EXAMINING THE ROLE OF TASK TECHNOLOGY FIT IN CONTINUANCE INTENTION OF MOBILE PAYMENT SYSTEM IN RETAIL STORES
Keywords:
Mobile Payment Systems, Continuance Intention, Task Technology Fit, Facilitating Conditions, Technology Continuance
Abstract
Over the past few years, the world has witnessed significant development in the area of mobile payment systems right from pre-paid mobile wallet to UPI based payment systems. The present study develops a research model to analyze mobile payment system continuance intention of consumers in the retail market. Data was collected among retail shoppers in India, through a survey-based questionnaire. The research hypotheses were tested using structural equation modeling approach. Results support that task technology fit; satisfaction, facilitating conditions and convenience influence the mobile payment system users’ continuance intention. The findings states that among the influencing variables task technology fit has stronger effect on the continuance intention among the users.
References
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Alwahaishi, S., and Snášel, V. (2013). Consumers’ Acceptance and Use of Information and Communications Technology: A UTAUT and Flow Based Theoretical Model. Journal of Technology Management and Innovation, Vol. 8, No. 2, pp. 61–73.
Anderson, James C., and Gerbing, David W. (1992). Assumptions and Comparative Strengths of the Two-Step Approach:Comment on Fornell and Yi. Sociological Methods & Research, doi:10.1177/0049124192020003002.
Bagozzi, Richard P., and Youjae Yi. (1988). On the Evaluation of Structural Equation Models. Journal of the Academy of Marketing Science, doi:10.1007/BF02723327.
Bandura, A. (1991). Social Cognitive Theory of Self-Regulation. Organizational Behavior and Human Decision Processes, doi:10.1016/0749-5978(91)90022-L.
Bentler, P. M., and Ping Chou, Chih. (1987). Practical Issues in Structural Modeling. Sociological Methods & Research, doi:10.1177/0049124187016001004.
Bhattacherjee, A. (2001). Understanding Information Systems Continuance: An Expectation - Confirmation Model. MIS Quarterly, Vol. 25, No. 3, September, p. 351, doi:10.2307/3250921.
Boden, Joe, et al. (2019). The Effect of Credit Card versus Mobile Payment on Convenience and Consumers’ Willingness to Pay. Journal of Retailing and Consumer Services, Vol. 52, doi:10.1016/j.jretconser.2019.101910.
Campbell, Donald T., and Fiske, Donald W. (1959). Convergent and Discriminant Validation by the MultitraitMultimethod Matrix. Psychological Bulletin, doi:10.1037/h0046016. Chen, X., and Shaorui Li. (2017). Understanding Continuance Intention of Mobile Payment Services: An Empirical
Study. Journal of Computer Information Systems, Vol. 57, No. 4, pp. 287–98, doi:10.1080/08874417.2016.1180649. Davis, Fred D. (1993). User Acceptance of Information Technology: System Characteristics, User Perceptions and Behavioral Impacts. International Journal of Man-Machine Studies, doi:10.1006/imms.1993.1022.
de Kerviler, Gwarlann, et al. (2016). Adoption of In-Store Mobile Payment: Are Perceived Risk and Convenience the Only Drivers? Journal of Retailing and Consumer Services, doi:10.1016/j.jretconser.2016.04.011.
Edwards, C. (2016). Crossing the CHASM. New Electronics, Vol. 49, No. 9.
Fishbein, M., and Ajzen, I. (1975). Beliefs, Attitude, Intention, and Behavior. Strategies of Change: Persuasive Communication, doi:10.2307/2065853.
Fornell, C., and David F. Larcker. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, doi:10.2307/3151312.
Garrett, Jamie Lynn, et al. (2014). Adoption of Mobile Payment Technology by Consumers. Family and Consumer Sciences Research Journal, Vol. 42, No. 4, pp. 358–68, doi:10.1111/fcsr.12069.
Gefen, David, et al. (2000. Structural Equation Modeling and Regression: Guidelines for Research Practice. Communications of the Association for Information Systems, doi:10.17705/1cais.00407.
Goodhue, Dale L., and Thompson, Ronald L. (1995). Task-Technology Fit and Individual Performance. MIS Quarterly: Management Information Systems, doi:10.2307/249689.
---. (1995). Task-Technology Fit and Individual Performance. MIS Quarterly, Vol. 19, No. 2, p. 213, doi:10.2307/249689.
Hooper, Daire, et al. (2008). Structural Equation Modelling: Guidelines for Determining Model Fit. Electronic Journal of Business Research Methods, doi:10.21427/D79B73.
