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Consumer Adoption of Proximity Mobile Payment: An Empirical Cross-Cultural Study

Yıl 2022, Cilt: 7 Sayı: 17, 173 - 201, 27.02.2022

Michael Fügl Elif Kocagöz

https://doi.org/10.25204/iktisad.1055044

Öz

The use of Proximity Mobile Payment (PMP) applications with several advantages over traditional payment methods is becoming more common. While a large diffusion of PMP is already apparent in South Korea, Germany is still lagging behind. The study examines the acceptance factors regarding the intention to use PMP in the mentioned countries to understand this divergence. A research model was developed to explain the behavioral intention, with an extensive literature analysis. An online survey was conducted, 186 Germans and 146 South Koreans participated the research. Results show that in both countries perceived usefulness, perceived risk, perceived compatibility, and personal innovativeness are the factors that determine the individuals’ intention to use PMP. In addition, the social influence has an impact on the intention only in Germany, while the factors perceived ease of use and trust had no influence in both countries. Lastly, the influence of factors perceived compatibility and social influence on intention varies due to cultural differences. The study provides theoretical and managerial implications as well as an insight for further research.

Anahtar Kelimeler

Consumer Behavior Proximity Mobile Payment (PMP) Behavioral Intention Germany South Korea

Kaynakça

  • Agarwal, R. & Prasad J. (1999). Are individual differences germane to the acceptance of new information technologies? Decision Sciences 30 (2): 361–391. DOI: 10.1111/j.1540-5915.1999.tb01614.x.
  • Akhtar, S., Irfan, M., Sarwar, A., Asma. & Rashid, Q. U. A. (2019). Factors influencing individuals' intention to adopt mobile banking in China and Pakistan: The moderating role of cultural values. Journal of Public Affairs 19 (1): e1884. DOI: 10.1002/pa.1884.
  • Allied Market Research. (2018). Mobile payment market outlook 2023. https://www.alliedmarketresearch.com/mobile-payments-market [21 April 2020].
  • Au, Y. A. & Kauffman, R. J. (2008). The economics of mobile payments: Understanding stakeholder issues for an emerging financial technology application. Electronic Commerce Research and Applications 7 (2): 141–164. DOI: 10.1016/j.elerap.2006.12.004.
  • Bailey, A. A., Pentina, I., Mishra, A. S. & Ben Mimoun, M. S. (2020). Exploring factors influencing US millennial consumers’ use of tap-and-go payment technology. The International Review of Retail, Distribution and Consumer Research 30 (2): 143–163. DOI: 10.1080/09593969.2019.1667854.
  • Bailey, A. A., Pentina, I., Mishra, A. S. & Mimoun, M. S. B. (2017). Mobile payments adoption by US consumers: an extended TAM. International Journal of Retail & Distribution Management 45 (6): 1–17. DOI: 10.1108/IJRDM-08-2016-0144.
  • Balz, B. (2019). Mobile Payments – In China Alltag, in Deutschland auf dem Vormarsch: Hangzhou, China.
  • Bauer, R. A. (1960). Consumer behavior as risk taking. Dynamic Marketing for a Changing World, 389–398.
  • Chandra, S., Srivastava, S. & Theng, Y. L. (2010). Evaluating the role of trust in consumer adoption of mobile payment systems: An empirical analysis. Communications of the Association for Information Systems 27. DOI: 10.17705/1CAIS.02729.
  • Chawla, D. & Joshi, H. (2019). Consumer attitude and intention to adopt mobile wallet in India – An empirical study. International Journal of Bank Marketing 37 (7): 1590–1618. DOI: 10.1108/IJBM-09-2018-0256.
  • Chen, L. (2008). A model of consumer acceptance of mobile payment. International Journal of Mobile Communications 6 (1): 32–52. DOI: 10.1504/IJMC.2008.015997.
  • Chin, W. W. (1998). The partial least squares approach for structural equation modeling. In Modern methods for business research. [Nachdr.], Marcoulides GA (ed). Lawrence Erlbaum: Mahwah, N.J; 294–336.
