Investigating a manufacturing ecosystem in transition toward electric vehicles – a business model perspective

2.1 BMI in ecosystems

While comprising different definitions, a business model can be subsumed in value offer, value creation and value capture (Foss and Saebi, 2017). Afuah and Tucci (2003) define a business model as a “method by which a firm builds and uses its resources (value creation) to offer its customers better value than its competitors (value offer) and to make money doing so (value capture).” BMI describes a significant change in multiple elements of an extant business model. Further, the value created by a given technology largely depends on the business model within which it is used, such as in the digital transformation of manufacturing or Industry 4.0 (Müller, 2019; Rachinger et al., 2018).

So far, empirical studies have primarily investigated business models as a company-centric construct (Hoch and Brad, 2020; Palmié et al., 2022; Yi et al., 2022). However, from early on, business models were conceptualized in the context of business environments (Amit and Zott, 2015) due to the boundary-spanning nature and dependence on external actors (Adner, 2017; Talmar et al., 2018).

As individual companies typically control different sets of resources and pursue specific activities, ecosystems are likely to emerge in situations where actors do not yield total control over their operations (Hoch and Brad, 2020; Palmié et al., 2022; Yi et al., 2022). Granstrand and Holgersson (2020) define an ecosystem as an “evolving set of actors, activities, and artifacts, and the institutions and relations, including complementary and substitute relations, that are important for the innovative performance of an actor or a population of actors.”

In manufacturing literature, the topic of manufacturing ecosystems has only recently emerged (Ates et al., 2023; Kazantsev et al., 2023). Several authors describe the formation of manufacturing ecosystems due to Industry 4.0 or post Covid-19 (e.g. Das and Dey, 2021; Schmidt et al., 2023). While other industries have seen developments toward ecosystems earlier, the automotive industry is characterized by traditional standards and is organized centrally by the automobile original equipment manufacturers (OEMs) (Kazantsev et al., 2023; Riasanow et al., 2021; Suuronen et al., 2022).

The research gap addressed is that there is little empirical evidence on entire ecosystems and detailed interactions of actors and their governance (Amit and Zott, 2015). xEV ecosystems represent a novel research context for an entire production ecosystem. Conclusively, the purpose of this study is to gain a better understanding of BMI in manufacturing ecosystems due to the transition of automotive manufacturing toward xEVs. In particular, the paper investigates Strategic Influences on Business Model Design (Abdelkafi et al., 2013; Aaldering et al., 2019). Further, how Business Model Design is manifested in the elements of a business model, Value Creation, Value Proposition and Delivery, and Value Capture, is investigated (Foss and Saebi, 2017).

2.2 Interaction and governance of BMI in ecosystems

Actors in an ecosystem require a vision for the overall ecosystem and appropriate governance to ensure their alignment (Adner, 2017; Iansiti and Levien, 2004; Moore, 1996) and the ecosystem’s overall health (Dattee et al., 2018). Subsequently, ecosystem actors must know their resources and respective business models and understand how ecosystems are governed. Then, they can align their business models to create BMI as an ecosystem (Adner, 2017). Ecosystem actors can be divided into (1) central ecosystem actors, in our case, automotive OEMs, (2) upstream actors, e.g. suppliers and (3) downstream actors, e.g. retailers (Dedehayir et al., 2018).

Several authors refer to a form of governance or orchestration of BMI in ecosystems to describe the interactions within ecosystems. Iansiti and Levien (2004) highlight the importance of keystone actors who create and share ecosystem value to attract and retain other actors, increasing stability and predictability. Thus, BMI in ecosystems requires the alignment of customer needs, technologies and infrastructures (Wang et al., 2022). In addition, as proposed by Russell and Smorodinskaya (2018), ecosystems typically emerge from collaboration by the actors. Hence, technological shifts require specific governance by actors and their interactions (Spieth and Meissner, 2018). We therefore investigate the Governance of Interactions and the Types of Interactions. The latter also reflects Spieth and Meissner (2018), who find incumbent companies increasingly relying on partners for additional resources, resulting in new forms of interactions and a Change of Ecosystem Value Creation Architecture as a further target of our investigation. The required integration, however, proves especially difficult (Baccarella et al., 2023).

Spieth and Meissner (2018) further indicate that taking an ecosystem-based approach to BMI can be a delicate prospect because it requires a thorough understanding of the entire company environment and external influences. Hence, we further investigate Strategic Influences on Interactions.

