Factors Affecting Consumers’ Usage Intention of Contactless Payment Systems

Main Article Content

Siriporn Mongkolrattanasit
Sasivimol Meeampol

Abstract

The purposes of this research were to study consumer behavior, factors that affect contactless payments, Demographic Factors Affecting Intention for Use, and to find relationships between various factors Data were collected by using online questionnaires from 463 residents of Bangkok and the Metropolitan Region between December 2020 and February 2021. Data were analyzed by using percentage, frequency, average values, standard deviations, independent sample analysis standard t-test, f-test (One-Way ANOVA), Least Significant Difference (LSD), and Multiple Linear Regression.


The number of male and female respondents was similar. Age 41-50 years, single, bachelor's degree. Being a company employee earning 15,001-30,000 baht/month. The survey found that they used to use a contactless payment service through a credit card 1-3 times a week, payment of 501-1,000 baht per time at the supermarket because it is convenient and fast. Interested in giving rebates and discounts. It was introduced by a bank employee, shop, cashier, and a contactless payment system.


The research results showed that the sample group strongly agreed that the performance expectancy and effort expectancy affect contactless payment. Price value, facility condition, social influences, trust, habit, hedonic motivation, and perceived risk were in descending order. From the hypothesis testing, it was found that different age, education level, occupation, and monthly income is significantly related to the usage intention of contactless payment systems at the significance level of .05. The study also shows the findings of a correlation between the factors found that satisfaction, value, price, and habit were positively correlated with the continued willingness to use contactless payment services.

Downloads

Download data is not yet available.

Article Details

How to Cite
Mongkolrattanasit, S., & Meeampol, S. (2022). Factors Affecting Consumers’ Usage Intention of Contactless Payment Systems. Journal of Humanities and Social Sciences, Rajapruk University, 8(2), 16–32. Retrieved from https://so03.tci-thaijo.org/index.php/rpu/article/view/262966
Section
Articles

References

กัลยา วานิชย์บัญชา. (2542). การวิเคราะห์สถิติ: สถิติเพื่อการตัดสินใจ. ภาควิชาสถิติ คณะพาณิชยศาสตร์และการบัญชี โรงพิมพ์แห่งจุฬาลงกรณ์มหาวิทยาลัย. ค้นเมื่อวันที่ 30 มีนาคม 2564, จาก https://koha.library.tu.ac.th/bib/316048

ธนาคารแห่งประเทศไทย. (2563). Payment Data Indicators. ค้นเมื่อวันที่ 30 มีนาคม 2564,จาก https://www.bot.or.th/Thai/PaymentSystems/Publication/payment_data_indicators/Pages/default.aspx

ธนาคารแห่งประเทศไทย. (2563). รายงานระบบการชำระเงิน 2562. ค้นเมื่อวันที่ 30 มีนาคม 2564, จาก https://www.bot.or.th/Thai/PaymentSystems/Pages/default.aspx

นันทนี ลักษมีการค้า. (2561). ปัจจัยการยอมรับเทคโนโลยีต่อการเข้าสู่สังคมไร้เงินสดของประชากรเจเนอเรชัน เอ็กซ์ ขึ้นไป: กรณีศึกษาจังหวัดกรุงเทพมหานคร. การค้นคว้าอิสระ หลักสูตรบริหารธุรกิจมหาบัณฑิต คณะพานิชยศาสตร์และการบัญชี มหาวืทยาลัยธรรมศาสตร์ ปีการศึกษา 2561. ค้นเมื่อวันที่ 15 พฤศจิกายน 2564, จากhttp://ethesisarchive.library.tu.ac.th/thesis/2018/TU_2018_6002030705_9868_9827.pdf

มาสเตอร์การ์ด. (2563). มาสเตอร์การ์ดเผยผู้บริโภค 79% ทั่วโลกใช้จ่ายแบบคอนแทคเลส. ค้นเมื่อวันที่ 10 สิงหาคม 2563, จาก https://www.nationtv.tv/main/content/378775670/

Bhattacherjee, A. (2001). Understanding Information Systems Continuance: An Expectation-Confirmation Model. MIS Quarterly, 25: 351-370.

Gefen, D., E. Karahanna and D. Straub. (2003). Trust and TAM in Online Shopping: An Integrated Model. MIS Quarterly, 27: 51-90.

Hong, S., J. Y. L. Thong and K. Y. Tam. (2006). Understanding continued information technology usage behavior: A comparison of three models in the context of mobile internet. Decision Support Systems, 42(3): 1819-1834.

Hsiao, C.-H., J.-J. Chang and K.-Y. Tang. (2016). Exploring the influential factors in continuance usage of mobile social Apps: Satisfaction, habit, and customer value perspectives. Telematics and Informatics, 33(2): 342-355.

Hsu, C.-L. and J. C.-C. Lin. (2015). What drives purchase intention for paid mobile apps? – An expectation confirmation model with perceived value. Electronic Commerce Research and Applications, 14(1): 46-57.

Kim, C., M. Mirusmonov and I. Lee. (2010). An empirical examination of factors influencing the intention to use mobile payment. Computers in Human Behavior, 26(3): 310-322.

Lee, M.-C. (2010). Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation–confirmation model. Computers & Education, 54(2): 506-516.

Leong, L.-Y., T.-S. Hew, G. W.-H. Tan and K.-B. Ooi. (2013). Predicting the determinants of the NFC-enabled mobile credit card acceptance: A neural networks approach. Expert Systems with Applications, 40(14): 5604-5620.

Liao, H., K. Toya, D. Lepak and Y. Hong. (2009). Do They See Eye to Eye? Management and Employee Perspectives of High-Performance Work Systems and Influence Processes on Service Quality. The Journal of applied psychology, 94: 371-391.

Lin, C. S., S. Wu and R. J. Tsai. (2005). Integrating perceived playfulness into expectation- Confirmation model for web portal context. Information & Management, 42(5): 683-693.

Olorunniwo, F. and G. Udo. (2002). The impact of management and employees on cellular manufacturing implementation. International Journal of Production Economics, 76(1): 27-38.

Porter, M. E. (1980). Competitive Strategy: Techniques for Analyzing Industries and Competitors. Book.

Tam, C., D. Santos and T. Oliveira. (2020). Exploring the influential factors of continuance intention to use mobile Apps: Extending the expectation confirmation model. Information Systems Frontiers, 22(1): 243-257.

Thong, J. Y. L., S.-J. Hong and K. Y. Tam. (2006). The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance. International Journal of Human-Computer Studies, 64(9): 799-810.

Venkatesh, V., J. Thong and X. Xu. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly, 36: 157-178.

Venkatesh, V., M. Morris, G. Davis and F. Davis. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27: 425-478.

Wang, Y.-M. (2008). Determinants Affecting Consumer Adoption of Contactless Credit Card: An Empirical Study. Cyberpsychology & behavior : the impact of the Internet, multimedia and virtual reality on behavior and society, 11 6: 687-689.

Xu, C., D. Peak and V. Prybutok. (2015). A customer value, satisfaction, and loyalty perspective of mobile application recommendations. Decision Support Systems, 79: 171-183.

Zhou, T. (2013). An empirical examination of continuance intention of mobile payment services. Decision Support Systems, 54(2): 1085-1091.