  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Lawrence Erlbaum Associates: Hillsdale.
  • Crabbe, M., Standing, C., Standing, S. & Karjaluoto, H. (2009). An adoption model for mobile banking in Ghana. International Journal of Mobile Communications 7 (5): 515. DOI: 10.1504/IJMC.2009.024391.
  • Dahlberg, T., Mallat, N, Ondrus, J. & Zmijewska, A. (2008). Past, present and future of mobile payments research: A literature review. Electronic Commerce Research and Applications 7 (2): 165–181. DOI: 10.1016/j.elerap.2007.02.001.
  • Daştan, İ. & Gürler, C. (2016). Factors affecting the adoption of mobile payment systems: An empirical analysis. EMAJ: Emerging Markets Journal 6 (1): 17–24. DOI: 10.5195/EMAJ.2016.95.
  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly 13 (3): 319. DOI: 10.2307/249008.
  • Dutot, V. (2015). Factors influencing Near Field Communication (NFC) adoption: An extended TAM approach. The Journal of High Technology Management Research 26 (1): 45–57. DOI: 10.1016/j.hitech.2015.04.005.
  • Efron B. (1979). Bootstrap methods: Another look at the jackknife. The Annals of Statistics 7 (1): 1–26. DOI: 10.1214/aos/1176344552. eMarketer. (2019). Global mobile payment users 2019. https://www.emarketer.com/content/global-mobile-payment-users-2019 [21 April 2020].
  • Falk, T., Kunz, W. H., Schepers, J. J. L. & Mrozek A. J. (2016). How mobile payment influences the overall store price image. Journal of Business Research 69 (7): 2417–2423. DOI: 10.1016/j.jbusres.2016.01.011.
  • Fan, J., Shao, M., Li, Y. & Huang, X. (2018). Understanding users’ attitude toward mobile payment use: A comparative study between China and the USA. Industrial Management & Data Systems 118 (3): 524–540. DOI: 10.1108/IMDS-06-2017-0268.
  • Featherman, M. S & Pavlou, P. A. (2003). Predicting e-services adoption: a perceived risk facets perspective. International Journal of Human-Computer Studies 59 (4): 451–474. DOI: 10.1016/S1071-5819(03)00111-3.
  • Fornell, C. & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research 18 (1): 39. DOI: 10.2307/3151312.
  • Gao, L. & Waechter, K. A. (2017). Examining the role of initial trust in user adoption of mobile payment services: an empirical investigation. Information Systems Frontiers 19 (3): 525–548. DOI: 10.1007/s10796-015-9611-0.
  • Hair, J. F, Sarstedt, M., Ringle C.M. & Gudergan, S. (2018). Advanced issues in partial least squares structural equation modeling. Sage: Los Angeles, London, New Delhi, Singapore, Washington DC, Melbourne.
  • Hair, J. F., Hult, G. T. M., Ringle, C. M. & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM). Second edition. Sage: Los Angeles, London, New Delhi, Singapore, Washington DC, Melbourne.
  • Hair, J. F., Ringle, C. M. & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice 19 (2): 139–152. DOI: 10.2753/MTP1069-6679190202.
  • Hair, J. F., Risher, J. J., Sarstedt, M. & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review 31 (1): 2–24. DOI: 10.1108/EBR-11-2018-0203.
  • Hair, J. F., Sarstedt, M., Hopkins, L. & Kuppelwieser, V. G. (2014). Partial least squares structural equation modeling (PLS-SEM). European Business Review 26 (2): 106–121. DOI: 10.1108/EBR-10-2013-0128.
  • Handarkho, Y. D. & Harjoseputro, Y. (2020). Intention to adopt mobile payment in physical stores Individual switching behavior perspective based on Push–Pull–Mooring (PPM) theory. Journal of Enterprise Information Management 33 (2): 285–308. DOI: 10.1108/JEIM-06-2019-0179.
  • Henseler, J., Ringle, C. M. & Sarstedt, M. (2016). Testing measurement invariance of composites using partial least squares. International Marketing Review 33 (3): 405–431. DOI: 10.1108/IMR-09-2014-0304.
  • Hofstede, G. (1991). Cultures and organizations: Software of the mind. McGraw-Hill: London.
