Digital payment system innovations: A marketing perspective on intention and actual use in the retail sector
-
DOIhttp://dx.doi.org/10.21511/im.17(3).2021.09
-
Article InfoVolume 17 2021, Issue #3, pp. 109-123
- Cited by
- 2168 Views
-
1543 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
This study empirically investigated the marketing perspectives of behavioral intention and the actual use of digital payment solutions as electronic innovation for retail purchases in Thailand. This is important as leveraging digital innovation can be applied to minimize physical contact between retailers and customers, especially in the COVID-19 era. The UTAUT model was used and extended to include attitude, social distancing, and perceived risk variables. The study was conducted using primary data collected from 467 Thai respondents who used digital payment systems as a means of payment in retail purchases. The study data were collected employing a structured questionnaire. Techniques used in data analysis include Confirmatory Factor Analysis and Structural Equation Modeling. The results from the data analysis highlighted that behavioral intention to use digital payment innovation in Thailand was influenced by Perceived Risk (PR), Facilitating Condition (FC), Performance Expectancy (PE), and Attitudes (AT) of people. The study also revealed that exploring the marketing perspectives, Behavioral Intention (BI) significantly influenced the Actual Use (AU) of digital payment systems. The implication is that stakeholders in retail and financial sectors, such as banks and other digital payment providers, should consider aspects of people’s attitudes and perceived risk as they influence the use and adoption of innovative digital payment solutions. Thus, it is, appropriate to propose policies and regulations that promote the effective use of digital payment systems in the Thai retail sector.
Acknowledgment
This work is supported by King Mongkut’s Institute of Technology Ladkrabang.
- Keywords
-
JEL Classification (Paper profile tab)C12, D90, M31, O31
-
References73
-
Tables3
-
Figures3
-
- Figure 1. Conceptual framework
- Figure 2. Confirmatory factor analysis
- Figure 3. Structural equation model analysis
-
- Table 1. Demographics and respondent adoption of digital payments
- Table 2. Reliability and validity statistics – Composite reliability and average variance extracted
- Table 3. Evaluation of study hypotheses
-
- Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs: Prentice-Hall.
- Akanfe, O., Rohit, V., & Rao, H. R. (2019). GDPR fitness assessment for Digital Payment Systems’ (DPS) privacy policies: A study of mobile wallet and remittance services. The 2019 Dewald Roode Workshop on Information Systems Security Research, 1-10.
- Alaeddin, O., Altounjy, R., Zainudin, Z., & Kamarudin, F. (2018). From physical to digital: Investigating consumer behaviour of switching to mobile wallet. Polish Journal of Management Studies, 17(2), 18-30.
- Al-Hujran, O., Al-Lozi, E., & Al-Debei, M. M. (2014). Get ready to mobile learning: Examining factors affecting college students’ behavioral intentions to use m-learning in Saudi Arabia. Journal of Business Administration, 10(1), 111-128.
- Ali, R.A., & Arshad, M.F.M. (2018). Empirical Analysis on Factors Impacting on Intention to Use M-learning in Basic Education in Egypt. International Review of Research in Open and Distributed Learning, 19(2).
- Al-Mamoorey, M., & Al-Rubaye, M. (2020). The role of electronic payment systems in Iraq in reducing banking risks: An empirical research on private banks. Polish Journal of Management Studies, 21(2), 49-59.
- Al-Okaily, M., Lutfi, A., Alsaad, A., Taamneh, A., & Alsyouf, A. (2020). The determinants of digital payment systems’ acceptance under cultural orientation differences: The case of uncertainty avoidance. Technology in Society, 63, 101367.
- Altounjy, R., Alaeddin, O., Hussain, H., & Kot, S. (2020). Moving from bricks to clicks: Merchants’ acceptance of the mobile payment in Malaysia. International Journal of EBusiness and EGovernment Studies, 12(2), 136-150.
- Chao, C. M. (2019). Factors Determining the Behavioral Intention to Use Mobile Learning: An Application and Extension of the UTAUT Model. Frontiers in Psychology, 16.
- Chaveesuk, S., Khalid, B, & Chaiyasoonthorn, W. (2019). Emergence of new business environment with big data and artificial intelligence. Proceedings of the 9th International Conference on Information Communication and Management, 181-185.
