Findings

In this section, the distinct roles that ecosystem actors take are described both visually and quantitatively. Therefore, visualizations were created, which, based on the calculated measures, helped to identify ecosystem roles. In total, this study identified five distinct roles. To distinguish between roles, the cluster analysis was consulted (see Table B-2). Regarding the CC measure, a span reaching from a minimum of .0 to a value of .424 with an arithmetic mean of .277 was noticed.

Moreover, EC has a minimum of .0086 to a maximum of .757 with an arithmetic mean of .172. BC values reach from .0 to 967.75 with an arithmetic mean of 110.74.

Thereby, the role of a dominator was observed, which is defined by at least two maximum values of either CC, BC, or EC. In the following, each role is outlined, guided by visualizations depicting the particular role type with nodes colored in red (see Figure B:10).

First, regarding role 1, actors positioned at the edges of the network were observed.

From a visual perspective, they are placed around the ecosystem’s periphery, occupying positions at the edge, as it is shown in Figure B:10, role 1. These actors exhibit both zero CC and zero BC. Their EC is less than 0,1. 13 actors of the ecosystem taking this role could be identified, namely Autonation, Inc., Mapfre, Vinci Energies SA, Henkel AG & Co KGaA, National Financial Partners, Sears Holdings, State Grid, GE Healthcare, Nippon Life Insurance, Iberdrola, Bitauto, Magna International and Fujifilm. Regarding their specific characteristics, it was noticed that

they have little information control and overall connections to other ecosystem actors.

Therefore, the actors taking this role were named Followers.

Second, regarding role 2, the presence of one actor standing out visually and in terms of its measures was noticed. From a visual point of view, this role takes a superior position, located in the center of the ecosystem. This actor shows the highest scores in BC, CC, and EC, compared to all other ecosystem actors. Microsoft could be identified in such a prominent position. Since the automotive ecosystem is subject of study, it is remarkable that a digital player dominates this ecosystem. Microsoft mainly provides computer software, which could be an indicator of the increasing interdependencies of car manufacturers and software companies. However, this still does not explain, why a software company dominates the automotive ecosystem.

Accordingly, in the discussion section it will be touched upon this. Microsoft’s scores are the highest in every measure taken as a basis. There is no other node in the network exhibiting similar features. This actor has a large number of connections to other network participants. Further, these connections are highly valuable, measured by a high score of EC. Moreover, as another characteristic, due to its high score of BC, it commands information flow within the ecosystem. These unique characteristics speak for the role type of a Dominator.

Third, regarding role 3, when analyzing the center of the network, ranked by BC, a group of firms occupying outstanding positions was found. From a visual perspective, these firms can be localized around the center respectively near the center, as it is depicted in Figure B:10, role 3. These firms show above-average measures of BC, CC and EC. There could be 25 identified of these firms, which are namely Royal Dutch Shell, British Petroleum, Total, Gazprom, Rosneft Oil, General Electric, Intel, Daimler, Siemens, Toyota Motor, Volkswagen, Mitsubishi, Guangzhou Auto Grp Co Ltd, Mitsui & Co Ltd, Robert Bosch, Chevron, Panasonic, NavInfo Co Ltd, Honda Motor, China FAW, Idemitsu Kosan, Dongfeng Motor, China United Network Commun, Uber Technologies and Didi Chuxing Technology. Because of their equally high scores in each of the relevant measures, they can be seen as the core of the ecosystem, having good connections, and participating in information flow. On the one hand, this core of leaders consists of firms which can be assigned to the traditional automotive ecosystem, namely Total, General Electric, Guangzhou Auto Grp Co Ltd, and Siemens. On the other hand, firms such as Didi Chuxing Technology or Uber Technologies represent new startups, which enable digitalization and

connectivity in the automotive ecosystem. Overall, these actors were named Leaders.

Fourth, arranging the network according to EC reveals a new perspective. When looking at Figure B:10, role 4, actors, which are neither located at the edge of the ecosystem, nor clearly assignable to the ecosystem’s core were identified.