  • Hsee, C. K & Weber, E. U. (1999). Cross-national differences in risk preference and lay predictions. Journal of Behavioral Decision Making 12 (2): 165–179. DOI: 10.1002/(SICI)1099-0771(199906)12:2<165:AID-BDM316>3.0.CO;2-N.
  • Humbani, M. & Wiese, M. (2018). A cashless society for all: Determining Consumers’ readiness to adopt mobile payment services. Journal of African Business 19 (3): 409–429. DOI: 10.1080/15228916.2017.1396792.
  • Im, I., Hong, S. & Kang, M. S. (2011). An international comparison of technology adoption. Information & Management 48 (1): 1–8. DOI: 10.1016/j.im.2010.09.001.
  • Kalinic, Z. & Marinkovic, V. (2016). Determinants of users’ intention to adopt m-commerce: an empirical analysis. Information Systems and e-Business Management 14 (2): 367–387. DOI: 10.1007/s10257-015-0287-2.
  • Keramati, A., Taeb, R., Larijani, A. M. & Mojir, N. (2012). A combinative model of behavioural and technical factors affecting ‘Mobile’-payment services adoption: an empirical study. The Service Industries Journal 32 (9): 1489–1504. DOI: 10.1080/02642069.2011.552716.
  • Kerviler, G. de., Demoulin, N. T. M. & Zidda, P. (2016). Adoption of in-store mobile payment: Are perceived risk and convenience the only drivers? Journal of Retailing and Consumer Services 31: 334–344. DOI: 10.1016/j.jretconser.2016.04.011.
  • Kim, C., Mirusmonov, M. & Lee, I. (2010). An empirical examination of factors influencing the intention to use mobile payment. Computers in Human Behavior 26 (3): 310–322. DOI: 10.1016/j.chb.2009.10.013.
  • Koenig-Lewis, N., Marquet, M., Palmer, A. & Zhao, A. L. (2015). Enjoyment and social influence: predicting mobile payment adoption. The Service Industries Journal 35 (10): 537–554. DOI: 10.1080/02642069.2015.1043278.
  • Koenig-Lewis, N., Palmer, A. & Moll, A. (2010). Predicting young consumers' take up of mobile banking services. International Journal of Bank Marketing 28 (5): 410–432. DOI: 10.1108/02652321011064917.
  • Lee, J. M. (2019). Determinants of mobile payment usage and the moderating effect of gender: Extending the utaut model with privacy risk. International Journal of Electronic Commerce Studies 10 (1): 43–64. DOI: 10.7903/ijecs.1644.
  • Leong, L. Y., Hew, T. S., Tan, G. W. H. & Ooi, K. B. (2013). Predicting the determinants of the NFC-enabled mobile credit card acceptance: A neural networks approach. Expert Systems with Applications 40 (14): 5604–5620. DOI: 10.1016/j.eswa.2013.04.018.
  • Liébana-Cabanillas, F. & Lara-Rubio, J. (2017). Predictive and explanatory modeling regarding adoption of mobile payment systems. Technological Forecasting and Social Change 120: 32–40. DOI: 10.1016/j.techfore.2017.04.002.
  • Liébana-Cabanillas, F., Marinkovic, V., Ramos-de-Luna, I. & Kalinic, Z. (2018). Predicting the determinants of mobile payment acceptance: A hybrid SEM-neural network approach. Technological Forecasting and Social Change 129: 117–130. DOI: 10.1016/j.techfore.2017.12.015.
  • Liébana-Cabanillas, F., Muñoz-Leiva, F. & Sánchez-Fernández, J. (2015a). Behavioral model of younger users in m-payment systems. Journal of Organizational Computing and Electronic Commerce 25 (2): 169–190. DOI: 10.1080/10919392.2015.1033947.
  • Liébana-Cabanillas, F., Muñoz-Leiva, F. & Sánchez-Fernández, J. (2015c). Payment systems in new electronic environments: Consumer behavior in payment systems via SMS. International Journal of Information Technology & Decision Making, 14 (02): 421-449. DOI: 10.1142/S0219622015500078.
  • 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. Economic Research-Ekonomska Istraživanja 30 (1): 892–910. DOI: 10.1080/1331677X.2017.1305784.
  • Liébana-Cabanillas, F., Ramos-de-Luna, I. & Montoro-Ríos, F. J. (2015b). User behaviour in QR mobile payment system: the QR Payment Acceptance Model. Technology Analysis & Strategic Management 27 (9): 1031–1049. DOI: 10.1080/09537325.2015.1047757.
  • 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. Computers in Human Behavior 35: 464–478. DOI: 10.1016/j.chb.2014.03.022.
