Findings and Analysis

suggests calculating different centrality metrics (Basole 2009). Within this context, established research was followed and network all-degree centralization and network betweenness centralization were applied (Freeman 1979). The first measure examines whether the ecosystem is focused on a few central nodes, as the latter tend to act as central connectors (Wasserman and Faust 1994). Regarding the second measure, the underlying concept is that a node can, even though it does not hold many connections, reside “between” other nodes, thereby acting as an important intermediary (Freeman 1979). It is measured by inspecting the number of geodesics within the ecosystem.

centralization measure, which provides information about the distribution of relations, reveals a substantial decrease of −70.8% (2002–2007: 0.2849; 2007–2012: 0.1419;

2012–2017: 0.0833).

To expand these initial impressions, the ecosystem’s segment development was examined. A company’s SIC code embodies an ecosystem segment extracted from the SDC Platinum database. Regarding the total number of SIC codes, there was an average increase of 14.0% over 16 years, with a clear spike in the second period (Figure B:3). Furthermore, contrary to the prior global analysis, an increase in ecosystem density of 26.5% (2002–2007: 0.030; 2007–2012: 0.034; 2012–2017:

0.038) was observed,

which equates to greater segment interaction. Meanwhile, network all-degree centralization decreased by 17.1% between 2002 and 2017, which aligns with the global trend. However, the reduction in all-degree centralization was evenly distributed (2002–2007: 0.2278; 2007–2012: 0.2030; 2012–2017: 0.1890). For network betweenness centralization, there was a decrease in asymmetrical relation distribution (−31.0%). Although there was a significant decline between the first and second periods, changes in the third period were significantly weaker compared to the global results (2002–2007: 0.2039; 2007–2012: 0.1475; 2012–2017: 0.1407).

Table B-1. Ecosystem Network Metrics

To derive more detailed information about the ecosystem’s development, a visualization of each time interval was created (Figures B:4, B:5, and B:6). Rather than modeling the ecosystem on a company level, this study focused on the SIC- code level, as the latter is more holistic in nature. Every node represents a SIC code, as each tie in between them represents a form of relation. The size of a node is related to its degree centrality and thereby implies its degree of connectance, where its position is related to the overall network importance. For each node, respective SIC code was added. Network visualizations were thereby created in a “top-down”

manner by first looking at the whole picture of the automotive ecosystem and then analyzing a particular segment of interest in a subset analysis (Figures B:7, B:8, and B:9).

Network Measure Period I 02-07

Period II 07-12

Period III 12-17

Relative Change

Period I - II

Relative Change

Period II - III

Relative Change

Period I - III Number of

Companies 2851 4437 5267 55,6% 18,7% 84,7%

Number of SIC Codes 494 573 563 16,0% -1,7% 14,0%

Number of

Connections 4228 6510 6894 54,0% 5,9% 63,1%

Network Density

(Company Level) 0,00101609 0,0006456 0,00048678 -36,5% -24,6% -52,1%

Network Density (SIC-

Code Level) 0,03016768 0,03465731 0,03816777 14,9% 10,1% 26,5%

Network All-Degree Centralization (Company Level)

0,10379339 0,03917492 0,03740323 -62,3% -4,5% -64,0%

Network All-Degree Centralization (Company Level)

0,22784017 0,20298397 0,18899271 -10,9% -6,9% -17,1%

Network Betweenness Centralization (Company Level)

0,28489557 0,14189231 0,08326242 -50,2% -41,3% -70,8%

Network Betweenness Centralization (SIC- Code Level)

0,20387161 0,14740904 0,14067946 -27,7% -4,6% -31,0%

Regarding the first period (2002–2007; see Figure B:4), the automotive ecosystem was dominated by seven segments, namely 3711 (Motor Vehicles and Passenger Car Bodies), 3714 (Motor Vehicle Parts and Accessories), 5051 (Metals Service Centers and Offices), 3312 (Steel Works, Blast Furnaces, and Rolling Mills), 5094 (Jewelry, Watches, Precious Stones, and Precious Metals), 2899 (Chemicals and Chemical Preparations), and 1311 (Crude Petroleum and Natural Gas). Together these

