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A Hybrid, Data-Driven Causality Exploration Method for Exploring the Key Factors Affecting Mobile Payment Usage Intention

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  • Ching Ching Fang

    (Department of International Business Administration, Chinese Culture University, Taipei 11114, Taiwan)

  • James J. H. Liou

    (Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei 10608, Taiwan)

  • Sun-Weng Huang

    (Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei 10608, Taiwan
    Graduate Institute of Urban Planning, College of Public Affairs, National Taipei University, New Taipei City 23741, Taiwan)

  • Ying-Chuan Wang

    (Faculty of Humanities and Social Sciences, City University of Macau, Macau 820004, China)

  • Hui-Hua Huang

    (Department of Marketing and Logistics, China University of Technology, Taipei 11607, Taiwan)

  • Gwo-Hshiung Tzeng

    (Graduate Institute of Urban Planning, College of Public Affairs, National Taipei University, New Taipei City 23741, Taiwan)

Abstract

Several methodologies for academically exploring causality have been addressed in recent years. The decision-making trial and evaluation laboratory (DEMATEL), one of the multiple criteria decision-making (MCDM) techniques, relies on expert judgements to construct an influential network relation map (INRM), revealing the mutual causes and effects of the criteria and dimensions for presentation of the results in a visual manner. The interactional impacts may be evaluated without considering the presumed hypotheses. The DEMATEL has been successfully utilized to assist in complex decision-making problems in various contexts. However, there is controversy about the reliance upon expert judgements, which could be subjective. Thus, this study seeks to overcome this dispute by developing a data-driven, concept-based novel hybrid model which the authors call SEM-DEMATEL. The model first constructs the direct effects between indicators based on structural equation modeling (SEM) and then utilizes DEMATEL to confirm the interdependence among the variables and identify their causes and effects. Finally, an empirical study exploring the key factors affecting mobile payment usage intention is further conducted to demonstrate the feasibility, validity, and reliability of the novel SEM-DEMATEL research approach. The results identify that the perceived value is the key influencing indicator of m-payment usage intention, and the objectivity and efficiency of the research results are compared.

Suggested Citation

  • Ching Ching Fang & James J. H. Liou & Sun-Weng Huang & Ying-Chuan Wang & Hui-Hua Huang & Gwo-Hshiung Tzeng, 2021. "A Hybrid, Data-Driven Causality Exploration Method for Exploring the Key Factors Affecting Mobile Payment Usage Intention," Mathematics, MDPI, vol. 9(11), pages 1-23, May.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:11:p:1185-:d:561222
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    2. Zhong, Junying & Chen, Tiao, 2023. "Antecedents of mobile payment loyalty: An extended perspective of perceived value and information system success model," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).

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