- Chaveesuk, S., Wutthirong, P., & Chaiyasoonthorn, W. (2018). The model of mobile payment system acceptance on social networks in Thailand. Proceedings of the 2018 10th International Conference on Information Management and Engineering – ICIME 2018.
- Chen, S.-C., Li, S.-H., & Li, C.-Y. (2011). Recent related research in Technology Acceptance Model: A literature review. Australian Journal of Business and Management Research, 1(9), 124-127.
- Chin, L. P., & Ahmad, Z. A. (2015). Perceived enjoyment and malaysian consumers’ intention to use a single platform e-payment. SHS Web of Conferences, 18, 01009.
- Chiou, J. S., Cheng, H. I., & Huang, C. Y. (2011). The effects of artist adoration and perceived risk of getting caught on attitude and intention to pirate music in the United States and Taiwan. Ethics & Behavior, 21(3), 182-196.
- Cselényi, J., Smid, L., & Kovács, G. (2002). Evaluation methods of storage capacity between manufacturing levels of EEES at changing product structure. MicroCAD 2002 International Scientific Conference. Miskolc, Hungary.
- Dahlström, P., & Edelman, D. (2013). The coming era of ’on-demand’ marketing. McKinsey Quarterly, 2, 24-39.
- Daragmeh, A., Sági, J., & Zéman, Z. (2021). Continuous intention to use e-Wallet in the context of the COVID-19 pandemic: Integrating the Health Belief Model (HBM) and Technology Continuous Theory (TCT). Journal of Open Innovation: Technology, Market, and Complexity, 7(2), 132.
- Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319.
- DesRoches, C. M., Charles, D., Furukawa, M. F., Joshi, M. S., Kralovec, P., Mostashari, F., Worzala, C., & Jha, A. K. (2013). Adoption of electronic health records grows rapidly, but fewer than half of US hospitals had at least a basic system in 2012. Health Affairs, 32(8), 1478-1485.
- Ferguson, K. K., Soutter, L., & Neubert, M. (2019). Digital payments in Africa – how demand, technology, and regulation disrupt digital payment systems. International Journal of Teaching and Case Studies, 10(4), 319.
- Feyen, E., Frost, J., Gambacorta, L., Natarajan, H., & Saal, M. (2021). Fintech and the digital transformation of financial services: implications for market structure and public policy (BIS Papers, No 117).
- Fichman, R., Dos Santos, B., & Zheng, Z. (2014). Digital Innovation as a Fundamental and Powerful Concept in the Information Systems Curriculum. MIS Quarterly, 38(2), 329-A15.
- Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Boston: Addison-Wesley.
- Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement Error. Journal of Marketing Research, 18(1), 39-50.
- Goodrich, M., & Boer, E. (2003). Model-based human-centered task automation: A case study in ACC system design. IEEE Transactions on Systems, Man, and Cybernetics – Part A: Systems and Humans, 33(3), 325-336.
- Hoehle, H., Scornavacca, E., & Huff, S. (2012). Three decades of research on consumer adoption and utilization of electronic banking channels: A literature analysis. Decision Support Systems, 54(1), 122-132.
- Hoque, R., & Sorwar, G. (2017). Understanding factors influencing the adoption of mHealth by the elderly: an extension of the UTAUT model. International Journal of Medical Information, 101, 75-84.
- Howcroft, B., Hewer, P., & Durkin, M. (2003). Banker-Customer Interactions in Financial Services. Journal of Marketing Management, 19(9-10), 1001-1020.
- Hu, L.-t., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1-55.
- Huang, H. M., & Liaw, S. S. (2005). Exploring users’ attitudes and intentions toward the web as a survey tool. Computers in Human Behavior, 21(5), 729-743.
- Hussein, Z. (2016). Leading to Intention: The Role of Attitude in Relation to Technology Acceptance Model in E-Learning. Procedia Computer Science, 105, 159-164.
- Iqbal, S., & Qureshi, I. A. (2012). M-learning adoption: A perspective from a developing country. The International Review of Research in Open and Distributed Learning, 13(3), 147-164.
- Iskandar, Y.H.P., Subramaniam, G., Majid, M.I.A., Ariff, A.M., & Rao, G.K.L. (2020). Predicting healthcare professionals’ intention to use poison information system in a Malaysian public hospital. Health Information Science and Systems, 8, 6.
