Title: Exploring the impact of e-commerce on mobile phone customer behaviour based on data mining techniques

Authors: Gia Minh Dao; Nhat Khang Nguyen; Thanh Bao Le; Song Thanh Quynh Le

Addresses: Department of Industrial Systems Engineering, Faculty of Mechanical Engineering, Ho Chi Minh City University of Technology (HCMUT), VNU-HCM, Ho Chi Minh City, Vietnam ' Department of Industrial Systems Engineering, Faculty of Mechanical Engineering, Ho Chi Minh City University of Technology (HCMUT), VNU-HCM, Ho Chi Minh City, Vietnam ' Department of Industrial Systems Engineering, Faculty of Mechanical Engineering, Ho Chi Minh City University of Technology (HCMUT), VNU-HCM, Ho Chi Minh City, Vietnam ' Department of Textile and Garment Engineering, Faculty of Mechanical Engineering, Ho Chi Minh City University of Technology (HCMUT), VNU-HCM, Ho Chi Minh City, Vietnam

Abstract: Nowadays, the internet and its associated technologies have created a low-cost and efficient means for businesses to build an electronic sales channel such as e-commerce. Despite the expeditious development of this approach, the contributions of e-commerce towards the mobile phone market are not efficiently utilised. Therefore, this study aims to investigate the behaviour of mobile phone customers in the e-commerce market and cluster the prioritised segments. First, we conduct a short review of literature among the 28 related researches to determine the appropriate factors and techniques. Then, the meaningful factors are adjusted into corresponding items for the questionnaire. Eventually, the data gathered from 339 undergraduate students in Vietnam National University, Ho Chi Minh City are analysed by k-means clustering and attached meaningful representation for the four cluster groups. Overall, the study applies cluster analysis in both mobile phone and e-commerce markets, then interprets four distinct customer groups with managerial implications.

Keywords: data mining; mobile phone market; e-commerce; internet shopping; market segmentation; cluster analysis; k-means; customer relationship management; customer segmentation; segmentation analysis.

DOI: 10.1504/IJSOM.2025.144758

International Journal of Services and Operations Management, 2025 Vol.50 No.3, pp.352 - 373

Received: 03 Jan 2023
Accepted: 11 Feb 2023
Published online: 03 Mar 2025 *

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