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

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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.

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References

  • Aiken, L. R. (1987). Formulas for equating ratings on different scales. Educational and Psychological Measurement, 47(1), 51–54.

    Article  Google Scholar 

  • Albashrawi, M. (2016). Detecting financial fraud using data mining techniques: a decade review from 2004 to 2015. Journal of Data Science, 14(3), 553–569.

    Google Scholar 

  • Baptista, G., & Oliveira, T. (2015). Understanding mobile banking: the unified theory of acceptance and use of technology combined with cultural moderators. Computers in Human Behavior, 50, 418–430.

    Article  Google Scholar 

  • Buckler, F., & Hennig-Thurau, T. (2008). Identifying hidden structures in marketing’s structural models through universal structure modeling: an explorative Bayesian neural network complement to LISREL and PLS. Marketing – Journal of Research in Management, 4(2), 49–68.

    Google Scholar 

  • Chan, F. K., Thong, J. Y., Venkatesh, V., Brown, S. A., Hu, P. J., & Tam, K. Y. (2010). Modeling citizen satisfaction with mandatory adoption of an e-government technology. Journal of the Association for Information Systems, 11(10), 519–549.

    Article  Google Scholar 

  • Chiang, W. Y. K., Zhang, D., & Zhou, L. (2006). Predicting and explaining patronage behavior toward web and traditional stores using neural networks: a comparative analysis with logistic regression. Decision Support Systems, 41(2), 514–531.

    Article  Google Scholar 

  • Chong, A. Y. L. (2013). A two-staged SEM-neural network approach for understanding and predicting the determinants of m-commerce adoption. Expert Systems with Applications, 40(4), 1240–1247.

    Article  Google Scholar 

  • Chung, N., & Kwon, S. J. (2009). The effects of customers' mobile experience and technical support on the intention to use mobile banking. Cyberpsychology & Behavior, 12(5), 539–543.

    Article  Google Scholar 

  • Davis, G. W. (1989). Sensitivity analysis in neural net solutions. IEEE Transactions on Systems Man and Cybernetics, 19, 1078–1082.

    Article  Google Scholar 

  • Delone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: a ten-year update. Journal of Management Information Systems, 19(4), 9–30.

    Article  Google Scholar 

  • Gupta, R., & Jain, K. (2014). Adoption of mobile telephony in rural India: an empirical study. Decision Sciences, 45(2), 281–307.

    Article  Google Scholar 

  • Hair, J., Hollingsworth, C. L., Randolph, A. B., & Chong, A. (2017). An updated and expanded assessment of PLS-SEM in information systems research. Industrial Management & Data Systems, 117(3).

  • Henseler, J., Hubona, G. S., & Ray, P. A. (2016). Using PLS path modeling in new technology research: Updated guidelines. Industrial Management & Data Systems, 116(1), 1–19.

    Article  Google Scholar 

  • Henseler, J., & Sarstedt, M. (2013). Goodness-of-fit indices for partial least squares path modeling. Computational Statistics, 28(2), 565–580.

    Article  Google Scholar 

  • Joreskog, K. G. (1993). Testing structural equation models. In K. A. Bollen & J. S. Lang (Eds.), Testing structural equation models. Newbury Park: Sage.

    Google Scholar 

  • Joreskog, K. G., & Yang, F. (1996). Nonlinear structural equation models: The Kenny-Judd Model with interaction effects. In G. A. Marcoulides & R. E. Schumacker (Eds.), Advanced structural equation modeling. Mahwah: Erlbaum.

    Google Scholar 

  • Lallmahomed, M. Z., Rahim, N. Z. A., Ibrahim, R., & Rahman, A. A. (2013). Predicting different conceptualizations of system use: acceptance in hedonic volitional context (Facebook). Computers in Human Behavior, 29(6), 2776–2787.

    Article  Google Scholar 

  • Lee, S., & Kim, B. G. (2009). Factors affecting the usage of intranet: A confirmatory study. Computers in Human Behavior, 25(1), 191–201.

