Advertisement

Soft Computing

, Volume 23, Issue 21, pp 10821–10836 | Cite as

What are the most influential factors of consumers’ intention to use NFC-enabled credit cards?

  • Feride Bahar KurtulmuşoğluEmail author
  • Ayhan Algüner
  • Kumru D. Atalay
Methodologies and Application
  • 128 Downloads

Abstract

The aim of this study is to determine the variables which affect the intention of Near-Field Communication (NFC)-enabled mobile credit card usage by estimating a fuzzy linear regression (FLR) model. The FLR model is used to test the proposed model. Four hundred and thirty-six participants having a smartphone participated in the study. The most effective variable on the intention of NFC-enabled mobile credit card usage was the dimension that defined the perceived risk and trust. Consumers’ intention to use NFC-enabled mobile credit cards increased as perceived risk decreased, and consumer trust increased. Another variable that had a high impact on the intention of using NFC-enabled mobile credit cards was identified as the perceived ease of use in the study. As the consumer’s perception of ease of use increased, the intention of using NFC-enabled mobile credit cards also increased. Based on the study results, as perceived usefulness of NFC-enabled mobile credit cards increased, their intentions to use NFC mobile cards also increased in a certain and determined manner defined. The study includes key findings on consumer adoption and the use of NFC-enabled mobile credit cards for different game players such as mobile phone manufacturers, mobile network operators, business and financial institutions, merchants, bank decision makers, software developers as designers of m-payment systems, governments. The research tries to strengthen the use of fuzzy Likert scales (FLSs) in the social studies in order to remove the limitations of the Likert scale and to introduce a suitable and accurate model using FLR. No other study in the previous literature has employed the data obtained from the FLSs used in FLR models.

Keywords

Near-Field Communication (NFC)-enabled mobile credit card Fuzzy linear regression (FLR) Fuzzy Likert scale (FLS) 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interests regarding the publication of this paper.

