Work

An empirical analysis of intention of use for bike-sharing system in China through machine learning techniques

Year: 2020

Type: article

Abstract: Sharing bicycles, as boosted by the advanced mobile technologies, is expected to mitigate the traffic congestion and air pollution issues in China. A survey study was conducted with 335 valid samples to identify the key factors that influence the customers' intention of use for bike-sharing system and quantify the corresponding importance. Five machine learning techniques for classification are applied and results are compared. The best performed technique is selected to prioritise and quantify the importance level of the influencing factors. The results indicate that the perceived ease of use is the most significant factor for the intention to use sharing bikes.

Source: Enterprise Information Systems

DOI: 10.1080/17517575.2020.1758796

Authors: Tao Zhou, Kris M. Y. Law, K. L. Yung

Institutions: Deakin University, University of Oulu, Hong Kong Polytechnic University

Language: English

Topic: Technology Adoption and User Behaviour

Subfield: Information Systems and Management

Field: Decision Sciences

Domain: Social Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: green

OpenAlex ID: W3024746468