Concerning their measures, they show above-average CC and rather low BC and EC. The vast majority of the ecosystem consists of actors showing these characteristics with a number of 68 actors. A diverse selection of firms could be identified, e.g., OMV, Fujitsu, Cisco Systems, Allianz, Badu Inc, NEC, Toyota Tsusho, DBJ and Continental. The industrial background of these firms mostly represents traditional segments, which are an essential part of the automotive ecosystem’s supply chain. However, there are also certain OEMs in this cluster, as for instance BMW or Ford Motor. While inspecting the Leaders cluster, it was already found that a lot of OEMs belong to this group. Hence, this finding will be discussed.

Concerning their measures, they indicate solid connections within the ecosystem, while at the same time little information control. These actors seem to be responsible for the ecosystem’s cohesion, bridging connections of core actors with those that are further away from the core. By drawing on these findings, these actors were named Intermediaries.

Fifth and finally, by analyzing the calculated measures, indications of another role in the ecosystem could be detected. These actors show high EC, above-average CC and little to zero BC, indicating, they are locally well-connected, yet do not participate on overall information flow. From a visual perspective, they are located close together, as shown in Figure B:10, role 5. Moreover, 14 firms taking this role, namely Chongqing Changan Auto Co Ltd, Shanghai Baolong Automotive, China Mobile Commun Grp Co, Beijing Auto Research Inst Co, Zhengzhou Yutong Group Co Ltd, China Natl Heavy Duty Truck, China Automotive Engineering, China Automotive Tech &, China N Inds Grp Corp, Shanghai Songhong Smart Auto, TUS International Ltd, Zhejiang Asia-Pacific, Wuhan Zhong Hai Ting Data, Beijing Shunyi Tech could be identified. They are locally good connected, forming a closed group. Therefore, these actors were named Local Champs.

1.5 Discussion of Findings

1.5.1 Ecosystem Roles in the Era of Digital Innovation

The analysis revealed that the established understanding of an ecosystem consisting of three different roles (Iansiti and Levien 2004), is not sufficient in the era of digital innovation. Building upon this, by conducting both a visual inspection of the data and drawing upon network measures, the characterization of the three established roles needs to be adjusted. While confirming one role, findings reveal the presence of four additional roles as a complement.

Role 1: Follower Role 2: Dominator

Role 3: Leader Role 4: Intermediary

Role 5: Local Champ

Figure B:10. Visualization of Ecosystem Roles

Regarding the role of a follower, rather few actors taking this role within the ecosystem were identified. By drawing upon the calculated network measures, as CC and EC are indicators for well-connected actors that have powerful connections to other actors within the network, it is assumed that followers are rather weakly connected to other network participants and do not command powerful connections.

In addition, as they show scores of zero BC, they are assumed to have little control of or participate in information flows. That, in turn, might be an indicator of less strategic foresight of these followers, as they simply do not command required information and connections. Since prior literature on ecosystem actors worked out a role typology, this study compares the role of a follower to the role of a niche player, as described by Iansiti and Levien (2004). This role shows the highest overlap to the role of a follower. Thereby, the assumption of followers representing the bulk of an ecosystem cannot be confirmed, as there are relatively few of them present according to the observation. Further, this study cannot agree with the statement of followers being responsible for a large part of innovation and value creation for the following reasons. First, a participation in information flow within the ecosystem is seen as a precondition for creating substantial value and providing innovation for the ecosystem. According to the characteristics of a follower, this is not the case. Second, the players representing followers do not seem to have a good basis for innovating, since their linked SIC codes reveal neither the presence of OEM, which have the conditions for value creation, nor new startups, which are linked to digital innovation.

This assumption is supported when looking at the visualization, clearly showing that followers occupying positions at the edge of the network, which seems to exclude them from the core. Third, this study assumes that leaders are responsible for value creation and innovation, which will be revealed further on. Since followers have no decision-making power due to a lack of information and connection within the ecosystem, they need to follow a leader.

