Investigating American iPhone Users’ Intentions to Use NFC Mobile Payments in Hotels

  • Cristian MorosanEmail author
  • Agnes DeFranco
Conference paper


Recently, a series of events (e.g., Near Field Communication (NFC) hardware becoming mainstream, the deadline given to merchants to accept EMV (chip-based) cards) precipitated the development of an infrastructure that increasingly accommodates NFC mobile payments (NFC-MP) in the U.S. Within the landscape of the American NFC-MP, an important role is occupied by the Apple NFC-MP ecosystem. Drawing from the neo-classic technology adoption and regulatory focus theory, this study developed a conceptual model that explicates iPhone users’ intentions to use NFC-MP in hotels. Using data collected from a sample of 347 U.S. iPhone users, the model was validated empirically, providing a mapping of the factors that influence intentions to use NFC-MP in hotels. The study validated hedonic motivation and performance expectancy as the most critical predictors of intentions, and recognized the more modest roles of privacy concerns and prevention focus in influencing intentions.


Mobile payments Technology adoption Regulatory focus Privacy Hotels 



This research has been conducted with the support of Hospitality Financial and Technology Professionals (HFTP).


  1. Aluri, A., & Palakurthi, R. R. (2011). The influence of demographic factors on intentions to use RFID technologies in the U.S. hotel industry. Journal of Hospitality and Tourism Technologies, 2(3), 188–203.CrossRefGoogle Scholar
  2. Apple (2015). Apple Pay. Your wallet. Without the Wallet. Retrieved on August 1, 2015 from
  3. Ary, D., Jacobs, L., & Razavieh, A. (1996). Introduction to research in education (5th ed.). Ft. Worth: Holt, Rinehart, and Winston, Inc.Google Scholar
  4. Avnet, T., & Higgins, E. T. (2008). Locomotion, assessment, and regulatory fit: Value transfer from “how” to “what”. Journal of Experimental Social Psychology, 39, 525–530.CrossRefGoogle Scholar
  5. Bagozzi, R. P. (2007). The legacy of the technology acceptance model and a proposal for a paradigm shift. Journal of the Association for Information Systems, 8(4), 244–254.Google Scholar
  6. Baptista, G., & Oliveira, T. (2015). Understanding mobile banking: The unified theory of acceptance and use of technology combined with cultural moderators. Computers in Human Behavior, 50, 418–430.CrossRefGoogle Scholar
  7. Benbasat, I., & Barki, H. (2007). Quo vadis, TAM? Journal of the Association of Information Systems, 8(4), 211–218.Google Scholar
  8. Brockner, J., Higgins, E. T., & Low, M. B. (2004). Regulatory focus theory and the entrepreneurial process. Journal of Business Venturing, 19, 203–220.CrossRefGoogle Scholar
  9. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.CrossRefGoogle Scholar
  10. Dwivedi, Y. K., Shareef, M. A., Simintiras, A. C., Lal, B., & Weerakkody, V. (2015). A generalized adoption model for services: A cross-country comparison of mobile health (m-health). Government Information Quarterly, in press.Google Scholar
  11. Eastlick, M. A., Lotz, S. L., & Warrington, P. (2006). Understanding online B-to-C relationships: An integrated model of privacy concerns, trust, and commitment. Journal of Business Research, 59(8), 877–886.CrossRefGoogle Scholar
  12. Fornell, C., & Larcker, D. (1981). Structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.CrossRefGoogle Scholar
  13. Furió, D., González-Gancedo, S., Juan, M. C., Seguí, I., & Rando, N. (2013). Evaluation of learning outcomes using an educational iPhone game vs. traditional game. Computers & Education, 64, 1–23.CrossRefGoogle Scholar
  14. Gebauer, H., Johnson, M., & Enquist, B. (2010). Value co-creation as a determinant of success in public transport services: A study of the Swiss Federal Railway operator (SBB). Managing Service Quality, 20, 511–530.CrossRefGoogle Scholar
  15. Hair, J. F., Black, W. C., Babin, B. B., & Anderson, R. E. (2009). Multivariate data analysis (7th ed.). Upper Saddle River, NJ: Prentice Hall.Google Scholar
  16. Heun, D. (2015). Is Apple Pay’s rising tide sinking other ships? PaymentsSource. Retrieved on August 1, 2015 from
  17. Higgins, E. T. (1997). Beyond pleasure and pain. American Psychologist, 52, 1280–1300.CrossRefGoogle Scholar
  18. Hirunyawipada, T., & Paswan, A. K. (2006). Consumer innovativeness and perceived risk: implications for high technology product adoption. Journal of Consumer Marketing, 23(4), 182–198.CrossRefGoogle Scholar
