Examining the factors influencing fintech adoption behaviour of gen Y in India
Open accessAbstract
The main impetus of the research is to inquire into the fintech adoption behaviour of India’s GenY population. The focus of this research paper is to enlarge the scope of the planned behaviour theory by including other exogenous variables, information quality (IQ), and readiness to pay for privileges. Responses from the 349 selected higher education students based on judgmental sampling were collected from various institutes of repute in India. The suggested framework was evaluated using the Smart PLS 4 software bootstrapping method. It was found that multiple hypotheses framed in the study exert notable impact besides the belief that there is a direct linkage between observed behavioural control (OBC) and actual intention (AI). The paper confirms the application of the theory of planned behaviour for the effective implementation of fintech in India. It was observed that information quality is one of the crucial elements which influences the fintech adoption behaviour. The research enriches past studies by expanding the blueprint of a theory of planned behaviour by identifying the information quality factor as the influencing intent and behaviour. The reliability and validity of the suggested framework were analyzed after keeping in mind the impact of a business environment’s constantly changing dynamic forces. The study identified the primary constructs affecting the fintech adoption behaviour with a particular focus on the GEN Y population of India. It will also help the financial technocrats to optimally utilize the immense underlying capabilities of the fintech users and strategically launch more user-friendly products and services.
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Fintech adoption among MSMEs is significantly influenced by cognitive, affective, and social factors.
“Based on a review of relevant literature, it is evident that Fintech adoption among MSMEs is significantly influenced by cognitive, affective, and social factors [34], [35].”
From Enhancing Digital Financial Literacy and Digital Trust for Sustainable Online Loan Adoption among Micro-Entrepreneurs in Developing Economies (2026) · cites this paper as [35]
The SN1 results report significant effects of attitude and social norms on fintech adoption intention, and the Discussion describes information quality, attitudes, social norms, and behavioural control as relevant factors. But the study concerns Gen Y users in India, not MSMEs, so it does not establish the claim for that population. Page numbers were not provided in our copy. (LLM, conf=0.86)
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Cites
Baron, R. M., & Kenny, D. A. (1986) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.51.6.1173 · Cited by 91,532 · see citing works
The theory of planned behavior
Ajzen, I. (1991) · Organizational Behavior and Human Decision Processes · doi:10.1016/0749-5978(91)90020-t · Cited by 90,587 · see citing works
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003) · Journal of Applied Psychology · doi:10.1037/0021-9010.88.5.879 · Cited by 82,194 · see citing works
A new criterion for assessing discriminant validity in variance-based structural equation modeling
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-014-0403-8 · Cited by 38,459 · see citing works
Pls-Sem: indeed a silver bullet
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011) · Journal of Marketing Theory and Practice · doi:10.2753/mtp1069-6679190202 · Cited by 21,631 · see citing works
Significance tests and goodness of fit in the analysis of covariance structures.
Bentler, P. M., & Bonett, D. G. (1980) · Psychological Bulletin · doi:10.1037/0033-2909.88.3.588 · Cited by 19,266 · see citing works
A primer on partial least squares structural equation modeling (PLS-SEM)
Leguina, A. (2015) · International Journal of Research & Method in Education · doi:10.1080/1743727x.2015.1005806 · Cited by 18,921 · see citing works
(2014) · DigitalCommons - Kennesaw State University (Kennesaw State University) · Cited by 12,785 · see citing works
A Caution Regarding Rules of Thumb for Variance Inflation Factors
O’brien, R. M. (2007) · Quality & Quantity · doi:10.1007/s11135-006-9018-6 · Cited by 11,217 · see citing works
Common Method Bias in PLS-SEM: A Full Collinearity Assessment Approach
Kock, N. (2015) · International Journal of e-Collaboration · doi:10.4018/ijec.2015100101 · Cited by 10,215 · see citing works
How to Write Up and Report PLS Analyses
Chin, W. W. (2009) · Springer eBooks · doi:10.1007/978-3-540-32827-8_29 · Cited by 5,398 · see citing works
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013) · Long Range Planning · doi:10.1016/j.lrp.2013.01.001 · Cited by 5,310 · see citing works
An index and test of linear moderated mediation
Hayes, A. F. (2015) · Multivariate Behavioral Research · doi:10.1080/00273171.2014.962683 · Cited by 3,979 · see citing works
Texas A&M International University, Kock, N., Lynn, G., & Stevens Institute of Technology (2012) · Journal of the Association for Information Systems · doi:10.17705/1jais.00302 · Cited by 3,783 · see citing works
When moderation is mediated and mediation is moderated.
