The purpose of this study is to investigate the factors that shape behavioural intentions of customers towards the adoption of payment banks in India. The conceptual framework of study is based upon integration of technology acceptance model with initial trust, facilitating conditions and social influence. Further, the study tests the moderating role of age, income and self-efficacy on the relationships between dependent variable and associated predicted variable.
A total of 507 responses were collected from the state of Punjab (India), using convenient sampling technique and were analysed using the structural equation modelling (SEM). The results revealed that perceived ease of use had the highest impact on the behavioural intentions, followed by initial trust and social influence. Facilitating conditions and perceived usefulness showed lower impact on the behavioural intentions towards the adoption of payment banks. Also, moderation analysis revealed that self-efficacy moderates the relation of perceived ease of use and perceived usefulness with behavioural intentions. Results imply that marketers should collaborate with developers to provide the customers with easy-to-operate solutions along with robust customer support mechanism to escalate the adoption intention of those having lower self-efficacy levels.
AbidS. (2017). Payment banks: A revolutionary step in Indian banking system. IOSR Journal of Economics and Finance (IOSR-JEF), 7(6), 81–83.
2.
AjzenI., (1991). The theory of planned behaviour. Organizational Behaviour and Human Decision Process, 50, 179–211.
3.
AlalwanA. A., DwivediY. K., RanaN. P., & AlgharabatR. (2018). Examining factors influencing Jordanian customers’ intentions and adoption of internet banking: Extending UTAUT2 with risk. Journal of Retailing and Consumer Services, 40, 125–138.
4.
AlfahlH. (2018). ‘Mobile commerce adoption’, in mobile commerce: Concepts, methodologies, tools, and applications (pp. 820–848). IGI Global.
5.
AltmanD. G., & BlandJ. M. (1995). Statistics notes: Absence of evidence is not evidence of absence. BMJ, 311(7003), 485.
6.
BaabdullahA., NasseefO., & AlalwanA. (2016, September). Consumer adoption of mobile government in the Kingdom of Saudi Arabia: The role of usefulness, ease of use, perceived risk and innovativeness in conference on e-business, e-services and e-society (pp. 267–279). Springer.
7.
BanduraA., & SchunkD. H. (1981). ‘Cultivating competence, self-efficacy, and intrinsic interest through proximal self-motivation’. Journal of Personality and Social Psychology, 41(3), 586.
8.
BenamatiJ. S., FullerM. A., ServaM. A., & BaroudiJ. A. (2010). Clarifying the integration of trust and TAM in e-commerce environments: Implications for systems design and management. IEEE Transactions on Engineering Management, 57(3), 380–393.
9.
BollenK. A. (1989). A new incremental fit index for general structural equation models. Sociological Methods and Research, 17(3), 303–316.
10.
BrislinR. W. (1970). Back-translation for cross-cultural research. Journal of Cross-Cultural Psychology, 1, 185–216.
11.
ChakisoC. B. (2019). Factors affecting attitudes towards adoption of mobile banking: Users and non-users perspectives. EMAJ: Emerging Markets Journal, 9(1), 54–62.
12.
ChangC. M., LiuL. W., HuangH. C., & HsiehH. H. (2019). Factors influencing online hotel booking: Extending UTAUT2 with age, gender, and experience as moderators. Information, 10(9), 281.
13.
DamghanianH., ZareiA., & KojuriM. A. S. (2016). Impact of perceived security on trust, perceived risk, and acceptance of online banking in Iran. Journal of Internet Commerce, 15(3), 214–238.
14.
DassR., & PalS. (2011). Exploring the factors affecting the adoption of mobile financial services among the rural under-banked (p. 246). In ECIS Proceedings. https://aisel.aisnet.org/ecis2011/246
15.
DavisF. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 19–340.
16.
DemirciA. E., & ErsoyN. F. (2008). Technology readiness for innovative high-tech products: How consumers perceive and adopt new technologies. The Business Review, 11(1), 302–308.
17.
DwivediY. K., RanaN. P., JanssenM., LalB., WilliamsM. D., & ClementR. M. (2017). An empirical validation of a unified model of electronic government adoption (UMEGA). Government Information Quarterly, 34(2), 211–230.
18.
Ernst & Young (2016). The business case for payments banks in India. GSMA.
19.
FishbeinM., & AjzenI. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley.
20.