- Kankanhalli, S., & Gomez, L. (2020, July 30). Why don’t small retailers adopt e-payments? New research suggests the reasons behind merchant aversion – and solutions for stimulating customer demand. Next Billion.
- Khalid, B., Chaveesuk, S., & Chaiyasoonthorn, W. (2021). MOOCS adoption in higher education: A management perspective. Polish Journal of Management Studies, 23(1), 239-256.
- Khalid, B., Lis, M., Chaiyasoonthorn, W., & Cheevasuk, S. (2021). Factors influencing behavioral intention to use MOOCs. Engineering Management in Production and Services, 13(2), 83-95.
- Kleczkowski, A., Maharaj, S., Rasmussen, S., Williams, L., & Cairns, N. (2015). Spontaneous social distancing in response to a simulated epidemic: a virtual experiment. BMC Public Health, 15, 973.
- Kovács, G., Cselényi, J., & Somogyvári, Z. (2007). Method and conception formation of microregional virtual logistics networks. OGET 2007 International Engineering Conference, 216-221. Cluj-Napoca, Romania. (In Romanian).
- Ligon, E., Malick, B., Sheth, K., & Trachtman, C. (2019). What explains low adoption of digital payment technologies? Evidence from small-scale merchants in Jaipur, India. PLOS ONE, 14(7), e0219450.
- Lin, Wan R., Lin, C.-H., & Ding, Y.-H. (2020). Factors affecting the behavioral intention to adopt mobile payment: An empirical study in Taiwan. Mathematics, 8(10), 1851.
- Macheel, T. (2017, February 3). Why retailers struggle to adopt mobile payments. Digiday.
- Montazemi, A. R., & Qahri-Saremi, H. (2015). Factors affecting adoption of online banking: A meta-analytic structural equation modeling study. Information & Management, 52(2), 210-226.
- Muangmee, C., Kot, S., Meekaewkunchorn, N., Kassakorn, N., & Khalid, B. (2021). Factors determining the behavioral intention of using food delivery apps during COVID-19 pandemics. Journal of Theoretical and Applied Electronic Commerce Research, 16(5), 1297-1310.
- Nathues, E. (2017). From interruption to interaction: Inspiration as a new marketing discipline. In E. Constantinides, & S. de Vries (Eds.), Marketing of the 21st Century Marketing Science Institute Research priorities 2016–2020 through the lens of the future marketer (pp. 38-48). University of Twente.
- Nwaolisa, E. F., & Kasie, E. G. (2012). Electronic retail payment systems: User acceptability and payment problems in Nigeria. Oman Chapter of Arabian Journal of Business and Management Review, 1(6), 18-35.
- Pearce, K. (2020, March 13). What is social distancing and how can it slow the spread of COVID-19?
- Peñarroja, V., Sánchez, J., Gamero, N., Orengo, V., & Abad, A. Z. (2019). The influence of organisational facilitating conditions and technology acceptance factors on the effectiveness of virtual communities of practice. Behavior & Information Technology, 38, 845-857.
- Pikkarainen, T., Pikkarainen, K., Karjaluoto, H., & Pahnila, S. (2004). Consumer acceptance of online banking: an extension of the technology acceptance model. Internet Research, 14(3), 224-235.
- Raharja, S. J., Sutarjo., Muhyi, H. A., & Herawaty, T. (2020). Digital payment as an enabler for business opportunities: A go-pay case study. Review of Integrative Business and Economics Research, 9(1), 319-329.
- Rangaswamy, A., Moch, N., Felten, C., van Bruggen, G., Wieringa, J. E., & Wirtz, J. (2020). The role of marketing in digital business platforms. Journal of Interactive Marketing, 51, 72-90.
- Roboff, G., & Charles, C. (1998). Privacy of financial information in cyberspace: banks addressing what consumers want. Journal of Retail Banking Services, XX(3), 51-60.
- Sanayei, A., & Noroozi, A. (2009). Security of internet banking services and its Linkage with Users’ Trust: A Case Study of Parsian Bank of Iran and CIMB Bank of Malaysia. 2009 International Conference on Information Management and Engineering.
