Skip to main content

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

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.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2

References

  • Al-Najjar B, Alsyouf I (2003) Selecting the most efficient maintenance approach using fuzzy multiple criteria decision making. Int J Prod Econ 84(1):85–100

    Article  Google Scholar 

  • Antoine P (2004) Understanding the mobile phone market drivers. In: Alcatel Telecommunications Review; 4th Quarter 2003/1st Quarter 2004, pp 1–7

  • Arvidsson N (2014) Consumer attitudes on mobile payment services–results from a proof of concept test. Int J Bank Mark 32(2):150–170

    Article  Google Scholar 

  • 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):141–164

    Article  Google Scholar 

  • Balachandran D, Tan GWH (2015) Regression modelling of predicting NFC mobile payment adoption in Malaysia. Int J Model Oper Manag 5(2):100–116

    Google Scholar 

  • Baser F, Gokten S, Gokten PO (2017) Using fuzzy c-means clustering algorithm in financial health scoring. Audit Financ 15(147):385–394

    Article  Google Scholar 

  • Benitez JM, Martín JC, Román C (2007) Using fuzzy number for measuring quality of service in the hotel industry. Tour Manag 28(2):544–555

    Article  Google Scholar 

  • Bit AK, Biswal MP, Alam SS (1993) An additive fuzzy programming model for multiobjective transportation problem. Fuzzy Sets Syst 57(3):313–319

    Article  MathSciNet  MATH  Google Scholar 

  • 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 1–5

  • 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):98–104

    Article  Google Scholar 

  • 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):995–1011

    Article  Google Scholar 

  • 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 27:561–588

    Google Scholar 

  • Chen LD (2008) A model of consumer acceptance of mobile payment. Int J Mobile Commun 6(1):32–52

    Article  MathSciNet  Google Scholar 

  • 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):1–17

    Article  Google Scholar 

  • 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):34–43

    Article  Google Scholar 

  • 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):360–369

    Article  Google Scholar 

  • 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):143–161

    Article  Google Scholar 

  • 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 37:897–910

    Article  Google Scholar 

  • 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 57:127–138

    Article  Google Scholar 

  • Clarke KC (2001) Cartography in a mobile internet age. In: Proceedings of the 20th international cartographic conference, pp 6–10

  • 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):165–181

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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):252–265

    Google Scholar 

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

    Article  Google Scholar 

  • 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):19–45

    Article  Google Scholar 

  • 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):221–230

    Article  Google Scholar 

  • DeVaus D (2000) Social surveys, vol 1–4. Cambridge, Boston

  • Dewan SG, Chen LD (2005) Mobile payment adoption in the US: a cross-industry, crossplatform solution. J Inf Priv Secur 1(2):4–28

    Google Scholar 

  • Dubois D, Prade H (1986) Weighted minimum and maximum operations in fuzzy set theory. Inf Sci 39(2):205–210

    Article  MathSciNet  MATH  Google Scholar 

  • Fishbein M, Ajzen Icek (1975) Belief, attitude, intention, and behavior: an introduction to theory and research. Addison-Wesley, Reading

    Google Scholar 

  • 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):291–314

    Article  Google Scholar 

  • Gefen D (2002) Reflections on the dimensions of trust and trustworthiness among online consumers. ACM Sigmis Database 33(3):38–53

    Article  Google Scholar 

  • Gerpott TJ, Meinert P (2017) Who signs up for NFC mobile payment services? Mobile network operator subscribers in Germany. Electron Commer Res Appl 23:1–13

    Article  Google Scholar 

  • 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):539–562

    Article  MathSciNet  MATH  Google Scholar 

  • 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):43–58

    Article  MATH  Google Scholar 

  • Hodge DR, Gillespie D (2003) Phrase completions: an alternative to Likert scales. Social Work Res 27:45–55

    Article  Google Scholar 

  • 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):263–275

    Article  Google Scholar 

  • 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 4844–4847

  • Kaiser MO (1974) Kaiser–Meyer–Olkin measure for identity correlation matrix. J R Stat Soc 52:296–298

    Google Scholar 

  • Kim KJ, Moskowitz H, Koksalan M (1996) Fuzzy versus statistical linear regression. Eur J Oper Res 92(2):417–434

    Article  MATH  Google Scholar 

  • Kim G, Shin B, Lee HG (2009) Understanding dynamics between initial trust and usage intentions of mobile banking. Inf Syst J 19(3):283–311

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Laukkanen T, Kiviniemi V (2010) The role of information in mobile banking resistance. Int J Bank Mark 28(5):372–388