Müller, D., Judd, C. M., & Yzerbyt, V. Y. (2005) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.89.6.852 · Cited by 3,332 · see citing works
Mediation analysis in partial least squares path modeling
Nitzl, C., Roldán, J. L., & Cepeda‐Carrión, G. (2016) · Industrial Management & Data Systems · doi:10.1108/imds-07-2015-0302 · Cited by 2,618 · see citing works
Lee, M.-C. (2009) · Electronic Commerce Research and Applications · doi:10.1016/j.elerap.2008.11.006 · Cited by 2,037 · see citing works
Modelagem de Equações Estruturais com Utilização do Smartpls
Ringle, C. M., da Silva, D., & Bido, D. D. S. (2014) · ReMark - Revista Brasileira de Marketing · doi:10.5585/remark.v13i2.2717 · Cited by 1,297 · see citing works
The Challenges of Data Quality and Data Quality Assessment in the Big Data Era
Cai, L., & Zhu, Y. (2015) · Data Science Journal · doi:10.5334/dsj-2015-002 · Cited by 1,044 · see citing works
Disentangling behavioral intention and behavioral expectation
Warshaw, P. R., & Davis, F. D. (1985) · Journal of Experimental Social Psychology · doi:10.1016/0022-1031(85)90017-4 · Cited by 963 · see citing works
Artificial Intelligence in FinTech: understanding robo-advisors adoption among customers
Belanche, D., Casaló, L. V., & Flavián, C. (2019) · Industrial Management & Data Systems · doi:10.1108/imds-08-2018-0368 · Cited by 851 · see citing works
Zhong-qing, H., Ding, S., Li, S., Chen, L., & Yang, S. (2019) · Symmetry · doi:10.3390/sym11030340 · Cited by 677 · see citing works
Chang, V. I. C., Baudier, P., Zhang, H., Xu, Q. A., Zhang, J., & Arami, M. (2020) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2020.120166 · Cited by 615 · see citing works
Escobar-Rodríguez, T., & Carvajal-Trujillo, E. (2014) · Tourism Management · doi:10.1016/j.tourman.2014.01.017 · Cited by 590 · see citing works
Philippon, T. (2016) · National Bureau of Economic Research · doi:10.3386/w22476 · Cited by 518 · see citing works
Less Is More in Presenting Quality Information to Consumers
Peters, E. M., Dieckmann, N. F., Dixon, A., Hibbard, J. H., & Mertz, C. K. (2007) · Medical Care Research and Review · doi:10.1177/10775587070640020301 · Cited by 514 · see citing works
Kim, A. J., & Johnson, K. K. P. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2015.12.047 · Cited by 491 · see citing works
Doing Statistical Mediation and Moderation
Jose, P. E. (2013) · CERN Document Server (European Organization for Nuclear Research) · Cited by 449 · see citing works
Kim, H., & Niehm, L. S. (2009) · Journal of Interactive Marketing · doi:10.1016/j.intmar.2009.04.009 · Cited by 442 · see citing works
What drives FinTech adoption? A multi-method evaluation using an adapted technology acceptance model
Singh, S., Sahni, M. M., & Kovid, R. K. (2020) · Management Decision · doi:10.1108/md-09-2019-1318 · Cited by 433 · see citing works
What makes users willing or hesitant to use Fintech?: the moderating effect of user type
Ryu, H.-S. (2018) · Industrial Management & Data Systems · doi:10.1108/imds-07-2017-0325 · Cited by 429 · see citing works
Explain the intention to use smartphones for mobile shopping
Agrebi, S., & Jallais, J. (2014) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2014.09.003 · Cited by 414 · see citing works
Consumers’ Resistance to Digital Innovations: A Systematic Review and Framework Development
Talwar, S., Talwar, M., Kaur, P., & Dhir, A. (2020) · Australasian Marketing Journal (AMJ) · doi:10.1016/j.ausmj.2020.06.014 · Cited by 372 · see citing works
Jeong, M., & Lambert, C. U. (2001) · International Journal of Hospitality Management · doi:10.1016/s0278-4319(00)00041-4 · Cited by 329 · see citing works
Zhang, Y., Xiao, C., & Zhou, G. (2019) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2019.118555 · Cited by 294 · see citing works