Hu, Li Tze, and Bentler, Peter M. (1999). Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria versus New Alternatives. Structural Equation Modeling, Vol. 6, No. 1, pp. 1–55,
doi:10.1080/10705519909540118.
Kim, Changsu, et al. (2010). An Empirical Examination of Factors Influencing the Intention to Use Mobile Payment. Computers in Human Behavior, Vol. 26, No. 3, pp. 310–22, doi:10.1016/j.chb.2009.10.013.
Liébana-Cabanillas, F., et al. (2014). Antecedents of the Adoption of the New Mobile Payment Systems: The Moderating Effect of Age. Computers in Human Behavior, Vol. 35, pp. 464–78, doi:10.1016/j.chb.2014.03.022.
Lin, Chihung, et al. (2012). Barriers to Physicians’ Adoption of Healthcare Information Technology: An Empirical Study on Multiple Hospitals. Journal of Medical Systems, Vol. 36, No. 3, pp. 1965–77, doi:10.1007/s10916-011- 9656-7.
Lu, June, et al. (2017). How Do Post-Usage Factors and Espoused Cultural Values Impact Mobile Payment Continuation? Behaviour & Information Technology, Vol. 36, No. 2, pp. 140–64,
doi:10.1080/0144929X.2016.1208773.
Oliveira, T, et al. (2014). Extending the Understanding of Mobile Banking Adoption: When UTAUT Meets TTF and ITM. International Journal of Information Management, Vol. 34, No. 5, pp. 689–703,
doi:10.1016/j.ijinfomgt.2014.06.004.
Oliveira, T., et al. (2014). Extending the Understanding of Mobile Banking Adoption: When UTAUT Meets TTF and ITM. International Journal of Information Management, Vol. 34, No. 5, pp. 689–703,
doi:10.1016/j.ijinfomgt.2014.06.004.
Oliveira, T., et al. (2016). Mobile Payment: Understanding the Determinants of Customer Adoption and Intention to Recommend the Technology. Computers in Human Behavior, Vol. 61, pp. 404–14, doi:10.1016/j.chb.2016.03.030.
Ondrus, J., and Pigneur, Y. (2012). Towards a Holistic Analysis of Mobile Payments: A Multiple Perspectives Approach. Electronic Commerce Research and Applications, Vol. 5, No. 3, pp. 246–57,
doi:10.1016/j.elerap.2005.09.003.
Peterson, Robert A. (1994). A Meta-Analysis of Cronbach’s Coefficient Alpha. Journal of Consumer Research, doi:10.1086/209405.
Shao, Zhen, et al. (2019). Antecedents of Trust and Continuance Intention in Mobile Payment Platforms: The Moderating Effect of Gender. Electronic Commerce Research and Applications, Vol. 33,
doi:10.1016/j.elerap.2018.100823.
Shaw, Heather, et al. (2018). The Technology Integration Model (TIM). Predicting the Continued Use of Technology. Computers in Human Behavior, Vol. 83, pp. 204–14, doi:10.1016/j.chb.2018.02.001.
Singh, N., and Sinha, N. (2020). How Perceived Trust Mediates Merchant’s Intention to Use a Mobile Wallet Technology. Journal of Retailing and Consumer Services, Vol. 52, doi:10.1016/j.jretconser.2019.101894.
Steiger, James H. (1980). Tests for Comparing Elements of a Correlation Matrix. Psychological Bulletin, doi:10.1037/0033-2909.87.2.245.
Sun, Heshan, et al. (2016). Choosing a Fit Technology: Understanding Mindfulness in Technology Adoption and Continuance. Journal of the Association for Information Systems, Vol. 17, No. 6, pp. 377–412,
doi:10.17705/1jais.00431.Sun, Yuan, and Jeyaraj, Anand. (2013). Information Technology Adoption and Continuance: A Longitudinal Study of Individuals’ Behavioral Intentions. Information and Management, Vol. 50, No. 7, pp. 457–65, doi:10.1016/j.im.2013.07.005.
Surry, Daniel W. et al. (2005). A Model for Integrating Instructional Technology into Higher Education. British Journal of Educational Technology, Vol. 36, No. 2, pp. 327–29, doi:10.1111/j.1467-8535.2005.00461.x.
Taherdoost, H. (2018). A Review of Technology Acceptance and Adoption Models and Theories. Procedia Manufacturing, Vol. 22, pp. 960–67, doi:10.1016/j.promfg.2018.03.137.