  19. Hof, R. (2015). Apple Pay starts to take off, leaving competition in the dust. Forbes/Tech. Retrieved on August 1, 2015 from
  20. Jia, H. M., Wang, Y., Ge, L., Shi, G., & Yao, S. (2012). Asymmetric effects of regulatory focus on expected desirability and feasibility of embracing self-service technologies. Psychology & Marketing, 29(4), 209–225.CrossRefGoogle Scholar
  21. Kassner, M. (2014). Apple Pay: More secure or just different? TechRepublic. Retrieved on May 20, 2015 from
  22. Kim, J., Brewer, P., & Bernhard, P. (2008). Hotel customers perceptions of biometric door locks. Journal of Hospitality Marketing & Management, 17(1-2), 162–183.CrossRefGoogle Scholar
  23. Kucukusta, D., Law, R., Besbes, A., & Legohérel, P. (2015). Re-examining perceived usefulness and ease of use in online booking. International Journal of Contemporary Hospitality Management, 27(2), 185–198.CrossRefGoogle Scholar
  24. Law, R., Leung, D., Au, N., & Lee, H. A. (2013). Progress and development of information technology in the hospitality industry: Evidence from Cornell Hospitality Quarterly. Cornell Hospitality Quarterly, 54(1), 10–24.CrossRefGoogle Scholar
  25. Lee, K. T., & Koo, D. M. (2012). Effects of attribute and valence of e-WOM on message adoption: Moderating roles of subjective knowledge and regulatory focus. Computers in Human Behavior, 28, 1974–1984.CrossRefGoogle Scholar
  26. Li, H., Sarathy, R., & Xu, H. (2011). The role of affect and cognition on online consumers’ decision to disclose personal information to unfamiliar online vendors. Decision Support Systems, 51(3), 434–445.CrossRefGoogle Scholar
  27. Liébana-Cabanillas, F., Sánchez-Fernández, J., & Munoz-Leiva, F. (2014). The moderating effect of experience in the adoption of mobile payment tools in Virtual Social Networks: The m-Payment Acceptance Model in Virtual Social Networks (MPAM-VSN). International Journal of Information Management, 34, 151–166.CrossRefGoogle Scholar
  28. Lu, J., Liu, C., Yu, C. S., & Wang, K. (2008). Determinants of accepting wireless mobile data services in China. Information & Management, 45(1), 52–64.CrossRefGoogle Scholar
  29. Montazemi, A. R., & Qahri-Saremi, H. (2015). Factors affecting adoption of online banking: A meta-analytic structural equation modeling study. Information & Management, 52, 210–226.CrossRefGoogle Scholar
  30. Morosan, C. (2014). Toward an integrated model of adoption of mobile phones for purchasing ancillary services in air travel. International Journal of Contemporary Hospitality Management, 26(2), 246–271.CrossRefGoogle Scholar
  31. Morosan, C., & Jeong, M. (2008). Users’ perceptions of two types of hotel reservation web sites. International Journal of Hospitality Management, 27(2), 284–292.CrossRefGoogle Scholar
  32. Muthén, L. K., & Muthén, B. O. (2007). Mplus user’s guide (5th ed.). Los Angeles, CA: Muthén & Muthén.Google Scholar
  33. Perez, S. (2014). iTunes App Store Now Has 1.2 Million Apps, Has Seen 75 Billion Downloads To Date. TechCrunch, Retrieved August 1, 2015 from
  34. Shin, D. H. (2009). Determinants of customer acceptance of multi-service network: An implication for IP-based technologies. Information & Management, 46(1), 16–22.CrossRefGoogle Scholar
  35. Shin, D. H. (2010). The effects of trust, security and privacy in social networking: A security-based approach to understand the pattern of adoption. Interacting with Computers, 22, 428–438.CrossRefGoogle Scholar
  36. Toh, R. S., Lee, E., & Hu, M. Y. (2006). Social desirability bias in diary panels is evident in panelists’ behavioral frequency. Psychological Reports, 99, 322–334.Google Scholar
  37. Venkatesh, V., & Bala, H. (2008). Technology Acceptance Model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315.CrossRefGoogle Scholar
  38. Venkatesh, V., Thong, J., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178.Google Scholar
  39. Yi, M. Y., Jackson, J. D., Park, J. S., & Probst, J. C. (2006). Understanding information technology acceptance by individual professionals: Toward an integrative view. Information & Management, 43, 350–363.CrossRefGoogle Scholar
  40. Zhao, G., & Pechmann, C. (2007). The impact of regulatory focus on adolescents’ response to antismoking advertising campaigns. Journal of Marketing Research, 44, 671–687.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Conrad N. Hilton College of Hotel & Restaurant ManagementUniversity of HoustonHoustonUSA

Personalised recommendations