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

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.

[1]  Paul Jen-Hwa Hu,et al.  Modeling Citizen Satisfaction with Mandatory Adoption of an E-Government Technology , 2011, J. Assoc. Inf. Syst..

[2]  Thorsten Hennig-Thurau,et al.  Identifying Hidden Structures in Marketing's Structural Models Through Universal Structure Modeling An Explorative Bayesian Neural Network Complement to LISREL and PLS , 2008 .

[3]  J.C. Principe,et al.  Innovating adaptive and neural systems instruction with interactive electronic books , 2000, Proceedings of the IEEE.

[4]  Dongsong Zhang,et al.  Predicting and explaining patronage behavior toward web and traditional stores using neural networks: a comparative analysis with logistic regression , 2006, Decis. Support Syst..

[5]  Roliana Ibrahim,et al.  Predicting different conceptualizations of system use: Acceptance in hedonic volitional context (Facebook) , 2013, Comput. Hum. Behav..

[6]  Alain Yee-Loong Chong,et al.  An updated and expanded assessment of PLS-SEM in information systems research , 2017, Ind. Manag. Data Syst..

[7]  Viswanath Venkatesh,et al.  Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology , 2012, MIS Q..

[8]  Gordon B. Davis,et al.  User Acceptance of Information Technology: Toward a Unified View , 2003, MIS Q..

[9]  Ian Davidson,et al.  Visual Data Mining: Techniques and Tools for Data Visualization and Mining , 2002 .

[10]  Anne P. Massey,et al.  Usability of Online Services: The Role of Technology Readiness and Context , 2007, Decis. Sci..

[11]  Garry Wei-Han Tan,et al.  Predicting the determinants of the NFC-enabled mobile credit card acceptance: A neural networks approach , 2013, Expert Syst. Appl..

[12]  Raj Prasanna,et al.  Factors affecting the acceptance of information systems supporting emergency operations centres , 2016, Comput. Hum. Behav..

[13]  G. W. Davis,et al.  Sensitivity analysis in neural net solutions , 1989, IEEE Trans. Syst. Man Cybern..

[14]  Ephraim R. McLean,et al.  The DeLone and McLean Model of Information Systems Success: A Ten-Year Update , 2003, J. Manag. Inf. Syst..

[15]  Joseph S. Valacich,et al.  The Effect of Perceived Novelty on the Adoption of Information Technology Innovations: A Risk/Reward Perspective , 2010, Decis. Sci..

[16]  Tiago Oliveira,et al.  Understanding mobile banking: The unified theory of acceptance and use of technology combined with cultural moderators , 2015, Comput. Hum. Behav..

[17]  M. Sathye,et al.  Mobile Phone Banking Usage Behaviour: An Australian Perspective , 2014 .

[18]  Garry Wei-Han Tan,et al.  Predicting the drivers of behavioral intention to use mobile learning: A hybrid SEM-Neural Networks approach , 2014, Comput. Hum. Behav..

[19]  Sujeet Kumar Sharma,et al.  A multi-analytical approach to understand and predict the mobile commerce adoption , 2016, J. Enterp. Inf. Manag..

[20]  Byung Gon Kim,et al.  Factors affecting the usage of intranet: A confirmatory study , 2009, Comput. Hum. Behav..

[21]  Vadlamani Ravi,et al.  Detection of financial statement fraud and feature selection using data mining techniques , 2011, Decis. Support Syst..

[22]  C. Yu.  Factors Affecting Individuals to Adopt Mobile Banking: Empirical Evidence from the Utaut Model , 2012 .

[23]  Asil Oztekin,et al.  A causal analytic approach to student satisfaction index modeling , 2016, Annals of Operations Research.

[24]  Alain Yee-Loong Chong,et al.  A two-staged SEM-neural network approach for understanding and predicting the determinants of m-commerce adoption , 2013, Expert Syst. Appl..

[25]  Marko Sarstedt,et al.  Gain more insight from your PLS-SEM results: The importance-performance map analysis , 2016, Ind. Manag. Data Syst..