    Article  Google Scholar 

  • Laukkanen T, Lauronen J (2005) Consumer value creation in mobile banking services. Int J Mob Commun 3(4):325–338

    Article  Google Scholar 

  • 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):130–141

    Article  Google Scholar 

  • 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):2109–2121

    Article  Google Scholar 

  • Li Q (2013) A novel Likert scale based on fuzzy sets theory. Expert Syst Appl 40(5):1609–1618

    Article  Google Scholar 

  • Liébana-Cabanillas F, Lara-Rubio J (2017) Predictive and explanatory modeling regarding adoption of mobile payment systems. Technol Forecast Soc Change 120:32–40

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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 129:117–130

    Article  Google Scholar 

  • Lin WS, Yeh JY, Chen YY, Chia-Yi N (2009) Determinants of user adoption of e-payment services. J Am Acad Bus 14(2):224–229

    Google Scholar 

  • Liou TS, Chen CW (2006) Subjective appraisal of service quality using fuzzy linguistic assessment. Int J Qual Reliab Manag 23(8):928–943

    Article  Google Scholar 

  • 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 1–6

  • 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):359–364

    Article  Google Scholar 

  • 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):393–403

    Article  Google Scholar 

  • Luarn P, Lin HH (2005) Toward an understanding of the behavioral intention to use mobile banking. Comput Hum Behav 21(6):873–891

    Article  Google Scholar 

  • 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 563–566

  • 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):79–104

    Article  Google Scholar 

  • Mallat N (2007) Exploring consumer adoption of mobile payments—a qualitative study. J Strateg Inf Syst 16(4):413–432

    Article  Google Scholar 

  • 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–42b

  • Moore GC, Benbasat I (1991) Development of an instrument to measure the perceptions of adopting an information technology innovation. Inf Syst Res 2(3):192–222

    Article  Google Scholar 

  • 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):25–38

    Google Scholar 

  • Nicole KL, Palmer A, Moll A (2010) Predicting young consumers’ take up of mobile banking services. Int J Bank Mark 28(5):342–371

    Article  Google Scholar 

  • Ondrus J, Pigneur Y (2006) Towards a holistic analysis of mobile payments: a multiple perspectives approach. Electron Commer Res Appl 5(3):246–257

    Article  Google Scholar 

  • 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 43–43

  • Ong JW, Poong YS, Ng TH (2008) 3G services adoption among university students: diffusion of innovation theory. Commun IBIMA 3(16):114–121

    Google Scholar 

  • Ooi K, Tan G (2016) Mobile technology acceptance model: an investigation using mobile users to explore smartphone credit card. Expert Syst Appl 59:33–46

    Article  Google Scholar 

  • Pasha E, Razzaghnia T, Allahviranloo T, Yari G, Mostafaei HR (2007) Fuzzy linear regression models with fuzzy entropy. Appl Math Sci 1(35):1715–1724

    MathSciNet  MATH  Google Scholar 

  • 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 43:159–172

    Article  Google Scholar 

  • Ragin CC (2000) Fuzzy-set social science. University of Chicago Press, Chicago

    Google Scholar 

  • Ram S, Sheth JN (1989) Consumer resistance to innovations: the marketing problem and its solutions. J Consum Mark 6(2):5–14

    Article  Google Scholar 

  • Rauyruen P, Miller KE (2007) Relationship quality as a predictor of B2B customer loyalty. J Bus Res 60(1):21–31

    Article  Google Scholar 

  • Rogers EM (1995) Difiusion of innovations. The Free, New York

    Google Scholar 

  • Ross TJ (2004) Fuzzy control systems. In: Fuzzy logic with engineering applications, 3rd edn. McgrawHill International edition, pp 437–500

  • Russell CJ, Bobko P (1992) Moderated regression analysis and Likert scales: too coarse for comfort. J Appl Psychol 77(3):336

    Article  Google Scholar 

  • Schierz PG, Schilke O, Wirtz BW (2010) Understanding consumer acceptance of mobile payment services: an empirical analysis. Electron Commer Res Appl 9(3):209–216

    Article  Google Scholar 

  • Shin DH (2010) Modeling the interaction of users and mobile payment system: conceptual framework. Int J Hum-Comput Interact 26(10):917–940

    Article  Google Scholar 

  • Siau K, Sheng H, Nah F, Davis S (2004) A qualitative investigation on consumer trust in mobile commerce. Int J Electron Bus 2(3):283–300

    Article  Google Scholar 

  • 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):1–14

    Google Scholar 

  • 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):299

    Google Scholar 

  • Symeonaki M, Kazani A (2011) Developing a fuzzy likert scale for measuring Xenophobia in Greece. ASMDA, Rome