  • Liu, Y., Wang, M., Huang, D., Huang, Q., Yang, H. & Li, Z. (2019b). The impact of mobility, risk, and cost on the users’ intention to adopt mobile payments. Information Systems and e-Business Management 17 (2): 319–342. DOI: 10.1007/s10257-019-00449-0.
  • Liu, Z., Ben, S. & Zhang, R. (2019a). Factors affecting consumers’ mobile payment behavior: a meta-analysis. Electronic Commerce Research 19 (3): 575–601. DOI: 10.1007/s10660-019-09349-4.
  • Lu, X. & Lu, H. (2020). Understanding chinese millennials' adoption intention towards third-party mobile payment. Information Resources Management Journal 33 (2): 40–63. DOI: 10.4018/IRMJ.2020040103.
  • Mallat N. (2007). Exploring consumer adoption of mobile payments – A qualitative study. The Journal of Strategic Information Systems 16 (4): 413–432. DOI: 10.1016/j.jsis.2007.08.001.
  • Martens, M., Roll, O. & Elliott, R. (2017). Testing the technology readiness and acceptance model for mobile payments across Germany and South Africa. International Journal of Innovation and Technology Management 14 (06): 1750033. DOI: 10.1142/S021987701750033X.
  • Moore, G. C. & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research 2 (3): 192–222. DOI: 10.1287/isre.2.3.192.
  • Musa, A., Khan, H. U. & Al-Share, K. A. (2015). Factors influence consumers' adoption of mobile payment devices in Qatar. International Journal of Mobile Communications 13 (6): 670. DOI: 10.1504/IJMC.2015.072100.
  • Nyaboga, A. B., Marwa, M. & Kabata, D. (2015). Motivational factors and use of mobile payment services in Kenya. Journal of business and economic studies 21 (1/2).
  • Oliveira, T., Thomas, M., Baptista, G. & Campos, F. (2016). Mobile payment: Understanding the determinants of customer adoption and intention to recommend the technology. Computers in Human Behavior 61: 404–414. DOI: 10.1016/j.chb.2016.03.030.
  • Ozturk, A. B., Bilgihan, A., Salehi-Esfahani, S. & Hua, N. (2017). Understanding the mobile payment technology acceptance based on valence theory. International Journal of Contemporary Hospitality Management 29 (8): 2027–2049. DOI: 10.1108/IJCHM-04-2016-0192.
  • Park, C. & Jun, J. K. (2003). A cross‐cultural comparison of Internet buying behavior. International Marketing Review 20 (5): 534–553. DOI: 10.1108/02651330310498771.
  • Park, J., Amendah, E., Lee, Y. & Hyun, H. (2018). M-payment service: Interplay of perceived risk, benefit, and trust in service adoption. Human Factors and Ergonomics in Manufacturing & Service Industries 29 (1): 31–43. DOI: 10.1002/hfm.20750.
  • Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce 7 (3): 101–134.
  • Qasim, H. & Abu-Shanab, E. (2016). Drivers of mobile payment acceptance: The impact of network externalities. Information Systems Frontiers 18 (5): 1021–1034. DOI: 10.1007/s10796-015-9598-6.
  • Ramos-de-Luna, I., Liébana-Cabanillas, F., Montoro-Ríos, F. & Luz, A. P. R. G. (2015). Nuevas perspectivas sobre el comportamiento del consumidor para la adopción del pago móvil sin contacto. Desenvolve Revista de Gestão do Unilasalle, 4(1): 65-86. DOI: http://dx.doi.org/10.18316/1649.
  • 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. Technological Forecasting and Social Change 146: 931–944. DOI: 10.1016/j.techfore.2018.09.018.
  • Ramos-de-Luna, I., Montoro-Ríos, F. & Liébana-Cabanillas, F. (2016). Determinants of the intention to use NFC technology as a payment system: an acceptance model approach. Information Systems and e-Business Management 14 (2): 293–314. DOI: 10.1007/s10257-015-0284-5.
  • Ramos-de-Luna, I., Montoro-Ríos, F., Liébana-Cabanillas, F. & Luna, J. G.de. (2017). NFC technology acceptance for mobile payments: A Brazilian Perspective. Review of Business Management 19 (63): 82–103. DOI: 10.7819/rbgn.v0i0.2315.
  • Rodríguez-Entrena, M., Schuberth, F. & Gelhard, C. (2018). Assessing statistical differences between parameters estimates in Partial Least Squares path modeling. Quality & Quantity 52 (1): 57–69. DOI: 10.1007/s11135-016-0400-8.