Figure:4. The Automotive Ecosystem in 2002-2007

segments form the automotive ecosystem’s core. For the related communities, the green nodes are metal industry components, while the red ones represent vehicle manufacturing. Furthermore, the yellow community can be assigned to automobile- related chemicals and lubricants, while the purple community stands for finance and insurance segments. First, from a visual point of view, these communities do not seem to be very blended, with every community almost occupying its own area, and

second, there seems to be additional scope for more connections. Visibly then, it is focused on a few nodes and still has further capacity for relations.

In the second interval (2007–2012; see Figure B:5), the previously dominating nodes are represented again, yet some newcomers are present. As indicated by the node sizes and positions, six nodes emerge as the dominant section within the network.

First, 3612 (Power, Distribution, and Specialty Transformers) and 4911 (Electric Services) dominate, representing an increased focus on vehicle connectivity and electrical engineering. Moreover, a further visual inspection reveals significant changes due to the rise of 3651 (Household Audio and Video Equipment) and 3663 (Radio and Television Broadcasting and Communications Equipment). A remarkable change took place with an increase in nodes 7372 (Prepackaged Software) and 3674 (Semiconductors and Related Devices), which expanded in this period. The first includes software companies and companies that base their business models on software and data, while the latter includes companies that produce computer-related products, such as microprocessors and chips. This indicates the increased importance of software and computers within the automotive ecosystem. Concluding with a review of the visual illustration clarifies a trend toward a considerably more connected ecosystem (regarding the number of observable ties), while the ecosystem’s core also seems to have expanded and is no longer concentrated only around a couple of nodes.

Figure B:5. The Automotive Ecosystem in 2007-2012

For the last period (2012–2017; see Figure B:6), the dominant segments from 2002– 2007 maintained an essential position within the ecosystem. Additionally, it is noticeable that the blue community became more interwoven in the network, as its nodes moved closer toward the network’s center, such as 3674 (Semiconductors and Related Devices), which moved closer to 3711 (Motor Vehicles and Passenger Car Bodies), and a particularly high increase in 7372 (Prepackaged Software), again highlighting the rising importance of software-related firms within the automotive ecosystem at the core of the blue community. The growing segments in this period were 4911 (Electric Services), 3663 (Radio and Television Broadcasting and Communications Equipment), 3651 (Household Audio and Video Equipment), 3612 (Power, Distribution, and Specialty Transformers), and 7372 (Prepackaged Software), which all achieved a comparable node size and a dominant position like the already established nodes. SIC code 7372 had an above-average growth rate.

This tendency toward

Figure B:6. The Automotive Ecosystem in 2012-2017

new important ecosystem participants is reinforced by the emergence of smaller SIC- code nodes in the core area. Here, vehicle connectivity-related segments expanded, with SIC codes such as 4813 (Telephone Communications) and 4812 (Radiotelephone Communications). A strengthening of the electrical engineering segments took place, demonstrated by the rise in 3674 (Semiconductors and Related Devices), 3679 (Electronic Components), and 8711 (Engineering Services). Besides the indicated strengthening of vehicle connectivity and electrification, there was substantial growth in vehicle-related software segments: 7373 (Computer Integrated Systems Design), 7375 (Information Retrieval Services), 7376 (Computer Facilities Management Services), 3577 (Computer Peripheral Equipment), further supported by additional SIC codes in the surrounding segment belt, namely 3571 (Electronic Computers), 3812 (Search, Detection, Navigation, Guidance, Aeronautical, and Nautical Systems and Instruments), and 3861 (Photographic Equipment and Supplies).

