Differences in Generation Y male and female customers’ perceived mobile banking trust, information, and system quality
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DOIhttp://dx.doi.org/10.21511/bbs.19(4).2024.04
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Article InfoVolume 19 2024, Issue #4, pp. 44-57
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Creative Commons Attribution 4.0 International License
Trust is key to mobile banking adoption, shaping customer confidence in the service. Trusting behaviors and expectations vary between genders and across generational cohorts, influenced by factors like system and information quality. As such, the purpose of this study was to determine whether South African Generation Y male and female banking customers differ in their mobile banking trust, system, and information quality. In accordance with a descriptive research design, self-administered surveys were voluntarily distributed to 334 South African mobile banking participants. Using structural equation modeling, the study found that, among the male participants, the system quality of mobile banking predicts their mobile banking trust (β = 0.72, p < .001) and perceived information quality of the system (β = 0.94, p < .001). However, their mobile banking trust had an insignificant influence on their perceived information quality (β = –0.04, p > .001). For female Generation Y banking customers, mobile banking system quality was a significant predictor of both trust (β = 0.53, p < .001) and information quality (β = 0.69, p < .001). In addition, the path between trust and information quality was statistically significant (β = 0.17, p < .05). Determining the role of trust and its relationship between information and system quality of mobile banking among males and females is essential for understanding customer behavior, enhancing user experience, mitigating perceived risk, and guiding strategic decision-making in the mobile banking industry.
- Keywords
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JEL Classification (Paper profile tab)G21, L81, M31
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References69
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Tables7
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Figures1
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- Figure 1. Structural paths
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- Table 1. Sample description
- Table 2. Descriptive statistics and independent samples t-test
- Table 3. One-sample statistics
- Table 4. Correlation and collinearity statistics
- Table 5. Measurement model statistics
- Table 6. Path analysis
- Table 7. Mediation analysis
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- Albashrawi, M., Kartal, H., Oztekin, A., & Motiwalla, L. (2019). Self-reported and computer-recorded experience in mobile banking: A multi-phase path analytic approach. Information Systems Frontiers, 21, 773-790.
- Baabdullah, A. M., Alalwan, A. A., Rana, N. P., Kizgin, H., & Patil, P. (2019). Consumer use of mobile banking (M-Banking) in Saudi Arabia: towards an integrated model. International Journal of Information Management, 44, 38-52.
- Berger, R. (2019). Decoding Gen Y means recoding your business model.
- Berraies, S., Ben Yahia, K., & Hannachi, M. (2017). Identifying the effects of perceived values of mobile banking applications on customers: comparative study between baby boomers, generation X and generation Y. International Journal of Bank Marketing, 35(6), 1018-1038.
- Chan, T. K. H., Cheung, C. M. K., Shi, N., & Lee, M. K. O. (2015). Gender differences in satisfaction with Facebook users. Industrial Management & Data Systems, 115(1), 182-206.
- Choudrie, J., Junior, C. O., McKenna, B., & Richter, S. (2018). Understanding and conceptualising the adoption, use and diffusion of mobile banking in older adults: a research agenda and conceptual framework. Journal of Business Research, 88, 449-465.
- Chung, N., & Kwon, S. J. (2009). Effect of trust level on mobile banking satisfaction: a multi-group analysis of information system success instruments. Behaviour Information Technology, 28, 549-562.
- Ciunova-Shuleska, A., Palamidovska-Sterjadovska, N., & Prodanova, J. (2022). What drives m-banking clients to continue using m-banking services? Journal of Business Research, 139, 731-739.
- Cohen. J. (1992). A power primer. Psychological Bulletin, 112(1), 155-159.
- Dang, V. T., Nguyen, N., & Pervan, S. (2020). Retailer corporate social responsibility and consumer citizenship behavior: the mediating roles of perceived consumer effectiveness and consumer trust. Journal of Retailing and Consumer Services, 55, 1-10.
- DeLone, W. H., & McLean, E. R. (1992). Information systems success: the quest for the dependent variable. Information Systems Research, 3(1), 60-95.
- 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.
- Gao, L., & Waechter, K. A. (2017). Examining the role of initial trust in user adoption of mobile payment services: an empirical investigation. Information Systems Frontiers, 19(3), 525-548.
- Geebren, A., Jabbar, A., & Luo, M. (2021). Examining the role of consumer satisfaction within mobile eco-systems: evidence from mobile banking services. Computers in Huma Behavior, 114, 1-12.
- Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: an integrated model. MIS Quarterly, 27(1), 51-90.