  • Sanakulov, N. & Karjaluoto, H. (2017). A cultural comparison study of smartphone adoption in Uzbekistan, South Korea and Turkey. International Journal of Mobile Communications 15 (1): 85. DOI: 10.1504/IJMC.2017.10001206.
  • Sarstedt, M., Ringle, C. M. & Hair, J. F. (2017). Treating unobserved heterogeneity in PLS-SEM: A Multi-method approach. In Partial Least Squares Path Modeling: Basic Concepts, Methodological Issues and Applications, Latan H, Noonan R (eds). Springer International Publishing; Imprint: Springer: Cham; 197–217.
  • Schierz, P. G., Schilke, O. & Wirtz, B. W. (2010). Understanding consumer acceptance of mobile payment services: An empirical analysis. Electronic Commerce Research and Applications 9 (3): 209–216. DOI: 10.1016/j.elerap.2009.07.005.
  • Sellitto, C., Fong, M.W.L. & Phonthanukitithaworn, C. (2016). A comparative study of current and potential users of mobile payment services. SAGE Open 6 (4): 215824401667539. DOI: 10.1177/2158244016675397.
  • Shin, D. H. (2009). Towards an understanding of the consumer acceptance of mobile wallet. Computers in Human Behavior 25 (6): 1343–1354. DOI: 10.1016/j.chb.2009.06.001.
  • Shin, S. & Lee, W. (2014). The effects of technology readiness and technology acceptance on Nfc mobile payment services in Korea. Journal of Applied Business Research (JABR) 30 (6): 1615. DOI: 10.19030/jabr.v30i6.8873.
  • Slade, E., Williams, M., Dwivedi, Y. & Piercy, N. (2015). Exploring consumer adoption of proximity mobile payments. Journal of Strategic Marketing 23 (3): 209–223. DOI: 10.1080/0965254X.2014.914075.
  • Smart Card Alliance. (2011). The Mobile Payments and NFC Landscape: A U.S. Perspective: A Smart Card Alliance Payments Council White Paper, Smart Card Alliance Inc., Princeton Junction.
  • Statista. (2016). Mobile payment revenue worldwide 2015-2019. https://www.statista.com/statistics/226530/mobile-payment-transaction-volume-forecast/ [21 April 2020].
  • Statista. (2019). Smartphone-Nutzung in Deutschland bis 2019. https://de.statista.com/statistik/daten/studie/198959/umfrage/anzahl-der-smartphonenutzer-in-deutschland-seit-2010/ [21 April 2020].
  • Steenkamp, J. B., Hofstede, F. T. & Wedel, M. (1999). A cross-national investigation into the individual and national cultural antecedents of consumer innovativeness. Journal of Marketing 63 (2): 55. DOI: 10.2307/1251945.
  • Straub, D., Keil, M. & Brenner, W. (1997). Testing the technology acceptance model across cultures: A three country study. Information & Management 33 (1): 1–11. DOI: 10.1016/S0378-7206(97)00026-8.
  • Streukens, S. & Leroi-Werelds, S. (2016). Bootstrapping and PLS-SEM: A step-by-step guide to get more out of your bootstrap results. European Management Journal 34 (6): 618–632. DOI: 10.1016/j.emj.2016.06.003.
  • Su, P., Wang, L. & Yan J. (2018). How users’ Internet experience affects the adoption of mobile payment: a mediation model. Technology Analysis & Strategic Management 30 (2): 186–197. DOI: 10.1080/09537325.2017.1297788.
  • Suh, B. & Han, I. (2002). Effect of trust on customer acceptance of Internet banking. Electronic Commerce Research and Applications 1 (3-4): 247–263. DOI: 10.1016/S1567-4223(02)00017-0.
  • Sun, H. & Zhang, P. (2006). The role of moderating factors in user technology acceptance. International Journal of Human-Computer Studies 64 (2): 53–78. DOI: 10.1016/j.ijhcs.2005.04.013.
  • Tan, K.L., Memon, M.A., Sim, P.L., Leong, C. M., Soetrisno, F.K. & Hussain, K. (2019). Intention to use mobile payment system by ethnicity: A partial least squares multi-group approach. Asian Journal of Business Research 9 (1). DOI: 10.14707/ajbr.190055.
  • Taylor, E. (2016). Mobile payment technologies in retail: a review of potential benefits and risks. International Journal of Retail & Distribution Management 44 (2): 159–177. DOI: 10.1108/IJRDM-05-2015-0065.