GoelU., & ManraiR. (2016, March). Payment banks: Sustainable game changer or a passing fad. Proceedings of NIDA International Business Conference 2016 Sustainability in Business Bangkok, Thailand, 302–309.
21.
HairJ. F., BlackW. C., BabinB. J., & AndersonR. E. (2015) Multivariate data analysis. Pearson.
22.
HernándezB., JiménezJ., & MartínM. J. (2011). Age, gender and income: Do they really moderate online shopping behavior?Online Information Review, 35(1), 113–133.
23.
HongS., ThongJ. Y., & TamK. Y. (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.
24.
HsuH. S., & KulviwatS. (2006). An integrative framework of technology acceptance model and personalisation in mobile commerce. International Journal of Technology Marketing, 1(4), 393–410.
25.
HundalB. S., & KumarV. (2015). Consumer perception towards green products: A factor analytic approach. Pacific Business Review International, 7(10), 1–7.
26.
IconaruC. (2013). The moderating role of perceived self-efficacy in the context of online buying adoption. BRAND. Broad Research in Accounting, Negotiation, and Distribution, 4(1), 20–29.
27.
IslamM. A., KhanM. A., RamayahT., & HossainM. M. (2011). The adoption of mobile commerce service among employed mobile phone users in Bangladesh: Self-efficacy as a moderator. International Business Research, 4(2), 80–89.
28.
JaradatM. I. R. M., & FaqihK. M. (2014). Investigating the moderating effects of gender and self-efficacy in the context of mobile payment adoption: A developing country perspective. International Journal of Business and Management, 9(11), 147–169
29.
KesharwaniA., & BishtS. S. (2012). The impact of trust and perceived risk on internet banking adoption in India: An extension of technology acceptance model. International Journal of Bank Marketing, 30(4), 303–322.
30.
KimG., ShinB., & LeeH. G. (2009). Understanding dynamics between initial trust and usage intentions of mobile banking. Information Systems Journal, 19, 283–311.
31.
KimC., MirusmonovM., & LeeI. (2010). An empirical examination of factors influencing the intention to use mobile payment. Computers in Human Behavior, 26(3), 310–322.
32.
KohliD. S., & PatelK. (2016). Payments banks–Changing Indian payments landscape an in-depth analysis. Sector Report, India Nivesh Research.
33.
KoufarisM., & Hampton-SoaW. (2004). The development of initial trust in an online company by new customers. Information and Management, 41(3), 377–397.
34.
KumarV., SyanA. S., KaurA., & HundalB. S. (2020). Determinants of farmers’ decision to adopt solar powered pumps. International Journal of Energy Sector Management, 14(4), 707–727.
35.
LeeM. C. (2009). Factors influencing the adoption of Internet banking: An integration of TAM and TPB with perceived risk and perceived benefit. Electronic Commerce Research and Applications, 8(3), 130–141.
36.
LiY.-M., & YehY.-S. (2010). Increasing trust in mobile commerce through design aesthetics. Computers in Human Behavior, 26(4), 673–684.
37.
LiuY. C., & HungY. Y. (2016). Self-efficacy as the moderator: Exploring driving factors of perceived social support for mainland Chinese students in Taiwan. Computers in Human Behavior, 64(1), 455–462.
38.
MadanK., & YadavR. (2016). Behavioural intention to adopt mobile wallet: A developing country perspective. Journal of Indian Business Research, 8(3), 227–244.
39.
MalhotraN. K., & DashS. (2014). Marketing research: An applied approach. Pearson.
40.
MalhotraY., & GallettaD. F. (1999, January5–8). Extending the technology acceptance model to account for social influence: Theoretical bases and empirical validation. Proceedings of the 32nd Hawaii International Conference on System Sciences, Maui, HI. https://doi.org/10.1109/HICSS.1999.772658
41.
MartinsG., OliveiraT., & PopovicA. (2014). 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.
42.
MasI., & MorawczynskiO. (2009). Designing mobile money services lessons from M-PESA. Innovations: Technology, Governance, Globalization, 4(2), 77–91.
43.
MorN. (2013). Committee on comprehensive financial services for small businesses and low income households, Mumbai. https://rbidocs.rbi.org.in/rdocs/PublicationReport/Pdfs/CFS070114RFL.pdf
44.
NaikV. R., FirdousP., & HarikaP. (2018). A study on role of payment banks in India—Financial inclusion. 5th International Conference on Recent Advances in Engineering, Science and Management YMCZ, Connaught Place, New Delhi. http://data.conferenceworld.in/3FEBYMCAICRAESM/24.pdf
45.