    Google Scholar 

  • Szmigin IT, Bourne H (1999) Electronic cash: a qualitative assessment of its adoption. Int J Bank Mark 17(4):192–203

    Article  Google Scholar 

  • Tan GWH, Ooi KB, Sim JJ, Phusavat K (2012) Determinants of mobile learning adoption: an empirical analysis. J Comput Inf Syst 52(3):82–91

    Google Scholar 

  • Tan GWH, Ooi KB, Chong SC, Hew TS (2014) NFC mobile credit card: the next frontier of mobile payment? Telemat Inform 31(2):292–307

    Article  Google Scholar 

  • Tanaka H, Vegima S, Asai K (1982) Linear regression analysis with fuzzy model. IEEE Trans Syst Man Cybernet 12(6):903–907

    Article  MATH  Google Scholar 

  • Tanaka H, Hayashi I, Watada J (1989) Possibilistic linear regression analysis for fuzzy data. Eur J Oper Res 40(3):389–396

    Article  MathSciNet  MATH  Google Scholar 

  • Teo TS, Pok SH (2003) Adoption of WAP-enabled mobile phones among Internet users. Omega 31(6):483–498

    Article  Google Scholar 

  • 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):478–497

    Article  Google Scholar 

  • Tode C (2016) Proximity payment is fastest growing segment of mobile payments: Forrester—Mobile Commerce Daily—Research. Mobilecommercedaily.com

  • 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):796–812

    Article  Google Scholar 

  • Tsaur SH, Chang TY, Yen CH (2002) The evaluation of airline service quality by fuzzy MCDM. Tour Manag 23(2):107–115

    Article  Google Scholar 

  • Tseng ML (2009) A causal and effect decision making model of service quality expectation using grey-fuzzy DEMATEL approach. Expert Syst Appl 36(4):7738–7748

    Article  Google Scholar 

  • Venkatesh V, Bala H (2008) Technology acceptance model 3 and a research agenda on interventions. Decis Sci 39(2):273–315

    Article  Google Scholar 

  • Venkatesh V, Davis FD (2000) A theoretical extension of the technology acceptance model: four longitudinal field studies. Manag Sci 46(2):186–204

    Article  Google Scholar 

  • Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: toward a unified view. MIS Q 27:425–478

    Article  Google Scholar 

  • 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):337–348

    Google Scholar 

  • Wang HF, Tsaur RC (1999) Outliers in fuzzy regression analysis. Int J Fuzzy Syst 1(2):113–119

    MathSciNet  Google Scholar 

  • Wang HF, Tsaur RC (2000) Insight of a fuzzy regression model. Fuzzy Sets Syst 112(3):355–369

    Article  MathSciNet  MATH  Google Scholar 

  • Wang YS, Lin HH, Luarn P (2006) Predicting consumer intention to use mobile service. Inf Syst J 16(2):157–179

    Article  Google Scholar 

  • 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):92–112

    Article  Google Scholar 

  • Wu JH, Wang SC (2005) What drives mobile commerce?: An empirical evaluation of the revised technology acceptance model. Inf Manag 42(5):719–729

    Article  MathSciNet  Google Scholar 

  • 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):63–79

    Article  Google Scholar 

  • 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):129–142

    Article  Google Scholar 

  • 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 50:9–24

    Article  Google Scholar 

  • Yen J, Langari R (2004) Fuzzy logic: Intelligence, control and information. Pearson Education, New Delhi

    Google Scholar 

  • Yousafzai SY, Pallister JG, Foxall GR (2003) A proposed model of e-trust for electronic banking. Technovation 23(11):847–860

    Article  Google Scholar 

  • 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):1172–1202

    Article  Google Scholar 

  • Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353

    MATH  Google Scholar 

  • 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):1902–1911

    Article  Google Scholar 

  • Zhou T (2011) Understanding online community user participation: a social influence perspective. Internet Res 21(1):67–81

    Article  Google Scholar 

  • Zimmermann HJ (1985) Applications of fuzzy set theory to mathematical programming. Inf Sci 36(1–2):29–58

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Feride Bahar Kurtulmuşoğlu.

Ethics declarations

Conflict of interest

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

Additional information

Communicated by V. Loia.

Publisher’s Note

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

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Kurtulmuşoğlu, F.B., Algüner, A. & Atalay, K.D. What are the most influential factors of consumers’ intention to use NFC-enabled credit cards?. Soft Comput 23, 10821–10836 (2019). https://doi.org/10.1007/s00500-018-3635-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-018-3635-6

Keywords

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