Strategy and policy of catch-up and post catch-up innovation in automobile industry's technology learning, business model and alliance network

Abstract

Determining how latecomer firms in a certain industry develop their technology, productivity, brand image and strategy to realize catch-up innovation is of great concern in various public and private sectors. Specifically, in the current rapidly changing global era with fierce competition, a thorough analysis of best practices is urgently needed. In my first chapter, I conducted a comparative analysis of most representative catch-up innovation models in the automobile industry ？ Hyundai and SAIC. By analyzing two cases of product development, productivity improvement, brand management, and market strategy, I was able to determine the following: 1) Hyundai’s catch-up innovation occurred in a closed innovation system with an independent R&D organization that nurtured its own technology capacity, brand power and productivity much earlier than SAIC. However, this high level of independence may be a disadvantage for Hyundai if it wishes to cooperate with MNC(Multi National Corporation)s in future transportation projects. 2) SAIC’s model for catch-up innovation of cooperating with Volkswagen and GM delayed SAIC’s own competence in product development and branding

however, its strategic alliance with GM and successful cooperation experience with MNCs gives SAIC an advantage in overseas expansion and the development of future transportation projects. The characteristics, advantages, disadvantages and cooperative characteristics of the two models are fully discussed in this paper. Second, I conducted a B/C analysis for adopting EV as a taxi service. In terms of sustainable development, EV (Electric Vehicle) is currently considered as one of the most promising alternatives due to its cleanness and inexhaustibility. However, the development and dissemination of EV has stagnated because it faces major constraints, such as battery performance and an excessively long charging time. Thus, this study examined the feasibility of using EVs as taxis by analyzing real data from a pilot project in Daejeon, a metropolitan city in South Korea, and proposes an effective way to adopt EV. To reflect reality and improve accuracy, we adopted scenarios and assumptions based on in-depth interviews with groups of experts. The resulting initial benefit-to-cost (B/C) ratio for EV taxis is approximately 0.4, which is quite low compared to 0.7 for traditional taxis. However, after incorporating further assumptions into the calculation, the B/C ratio shifts to approximately 0.7, which is more appropriate for EV adoption. For this improvement to be achieved, the dissemination of a charging infrastructure, improvement of the business model and policy support are strongly needed. Limitations to this work and potential areas for future study are also fully discussed.Third, I used simulations to examine the traffic efficiency improvement of autonomous cars. By using real traffic data, such as arrival rates, average status, green light information from the Daejeon metropolitan city government, I designed the most reliable and realistic autonomous vehicle simulation based on an ABM (agent-based model). By expanding a single agent to all cars on the road, I investigated the throughput, average distance, acceleration and deceleration of 4 roads in 2 periods of time (rush hour and non-rush hour). According to the results, in most cases, traffic efficiency increases a great deal by adopting autonomous cars with safe distance. However, roads leading to suburbs need some changes in speed limits due to the road characteristics, and small merging lines need to limit their total car input because chaos erupts beyond a given input. Acceleration and deceleration does not have significant changes on the traffic efficiency. Fourth, I examined car-sharing services as a business model for future transportation. With the advent of mobile communication devices and services, users have become more accessible to services that are closely related to the sharing economy. This study identifies and investigates numerous factors that contribute to forming user perceptions and attitudes toward car sharing services, which are a representative social sharing platform service. In order to examine the relationship between car sharing services and factors, the technology acceptance model, as well as the structural equation modeling, have been applied to data collected from surveys. According to the results, the user acceptance of social sharing services is affected mainly by their attitude, usefulness, enjoyment, user innovativeness, reliability, compatibility, privacy concern and cost. Based on the collected findings, this study discusses both implications and future suggestions. Finally, I investigated the characteristics of firms’ activity on autonomous car development by using patent data and alliance data. Using data on 440 US patents extracted with the data mining method, I found that, among the companies holding the most US patents, Google focused mostly on the information area, Ford on control, and GM mostly on detection. Accordingly, with a network analysis of data on 120 alliances, I found that Google has the highest degree centrality, that is, of direct cooperation with other firms, while GM, Ford, Nissan, Hyundai, Toyota also have the relatively high degrees of centrality. Finally, from the community analysis of alliances, I found several significant groups: Google-German, GM-China, GM-Ford, Korean and Japanese alliances. Among those groups, the Google-German alliance has a very complete structure, while the Japanese alliance has the highest solidarity. The Chinese-related alliances can be categorized into two competitive groups: GM-SOEs (State-Owned Enterprises) and Ford-private enterprises. In all chapters, the different strategic behaviors of the companies and groups are fully discussed and the value and feasibility of EV and autonomous cars are fully examined, while the problems and implications of future strategies and policies are suggested in the final conclusion section.