Willingness to pay for green electricity in Japan as estimated through contingent valuation method
Nomura, N., & Akai, M. (2003) · Applied Energy · doi:10.1016/j.apenergy.2003.10.001 · Cited by 268 · see citing works
User Innovativeness and Fintech Adoption in Indonesia
Setiawan, B., Nugraha, D. P., Irawan, A., Nathan, R. J., & Zoltan, Z. (2021) · Journal of Open Innovation: Technology, Market, and Complexity · doi:10.3390/joitmc7030188 · Cited by 267 · see citing works
Shopping and word-of-mouth intentions on social media
Mikalef, P., Giannakos, M., & Pateli, A. (2013) · Journal of theoretical and applied electronic commerce research · doi:10.4067/s0718-18762013000100003 · Cited by 265 · see citing works
Gao, J., Zhang, C., Wang, K., & Ba, S. (2012) · Decision Support Systems · doi:10.1016/j.dss.2012.05.011 · Cited by 237 · see citing works
Quality-driven information filtering using the WIQA policy framework
Bizer, C., & Cyganiak, R. (2008) · Journal of Web Semantics · doi:10.1016/j.websem.2008.02.005 · Cited by 219 · see citing works
Alnawayseh, M. (2020) · Journal of Open Innovation Technology Market and Complexity · doi:10.3390/joitmc6040153 · Cited by 218 · see citing works
Influences of gender and product type on online purchasing
Pascual-Miguel, F. J., Agudo-Peregrina, Á. F., & Chaparro-Peláez, J. (2015) · Journal of Business Research · doi:10.1016/j.jbusres.2015.01.050 · Cited by 214 · see citing works
Predictors of investment intention in Indian stock markets
Akhtar, F., & Das, N. (2018) · International Journal of Bank Marketing · doi:10.1108/ijbm-08-2017-0167 · Cited by 211 · see citing works
Shaikh, I. M., Qureshi, M. A., Noordin, K., Shaikh, J. M., Khan, A., & Shahbaz, M. S. (2020) · foresight · doi:10.1108/fs-12-2019-0105 · Cited by 204 · see citing works
Mobile social commerce: The booster for brand loyalty?
Hew, J.-J., Lee, V., Ooi, K., & Lin, B. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2016.01.027 · Cited by 198 · see citing works
Predicting FinTech innovation adoption in South Africa: the case of cryptocurrency
Mazambani, L., & Mutambara, E. (2019) · African Journal of Economic and Management Studies · doi:10.1108/ajems-04-2019-0152 · Cited by 195 · see citing works
Yan, C., Siddik, A. B., Akter, N., & Dong, Q. (2021) · Environmental Science and Pollution Research · doi:10.1007/s11356-021-17437-y · Cited by 189 · see citing works
Factors determining behavioral intentions to use Islamic financial technology
Darmansyah, D., Fianto, B. A., Hendratmi, A., & Aziz, P. F. (2020) · Journal of Islamic marketing · doi:10.1108/jima-12-2019-0252 · Cited by 182 · see citing works
Do Electronic Word-of-Mouth and Elaboration Likelihood Model Influence Hotel Booking?
Leong, L.-Y., Hew, J.-J., Ooi, K., & Lin, B. (2017) · Journal of Computer Information Systems · doi:10.1080/08874417.2017.1320953 · Cited by 178 · see citing works
Sujood, S., Bano, N., & Siddiqui, S. (2022) · Journal of Hospitality and Tourism Insights · doi:10.1108/jhti-06-2022-0267 · Cited by 176 · see citing works
Yan, C., Siddik, A. B., Li, Y., Dong, Q., Zheng, G.-W., & Rahman, M. N. (2022) · Systems · doi:10.3390/systems10050148 · Cited by 161 · see citing works
Myers, M. B., Calantone, R. J., Page, T. J., et al. (2000) · Journal of International Marketing · doi:10.1509/jimk.8.4.108.19790 · Cited by 160 · see citing works
Sustainability reporting and the theory of planned behaviour
Thoradeniya, P., Lee, J., Tan, R., & Ferreira, A. (2015) · Accounting Auditing & Accountability Journal · doi:10.1108/aaaj-08-2013-1449 · Cited by 153 · see citing works
Tsai, M., Cheng, N., & Chen, K. (2011) · Total Quality Management & Business Excellence · doi:10.1080/14783363.2011.614870 · Cited by 134 · see citing works
Can the demographic and subjective norms influence the adoption of mobile banking?