NunnallyJ. C. (1978). Psychometric theory. McGraw-Hill.
46.
OliveiraT., FariaaM., & ThomasM. A. (2014). Extending the understanding of mobile banking adoption: When UTAUT meets TTF and ITM international. Journal of Information Management, 34, 689–703.
47.
OliveiraT., ThomasM., & BaptistaG. (2016). Mobile payment: Understanding the determinants of customer adoption and intention to recommend the technology. Computers in Human Behavior, 61, 404–414.
48.
PallantJ. (2001). SPSS survival manual: A step by step guide to data analysis using SPSS for windows. McPherson’s Printing Group.
49.
PatelK. J., & PatelH. J. (2018). Adoption of internet banking services in Gujarat: An extension of TAM with perceived security and social influence. International Journal of Bank Marketing, 36(1), 147–169.
50.
PavlouP. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal Electronic Commerce, 7(3), 69–103.
51.
PorterC. E., & DonthuN. (2006). Using the technology acceptance model to explain how attitudes determine Internet usage: The role of perceived access barriers and demographics. Journal of Business Research, 59(9), 999–1007.
52.
RabinowitzY. G., MausbachB. T., & Gallagher-ThompsonD. (2009). Self-efficacy as a moderator of the relationship between care recipient memory and behavioral problems and caregiver depression in female dementia caregivers. Alzheimer Disease and Associated Disorders, 23(4), 389–394.
53.
ReddyN. S. N. (2015). Payment banks: New kids on the block. The Indian Banker, 3, 25–29.
54.
RocaJ. C., GarcíaJ. J., & de la VegaJ. J. (2009). The importance of perceived trust, security and privacy in online trading systems. Information Management and Computer Security, 17(2), 96–113.
55.
RogersE. M. (2003). Diffusion of innovations (5th ed.). The Free Press.
56.
ShankarA., & KumariP. (2016). Factors affecting mobile banking adoption behavior in India. The Journal of Internet Banking and Commerce, 21(1), 1–24.
57.
SharmaS. K., ManglaS. K., LuthraS., & Al-SaltiZ. (2018). Mobile wallet inhibitors: Developing a comprehensive theory using an integrated model. Journal of Retailing and Consumer Services, 45, 52–63.
58.
SikdarP., & KumarA. (2016). Payment bank: A catalyst for financial inclusion. Asia-Pacific Journal of Management Research and Innovation, 12(3–4), 226–231.
59.
SinghS., & SrivastavaR. K. (2018). Predicting the intention to use mobile banking in India. International Journal of Bank Marketing, 36(2), 357–378.
60.
SyanA. S., KumarV., SandhuV., & HundalB. S. (2019). Empirical analysis of farmers’ intention to adopt sustainable agricultural practices. Asia-Pacific Journal of Management Research and Innovation, 15(1–2), 39–52.
61.
TeohW. M.-Y., ChongS. C., LinB., & ChuaJ. W. (2013). Factors affecting consumers’ perception of electronic payment: An empirical analysis. Internet Research, 23(4), 465–485.
62.
VaishnaviJ., & ShruthiN. (2017). Payment banks—An innovative initiative by RBI. Paper presented at International Conference on ‘Paradigm Shift in Taxation, Accounting’ (pp. 60–66). http://iosrjournals.org/iosr-jbm/papers/Conf.17037-2017/Volume-7/12.%2060-66.pdf
63.
VenkateshV., & MorrisG. M. (2000). Why don’t men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS Quarterly, 115–139. https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.361.4468&rep=rep1&type=pdf
64.
VenkateshV., MorrisM. G., DavisG. B., & DavisF. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.
65.
VenkateshV., ThongJ. Y., & XuX. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–17
66.
WangY. S., WuM. C., & WangH. Y. (2009). Investigating the determinants and age and gender differences in the acceptance of mobile learning. British Journal of Educational Technology, 40(1), 92–118.
67.
WoodR., & BanduraA. (1989). Social cognitive theory of organizational management. Academy of Management Review, 14(3), 361–384.
68.
YenD. C., WuC. S., ChengF. F., & HuangY. W. (2010). Determinants of users’ intention to adopt wireless technology: An empirical study by integrating TTF with TAM. Computers in Human Behavior, 26(5), 906–915.
69.
ZhouT., LuY., & WangB. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in Human Behavior, 26, 760–767.
