Explaining the Factors Influencing Consumer Perception,Adoption Readiness,and Perceived Usefulness toward Digital Transactions: Online Retailing Experience of Millennials in India
Restricted accessResearch articleFirst published online December, 2021
Explaining the Factors Influencing Consumer Perception,Adoption Readiness,and Perceived Usefulness toward Digital Transactions: Online Retailing Experience of Millennials in India
The present study intends to identify and evaluate the factors affecting the perception and readiness of young online shoppers regarding digital transactions. Thirty variables were identified through systematic literature review that can influence consumers’ perception of digital transactions. Data were collected from 525 millennials from north India. After performing factor analysis, five broad factors were identified namely (a) personal characteristics and incentives, (b) knowledge technical capability, (c) perceived usefulness, (d) sense of security, and (e) transaction risk, which affect the perception and intention as well as adaption of young consumers regarding the digital transactions. Using regression analysis, factors affecting consumer’s perception and readiness regarding digital transactions are modeled out. The study also examines the influence on intention to adopt digital transactions and the actual adaption of digital transactions. Further, the study concludes that consumer intention to make digital transactions mediates the relationship between factors of digital transactions and digital transaction adaption behavior.
AhmmadiP., RahimianM., & MovahedR. G. (2021). Theory of planned behavior to predict consumer behavior in using products irrigated with purified wastewater in Iran consumer. Journal of Cleaner Production, 296, 126359.
2.
AlabiS., WhiteM., & BeloffN. (2020, July 16–17). Contactless palm vein authentication security technique for better adoption of e-commerce in developing countries. In Science and Information Conference (pp. 380–390). Intelligent Computing.
3.
AlrahbiD. A., KhanM., GuptaS., ModgilS., & JabbourC. J. C. (2020). Challenges for developing health-care knowledge in the digital age. Journal of Knowledge Management. https://doi.org/10.1108/JKM-03-2020-0224
4.
AminK. P., GriffithsM. D., & DsouzaD. D. (2020). Online gaming during the COVID-19 pandemic in India: Strategies for work-life balance. International Journal of Mental Health and Addiction, 1–7. https://doi.org/10.1007/s11469-020-00358-1
5.
AmirthaR., SivakumarV. J., & HwangY. (2021). Influence of perceived risk dimensions on e-shopping behavioural intention among women—a family life cycle stage perspective. Journal of Theoretical and Applied Electronic Commerce Research, 16(3), 320–355.
6.
BaharuddinH. E. A., OthmanA. F., AdnanH., & IsmailN. A. A. (2021). Evaluating the influence of training on attitudes to building information modelling (BIM) adoption in Malaysian construction industry by using extended technology acceptance model (TAM). In Collaboration and Integration in Construction, Engineering, Management and Technology (pp. 577–582). Springer.
7.
BangunC. S., & HandraT. (2021). How theory of planned behavior and perceived risk affect online shopping behavior. Aptisi Transactions on Management (ATM), 5(2), 169–179.
8.
BelancheD., FlaviánC., & Pérez-RuedaA. (2020). Consumer empowerment in interactive advertising and eWOM consequences: The PITRE model. Journal of Marketing Communications, 26(1), 1–20.
9.
BudiharsenoR. S., HandaniN. D., & HwanS. J. (2020). Impact of utilitarian value and hedonic value on purchase in online store in Indonesia. Research, Society and Development, 9(10), e1159108305.
10.
ChoiY.-J., & ParkJ.-W. (2020). Investigating factors influencing the behavioral intention of online duty-free shop users. Sustainability, 12(17), 7108.
11.
D’AgostinA., de MedeirosJ. F., VidorG., ZulpoM., & MorettoC. F. (2020). Drivers and barriers for the adoption of use-oriented product-service systems: A study with young consumers in medium and small cities. Sustainable Production and Consumption, 21, 92–103.
12.
DarochB., NagrathG., & GuptaA. (2021). A study on factors limiting online shopping behaviour of consumers. Rajagiri Management Journal, 15, 39–52.
13.
de Sena AbrahãoR., MoriguchiS. N., & AndradeD. F. (2016). Intention of adoption of mobile payment: An analysis in the light of the Unified Theory of Acceptance and Use of Technology (UTAUT). RAI Revista de Administração e Inovação, 13(3), 221–230.
14.
DedrickJ., KraemerK. L., & ShihE. (2013). Information technology and productivity in developed and developing countries. Journal of Management Information Systems, 30(1), 97–122.
15.
FernandesS., VenkateshV. G., PandaR., & ShiY. (2021). Measurement of factors influencing online shopper buying decisions: A scale development and validation. Journal of Retailing and Consumer Services, 59, 102394.