Taylor, Shirley, and Peter A. Todd. (1995). Understanding Information Technology Usage: A Test of Competing Models. Information Systems Research, doi:10.1287/isre.6.2.144.
Teo, Aik Chuan, et al. (2015). Why Consumers Adopt Mobile Payment? A Partial Least Squares Structural Equation Modelling (PLS-SEM) Approach. International Journal of Mobile Communications, Vol. 13, No. 5, p. 478, doi:10.1504/IJMC.2015.070961.
Thompson, Ronald L., et al. (1991). Personal Computing: Toward a Conceptual Model of Utilization. MIS Quarterly: Management Information Systems, doi:10.2307/249443.
Venkatesh, et al. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, Vol. 27, No. 3, p. 425, doi:10.2307/30036540.
Venkatesh, V., et al. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly: Management Information Systems, Vol. 36, No. 1, pp. 157–78,
Venkatesh, V., et al. (2012). Consumer Acceptance and User of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly, Vol. 36, No. 1, pp. 157–78, doi:10.1111/j.1365- 2729.2006.00163.x.
Venkatesh, V., et al. (2016). Unified Theory of Acceptance and Use of Technology: A Synthesis and the Road Ahead. Journal of the Association for Information Systems, Vol. 17, No. 5, pp. 328–76,
Wu, Bing, and Xiaohui Chen. (2017). Continuance Intention to Use MOOCs: Integrating the Technology Acceptance Model (TAM) and Task Technology Fit (TTF) Model. Computers in Human Behavior,
doi:10.1016/j.chb.2016.10.028.
Zhou, T, et al. (2010). Integrating TTF and UTAUT to Explain Mobile Banking User Adoption. Computers in Human Behavior, Vol. 26, No. 4, pp. 760–67, doi:10.1016/j.chb.2010.01.013.
Zhou, Tao. (2013). An Empirical Examination of Continuance Intention of Mobile Payment Services. Decision Support Systems, Vol. 54, No. 2, pp. 1085–91, doi:10.1016/j.dss.2012.10.034.
Zhou, Y. (2008). Voluntary Adopters versus Forced Adopters: Integrating the Diffusion of Innovation Theory and the Technology Acceptance Model to Study Intra-Organizational Adoption. New Media and Society, Vol. 10, No. 3, pp. 475–96, doi:10.1177/1461444807085382.
Zmud, R., et al. (1999). Information Technology Adoption across Time: A Cross-Sectional Comparison of PreAdoption and Post-Adoption Beliefs. MIS Quarterly, Vol. 23, No. 2, p. 145.
Alwahaishi, S., and Snášel, V. (2013). Consumers’ Acceptance and Use of Information and Communications Technology: A UTAUT and Flow Based Theoretical Model. Journal of Technology Management and Innovation, Vol. 8, No. 2, pp. 61–73.
Anderson, James C., and Gerbing, David W. (1992). Assumptions and Comparative Strengths of the Two-Step Approach:Comment on Fornell and Yi. Sociological Methods & Research, doi:10.1177/0049124192020003002.
Bagozzi, Richard P., and Youjae Yi. (1988). On the Evaluation of Structural Equation Models. Journal of the Academy of Marketing Science, doi:10.1007/BF02723327.
Bandura, A. (1991). Social Cognitive Theory of Self-Regulation. Organizational Behavior and Human Decision Processes, doi:10.1016/0749-5978(91)90022-L.
Bentler, P. M., and Ping Chou, Chih. (1987). Practical Issues in Structural Modeling. Sociological Methods & Research, doi:10.1177/0049124187016001004.
Bhattacherjee, A. (2001). Understanding Information Systems Continuance: An Expectation - Confirmation Model. MIS Quarterly, Vol. 25, No. 3, September, p. 351, doi:10.2307/3250921.
Boden, Joe, et al. (2019). The Effect of Credit Card versus Mobile Payment on Convenience and Consumers’ Willingness to Pay. Journal of Retailing and Consumer Services, Vol. 52, doi:10.1016/j.jretconser.2019.101910.
Campbell, Donald T., and Fiske, Donald W. (1959). Convergent and Discriminant Validation by the MultitraitMultimethod Matrix. Psychological Bulletin, doi:10.1037/h0046016. Chen, X., and Shaorui Li. (2017). Understanding Continuance Intention of Mobile Payment Services: An Empirical
Study. Journal of Computer Information Systems, Vol. 57, No. 4, pp. 287–98, doi:10.1080/08874417.2016.1180649. Davis, Fred D. (1993). User Acceptance of Information Technology: System Characteristics, User Perceptions and Behavioral Impacts. International Journal of Man-Machine Studies, doi:10.1006/imms.1993.1022.
de Kerviler, Gwarlann, et al. (2016). Adoption of In-Store Mobile Payment: Are Perceived Risk and Convenience the Only Drivers? Journal of Retailing and Consumer Services, doi:10.1016/j.jretconser.2016.04.011.