3.1 Choice of method

Case study research represents an appropriate research method for gaining insights into contemporary phenomena in their real-life setting. Further, “How and Why” questions that do not require control of behavioral events should be answered (Yin, 2017). Swanborn (2010) adds that case studies are an appropriate research method to investigate phenomena that are not isolatable. Thus, we regard the interactions within an ecosystem to create BMI as appropriate for conducting a case study. Further, the transition toward xEVs represents a contemporary phenomenon. The automotive industry is in transition toward xEV due to legislation, especially in the form of environmental and emission regulations (Abdelkafi et al., 2013; Bohnsack and Pinkse, 2017; Monios and Bergqvist, 2020). Due to geographic and cultural proximity, we chose an automotive manufacturing ecosystem in transition toward xEV in Germany and Austria as our case study. Germany represents the most important manufacturing country within the European Union and is also leading in patents and technologies toward xEVs (European Commission, 2020). Austria was included due to its essential roles in the automotive manufacturing ecosystem and geographical proximity, e.g. the majority of BMW’s engines are manufactured in Steyr, Austria (BMW, 2023).

3.2 Participants’ selection

We chose four automotive OEMs as a starting point to investigate the ecosystem since Eisenhardt (1989) suggested four as a minimum number for a case study. As Strang (2015) noted, the final number of participants cannot be stated at the outset of the research but develops during the course of it. Following the recommendation of Yin (2017), this study was designed to include at least two participants for each ecosystem actor type in addition to the four OEMs representing central ecosystem actors. Considering the insight into relevant ecosystem roles provided by Dedehayir et al. (2018), both upstream and downstream actors were included. In addition to the participant selection procedure described above, participants had to fulfill the following selection criteria: (1) companies needed to be either actors in the xEV ecosystem or directly affected by actors in the xEV ecosystem, (2) data collection focused on organizational units providing value toward the xEV ecosystem value proposition and (3) only data from companies with a headquarter in Germany or Austria was collected for the reason of geographical and cultural ecosystem proximity to ensure comparability.

For selecting participants, we started by including four major automotive OEMs, as described above. We continued with their most important automotive suppliers, which are among the largest worldwide. The participants were identified using professional networking platforms (i.e. LinkedIn and Xing). Additional participants like Tier 2 suppliers as well as engineering and technology providers, partnering research institutions (RI), automotive retail companies and corporate vehicle fleet operators were either recommended by participants or identified through desk research. A similar approach was taken to identify further participants such as energy and infrastructure companies. While this selection of informants potentially bears some bias since participants were partially recommended by each other, we were able to identify those who actually cooperate directly. This is especially important for the intended ecosystem perspective, which is further strengthened by the fact that several cross-relationships exist, such as Tier 1 suppliers providing products and services to several of the four OEMs, as well as others such as engineering and technology providers, infrastructure companies or RI.

We stopped data collection for the empirical study if additional insights from each actor type did not occur, i.e. when saturation was reached. Since we do not aim to derive importance from a number of occurrences and mainly analyze each actor type individually, we argue that it is acceptable to include different numbers of participants for each actor type. We stopped data collection after gathering data on 27 ecosystem members and insight from 46 interview respondents.

Potential informants were contacted and provided with a short research project summary. The main requirement was their knowledge about and interaction with further ecosystem actors. In addition, we aimed for at least a team leader level to ensure some level of involvement in strategic considerations. While this approach did not aim for completeness regarding the representation of all actor types equally, we aimed to gain an overview of as many actors as possible.

3.3 Participants’ description

Table 1 summarizes the case study participants. In addition, Appendix 1 gives more details on the individual participants, their roles and interview types and lengths. Due to demands for anonymity, we had to generalize several roles while others are stated with more details.

Figure 1 further illustrates the ecosystem investigated.

3.4 Data collection and analysis

We conducted semi-structured interviews based on an interview guideline asking for Strategic Influences on Interactions, Governance of Interactions, Change of Ecosystem Value Creation Architecture and the Types of Interactions relating to Research Question 1. For Research Question 2, the questions related to Strategic Influences on Business Model Design, Value Creation, Value Proposition and Delivery, and Value Capture. Appendix 2 shows the detailed interview guideline.

The data for the main study were collected from December 2018 to September 2019. All interviews were recorded and transcribed in full to establish a solid basis for further analysis. The collected transcripts were used to perform a qualitative content analysis (Gioia et al., 2013). The coding procedure was performed considering individual sense-bearing phrases using the software MAXQDA2018. A structuring logic proposed by Gioia et al. (2013) was employed to evaluate and structure relevant findings into categories. A category was considered saturated when no new properties, dimensions, conditions, actions/interactions or consequences emerged from the data.