- Giovanis, A., Athanasopoulou, P., Assimakopoulos, C., & Sarmaniotis, C. (2019). Adoption of mobile banking services. International Journal of Bank Marketing, 37(5), 1165-1189.
- Gorla, N., Somers, T. M., & Wong, B. (2010). Organizational impact of system quality, information quality, and service quality. Journal of Strategic Information Systems, 19, 207-228.
- Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis. Hampshire, UK.: Cengage Learning.
- Hasan, Y., Shamsuddin, A., & Aziati, N. (2013). The impact of management information systems adoption in managerial decision making: a review. The International Scientific Journal of Management Information Systems, 8(4), 10-17.
- Hayes, A. F. (2018). Partial, conditional, and moderated moderated mediation: Quantification, inference, and interpretation. Communication Monographs, 85(1), 4-40.
- Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115-135.
- Ivanova, A., & Kim, J. Y. (2022). Acceptance and use of mobile banking in Central Asia: evidence from modified UTAUT model. The Journal of Asian Finance, Economics and Business, 9(2), 217-227.
- Kanani, R., & Glavee-Geo, R. (2021). Breaking the uncertainty barrier in social commerce: the relevance of seller and customer-based signals. Electronic Commerce Research and Applications, 48, 1-17.
- Kassim, E. S., Jailani, S. F. A. K., Hairuddin, H., & Zamzuri, N. H. (2012). Information system acceptance and user satisfaction: the mediating role of trust. Procedia Social and Behavioral Sciences, 57, 412-418.
- Khare, A. (2012). Moderating effect of age and gender on consumer style inventory in predicting Indian consumers’ local retailer loyalty. The International Review of Retail, Distribution and Consumer Research, 22(2), 223-239.
- Kim, Y., Seok, J., & Roh, T. (2023). The linkage between quality of information systems and the impact of trust-based privacy on behavioral outcomes in unmanned convenience store: moderating effect of gender and experience. Technological Forecasting & Social Change, 196, 1-14.
- Klimenko, A. (2023). Digital transformation in banking and financial services.
- Latinia. (2024). Mobile banking evolution: 2024 trends and predictions.
- Laukkanen, T. (2016). Consumer adoption versus rejection decisions in seemingly similar service innovations: the case of the Internet and mobile banking. Journal of Business Research, 7, 2432-2439.
- Liao, C. H. (2015). Exploring the impacts of Age and usage experience of e-service on user perceived web quality. In Zhou, J., & Salvendy, G. (Eds.), Human Aspects of IT for the Aged Population. Design for Aging. ITAP 2015. Lecture Notes in Computer Science (vol. 9193). Springer, Cham.
- Lin, X., Wang, X., & Hajli, N. (2019). Building E-commerce satisfaction and boosting sales: the role of social commerce trust and its antecedents. International Journal of Electronic Commerce, 23(3), 328-363.
- Liu, B., Zhou, Q., Ding, R. X., Palomares, I., & Herrera, F. (2019). Large-scale group decision making model based on social network analysis: trust relationship-based conflict detection and elimination. European Journal of Operational Research, 275(2), 737-754.
- Malaquias, R. F., & Hwang, Y. (2019). Mobile banking use: a comparative study with Brazilian and US participants. International Journal of Information Management, 44, 132-140.
- Malhotra, N. K. (2020). Marketing research: an applied orientation. New Jersey: Pearson Prentice-Hall.
- Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An integrative model of organizational trust. Academy of Management Review, 20(3), 709-734.
- McCorvey, J. J., & Cheung, B. (2024). Young adults are getting used to living on a financial cliff.
- McKnight, D. H., & Chervany, N. L. (2001). Conceptualizing trust: a typology and ecommerce customer relationships model. Proceedings of the 34th Annual Hawaii International Conference on System Sciences. Maui, HI, USA.
- McKnight, D. H., Lankton, N. K., Nicolaou, A., & Price, J. (2017). Distinguishing the effects of B2B information quality, system quality, and service outcome quality on trust and distrust. The Journal of Strategic Information Systems, 2, 118-141.
- Miller, L., & Lu, W. (2018). Gen Z is set to outnumber millennials within a year. Bloomberg.
- Mokhlis, S. (2012). The influence of service quality on satisfaction: A gender comparison. Public Administration Research, 1(1), 103-112.
- Motiwalla, L. F., Albashrawi, M., & Kartal, H. B. (2019). Uncovering unobserved heterogeneity bias: measuring mobile banking system success. International Journal of Information Management, 49, 439-451.
- Nor, K. M. & Pearson, J. M. (2008). An exploratory study into the adoption of Internet banking in a developing country: Malaysia. Journal of Internet Commerce, 7(1), 29-73.