- Sang, G. Y., Valcke, M., van Braak, J., & Tondeur, J. (2010). Student teachers’ thinking processes and ICT integration: Predictors of prospective teaching behaviors with educational technology. Computers & Education, 54(1), 103-112.
- Scholnick, B., Massoud, N., Saunders, A., Carbo-Valverde, S., & Rodríguez-Fernández, F. (2008). The economics of credit cards, debit cards and ATMs: A survey and some new evidence. Journal of Banking & Finance, 32(8), 1468-1483.
- Seethamraju, R., & Diatha, K. S. (2018). Adoption of digital payments by small retail stores. Australasian Conference on Information Systems. Sydney, Australia.
- Seethamraju, R., & Diatha, K.S. (2019). Digitalization of small retail stores – challenges in digital payments. Proceedings of the 52nd Hawaii International Conference on System Sciences, HICSS 2019. Grand Wailea, Maui, Hawaii, USA.
- Shiau, W.-L., & Chau, P. Y. K. (2016). Understanding behavioral intention to use a cloud computing classroom: a multiple model comparison approach. Cell, 53, 355-365.
- Singh, S., & Rana, R. (2017). Study of consumer perceptive of digital payment mode. Journal of Internet Banking and Commerce, 22(3), 1-14.
- Sivathanu, B. (2019). Adoption of digital payment systems in the era of demonetization in India. Journal of Science and Technology Policy Management, 10(1), 143-171.
- Solomon, E. M., & van Klyton, A. (2020). The impact of digital technology usage on economic growth in Africa. Utilities Policy, 67, 101104.
- Staykova, K. S., & Damsgaard, J. (2016). Adoption of mobile payment platforms: managing reach and range. Journal of Theoretical and Applied Electronic Commerce Research, 11(3).
- Technology and Innovation Report. (2018). Harnessing Frontier Technologies for Sustainable Development. United Nations Conference on Trade and Development.
- Teo, T., Lee, C. B., Chai, C. S., & Wong, S. L. (2009). Assessing the intention to use technology among pre-service teachers in Singapore and Malaysia: A multigroup invariance analysis of the technology acceptance model. Computers & Education, 53(3), 1000-1009.
- Tounekti, O., Ruiz-Martinez, A., & Skarmeta Gomez, A. F. (2020). Users supporting multiple (mobile) electronic payment systems in online purchases: An empirical study of their payment transaction preferences. IEEE Access, 8, 735-766.
- Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: toward a unified view. MIS Quarterly, 27, 425-478.
- Wei, Y., Wang, C., Zhu, S., Xue, H., & Chen, F. (2018). Online purchase intention of fruits: antecedents in an integrated model based on technology acceptance model and perceived risk theory. Frontiers in Psychology, 9, 1521.
- Williams, M. D., Rana, N. P., & Dwivedi, Y. K. (2015). The Unified Theory of Acceptance and Use of Technology (UTAUT): A literature review. Journal of Enterprise Information Management, 28(3), 443-488.
- Wong, W. H., & Mo, W. Y. (2019). A study of consumer intention of mobile payment in Hong Kong, based on perceived risk, perceived trust, perceived security and Technological Acceptance Model. Journal of Advanced Management Science, 7(2), 33-38.
- Yang, M., Mamun, A. A., Mohiuddin, M., Nawi, N. C., & Zainol, N. R. (2021). Cashless transactions: A study on intention and adoption of e-Wallets. Sustainability, 13, 831.
- Yang, S., Lu, Y., Gupta, S., Cao, Y., & Zhang, R. (2012). Mobile payment services adoption across time: An empirical study of the effects of behavioral beliefs, social influences, and personal traits. Computers in Human Behavior, 28(1), 129-142.
- Yi, M. Y., Jackson, J. D., Park, J. S., & Probst, J. C. (2006). Understanding information technology acceptance by individual professionals? Toward an integrative view. Information & Management, 43(3), 350-363.
- Yu, H. C., Hsi, K. H., & Kuo, P. J. (2002). Electronic payment systems: an analysis and comparison of types. Technology in Society, 24(3), 331-347.
- Zhang, X., & Yu, X. (2020). The Impact of Perceived Risk on Consumers’ Cross-Platform Buying Behavior. Frontiers in Psychology, 11, 2835.