References

  1. Al-Najjar B, Alsyouf I (2003) Selecting the most efficient maintenance approach using fuzzy multiple criteria decision making. Int J Prod Econ 84(1):85–100CrossRefGoogle Scholar
  2. Antoine P (2004) Understanding the mobile phone market drivers. In: Alcatel Telecommunications Review; 4th Quarter 2003/1st Quarter 2004, pp 1–7Google Scholar
  3. Arvidsson N (2014) Consumer attitudes on mobile payment services–results from a proof of concept test. Int J Bank Mark 32(2):150–170CrossRefGoogle Scholar
  4. Au YA, Kauffman RJ (2008) The economics of mobile payments: understanding stakeholder issues for an emerging financial technology application. Electron Commer Res Appl 7(2):141–164CrossRefGoogle Scholar
  5. Balachandran D, Tan GWH (2015) Regression modelling of predicting NFC mobile payment adoption in Malaysia. Int J Model Oper Manag 5(2):100–116Google Scholar
  6. Baser F, Gokten S, Gokten PO (2017) Using fuzzy c-means clustering algorithm in financial health scoring. Audit Financ 15(147):385–394CrossRefGoogle Scholar
  7. Benitez JM, Martín JC, Román C (2007) Using fuzzy number for measuring quality of service in the hotel industry. Tour Manag 28(2):544–555CrossRefGoogle Scholar
  8. Bit AK, Biswal MP, Alam SS (1993) An additive fuzzy programming model for multiobjective transportation problem. Fuzzy Sets Syst 57(3):313–319CrossRefMathSciNetzbMATHGoogle Scholar
  9. Brohi IA, Ali NI, Karbasi M, Shah A, Akbar A, Gharamah AR, Ali A (2017) Near field communication enabled payment system adoption: a proposed framework. In: 2017 IEEE 3rd international conference on engineering technologies and social sciences (ICETSS). IEEE, pp 1–5Google Scholar
  10. Busu S, Karim NA, Haron H (2018) Factors of adoption intention for near field communication mobile payment. Indones J Electr Eng Comput Sci 11(1):98–104CrossRefGoogle Scholar
  11. Carrasco RA, Sánchez-Fernández J, Muñoz-Leiva F, Blasco MF, Herrera-Viedma E (2017) Evaluation of the hotels e-services quality under the user’s experience. Soft Comput 21(4):995–1011CrossRefGoogle Scholar
  12. Chandra S, Srivastava SC, Theng YL (2010) Evaluating the role of trust in consumer adoption of mobile payment systems: an empirical analysis. Commun Assoc Inf Syst 27:561–588Google Scholar
  13. Chen LD (2008) A model of consumer acceptance of mobile payment. Int J Mobile Commun 6(1):32–52CrossRefMathSciNetGoogle Scholar
  14. Chen X, Choi K, Chae K (2017) A secure and efficient key authentication using bilinear pairing for NFC mobile payment service. Wirel Pers Commun 97(1):1–17CrossRefGoogle Scholar
  15. Chong AYL, Chan FT, Ooi KB (2012a) Predicting consumer decisions to adopt mobile commerce: cross country empirical examination between China and Malaysia. Decis Support Syst 53(1):34–43CrossRefGoogle Scholar
  16. Chong AYL, Ooi KB, Lin B, Bao H (2012b) An empirical analysis of the determinants of 3G adoption in China. Comput Hum Behav 28(2):360–369CrossRefGoogle Scholar
  17. Chung JE, Stoel L, Xu Y, Ren J (2012) Predicting Chinese consumers’ purchase intentions for imported soy-based dietary supplements. Br Food J 114(1):143–161CrossRefGoogle Scholar
  18. Cid-López A, Hornos MJ, Carrasco RA, Herrera-Viedma E (2015) SICTQUAL: a fuzzy linguistic multi-criteria model to assess the quality of service in the ICT sector from the user perspective. Appl Soft Comput 37:897–910CrossRefGoogle Scholar
  19. Cid-López A, Hornos MJ, Carrasco RA, Herrera-Viedma E (2016) Applying a linguistic multi-criteria decision-making model to the analysis of ICT suppliers’ offers. Expert Syst Appl 57:127–138CrossRefGoogle Scholar
  20. Clarke KC (2001) Cartography in a mobile internet age. In: Proceedings of the 20th international cartographic conference, pp 6–10Google Scholar
  21. Dahlberg T, Mallat N, Ondrus J, Zmijewska A (2008) Past, present and future of mobile payments research: a literature review. Electron Commer Res Appl 7(2):165–181CrossRefGoogle Scholar