Edwards, C. (2016). Crossing the CHASM. New Electronics, Vol. 49, No. 9.
Fishbein, M., and Ajzen, I. (1975). Beliefs, Attitude, Intention, and Behavior. Strategies of Change: Persuasive Communication, doi:10.2307/2065853.
Fornell, C., and David F. Larcker. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, doi:10.2307/3151312.
Garrett, Jamie Lynn, et al. (2014). Adoption of Mobile Payment Technology by Consumers. Family and Consumer Sciences Research Journal, Vol. 42, No. 4, pp. 358–68, doi:10.1111/fcsr.12069.
Gefen, David, et al. (2000. Structural Equation Modeling and Regression: Guidelines for Research Practice. Communications of the Association for Information Systems, doi:10.17705/1cais.00407.
Goodhue, Dale L., and Thompson, Ronald L. (1995). Task-Technology Fit and Individual Performance. MIS Quarterly: Management Information Systems, doi:10.2307/249689.
---. (1995). Task-Technology Fit and Individual Performance. MIS Quarterly, Vol. 19, No. 2, p. 213, doi:10.2307/249689.
Hooper, Daire, et al. (2008). Structural Equation Modelling: Guidelines for Determining Model Fit. Electronic Journal of Business Research Methods, doi:10.21427/D79B73.
Hu, Li Tze, and Bentler, Peter M. (1999). Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria versus New Alternatives. Structural Equation Modeling, Vol. 6, No. 1, pp. 1–55,
doi:10.1080/10705519909540118.
Kim, Changsu, et al. (2010). An Empirical Examination of Factors Influencing the Intention to Use Mobile Payment. Computers in Human Behavior, Vol. 26, No. 3, pp. 310–22, doi:10.1016/j.chb.2009.10.013.
Liébana-Cabanillas, F., et al. (2014). Antecedents of the Adoption of the New Mobile Payment Systems: The Moderating Effect of Age. Computers in Human Behavior, Vol. 35, pp. 464–78, doi:10.1016/j.chb.2014.03.022.
Lin, Chihung, et al. (2012). Barriers to Physicians’ Adoption of Healthcare Information Technology: An Empirical Study on Multiple Hospitals. Journal of Medical Systems, Vol. 36, No. 3, pp. 1965–77, doi:10.1007/s10916-011- 9656-7.
Lu, June, et al. (2017). How Do Post-Usage Factors and Espoused Cultural Values Impact Mobile Payment Continuation? Behaviour & Information Technology, Vol. 36, No. 2, pp. 140–64,
doi:10.1080/0144929X.2016.1208773.
Oliveira, T, et al. (2014). Extending the Understanding of Mobile Banking Adoption: When UTAUT Meets TTF and ITM. International Journal of Information Management, Vol. 34, No. 5, pp. 689–703,
doi:10.1016/j.ijinfomgt.2014.06.004.
Oliveira, T., et al. (2014). Extending the Understanding of Mobile Banking Adoption: When UTAUT Meets TTF and ITM. International Journal of Information Management, Vol. 34, No. 5, pp. 689–703,
doi:10.1016/j.ijinfomgt.2014.06.004.
Oliveira, T., et al. (2016). Mobile Payment: Understanding the Determinants of Customer Adoption and Intention to Recommend the Technology. Computers in Human Behavior, Vol. 61, pp. 404–14, doi:10.1016/j.chb.2016.03.030.
Ondrus, J., and Pigneur, Y. (2012). Towards a Holistic Analysis of Mobile Payments: A Multiple Perspectives Approach. Electronic Commerce Research and Applications, Vol. 5, No. 3, pp. 246–57,
doi:10.1016/j.elerap.2005.09.003.
Peterson, Robert A. (1994). A Meta-Analysis of Cronbach’s Coefficient Alpha. Journal of Consumer Research, doi:10.1086/209405.
Shao, Zhen, et al. (2019). Antecedents of Trust and Continuance Intention in Mobile Payment Platforms: The Moderating Effect of Gender. Electronic Commerce Research and Applications, Vol. 33,
doi:10.1016/j.elerap.2018.100823.
