Cash rich to cashless market: Segmentation and profiling of Fintech-led-Mobile payment users
Tài liệu tham khảo
Alalwan, 2017, Factors influencing adoption of mobile banking by Jordanian bank customers: extending UTAUT2 with trust, Int. J. Inf. Manag., 37, 99, 10.1016/j.ijinfomgt.2017.01.002
Alfansi, 2000, Market segmentation in the Indonesian banking sector: the relationship between demographics and desired customer benefits, Int. J. Bank Mark., 18, 64, 10.1108/02652320010322976
Ali, 2016, An assessment of students' acceptance and usage of computer-supported collaborative classrooms in hospitality and tourism schools, J. Hosp. Leis. Sport Tour. Educ., 18, 51
Al-Saedi, 2020, Developing a general extended UTAUT model for M-payment adoption, Tech. Soc., 62, 10.1016/j.techsoc.2020.101293
Awasthi
Baptista, 2015, Understanding mobile banking: the unified theory of acceptance and use of technology combined with cultural moderators, Comput. Hum. Behav., 50, 418, 10.1016/j.chb.2015.04.024
Beane, 1987, Market segmentation: a review, Eur. J. Mark., 21, 20, 10.1108/EUM0000000004695
Calvo-Porral, 2020, An emotion-based segmentation of bank service customers, Int. J. Bank Mark., 38, 1441, 10.1108/IJBM-05-2020-0285
Chamberlain, 1933
Chauhan, 2018, Analyzing the impact of consumer innovativeness and perceived risk in internet banking adoption: a study of Indian consumers, Int. J. Bank Market., 37, 323, 10.1108/IJBM-02-2018-0028
Chawla, 2017, Consumer perspectives about mobile banking adoption in India–a cluster analysis, Int. J. Bank Mark., 35, 616, 10.1108/IJBM-03-2016-0037
Chawla, 2019, Consumer attitude and intention to adopt mobile wallet in India–an empirical study, Int. J. Bank Mark., 37, 1590, 10.1108/IJBM-09-2018-0256
Chawla, 2021, Segmenting mobile banking users based on the usage of mobile banking services, Glob. Bus. Rev., 22, 689, 10.1177/0972150918811257
Davis, 1989, User acceptance of computer technology: A comparison of two theoretical models, Manag. Sci., 35, 982, 10.1287/mnsc.35.8.982
Duane, 2014, Realising M-Payments: modelling consumers’ willingness to M-pay using Smart Phones, Behav. Inform. Tech., 33, 318, 10.1080/0144929X.2012.745608
Dwivedi, 2020, A meta-analysis based modified unified theory of acceptance and use of technology (meta-UTAUT): a review of emerging literature, Curr. Opin. Psychol., 36, 13, 10.1016/j.copsyc.2020.03.008
Fornell, 1981, Evaluating structural equation models with unobservable variables and measurement error, J. Mark. Res., 18, 39, 10.1177/002224378101800104
Frost, 2020, The economic forces driving fintech adoption across countries. The technological revolution in financial services: how banks, fintechs, and customers win together, 838, 70
Gao, 2015, The adoption of smartphones among older adults in China, 449
Gefen, 1997, Gender differences in the perception and use of e-mail: An extension to the technology acceptance model, MIS Quart., 389, 10.2307/249720
Gupta, 2019, Factors influencing adoption of payments banks by indian customers: extending UTAUT with perceived credibility, J. Asia Bus. Stud., 13, 173, 10.1108/JABS-07-2017-0111
Hair, 2015
Haley, 1968, Benefit segmentation: a decision-oriented research tool, J. Mark., 32, 30, 10.1177/002224296803200306
Hartigan, 1985, Statistical theory in clustering, J. Classif., 2, 63, 10.1007/BF01908064
Hasan, 2020, Exploring tourists' behavioral intentions towards the use of select mobile wallets for digital payments, Paradigm, 24, 177
Hasan, 2021, Evaluating drivers of fintech adoption in the Netherlands, Glob. Bus. Rev., 10.1177/09721509211027402
Ho, 2020, Factors affecting the behavioral intention to adopt mobile banking: an international comparison, Technol. Soc., 63, 10.1016/j.techsoc.2020.101360
Hong, 2020