  22. Dahlberg T, Guo J, Ondrus J (2015) A critical review of mobile payment research. Electron Commer Res Appl 14:265–284CrossRefGoogle Scholar
  23. Daud NM, Kassim NEM, Rahayu WS, Said WM, Noor MMM (2011) Determining critical success factors of mobile banking adoption in Malaysia. Aust J Basic Appl Sci 5(9):252–265Google Scholar
  24. Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 13:319–340CrossRefGoogle Scholar
  25. Davis FD, Venkatesh V (1996) A critical assessment of potential measurement biases in the technology acceptance model: three experiments. Int J Hum Comput Stud 45(1):19–45CrossRefGoogle Scholar
  26. De Sena Abrahão R, Moriguchi SN, Andrade DF (2016) Intention of adoption of mobile payment: an analysis in the light of the Unified Theory of Acceptance and Use of Technology (UTAUT). RAI Revista de Administração e Inovação 13(3):221–230CrossRefGoogle Scholar
  27. DeVaus D (2000) Social surveys, vol 1–4. Cambridge, BostonGoogle Scholar
  28. Dewan SG, Chen LD (2005) Mobile payment adoption in the US: a cross-industry, crossplatform solution. J Inf Priv Secur 1(2):4–28Google Scholar
  29. Dubois D, Prade H (1986) Weighted minimum and maximum operations in fuzzy set theory. Inf Sci 39(2):205–210CrossRefMathSciNetzbMATHGoogle Scholar
  30. Fishbein M, Ajzen Icek (1975) Belief, attitude, intention, and behavior: an introduction to theory and research. Addison-Wesley, ReadingGoogle Scholar
  31. Ford JK, MacCallum RC, Tait M (1986) The application of exploratory factor analysis in applied psychology: a critical review and analysis. Pers Psychol 39(2):291–314CrossRefGoogle Scholar
  32. Gefen D (2002) Reflections on the dimensions of trust and trustworthiness among online consumers. ACM Sigmis Database 33(3):38–53CrossRefGoogle Scholar
  33. Gerpott TJ, Meinert P (2017) Who signs up for NFC mobile payment services? Mobile network operator subscribers in Germany. Electron Commer Res Appl 23:1–13CrossRefGoogle Scholar
  34. Herrera F, Martinez L (2000) An approach for combining linguistic and numerical information based on the 2-tuple fuzzy linguistic representation model in decision-making. Int J Uncertain Fuzziness Knowl-Based Syst 8(05):539–562CrossRefMathSciNetzbMATHGoogle Scholar
  35. Herrera F, Herrera-Viedma E, Martı́nez L (2000) A fusion approach for managing multi-granularity linguistic term sets in decision making. Fuzzy Sets Syst 114(1):43–58CrossRefzbMATHGoogle Scholar
  36. Hodge DR, Gillespie D (2003) Phrase completions: an alternative to Likert scales. Social Work Res 27:45–55CrossRefGoogle Scholar
  37. Hsieh BZ, Lewis C, Lin ZS (2005) Lithology identification of aquifers from geophysical well logs and fuzzy logic analysis: Shui-Lin Area, Taiwan. Comput Geosci 31(3):263–275CrossRefGoogle Scholar
  38. Huang YC, Tsay WD, Huang CH, Lin YH, Lai MC (2011) The influence factors of electronic bill presentment and payment—a case study of mobile phone bill. In: 2011 2nd international conference on artificial intelligence, management science and electronic commerce (AIMSEC). IEEE, pp 4844–4847Google Scholar
  39. Kaiser MO (1974) Kaiser–Meyer–Olkin measure for identity correlation matrix. J R Stat Soc 52:296–298Google Scholar
  40. Kim KJ, Moskowitz H, Koksalan M (1996) Fuzzy versus statistical linear regression. Eur J Oper Res 92(2):417–434CrossRefzbMATHGoogle Scholar
  41. Kim G, Shin B, Lee HG (2009) Understanding dynamics between initial trust and usage intentions of mobile banking. Inf Syst J 19(3):283–311CrossRefGoogle Scholar
  42. Kim C, Mirusmonov M, Lee I (2010) An empirical examination of factors influencing the intention to use mobile payment. Comput Hum Behav 26(3):310–322CrossRefGoogle Scholar
  43. Laukkanen T, Kiviniemi V (2010) The role of information in mobile banking resistance. Int J Bank Mark 28(5):372–388CrossRefGoogle Scholar