Regarding the role type of a dominator, this study was able to empirically confirm the existence of this role, as described in literature on ecosystem role types (Iansiti and Levien 2004). However, going beyond existing literature, this study characterizes a dominator based upon inductively analyzed data. Once again by drawing on the calculated measures one actor within the network, whose particular measures of BC, EC and CC are equally the highest compared to all other actors was identified. Since these measures allow to gain knowledge about the particular position,

connectedness and information controlling capabilities of an actor within the ecosystem, a dominator can be characterized by the following. First, a firm carrying out this role type, is well-connected to every other actor within the ecosystem, which promotes a dominator to potentially influence many other actors. Second, since the firm, which takes the role of a dominator exhibits the highest scores of BC, it has access to great quantities of information flows within the ecosystem, promoting it to take a superior position in terms of strategic foresight. In addition, this statement is supported when looking at role 2 in Figure B:10, where a single outstanding actor can be clearly identified. And last, as this role type is characterized by the highest EC in its respective ecosystem, a dominator is not only well-connected with a superior access to information but also commands higher quality connections, since it has influential contacts, which in turn are well-connected. In sum, this study is able to empirically confirm the presence of a dominator within an ecosystem, as it is described by literature. Especially drawing on superior connections and information access might enable a dominator to create and capture a large amount of value within the ecosystem. In this case, in line with Iansiti and Levien (2004), there is potential danger for the ecosystem in terms of its development and vital growth. In this sense, the greatest similarity of results is that with a physical dominator, as it controls much of an ecosystem, yet, is responsible for creating the value they capture. However, this study sees no indications of a value dominator being present in the ecosystem, as it is described by existing literature.

Furthermore, Iansiti and Levien (2004) emphasize the presence of one particular role, named keystone, that is crucial for the ecosystem’s health. Keystones are described as promoting the overall health of the ecosystem by providing common assets for the whole network. Moreover, they constantly contribute by both incorporating innovative technologies but also providing innovations to the ecosystem’s actors. They are an essential part of the ecosystem in terms of creating and sharing value. Keystones reach this outstanding position by providing assets, which enable them to construct an ecosystem around their core feature. In this study, the presence of several actors showing characteristics similar to these of a keystone is observable. These actors exhibit equally high values in CC, BC, and EC, which indicates good and influential connections and access to information flow within the ecosystem. However, this study indicates, that especially a heterogeneous core of these type of ecosystem actors is responsible for value creation and innovation,

contrary to existing literature, which predominantly emphasizes the presence of one keystone actor. This view is supported by looking at role 3 in Figure B:10, which shows several actors are positioned around the ecosystem’s core. By linking the actors to their respective SIC code different industrial background were disclosed. As the observation period is associated with the rise of software-based firms, there are actors which represent the upcoming software and electrification segment (SIC codes 7372, 3577 and 3612). But in addition, there exist more established actors (SIC codes 3711, 2911 and 2899), which represent the traditional core of an automotive ecosystem. Together these actors, on the one hand, make an essential contribution to value creation within the ecosystem. On the other hand, since there are many startups driving digitalization, it is concluded that these actors contribute in terms of providing innovation to the ecosystem. In this sense, contrary to literature, which predominantly describes the presence of a single actor constructing an ecosystem by providing a common asset, this study argues for a similar, yet more accurate description of these actors and call them leaders. Further, in contrast to Iansiti and Levien (2004), who emphasize the dependence on one actor, once removed, would lead to the collapse of the whole ecosystem, the ecosystem’s overall dependence on a single actor is not observed. Even if one of the described leaders would be removed, another leader would fill in its position and ensure further value creation.

The analysis reveals two further roles actors take within an ecosystem in the era of digital innovation that have not been described in prior literature. First, the role of an intermediary could be outlined. With respect to this study, there are several actors which cannot clearly be assigned to the groups of followers, leaders, or dominators.

Thus, based on their particular measure scores, intermediaries are well-connected and have a certain kind of influence in the ecosystem, but they are excluded from overall information flow. As supported by the visual perspective, these actors are located between the edge of the ecosystem, where niche players operate, and the core of the ecosystem, where leaders or dominators can be identified. By considering both, the calculated measures and the network visualization, there is indication that intermediaries must be important for the ecosystem’s overall cohesion. As they are connected to both followers and leaders, they serve as a bridge of specifically information flow between both. In this sense, they behave like boundary spanners, filling the gap of connecting actors taking leadership roles, who belong to the

ecosystem’s core and niche players, finding themselves at the periphery of the ecosystem.

