Skip to main content
SpringerLink
Log in

Examining the determinants of continuance intention to use and the moderating effect of the gender and age of users of NFC mobile payments: a multi-analytical approach

  • Published:
Information Technology and Management Aims and scope Submit manuscript

Abstract

The interest in m-payments through mobile phones to replace the use of cash, credit cards or cheques is rapidly increasing in our society. The present study aims to examine the situation of near field communication (NFC) m-payment services along with the determinants of users’ continuance intention. To this intent, a sample of 1840 respondents with experience in using NFC payments participated in an online survey. During the first phase of this research, an structural equation modelling (SEM) technique was used to identify the acceptance predictors of mobile payments as well as to analyse the eventual moderating effect of the gender and age of the users of this tool. The second phase focused on the neural network model’s proficiency in assessing the relative impact of the most relevant predictors stemming from the aforementioned SEM analysis. The results obtained revealed subjective norms, risk, perceived usefulness, customer brand engagement and trust as the most significant antecedents of continuance intention towards NFC payments. The study also discusses the managerial implications derived from this research while assessing and suggesting potential user behaviour-based business opportunities for service providers.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Agarwal R, Ahuja M, Carter PE, Gans M (1998) Early and late adopters of IT innovations: extensions to innovation diffusion theory. In: Proceedings of the DIGIT Conference, pp 1–18

  2. Agarwal R, Prasad J (1998) A conceptual and operational definition of personal innovativeness in the domain of information technology. Inf Syst Res 9(2):204–215

    Google Scholar 

  3. Al-Amri R, Maarop N, Jamaludin R, Samy GN, Magalingam P, Hassan NH, Daud SM (2018) Correlation analysis between factors influencing the usage intention of NFC mobile wallet payment. J Fundam Appl Sci 10(2S):215–228

    Google Scholar 

  4. Alonso-Dos-Santos M, Jiménez MA, Carvajal-Trujillo E (2019) Facebook commerce usage intention: a symmetric and asymmetric approach. Inf Technol Manag 21:145–156

    Google Scholar 

  5. Arbore A, Busacca B (2009) Customer satisfaction and dissatisfaction in retail banking: exploring the asymmetric impact of attribute performances. J Retail Consum Serv 16(4):271–280

    Google Scholar 

  6. Barroso-Castro MC, Cepeda-Carrión GA, Roldán JL (2007) Constructos latentes y agregados en la economía de la empresa. In: Decisiones Basadas en el Conocimiento y en el Papel Social de la Empresa: XX Congreso Anual de AEDEM. Asociación Española de Dirección y Economía de la Empresa (AEDEM), p 67

  7. Bejou D, Ennew CT, Palmer A (1998) Trust, ethics and relationship satisfaction. Int J Bank Mark 16(4):170–175

    Google Scholar 

  8. Belanche Gracia D, Casalo LV, Flavián C, Schepers JJL (2014) Trust transfer in the continued usage of public e-services. Inf Manag 51(6):627–640

    Google Scholar 

  9. Bhattacherjee A (2001) Understanding information systems continuance: an expectation-confirmation model. MIS Q 25:351–370

    Google Scholar 

  10. Bhattacherjee A (2001) An empirical analysis of the antecedents of electronic commerce service continuance. Decis Support Syst 32(2):201–214

    Google Scholar 

  11. Bhattacherjee A, Premkumar G (2004) Understanding changes in belief and attitude toward information technology usage: a theoretical model and longitudinal test. MIS Q 28:229–254

    Google Scholar 

  12. Braun C, Hadwich K, Bruhn M (2017) How do different types of customer engagement affect important relationship marketing outcomes? An empirical analysis. J Cust Behav 16(2):111–144

    Google Scholar 

  13. Brown H (2016) Online apparel consumer behavior: hedonic motivation & post-purchase communication. Major Papers by Master of Science Students. Paper 5. https://digitalcommons.uri.edu/tmd_major_papers/5. Accessed 24 Mar 2019

  14. Cao X, Yu L, Liu Z, Gong M, Adeel L (2018) Understanding mobile payment users’ continuance intention: a trust transfer perspective. Internet Res 28(2):456–476

    Google Scholar 

  15. Casado-Aranda LA, Liébana-Cabanillas F, Sánchez-Fernández J (2018) A neuropsychological study on how consumers process risky and secure E-payments. J Interact Mark 43:151–164

    Google Scholar 

  16. Chan FT, Chong AY (2012) A SEM–neural network approach for understanding determinants of interorganizational system standard adoption and performances. Decis Support Syst 54(1):621–630

    Google Scholar 

  17. Chawla D, Joshi H (2020) The moderating role of gender and age in the adoption of mobile wallet. Foresight 22(4):483–504

    Google Scholar 

  18. Chen SC, Chung KC, Tsai MY (2019) How to achieve sustainable development of mobile payment through customer satisfaction—the SOR model. Sustainability 11(22):6314

    Google Scholar 

  19. Chen X, Li S (2017) Understanding continuance intention of mobile payment services: an empirical study. J Comput Inf Syst 57(4):287–298

    Google Scholar 

  20. Chin WW (1998) The partial least squares approach to structural equation modeling. Mod Methods Bus Res 295(2):295–336

    Google Scholar 

  21. Chong AYL (2013) A two-staged SEM-neural network approach for understanding and predicting the determinants of m-commerce adoption. Expert Syst Appl 40(4):1240–1247