  • Teo, A.C., Tan, GW.H., Ooi, K.B., Hew, T.S. & Yew, K. T. (2015). The effects of convenience and speed in m-payment. Industrial Management & Data Systems 115 (2): 311–331. DOI: 10.1108/IMDS-08-2014-0231.
  • Thakur, R. & Srivastava, M. (2014). Adoption readiness, personal innovativeness, perceived risk and usage intention across customer groups for mobile payment services in India. Internet Research 24 (3): 369–392. DOI: 10.1108/IntR-12-2012-0244.
  • Thatcher, J. B., Srite, M., Stepina, L.P. & Yongmei, L. I. U. (2003). Culture, overload and personal innovativeness with information technology: Extending the nomological net. Journal of Computer Information Systems 44 (1): 74–81.
  • van der Heijden, H., Verhagen, T. & Creemers, M. (2003). Understanding online purchase intentions: contributions from technology and trust perspectives. European Journal of Information Systems 12 (1): 41–48. DOI: 10.1057/palgrave.ejis.3000445.
  • van Herk, H., Poortinga, Y. H. & Verhallen, T.M.M. (2005). Equivalence of survey data: relevance for international marketing. European Journal of Marketing 39 (3/4): 351–364. DOI: 10.1108/03090560510581818.
  • Venkatesh V., Morris, M. G., Davis, G. B. & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly 27 (3): 425. DOI: 10.2307/30036540.
  • Venkatesh, V., Thong, J. & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. Management Information Systems Quarterly 36 (1): 157–178.
  • Verkijika, S. F. (2020). An affective response model for understanding the acceptance of mobile payment systems. Electronic Commerce Research and Applications 39: 100905. DOI: 10.1016/j.elerap.2019.100905.
  • Webster, J. & Watson, R. T. (2002). Analyzing the past to prepare for the future: Writing a literature review. MIS Quarterly 26 (2): 8–23.
  • Weiber, R. & Mühlhaus, D. (2014). Strukturgleichungsmodellierung: Eine anwendungsorientierte Einführung in die Kausalanalyse mit Hilfe von AMOS, SmartPLS und SPSS. 2, extended and revised. Springer Gabler: Berlin.
  • Wu, J., Liu, L. & Huang, L. (2017). Consumer acceptance of mobile payment across time. Industrial Management & Data Systems 117 (8): 1761–1776. DOI: 10.1108/IMDS-08-2016-0312.
  • Xin, H., Techatassanasoontorn, A. A. & Tan F. B. (2015). Antecedents of Consumer Trust in Mobile Payment Adoption. Journal of Computer Information Systems 55 (4): 1–10. DOI: 10.1080/08874417.2015.11645781.
  • Ya, P. C., Li, Y. L.& Dong, H. Z. (2017). Understanding the intention to continue use of a mobile payment provider: An examination of Alipay wallet in China. International Journal of Business and Information 12 (4): 369–390.
  • 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. Computers in Human Behavior 28 (1): 129–142. DOI: 10.1016/j.chb.2011.08.019.
  • Yang, Y., Liu, Y., Li, H. & Yu, B. (2015). Understanding perceived risks in mobile payment acceptance. Industrial Management & Data Systems 115 (2): 253–269. DOI: 10.1108/IMDS-08-2014-0243.
  • Zhang, J. & Mao, E. (2020). Cash, credit, or phone? An empirical study on the adoption of mobile payments in the United States. Psychology & Marketing 37 (1): 87–98. DOI: 10.1002/mar.21282.
  • Zhou, T. (2011). The effect of initial trust on user adoption of mobile payment. Information Development 27 (4): 290–300. DOI: 10.1177/0266666911424075.
  • Zhou, T. (2013). An empirical examination of continuance intention of mobile payment services. Decision Support Systems 54 (2): 1085–1091. DOI: 10.1016/j.dss.2012.10.034.
  • Zhou, T. (2014). Understanding the determinants of mobile payment continuance usage. Industrial Management & Data Systems 114 (6): 936–948. DOI: 10.1108/IMDS-02-2014-0068.
  • Zhu, K., Dong, S., Xu, S. X. & Kraemer, K. L. (2006). Innovation diffusion in global contexts: determinants of post-adoption digital transformation of European companies. European Journal of Information Systems 15 (6): 601–616. DOI: 10.1057/palgrave.ejis.3000650.

