Digitalization—a technology-driven innovation—is shaping the future of the economy and society at large. The impact of digitalization on digital payment industry has been phenomenal. The purpose of this article is (i) to examine the degree of awareness associated with mobile wallets and its relationship with demographic variables and (ii) to build a logistic model to predict the attitude towards mobile wallet using a group of antecedents. A total of 744 responses were obtained. Convenience and purposive sampling were used. The results show that 69% of the respondents had high awareness level with respect to mobile wallets. Further, it was found that out of the various demographic variables under consideration only gender, occupation and income significantly impact awareness levels. It was also revealed that awareness levels, perceived usefulness, trust, security and lifestyle compatibility are strong and significant predictors of attitude towards mobile wallets. The predictive ability of the model as is 89.11%, which is satisfactory. This study provides a framework for academicians to understand awareness, user behaviours and attitude related to mobile wallets. It can also guide mobile service providers to focus on important factors affecting user’s adoption decision.
AgagG., & El-MasryA. A. (2016). Understanding the determinants of hotel booking intentions and moderating role of habit. International Journal of Hospitality Management, 54, 52–67.
2.
AkinciS., AksoyS., & AtilganE. (2004). Adoption of internet banking among sophisticated consumer segments in an advanced developing country. International Journal of Bank Marketing, 22(3), 212–232.
3.
AkturanU., & TezcanN. (2012). Mobile banking adoption of the youth market. Marketing Intelligence & Planning, 30(4), 444–459.
4.
AminH., HamidM. R., TanakinjalG., & LadaS. (2006). Undergraduate attitudes and expectations for mobile banking. Journal of Internet Banking and Commerce, 11(3). http://www.icommercecentral.com/open-access/undergraduate-attitudes-and-expectations-for-mobile-banking-1-9.php?aid=38550
5.
Assocham. (2016). M-Wallet: scenario post demonetisation. https://www.assocham.org/upload/docs/M-Wallet_Report_press.pdf
6.
BashirI., & MadhavaiahC. (2014). Determinants of young consumers’ intention to use internet banking services in India. Vision, 18(3), 153–163.
7.
BelancheD., CasaloL. V., & FlavianC. (2012). Integrating trust and personal values into the technology acceptance model: The case of e-government service adoption. Cuadernos de Economia y Direccion de la Empresa, 15(4), 192–204.
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.
BerkowskyR. W., SharitJ., & CzajaS. J. (2017). Factors predicting decisions about technology adoption among older adults. Innovation in Aging, 1(3). https://doi.org/10.1093/geroni/igy002
10.
Broadband Commission. (2016). The State of Broadband: Broadband catalyzing sustainable development. https://www.broadbandcommission.org/Documents/reports/bb-annualreport2016.pdf
11.
CalantoneR. J., GriffithD. A., & YalcinkayaG. (2006). An empirical examination of a technology adoption model for the context of China. Journal of International Marketing, 14(4), 1–27.
12.
ChangC. C., YanC. F., & TsengJ. S. (2012). Perceived convenience in an extended technology acceptance model: Mobile technology and English learning for college students. Australasian Journal of Educational Technology, 28(5), 809–826.
13.
ChaoC.-M. (2019). Factors determining the behavioral intention to use mobile learning: an application and extension of the UTAUT model. Frontiers in Psychology, 10, 1–14.
14.
ChaudharyA. K., & IsraelG. D. (2017). The savvy survey #8: Pilot testing and pretesting (#AEC402). University of Florida Institute of Food and Agricultural Sciences. https://edis.ifas.ufl.edu/pdffiles/PD/PD07200.pdf
15.
ChauhanS. (2015). Acceptance of mobile money by poor citizens of India: Integrating trust into the technology acceptance model. Info, 17(3),58–68.
16.
ChauhanS., & JaiswalM. (2016). Determinants of acceptance of ERP software training in business schools: Empirical investigation using UTAUT model. The International Journal of Management Education, 14(3), 248–262.
17.