Teo, A. C., Tan, G. W., Cheah, C.-M., Ooi, K., & Yew, K. T. (2012) · International Journal of Mobile Communications · doi:10.1504/ijmc.2012.049757 · Cited by 131 · see citing works
Chuah, S. H., Aw, E. C., & Cheng, C.-F. (2021) · Journal of Hospitality Marketing & Management · doi:10.1080/19368623.2021.1926038 · Cited by 130 · see citing works
Le, M. T. H. (2021) · Heliyon · doi:10.1016/j.heliyon.2021.e07821 · Cited by 130 · see citing works
The Influence of Perceived Risk and Trust in Adoption of FinTech Services in Indonesia
Meyliana, M., Fernando, E., & Surjandy, S. (2019) · CommIT (Communication and Information Technology) Journal · doi:10.21512/commit.v13i1.5708 · Cited by 119 · see citing works
Hew, T.-S., & Kadir, S. L. S. A. (2016) · Computers & Education · doi:10.1016/j.compedu.2016.06.004 · Cited by 104 · see citing works
Toward understanding FinTech and its industry
Knewtson, H. S., & Rosenbaum, Z. (2020) · Managerial Finance · doi:10.1108/mf-01-2020-0024 · Cited by 103 · see citing works
MUSLIM MILLENNIAL’S INTENTION OF DONATING FOR CHARITY USING FINTECH PLATFORM
Niswah, F. M., Mutmainah, L., & Legowati, D. A. (2019) · Journal of Islamic Monetary Economics and Finance · doi:10.21098/jimf.v5i3.1080 · Cited by 87 · see citing works
Digitalisation and Development: Issues for India and Beyond
Maiti, D., Castellacci, F., & Melchior, A. (2019) · Springer eBooks · doi:10.1007/978-981-13-9996-1_1 · Cited by 67 · see citing works
Fintech and Islamic Finance-challenges and Opportunities
Saba, I., Kouser, R., & Chaudhry, I. S. (2019) · Review of Economics and Development Studies · doi:10.26710/reads.v5i4.887 · Cited by 66 · see citing works
Nakagawa, K., & Yellowlees, P. (2020) · Current Psychiatry Reports · doi:10.1007/s11920-020-01171-2 · Cited by 65 · see citing works
PREDICTING MOBILE BANKING ADOPTION: AN INTEGRATION OF TAM AND TPB WITH TRUST AND PERCEIVED RISK
Aldammagh, Z., Abdaljawad, R. Y. R., & Obaid, T. (2021) · Financial Internet Quarterly · doi:10.2478/fiqf-2021-0017 · Cited by 64 · see citing works
Sahni, J. (2021) · SAGE Open · doi:10.1177/21582440211002208 · Cited by 54 · see citing works
Determinant Factors in Adopting Mobile Technology-based Services by Academic Librarians
Gholami, Z., Abdekhoda, M., & Gavgani, V. Z. (2018) · DESIDOC Journal of Library & Information Technology · doi:10.14429/djlit.38.4.12676 · Cited by 44 · see citing works
Effect of information quality of employment website on attitude toward the website
Priyadarshini, C., Sreejesh, S., & Anusree, M. R. (2017) · International Journal of Manpower · doi:10.1108/ijm-12-2015-0235 · Cited by 43 · see citing works
Alshari, H. A., & Lokhande, M. A. (2022) · Cogent Business & Management · doi:10.1080/23311975.2022.2114305 · Cited by 42 · see citing works
Prioritization of data quality dimensions and skills requirements in genome annotation work
Huang, H., Stvilia, B., Jörgensen, C., & Bass, H. W. (2011) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.21652 · Cited by 39 · see citing works
The Role of Religiosity in Explaining the Intention to use Islamic FinTech Among MSME Actors
Majid, R. (2021) · International Journal of Islamic Economics and Finance (IJIEF) · doi:10.18196/ijief.v4i2.11833 · Cited by 39 · see citing works
Lee, E. (2021) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2021.102872 · Cited by 38 · see citing works
Nayanajith, G., & Damunupola, K. A. (2019) · Asian Journal of Multidisciplinary Studies · Cited by 27 · see citing works