Tüketicilerin Yakından Mobil Ödeme Kullanım Kabulü: Kültürlerarası Ampirik Bir Çalışma

Yıl 2022, Cilt: 7 Sayı: 17, 173 - 201, 27.02.2022

Michael Fügl Elif Kocagöz

https://doi.org/10.25204/iktisad.1055044

Öz

Yakından Mobil Ödeme (YMÖ) uygulamalarının kullanımı, geleneksel ödeme yöntemlerine göre birçok avantaj sağlaması nedeniyle giderek yaygınlaşmaktadır. Güney Kore'de YMÖ büyük bir yayılma gösterse de Almanya’da henüz yaygınlaşmadığı söylenebilir. Bu farklılığı anlamak için çalışma, her iki ülkede de YMÖ kullanma niyetiyle ilgili kabul faktörlerini incelemektedir. Kapsamlı bir literatür analizi ile YMÖ kullanım niyetini açıklamak için bir araştırma modeli geliştirilmiştir. Çevrimiçi bir anket yapılmış, araştırmaya 186 Alman ve 146 Güney Koreli katılmıştır. Sonuçlar, her iki ülkede de algılanan fayda, algılanan risk, algılanan uyumluluk ve kişisel yenilikçiliğin bireylerin davranışsal niyetini belirleyen faktörler olduğunu göstermiştir. Ayrıca sonuçlar, sosyal etkinin yalnızca Almanya'da YMÖ kullanma niyeti üzerinde bir etkisinin olduğunu, algılanan kullanım kolaylığı ve güven faktörlerinin her iki ülkede de anlamlı bir etkisinin olmadığını ortaya koymuştur. Son olarak, YMÖ kullanmaya yönelik davranışsal niyette, algılanan uyumluluk ve sosyal etki faktörlerinin etkilerinin kültür baz alınarak yapılan incelemede anlamlı olarak farklılaştığı bulunmuştur. Çalışma, teorik ve yönetimsel çıkarımların yanı sıra izleyen araştırmalar için bazı bilgiler sunmaktadır.