Second, the study indicates the presence of another role, actors might choose to take in an ecosystem. Relating to the network measures, this role is characterized by rather high EC and CC. Hence, this is an indicator of good connections to actors which in turn have good connections to other actors. Further, since these actors show low scores of BC, they are expected to be excluded of the overall ecosystem’s information flow. From a visual point of view, a cluster of actors can be identified, which are closely linked to each other, as observable in Figure B:10, role 5.

Especially, as they are located close together in the ecosystem, they are assumed to represent a rather homogeneous cluster. Supported by the measures, this might be an indicator of having influential connections on a local basis. They seem to form a strong sub-network within the ecosystem, which might be a competitive advantage.

Possibly, they formed alliances, which help them to achieve benefits within the ecosystem. By concluding from the names of these actors, it is assumed that the majority of them has origins in one single country, maybe even in one region. Taking this information into account, they are named local champs. Although they seem to contribute value, this is assumed to be bounded to a restricted area, which limits their impact on the whole ecosystem.

1.5.2 Role-Taking Patterns by Ecosystem Participants

In what follows, this study discusses which roles particular firms occupy and links them to their related SIC codes. Further, explanations for the patterns of ecosystem role types observed are provided. For a better comprehensibility Table B-2 was included, providing definitions, characteristics and empirical examples of ecosystem role types.

Concerning followers, there are firms representing sectors that are clearly automotive-related. These are, for instance, the automotive supplier Magna International or the car comparison platform Bitauto (SIC codes 3714 and 7374). On the other hand, many firms related to electrification and electric services such as Vinci Energies SA, GE Healthcare, Iberdrola or State Grid (SIC codes 3629, 3845 and 4911) can be identified. Although this appearance can be related to the growing trend of electrification and the increasing demand of electric components within the automotive ecosystem, it seems that these actors are not taking more decisive

positions. To conclude, the group of followers appears to consist primarily of accessory manufacturers, which are part of the value creation within the ecosystem.

In this sense, however, their contribution is limited.

Since a dominator was defined as an actor showing the highest values of BC, CC, and EC measures compared to any other actor in the ecosystem, Microsoft can be identified as occupying this position. By referring to Microsoft’s segment (SIC code 7372), it is a software-related company that is dominating the automotive ecosystem.

First of all, this is remarkable, since this study is able to observe how a software company dominates a traditional ecosystem that is predominantly characterized by established players such as OEMs, component suppliers etc. One possible explanation would be that there exist strong interconnections of software-related companies and traditional automotive-related companies. However, since Microsoft dominates this ecosystem, another possible explanation would be that there are strong dependencies on Microsoft’s products and services along the whole automotive value chain. By taking advantage of this dependencies, Microsoft takes the role of a dominator within the ecosystem. As Microsoft occupies such a superior position within the ecosystem, it thereby has potential to affect many actors of the network due to its large number of powerful connections. This is possible by drawing on its unique access to information. In this regard, Microsoft can retrieve exclusive insights, other ecosystem actors in the automotive sector might not have. This can be related to the increasing dependence on software capabilities, players of the automotive ecosystem, such as traditional OEMs, rely on. This enables Microsoft to actively influence the ecosystem’s strategic direction. By summarizing these insights, Microsoft is in a position of unique power and control, which might be jeopardizing for the overall health of the ecosystem. Comparing to literature, as described by Iansiti and Levien (2004), a dominator seeks to either take over a network or drain value from it. From what it is known, one cannot evaluate Microsoft’s future actions, however, Microsoft appears to be in a position to potentially do so. From a more general perspective, this finding lends support to the view that in industrial-age contexts, there is an “emergence of a new digital layer while the former, physical one remains relevant” (Piccinini et al. 2015, p. 14). Players occupying the digital layer seem to be in a powerful position.

Regarding the role of a leader, a heterogeneous picture emerges. Since several central actors with equally high measures were identified, this group seems to