After analyzing data from 46 respondents, a workshop with industry professionals was held to verify and extend the results. Hence, Figures 2 and 3 attempt to show overarching themes across categories and across actors. Further, Figures 2 and 3 show the most important interrelations regarding both research question 1 and research question 2. The figures do not aim for completeness but to get an overview of the most important aspects, their interrelations and the ecosystem structure. Tables 2 and 3 summarize the interview data for each actor type, as explained in the following section. Since we do not aim for a cross-actor analysis but rather interrelationships of the entire ecosystem, we do not display the detailed constructs according to Gioia et al. (2013).

4.1 Influences and governance on interactions, change of ecosystem architecture (RQ 1)

Table 2 gives an overview of Strategic Influences on Interactions, Governance of Interactions, Change of Ecosystem Value Creation Architecture and the Types of Interactions relating to Research Question 1. The results are separated for the different ecosystem roles (abbreviations can be found in Table 1) and show the condensed categories for each actor type. Below, we briefly summarize the findings for each actor type.

OEMs as central actors rely on collaboration with other ecosystem actors and reorient their strategies both upstream and downstream in the ecosystem.

Engineering and Technology Providers (ETPs) face the challenge of establishing themselves as integral ecosystem members. While they possess crucial technological expertise, they find themselves in a position of limited influence compared to OEMs. Still, their experience with xEV-related technologies positions them as valuable partners in the ecosystem.

Established automotive suppliers (SUP(e)) are capitalizing on the growing demand for sustainability-driven solutions. They collaborate closely with OEMs, albeit with an eye on the emergence of new competitors in the xEV market. Meanwhile, focused technology suppliers (SUP(f)) pursue market leadership in xEV components, collaborating with large customers to influence OEMs and aiming for cooperation via technology licensing and partnerships. RI report increased regulations and shifting competencies required for xEVs.

Retailers (RET) are focusing on complementary solutions to support the xEV ecosystem, such as charging infrastructure, while reporting dependencies on OEMs and energy providers. Fleet Operators (FO) are leveraging governmental incentives and forming partnerships to integrate xEVs into their vehicle fleets. However, they face challenges related to infrastructure availability and are dependent on OEMs for supply.

Electric energy companies (EC(e)) monitor the environment to align activities with other ecosystem actors and collaborate to offer seamless charging infrastructure coverage. However, they face challenges in exerting significant influence on central ecosystem actors like OEMs. Petrol energy companies (EC(p)) face technological and financial risks alongside the low availability of xEVs, hindering investment in the charging infrastructure.

Infrastructure Companies (INF) are tasked with providing user-friendly charging solutions and navigating the influence of OEMs in the ecosystem. As OEMs increasingly enter the charging infrastructure sector, INF find themselves engaged in a balance of cooperation and competition.

Condensing Table 2, Figure 2 presents the identified most significant interactions and dependencies in the xEV ecosystem based on the follow-up workshops.

4.2 Changes in business model design and strategic influences (RQ 2)

Table 3 gives an overview of Strategic Influences on BM Design, Value Creation, Value Proposition and Delivery, and Value Capture, separated for the different ecosystem roles and shows the condensed categories for each actor type. As for Table 2, we summarize the most prominent contents below.

OEMs are strategically focusing on scaling up xEV technology and charging infrastructure. They recognize the critical importance of timing in scaling up these technologies, such as incorporating electric drivetrains. Additionally, OEMs are increasingly reliant on partnerships with other ecosystem actors to offer complementary solutions, such as electric charging infrastructure, in their pursuit of holistic xEV offerings to satisfy evolving customer demands and regulatory requirements.

ETPs face significant uncertainty stemming from competing technologies in the xEV landscape. They rely on profits from traditional petrol-based technologies to finance a diverse portfolio of xEV-related innovations. Collaborative efforts with RI, start-ups and other partners, as well as acquiring companies, are measures to augment ETPs' capabilities.

RI observed that xEVs are primarily utilized for portraying a positive image and report uncertainty surrounding the dominance of specific xEV technologies. They report from endeavors to maximize profits from traditional petrol-based vehicles while striving to make xEVs commercially viable.

SUPs, especially those focusing on xEV technologies (SUP(e)), establish independent organizational units and aim for modularization to streamline component production for both xEVs and traditional vehicles. They collaborate extensively with RI, engineering and technology providers, and other partners to enhance their capabilities and navigate the complexities of the xEV ecosystem.

