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Self-Reported and Computer-Recorded Experience in Mobile Banking: a Multi-Phase Path Analytic Approach

Author

Listed:
  • Mousa Albashrawi

    (King Fahd University of Petroleum & Minerals)

  • Hasan Kartal

    (University of Illinois at Springfield)

  • Asil Oztekin

    (University of Massachusetts Lowell)

  • Luvai Motiwalla

    (University of Massachusetts Lowell)

Abstract

Mobile banking (MB) has emerged as a strategic differentiator for financial institutions. This study explores the limitations associated with using subjective measures in MB studies that solely rely on survey-based approaches and traditional structural analysis models. We incorporate an objective data analytic approach into measuring usage experiences in MB to overcome potential limitations and to provide further insight for practitioners. We first utilize a multi-phase path analytical approach to validate the UTAUT model in order to reveal critical factors determining the success of MB use and disclose any nonlinearities within those factors. Proposed data analytics approach also identifies non-hypothesized paths and interaction effects. Our sample is collected from computer-recorded log data and self-reported data of 472 bank customers in the northeastern region of USA. We have analyzed the data using the conventional structural equation modeling (SEM) and the Bayesian neural networks-based universal structural modeling (USM). This holistic approach reveals non-trivial, implicit, previously unknown, and potentially useful results. To exemplify, effort expectancy is found to relate positively (but nonlinearly) with behavioral intention and is also ranked as the most important driving factor in UTAUT affecting the MB system usage. Theoretical and practical implications are discussed and presented in terms of both academic and industry-based perspectives.

Suggested Citation

  • Mousa Albashrawi & Hasan Kartal & Asil Oztekin & Luvai Motiwalla, 2019. "Self-Reported and Computer-Recorded Experience in Mobile Banking: a Multi-Phase Path Analytic Approach," Information Systems Frontiers, Springer, vol. 21(4), pages 773-790, August.
    • Handle: RePEc:spr:infosf:v:21:y:2019:i:4:d:10.1007_s10796-018-9892-1
    DOI: 10.1007/s10796-018-9892-1
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    References listed on IDEAS

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    1. Rajiv Sabherwal & Anand Jeyaraj & Charles Chowa, 2006. "Information System Success: Individual and Organizational Determinants," Management Science, INFORMS, vol. 52(12), pages 1849-1864, December.
    2. Jörg Henseler & Marko Sarstedt, 2013. "Goodness-of-fit indices for partial least squares path modeling," Computational Statistics, Springer, vol. 28(2), pages 565-580, April.
    3. Tenenhaus, Michel & Vinzi, Vincenzo Esposito & Chatelin, Yves-Marie & Lauro, Carlo, 2005. "PLS path modeling," Computational Statistics & Data Analysis, Elsevier, vol. 48(1), pages 159-205, January.
    4. Caroline Lancelot Miltgen & Ales Popovic & Tiago Oliveira, 2013. "Determinants of end-user acceptance of biometrics: Integrating the “Big 3” of technology acceptance with privacy context," Post-Print hal-01116141, HAL.
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    Cited by:

    1. Nisreen Ameen & Nnamdi O. Madichie & Amitabh Anand, 2023. "Between Handholding and Hand-held Devices: Marketing Through Smartphone Innovation and Women’s Entrepreneurship in Post Conflict Economies in Times of Crisis," Information Systems Frontiers, Springer, vol. 25(1), pages 401-423, February.
    2. Micus, Christian & Schramm, Simon & Boehm, Markus & Krcmar, Helmut, 2023. "Methods to analyze customer usage data in a product decision process:A systematic literature review," Operations Research Perspectives, Elsevier, vol. 10(C).
    3. Sahar Karimi, 2021. "Cross-visiting Behaviour of Online Consumers Across Retailers’ and Comparison Sites, a Macro-Study," Information Systems Frontiers, Springer, vol. 23(3), pages 531-542, June.
    4. Ashraf Hilal & Concepción Varela-Neira, 2022. "Understanding Consumer Adoption of Mobile Banking: Extending the UTAUT2 Model with Proactive Personality," Sustainability, MDPI, vol. 14(22), pages 1-23, November.
    5. Luvai Motiwalla & Amit V. Deokar & Surendra Sarnikar & Angelika Dimoka, 2019. "Leveraging Data Analytics for Behavioral Research," Information Systems Frontiers, Springer, vol. 21(4), pages 735-742, August.
    6. Sener, Abdurrezzak & Barut, Mehmet & Oztekin, Asil & Avcilar, Mutlu Yuksel & Yildirim, Mehmet Bayram, 2019. "The role of information usage in a retail supply chain: A causal data mining and analytical modeling approach," Journal of Business Research, Elsevier, vol. 99(C), pages 87-104.
    7. Jadil, Yassine & Rana, Nripendra P. & Dwivedi, Yogesh K., 2021. "A meta-analysis of the UTAUT model in the mobile banking literature: The moderating role of sample size and culture," Journal of Business Research, Elsevier, vol. 132(C), pages 354-372.

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