After analyzing the whole automotive ecosystem, the large network was broken down into smaller subsets to derive additional insights. As the SIC-code analysis of the whole automotive ecosystem revealed, the codes related to 7372, as indicated by the community structure and close position to one another, were 7373 (Computer Integrated Systems Design), 7374 (Computer Processing and Data Preparation and Processing Services), and 7375 (Information Retrieval Services). These were matched to one software group to derive insights into developments in the underlying structures and firms playing a significant role within this segment. Here, content- related proximity was also considered. As the group of software-related segments grew over the respective periods and experienced a change in the core toward companies with business models focusing on mobility services and autonomous driving applications, an investigation was undertaken of this segment together with the OEM segment by juxtaposing both networks. The OEM segment was chosen due to its prominent position within the whole automotive ecosystem, which becomes especially clear when recapitulating Figures B:4–B:6. The visual depictions show a significant shift in SIC code 3711 (Motor Vehicles and Passenger Car Bodies) into the center of the ecosystem. The following visual inspection will analyze which firms occupy important positions, and the underlying connections between both groups will be considered. Importantly, the assumptions are that the software-related segments are matched, as described previously, and second, that the OEM-related segments 3711 and 3713 (Truck and Bus Bodies) are matched to conduct a proper study of OEMs.

The first time span (2002–2007; see Figure B:7) shows a network dominated by OEMs, such as General Motors, Ford Motors, Toyota Motors, and DaimlerChrysler.

While a few software-related firms can be found (e.g., NTT Data Corp, Toshiba, Nifty Corp, TBS), all their nodes were similar in size, indicating no firm had an exceptional number of connections. In total, except for NEC, the second largest node was Toyota Motors (an OEM), which was very well connected. Generally speaking, neither segment was well connected; thus, the network was not closely interwoven.

However, minor exceptions were SAP AG, UGS Corp, and RiskWatch Inc, which were assigned to the software segment, yet interconnected within the automobile segment. Visually, there is a spatial separation. A large area of automobile-related nodes, which are well interconnected, dominates the network compared to a small area of red nodes on the network’s edge.

Figure B:7. OEM and Software Group 2002-2007

For the 2007–2012 period (Figure B:8), there were three notable aspects. First, many large nodes still existed in the OEM segment. General Motors and Toyota Motors maintained their strong position, while Volkswagen and Fiat became comparable in size. Daimler maintained its dominant position and size (DaimlerChrysler separated in 2007, leaving Daimler as a single firm). Second, for the software-related segment, a few nodes increased in size. Besides NEC, which can now be clearly assigned to this segment type, Nokia, Intel, Sony, Fujitsu, Hitachi, and Motorola reached large node sizes, indicating stronger connections than for the previous period, thus increasing interconnections between both segment types compared to the first period. It is noticeable that red nodes encroached on more turquoise areas, such as Toshiba Corp or China FAW, while some turquoise nodes were interwoven with red nodes. Thereby, the border between red and turquoise nodes, which was previously clearly visible, is now blurred.

Figure B:8. OEM and Software Group 2007-2012

While the boundaries between both segment types began to blur in the second period, an interconnected network developed in the third period (2012–2017; see Figure B:9). Visually, in the OEM segment, Toyota Motors maintained its central position while simultaneously the size of its node shows a good connection within the whole network. Volkswagen moved toward the center of the network, while Daimler still maintained a central position and large node size. Besides this, Nissan Motors, Honda Motors, Ford Motors, Volvo, General Motors, Isuzu Motors Ltd, and BMW were of comparable size, indicating a similar connection within the network. The software-related segment reveals a different picture. The largest node was Cisco Systems, followed by NEC, Fujitsu, and IBM Corp. The latter, together with Siemens, Panasonic, and Hitachi, encircled the most centered automobile-related nodes.

Furthermore, Uber Technologies, Mobileye, Gett Taxi, Lyft, Nvidia Corp, Careem, and Mobvoi moved closer to or even into the main OEM area. Additionally, there is a distinct area of software-related segments.

Within this area, Didi Chuxing is represented by a relatively large node and, visually, it has the best connection to the remaining network among these nodes. The whole area is separated from the rest of the network, including firms such as China FAW, Dongfeng Motors, Jiangling Motors Group Co, and China Automotive Engineering.

To conclude, the combination of both segment types moved closer together over the time spans.

Figure B:9. OEM and Software Group 2012-2017