    Google Scholar 

  22. Chong AYL (2013) Predicting m-commerce adoption determinants: a neural network approach. Expert Syst Appl 40(2):523–530

    Google Scholar 

  23. Chong AYL, Chan FT, Ooi KB (2012) Predicting consumer decisions to adopt mobile commerce: cross country empirical examination between China and Malaysia. Decis Support Syst 53(1):34–43

    Google Scholar 

  24. Chong AYL, Bai R (2014) Predicting open IOS adoption in SMEs: an integrated SEM-neural network approach. Expert Syst Appl 41(1):221–229

    Google Scholar 

  25. Chou SW, Min HT, Chang YC, Lin CT (2010) Understanding continuance intention of knowledge creation using extended expectation–confirmation theory: an empirical study of Taiwan and China online communities. Behav Inf Technol 29(6):557–570

    Google Scholar 

  26. Chow WS, Shi S (2014) Investigating students’ satisfaction and continuance intention toward E-learning: an extension of the expectation–confirmation model. Procedia Soc Behav Sci 141:1145–1149. https://doi.org/10.1016/j.sbspro.2014.05.193

    Article  Google Scholar 

  27. Christian L, Juwitasary H, Chandra YU, Putra EP (2019) Evaluation of the E-service quality for the intention of community to use NFC technology for mobile payment with TAM. In: 2019 International Conference on Information Management and Technology (ICIMTech), vol 1. IEEE, pp 24–29

  28. Cocosila M, Trabelsi H (2016) An integrated value-risk investigation of contactless mobile payments adoption. Electron Commer Res Appl 20:159–170

    Google Scholar 

  29. Cronbach LJ (1951) Coefficient alpha and the internal structure of tests. Psychometrika 16(3):297–334

    Google Scholar 

  30. Dahlberg T, Guo J, Ondrus J (2015) A critical review of mobile payment research. Electron Commer Res Appl 14:265–284

    Google Scholar 

  31. Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 13:319–340

    Google Scholar 

  32. Euromonitor International (2014) Business Environment: Malaysia.

  33. Farivar S, Turel O, Yuan Y (2017) A trust-risk perspective on social commerce use: an examination of the biasing role of habit. Internet Res 27(3):586–607

    Google Scholar 

  34. Fishbein M, Ajzen I (1975) Intention and behavior: an introduction to theory and research

  35. Flavián C, Guinalíu M, Gurrea R (2006) The role played by perceived usability, satisfaction and consumer trust on website loyalty. Inf Manag 43(1):1–14

    Google Scholar 

  36. Gao L, Waechter KA, Bai X (2015) Understanding consumers’ continuance intention towards mobile purchase: a theoretical framework and empirical study—a case of China. Comput Hum Behav 53:249–262

    Google Scholar 

  37. Gbongli K, Xu Y, Amedjonekou KM (2019) Extended technology acceptance model to predict mobile-based money acceptance and sustainability: a multi-analytical structural equation modeling and neural network approach. Sustainability 11(13):3639

    Google Scholar 

  38. Gefen D, Karahanna E, Straub DW (2003) Trust and TAM in online shopping: an integrated model. MIS Q 27(1):51–90

    Google Scholar 

  39. Geisser S (1975) The predictive sample reuse method with applications. J Am Stat Assoc 70(350):320–328

    Google Scholar 

  40. GSMA (2018) The mobile economy 2018. https://www.gsma.com/mobileeconomy/wp-content/uploads/2018/05/The-Mobile-Economy-2018.pdf. Accessed 26 Mar 2019

  41. Ha I, Yoon Y, Choi M (2007) Determinants of adoption of mobile games under mobile broadband wireless access environment. Inf manage 44(3):276–286

    Google Scholar 

  42. Hair J Jr, Sarstedt M, Hopkins L, Kuppelwieser V (2014) Partial least squares structural equation modeling (PLS-SEM) An emerging tool in business research. Eur Bus Rev 26(2):106–121

    Google Scholar 

  43. Hair JF, Ringle CM, Sarstedt M (2011) PLS-SEM: indeed a silver bullet. J Mark Theory Pract 19(2):139–152

    Google Scholar 

  44. Halzack S (2015) The staggering challenges of the online grocerybusiness. Online: The Washington Post

  45. Hamzah ZL, Lee SP, Moghavvemi S (2017) Elucidating perceived overall service quality in retail banking. Int J Bank Mark 35(5):781–804

    Google Scholar 

  46. HelpMyCash (2019) Los bancos se abonan al pago móvil: más del 90% permiten esta operación con el ‘smartphone’. https://www.helpmycash.com/blog/los-bancos-se-abonan-al-pago-movil-mas-del-90-permiten-esta-operacion-con-el-smartphone/

  47. Henseler J, Dijkstra TK, Sarstedt M, Ringle CM, Diamantopoulos A, Straub DW, Calantone RJ (2014) Common beliefs and reality about PLS: comments on Rönkkö and Evermann (2013). Organ Res Methods 17(2):182–209

    Google Scholar 

  48. Henseler J, Hubona G, Ray PA (2016) Using PLS path modeling in new technology research: updated guidelines. Ind Manag Data Syst 116(1):2–20