Shaw, Heather, et al. (2018). The Technology Integration Model (TIM). Predicting the Continued Use of Technology. Computers in Human Behavior, Vol. 83, pp. 204–14, doi:10.1016/j.chb.2018.02.001.
Singh, N., and Sinha, N. (2020). How Perceived Trust Mediates Merchant’s Intention to Use a Mobile Wallet Technology. Journal of Retailing and Consumer Services, Vol. 52, doi:10.1016/j.jretconser.2019.101894.
Steiger, James H. (1980). Tests for Comparing Elements of a Correlation Matrix. Psychological Bulletin, doi:10.1037/0033-2909.87.2.245.
Sun, Heshan, et al. (2016). Choosing a Fit Technology: Understanding Mindfulness in Technology Adoption and Continuance. Journal of the Association for Information Systems, Vol. 17, No. 6, pp. 377–412,
doi:10.17705/1jais.00431.Sun, Yuan, and Jeyaraj, Anand. (2013). Information Technology Adoption and Continuance: A Longitudinal Study of Individuals’ Behavioral Intentions. Information and Management, Vol. 50, No. 7, pp. 457–65, doi:10.1016/j.im.2013.07.005.
Surry, Daniel W. et al. (2005). A Model for Integrating Instructional Technology into Higher Education. British Journal of Educational Technology, Vol. 36, No. 2, pp. 327–29, doi:10.1111/j.1467-8535.2005.00461.x.
Taherdoost, H. (2018). A Review of Technology Acceptance and Adoption Models and Theories. Procedia Manufacturing, Vol. 22, pp. 960–67, doi:10.1016/j.promfg.2018.03.137.
Taylor, Shirley, and Peter A. Todd. (1995). Understanding Information Technology Usage: A Test of Competing Models. Information Systems Research, doi:10.1287/isre.6.2.144.
Teo, Aik Chuan, et al. (2015). Why Consumers Adopt Mobile Payment? A Partial Least Squares Structural Equation Modelling (PLS-SEM) Approach. International Journal of Mobile Communications, Vol. 13, No. 5, p. 478, doi:10.1504/IJMC.2015.070961.
Thompson, Ronald L., et al. (1991). Personal Computing: Toward a Conceptual Model of Utilization. MIS Quarterly: Management Information Systems, doi:10.2307/249443.
Venkatesh, et al. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, Vol. 27, No. 3, p. 425, doi:10.2307/30036540.
Venkatesh, V., et al. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly: Management Information Systems, Vol. 36, No. 1, pp. 157–78,
Venkatesh, V., et al. (2012). Consumer Acceptance and User of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly, Vol. 36, No. 1, pp. 157–78, doi:10.1111/j.1365- 2729.2006.00163.x.
Venkatesh, V., et al. (2016). Unified Theory of Acceptance and Use of Technology: A Synthesis and the Road Ahead. Journal of the Association for Information Systems, Vol. 17, No. 5, pp. 328–76,
Wu, Bing, and Xiaohui Chen. (2017). Continuance Intention to Use MOOCs: Integrating the Technology Acceptance Model (TAM) and Task Technology Fit (TTF) Model. Computers in Human Behavior,
doi:10.1016/j.chb.2016.10.028.
Zhou, T, et al. (2010). Integrating TTF and UTAUT to Explain Mobile Banking User Adoption. Computers in Human Behavior, Vol. 26, No. 4, pp. 760–67, doi:10.1016/j.chb.2010.01.013.
Zhou, Tao. (2013). An Empirical Examination of Continuance Intention of Mobile Payment Services. Decision Support Systems, Vol. 54, No. 2, pp. 1085–91, doi:10.1016/j.dss.2012.10.034.
Zhou, Y. (2008). Voluntary Adopters versus Forced Adopters: Integrating the Diffusion of Innovation Theory and the Technology Acceptance Model to Study Intra-Organizational Adoption. New Media and Society, Vol. 10, No. 3, pp. 475–96, doi:10.1177/1461444807085382.
Zmud, R., et al. (1999). Information Technology Adoption across Time: A Cross-Sectional Comparison of PreAdoption and Post-Adoption Beliefs. MIS Quarterly, Vol. 23, No. 2, p. 145.
How to Cite
Pradeepkumar Chokkannan, & Vivekanandan Kaniappan. (2020). EXAMINING THE ROLE OF TASK TECHNOLOGY FIT IN CONTINUANCE INTENTION OF MOBILE PAYMENT SYSTEM IN RETAIL STORES. International Journal of Applied Service Marketing Perspectives, 9(01), 3744-3753. Retrieved from https://asmp.gfer.org/index.php/asmp/article/view/43
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