not have to be assigned to one player, usually a large focal firm (Spelmeyer and Lingens 2018).

Regarding the direct and supporting contribution to value creation, our informants suggest that although the direct value creation role can be assigned to all actors in the innovation ecosystem, the supportive role seems only to be applicable to those actors not implementing innovation processes i.e. co-innovation processes within the innovation ecosystem. Similarly, the role of encouraging entrepreneurship seems to least suit firms responsible for launching innovation on the market (video game developers).

Our data also shows that independently from taking on a role, some actors are more likely to strongly engage in that particular role than others (Table 3). Thus we map the perception of engagement by GIE actors.

Table 3 The engagement of actors into gaming innovation ecosystem
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4.3 When and How: varied involvement in innovation processes

The differences in actors’ involvement can be seen through the lens of their involvement across the stages of the co-innovation processes implemented within the innovation ecosystem boundaries. Our data substantiates prior theoretical suggestions (Dedehayir et al. 2016; Spelmeyer and Lingens 2018) that various actors inside GIEs can be more, less, or not at all involved in the co-innovation processes (Table 4). As one of our interviewees, a developer of AA console games, stated “(…) it depends on what kind of game is being made. Also, it is important at what moment the game is created, these are two things that decide what this co-creation is like and with whom” (VGD_11). This remains in line with prior suggestions linking different actors and the performing of different roles with different impacts on the ecosystem’s value proposition (Walrave et al. 2018).

Our informants structure the phases of the co-innovation process according to actors' involvement: co-discovery, co-development, co-deployment, co-delivery and co-dissemination. Co-discovery is focused on concept creation and idea generation, but also on collecting heterogeneous resource support inside the IE, including intellectual, cognitive and financial resources. The co-development stage refers to operational work on product development including its prototyping and testing. As reported by our informants, intensive co-creation actions are also aimed at product testing. These tests are carried out at different stages of product development, such as early testing, alpha testing, and beta testing run just before the market launch. The co-deployment stage, which seems to be the shortest sub-phase of the co-innovation process, is aimed at intensive product presentation through promotion assistance, mutual support during trade fairs, etc. given mainly by organizational co-creators and market launch. The co-delivery stage covers mid-term promotional activities aimed at full marketization and game commercialization, mainly carried out with individuals and collectives of individuals (gamers and gaming communities, for instance, in the form of staging game playing with the most popular influencers). The co-dissemination phase refers to involving ecosystem actors in such activities as post-launch promotion, different forms of market uptakes, after-sales support and further improvements of products, and finally monitoring of competitors' reactions and customers’ opinions. This stage can be implemented over a very long-term perspective (referring to the extension of PC/console premium games) or even permanently (referring to the retention of mobile freemium games to maximize product performance).

Summing up, the theoretical path of co-innovation process (Fig. 1) generally matches our data. However, more activities are indicated by our informants than prior literature suggested. For instance, exploitation of resource support in the co-discovery phase, exploiting testing of products in the co-development phase, gaining from inputs into after-sales support and further product improvements, as well as from ongoing customer feedback in the co-dissemination phase (Fig. 1 and Table 4).

Table 4 The stages of the co-innovation process – variety of co-creative relationships among ecosystems actors
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Furthermore, depending on the intensity of the involvement of actors across the co-innovation process, some types of actors may be perceived as most desirable and adding contributions at subsequent stages of the process. The engagement of a particular actor type may differ in different co-innovation processes – “ they (ref. to gamers or players) can be a co-creator, but on a different scale. As far as the developer allows them, so sometimes, in some areas, it's going to be a kind of consultative influence, but in other situations, it can be a strong impact including an impact on the gameplay or narration style. So, we ask gamers how to balance these weapons, do you like it, is this weapon too strong, is there a sword or a vehicle that’s nice, how in the next patch we're going to balance the game, do you see any room for gameplay improvements? It can be extended again with the possibility of creating skins, it can be extended with the possibility of making locations, it can be extended with the possibility of making whole mods, yes, that is a completely different game, the inner game you can say, some advanced scenarios and so on. So, as I said, it’s just a matter of what scope gamers will be allowed, what freedom they will be given.” (video game developer producing AA and VR games; VGD_17).

5 Discussion and conclusions

The aim of this study was to identify innovation ecosystems from the perspective of those engaged in innovation processes by addressing the variety of actors (who?), the distinct roles (what?), the different stages (when?), and the diverse engagement into co-innovation processes (how?) as perceived by those involved in innovation ecosystems. We flexibly matched (Bouncken et al. 2021) literature-driven categories with the reported perceptions of those involved in innovation processes in order to advance the current understanding of the innovation ecosystem structure and dynamics, as well as the roles and involvement of actors in distinct phases of the co-innovation process.

