Developing A New Model for Cyber Security Behavior of E-Hailing Services (S/O 14166)

E-hailing is a platform where the drivers and passengers can be directly connected through third-party mobile phone applications. In Malaysia, there are about 41 e-hailing firms which have attracted 7.6 million users and expected to increase to 7.7 million users by 2027 and earnings of MYR5.5 billio...

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Bibliographic Details
Main Authors: Nik Mat, Nik Kamariah, Sulaiman, Yaty, Perumal, Selvan
Format: Monograph
Language:English
Published: UUM 2023
Subjects:
Online Access:https://repo.uum.edu.my/id/eprint/30571/1/14166.pdf
Description
Summary:E-hailing is a platform where the drivers and passengers can be directly connected through third-party mobile phone applications. In Malaysia, there are about 41 e-hailing firms which have attracted 7.6 million users and expected to increase to 7.7 million users by 2027 and earnings of MYR5.5 billion (USD1.27 billion) in 2023. However, this new industry is not without problems. It was reported that incidents of cybercrime have increased from 3,564 cases to 8,090 cases. Hence, the main objective of this research is to examine the predictors of cybersecurity behavior when using e-hailing services. The research design is quantitative (survey) and qualitative (interviews) research designs. The measurement of the survey data consists of twelve main variables using a total of 69 items based on a 7-point Likert scale. The sampling technique is collecting data from 400 passengers and 400 drivers of e-hailing services. The sampling frame used is cluster sampling from five main cities of Kuala Lumpur, Selangor, Penang, Ipoh and Alor Setar. A total of 235 (passengers) and 219 (drivers) responses were collected, representing a 58.75% and 54.75% response rate respectively. The quantitative data was collected using a self-administered survey while interviews with e-hailing firms and government ministry were conducted. Data were analyzed using Smart PLS 3.0. The findings indicate that the direct predictors of cybersecurity behavior of passengers are perceived threat of substitute, security self-efficacy and government policy while the predictors of cybersecurity behavior of drivers are perceived threat of substitute, perceived privacy, security self-efficacy and government policy. Generation gap has no moderation effect on the specified linkages. The implication of the finding indicates that although e-hailing services are preferred by netizens, e-hailing industry should be more aware of increased threat from substitutes such as LRT and city buses, more concern for privacy, security self-efficacy and rising monitoring from government sector from both passengers and drivers of e-hailing services.