[26]  Fan Yang,et al.  Nonlinear structural equation models: The Kenny-Judd model with Interaction effects , 1996 .

[27]  J. S. Long,et al.  Testing Structural Equation Models , 1993 .

[28]  Brian D. Ripley,et al.  Pattern Recognition and Neural Networks , 1996 .

[29]  Hossein Mohammadi,et al.  A study of mobile banking loyalty in Iran , 2015, Comput. Hum. Behav..

[30]  Charles Chowa,et al.  Information System Success: Individual and Organizational Determinants , 2006, Manag. Sci..

[31]  Tao Zhou,et al.  Examining mobile banking user adoption from the perspectives of trust and flow experience , 2012, Inf. Technol. Manag..

[32]  M. Albashrawi.  Detecting Financial Fraud Using Data Mining Techniques: A Decade Review from 2004 to 2015 , 2021, Journal of Data Science.

[33]  Michel Tenenhaus,et al.  PLS path modeling , 2005, Comput. Stat. Data Anal..

[34]  T. Ramayah,et al.  PC USAGE AMONG STUDENTS IN A PRIVATE INSTITUTION OF HIGHER LEARNING: THE MODERATING ROLE OF PRIOR EXPERIENCE , 2005 .

[35]  Marko Sarstedt,et al.  Structural modeling of heterogeneous data with partial least squares , 2010 .

[36]  Marko Sarstedt,et al.  Goodness-of-fit indices for partial least squares path modeling , 2013, Comput. Stat..

[37]  Viswanath Venkatesh,et al.  Predicting Different Conceptualizations of System Use: The Competing Roles of Behavioral Intention, Facilitating Conditions, and Behavioral Expectation , 2008, MIS Q..

[38]  A. Saltelli,et al.  Making best use of model evaluations to compute sensitivity indices , 2002 .

[39]  Lewis R. Aiken.  Formulas for Equating Ratings on Different Scales , 1987 .

[40]  Selim Zaim,et al.  Universal structure modeling approach to customer satisfaction index , 2013, Ind. Manag. Data Syst..

[41]  Dursun Delen,et al.  Development of a structural equation modeling-based decision tree methodology for the analysis of lung transplantations , 2011, Decis. Support Syst..

[42]  Geoffrey S. Hubona,et al.  Using PLS path modeling in new technology research: updated guidelines , 2016, Ind. Manag. Data Syst..

[43]  Jeffrey R. Edwards,et al.  Reflections on Partial Least Squares Path Modeling , 2014 .

[44]  Tao Zhou,et al.  Integrating TTF and UTAUT to explain mobile banking user adoption , 2010, Comput. Hum. Behav..

[45]  Anníbal C. Sodero,et al.  Customer Use of Virtual Channels in Multichannel Services: Does Type of Activity Matter? , 2015, Decis. Sci..

[46]  Soon Jae Kwon,et al.  The Effects of Customers' Mobile Experience and Technical Support on the Intention to Use Mobile Banking , 2009, Cyberpsychology Behav. Soc. Netw..

[47]  Keith C. C. Chan,et al.  An Effective Data Mining Technique for Reconstructing gene Regulatory Networks from Time Series Expression Data , 2007, J. Bioinform. Comput. Biol..

[48]  Tiago Oliveira,et al.  Determinants of end-user acceptance of biometrics: Integrating the "Big 3" of technology acceptance with privacy context , 2013, Decis. Support Syst..

[49]  Ruchita Gupta,et al.  Adoption of Mobile Telephony in Rural India: An Empirical Study , 2014, Decis. Sci..

[50]  Hsiu-Fen Lin,et al.  An empirical investigation of mobile banking adoption: The effect of innovation attributes and knowledge-based trust , 2011, Int. J. Inf. Manag..

[51]  Judy E. Scott,et al.  Cognitive engagement with a multimedia ERP training tool: Assessing computer self-efficacy and technology acceptance , 2009, Inf. Manag..