5.1 Theory contributions

Our study offers three noteworthy contributions to innovation ecosystems literature: conceptual, structural and dynamic. We refine Klimas and Czakon’s (2021) conceptualization of IE and Granstrand and Holgersson’s (2020) three-dimensional framework by developing a more operational definition of innovation ecosystems: a multi-element cooperation environment surrounding the framework of innovative activity of its co-evolving actors, organized across the co-innovation process, and resulting in co-creation of new value, delivered to the market in the form of innovation. By introducing co-evolution and co-innovation, we shed light on the interdependencies relevant in ecosystems, and emphasize the variety of roles and interactions that unfold with time.

Secondly, we advance the structural perspective on innovation ecosystems by capturing the respective roles various actors play in innovation co-creation, as perceived by their participants. Prior literature listed actors, roles and processes from the perspective of an external, objective observer grounded in the positivist stance in management research. By adopting a social-constructivist stance, we complement, refine and extend prior claims with the perceptions of those involved in innovation ecosystems. As a result, we develop a two-dimensional view of the innovation ecosystem structure, singling out its actors and innovation co-creation relationships. On the one hand, we confirm and expand the structural view of IE in terms of the engaged actors (e.g. Adner 2017; Granstrand and Holgersson 2020). We identify 12 types of collective actors, 9 types of individual actors, and 1 community of individuals. Our study substantiates that individual customers and their communities are important value co-creators (Prahalad 2004), especially for co-innovation. Interestingly, some actors indicated in the literature, as well as some network organizations, were not identified by our informants, while actors not identified in the literature appeared in our study (e.g. regulators and NGOs). We substantiate respective actors’ roles in practice (Dedehayir et al. 2016; Spelmeyer and Lingens 2018). In particular, we found four distinct roles that actors may play in co-creation processes, that is: direct value creation, supporting value creation, encouraging entrepreneurship and leadership. Our data helps to map various actors’ engagement in the four identified roles. As in the case of entrepreneurial ecosystems (Bacon and Williams 2021), our findings show individuals and communities of individuals with outstanding commitment to be critically important in terms of the intense implementation of an intra-ecosystem.

Thirdly, we also advance the dynamic perspective on innovation ecosystems (Beliaeva et al. 2020; Bouncken et al. 2020; Bouncken and Kraus 2021) by identifying the various degrees of engagement that actors take during the co-innovation process. Based on the GIE participants’ perceptions, we structure the co-innovation process (Lee et al. 2012) into five stages: co-discovery, co-development, co-deployment, co-delivery and co-dissemination. We find that actors perceive others and engage in these processes to various degrees over time. Therefore, the roles of actors are not perceived as stable, and depend on the category to which an actor can be assigned. Instead, GIE actors have a broad understanding of co-innovation processes and their potential roles, and that they strategize within these frames and purposefully position themselves in the innovation ecosystem. Such a broad perspective suggests that GIE actors may act as prosumers actively engaged at different stages of co-prosumption (Bouncken and Tiberius, 2021). Furthermore, we find support for prior theoretical claims showing differences in the roles performed by particular actors (Dedehayir et al. 2016), but also uncover evidence that there is not an equal distribution of challenges across innovation ecosystem actors (Autio and Thomas 2014; Adner and Kapoor 2010). In line with Dedehayir et al. (2016), the highest engagement in co-innovation processes can be assigned to the companies responsible for putting the co-created value onto the market (video game developers—Table 4). Moreover, our findings show that co-innovation processes, just like innovation processes, require cross-functional cooperation (Ernst et al. 2010; Song et al. 1998), as particular actors engage in different or the same stages while undertaking different or the same roles across those stages. Interestingly, the trust-based rules underlying the very high engagement of competitors at every stage of the co-innovation process, show coopetition and the perception of competitors in coopetition as quite similar within ecosystems and networks (Czakon and Czernek-Marszałek 2021).

5.2 Practical, regional and social implications

Our study aims to understand IEs as perceived by their participants. The methodological implications of this stance do not allow for normative statements, as perceptions may vary across empirical settings. Additionally, we focused on identifying what GIE participants actually perceive, not what they should perceive. Therefore we refrain from setting forth normative implications for practice.

However, by finding that roles and degrees of involvement vary over time, we tap into a very important strategic decision that GIE participating actors take. Depending on the role and their engagement, their goals and value capture is likely to differ. Therefore, we encourage GIE actors to develop a broad understanding of the co-innovation process, to map possible positions they may be willing to take, and to purposefully strategize around this map. This may help in sensing opportunities and in effectively exploiting those opportunities.

Additionally, our method follows the social constructivist approach to the study of organizations. Its distinctive feature is that social structures and processes are enacted by the actors involved. Therefore, such constructs as social norms, identities and legitimate interaction processes come into focus. Innovation ecosystems are not just systems of interaction between innovation processes, but are embedded in social reality. This opens ways for the scrutiny of IE as socially embedded, and helps in fostering the creation and functioning of IE through social action.

