UNRAVELLING THE DETERMINANTS OF SOCIAL MEDIA PAYMENT PLATFORM (SMPP) USAGE: A QUALITATIVE STUDY ON USER INTENTIONS AND ADOPTION
Abstract
Lately, social media companies worldwide have introduced payment features on their social media platforms. This has given social media users the option to make payments to their near and dear ones without changing the platform while engaged in chatting. The adoption of Social Media Payment Platforms (SMPP) varies across user groups and is influenced by socio-demographic characteristics, convenience, and perceived risk. Understanding these factors is essential for enhancing user engagement and trust, thereby addressing the gaps in current SMPP adoption research. The purpose of this study is to ascertain the behavioural intention and perception level of users toward Social Media Payment Platforms (SMPP) adoption and to identify the factors which motivate and inhibit from using social payment apps. To understand the intention of the users to behave and adopt SMPP, we conducted in-depth structured interviews. Qualitative analysis was used to analyse the data, using pre-defined categories (deductive) and code emerging from the respondent discussions (inductively) to identify the relevant influencing factors. Through data analysis, we identified three key themes that categorised user behaviour: socio-demographic, convivence and vulnerability to risks, which is further divided into several sub-themes such as age, gender, education, occupation under socio-demographic, while perceived usefulness (PU) and perceived ease of use (PEOU) comes under the theme of convivence and lastly, under Vulnerability of risk includes privacy, security and platform features. The findings of the study can be validated and explored in future research, which will provide a clear understanding of identifying factors that influence SMPP adoption and behaviour intention.
JEL Classification Codes: R1, M2, M4, B4.
References
Agur, I., Peria, S. M., & Rochon, C. (2020). Digital financial services and the pandemic: Opportunities and risks for emerging and developing economies. International Monetary Fund Special Series on COVID-19, Transactions, 1, 2-1.
Braun, V., & Clarke, V. (2016). (Mis) conceptualising themes, thematic analysis, and other problems with Fugard and Potts’ (2015) sample-size tool for thematic analysis. International Journal of social research methodology, 19(6), 739-743.
Castleberry, A., & Nolen, A. (2018). Thematic analysis of qualitative research data: Is it as easy as it sounds? Currents in pharmacy teaching and learning, 10(6), 807-815.
Chua, J. J., Wang, A., & Ling, P. (2019). Acceptance of WeChat Pay among consumers in Malaysia. Inti Journal, 2019(48). Retrieved from http://intijournal.newinti.edu.my
Devi, B., & Singh, C. (2022). Potential impact of covid-19 on occupational status, mobility and digital payment system in India. Pragati: Journal of Indian Economy, 9(1), 125-139.
El Haddad, G., Aimeur, E., & Hage, H. (2018). Understanding trust, privacy and financial fears in online payment. In 2018 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications/12th IEEE International Conference on Big Data Science and Engineering (TrustCom/BigDataSE) (pp. 28-36). IEEE.
Ghosh, N., Choudhury, P., Kaushik, S. R., Arya, R., Nanda, R., Bhattacharyya, P., ... & Chaudhury, K. (2020). Metabolomic fingerprinting and systemic inflammatory profiling of asthma COPD overlap (ACO). Respiratory Research, 21, 1-16.
Guest, G., MacQueen, K. M., & Namey, E. E. (2012). Applied thematic analysis. Sage.
Gutierrez-Leefmans, M., & Adewale Olaleye, S. (2021). Social influence and mPOS use: The effects of gender. Journal of technology management & innovation, 16(4), 3-10.
Han, B. O., & Windsor, J. (2011). User's willingness to pay on social network sites. Journal of computer information systems, 51(4), 31-40.
He, X., Lau, E. H., Wu, P., Deng, X., Wang, J., Hao, X., & Leung, G. M. (2020). Temporal Dynamics in Viral Shedding and Transmissibility of COVID-19. Nature Medicine, 26(5), 672-675. https://doi.org/10.1038/s41591-020-1016-z
Hennink, M., & Kaiser, B. N. (2022). Sample sizes for saturation in qualitative research: A systematic review of empirical tests. Social science & medicine, 292, 114523.
Horn, M., Oehler, A., & Wendt, S. (2020). FinTech for Consumers and Retail Investors: Opportunities and Risks of Digital Payment and Investment Services. Ecological, Societal, and Technological Risks and the Financial Sector, 309-327. https://doi.org/10.1007/978-3-030-38858-4_14
Huang, W., & Tang, J. (2015). Explore the development of WeChat payment from user behavior. In 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) (Vol. 3, pp. 118-121). IEEE.