    Article  Google Scholar 

  • Leong, L. Y., Hew, T. S., Tan, G. W. H., & Ooi, K. B. (2013). Predicting the determinants of the NFC-enabled mobile credit card acceptance: a neural networks approach. Expert Systems with Applications, 40(14), 5604–5620.

    Article  Google Scholar 

  • Lin, H. F. (2011). An empirical investigation of mobile banking adoption: the effect of innovation attributes and knowledge-based trust. International Journal of Information Management, 31(3), 252–260.

    Article  Google Scholar 

  • Ma, P. C., & Chan, K. C. (2007). An effective data mining technique for reconstructing gene regulatory networks from time series expression data. Journal of Bioinformatics and Computational Biology, 5(3), 651–668.

    Article  Google Scholar 

  • Massey, A. P., Khatri, V., & Montoya-Weiss, M. M. (2007). Usability of online services: the role of technology readiness and context. Decision Sciences, 38(2), 277–308.

    Article  Google Scholar 

  • McIntosh, C. N., Edwards, J. R., & Antonakis, J. (2014). Reflections on partial least squares path modeling. Organizational Research Methods, 17, 210–251.

    Article  Google Scholar 

  • Miltgen, C. L., Popovič, A., & Oliveira, T. (2013). Determinants of end-user acceptance of biometrics: integrating the “big 3” of technology acceptance with privacy context. Decision Support Systems, 56, 103–114.

    Article  Google Scholar 

  • Mohammadi, H. (2015). A study of mobile banking loyalty in Iran. Computers in Human Behavior, 44(44), 35–47.

    Article  Google Scholar 

  • Oztekin, A., Kong, Z. J., & Delen, D. (2011). Development of a structural equation modeling-based decision tree methodology for the analysis of lung transplantations. Decision Support Systems, 51(1), 155–166.

    Article  Google Scholar 

  • Plate, T. (1998). Controlling the hyper-parameter search in MacKay’s Bayesian neural network framework, neural networks: tricks of the Trade, Eds.: Genevieve Orr, Klaus-Robert Müller, and Rich Caruana (pp. 93–112). Berlin: Springer.

    Google Scholar 

  • Prasanna, R., & Huggins, T. J. (2016). Factors affecting the acceptance of information systems supporting emergency operations centers. Computers in Human Behavior, 57, 168–181.

    Article  Google Scholar 

  • Principe, J. C., Euliano, N. R., & Lefebvre, W. C. (2000). Innovating adaptive and neural systems instruction with interactive electronic books. Proceedings of the IEEE, 88, 81–95.

    Article  Google Scholar 

  • Ramayah, T., Ignatius, J., & Aafaqi, B. (2005). PC usage among students in a private institution of higher learning: the moderating role of prior experience. Educators and Education Journal, 20(3), 131–152.

    Google Scholar 

  • Raschka, S. (2014). About feature scaling and normalization. Sebastian Racha. Disques, nd Web. Dec.

  • Ravisankar, P., Ravi, V., Rao, G. R., & Bose, I. (2011). Detection of financial statement fraud and feature selection using data mining techniques. Decision Support Systems, 50, 491–500.

    Article  Google Scholar 

  • Rigdon, E. E., Ringle, C. M., & Sarstedt, M. (2010). Structural modeling of heterogeneous data with partial least squares. In N. K. Malhotra (Ed.) Review of marketing volume 7, Emerald Group Publishing Limited, pp.255 – 296.

  • Ringle, C. M., Ringle, C. M., & Sarstedt, M. (2016). Gain more insight from your PLS-SEM results: The importance-performance map analysis. Industrial Management & Data Systems, 116(9), 1865–1886.

    Article  Google Scholar 

  • Ripley, B. D. (1996). Pattern recognition and neural networks. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Sabherwal, R., Jeyaraj, A., & Chowa, C. (2006). Information system success: individual and organizational determinants. Management Science, 52(12), 1849–1864.

    Article  Google Scholar 

  • Saltelli, A. (2002). Making best use of model evaluations to compute sensitivity indices. Computer Physics Communications, 145, 280–297.

