Mobile payments adoption by US consumers: an extended TAM
Tóm tắt
The purpose of this paper is to incorporate mobile payment (MP) self-efficacy, new technology anxiety, and MP privacy concerns into the basic TAM to explore MP adoption, particularly tap-and-go payment, among US consumers.
Data were collected through an online survey conducted among students at a Midwestern University in the USA. A total of 254 participants provided 240 useable responses.
MP self-efficacy significantly impacts perceived ease of use (PEOUMP) and perceived usefulness of MP (PUMP). These in turn impact MP attitude, which affects intention to use MP. Privacy concerns also impact attitude towards MP and MP use intention. New technology anxiety impacts PEOUMP, but not PUMP.
The study uses a convenience sample of young US consumers, which could limit the generalisability of the results. The study is also limited to tap-and-go payment.
US retailers have information on some of the factors that encourage MP adoption. Retailers need to address self-efficacy concerns, MP privacy concerns, and consumers’ perceptions of usefulness of the technology.
There has been little research on factors impacting tap-and-go payment adoption in the USA. The study highlights the roles of self-efficacy and privacy concerns. It focusses on tap-and-go payment, since this technology can enhance consumers’ retail experience.
Từ khóa
Tài liệu tham khảo
2009, Adoption of electronic health records in the presence of privacy concerns: the elaboration likelihood model and individual persuasion, MIS Quarterly, 33, 339, 10.2307/20650295
AT Kearney (2014), “On solid ground: brick-and-mortar is the foundation of omnichannel retailing”, available at: www.atkearney.com/consumer-products-retail/on-solid-ground (accessed 14 April 2016).
1997, Self-Efficacy: The Exercise of Control
Browne, M.W. and Cudeck, R. (1993), “Alternative ways of assessing model fit”, in Bollen, K.A. and Long, J.S. (Eds), Testing Structural Equation Models, Sage, Newbury Park, CA, pp. 136-162.
2014, Understanding organic food consumption: attitude as a mediator, International Journal of Consumer Studies, 38, 337, 10.1111/ijcs.12094
2013, User acceptance of ‘near field communication’ mobile phone service: an investigation based on the ‘unified theory of acceptance and use of technology’ model, Service Industries Journal, 33, 609, 10.1080/02642069.2011.622369
2008, A model of consumer acceptance of mobile payment, International Journal of Mobile Communications, 6, 32, 10.1504/IJMC.2008.015997
2005, Gender differs: assessing a model of online purchase intentions in e-tail service, International Journal of Service Industry Management, 16, 416, 10.1108/09564230510625741
2010, Early versus potential adopters, International Journal of Retail & Distribution Management, 38, 443, 10.1108/09590551011045357
2009, Acceptance of online customization for apparel shopping, International Journal of Retail & Distribution Management, 37, 389, 10.1108/09590550910954892
2011, The intranet’s role in newcomer socialization in the hotel industry in Taiwan – technology acceptance model analysis, International Journal of Human Resource Management, 22, 1163, 10.1080/09585192.2011.556795
2002, An attitudinal model of technology-based self-service: moderating effects of consumer traits and situational factors, Journal of the Academy of Marketing Science, 30, 184, 10.1177/0092070302303001
1989, User acceptance of computer technology: a comparison of two theoretical models, Management Science, 35, 982, 10.1287/mnsc.35.8.982
2016, An integrated model of self-service technology (SST) usage in a retail context, International Journal of Retail & Distribution Management, 44, 540, 10.1108/IJRDM-08-2015-0122
2010, Linking trust to use intention for technology-enabled bank channels: the role of trusting intentions, Psychology & Marketing, 27, 799, 10.1002/mar.20358
2015, Consumer trial, continuous use, and economic benefits of a retail service innovation: the case of the personal shopping assistant, Journal of Product Innovation Management, 32, 459, 10.1111/jpim.12241
2013, Exploring the influence of perceived risk and internet self-efficacy on consumer online shopping intentions: perspective of technology acceptance model, International Management Review, 9, 67
Farrell, A.M. and Rudd, J.M. (2009), “Factor analysis and discriminant validity: a brief review of some practical issues”, in Tojib, D. (Ed.), ANZMAC 2009 Conference Proceedings, ANZMAC, Melbourne.