Retailers (RET) rely on collaborations with OEMs to provide charging solutions, aiming to reduce dependence on external infrastructure providers. FO gradually integrate xEVs into their fleets, with a particular emphasis on establishing proprietary charging infrastructure to ensure reliability and cost-effectiveness.

Electric energy companies (EC(e)) focus on offering reliable technological solutions to recoup investments while increasing overall coverage of charging infrastructure through collaboration with other ecosystem partners. They aim to deliver value to customers through efficient monitoring, booking and billing of infrastructure. Meanwhile, petrol energy companies (EC(p)) endeavor to build new competencies while primarily relying on suppliers for technological solutions.

INF play a pivotal role in coordinating suppliers and offering charging solutions to both business and consumer customers, generating revenue through various fee structures while leveraging intelligent solutions to mitigate investments in the energy grid.

Figure 3 subsumes Table 3 regarding the alignment of Business Models in the xEV ecosystem b and highlights the most important interdependencies across actors.

Table 4 presents the degree of business model changes for the investigated actor types. Change intensity is characterized by the scope of business model change and the novelty of a business model to a company or industry. The typology used is based on characterizations of business model changes by Foss and Saebi (2017). RI were left out of this analysis due to a lack of changes in the business models of the investigated actors.

5.1 Ecosystem roles and degree of BMI

The ability and incentive to change business models may depend on the actor’s specific type, role and ecosystem position. Upstream actors tend to perform more evolutionary or adaptive approaches in changing their business models. Downstream ecosystem actors rather perform more focused or complex BMI, as described in Table 4 (Saebi et al., 2017; Foss and Saebi, 2017). The results of the actor interactions extend recent findings in emerging manufacturing ecosystems literature, such as Kazantsev et al. (2023), as an individual actor becomes increasingly challenging. Hence, companies must (1) develop their business models with respect to their ecosystem and (2) rely on upstream and downstream external actors for network-based BMI.

The results indicate that the influence of individual actors on other ecosystem actors' business models is closely related to their ecosystem position and the actor’s ability to create value (Adner and Kapoor, 2010). Central actors – in our case, mainly automotive OEMs – taking on the role of an ecosystem leader could be particularly well suited to pursuing this undertaking. Specifically, central ecosystem leaders could influence upstream suppliers to ensure the alignment of the actors' business models (Adner, 2017). As indicated in Table 4, we confirm extant research that upstream actors adopted or evolved their business models rather than downstream actors (Saebi et al., 2017). Concerning downstream actors offering complementary value, ecosystem leaders could pursue a keystone approach (Iansiti and Levien, 2004) to improve ecosystem health. Our data suggest that aligning downstream actors to fulfill the ecosystem value proposition requires high degrees of BMI or even actors who introduce new business models to the industry (Saebi et al., 2017).

These results extend the insights provided by Foss and Saebi (2017) in two ways. First, our data on the actors' business models indicate that the actors' influence and ecosystem position affected their incentive and ability to influence the business models of other ecosystem actors. Second, the degree of business model change necessary to establish alignment to fulfill an ecosystem value proposition seems to depend on the actor’s ecosystem position. As Fjeldstad and Snow (2018) note, for ecosystems confronted with technological shiftsincremental BMI to match external conditions might be insufficient. We give further insights regarding this evaluation, highlighting that downstream ecosystem actors tend to require focused or complex BMI. In contrast, upstream actors are instead often engaged in business model adaptation, as illustrated in Table 4. This outcome highlights the relationship between the structure and governance of an ecosystem with the changes in the involved actors' business models.

For some ecosystem members, the involved technologies pose considerable challenges while they provide little objective value. As the results suggest, smaller suppliers with different business models have less impact and benefits and must thus be integrated into the entire ecosystem (Kazantsev et al., 2023; Schmidt et al., 2023).

5.2 Ecosystem change and governance

The results demonstrate that aligning business models to overcome bottlenecks in creating ecosystem value might be particularly challenging, as this alignment must be established nearby, i.e. locally and temporally. Actors that aim to contribute value to an ecosystem focused on technological innovation are confronted with many uncertainties influencing their business model change activities. These uncertainties are primarily rooted in customer requirements and the type and timing of technologies when participating in an ecosystem, as illustrated in Strategic Influences on Interactions in Table 2.

Regarding ecosystem governance, OEMs facilitate changes in the ecosystem’s architecture, as described for OEMs in Table 2. These changes aim, e.g. to shorten development processes and modularize offers. Companies pursue modularized offers to adjust their business models in response to technological developments and customer demand. Moreover, modularity could support the coordination and exchange of values between ecosystem actors (Jacobides et al., 2018).