    Google Scholar 

  49. Hew J-J, Leong L-Y, Tan GW-H, Ooi K-B, Lee V-H (2019) The age of mobile social commerce: an Artificial Neural Network Analysis on its resistances. Technol Forecast Soc Chang 144:311–324

    Google Scholar 

  50. Hew TS, Kadir SLSA (2016) Understanding cloud-based VLE from the SDT and CET perspectives: development and validation of measurement instrument. Comput Educ 101:132–149

    Google Scholar 

  51. Higueras-Castillo E, Kalinić Z, Marinkovic V, Liébana-Cabanillas F (2020) A mixed analysis of perceptions of electric and hybrid vehicles. Energy Policy 136:111076

    Google Scholar 

  52. Hossain MA, Hossain MS, Jahan N (2018) Predicting continuance usage intention of mobile payment: an experimental study of Bangladeshi customers. Asian Econ Financ Rev 8(4):487

    Google Scholar 

  53. Hsiao CH, Chang JJ, Tang KY (2016) Exploring the influential factors in continuance usage of mobile social Apps: satisfaction, habit, and customer value perspectives. Telematics Inform 33(2):342–355

    Google Scholar 

  54. Hsu CL, Lin JCC (2015) What drives purchase intention for paid mobile apps? An expectation confirmation model with perceived value. Electron Commer Res Appl 14(1):46–57

    Google Scholar 

  55. Hsu HM, Hsu JSC, Wang SY, Chang IC (2016) Exploring the effects of unexpected outcome on satisfaction and continuance intention. J Electron Commer Res 17(3):239–255

    Google Scholar 

  56. Hung MC, Yang ST, Hsieh TC (2012) An examination of the determinants of mobile shopping continuance. Int J Electron Bus Manag 10(1):29–37

    Google Scholar 

  57. Ibrahim N, Maarop N, Samy GN, Hassan NH, Abas H, Zainuddin NMM, Ten DWH (2018) Descriptive findings regarding factors influencing mobile application acceptance among Millennial in Malaysia. Open Int J Inform 5(1):1–10

    Google Scholar 

  58. Jamal A, Naser K (2002) Customer satisfaction and retail banking: an assessment of some of the key antecedents of customer satisfaction in retail banking. Int J Bank Mark 20(4):146–160

    Google Scholar 

  59. Kalinić Z, Liébana-Cabanillas FJ, Muñoz-Leiva F, Marinković V (2019) The moderating impact of gender on the acceptance of peer-to-peer mobile payment systems. Int J Bank Mark 38:138–158

    Google Scholar 

  60. Kalinić Z, Marinkovic V, Molinillo S, Liébana-Cabanillas F (2019) A multi-analytical approach to peer-to-peer mobile payment acceptance prediction. J Retail Consum Serv 49:143–153

    Google Scholar 

  61. Karjaluoto H, Shaikh AA, Leppäniemi M, Luomala R (2019) Examining consumers’ usage intention of contactless payment systems. Int J Bank Mark 38(2):332–351. https://doi.org/10.1108/IJBM-04-2019-0155

    Article  Google Scholar 

  62. Karnouskos S (2004) Mobile payment: a journey through existing procedures and standardization initiatives. IEEE Communications Surveys & Tutorials 6(4):44–66

    Google Scholar 

  63. Khalilzadeh J, Ozturk AB, Bilgihan A (2017) Security-related factors in extended UTAUT model for NFC based mobile payment in the restaurant industry. Comput Hum Behav 70:460–474

    Google Scholar 

  64. Kim B, Choi M, Han I (2009). User behaviors toward mobile data services: The role of perceived fee and prior experience. Expert Syst Appl 36(4):8528–8536

    Google Scholar 

  65. Kim C, Mirusmonov M, Lee I (2010) An empirical examination of factors influencing the intention to use mobile payment. Comput Hum Behav 26(3):310–322

    Google Scholar 

  66. Kim HW, Chan HC, Gupta S (2007) Value-based adoption of mobile internet: an empirical investigation. Decis Support Syst 43(1):111–126

    Google Scholar 

  67. Kim YH, Kim JD, Wachter K (2013) A study of mobile user engagement (MoEN): engagement motivations, perceived value, satisfaction, and continued engagement intention. Decis Support Syst 56:361–370. https://doi.org/10.1016/j.dss.2013.07.002

    Article  Google Scholar 

  68. Kleijnen M, de Ruyter K, Wetzels M (2007) An assessment of value creation in mobile service delivery and the moderating role of time consciousness. J Retail 83:33–46. https://doi.org/10.1016/j.jretai.2006.10.004

    Article  Google Scholar 

  69. Kumar A (2011) Near field communication. http://dspace.cusat.ac.in/jspui/bitstream/123456789/2214/1/NEAR%20FIELD%20COMMUNICATION.pdf. Accessed 25 Mar 2019

  70. Lee JM, Lee B, Rha JY (2019) Determinants of mobile payment usage and the moderating effect of gender: extending the UTAUT model with privacy risk. Int J Electron Commerce Stud 10(1):43–64

    Google Scholar 

  71. Lee V-H, Hew J-J, Leong L-Y, Tan GW-H, Ooi K-B (2020) Wearable payment: a deep learning-based dual-stage SEM-ANN analysis. Expert Syst Appl 157:113477

