The Impact of E-Hailing Competition on the Urban Taxi Ecosystem and Governance Strategy from a Rent-Seeking Perspective: The China E-Hailing Platform

Ma, Liang; Tao, Li; Wu, Jinxi; Yan, Dandan

doi:10.3390/joitmc4030035

Cited by 12 publications

(11 citation statements)

References 12 publications

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“…The recall of an open innovation approach is the base of a competitive advantage for both tourist destinations and companies. This represents the synthesis of different interests and a shared willingness to act according to a sustainable manner [116][117][118].…”

Section: Discussionmentioning

confidence: 99%

Sustainable Tourism in the Open Innovation Realm: A Bibliometric Analysis

et al. 2019

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This study evaluates bibliometric analysis of sustainable tourism in the open innovation realm, depicts emerging themes, and offers critical discussion for theory development and further research. Through the use of bibliometrix, this paper investigates the amount of studies conducted in this area and verifies if such studies have represented a contribution to the evolving research in the field of sustainable tourism. Specifically, the paper identifies whether and to what extent scholars have explored these interconnections and maps to get to a conceptual structure of the field under investigation. The results identify the development status and the leading trends in terms of impact, main journals, papers, topics, authors, and countries. The analysis and the graphical presentations are crucial, as they can help both researchers and practitioners to better understand the state of the art of sustainable tourism in the experiential and digital era.

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Section: Discussionmentioning

confidence: 99%

Sustainable Tourism in the Open Innovation Realm: A Bibliometric Analysis

et al. 2019

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“…The carsharing industry can also motivate economical sustainability in several aspects as follows [59]. First, this industry can be a new change for job creation in the fourth industrial revolution, which normally motivates economy growth with the increase of unemployment according to interviews of several car-share drivers [20,60,61]. Second, the carsharing industry can increase the revenue of a household without investing additional money to create new chances.…”

Section: Implication-environmental and Economical Sustainabilitymentioning

confidence: 99%

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

Yun

Zhao

et al. 2020

Sustainability

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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.

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“…Non-peak time (0,0), Peak time (0,1), Midnight (1,0) -Day of the week (1) Weekday (0), Weekend (1) -…”

Section: Time Zonementioning

confidence: 99%

“…Non-station influence area (0), Station influence area (1) - (1) Weekday: Monday-Thursday, Weekend: Friday-Sunday. (2) Others: Natural green area, Semi-industrial area, Productive green area.…”

Section: Station Influence Areamentioning

confidence: 99%

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

Byun

Shin

et al. 2019

Sustainability

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It is common to call a taxi by taxi-apps in Korea and it was believed that an app-taxi service would provide customers with more convenience. However, customers’ requests can often be denied, as taxi drivers can decide whether to take calls from customers or not. Therefore, studies on factors that determine whether taxi drivers refuse or accept calls from customers are needed. This study investigated why taxi drivers might refuse calls from customers and factors that influence the success of matching within the service. This study used origin-destination data in Seoul and Daejeon obtained from T-map Taxis, which was analyzed via a decision tree using machine learning. Cross-validation was also performed. Results showed that distance, socio-economic features, and land uses affected matching success rate. Furthermore, distance was the most important factor in both Seoul and Daejeon. The matching success rate in Seoul was lowest for trips shorter than the average at midnight. In Daejeon, the rate was lowest when the calls were made for trips either shorter or longer than the average distance. This study showed that the matching success for ride-hailing services can be differentiated particularly by the distance of the requested trip depending on the size of the city.

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The Impact of E-Hailing Competition on the Urban Taxi Ecosystem and Governance Strategy from a Rent-Seeking Perspective: The China E-Hailing Platform

Cited by 12 publications

References 12 publications

Sustainable Tourism in the Open Innovation Realm: A Bibliometric Analysis

Sustainable Tourism in the Open Innovation Realm: A Bibliometric Analysis

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

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

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