The results concerning the actors' business models indicate that their position in the ecosystem (Table 4) also influences their abilities to change their business models and the respective drivers (Table 3). OEMs pursued different strategies in the upstream and downstream ecosystems. This finding is also reflected in the respective business models they pursued. Their business models were designed to introduce xEVs in the market. They were primarily adapted concerning how they could create value compared to their further business models, as described in Table 3. However, OEMs as central ecosystem actors must introduce additional business models to provide complementary offers for xEVs on a large scale. In our case, they do so by forging collaborations and facilitating the introduction of separate actors who could provide complementary offers as the main value proposition, making the ecosystem value proposition for xEVs more attractive (Aaldering et al., 2019). The results further indicate that upstream actors performed lower degrees of business model change than downstream actors, as shown in Table 4.

Moreover, the results suggest that ecosystem leaders, such as OEMs, must coordinate actors and align their business models with overcoming relevant bottlenecks simultaneously, ensuring the ecosystem’s overall health (Dattee et al., 2018). Therefore, successful ecosystem strategies must consider all the necessary actors' business models to be critical (Adner, 2017). Establishing good alignment among actors' business models might help create an attractive value offer (Adner, 2017) and encourage additional actors to participate in the ecosystem (Dattee et al., 2018).

Finally, the findings support the notion that management commitment is a prerequisite for changing business models (Saebi et al., 2017; Witschel et al., 2023) and aligning them to fulfill a joint ecosystem value proposition (Adner, 2017; Talmar et al., 2018). The data indicate that this particularly applies to business models contributing to an ecosystem for technological innovation in its early stages, as the actors potentially face substantial uncertainty. This extends the findings on challenges of bottlenecks and requirements of orchestrating different organizational cultures and approaches to ecosystems (Kazantsev et al., 2023). This is particularly relevant as different types of ecosystem bottlenecks (Adner and Kapoor, 2010) may exist simultaneously, e.g. upstream and downstream complement bottlenecks. Adner (2017) noted that some bottlenecks could be partly due to technological difficulties. Other bottlenecks might stem from difficulties coordinating systems and the late emergence of respective markets (Adner, 2017). The latter two factors arguably relate to the misalignment of the ecosystem actors' business models in our research setting.

5.3 Theoretical contribution

This research derives results from 27 ecosystem members covering the most relevant roles of an xEV ecosystem, representing one of the most detailed analyses of an ecosystem to this date in comparison to extant research (e.g. Hoch and Brad, 2020; Palmié et al., 2022; Yi et al., 2022).

Our research contributes to the nascent research stream of manufacturing ecosystems. While Kazantsev et al. (2023), Schmidt et al. (2023) or Suuronen et al. (2022) describe Industry 4.0 technologies, such as digital platforms, as enablers for manufacturing ecosystems, we highlight the shift to a new vehicle propulsion technology as supporting the further transition of a manufacturing ecosystem. This is because new entrants are required for xEV technologies to enter the ecosystem, as mentioned in Table 2 and 3,for providers of engineering and technology, RI and established automotive suppliers.

We further contribute to understanding ecosystem governance (Ates et al., 2023) and extend Riasanow et al. (2021) in understanding distinct manufacturing ecosystem characteristics, especially automotive manufacturing ecosystems. The role of the central actor and the trust and interaction with the OEM seems particularly important in the automotive industry due to its often still hierarchical structure, extending Das and Dey’s (2021) non-industry-specific assessment. Further, while several authors rely on literature reviews (e.g. Das and Dey, 2021; Schmidt et al., 2023; Suuronen et al., 2022), we are able to present empirical insights from an entire ecosystem rather than single actors (e.g. Ates et al., 2023).

The empirical results connect two largely distinct constructs: business models and ecosystems (Moore, 1996; Iansiti and Levien, 2004). The results extend the understanding of business model change to align with the environment (Saebi et al., 2017; Foss and Saebi, 2017). The findings further contribute to this nascent field in the literature by providing evidence that the environmental alignment of individual business models is insufficient. Instead, individual actors must consider business models in their ecosystem (Adner, 2017) and ensure the overall alignment of their individual business models in the automotive industry (Secinaro et al., 2020).

5.4 Managerial implications

The study offers insights into the automotive industry’s transition to electric vehicles. Practitioners must be aware of the chosen ecosystem’s characteristics concerning their business models, which is relevant because ecosystems can involve actors and their business models from several newly included industries.