Johnson, M. (2023). Mobile technology and the future of digital banking. Journal of Financial Services, 45(2), 123-145.
Kaplan, A. M., & Haenlein, M. (2009). The fairyland of Second Life: Virtual social worlds and how to use them. Business horizons, 52(6), 563-572.
Kemp, R. (2013). Mobile payments: Current and emerging regulatory and contracting issues. Computer Law & Security Review, 29(2), 175-179. https://doi.org/10.1016/j.clsr.2013.01.009
Kiger, M. E., & Varpio, L. (2020). Thematic analysis of qualitative data: AMEE Guide No. 131. Medical Teacher, 42(8), 846-854.
Kullmann, M. (2018). Platform work, algorithmic decision-making, and EU gender equality law. International Journal of Comparative Labour Law and Industrial Relations, 34(1), 1 – 21. https://doi.org/10.54648/ijcl2018001
Li, X., Zhu, X., Lu, Y., Shi, D., & Deng, W. (2023). Understanding the Continuous Usage of Mobile Payment Integrated into Social Media Platform: The Case of WeChat Pay. Electronic Commerce Research and Applications, 60, 101275. https://doi.org/10.1016/j.elerap.2023.101275
Liébana-Cabanillas, F., Japutra, A., Molinillo, S., Singh, N., & Sinha, N. (2020). Assessment of mobile technology use in the emerging market: Analyzing intention to use m-payment services in India. Telecommunications Policy, 44(9), 102009.
Liébana-Cabanillas, F., Marinkovic, V., De Luna, I. R., & Kalinic, Z. (2018). Predicting the Determinants of Mobile Payment Acceptance: A Hybrid SEM-neural Network Approach. Technological Forecasting and Social Change, 129, 117-130. https://doi.org/10.1016/j.techfore.2017.12.015
Ma, L., Su, X., Yu, Y., Wang, C., Lin, K., & Lin, M. (2018). What drives the use of M-Payment? An Empirical Study about Alipay and WeChat Payment. In 2018 15th International Conference on Service Systems and Service Management (ICSSSM) (pp. 1-6). IEEE. https://doi.org/10.1109/ICSSSM.2018.8465029
McDonald, T. (2020). Social Money and Working-class Subjectivities: Digital Money and Migrant Labour in Shenzhen, China. The China Quarterly, 242, 397-417. https://doi.org/10.1017/S0305741019001073
McLean, G., Osei-Frimpong, K., Al-Nabhani, K., & Marriott, H. (2020). Examining Consumer Attitudes Towards Retailers' M-commerce Mobile Applications – An Initial Adoption vs. Continuous Use Perspective. Journal of Business Research, 106(C), 139-157. https://doi.org/10.1016/j.jbusres.2019.08.032
Miraz, M. H., & Haikel-Elsabeh, M. (2019). Analysis of Users' Behaviour and Adoption Trends of Social Media Payment Platforms. In 2019 International Conference on Computing, Electronics & Communications Engineering (iCCECE) (pp. 197-202). IEEE.
Mohan, A., Tiwari, P., Bhatnagar, S., Patel, A., Maurya, A., Dar, L., ... &Wundavalli, L. (2020). Clinico-demographic profile & hospital outcomes of COVID-19 patients admitted at a tertiary care centre in north India. Indian Journal of Medical Research, 152(1-2), 61-69.
Mu, H. L., & Lee, Y. C. (2017). Examining the Influencing Factors of third-party Mobile Payment Adoption: A Comparative study of Alipay and WeChat Pay. The Journal of Information Systems, 26(4), 247-284. http://dx.doi.org/10.5859/KAIS.2017.26.4.247
Nan, D., Kim, Y., Park, M. H., & Kim, J. H. (2020). What Motivates Users to keep using Social Mobile Payments? Sustainability, 12(17), 6878. https://doi.org/10.3390/su12176878
Nandru, P., & Rentala, S. (2020). Demand-side analysis of measuring financial inclusion: Impact on socio-economic status of primitive tribal groups (PTGs) in India. International Journal of Development Issues, 19(1), 1-24.
Nowell, L. S., Norris, J. M., White, D. E., & Moules, N. J. (2017). Thematic analysis: Striving to meet the trustworthiness criteria. International journal of qualitative methods, 16(1), 1609406917733847.
Ofori-Okyere, I., Edghiem, F., & Kumah, S. P. (2023). Marketing inclusive banking services to financially vulnerable consumers: a service design approach. Journal of Services Marketing, 37(2), 232-247.
Ofori-Okyere, I. (2019). The role of Social Media platforms in the promotion of beach resorts in the Western Region of Ghana. ADRRI Journal of Arts and Social Sciences, 16(6), 99-124.