    Article  Google Scholar 

  • Scott, J. E., & Walczak, S. (2009). Cognitive engagement with a multimedia ERP training tool: assessing computer self-efficacy and technology acceptance. Information and Management, 46(4), 221–232.

    Article  Google Scholar 

  • Soukup, T., & Davidson, I. (2002). Visual data mining: Techniques and tools for data visualization and mining. New York: John Wiley & Sons.

    Google Scholar 

  • Sousa, R., Amorim, M., Rabinovich, E., & Sodero, A. C. (2015). Customer use of virtual channels in multichannel services: does type of activity matter? Decision Sciences, 46(3), 623–657.

    Article  Google Scholar 

  • Talukder, M., Quazi, A., & Sathye, M. (2014). Mobile phone banking usage behaviour: an Australian perspective. Australasian Accounting Business and Finance Journal, 8(4), 83–104.

    Article  Google Scholar 

  • Tan, G. W. H., Ooi, K. B., Leong, L. Y., & Lin, B. (2014). Predicting the drivers of behavioral intention to use mobile learning: a hybrid SEM-neural networks approach. Computers in Human Behavior, 36, 198–213.

    Article  Google Scholar 

  • Tenenhaus, M., Vinzi, V. E., Chatelin, Y.-M., & Lauro, C. (2005). PLS path modeling. Computational Statistics & Data Analysis, 48, 159–205.

    Article  Google Scholar 

  • Turkyilmaz, A., Oztekin, A., Zaim, S., & Demirel, O. F. (2013). Universal structure modeling approach to customer satisfaction index. Industrial Management & Data Systems, 113(7), 932–949.

    Article  Google Scholar 

  • Turkyilmaz, A., Temizer, L., & Oztekin, A. (2016). A causal analytic approach to student satisfaction index modeling. Annals of Operations Research. https://doi.org/10.1007/s10479-016-2245-x.

  • Venkatesh, V., Brown, S. A., Maruping, L. M., & Bala, H. (2008). Predicting different conceptualizations of system use: the competing roles of behavioral intention, facilitating conditions, and behavioral expectation. MIS Quarterly, 32(3), 483–502.

    Article  Google Scholar 

  • Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: toward a unified view. MIS Quarterly, 27(3), 425–478.

    Article  Google Scholar 

  • Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178.

    Google Scholar 

  • Wells, J. D., Campbell, D. E., Valacich, J. S., & Featherman, M. (2010). The effect of perceived novelty on the adoption of information technology innovations: A risk/reward perspective. Decision Sciences, 41(4), 813–843.

    Article  Google Scholar 

  • Yadav, R., Sharma, S. K., & Tarhini, A. (2016). A multi-analytical approach to understand and predict the mobile commerce adoption. Journal of Enterprise Information Management, 29(2), 222–237.

    Article  Google Scholar 

  • Yu, C. (2012). Factors affecting individuals to adopt mobile banking: empirical evidence from the UTAUT model. Journal of Electronic Commerce Research, 13(2), 104–121.

    Google Scholar 

  • Zhou, T. (2012). Examining mobile banking user adoption from the perspectives of trust and flow experience. Information Technology and Management, 13(1), 27–37.

    Article  Google Scholar 

  • Zhou, T., Lu, Y., & Wang, B. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in Human Behavior, 26(4), 760–767.

    Article  Google Scholar 

  • Zimmermann, H. G. (1994). Neuronale Netze als Entscheidungskalkü. Neuronale Netze in der Ökonomie, Eds. Heinz Rehkugler and Hans G. Zimmermann, München: Vahlen, 1–87.

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Correspondence to Mousa Albashrawi.

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Albashrawi, M., Kartal, H., Oztekin, A. et al. Self-Reported and Computer-Recorded Experience in Mobile Banking: a Multi-Phase Path Analytic Approach. Inf Syst Front 21, 773–790 (2019). https://doi.org/10.1007/s10796-018-9892-1

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