A Conceptual Framework Integration of UTAUT and HBM on Evaluating the Adoption of Electronic Payment System in Malaysia
Abstract views: 864
/ 
PDF downloads: 626
DOI:
https://doi.org/10.46291/IJOSPERvol7iss4pp1164-1176Keywords:
electronic payment services; health benefit model; intention use; perceived health risk; Intention to use.Abstract
Given the ongoing epidemic coupled with low acceptance of electronic payment system, such could affect individual behavior. It is through the identification of this factors that affect individual behavior that aide toward overcoming the present challenges faced in influencing individual participation in electronic payment system. The main aim of this study is to propose a conceptual framework on the term of improving the adoption of electronic payment system. Through the incorporation of grounding theory of unified theory of acceptance model and health benefit model from both quantitative and qualitative studies, we select three influencing variables perceived susceptibility, perceived severity, perceived health risk which affect electronic payment adoption. This paper further explores the impact of identified variables perceived susceptibility, perceived severity the role of perceived health risk as mediator. Finally, this paper finalized a conceptual model after exploring previous studies and propose an empirical investigation for validation in future for researchers and practitioners.
References
Ahadzadeh, A. S., Sharif, S. P., Ong, F. S., & Khong, K. W. (2015). Integrating Health Belief Model and Technology Acceptance Model : An Investigation of Health-Related Internet Use Corresponding Author : Journal of Medical Internet Research, 17(2), 1–17. https://doi.org/10.2196/jmir.3564
Ajzen, I. (1985). (1985). From intentions to actions: A theory of planned behavior. In Action Control, 11–39.
Alalwan, A. A., Dwivedi, Y. K., Rana, N. P., & Algharabat, R. (2018). Journal of Retailing and Consumer Services Examining factors in fl uencing Jordanian customers ’ intentions and adoption of internet banking : Extending UTAUT2 with risk. Journal of Retailing and Consumer Services, 40, 125–138. https://doi.org/10.1016/j.jretconser.2017.08.026
AlAwadhi, S., & Morris, A. (2008). The Use of the UTAUT Model in the Adoption of E-Government Services in The Use of the UTAUT Model in the Adoption of E-government Services in Kuwait Suha AlAwadhi Department of Information Science S.Alawadhi@lboro.ac.uk Department of Information Science. In In Kuwait Paper presented at the Proceedings of the 41st Hawaii International Conference (p. 219). IEEE. https://doi.org/10.1109/HICSS.2008.452
Bagozzi, R. P. (2016). The Legacy of the Technology Acceptance Model and a Proposal for a Paradigm Shift. Journal of the Association for Information Systems, 8(4), 3. https://doi.org/10.17705/1jais.00122
Bilton, N. (2015, March 19). The health concerns in wearable tech. The New York Times. Retrieved from https://www.nytimes.com/2015/03/19/style/couldwearable-computers-be-as- harmful-as-cigarettes.html?_r¼0.
Chang, T.-Y., Tsai, C.-J., & Lin, J.-H. (2012). The Journal of Systems and Software A graphical-based password keystroke dynamic authentication system for touch screen handheld mobile devices. The Journal of Systems & Software, 85(5), 1157–1165. https://doi.org/10.1016/j.jss.2011.12.044
Chen, C. (2013). Perceived risk , usage frequency of mobile banking services. Managing Service Quality, 23(5), 410–436. https://doi.org/10.1108/MSQ-10-2012-0137
Chopdar, P. K., Korfiatis, N., Sivakumar, V. J., & Lytras, M. D. (2018). Mobile shopping apps adoption and perceived risks : A cross-country perspective utilizing the Unified Theory of Acceptance and Use of Technology. Computers in Human Behavior, 86, 109–128. https://doi.org/10.1016/j.chb.2018.04.017
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (2014). User Acceptance of Computer Technology : A Comparison of Two Theoretical Models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982
Dishion, T. J., & Tipsord, J. M. (2011). Peer Contagion in Child and Adolescent Social and Emotional Development. Annual Review of Psychology, 62, 189–217. https://doi.org/10.1146/annurev.psych.093008.100412
Dowling, G. R., & Staelin, R. (1994). A Model of Perceived Risk and Intended Risk-Handling Activity. Journal of Consumer Research, 21(1), 119–134. https://doi.org/10.1086/209386
Faqih, K. M. (2011). Integrating Perceived Risk and Trust with Technology Acceptance Model : An Empirical Assessment of Customers â€TM Acceptance of Online Shopping in Jordan. In In 2011 International Conference on Research and Innovation in Information Systems (pp. 1–5). IEEE.
Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption : a perceived risk facets perspective. International Journal of Human-Computer Studies, 59(4), 451–474. https://doi.org/10.1016/S1071-5819(03)00111-3
Gardner, M., & Steinberg, L. (2005). Peer Influence on Risk Taking , Risk Preference , and Risky Decision Making in Adolescence and Adulthood : An Experimental Study. Developmental Psychology, 41(4), 625–635. https://doi.org/10.1037/0012-1649.41.4.625
Geva, B. (2012). The Wireless Wire Do M-Payments and UNCITRAL Model Law on International Credit Transfers Match , Raw ? Model Law on International Credit Transfers. Banking & Finance Law Review, 27, 249–264.
Hanafizadeh, P., Behboudi, M., Koshksaray, A. A., & Tabar, M. J. S. (2014). Mobile-banking adoption by Iranian bank clients. Mobile-Banking Adoption by Iranian Bank Clients. Telematics and Informatics, 31(1), 62–78. https://doi.org/10.1016/j.tele.2012.11.001
Im, I., Kim, Y., & Han, H. J. (2008). The effects of perceived risk and technology type on users ’ acceptance of technologies §. Information & Management, 45(1), 1–9. https://doi.org/10.1016/j.im.2007.03.005
Islam, R., Islam, R., & Mazumder, T. (2017). Mobile application and its global impact. International Journal of Engineering & Technology (IJEST), 10(6), 72–78.
Ivarsson, S. (2008). Mobile payment with customer controlled connection - Can it be constructed to be safe enough ? Blekinge Institute of Technology.
Kansal, P. (2016). Perceived Risk and Technology Acceptance Model in Self-service Banking : A Study on the Nature of Mediation. South Asian Journal of Management, 23(2), 51.
Kim, J., & Park, H. A. (2012). Development of a Health Information Technology Acceptance Model Using Consumers ’ Health Behavior Intention Corresponding Author : Journal of Medical Internet Research, 14(5), 1–14. https://doi.org/10.2196/jmir.2143
Klucharev, V., Hytönen, K., Rijpkema, M., Smidts, A., & Fernández, G. (2009). Reinforcement Learning Signal Predicts Social Conformity ´. Neuron, 61(1), 140–151. https://doi.org/10.1016/j.neuron.2008.11.027
Madan, K., & Yadav, R. (2016). Behavioural intention to adopt mobile wallet : a developing country perspective. Journal of Indian Business Research, 8(3), 227–244. https://doi.org/10.1108/JIBR-10-2015-0112
Martins, C., Oliveira, T., & Popovič, A. (2014). International Journal of Information Management Understanding the Internet banking adoption : A unified theory of acceptance and use of technology and perceived risk application. International Journal of Information Management, 34(1), 1–13. https://doi.org/10.1016/j.ijinfomgt.2013.06.002
Menon, G., Raghubir, P., & Agrawal, N. (2008). Health risk perceptions and consumer psychology. ( and K. Haugtvvedlt, Herr, Ed.). Available at SSRN 945673.
Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192–222.
Mustafaoğlu, R., Zirek, E., Yasacı, Z., & Özdinçler, A. R. (2018). The Negative Effects of Digital Technology Usage on Children ’ s Development and Health The Negative Effects of Digital Technology Usage on Children ’ s Development and Health *. Addicta: The Turkish Journal on Addictions, 5(2), 13–21.
