Factors Influencing Matching of Ride-Hailing Service Using Machine Learning Method

The Culture for Open Innovation Dynamics

Culture, in its various forms, has always been a critical driver of innovation. This paper focuses on generating some insights into the role of “culture for open innovation dynamics”. First, because the requirement to understand culture, which can control open innovation complexity, has been augmented, we want to answer the following research question in this study: How can we define or organize “culture for open innovation dynamics”, which can motivate open innovation dynamics, and control open innovation complexity? Second, we propose a concept model of culture for open innovation dynamics by reviewing the literature on the culture of firms in terms of their traits, organization, static innovation, and dynamic aspects regarding their innovation in entrepreneurship, and we validate said model through an indirect social experiment using the research results of 23 Special Issue papers. Third, the concept model of culture for open innovation dynamics is explained as the interaction between three different entrepreneurship dimensions: Entrepreneurship of novice entrepreneurs, intrapreneurship of employees of an existing firm, and organizational entrepreneurship by the firm itself. According to the balance of three sub-entrepreneurship types, culture for open innovation dynamics can have different aspects, namely, entrepreneurship leading culture for open innovation dynamics, intrapreneurship leading culture for open innovation dynamics, or organizational entrepreneurship leading culture for open innovation dynamics. This paper helps organizations and entrepreneurs to better understand the role that culture plays in boosting open innovation dynamics.

Business Model, Open Innovation, and Sustainability in Car Sharing Industry—Comparing Three Economies

This paper discusses dynamics and differences of business models in the car-sharing industry and focuses on open innovation as the trigger of diverse business models among Uber in the U.S., DiDi Chuxing in China, and KakaoT in Korea. We seek to answer the following two questions: What creates the differences in the business models of the car-sharing industry? Do the differences in open innovation motivate the diversity of business models among Uber, DiDi Chuxing, and KakaoT? We incorporated participatory observation, interviews, and semi-structured questionnaire methods in our study. We used two-step participatory observation and interview methods, hence carrying out observation and interviews two times by different researchers with Uber drivers in the U.S., DiDi-Chuxing drivers in Beijing, and KakaoT taxi drivers in Korea to confirm the interview and participatory observation results. First, business models of the car-sharing firms Uber, DiDi-Chuxing, and KakaoT are not fixed but rather are dynamically changing. Second, business models of car-sharing firms are the result of interaction with government regulations, the taxi industry, public transportation, and the automotive car industry. Third, open innovation strategies of car-sharing firms determine the contents and dynamics of car-sharing business models, such as the revenue business model, responsibility business model, and system business model upon interaction with four agencies.