Federal Reserve, 2014, Consumers and mobile financial services 2014
Federal Reserve (2015), “Consumers and mobile financial services 2015”, available at: www.federalreserve.gov/econresdata/consumers-and-mobile-financial-services-report-201503.pdf (accessed 14 April 2016).
1981, Evaluating structural equation models with unobservable variables and measurement error, Journal of Marketing Research, 8, 39
2015, Exploring the acceptance of technology for mobile shopping: an empirical investigation among smartphone users, International Review of Retail, Distribution and Consumer Research, 25, 215
2009, Consumer e-shopping acceptance: antecedents in a technology acceptance model, Journal of Business Research, 62, 565, 10.1016/j.jbusres.2008.06.016
2014, Determinants of mobile coupon service adoption: assessment of gender difference, International Journal of Retail & Distribution Management, 42, 441, 10.1108/IJRDM-08-2012-0074
2009, Multivariate Data Analysis
2007, Determining factors of academic library web site usage, Journal of the American Society for Information Science & Technology, 58, 2325, 10.1002/asi.20710
Heller, L. (2015), “Mobile payment adoption slower than you think”, available at: www.fierceretail.com/operations/mobile-payment-adoption-slower-than-you-think (accessed 29 August 2016).
2010, An empirical examination of factors influencing the intention to use mobile payment, Computers in Human Behaviour, 26, 310, 10.1016/j.chb.2009.10.013
2009, Towards an understanding of the behavioural intention to use 3G mobile value-added services, Computers in Human Behaviour, 25, 103, 10.1016/j.chb.2008.07.007
2010, An empirical study of the factors affecting social network service use, Computers in Human Behaviour, 26, 254, 10.1016/j.chb.2009.04.011
2002, The dual credibility model: the influence of corporate and endorser credibility on attitudes and purchase intentions, Journal of Marketing Theory & Practice, 10, 1, 10.1080/10696679.2002.11501916
2014, Technology usage intent among apparel retail employees, International Journal of Retail & Distribution Management, 42, 422, 10.1108/IJRDM-07-2012-0067
2013, Antecedents of travellers’ electronic word-of-mouth communication, Journal of Marketing Management, 29, 584
2012, An integrated attitude model of self-service technologies: evidence from online stock trading systems brokers, Service Industries Journal, 32, 1823, 10.1080/02642069.2011.574695
1989, An empirical examination of the structural antecedents of attitude toward the ad in an advertising pretesting context, Journal of Marketing, 53, 48, 10.1177/002224298905300204
2003, The influence of technology anxiety on consumer use and experiences with self-service technologies, Journal of Business Research, 56, 899, 10.1016/S0148-2963(01)00276-4
2014, Adoption of M-commerce in India: applying theory of planned behaviour model, Journal of Internet Banking & Commerce, 19, 1
2014, Mplus: Statistical Analysis with Latent Variables
2015, Factors affecting adoption of internet banking in Jordan, International Journal of Bank Marketing, 33, 510, 10.1108/IJBM-03-2014-0043
2008, Integrating trust and computer self-efficacy with TAM: an empirical assessment of customers’ acceptance of banking information systems (BIS) in Jamaica, Journal of Internet Banking & Commerce, 13, 1
2010, Understanding consumer acceptance of mobile payment services: an empirical analysis, Electronic Commerce Research and Applications, 9, 209, 10.1016/j.elerap.2009.07.005
Schwarzer, R. and Jerusalem, M. (1995), “Generalised self-efficacy scale”, in Weinman, J., Wright, S. and Johnston, M. (Eds), Measures in Health Psychology: A User’s Portfolio. Causal and Control Beliefs, NFER-Nelson, Windsor, pp. 35-37.