First, when multiple actors start to engage in activities to contribute to a joint ecosystem value proposition (Adner, 2017; Talmar et al., 2018), practitioners might need to consider that the individual actors' business model change activities (Saebi et al., 2017; Foss and Saebi, 2017) must be governed to ensure that individual contributions add to the ecosystem value proposition. Practitioners must also be aware of the potential misalignment of the individual actors' business models.

Second, the analyses presented in Figures 2 and 3 could serve as a starting point to consider the state of an ecosystem and could offer guidance to practitioners who aim to address misaligned business models in a coordinated manner. This could prevent spending time or resources on resolving isolated bottlenecks and creating ecosystem value while failing to ensure an attractive value offer. Leading ecosystem actors who take on the role of ecosystem governance could be particularly well suited for this undertaking.

Third, the data indicate that changes in business models to establish alignment with other actors and provide a joint ecosystem value proposition seem to depend on the specific position and type of ecosystem actor. As discussed, upstream actors tend to take more evolutionary or adaptive approaches to change their business models. In contrast, actors downstream pursue focused or complex BMI (see Table 4). In addition, central ecosystem actors wield substantial influence and control a significant number of resources. Thus, they possess the position to adopt or introduce business models to fulfill specific ecosystem functions. Therefore, practitioners are presented with a starting point when considering which approach to BMI could suit their particular circumstances.

5.5 Limitations and further research

The major limitation of this research is that it exclusively relies on qualitative data. Publications combining business models and ecosystems represent a nascent stream in extant literature (Adner, 2017). Thus, a qualitative approach was deemed suitable to generate novel insights through a case-study approach (Yin, 2017). An extensive database of 27 ecosystem actors was established, relying on data from interviews with 46 respondents and a workshop with industry professionals. Further, detailed arguments were provided regarding the chosen methodological approach and data-source selection. Rich data were gathered from multiple sources, allowing for the replication of findings between similar cases. In addition, triangulation in terms of the chosen methods, informants and researchers was applied whenever possible.

The case study within Germany and Austria limits the generalizability of the study findings. Due to the likely differences in market behavior, industry structure and regulatory regimes between individual geographic regions and technological settings, the findings might not fully apply in other regions or technological settings. To explore the issue further, future research could be performed that considers the insight from additional ecosystem actors (e.g. private customers of xEVs or new OEMs and suppliers entering the ecosystem). Moreover, this study could be repeated in diverse empirical settings to gain broader insight into the relationships between business models and ecosystems. This could include different geographical settings, e.g. automotive manufacturing ecosystems worldwide or alternative technological transitions. In addition to qualitative approaches, quantitative inquiries on comparable ecosystems could provide interesting insights in this context.

Further, as the data were collected over a limited time frame, they do not allow for a process perspective. Important further influences, such as the governmental or political perspective, were only uncovered in the later stages of research. Those aspects could be enhanced in a long-term perspective on the evolution of the ecosystem. As the collected primary data only allow for a snapshot of the ecosystem state and the involved actors' business models, process studies on the investigated relationships between business models and ecosystems might yield novel insights. Therefore, investigations that rely on consistently available secondary data and cover more extended periods might be examples of a feasible approach that can be taken to investigate the processes and dynamics of an ecosystem and its actors' business models. While qualitative research is well suited for nascent research fields, a mix of qualitative and quantitative or even fully quantitative investigations based on this research could provide additional insights.

The findings could further be transferred to other settings concerning the literature emphasizing the external orientation of business models (Saebi et al., 2017) and the alignment of ecosystem actors (Adner, 2017; Talmar et al., 2018). Examples include ecosystems that have formed around a technological innovation (Dattee et al., 2018; Dedehayir et al., 2018). Further, those examples could face similar shortcomings concerning the structure of actors providing components and complementary offerings to add value with their business models to the ecosystem value proposition (Adner, 2017; Talmar et al., 2018; Jacobides et al., 2018). This situation might be particularly applicable where regulators, standardization bodies, laws, social behaviors and business ethics impose similar constraints on ecosystem actors. In these cases, actors are confronted with similar rules, providing productive grounds to transfer the insights to the respective settings.

Finally, an extension to the ecosystem perspective on xEVs could be the recycling or Circular Economy aspect of batteries, xEVs as part of hydrogen ecosystems, or including sustainable business models driven by regulation in the automotive industry. Such a perspective could help to better understand recent developments in sustainable mobility and academic research toward sustainability aspects in BMI.