  44. Laukkanen T, Lauronen J (2005) Consumer value creation in mobile banking services. Int J Mob Commun 3(4):325–338CrossRefGoogle Scholar
  45. Lee MC (2009) Factors influencing the adoption of internet banking: an integration of TAM and TPB with perceived risk and perceived benefit. Electron Commer Res Appl 8(3):130–141CrossRefGoogle Scholar
  46. Leong LY, Ooi KB, Chong AYL, Lin B (2013) Modeling the stimulators of the behavioral intention to use mobile entertainment: does gender really matter? Comput Hum Behav 29(5):2109–2121CrossRefGoogle Scholar
  47. Li Q (2013) A novel Likert scale based on fuzzy sets theory. Expert Syst Appl 40(5):1609–1618CrossRefGoogle Scholar
  48. Liébana-Cabanillas F, Lara-Rubio J (2017) Predictive and explanatory modeling regarding adoption of mobile payment systems. Technol Forecast Soc Change 120:32–40CrossRefGoogle Scholar
  49. 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 35:464–478CrossRefGoogle Scholar
  50. 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 Change 129:117–130CrossRefGoogle Scholar
  51. Lin WS, Yeh JY, Chen YY, Chia-Yi N (2009) Determinants of user adoption of e-payment services. J Am Acad Bus 14(2):224–229Google Scholar
  52. Liou TS, Chen CW (2006) Subjective appraisal of service quality using fuzzy linguistic assessment. Int J Qual Reliab Manag 23(8):928–943CrossRefGoogle Scholar
  53. Liu J, Wang Z, Peng Z, Zuba M, Cui JH, Zhou S (2011) TSMU: a time synchronization scheme for mobile underwater sensor networks. In: Global telecommunications conference (GLOBECOM 2011), IEEE, pp 1–6Google Scholar
  54. López-Nicolás C, Molina-Castillo FJ, Bouwman H (2008) An assessment of advanced mobile services acceptance: contributions from TAM and diffusion theory models. Inf Manag 45(6):359–364CrossRefGoogle Scholar
  55. Lu Y, Yang S, Chau PY, Cao Y (2011) Dynamics between the trust transfer process and intention to use mobile payment services: a cross-environment perspective. Inf Manag 48(8):393–403CrossRefGoogle Scholar
  56. Luarn P, Lin HH (2005) Toward an understanding of the behavioral intention to use mobile banking. Comput Hum Behav 21(6):873–891CrossRefGoogle Scholar
  57. Madlmayr G (2008) A mobile trusted computing architecture for a near field communication ecosystem. In: Proceedings of the 10th international conference on information integration and web-based applications & services. ACM, pp 563–566Google Scholar
  58. Malhotra Y (1999) Bringing the adopter back into the adoption process: a personal construction framework of information technology adoption. J High Technol Manag Res 10(1):79–104CrossRefGoogle Scholar
  59. Mallat N (2007) Exploring consumer adoption of mobile payments—a qualitative study. J Strateg Inf Syst 16(4):413–432CrossRefGoogle Scholar
  60. Mallat N, Rossi M, Tuunainen VK, Oorni A (2006) The impact of use situation and mobility on the acceptance of mobile ticketing services. In: Proceedings of the 39th annual Hawaii international conference on system sciences, 2006. HICSS’06, vol 2. IEEE, pp 42b–42bGoogle Scholar
  61. Moore GC, Benbasat I (1991) Development of an instrument to measure the perceptions of adopting an information technology innovation. Inf Syst Res 2(3):192–222CrossRefGoogle Scholar
  62. Muñoz-Leiva F, Climent-Climent S, Liébana-Cabanillas F (2017) Determinants of intention to use the mobile banking apps: an extension of the classic TAM model. Span J Mark-ESIC 21(1):25–38Google Scholar
  63. Nicole KL, Palmer A, Moll A (2010) Predicting young consumers’ take up of mobile banking services. Int J Bank Mark 28(5):342–371CrossRefGoogle Scholar
  64. Ondrus J, Pigneur Y (2006) Towards a holistic analysis of mobile payments: a multiple perspectives approach. Electron Commer Res Appl 5(3):246–257CrossRefGoogle Scholar
  65. Ondrus J, Pigneur Y (2007) An assessment of NFC for future mobile payment systems. In: International conference on the management of mobile business, 2007. ICMB 2007. IEEE, pp 43–43Google Scholar