Oliveira, T., Thomas, M., Baptista, G., & Campos, F. (2016). Computers in Human Behavior Mobile payment : Understanding the determinants of customer adoption and intention to recommend the technology. Computers in Human Behavior, 61(2016), 404–414. https://doi.org/10.1016/j.chb.2016.03.030
Peter, J. P., & Ryan, M. J. (1976). An Investigation of Perceived Risk at the. Journal of Marketing Research, 13(2), 184–188.
Qian, Y., Fang, Y., & Gonzalez, J. J. (2012). Managing information security risks during new technology adoption. Computers & Security, 31(8), 859–869. https://doi.org/10.1016/j.cose.2012.09.001
Rosenstock, I. M., Strecher, V. J., & Becker, M. H. (1988). Social Learning Theory and the Health Belief Model. Health Education Quarterly, 15(2), 404–414.
Simons-Morton, B., Lerner, N., & Singer, J. (2005). The observed effects of teenage passengers on the risky driving behavior of teenage drivers. Accident Analysis and Prevention, 37, 973–982. https://doi.org/10.1016/j.aap.2005.04.014
Sitkin, S. B., & Pablo, A. L. (1992). Reconceptualizing the Determinants of Risk Behavior. Academy Oi Management Review, 17(1), 9–38.
Spoth, R., & Redmond, C. (1995). Parent Motivation to Enroll in Parenting Skills Programs : A Model of Family Context and Health Belief Predictors. Journal of Family Psychology, 9(3), 294–310.
Tak, P., & Panwar, S. (2017). Using UTAUT 2 model to predict mobile app based shopping : evidences from India. Journal of Indian Business Research, 9(3), 248–264. https://doi.org/10.1108/JIBR-11-2016-0132
Tandon, U., Kiran, R., & Sah, A. N. (2016). Understanding Online Shopping Adoption in India : Unified Understanding Online Shopping Adoption in India : Unified Theory of Acceptance and Use of. Service Science, 8(4), 420–437.
Tavares J, O. T. (2016). Electronic health record portals definition and usage. In Encyclopedia of E-Health and Telemedicine, 555–62.
Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144–176.
Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal Computing : Toward a Conceptual Model of Utilization. MIS Quarterly, 125–143.
Thornton, S., & Calam, R. (2010). Predicting intention to attend and actual attendance at a universal parent-training programme : A comparison of social cognition models. Clinical Child Psychology and Psychiatry, 16(3), 365–383. https://doi.org/10.1177/1359104510366278
van de Kar, A., Knottnerus, A. N. D. R. E., Meertens, R., Dubois, V., & Kok, G. E. R. J. O. (1992). Why do patients consult the general practitioner ? Determinants of their decision. British Journal of General Practice, 42, 313–316.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 425–478.
Venkatesh, V., Thong, J. Y., & Xu, X. (2012). CONSUMER A CCEPTANCE AND U SE OF I NFORMATION T ECHNOLOGY : E XTENDING THE U NIFIED T HEORY. MIS Quarterly, 36(1), 157–178.
Walker, M. B., & Andrade, M. G. (1996). Conformity in the Asch task as a function of age. The Journal of Social Psychology, 136(3), 367–372.
Wilson, E. V., & Lankton, N. K. (2004). Modeling Patients ’ Acceptance of Provider-delivered E-health. Journal of the American Medical Informatics Association, 11(4), 241–248. https://doi.org/10.1197/jamia.1475.Health-related
Yang, K. (2012). Journal of Retailing and Consumer Services Consumer technology traits in determining mobile shopping adoption : An application of the extended theory of planned behavior. Journal of Retailing and Consumer Services, 19(5), 484–491. https://doi.org/10.1016/j.jretconser.2012.06.003
Yüksel, A., & Yüksel, F. (2007). Shopping Risk Perceptions : Effects on Tourists â€TM Emotions , Satisfaction and Expressed Loyalty Intentions. Tourism Management, 28, 703–713. https://doi.org/10.1016/j.tourman.2006.04.025
Zaki, J., Schirmer, J., & Mitchell, J. P. (2011). Computation of Value. Psychological Science, 22(7), 894–900. https://doi.org/10.1177/0956797611411057
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2020 International Journal of Social, Political and Economic Research
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