Hsieh, 2021, Understanding medical consumers’ intentions to switch from cash payment to medical mobile payment: a perspective of technology migration, Technol. Forecast. Soc. Chang., 173, 10.1016/j.techfore.2021.121074
Hubona, 1996, IEEE. The influence of external variables on information technology usage behavior, 4, 166
Iman, 2018, Is mobile payment still relevant in the fintech era?, Electron. Commer. Res. Appl., 30, 72, 10.1016/j.elerap.2018.05.009
Jadil, 2021, A meta-analysis of the UTAUT model in the mobile banking literature: the moderating role of sample size and culture, J. Bus. Res., 132, 354, 10.1016/j.jbusres.2021.04.052
Jain, 1999, Data clustering: a review, ACM Comput. Surv., 31, 264, 10.1145/331499.331504
Jaiswal, 2022, Who will adopt electric vehicles? Segmenting and exemplifying potential buyer heterogeneity and forthcoming research, J. Retail. Consum. Serv., 67, 10.1016/j.jretconser.2022.102969
Jaiswal, 2021, Consumer adoption intention for electric vehicles: insights and evidence from indian sustainable transportation, Technol. Forecast. Soc. Chang., 173, 10.1016/j.techfore.2021.121089
Jaiswal, 2022, Mobile wallets adoption: pre- and post-adoption dynamics of mobile wallets usage, Mark. Intell. Plan., 40, 573, 10.1108/MIP-12-2021-0466
Jaiswal, 2022, What drives electric vehicles in an emerging market?, Mark. Intell. Plan., 40, 738, 10.1108/MIP-11-2021-0406
Jansson, 2009, Elucidating green consumers: a cluster analytic approach on proenvironmental purchase and curtailment behaviors, J. Euro Market., 18, 245, 10.9768/0018.04.245
Kant, 2019, A model of customer loyalty: an empirical study of Indian retail banking customer, Glob. Bus. Rev., 20, 1248, 10.1177/0972150919846813
Kanungo, 2021, Financial inclusion through digitalisation of services for well-being, Technol. Forecast. Soc. Chang., 167, 10.1016/j.techfore.2021.120721
Kar, 2021, What affects usage satisfaction in mobile payments? Modelling user generated content to develop the “digital service usage satisfaction model”, Inf. Syst. Front., 23, 1341, 10.1007/s10796-020-10045-0
Kaushal, 2021, Determinants of university reputation: conceptual model and empirical investigation in an emerging higher education market, Intern. J. Emerg. Mark.
Kim, 2009, Understanding dynamics between initial trust and usage intentions of mobile banking, Inf. Syst. J., 19, 283, 10.1111/j.1365-2575.2007.00269.x
Kline, 2015
Kock, 2021, Understanding and managing the threat of common method bias: detection, prevention and control, Tour. Manag., 86, 10.1016/j.tourman.2021.104330
Kotler, 2016
Koufaris, 2002, Applying the technology acceptance model and flow theory to online consumer behavior, Inf. Syst. Res., 13, 205, 10.1287/isre.13.2.205.83
KPMG
Landau, 2004, Analysis of repeated measures II: linear mixed model, 194
Lavuri, 2023, Extrinsic and intrinsic motives: panic buying and impulsive buying during a pandemic, Int. J. Retail Distrib. Manag., 51, 190, 10.1108/IJRDM-01-2022-0010
Leong, 2020, Predicting mobile wallet resistance: a two-staged structural equation modeling-artificial neural network approach, Int. J. Inf. Manag., 51, 10.1016/j.ijinfomgt.2019.102047
Liébana-Cabanillas, 2017, Predictive and explanatory modeling regarding adoption of mobile payment systems, Technol. Forecast. Soc. Chang., 120, 32, 10.1016/j.techfore.2017.04.002
Machauer, 2001, Segmentation of bank customers by expected benefits and attitudes, Int. J. Bank Mark., 19, 6, 10.1108/02652320110366472
Malhotra, 2007, 10.1108/S1548-6435(2007)3
Malodia, 2021, Future of e-government: an integrated conceptual framework, Technol. Forecast. Soc. Chang., 173, 10.1016/j.techfore.2021.121102
Marriott, 2017, What do we know about consumer m-shopping behaviour?, Int. J. Retail Distrib. Manag., 45, 568, 10.1108/IJRDM-09-2016-0164
Minhas, 1996, Benefit segmentation by factor analysis: an improved method of targeting customers for financial services, Int. J. Bank Mark., 14, 3, 10.1108/02652329610113126