Indonesian Consumers’ Intention of Adopting Islamic Financial Technology Services
Purwantini, A. H., Athief, F. H. N., & Waharini, F. M. (2020) · Shirkah Journal of Economics and Business · doi:10.22515/shirkah.v5i2.304 · Cited by 27 · see citing works
Bahl, K., Kiran, R., & Sharma, A. (2022) · SAGE Open · doi:10.1177/21582440221097393 · Cited by 23 · see citing works
The Impact of Marketing Influencer and Information Quality to Purchase Intention of Instagram Users
Halim, E., P, R. A. A. W., Rianto, A., et al. (2020) · International Conference on Information Management and Technology (ICIMTech) · doi:10.1109/icimtech50083.2020.9211167 · Cited by 21 · see citing works
Fanoberova, A., & Kuczkowska, H. (2016) · KTH Publication Database DiVA (KTH Royal Institute of Technology) · Cited by 18 · see citing works
Does online social presence lead to purchase intentions
Khwaja, M. G., Jusoh, A., & Nor, K. M. (2019) · International Journal of Economic Policy in Emerging Economies · doi:10.1504/ijepee.2019.099730 · Cited by 16 · see citing works
Bhatt, E. A. D. V. (2021) · Türk bilgisayar ve matematik eğitimi dergisi · doi:10.17762/turcomat.v12i10.5336 · Cited by 12 · see citing works
Bhasin, N. K., & Gulati, K. (2021) · Advances in social networking and online communities book series · doi:10.4018/978-1-7998-7764-6.ch011 · Cited by 11 · see citing works
Predicting Intention of Using Fintech Lending to Bank Users in Indonesia
Putranto, B. D., & Sobari, N. (2021) · Advances in economics, business and management research/Advances in Economics, Business and Management Research · doi:10.2991/aebmr.k.210628.034 · Cited by 8 · see citing works
Lestari, M. R., & Nita, A. (2021) · Malaysian Journal of Social Sciences and Humanities (MJSSH) · doi:10.47405/mjssh.v6i8.981 · Cited by 8 · see citing works
Antecedents of mobile banking app adoption during COVID19: A perspective of Jordanian consumer
Almajali, D., Al-Radaideh, A. T., Nussir, N. A., et al. (2022) · International Journal of Data and Network Science · doi:10.5267/j.ijdns.2022.8.011 · Cited by 8 · see citing works
Njanja, L. W., Ogutu, M., & Ogutu, R. (2014) · Cited by 7 · see citing works
The impact of customer images on online purchase decisions: Evidence from a Chinese C2C Web site
dai, M., & Hove, L. V. (2017) · First Monday · doi:10.5210/fm.v22i10.7120 · Cited by 7 · see citing works
Brand Loyalty at Smartphones Market: Linking Between Brand Passion, Hedonic and Utilitarian Values
Yalçıntekin, T., & Saygılı, M. (2020) · Marketing and Management of Innovations · doi:10.21272/mmi.2020.1-23 · Cited by 6 · see citing works
Jain, A., Joshi, N., & Mayee, A. J. (2021) · Jindal Journal of Business Research · doi:10.1177/22786821211045196 · Cited by 5 · see citing works
Assessing consumer behavior from a main website of tourism product
Razak, M. I. M., Aminudin, N., Amir, A., et al. (2013) · CRC Press eBooks · doi:10.1201/b16064-89 · Cited by 1 · see citing works
Dressing in white, a true brazilian tradition: social influence, values, and symbolic consumption
Melo, F. V. S., de Farias, S. A., & Barbosa, O. T. (2019) · Revista de Administração da UFSM · doi:10.5902/1983465917984 · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 14
- Afandi, M. Y. (2021). Antecedents of digitizing ZIS pay- ments: A TAM and THEORY of PLANNED BEHAVIOUR Approaches. Journal of Finance and Islamic Banking, 41, 55-76
- Chuang, L.M., Liu, C.C., & Kao, H.K. (2016). The adoption of fintech service: TAM perspective. International Journal of Management and Administrative Sciences, 3(07), 01-15.