Finally, our study reveals the multi-actor facet of IE, and differentiates this facet in terms of the roles and engagement in co-innovation processes. At the meso or macro levels, these findings can be used by local authorities or policy makers to screen, select and work with either the most influential actors, or those undertaking specific actions to expand innovativeness in a given region. The ecosystem concept assumes that the co-creation outputs of ecosystems are beneficial not only within their boundaries. We suggest that the engagement, roles and actions carried out by a wide range of IE actors can generate more general ecosystem effects by fostering regional entrepreneurship (Bacon and Williams 2021; Scott et al. 2021) or digitalization of a particular region or country (Beliaeva et al. 2019).

5.3 Limitations and further research

The limitations of our study stem from the methodological choices. Our exploratory and qualitative approach provides limited options for drawing general conclusions, but allows us to make moderate generalizations (Payne and Williams 2005) relevant in social sciences (Finfgeld-Connett 2010). The conceptual framework underlying our exemplification was based on a synthesis of prior systematic literature reviews. Although such an approach is suitable in a rapidly growing field of interest (Czakon et al. 2019), the results of such reviews, as any findings from a review of the literature, are not free from subjectivity (Hagen-Zanker and Mallett 2013; Kraus et al. 2020). In other words, our study's conceptual rigor is indirectly influenced by the emerging stage of innovation ecosystems literature.

Secondly, by taking an in-depth qualitative approach, we were able to map its relevant components as seen by those involved. We relied on individual perceptions, which revealed stability over time and across various respondents. However, we did not explicitly focus on collective understanding, social identity and norms. This opens avenues for further qualitative scrutiny at the collective level of analysis. Additionally, further scrutiny from a more objectivist stance, could be useful in complementing the subjectivity of those involved in IEs.

Qualitative studies are prone to subjectivity biases by the informants and the researchers. In order to address this we have deployed multiple triangulation types, and proceeded with member-checks to validate the accuracy of our findings with our informants to strengthen the rigor and trustworthiness of our findings. We also recognize that perceptions of those involved in GIE may not be stable over time. To address this concern we have run a longitudinal data collection process on a period of 3 years. Nevertheless, we encourage further research across different contexts, periods of time, and types of innovation ecosystems in order to gather additional insights before proceeding with large sample studies.

Another limitation of our study stems from the industry focus. While the global nature of the gaming industry (Rodzińska-Szary et al. 2016; Niemand et al. 2021) encourages generalizations, they are bound to the context of gaming industries only. We encourage further scrutiny across other industries as well as dig deeper into recognition if there are any other types of actors relevant in different national context (e.g. probably software complementors including firms providing game engines can be recognized as important co-innovators while in Poland they have not been identified at all).

We also recognize that value co-creation, including the co-creation based on innovating experience (Prahalad and Ramaswamy 2004) that is so important in innovation ecosystems (Klimas and Czakon 2021), is highly contextual. This high level of contextuality can be seen as specific for studies on ecosystems in terms of both the national context (e.g. the Brazilian context – Beliaeva et al. 2019; Italian context – Del Bosco et al. 2020), as well as the industry context, as industry characteristics can also play a role (e.g. position inside the industry, industry dynamics – Bouncken et al, 2020; intensity of competition—Beliaeva et al. 2019; intensity and type of coopetition – Le Roy et al. 2021). Therefore, running a non-contextually limited investigation would be seen as unreasoned at this stage.

Further research could also investigate actors, their roles and their engagement in co-innovation or co-creation processes. Actors’ roles (Dedehayir et al. 2016; Spelmeyer and Lingens 2018) and intra-ecosystem dynamics are shown to be relevant and under-researched issues in the entire ecosystem approach (Bouncken and Kraus 2021). This study sheds some light on these issues within innovation ecosystems. Therefore, future replication studies could be run in other ecosystems (Aarikka-Stenroos and Ritala 2017; Scaringella and Radziwon 2018; Tsujimoto et al. 2018; Valkokari 2015), including for instance entrepreneurial ecosystems (Scott et al. 2021), as a recent literature review showed “innovation and dynamics: actors and norms” to be one of the most significant trends in the literature (Fernandes and Ferreira 2021: (1). Furthermore, as the behavioural and cognitive issues have been shown as important for game-playing entrepreneurs (Niemand et al. 2021) they can be also explored in the context of innovation ecosystems. Replication research could also be run in other types of innovation ecosystems (Klimas and Czakon 2022; Pattinson et al. 2018) that are likely to differ in terms of global success, product type and creative nature. Both the choice of empirical setting following the purposeful criterion-intensity and the industry choice call for replicating our study in different contexts. We concur with prior suggestions (Durst and Poutanen 2013) that developing the understanding of innovation ecosystems requires explorative studies focused on delivering empirical data useful in country comparisons as “comparative settings would clarify what factors are likely to remain constant under different conditions” (p.11). We do believe that further replication (Finfgeld-Connett 2010) and explorative (Thomas and Autio 2020) investigations will cumulatively create a coherent and generalizable knowledge base on innovation ecosystems.