ChawlaD., & JoshiH. (2017a). Consumer perspectives about mobile banking adoption in India – A cluster analysis. International Journal of Bank Marketing, 35(4), 616–636.
18.
ChawlaD., & JoshiH. (2017b). High versus low consumer attitude and intention towards adoption of mobile banking in India: An empirical study. Vision: The Journal of Business Perspective, 21(4), 410–424.
19.
ChawlaD., & JoshiH. (2019). Consumer attitude and intention to adopt mobile wallet in India – An empirical study. International Journal of Bank Marketing, 37(7), 1590–1618.
20.
ChawlaD., & SondhiN. (2016). Research methodology: Concept and cases (2nd ed.). Vikas Publishing House.
21.
CorreaT., HinsleyA. W., & De ZunigaH. G. (2010). Who interacts on the web? The intersection of users’ personality and social media use. Computers in Human Behavior, 26(2), 247–253.
22.
CzajaS. J., CharnessN., FiskA. D., HertzogC., NairS. N., RogersW. A., & SharitJ. (2006). Factors predicting the use of technology: Findings from the Center for Research and Education on Aging and Technology, Enhancement (CREATE). Psychology and Aging, 21(2), 333–352.
23.
DavisF. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
24.
DavisF. D. (1993). User acceptance of information technology: System characteristics, user perceptions and behavioral impacts. International Journal of Man–Machine Studies, 38(3), 475–487.
25.
DavisF. D., BagozziR. P., & WarshawP. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003.
26.
DebM., & DavidE. L. (2014). An empirical examination of customers’ adoption of m-banking in IndiaMarketing Intelligence & Planning, 32(4), 475–494.
27.
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.
28.
DimitriadisS., & KyrezisN. (2010). Linking trust to use intention for technology-enabled bank channels: The role of trusting intentions. Psychology and Marketing, 27(8), 799–820.
29.
EappenN. J. (2019). Mobile wallet adoption in India: Impact of trust and information sharing. South Asian Journal of Management, 26(1), 32–49.
30.
EnckW., OngtangM., & McDanielP. (2009). On lightweight mobile phone application certification. In: Proceedings of the 16th ACM Conference on Computer and Communications Security, ACM, 2009.
31.
FlaviánC., GuinaliuM., & TorresE. (2006). How bricks-and-mortar attributes affect online banking adoption. International Journal of Bank Marketing, 24(6), 406–423.
32.
GeorgeA., & SunnyP. (2021). Developing a research model for mobile wallet adoption and usage. IIM Kozhikode Society and Management Review, 10(1), 82–98.
33.
GuJ.-C., LeeS.-C., & SuhY.-H. (2009). Determinants of behavioral intention to mobile banking. Expert Systems with Applications, 36(9), 11605–11616.
34.
HertzogM. A. (2008). Considerations in determining sample size for pilot studies. Research in Nursing and Health, 31(2), 180–191.
35.
Internet & Mobile Association of India (IAMAI) and Nielsen. (2019). Digital in India – Round 2 report. https://cms.iamai.in/Content/ResearchPapers/2286f4d7-424f-4bde-be88-6415fe5021d5.pdf
36.
JohansonG., & BrooksG. B. (2009). Initial scale development: Sample size for pilot studies. Educational and Psychological Measurement, 70(3), 394–400.
37.
KarjaluotoH., MattilaM., & PentoT. (2002). Factors underlying attitude formation towards online banking in Finland. International Journal of Bank Marketing, 20(6), 261–272.
38.
KhanM. R., RanaS., & HosenM. I. (2021). Impact of trustworthiness on the usage of m-banking apps: A study on Bangladeshi consumers. Business Perspectives and Research. doi: 10.1177/22785337211001969
39.
KhasawnehM. H. A. (2015). A mobile banking adoption model in the Jordanian market: An Integration of TAM with perceived risks and perceived benefits. The Journal of Internet Banking and Commerce, 20(3), 1–13.
40.
KrishananD., KhinA. A., TengK. L. L., & ChinnaK. (2016). Consumers’ perceived interactivity and intention to use mobile banking in structural equation modeling. International Review of Management and Marketing, 6(4), 883–890.