  66. Ong JW, Poong YS, Ng TH (2008) 3G services adoption among university students: diffusion of innovation theory. Commun IBIMA 3(16):114–121Google Scholar
  67. Ooi K, Tan G (2016) Mobile technology acceptance model: an investigation using mobile users to explore smartphone credit card. Expert Syst Appl 59:33–46CrossRefGoogle Scholar
  68. Pasha E, Razzaghnia T, Allahviranloo T, Yari G, Mostafaei HR (2007) Fuzzy linear regression models with fuzzy entropy. Appl Math Sci 1(35):1715–1724MathSciNetzbMATHGoogle Scholar
  69. Pham TT, Ho JC (2015) The effects of product-related, personal-related factors and attractiveness of alternatives on consumer adoption of NFC-based mobile payments. Technol Soc 43:159–172CrossRefGoogle Scholar
  70. Ragin CC (2000) Fuzzy-set social science. University of Chicago Press, ChicagoGoogle Scholar
  71. Ram S, Sheth JN (1989) Consumer resistance to innovations: the marketing problem and its solutions. J Consum Mark 6(2):5–14CrossRefGoogle Scholar
  72. Rauyruen P, Miller KE (2007) Relationship quality as a predictor of B2B customer loyalty. J Bus Res 60(1):21–31CrossRefGoogle Scholar
  73. Rogers EM (1995) Difiusion of innovations. The Free, New YorkGoogle Scholar
  74. Ross TJ (2004) Fuzzy control systems. In: Fuzzy logic with engineering applications, 3rd edn. McgrawHill International edition, pp 437–500Google Scholar
  75. Russell CJ, Bobko P (1992) Moderated regression analysis and Likert scales: too coarse for comfort. J Appl Psychol 77(3):336CrossRefGoogle Scholar
  76. Schierz PG, Schilke O, Wirtz BW (2010) Understanding consumer acceptance of mobile payment services: an empirical analysis. Electron Commer Res Appl 9(3):209–216CrossRefGoogle Scholar
  77. Shin DH (2010) Modeling the interaction of users and mobile payment system: conceptual framework. Int J Hum-Comput Interact 26(10):917–940CrossRefGoogle Scholar
  78. Siau K, Sheng H, Nah F, Davis S (2004) A qualitative investigation on consumer trust in mobile commerce. Int J Electron Bus 2(3):283–300CrossRefGoogle Scholar
  79. Sim JJ, Tan GWH, Ooi KB, Lee VH (2011) Exploring the individual characteristics on the adoption of broadband: an empirical analysis. Int J Netw Mob Technol 2(1):1–14Google Scholar
  80. Suntornpithug N, Khamalah J (2010) Machine and person interactivity: the driving forces behind influences on consumers’willingness to purchase online. J Electron Commer Res 11(4):299Google Scholar
  81. Symeonaki M, Kazani A (2011) Developing a fuzzy likert scale for measuring Xenophobia in Greece. ASMDA, RomeGoogle Scholar
  82. Szmigin IT, Bourne H (1999) Electronic cash: a qualitative assessment of its adoption. Int J Bank Mark 17(4):192–203CrossRefGoogle Scholar
  83. Tan GWH, Ooi KB, Sim JJ, Phusavat K (2012) Determinants of mobile learning adoption: an empirical analysis. J Comput Inf Syst 52(3):82–91Google Scholar
  84. Tan GWH, Ooi KB, Chong SC, Hew TS (2014) NFC mobile credit card: the next frontier of mobile payment? Telemat Inform 31(2):292–307CrossRefGoogle Scholar
  85. Tanaka H, Vegima S, Asai K (1982) Linear regression analysis with fuzzy model. IEEE Trans Syst Man Cybernet 12(6):903–907CrossRefzbMATHGoogle Scholar
  86. Tanaka H, Hayashi I, Watada J (1989) Possibilistic linear regression analysis for fuzzy data. Eur J Oper Res 40(3):389–396CrossRefMathSciNetzbMATHGoogle Scholar
  87. Teo TS, Pok SH (2003) Adoption of WAP-enabled mobile phones among Internet users. Omega 31(6):483–498CrossRefGoogle Scholar
  88. Teo AC, Tan GWH, Ooi KB, Lin B (2015) Why consumers adopt mobile payment? A partial least squares structural equation modelling (PLS-SEM) approach. Int J Mob Commun 13(5):478–497CrossRefGoogle Scholar
  89. Tode C (2016) Proximity payment is fastest growing segment of mobile payments: Forrester—Mobile Commerce Daily—Research. Mobilecommercedaily.comGoogle Scholar
