Description
Over the last decade, bank industry has made a significant investment on mobile banking (MB) as an innovative tool with an expectation that MB services are being frequently used and increasing customer satisfaction. While the focus has been on increasing MB adoption, banking research shows more value is generated with frequent and continued usage of MB services, an area that has been given little attention. We develop a research model that integrates privacy and personalization with satisfaction and technology acceptance model (TAM) factors to address this gap. Using a sample of 486 MB customers from a US local bank, our regression results reveal that perceived usefulness and perceived ease of use are significant predictors of satisfaction, which leads to continued usage of MB. However, the interaction effects used in our model show statistical significance for privacy while not for personalization. Limitations and practical implications are discussed.
Recommended Citation
Albashrawi, Mousa and Motiwalla, Luvai, "The Moderating Effect of Privacy and Personalization in Mobile Banking: A Structural Equation Modeling Analysis" (2015). AMCIS 2015 Proceedings. 31.
https://aisel.aisnet.org/amcis2015/AdoptionofIT/GeneralPresentations/31
The Moderating Effect of Privacy and Personalization in Mobile Banking: A Structural Equation Modeling Analysis
Over the last decade, bank industry has made a significant investment on mobile banking (MB) as an innovative tool with an expectation that MB services are being frequently used and increasing customer satisfaction. While the focus has been on increasing MB adoption, banking research shows more value is generated with frequent and continued usage of MB services, an area that has been given little attention. We develop a research model that integrates privacy and personalization with satisfaction and technology acceptance model (TAM) factors to address this gap. Using a sample of 486 MB customers from a US local bank, our regression results reveal that perceived usefulness and perceived ease of use are significant predictors of satisfaction, which leads to continued usage of MB. However, the interaction effects used in our model show statistical significance for privacy while not for personalization. Limitations and practical implications are discussed.