- Nyoka, C. (2018). An examination of the factors that determine consumers’ adoption of mobile banking services in South Africa. EuroEconomica, 3(37), 116-129.
- Pallant, J. (2020). SPSS survival manual: a step by step guide to data analysis using the IBM SPSS. Berkshire: McGraw-Hill.
- Purani, K., Kumar, D. S., & Sahadev, S. (2019). e-Loyalty among millennials: personal characteristics and social influences. Journal of Retailing and Consumer Services, 48, 215-223.
- Roh, T., Yang, Y. S., Xiao, S., & Park, B. I. I. (2024). What makes consumers trust and adopt fintech? An empirical investigation in China. Electronic Commerce Research, 24, 3-35.
- Sarkar, S., Chauhan, S., & Khare, A. (2020). A meta-analysis of antecedents and consequences of trust in mobile commerce. International Journal of Information Management, 50, 286-301.
- Shankar, A., & Rishi, B. (2020). Convenience matter in mobile banking adoption intention? Australasian Marketing Journal, 28(4), 273-285.
- Shankar, A., Jebarajakirhy, C., Ashaduzzaman, M. D. (2020). How do electronic word of mouth practices contribute to mobile banking adoption? Journal of Retailing and Consumer Services, 52, 1-14.
- Sharma, S. K., & Sharma, M. (2019). Examining the role of trust and quality dimensions in the actual usage of mobile banking services: an empirical investigation. International Journal of Information Management, 44, 65-75.
- Sharma, S. K., Govindaluri, S. M., Al-Muharrami, S., & Tarhini, A. (2017). A multi-analytical model for mobile banking adoption: a developing country perspective. Review of International Business and Strategy, 27(1), 133-148.
- Shrout, P. E., & Bolger, N. (2002). Mediation in experimental and nonexperimental studies: new procedures and recommendations. Psychological Methods, 7(4), 422-445.
- Souiden, N., Ladhari, R., & Chaouali, W. (2021). Mobile banking adoption: a systematic review, International Journal of Bank Marketing, 39(2), 214-241.
- Statistics South Africa. (2023). Mid-year population estimates 2022 (Statistical release P0302).
- Stouthuysen, K. (2020). A 2020 perspective on “The building of online trust in e-business relationships”. Electronic Commerce Research and Applications, 40, 1-2.
- Tam, C., & Oliveira, T. (2017). Understanding mobile banking individual performance: The DeLone & McLean model and the moderating effects of individual culture. Internet Research, 27(3), 538-562.
- Thusi, P., Maduku, D. K. (2020). South African millennials’ acceptance and use of retail mobile banking apps: an integrated perspective. Computers in Human Behavior, 111, 1-10.
- Trabelsi-Zoghlami, A., Berraies, S., & Yahia, K. B. (2020). Service quality in a mobile-banking-applications context: do users’ age and gender matter? Total Quality Management & Business Excellence, 31(15-16), 1639-1668.
- Ubl, H. L., Walden, L. X., & Arbit, D. (2023). Embracing tech-savvy millennials in the workplace.
- Wavetec. (2024). Banking channels of the future – digital banking.
- Werenowska, A., & Rzepka, M. (2020). The role of social media in Generation Y travel decision-making process (case study in Poland). Information, 11(8), 1-14.
- Windasari, N. A., Kusumawati, N., Larasati, N., & Amelia, R. P. (2022). Digital-only banking experience: insights from gen Y and gen Z. Journal of Innovation & Knowledge, 7(2), 1-10.
- Yuen, M. (2022). State of mobile banking in 2022: top apps, features, statistics ad market trends.
- Zhang, T., Lu, C., & Kizildag, M. (2018). Banking “on-the-go”: examining consumers’ adoption of mobile banking services. International Journal of Quality and Service Sciences, 10(3), 279-295.
- Zhou, T. (2011). An empirical examination of initial trust in mobile banking. Internet Research, 21(5), 527-540.
- Zhou, T. (2012a). Understanding users’ initial trust in mobile banking: an elaboration likelihood perspective. Computers in Human Behavior, 28, 1518-1525.
- Zhou, T. (2012b). Examining mobile banking user adoption from the perspectives of trust and flow experience. Information Technology and Management, 13(1), 27-37.
- Zhou, T. (2013). An empirical examination of continuance intention of mobile payment services. Decision Support Systems, 54(2), 1085-1091.
- Zhu, J., & Wang, M. (2022). Analyzing the effect of people utilizing mobile technology to make banking services more accessible. Frontiers in Public Health, 10, 1-9.