  90. Tsaur SH, Tzeng GH, Wang GC (1997) The application of AHP and fuzzy MCDM on the evaluation study of tourist risk. Ann Tour Res 24(4):796–812CrossRefGoogle Scholar
  91. Tsaur SH, Chang TY, Yen CH (2002) The evaluation of airline service quality by fuzzy MCDM. Tour Manag 23(2):107–115CrossRefGoogle Scholar
  92. Tseng ML (2009) A causal and effect decision making model of service quality expectation using grey-fuzzy DEMATEL approach. Expert Syst Appl 36(4):7738–7748CrossRefGoogle Scholar
  93. Venkatesh V, Bala H (2008) Technology acceptance model 3 and a research agenda on interventions. Decis Sci 39(2):273–315CrossRefGoogle Scholar
  94. Venkatesh V, Davis FD (2000) A theoretical extension of the technology acceptance model: four longitudinal field studies. Manag Sci 46(2):186–204CrossRefGoogle Scholar
  95. Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: toward a unified view. MIS Q 27:425–478CrossRefGoogle Scholar
  96. Vishwakarma P, Tripathy AK, Vemuru S (2016) A hybrid security framework for near field communication driven mobile payment model. Int J Comput Sci Inf Secur 14(12):337–348Google Scholar
  97. Wang HF, Tsaur RC (1999) Outliers in fuzzy regression analysis. Int J Fuzzy Syst 1(2):113–119MathSciNetGoogle Scholar
  98. Wang HF, Tsaur RC (2000) Insight of a fuzzy regression model. Fuzzy Sets Syst 112(3):355–369CrossRefMathSciNetzbMATHGoogle Scholar
  99. Wang YS, Lin HH, Luarn P (2006) Predicting consumer intention to use mobile service. Inf Syst J 16(2):157–179CrossRefGoogle Scholar
  100. Wong CH, Tan GWH, Ooi KB, Lin B (2014) Mobile shopping: the next frontier of the shopping industry? An emerging market perspective. Int J Mob Commun 13(1):92–112CrossRefGoogle Scholar
  101. Wu JH, Wang SC (2005) What drives mobile commerce?: An empirical evaluation of the revised technology acceptance model. Inf Manag 42(5):719–729CrossRefMathSciNetGoogle Scholar
  102. Xu G, Gutierrez JA (2006) An exploratory study of Killer applications and critical success factors in M-commerce. J Electron Commer Org (JECO) 4(3):63–79CrossRefGoogle Scholar
  103. Yang S, Lu Y, Gupta S, Cao Y, Zhang R (2012) Mobile payment services adoption across time: an empirical study of the effects of behavioral beliefs, social influences, and personal traits. Comput Hum Behav 28(1):129–142CrossRefGoogle Scholar
  104. Yang Q, Pang C, Liu L, Yen DC, Tarn JM (2015) Exploring consumer perceived risk and trust for online payments: an empirical study in China’s younger generation. Comput Hum Behav 50:9–24CrossRefGoogle Scholar
  105. Yen J, Langari R (2004) Fuzzy logic: Intelligence, control and information. Pearson Education, New DelhiGoogle Scholar
  106. Yousafzai SY, Pallister JG, Foxall GR (2003) A proposed model of e-trust for electronic banking. Technovation 23(11):847–860CrossRefGoogle Scholar
  107. Yousafzai SY, Foxall GR, Pallister JG (2010) Explaining internet banking behavior: theory of reasoned action, theory of planned behavior, or technology acceptance model? J Appl Soc Psychol 40(5):1172–1202CrossRefGoogle Scholar
  108. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353zbMATHGoogle Scholar
  109. Zhang L, Zhu J, Liu Q (2012) A meta-analysis of mobile commerce adoption and the moderating effect of culture. Comput Hum Behav 28(5):1902–1911CrossRefGoogle Scholar
  110. Zhou T (2011) Understanding online community user participation: a social influence perspective. Internet Res 21(1):67–81CrossRefGoogle Scholar
  111. Zimmermann HJ (1985) Applications of fuzzy set theory to mathematical programming. Inf Sci 36(1–2):29–58CrossRefMathSciNetzbMATHGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Feride Bahar Kurtulmuşoğlu
    • 1
    Email author
  • Ayhan Algüner
    • 1
  • Kumru D. Atalay
    • 2
  1. 1.Faculty of Economics and Administrative SciencesBaskent UniversityAnkaraTurkey
  2. 2.Faculty of EngineeringBaskent UniversityAnkaraTurkey

Personalised recommendations