    Google Scholar 

  72. Leong LY, Jaafar NI, Ainin S (2018) Understanding Facebook commerce (f-commerce) actual purchase from an artificial neural network perspective. J Electron Commer Res 19(1):75–103

    Google Scholar 

  73. Leong L-Y, Hew T-S, Ooi K-B, Lee V-H, Hew J-J (2019) A hybrid SEM-neural network analysis of social media addiction. Expert Syst Appl 133:296–316

    Google Scholar 

  74. Li Y, Yang S, Zhang S, Zhang W (2019) Mobile social media use intention in emergencies among Gen Y in China: an integrative framework of gratifications, task-technology fit, and media dependency. Telematics Inform 42:101244

    Google Scholar 

  75. Liébana-Cabanillas F, Marinkovic V, de Luna IR, Kalinic Z (2018) Predicting the determinants of mobile payment acceptance: A hybrid SEM neural network approach. Technol Forecast Soc 129:117–130

    Google Scholar 

  76. Liébana-Cabanillas FJ, Sánchez-Fernández J, Muñoz-Leiva F (2014) Role of gender on acceptance of mobile payment. Ind Manag Data Syst 14(2):220–240

    Google Scholar 

  77. Liébana-Cabanillas F (2012) El papel de los medios de pago en los nuevos entornos electrónicos. Doctoral dissertation

  78. Liébana-Cabanillas F, Marinković V, Kalinić Z (2017) A SEM-neural network approach for predicting antecedents of m-commerce acceptance. Int J Inf Manag 37(2):14–24

    Google Scholar 

  79. Liébana-Cabanillas F, Molinillo S, Ruiz-Montañez M (2019) To use or not to use, that is the question: analysis of the determining factors for using NFC mobile payment systems in public transportation. Technol Forecast Soc Change 139:266–276

    Google Scholar 

  80. Liébana-Cabanillas F, Ramos de Luna I, Montoro-Ríos F (2017) Intention to use new mobile payment systems: a comparative analysis of SMS and NFC payments. Econ Res 30(1):892–910

    Google Scholar 

  81. Liébana-Cabanillas F, Sánchez-Fernández J, Muñoz-Leiva F (2014) Antecedents of the adoption of the new mobile payment systems: the moderating effect of age. Comput Hum Behav 35:464–478

    Google Scholar 

  82. Lin J, Wang B, Wang N, Lu Y (2014) Understanding the evolution of consumer trust in mobile commerce: a longitudinal study. Inf Technol Manag 15(1):37–49

    Google Scholar 

  83. Lin WR, Lin CY, Ding YH (2020) Factors affecting the behavioral intention to adopt mobile payment: an empirical study in Taiwan. Mathematics 8(10):1851

    Google Scholar 

  84. Lin X, Wu R, Lim YT, Han J, Chen SC (2019) Understanding the sustainable usage intention of mobile payment technology in Korea: cross-countries comparison of Chinese and Korean users. Sustainability 11(19):5532

    Google Scholar 

  85. Liu C, Forsythe S (2010) Sustaining online shopping: moderating role of online shopping motives. J Internet Commerce 9(2):83–103

    Google Scholar 

  86. Lu J (2014) Are personal innovativeness and social influence critical to continue with mobile commerce? Internet Res 24(2):134–159

    Google Scholar 

  87. Lu J, Wei J, Yu CS, Liu C (2017) How do post-usage factors and espoused cultural values impact mobile payment continuation? Behav Inf Technol 36(2):140–164

    Google Scholar 

  88. Lu J, Yao JE, Yu CS (2005) Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology. J Strateg Inf Syst 14(3):245–268

    Google Scholar 

  89. Lu Y, Zhou T, Wang B (2009) Exploring Chinese users’ acceptance of instant messaging using the theory of planned behavior, the technology acceptance model, and the flow theory. Comput Hum Behav 25(1):29–39

    Google Scholar 

  90. Magni M, Taylor MS, Venkatesh V (2010) ‘To play or not to play’: a cross-temporal investigation using hedonic and instrumental perspectives to explain user intentions to explore a technology. Int J Hum Comput Stud 68(9):572–588

    Google Scholar 

  91. Marinković V, Đorđević A, Kalinić Z (2020) The moderating effects of gender on customer satisfaction and continuance intention in mobile commerce: a UTAUT-based perspective. Technol Anal Strateg Manag 32(3):306–318

    Google Scholar 

  92. Mastercard Barometer (2019). https://newsroom.mastercard.com/eu/es/press-releases/mas-de-la-mitad-de-los-consumidores-escoge-el-pago-contactless-de-forma-habitual/

  93. Mateos-Aparicio G (2011) Partial least squares (PLS) methods: origins, evolution, and application to social sciences. Commun Stat Theory Methods 40(13):2305–2317

    Google Scholar 

  94. McKnight DH, Chervany NL (2001) What trust means in e-commerce customer relationships: an interdisciplinary conceptual typology. Int J Electron Commer 6(2):35–59

    Google Scholar 

  95. McLean G, Wilson A (2019) Shopping in the digital world: examining customer engagement through augmented reality mobile applications. Comput Hum Behav 101:210–224

    Google Scholar 

  96. Merhi M, Hone K, Tarhini A (2019) A cross-cultural study of the intention to use mobile banking between Lebanese and British consumers: extending UTAUT2 with security, privacy and trust. Technol Soc 59:101151