16.
GhobakhlooM., & ChingN. T. (2019). Adoption of digital technologies of smart manufacturing in SMEs. Journal of Industrial Information Integration, 16, 100107.
17.
HaggerM. S., & HamiltonK. (2021). Effects of socio-structural variables in the theory of planned behavior: A mediation model in multiple samples and behaviors. Psychology & Health, 36(3), 307–333.
18.
HassanM., KazmiS., RehmanM. A., AmaadH., & PadleeS. F. (2021). The online shoppers’ behavioural intentions, e-satisfaction, pathway to repurchase behaviour: An empirical investigation from Pakistan. Studies of Applied Economics, 39(4), 10–23.
19.
HepolaJ., LeppäniemiM., & KarjaluotoH. (2020). Is it all about consumer engagement? Explaining continuance intention for utilitarian and hedonic service consumption. Journal of Retailing and Consumer Services, 57, 102232.
20.
IqbalM. S., ZulkernineM., JaafarF., & GuY. (2018). Protecting Internet users from becoming victimized attackers of click-fraud. Journal of Software: Evolution and Process, 30(3), e1871.
21.
IzogoE. E., & JayawardhenaC. (2018). Online shopping experience in an emerging e-retailing market. Journal of Research in Interactive Marketing. https://doi.org/10.1108/JRIM-02-2017-0015
22.
JonsonD. (2021). How will the perceived usefulness, ease of use and security of Asian millennials on e-commerce influence online residential real estate purchase intention?Universitas Pelita Harapan.
23.
JoshiS. (2013). E-supply chain collaboration and integration: Implementation issues and challenges. E-logistics and e-supply chain management: Applications for evolving business. https://doi.org/10.4018/978-1-4666-3914-0.ch002
24.
JoshiS., SharmaM., & RathiS. (2017). Forecasting in service supply chain systems: A state-of-the-art review using latent semantic analysis. Advances in Business and Management Forecasting.
25.
JoshiS., & SharmaM. (2018). Blending green with lean-incorporating best-of-the-breed practices to formulate an optimum global supply chain management framework: Issues and concerns. In Operations and Service Management: Concepts, Methodologies, Tools, and Applications (pp. 230–249). IGI Global.
26.
JoshiS., SharmaM., & SinghR. K. (2020). Performance evaluation of agro-tourism clusters using AHP–TOPSIS. Journal of Operations and Strategic Planning, 3(1), 7–30.
27.
JoshiS., SinghR. K., & SharmaM. (2020). Sustainable agri-food supply chain practices: Few empirical evidences from a developing economy. Global Business Review. https://doi.org/10.1177/0972150920907014
28.
JoshiS., & SharmaM. (2021). Digital technologies (DT) adoption in agri-food supply chains amidst COVID-19: an approach towards food security concerns in developing countries. Journal of Global Operations and Strategic Sourcing. https://doi.org/10.1108/JGOSS-02-2021-0014
29.
JungJ., ParkE., MoonJ., & LeeW. S. (2021). Exploration of sharing accommodation platform Airbnb using an extended technology acceptance model. Sustainability, 13(3), 1185.
30.
KhareA., KhareA., MukherjeeS., & GoyalT. (2016). Do consumer shopping styles influence consumer attitudes toward services offered by shopping websites? Journal of International Consumer Marketing, 28(1), 28–41.
31.
LalK. (2007). E-business and manufacturing sector: A study of small and medium-sized enterprises in India. In Information and Communication Technologies in the Context of Globalization (pp. 16–33). Springer.
32.
LiaoL., & WangK. (2010). Strategy for building initial trust in B2C electronic commerce. In 2010 International Conference on Networking and Digital Society (Vol. 1, pp. 282–285). IEEE.
33.
LiuX., WangQ.-C., JianI. Y., ChiH.-L., YangD., & ChanE. H.-W. (2021). Are you an energy saver at home? The personality insights of household energy conservation behaviors based on theory of planned behavior. Resources, Conservation and Recycling, 174, 105823.
34.
MatuteJ., Polo-RedondoY., & UtrillasA. (2016). The influence of EWOM characteristics on online repurchase intention: Mediating roles of trust and perceived usefulness. Online Information Review, 40(7), 1090–1110.
35.
MoslehpourM., PhamV. K., WongW.-K., & BilgiçliI. (2018). E-purchase intention of Taiwanese consumers: Sustainable mediation of perceived usefulness and perceived ease of use. Sustainability, 10(1), 234.
36.
NiebelT. (2018). ICT and economic growth–Comparing developing, emerging and developed countries. World Development, 104, 197–211.
37.