41.
LeeH.-J., ChoH. J., XuW., & FairhurstA. (2010). The Influence of consumer traits and demographics on intention to use retail self-service checkouts. Marketing Intelligence & Planning, 28(1), 46–58.
42.
LewS., TanG. W.-H., LohX.-M., HewJ.-J., & OoiK.-B. (2020). The disruptive mobile wallet in the hospitality industry: An extended mobile technology acceptance model. Technology in Society, 63, 101430.
43.
LewisN. K., PalmerA., & MollA. (2010). Predicting young consumers’ take up of mobile banking services. International Journal of Bank Marketing, 28(5), 410–432.
44.
LinH.-F. (2011). An empirical investigation of mobile banking adoption: The effect of innovation attributes and knowledge-based trust. International Journal of Information Management, 31(3), 252–260.
45.
LinckK., PousttchiK., & WiedemannD. G. (2006). Security issues in mobile payment from the customer viewpoint. In Proceedings of the 14th European Conference on Information Systems (ECIS 2006).
46.
MadanK., & YadavR. (2016). Behavioural intention to adopt mobile wallet: A developing country perspective. Journal of Indian Business Research, 8(3), 227–244.
47.
MannB. J. S., & SahniS. K. (2012). Profiling adopter categories of internet banking in India: An empirical study. Vision, 16(4), 283–295.
48.
MartinsC., 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.
49.
MerzM. (2002). E-commerce und e-business: Marktmodelle, Anwendungen und Technologien (2nd ed.). Dpunkt Verlag.
50.
MohammadiH. (2015). A study of mobile banking usage in Iran. International Journal of Bank Marketing, 33(6), 733–759.
51.
MooreG. C., & BenbasatI. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192–222.
52.
O’CassA., & FenechT. (2003). Web retailing adoption: Exploring the nature of internet users web retailing behaviour. Journal of Retailing and Consumer Services, 10(2), 81–94.
53.
PalA., DeR., HerathT., & RaoH. R. (2019). A review of contextual factors affecting mobile payment adoption and use. Journal of Banking and Financial Technology, 3, 43–57.
54.
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.
55.
PavlouP. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 69–103.
56.
PhutelaN., & AltekarS. (2019). Mobile wallets in India: A framework for consumer adoption. International Journal of Online Marketing (IJOM), 9(2), 27–38.
57.
PowellE. T., & RennerM. (2003). Analyzing qualitative data. University of Wisconsin-Extension. http://learningstore.uwex.edu/assets/pdfs/G3658-12.pdf
58.
RiquelmeH. E., & RiosR. E. (2010). The moderating effect of gender in the adoption of mobile banking. International Journal of Bank Marketing, 28(5), 328–341.
59.
RowleyJ., & SlackF. (2004). Conducting a literature review. Management Research News, 27(6), 31–39.
60.
SchierzP. G., SchilkeO., & WirtzB. W. (2010). Understanding consumer acceptance of mobile payment services: An empirical analysis. Electronic Commerce Research and Applications, 9(3), 209–216.
61.
SeetharamanA., KumarK. N., PalaniappanS., & WeberG. (2017). Factors influencing behavioural intention to use the mobile wallet in Singapore. Journal of Applied Economics & Business Research, 7(2), 116–136.
62.
ShankarA., & DattaB. (2018). Factors affecting mobile payment adoption intention: An Indian perspective. Global Business Review, 19(3_suppl), S72–S89.
63.
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.
64.
ShawN. (2014). The mediating influence of trust in the adoption of mobile wallet. Journal of Retailing and Consumer Services, 21(4), 449–459.
65.
ShawN. (2015). The mediating role of perceived security: An empirical study of mobile wallet adoption in USA. In: Fui-HoonNah F., TanCH. (eds.), HCI in business. HCIB 2015. Lecture Notes in Computer Science, Vol. 9191. Springer.
66.
ShinD. H. (2009). Towards an understanding of the consumer acceptance of mobile wallet. Computers in Human Behaviour, 25(6), 1343–1354.