    Google Scholar 

  97. Mohsan F, Nawaz MM, Khan MS, Shaukat Z, Aslam N (2011) Impact of customer satisfaction on customer loyalty and intentions to switch: evidence from banking sector of Pakistan. Int J Bus Soc Sci 2(16):263–270

  98. Molinillo S, Aguilar-Illescas R, Anaya-Sánchez R, Liébana-Cabanillas F (2021) Social commerce website design, perceived value and loyalty behavior intentions: the moderating roles of gender, age and frequency of use. J Retail Consumer Serv 102404

  99. Morosan C, DeFranco A (2016) Investigating American iPhone users’ intentions to use NFC mobile payments in hotels. In: Information and Communication Technologies in Tourism 2016. Springer, Cham, pp 427–440

  100. Morosan C, De Franco A (2016) It’s about time: revisiting UTAUT2 to examine consumers’ intentions to use NFC mobile payments in hotels. Int J Hosp Manag 53:17–29

    Google Scholar 

  101. Mou Y, Lin CA (2014) Communicating food safety via the social media: the role of knowledge and emotions on risk perception and prevention. Sci Commun 36(5):593–616

    Google Scholar 

  102. Museli A, Navimipour NJ (2018) A model for examining the factors impacting the near field communication technology adoption in the organizations. Kybernetes 47(7):1378–1400

    Google Scholar 

  103. Negnevitsky M (2011) Artificial intelligence: a guide to intelligent systems, 3rd edn. Pearson Education, Essex

    Google Scholar 

  104. Nunnally JC, Bernstein IH (1994) Psychological theory. MacGraw-Hill, New York, pp 131–147

    Google Scholar 

  105. O’Brien HL (2010) The influence of hedonic and utilitarian motivations on user engagement: the case of online shopping experiences. Interact Comput 22(5):344–352

    Google Scholar 

  106. Oliveira T, Thomas M, Baptista G, Campos F (2016) Mobile payment: understanding the determinants of customer adoption and intention to recommend the technology. Comput Hum Behav 61:404–414

    Google Scholar 

  107. Oliver RL (1997) Satisfaction: a behavioral perspective on the consumer. Irwin/McGraw-Hill, New York

    Google Scholar 

  108. Oliver RL (1999) Whence consumer loyalty? J Mark 63:33–44

    Google Scholar 

  109. Ozturk AB, Bilgihan A, Nusair K, Okumus F (2016) What keeps the mobile hotel booking users loyal? Investigating the roles of self-efficacy, compatibility, perceived ease of use, and perceived convenience. Int J Inf Manag 36(6):1350–1359

    Google Scholar 

  110. Passyn KA, Diriker M, Settle RB (2011) Images of online versus store shopping: have the attitudes of men and women, young and old really changed? J Bus Econ Res 9(1):99–110

    Google Scholar 

  111. Pavlou PA (2002) A theory of planned behavior perspective to the consumer adoption of electronic commerce. MIS Q 30(1):115–143

    Google Scholar 

  112. Philips P, Zigan K, Silva MMS, Schegg R (2015) The interactive effects of online reviews on the determinants of Swiss hotel performance: a neural network approach. Tour Manag 50:130–141

    Google Scholar 

  113. Pikkarainen T, Pikkarainen K, Karjaluoto H, Seppo P (2004) Consumer acceptance of online banking: an extension of the Technology Acceptance Model. Internet Res 14:224–235. https://doi.org/10.1108/10662240410542652

    Article  Google Scholar 

  114. Podsakoff PM, MacKenzie SB, Lee JY, Podsakoff NP (2003) Common method biases in behavioral research: a critical review of the literature and recommended remedies. J Appl Psychol 88(5):879

  115. Raman A, Don Y (2013) Preservice teachers’ acceptance of learning management software: an application of the UTAUT2 model. Int Educ Stud 6(7):157–164. https://doi.org/10.5539/ies.v6n7p157

    Article  Google Scholar 

  116. Ramírez-Correa PE, Grandón EE, Arenas-Gaitán J (2019) Assessing differences in customers’ personal disposition to e-commerce. Ind Manag Data Syst 119(4):792–820

  117. Ramos de Luna I, Liébana-Cabanillas F, Sánchez-Fernández J, Muñoz-Leiva F (2019) Mobile payment is not all the same: the adoption of mobile payment systems depending on the technology applied. Technol Forecast Soc Chang 146:931–944

    Google Scholar 

  118. Raut RD, Priyadarshinee P, Gardas BB, Jha MK (2018) Analyzing the factors influencing cloud computing adoption using three stage hybrid SEM-ANN-ISM (SEANIS) approach. Technol Forecast Soc Chang 134:98–123

    Google Scholar 

  119. Reichheld F, Aspinall K (1993) Building high-loyalty business systems. J retail Banking 15(4):21–30

  120. Ringle CM, Wende S, Becker JM (2015) SmartPLS 3. Boenningstedt: SmartPLS GmbH. http://www.smartpls.com

  121. Riquelme HE, Rios RE (2010) The moderating effect of gender in the adoption of mobile banking. Int J Bank Mark 28(5):328–341

    Google Scholar 

  122. Roger EM (1995) Diffusion of innovations. The Free Press, New York

    Google Scholar 

  123. Ruangkanjanases A, Sirikulprasert N (2018) Predicting consumer intention to adopt near field communication enabled mobile payment in Thailand. J Telecommun Electron Comput Eng 10(2–7):147–152