OstrowskiS. (2021). Commitment ladder in the relationship between service providers and customers as added value in sustainable services development. Sustainability, 13(9), 5079.
38.
PersadaS. F., DalimunteI., NadlifatinR., MirajaB. A., RediA. A. N. P., PrasetyoY. T., ChinJ., & LinS.-C. (2021). Revealing the behavior intention of tech-savvy generation Z to use electronic wallet usage: A theory of planned behavior based measurement. International Journal of Business and Society, 22(1), 213–226.
39.
PrasadM., LiD.-L., LinC.-T., PrakashS., SinghJ., & JoshiS. (2015). Designing Mamdani-type fuzzy reasoning for visualizing prediction problems based on collaborative fuzzy clustering. IAENG International Journal of Computer Science, 42(4), 4–12.
40.
SharmaM., GuptaM., & JoshiS. (2019). Adoption barriers in engaging young consumers in the Omni-channel retailing. Young Consumers, 21(2), 193–210.
41.
SharmaM., & JoshiS. (2019). Brand sustainability among young consumers: An AHP-TOPSIS approach. Young Consumers, 20(4), 314–337. https://doi.org/10.1108/YC-12-2018-0914
42.
SharmaM., & JoshiS. (2020). Digital supplier selection reinforcing supply chain quality management systems to enhance firm’s performance. TQM Journal. https://doi.org/10.1108/TQM-07-2020-0160
43.
SharmaM., & JoshiS. (2021). Barriers to blockchain adoption in health-care industry: An Indian perspective. Journal of Global Operations and Strategic Sourcing. https://doi.org/10.1108/JGOSS-06-2020-0026
44.
SharmaM., JoshiS., LuthraS., & KumarA. (2021a). Managing disruptions and risks amidst COVID-19 outbreaks: Role of blockchain technology in developing resilient food supply chains. Operations Management Research, 1–14. https://doi.org/10.1007/s12063-021-00198-9.
45.
SharmaM., LuthraS., JoshiS., & KumarA. (2021b). Accelerating retail supply chain performance against pandemic disruption: Adopting resilient strategies to mitigate the long-term effects. Journal of Enterprise Information Management. https://doi.org/10.1108/JEIM-07-2020-0286
46.
SinghR. K., JoshiS., & SharnaM. (2020). Modelling supply chain flexibility in the Indian personal hygiene industry: An ISM-Fuzzy MICMAC approach. Global Business Review. https://doi.org/10.1177/0972150920923075
47.
SohnY. S., & KoM. T. (2021). The impact of planned vs. unplanned purchases on subsequent purchase decision making in sequential buying situations. Journal of Retailing and Consumer Services, 59, 102419.
48.
UrbinatiA., RosaP., SassanelliC., ChiaroniD., & TerziS. (2020). Circular business models in the European manufacturing industry: A multiple case study analysis. Journal of Cleaner Production, 274, 122964.
49.
UrdeaA.-M., ConstantinC. P., & PurcaruI.-M. (2021). Implementing experiential marketing in the digital age for a more sustainable customer relationship. Sustainability, 13(4), 1865.
50.
VarahF., MahongnaoM., PaniB., & KhamrangS. (2021). Exploring young consumers’ intention toward green products: Applying an extended theory of planned behavior. Environment, Development and Sustainability, 23(6), 9181–9195.
51.
WuJ., & SongS. (2021). Older adults’ online shopping continuance intentions: Applying the technology acceptance model and the theory of planned behavior. International Journal of Human–Computer Interaction, 37(10), 938–948.
52.
XingZ. (2018). The impacts of information and communications technology (ICT) and e-commerce on bilateral trade flows. International Economics and Economic Policy, 15(3), 565–586.
53.
YangH., WangS., & ZhengY. (2021). Spatial-temporal variations and trends of Internet users: assessment from global perspective. Information Development. https://doi.org/10.1177/02666669211035479
54.
YuenK. F., CaiL., QiG., & WangX. (2021). Factors influencing autonomous vehicle adoption: An application of the technology acceptance model and innovation diffusion theory. Technology Analysis & Strategic Management, 33(5), 505–519.
55.
ZhangY., XiongZ., NiyatoD., WangP., & HanZ. (2020). Information trading in internet of things for smart cities: A market-oriented analysis. IEEE Network, 34(1), 122–129.
56.
Zielińska-TomczakŁ., Cerbin-KoczorowskaM., PrzymuszałaP., & MarciniakR. (2021). How to effectively promote interprofessional collaboration?—A qualitative study on physicians’ and pharmacists’ perspectives driven by the theory of planned behavior. BMC Health Services Research, 21(1), 1–13.