2009, Towards an understanding of the consumer acceptance of mobile wallet, Computers in Human Behaviour, 25, 1343, 10.1016/j.chb.2009.06.001
Silbert, S. (2015), “How mobile payments will grow in 2016”, available at: http://fortune.com /2015/10/29/ mobile-payments-grow-2016 (accessed 29 August 2016).
2015, Modelling consumers’ adoption intentions of remote mobile payments in the United Kingdom: extending UTAUT with innovativeness, risk, and trust, Psychology & Marketing, 32, 860, 10.1002/mar.20823
2014, Using an old dog for new tricks: a regulatory focus perspective on consumer acceptance of RFID applications, Journal of Service Research, 17, 85, 10.1177/1094670513501394
2009, Factors influencing consumer acceptance of mobile marketing: a two-country study of youth markets, Journal of Interactive Marketing, 23, 308, 10.1016/j.intmar.2009.07.003
2013, Using Multivariate Statistics
2016, Mobile payment technologies in retail: a review of potential benefits and risks, International Journal of Retail & Distribution Management, 44, 159, 10.1108/IJRDM-05-2015-0065
2003, Factors influencing the usage of websites: the case of a generic portal in the Netherlands, Information and Management, 40, 541, 10.1016/S0378-7206(02)00079-4
2000, Determinants of perceived ease of use: integrating control, intrinsic motivation, and emotion into the technology acceptance model, Information Systems Research, 11, 342, 10.1287/isre.11.4.342.11872
2008, Technology acceptance model 3 and a research agenda on interventions, Decision Sciences, 39, 273, 10.1111/j.1540-5915.2008.00192.x
2000, A theoretical extension of the technology acceptance model: four longitudinal field studies, Management Science, 46, 186, 10.1287/mnsc.46.2.186.11926
2003, User acceptance of information technology: toward a unified view, MIS Quarterly, 27, 425, 10.2307/30036540
2015, Exploring the acceptance of technology for mobile shopping: an empirical investigation among smartphone users, The International Review of Retail, Distribution and Consumer Research, 25, 215, 10.1080/09593969.2014.988280
2005, What drives mobile commerce? An empirical evaluation of the revised technology acceptance model, Information and Management, 42, 719
2015, Intention to adopt internet banking in an emerging economy: a perspective of Indian youth, International Journal of Bank Marketing, 33, 530, 10.1108/IJBM-06-2014-0075
2010, The effects of technology self-efficacy and innovativeness on consumer mobile data service adoption between American and Korean consumers, Journal of International Consumer Marketing, 22, 117, 10.1080/08961530903476147
2012, Consumer technology traits in determining mobile shopping adoption: an application of the extended theory of planned behaviour, Journal of Retailing and Consumer Services, 19, 484, 10.1016/j.jretconser.2012.06.003
2009, The effects of consumer perceived value and subjective norm on mobile data service adoption between American and Korean consumers, Journal of Retailing and Consumer Services, 16, 502, 10.1016/j.jretconser.2009.08.005
2005, Exploring factors affecting the adoption of mobile commerce in Singapore, Telematics and Informatics, 22, 257, 10.1016/j.tele.2004.11.003
YCharts.com (2015), available at: http://ycharts.com/indicators/ecommerce_ sales_as_percent _retail_sales (accessed 14 April 2016).
1990, Cognitive and affective priming effects of the context for print advertisements, Journal of Advertising, 19, 40, 10.1080/00913367.1990.10673186
2012, Examining location-based services usage from the perspectives of unified theory of acceptance and use of technology and privacy risk, Journal of Electronic Commerce Research, 13, 135
2010, Modelling the interaction of users and mobile payment system: conceptual framework, International Journal of Human-Computer Interaction, 26, 917, 10.1080/10447318.2010.502098
2012, A preliminary model for mobile payment acceptance, International Journal of Mobile Marketing, 7, 37