    Google Scholar 

  124. Sarstedt M, Henselar J, Ringle CM (2011) Multi-group analysis in Partial Least Squares (PLS) path modeling: alternative methods and empirical results. Adv Int Mark 22:195–218

    Google Scholar 

  125. Schloderer MP, Sarstedt M, Ringle CM (2014) The relevance of reputation in the nonprofit sector: the moderating effect of socio-demographic characteristics. Int J Nonprofit Volunt Sector Mark 19(2):110–126

    Google Scholar 

  126. Shaikh AA, Hanafizadeh P, Karjaluoto H (2017) Mobile banking and payment system: a conceptual standpoint. Int J E-Bus Res 13(2):14–27

    Google Scholar 

  127. Sharma SK (2019) Integrating cognitive antecedents into TAM to explain mobile banking behavioral intention: a SEM-neural network modeling. Inf Syst Front 21(4):815–827

    Google Scholar 

  128. Sharma SK, Govindaluri SM, Al Balushi SM (2015) Predicting determinants of Internet banking adoption. Manage Res Rev 38(7):750–766

  129. Sharma SK, Joshi A, Sharma H (2016) A multi-analytical approach to predict the Facebook usage in higher education. Comput Hum Behav 55:340–353

    Google Scholar 

  130. Sharma SK, Sharma H, Dwivedi YK (2019) A hybrid SEM-neural network model for predicting determinants of mobile payment services. Inf Syst Manag 36:243–261

    Google Scholar 

  131. Sharma SK, Al-Badi A, Rana NP, Al-Azizi L (2018) Mobile applications in government services (mG-App) from user’s perspectives: a predictive modelling approach. Gov Inf Q 35:557–568

    Google Scholar 

  132. Sharma SK, Al-Badi AH, Govindaluri SM, Al-Kharusi MH (2016) Predicting motivators of cloud computing adoption: a developing country perspective. Comput Hum Behav 62:61–69

    Google Scholar 

  133. Sharma SK, Sharma M (2019) Examining the role of trust and quality dimensions in the actual usage of mobile banking services: an empirical investigation. Int J Inf Manag 44:65–75

    Google Scholar 

  134. Siddiqi KO (2011) Interrelations between Service Quality Attributes, customer satisfaction and customer loyalty in retail banking sector in Bangladesh. Int J Bus Manag 1(1):19–25

    Google Scholar 

  135. Singh N, Sinha N, Liébana-Cabanillas FJ (2020) Determining factors in the adoption and recommendation of mobile wallet services in India: analysis of the effect of innovativeness, stress to use and social influence. Int J Inf Manag 50:191–205

    Google Scholar 

  136. Singh N, Srivastava S, Sinha N (2017) Consumer preference and satisfaction of M-wallets: a study on North Indian consumers. Int J Bank Mark 35(6):944–965

    Google Scholar 

  137. Slade EL, Dwivedi YK, Piercy NC, Williams MD (2015) Modeling consumers’ adoption intentions of remote mobile payments in the United Kingdom: extending UTAUT with innovativeness, risk, and trust. Psychol Mark 32(8):860–873

    Google Scholar 

  138. Stone M (1974) Cross validatory choice and assessment of statistical predictions. J R Stat Soc 36(2):111–147

    Google Scholar 

  139. Sun H (2012) Understanding user revisions when using information system features: adaptive system use and triggers. MIS Q 36(2):453–478

    Google Scholar 

  140. Talwar S, Dhir A, Khalil A, Mohan G, Islam AN (2020) Point of adoption and beyond. Initial trust and mobile-payment continuation intention. J Retail Consumer Serv 55:102086

    Google Scholar 

  141. Tan GWH, Ooi KB, Leong LY, Lin B (2014) Predicting the drivers of behavioral intention to use mobile learning: a hybrid SEM-Neural Networks approach. Comput Hum Behav 36:198–213

    Google Scholar 

  142. Taylor S, Todd PA (1995) Understanding information technology usage: a test of competing models. Inf Syst Res 6(2):144–176

    Google Scholar 

  143. Telefónica (2017) Digital Society in Spain. https://www.fundaciontelefonica.com/cultura-digital/publicaciones/sociedad-digital-en-espana-2017/625/

  144. Thakur R, Srivastava M (2014) Adoption readiness, personal innovativeness, perceived risk and usage intention across customer groups for mobile payment services in India. Internet Res 24(3):369–392

    Google Scholar 

  145. Thong JY, Hong SJ, Tam KY (2006) The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance. Int J Hum Comput Stud 64(9):799–810

    Google Scholar 

  146. Thominathan S, Ramayah T (2015) The effect of trust on the continuance intention of E-filing usage: a review of literatures. In: Public affairs and administration: concepts, methodologies, tools, and applications. IGI Global, pp 1213–1234

  147. Turel O, Serenko A, Bontis N (2007) User acceptance of wireless short messaging services: deconstructing perceived value. Inf Manag 44(1):63–73

    Google Scholar 

  148. 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 Q 36(1):157–178

    Google Scholar 

  149. Wang Y-Y, Wang Y-S, Lin T-C (2018) Developing and validating a technology upgrade model. Int J Inf Manag 38(1):7–26

    Google Scholar 

  150. Wiese M, Humbani M (2020) Exploring technology readiness for mobile payment app users. Int Rev Retail Distrib Consumer Res 30(2):123–142