Mombeuil, 2020, An exploratory investigation of factors affecting and best predicting the renewed adoption of mobile wallets, J. Retail. Consum. Serv., 55, 10.1016/j.jretconser.2020.102127
Mooi, 2011
Moore, 1991, Development of an instrument to measure the perceptions of adopting an information technology innovation, Inf. Syst. Res., 2, 192, 10.1287/isre.2.3.192
Oliveira, 2014, Extending the understanding of mobile banking adoption: when UTAUT meets TTF and ITM, Int. J. Inf. Manag., 34, 689, 10.1016/j.ijinfomgt.2014.06.004
Park, 2019, M-payment service: interplay of perceived risk, benefit, and trust in service adoption, Hum. Factors Ergon. Manuf. Serv. Ind., 29, 31, 10.1002/hfm.20750
PwC
Robinson, 1933
Sankaran, 2020, Why customers make mobile payments? Applying a means-end chain approach, Mark. Intell. Plan., 39, 109, 10.1108/MIP-12-2019-0622
Sharma, 2019, Mobile wallet adoption in India: an analysis, IUP J. Bank Manag., 18
Sharma, 2019, Examining the role of trust and quality dimensions in the actual usage of mobile banking services: an empirical investigation, Int. J. Inf. Manag., 44, 65, 10.1016/j.ijinfomgt.2018.09.013
Shaw, 2014, The mediating influence of trust in the adoption of the mobile wallet, J. Retail. Consum. Serv., 21, 449, 10.1016/j.jretconser.2014.03.008
Shiferaw, 2019, Modeling predictors of acceptance and use of electronic medical record system in a resource limited setting: using modified UTAUT model, Informatics Med. Unlocked, 17, 10.1016/j.imu.2019.100182
Shukla, 2022, Prospects of mobile payments in northern India: customer segmentation and profiling, Int. J. Bus. Excell., 27, 23, 10.1504/IJBEX.2022.123029
Singh, 2021, Assessing determinants influencing continued use of live streaming services: An extended perceived value theory of streaming addiction, Expert Syst. Appl., 168, 10.1016/j.eswa.2020.114241
Smith, 1956, Product differentiation and market segmentation as alternative marketing strategies, J. Mark., 21, 3, 10.1177/002224295602100102
Statista
Talwar, 2020, Point of adoption and beyond. Initial trust and mobile-payment continuation intention, J. Retail. Consum. Serv., 55, 10.1016/j.jretconser.2020.102086
Tamilmani, 2021, Consumer acceptance and use of information technology: a meta-analytic evaluation of UTAUT2, Inf. Syst. Front., 23, 987, 10.1007/s10796-020-10007-6
Tan, 2016, Behavioral intention to adopt mobile banking among the millennial generation, Young Consum., 17, 18, 10.1108/YC-07-2015-00537
Teng, 2021, Examining actual consumer usage of e-wallet: a case study of big data analytics, Comput. Hum. Behav., 121, 10.1016/j.chb.2021.106778
Thakur, 2013, Customer usage intention of mobile commerce in India: an empirical study, J. Indian Bus. Res., 5, 52, 10.1108/17554191311303385
Vallespín, 2018, Who relies on mobile payment systems when they are on vacation? A segmentation analysis, Tourism, 66, 6
Venkatesh, 2003, User acceptance of information technology: toward a unified view, Manag. Inf. Syst. Q., 27, 5, 10.2307/30036540
Venkatesh, 2012, Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology, MIS Q., 36, 157, 10.2307/41410412
Wei, 2021, Young generation's mobile payment adoption behavior: analysis based on an extended UTAUT model, J. Theor. Appl. Electron. Commer. Res., 16, 618, 10.3390/jtaer16040037
Wells, 1971, Activities, interests and opinions, J. Advert. Res., 11, 27
Xu, 2017, The impact of informational incentives and social influence on consumer behavior during Alibaba's online shopping carnival, Comput. Hum. Behav., 76, 245, 10.1016/j.chb.2017.07.018
Zheng, 2021, Impacts of market segmentation on the over-capacity of the thermal electricity generation industry in China, J. Environ. Manag., 279, 10.1016/j.jenvman.2020.111761
Ziff, 1971, Psychographics for market segmentation, J. Advert. Res., 11, 3
