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

Do, Myungsik; Byun, Wanhee; Shin, Doh Kyoum; Jin, Hyeryun

doi:10.3390/su11205615

Cited by 13 publications

(14 citation statements)

References 11 publications

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“…The 11th paper analyzes the sustainable development of land-lost peasants' citizenization, that is, compulsory urbanization, in China, which is related to organizational entrepreneurship leading culture for open innovation dynamics, in addition to smart cities and automated driving systems [121][122][123].…”

Section: Organizational Entrepreneurship Leading Culture For Open Innovation Dynamicsmentioning

confidence: 99%

The Culture for Open Innovation Dynamics

et al. 2020

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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.

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Section: Organizational Entrepreneurship Leading Culture For Open Innovation Dynamicsmentioning

confidence: 99%

The Culture for Open Innovation Dynamics

et al. 2020

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“…DT split can be present in binary or range mode, with numerous answers taken from each of the trait inputs in the DT (Kingsford and Salzberg, 2008;Song and Lu, 2015;Hartman, 2021). The decision tree structure diagram is presented in Figure 2 (Do et al, 2019).…”

Section: Decision Tree (Dt)mentioning

confidence: 99%

Predictive Analytics in Business Analytics: Decision Tree

2022

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Purpose: Business Analytics was defined as one of the most important aspects of combinations of skills, technologies and practices which scrutinize a corporation's data and performance to transpire data-driven decision-making analytics for a corporation's future direction and investment plans. In this paper, much of the focus will be given to predictive analytics, which is a branch of business analytics that scrutinize the application of input data, statistical combinations and intelligence machine learning statistics on predicting the plausibility of a particular event happening, forecast future trends or outcomes utilizing on-hand data with the final objective of improving the performance of the corporation. While it has been around for decades, predictive analytics has gained much attention in the late 20th century. This technique includes data mining and big data analytics. Last but not least, the decision tree methodology, a supervised simple classification tool for predictive analytics, is fully scrutinized below for applying predictive business analytics and decision tree in business applications.Design/ Methodology/Approach: A systematic literature review was conducted in predictive analytics and decision tree. The literature review explains various fields' latest predictive analytics and decision trees. All the research papers are obtained from two databases: Web of Science and Scopus, which are widely acknowledged by the scientific and research communities that contain top-quality peer-reviewed journals.Findings: This study reviews the application of predictive analytics and decision tree in business decision-making across various fields.Practical implications: This paper will strongly contribute to providing significant inputs to analysts or researchers in business analytics, predictive analytics and decision tree as it presents recent evidence of the applications of various fields. This review will be in the interest of academics and practitioners in business analytics, especially predictive analytics.

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“…Third, the system model goes same direction with government regulation according to Uber, and DiDi Chuxing. So, the system model of carsharing should consider government regulations in addition to factors influencing matching of ride-hailing services using machine learning methods [55].…”

Section: Comparing Three Car-sharing Firms In Different Economiesmentioning

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

Cited by 13 publications

References 11 publications

The Culture for Open Innovation Dynamics

The Culture for Open Innovation Dynamics

Predictive Analytics in Business Analytics: Decision Tree

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

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