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

  • Mousa AlbashrawiEmail author
  • Hasan Kartal
  • Asil Oztekin
  • Luvai Motiwalla


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.


Mobile banking UTAUT model Behavioral analytics Structural equation modeling 



  1. Aiken, L. R. (1987). Formulas for equating ratings on different scales. Educational and Psychological Measurement, 47(1), 51–54.Google Scholar
  2. 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
  3. 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.Google Scholar
  4. 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
  5. 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.Google Scholar
  6. 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.Google Scholar
  7. 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.Google Scholar
  8. 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.Google Scholar
  9. Davis, G. W. (1989). Sensitivity analysis in neural net solutions. IEEE Transactions on Systems Man and Cybernetics, 19, 1078–1082.Google Scholar
  10. 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.Google Scholar
  11. Gupta, R., & Jain, K. (2014). Adoption of mobile telephony in rural India: an empirical study. Decision Sciences, 45(2), 281–307.Google Scholar
  12. 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).Google Scholar
  13. 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.Google Scholar
  14. Henseler, J., & Sarstedt, M. (2013). Goodness-of-fit indices for partial least squares path modeling. Computational Statistics, 28(2), 565–580.Google Scholar
  15. 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
  16. 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
  17. 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.Google Scholar
  18. Lee, S., & Kim, B. G. (2009). Factors affecting the usage of intranet: A confirmatory study. Computers in Human Behavior, 25(1), 191–201.Google Scholar
  19. 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.Google Scholar
  20. 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.Google Scholar
  21. 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.Google Scholar
  22. 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.Google Scholar
  23. McIntosh, C. N., Edwards, J. R., & Antonakis, J. (2014). Reflections on partial least squares path modeling. Organizational Research Methods, 17, 210–251.Google Scholar
  24. 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.Google Scholar
  25. Mohammadi, H. (2015). A study of mobile banking loyalty in Iran. Computers in Human Behavior, 44(44), 35–47.Google Scholar
  26. 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.Google Scholar
  27. 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
  28. Prasanna, R., & Huggins, T. J. (2016). Factors affecting the acceptance of information systems supporting emergency operations centers. Computers in Human Behavior, 57, 168–181.Google Scholar
  29. 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.Google Scholar
  30. 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
  31. Raschka, S. (2014). About feature scaling and normalization. Sebastian Racha. Disques, nd Web. Dec.Google Scholar
  32. 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.Google Scholar
  33. 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.Google Scholar
  34. 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.Google Scholar
  35. Ripley, B. D. (1996). Pattern recognition and neural networks. Cambridge: Cambridge University Press.Google Scholar
  36. Sabherwal, R., Jeyaraj, A., & Chowa, C. (2006). Information system success: individual and organizational determinants. Management Science, 52(12), 1849–1864.Google Scholar
  37. Saltelli, A. (2002). Making best use of model evaluations to compute sensitivity indices. Computer Physics Communications, 145, 280–297.Google Scholar
  38. 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.Google Scholar
  39. Soukup, T., & Davidson, I. (2002). Visual data mining: Techniques and tools for data visualization and mining. New York: John Wiley & Sons.Google Scholar
  40. 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.Google Scholar
  41. 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.Google Scholar
  42. 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.Google Scholar
  43. Tenenhaus, M., Vinzi, V. E., Chatelin, Y.-M., & Lauro, C. (2005). PLS path modeling. Computational Statistics & Data Analysis, 48, 159–205.Google Scholar
  44. 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.Google Scholar
  45. Turkyilmaz, A., Temizer, L., & Oztekin, A. (2016). A causal analytic approach to student satisfaction index modeling. Annals of Operations Research.
  46. 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.Google Scholar
  47. 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.Google Scholar
  48. 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
  49. 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.Google Scholar
  50. 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.Google Scholar
  51. 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
  52. Zhou, T. (2012). Examining mobile banking user adoption from the perspectives of trust and flow experience. Information Technology and Management, 13(1), 27–37.Google Scholar
  53. 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.Google Scholar
  54. Zimmermann, H. G. (1994). Neuronale Netze als Entscheidungskalkü. Neuronale Netze in der Ökonomie, Eds. Heinz Rehkugler and Hans G. Zimmermann, München: Vahlen, 1–87.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Mousa Albashrawi
    • 1
    Email author
  • Hasan Kartal
    • 2
  • Asil Oztekin
    • 3
  • Luvai Motiwalla
    • 3
  1. 1.King Fahd University of Petroleum & MineralsDhahranSaudi Arabia
  2. 2.University of Illinois at SpringfieldSpringfieldUSA
  3. 3.University of Massachusetts LowellLowellUSA

Personalised recommendations