    Google Scholar 

  151. Wong L-W, Leong L-Y, Hew J-J, Tan GW-H, Ooi K-B (2020) Time to seize the digital evolution: adoption of blockchain in operations and supply chain management among Malaysian SMEs. Int J Inf Manag 52:101977

    Google Scholar 

  152. Yadav R, Sharma SK, Tarhini A (2016) A multi-analytical approach to understand and predict the mobile commerce adoption. J Enterp Inf Manag 29(2):222–237

    Google Scholar 

  153. Yang Y, Liu Y, Li H, Yu B (2015) Undestanding perceived risks in mobile payment accpetance. Ind Manag Data Syst 115(2):253–269

    Google Scholar 

  154. Yi MY, Fiedler KD, Park JS (2006) Understanding the role of individual innovativeness in the acceptance of IT-based innovations: comparative analyses of models and measures. Decis Sci 37(3):393–426

    Google Scholar 

  155. Yu L, Cao X, Liu Z, Gong M, Adeel L (2018) Understanding mobile payment users' continuance intention: a trust transfer perspective. Int Res 28(2):456–476

  156. Zeithaml V (1988) Consumer perceptions of price, quality, and value. J Mark 55:2–22

    Google Scholar 

  157. Zhao H, Anong ST, Zhang L (2019) Understanding the impact of financial incentives on NFC mobile payment adoption: an experimental analysis. Int J Bank Mark 37:1296–1312

    Google Scholar 

  158. Zhou T (2012) Examining mobile banking user adoption from the perspectives of trust and flow experience. Inf Technol Manag 13(1):27–37

    Google Scholar 

  159. Zhou T (2014) Examining continuance usage of mobile Internet services from the perspective of resistance to change. Inf Dev 30(1):22–31

    Google Scholar 

  160. Zhou T (2014) Understanding the determinants of mobile payment continuance usage. Ind Manag Data Syst 114(6):936–948

    Google Scholar 

  161. Zhou T, Lu Y (2011) Examining mobile instant messaging user loyalty from the perspectives of network externalities and flow experience. Comput Hum Behav 27(2):883–889

    Google Scholar 

Download references

Acknowledgements

This research was funded by the Spanish Ministry of Science and Innovation, National R&D&I Plan and FEDER under Grant [B-SEJ-209-UGR18] and Research Project III-44010 of the Ministry of Education, Science and Technological Development of the Republic of Serbia.

Author information

Authors and Affiliations

  1. Department of Marketing and Market Research, Faculty of Economics and Business, University of Granada, Campus La Cartuja s/n, 18071, Granada, Spain

    Francisco Liébana-Cabanillas

  2. Department of Finance, Jaipuria Institute of Management Noida, Noida, India

    Nidhi Singh

  3. Faculty of Economics, University of Kragujevac, Kragujevac, Serbia

    Zoran Kalinic

  4. Department of Business Administration and Marketing, Faculty of Management and Tourism, University of Huelva, Plaza de la Merced s/n, 21071, Huelva, Spain

    Elena Carvajal-Trujillo

Authors
  1. Francisco Liébana-Cabanillas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nidhi Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zoran Kalinic
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Elena Carvajal-Trujillo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Francisco Liébana-Cabanillas.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendices

Appendix 1: Research on m-payment systems

References

Dependent variable

Theory

Country

Independent variables

Al-Amri et al. [3]

Intention to use

None

Malaysia

Perceived ease of use, perceived usefulness, ubiquity, awareness, perceived risk, structural assurance, security, privacy and trust

Ruangkanjanases and Sirikulprasert [117]

Intention to use

IDT

TAM

Thailand

Complexity, trust and security, relative advantage, compatibility, social influence and cost

Liébana-Cabanillas et al. (2018)

Intention to use

TAM extended

Spain

Perceived usefulness, perceived ease of use, perceived security, perceived compatibility, subjective norms, personal innovativeness and individual mobility

Museli and Jafari Navimipour [98]

Intention to use

TAM extended

Azerbaijan

Perceived ease of use, potential risk, perceived usefulness and cost

Zhao et al. [149]

Intention to use

TAM extended

United States

Perceived risk, perceived ease of use and perceived usefulness

Ramos de Luna et al. [112]

Intention to use

TAM extended

Spain

Subjective norms, perceived ease of use, perceived usefulness, attitude and perceived security

Christian et al. [27]

Intention to use

TAM extended

Indonesia

E-service Quality, NFC indicators, perceived ease of use and perceived usefulness

Gbongli et al. [36]

Intention to use

TRA

IDT

TAM

UTAUT

Togo

Mobile money self-efficacy, Mobile money technology anxiety, Perceived ease-of-use, perceived usefulness, Attitude and Personal innovativeness

Lee et al. [66]

Intention to use

UTAUT

South Korean

Performance expectancy, Effort expectancy, Social influence, Facilitating conditions and Privacy risk

Chen et al. [18]

Intention to use

SOR

Taiwan

Utilitarian Value, Hedonic Value, Salespersons behaviors and Satisfaction

Lin et al. [79]