Poudel, O., & Sapkota, M. P. (2022). Consumer perception toward digital payment system. Management Dynamics, 25(1), 39-50.
Qu, Y., Rong, W., Chen, H., Ouyang, Y., & Xiong, Z. (2018). Influencing factors analysis for a social network web-based payment service in China. Journal of theoretical and applied electronic commerce research, 13(3), 99-113. https://doi.org/10.4067/S0718-18762018000300106
Qu, Y., Rong, W., Ouyang, Y., Chen, H., & Xiong, Z. (2015). Social Aware Mobile Payment Service Popularity Analysis: The Case of WeChat payment in China. In Advances in Services Computing: 9th Asia-Pacific Services Computing Conference, APSCC 2015, Bangkok, Thailand, December 7-9, 2015, Proceedings 9 (pp. 289-299). https://doi.org/10.1007/978-3-319-26979-5_22
Rohilla, A. (2017). State-wise Assessment of Banking Frauds in India: A Study of Trends in 21st Century. MUDRA: Journal of Finance and Accounting, 4(2), 127-144.
Ramasamy, M., Subbiah, J., Bordoloi, D., & Ramalingam, P. (2024). BANKERS’ PERSPECTIVE ON SOCIAL MEDIA BANKING: A STUDY WITH SPECIAL REFERENCE TO SELECT INDIAN BANKS. Bangladesh Journal of Multidisciplinary Scientific Research, 9(2), 1-9. https://doi.org/10.46281/bjmsr.v9i2.2210
Seldal, M. N., & Nyhus, E. K. (2022). Financial Vulnerability, Financial Literacy, and the Use of Digital Payment Technologies. Journal of Consumer Policy, 45(2), 281-306. https://doi.org/10.1007/s10603-022-09512-9
Singhal, J., & Chauhan, V. (2021). Analytical study of PMC (Punjab & Maharashtra Co-operative bank) by considering financial failure of bank. Globus-An International Journal of Management and IT, 12(2), 6-10.
Smith, J. (2022). The rise of mobile banking: How smartphones are transforming financial transactions. Financial Times.
Sun, S., Law, R., & Zhong, L. (2021). Mobile payment failure during travel. Journal of China Tourism Research, 17(1), 73-89.
Tang, Y. M., Chau, K. Y., Hong, L., Ip, Y. K., & Yan, W. (2021). Financial Innovation in Digital Payment with WeChat towards Electronic Business Success. Journal of Theoretical and Applied Electronic Commerce Research, 16(5), 1844-1861. https://doi.org/10.3390/jtaer16050103
Tavera-Mesías, J. F., van Klyton, A., & Collazos, A. Z. (2023). Technology Readiness, Mobile Payments and Gender-A Reflective-Formative Second Order Approach. Behaviour & Information Technology, 42(7), 1005-1023. https://doi.org/10.1080/0144929X.2022.2054729
Tinmaz, H., & Doan, V. P. (2023). User perceptions of WeChat and WeChat pay in China. Global Knowledge, Memory and Communication, 72(8/9), 797-812. https://doi.org/10.1108/GKMC-12-2021-0195
Unger, C., Murthy, D., Acker, A., Arora, I., & Chang, A. (2020). Examining the evolution of mobile social payments in Venmo. In International Conference on Social Media and Society (pp. 101-110).
Wang, J., & Gu, L. (2017). Why is WeChat Pay so Popular.? Issues in Information Systems, 18(4), 1-8. https://doi.org/10.48009/4_iis_2017_1-8
Wei, R., Zheng, F., Gao, L., Dong, J., Fan, G., Wan, L., ... & Wang, Y. (2021, December). Heterogeneous-PAKE: Bridging the gap between PAKE protocols and their real-world deployment. In Proceedings of the 37th Annual Computer Security Applications Conference (pp. 76-90).
Wong, D., Liu, H., Meng-Lewis, Y., Sun, Y., & Zhang, Y. (2022). Gamified Money: Exploring the Effectiveness of Gamification in Mobile Payment Adoption among the Silver Generation in China. Information Technology & People, 35(1), 281-315. https://doi.org/10.1108/ITP-09-2019-0456
Wu, Y., Schuster, M., Chen, Z., Le, Q. V., Norouzi, M., Macherey, W., ... & Dean, J. (2016). Google's neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144
Copyright (c) 2024 Lata Kumari Pandey, Jayashree Bhattacharjee, Ranjit Singh, Ambrish Singh
This work is licensed under a Creative Commons Attribution 4.0 International License.