67.
SinghS., & SrivastavaR. K. (2014). Factors influencing the adoption of mobile banking in India. International Journal of E-Services and Mobile Applications, 6(4), 1–15.
68.
SinghN., SrivastavaS., & SinhaN. (2017). Consumer preference and satisfaction of M-wallets: A study on North Indian consumers. International Journal of Bank Marketing, 35(6), 944–965.
69.
SinghN., SinhaN., & Liébana-CabanillasF. J. (2020). Determining factors in the adoption and recommendation of mobile wallet services in India: Analysis of the effect of innovativeness, stress to use and social influence. International Journal of Information Management, 50, 191–205.
70.
SobtiN. (2019). Impact of demonetization on diffusion of mobile payment service in India: Antecedents of behavioral intention and adoption using extended UTAUT model. Journal of Advances in Management Research, 16(4), 472–497.
71.
Statistica. (2018). Share of consumers using mobile wallets across India as of January 2018, by age group. https://www.statista.com/statistics/870582/india-consumer-usage-of-mobile-wallets-by-age-group/
72.
Statistica. (2020). Number of internet users in India from 2015 to 2020 with a forecast until 2025. https://www.statista.com/statistics/255146/number-of-internet-users-in-india/
73.
StavinsJ. (2001). Effect of consumer characteristics on the use of payment instruments,New England Economic Review, 3, 19–31.
74.
SuP., Le WangL., & YanJ. (2017). How users’ Internet experience affects the adoption of mobile payment: A mediation model. Technology Analysis & Strategic Management, 30(2), 186–197.
75.
SuhB., & HanI. (2003). Effect of trust on customer acceptance of Internet banking. Electronic Commerce Research and Applications, 1(3), 247–263.
76.
SusantoA., LeeH., ZoH., & CiganekA. (2012). User acceptance of internet banking in Indonesia: Initial trust formation. Information Development, 29(4), 309–322.
77.
SzopińskiT. S. (2016). Factors affecting the adoption of online banking in Poland. Journal of Business Research, 69(11), 4763–4768.
78.
TeoT. (2010). Examining the influence of subjective norm and facilitating conditions on the intention to use technology among pre-service teachers: A structural equation modeling of an extended technology acceptance model. Asia Pacific Education Review, 11(2), 253–262.
79.
TeoT., LeeC. B., & ChaiC. S. (2008). Understanding pre-service teachers’ computer attitudes: Applying and extending the technology acceptance model. Journal of Computer Assisted Learning, 24(2), 128–143.
80.
ThakurR., & SrivastavaM. (2014). Adoption readiness, personal innovativeness, perceived risk and usage intention across customer groups for mobile payment services in India. Internet Research, 24(3), 369–392.
81.
TiwariP., & TiwariS. K. (2020). Integration of technology acceptance model with perceived risk, perceived trust and perceived cost: Customers’ adoption of m-banking. International Journal on Emerging Technologies, 11(2), 447–452.
82.
ThompsonR., HigginsC., & HowellJ. (1991). Personal computing: Toward a conceptual model of utilization. MIS Quarterly, 15(1), 125–143.
83.
UpadhyayP., & JahanyanS. (2016). Analyzing user perspective on the factors affecting use intention of mobile based transfer payment. Internet Research, 26(1), 38–56.
84.
VenkateshV., & DavisF. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204.
85.
VenkateshV., MorrisM. G., DavisG. B., & DavisF. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.
86.
VijayasarathyL. R. (2004). Predicting consumer intentions to use on-line shopping: The case for an augmented technology acceptance model. Information & Management, 41(6), 747–762.
87.
WanW. W. N., LukC.-L., & ChowC. W. C. (2005). Customers’ adoption of banking channels in Hong Kong. International Journal of Bank Marketing, 23(3), 255–272.
88.
WangQ., & SunX. (2016). Investigating gameplay intention of the elderly using an Extended Technology Acceptance Model (ETAM). Technological Forecasting and Social Change, 107, 59–68.