Intention to use

UTAUT

ISS

TTF

China and Korea

Effort expectancy, Facilitating conditions, Information quality, Performance expectancy, Service quality, Social influence, System quality, Task characteristics, Task-technology Fit, Technology characteristics and User satisfaction

Lu et al. [82]

Continuance Intention

IS continuance theory

China

Social influence, privacy, mobility, privacy protection, mobility, usefulness and satisfaction

Chen and Li [19]

Continuance Intention

IT continuance theory

China

Post perceived usefulness, disconfirmation of pre-perceived usefulness, post perceived risk, disconfirmation of pre-perceived risk, trust and satisfaction

Hossain et al. [50]

Continuance Intention

None

Bangladesh

Perceived usefulness, perceived ease of use, credibility, mobility, perceived risk and satisfaction

Liébana-Cabanillas et al. [74]

Continuance Intention

None

Spain

Convenience, effort expectancy, perceived trust, service quality, social value, satisfaction and perceived risk

Talwar et al. [133]

Continuance Intention

ISS

TCE

IT continuance theory

India

Initial trust, Perceived information quality, Perceived service quality, Perceived asset specificity, Perceived uncertainty, Confirmation, Perceived usefulness and Dissatisfaction

Wiese and Humbani [143]

Continuance Intention

None

South African

Optimism, Innovativeness, Discomfort, Insecurity, Adoption, Usefulness, Ease of use and Attitude

IDT diffusion of innovations theory, TAM technology acceptance model, TRA theory of reasoned action, TPB theory of planned behavior, UTAUT unified theory of acceptance and use of technology, SOR stimulus–response model, TTF task-technology fit model, ISS information systems success model, TCE transaction cost economics

Appendix 2: Constructs and measurement items

Construct

Item

Scale

Subjective norms

SN1

The people whose opinions I value would approve of me using NFC mobile payment systems to purchase products

 

SN2

Most of the people I have in mind think that I should use NFC mobile payment systems to purchase products

 

SN3

They hope that I use NFC mobile payment systems to purchase products

 

SN4

The people who are close to me would agree with me using NFC mobile payment systems to purchase products

Performance/quality value

PQV1

NFC mobile payment systems have an acceptable standard of quality

 

PQV2

NFC mobile payment systems services make me want to use them

 

PQV3

The quality of NFC mobile payment systems is good relative to the price

 

PQV4

The fact I use NFC mobile payment systems makes a good impression on other people

Perceived usefulness

PU1

Using NFC mobile payment systems could help me make purchases

 

PU2

Using NFC mobile payment systems could increase the efficiency of making my purchases

 

PU3

Using NFC mobile payment systems for my purchases could increase my productivity

 

PU4

In general, NFC mobile payment systems could be useful for me to make purchases

Hedonic motivation

HM1

Using NFC mobile payment systems is fun

 

HM2

Using NFC mobile payment systems is enjoyable

 

HM3

Using NFC mobile payment systems is very entertaining

 

HM4

Using NFC mobile payment systems gives me pleasure

 

HM5

Using NFC mobile payment systems is exciting

Personal innovation

PI1

If I find out about new information technology, I seek ways to experience it

 

PI2

I am usually one of the first among my colleagues/peers to explore new information technology

 

PI3

In general, I am reluctant to try new information technologies

Consumer-brand engagement

CBE1

I am enthusiastic about NFC mobile payment systems

 

CBE2

When I am engaging myself with NFC mobile payment systems, I feel strong and vigorous

 

CBE3

I can continue engaging myself with NFC mobile payment systems for very long periods of time

 

CBE4

I keep on engaging myself with NFC mobile payment systems, even when things do not go well

 

CBE5

The quality of NFC mobile payment systems is high

 

CBE6

My expectations with regard to the offer of NFC mobile payment systems are surpassed

 

CBE7

The performance of NFC mobile payment systems with regard to other mobile payments is high

Perceived trust

TRUST1

I think that NFC mobile payment systems will keep the promises and commitments they put forward

 

TRUST2

NFC mobile payment systems are trustworthy

 

TRUST3

I would qualify NFC mobile payment systems as honest

 

TRUST4

I think that NFC mobile payment systems are responsible

 

TRUST5

In general, I trust NFC mobile payment systems

Perceived Risk

PR1

Other people may see the information about my online transactions if I use NFC mobile payment systems

 

PR2

There is a high chance of wasting money if I make purchases using NFC mobile payment systems

 

PR3

There is significant risk involved in purchasing through NFC mobile payment systems

 

PR4

I think that making purchases with NFC mobile payment systems is risky

Satisfaction

SAT1

Disgusted-Pleased

 

SAT2

Frustrated-happy/pleased/gratified

 

SAT3

Appalling/awful/terrible-delighted/happy

 

SAT4

Unsatisfied-satisfied

Continuance intention

CI1

I intend to continue using NFC mobile payment systems in the future

 

CI2

I will try to use NFC mobile payment systems in my daily life

 

CI3

I will continue to use NFC mobile payment systems as often as I do now

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liébana-Cabanillas, F., Singh, N., Kalinic, Z. et al. Examining the determinants of continuance intention to use and the moderating effect of the gender and age of users of NFC mobile payments: a multi-analytical approach. Inf Technol Manag 22, 133–161 (2021). https://doi.org/10.1007/s10799-021-00328-6

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10799-021-00328-6

Keywords

Search

Navigation