- Cohen, J. (1988). Statistical power analysis for the beha- vioural sciences (2nd ed.). Routledge.
- Ferdaous, J., & Rahman, M. N. (2021). Banking Goes Digital: Unearthing the Adoption of fintech by Bangladeshi Households. Journal of Innovation in Business Studies, 1(1), 7-42.
- Gupta, S., & Agrawal, A. (2021). Analytical Study of fintech in India: Pre & Post Pandemic Covid-19. Indian Journal of Economics and Business, 20(3), 33-71.
- Lee, T. S., Ariff, M. S. M., Zakuan, N., Sulaiman, Z., & Saman, M. Z. M. (2016). Online sellers' website quality influencing online buyers' purchase intention. [Paper presentation] IOP Conference Series: Materials Science and Engineering, Bali, Indonesia
- Mehta, D., & Kumari, S. (2021). Drivers fintech in India - a study of customers' attitude and adoption. Zenith International Journal of Multidisciplinary, 11(1), 29-41.
- Mohanasundaram, T., Sathyanarayana, S., & Rizwana, M. (2021), "Disruption on India's fintech landscape: The 5G wave", International Conference on Innovative Technology for Sustainable Development,Vol.37, https://www.itmconferences.org/articles/itmconf/ abs/2021/02/itmconf_icitsd2021_01008/itmconf_ icitsd2021_01008.html (Retrieved October 10, 2022).
- 6.852 · DOI
- Nagaraju, S. (2015). Mobile Banking-Perception of Customers and Bankers. International Journal of Business Administration Research Review, 3(9), 236.
- Ramchandran, T., & Stella, M. (2022). Behavioural inten- tion towards cryptocurrency adoption among stu- dents: A fintech innovation. Journal of Positive School Psychology, 5(6), 5046-5053.
- Setiawan. (2020). Digital financial literacy, current beha- vior of saving and spending and its future foresight, Econ (pp. 1-19). Innovation and Technology.
- Sheokand, K., & Gupta, N. (2017). Digital India pro- gramme and impact of digitalization on the Indian economy. Indian Journal of Economics and Development, 5(5).
- Tun-Pin, Keng-Soon, C., Choo Yen-San, W., Pui-Yee, Y., Hong Leong, C., & Teh Shwu-Shing, J. (2019). An Adoption of fintech Service in Malaysia, South East Asia journal of contemporary business. Economics and Law, 18, N0 5, pp.134-147.
Cited by
Sorted by citations
UTHAILEANG, W., Setthajit, R., Santoso, H. B., Figueroa, R. B., Buaprommee, N., & Sompaeng, A. (2026) · doi:10.21203/rs.3.rs-9679264/v1
- Authors
- Aggarwal, M., Nayak, K. M., & Bhatt, V.
- Affiliations
- Guru Nanak Dev University (IN)
- University of Mumbai (IN)
- Sal Institute of Management
- Corresponding author
- Nayak, Keyurkumar M
- Venue
- Cogent Economics & Finance
- Publisher
- Informa UK Limited
- Published
- 2023-04-26
- Volume
- 11
- Issue
- 1
- ISSN
- 2332-2039 (ISSN-L)
- OpenAlex
- W4367040458
- Cited by
- 54
- References
- 91
- Citations by year
- 2026: 15 · 2025: 20 · 2024: 16 · 2023: 3
- FWCI
- 13.77
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Cogent Economics & Finance (publishedVersion, cc-by)
- Econstor (Econstor) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Social Sciences › Business, Management and Accounting › Management Information Systems › FinTech, Crowdfunding, Digital Finance
- Health Sciences › Health Professions › Health Information Management › Health, Technology, Consumer Behavior
- Keywords
- fintech adoption, millennials, theory of planned behavior, information quality, behavioral control, adoption intention, university students, India
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