Anahtar Kelimeler

Tüketici Davranışı Yakından Mobil Ödeme Davranışsal Niyet Almanya Güney Kore

Kaynakça

  • Agarwal, R. & Prasad J. (1999). Are individual differences germane to the acceptance of new information technologies? Decision Sciences 30 (2): 361–391. DOI: 10.1111/j.1540-5915.1999.tb01614.x.
  • Akhtar, S., Irfan, M., Sarwar, A., Asma. & Rashid, Q. U. A. (2019). Factors influencing individuals' intention to adopt mobile banking in China and Pakistan: The moderating role of cultural values. Journal of Public Affairs 19 (1): e1884. DOI: 10.1002/pa.1884.
  • Allied Market Research. (2018). Mobile payment market outlook 2023. https://www.alliedmarketresearch.com/mobile-payments-market [21 April 2020].
  • Au, Y. A. & Kauffman, R. J. (2008). The economics of mobile payments: Understanding stakeholder issues for an emerging financial technology application. Electronic Commerce Research and Applications 7 (2): 141–164. DOI: 10.1016/j.elerap.2006.12.004.
  • Bailey, A. A., Pentina, I., Mishra, A. S. & Ben Mimoun, M. S. (2020). Exploring factors influencing US millennial consumers’ use of tap-and-go payment technology. The International Review of Retail, Distribution and Consumer Research 30 (2): 143–163. DOI: 10.1080/09593969.2019.1667854.
  • Bailey, A. A., Pentina, I., Mishra, A. S. & Mimoun, M. S. B. (2017). Mobile payments adoption by US consumers: an extended TAM. International Journal of Retail & Distribution Management 45 (6): 1–17. DOI: 10.1108/IJRDM-08-2016-0144.
  • Balz, B. (2019). Mobile Payments – In China Alltag, in Deutschland auf dem Vormarsch: Hangzhou, China.
  • Bauer, R. A. (1960). Consumer behavior as risk taking. Dynamic Marketing for a Changing World, 389–398.
  • Chandra, S., Srivastava, S. & Theng, Y. L. (2010). Evaluating the role of trust in consumer adoption of mobile payment systems: An empirical analysis. Communications of the Association for Information Systems 27. DOI: 10.17705/1CAIS.02729.
  • Chawla, D. & Joshi, H. (2019). Consumer attitude and intention to adopt mobile wallet in India – An empirical study. International Journal of Bank Marketing 37 (7): 1590–1618. DOI: 10.1108/IJBM-09-2018-0256.
  • Chen, L. (2008). A model of consumer acceptance of mobile payment. International Journal of Mobile Communications 6 (1): 32–52. DOI: 10.1504/IJMC.2008.015997.
  • Chin, W. W. (1998). The partial least squares approach for structural equation modeling. In Modern methods for business research. [Nachdr.], Marcoulides GA (ed). Lawrence Erlbaum: Mahwah, N.J; 294–336.
  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Lawrence Erlbaum Associates: Hillsdale.
  • Crabbe, M., Standing, C., Standing, S. & Karjaluoto, H. (2009). An adoption model for mobile banking in Ghana. International Journal of Mobile Communications 7 (5): 515. DOI: 10.1504/IJMC.2009.024391.
  • Dahlberg, T., Mallat, N, Ondrus, J. & Zmijewska, A. (2008). Past, present and future of mobile payments research: A literature review. Electronic Commerce Research and Applications 7 (2): 165–181. DOI: 10.1016/j.elerap.2007.02.001.
  • Daştan, İ. & Gürler, C. (2016). Factors affecting the adoption of mobile payment systems: An empirical analysis. EMAJ: Emerging Markets Journal 6 (1): 17–24. DOI: 10.5195/EMAJ.2016.95.
  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly 13 (3): 319. DOI: 10.2307/249008.
  • Dutot, V. (2015). Factors influencing Near Field Communication (NFC) adoption: An extended TAM approach. The Journal of High Technology Management Research 26 (1): 45–57. DOI: 10.1016/j.hitech.2015.04.005.
  • Efron B. (1979). Bootstrap methods: Another look at the jackknife. The Annals of Statistics 7 (1): 1–26. DOI: 10.1214/aos/1176344552. eMarketer. (2019). Global mobile payment users 2019. https://www.emarketer.com/content/global-mobile-payment-users-2019 [21 April 2020].
  • Falk, T., Kunz, W. H., Schepers, J. J. L. & Mrozek A. J. (2016). How mobile payment influences the overall store price image. Journal of Business Research 69 (7): 2417–2423. DOI: 10.1016/j.jbusres.2016.01.011.
  • Fan, J., Shao, M., Li, Y. & Huang, X. (2018). Understanding users’ attitude toward mobile payment use: A comparative study between China and the USA. Industrial Management & Data Systems 118 (3): 524–540. DOI: 10.1108/IMDS-06-2017-0268.
  • Featherman, M. S & Pavlou, P. A. (2003). Predicting e-services adoption: a perceived risk facets perspective. International Journal of Human-Computer Studies 59 (4): 451–474. DOI: 10.1016/S1071-5819(03)00111-3.
  • Fornell, C. & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research 18 (1): 39. DOI: 10.2307/3151312.
  • Gao, L. & Waechter, K. A. (2017). Examining the role of initial trust in user adoption of mobile payment services: an empirical investigation. Information Systems Frontiers 19 (3): 525–548. DOI: 10.1007/s10796-015-9611-0.
  • Hair, J. F, Sarstedt, M., Ringle C.M. & Gudergan, S. (2018). Advanced issues in partial least squares structural equation modeling. Sage: Los Angeles, London, New Delhi, Singapore, Washington DC, Melbourne.
  • Hair, J. F., Hult, G. T. M., Ringle, C. M. & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM). Second edition. Sage: Los Angeles, London, New Delhi, Singapore, Washington DC, Melbourne.
  • Hair, J. F., Ringle, C. M. & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice 19 (2): 139–152. DOI: 10.2753/MTP1069-6679190202.
  • Hair, J. F., Risher, J. J., Sarstedt, M. & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review 31 (1): 2–24. DOI: 10.1108/EBR-11-2018-0203.
  • Hair, J. F., Sarstedt, M., Hopkins, L. & Kuppelwieser, V. G. (2014). Partial least squares structural equation modeling (PLS-SEM). European Business Review 26 (2): 106–121. DOI: 10.1108/EBR-10-2013-0128.
  • Handarkho, Y. D. & Harjoseputro, Y. (2020). Intention to adopt mobile payment in physical stores Individual switching behavior perspective based on Push–Pull–Mooring (PPM) theory. Journal of Enterprise Information Management 33 (2): 285–308. DOI: 10.1108/JEIM-06-2019-0179.
  • Henseler, J., Ringle, C. M. & Sarstedt, M. (2016). Testing measurement invariance of composites using partial least squares. International Marketing Review 33 (3): 405–431. DOI: 10.1108/IMR-09-2014-0304.
  • Hofstede, G. (1991). Cultures and organizations: Software of the mind. McGraw-Hill: London.
  • Hsee, C. K & Weber, E. U. (1999). Cross-national differences in risk preference and lay predictions. Journal of Behavioral Decision Making 12 (2): 165–179. DOI: 10.1002/(SICI)1099-0771(199906)12:2<165:AID-BDM316>3.0.CO;2-N.
  • Humbani, M. & Wiese, M. (2018). A cashless society for all: Determining Consumers’ readiness to adopt mobile payment services. Journal of African Business 19 (3): 409–429. DOI: 10.1080/15228916.2017.1396792.
  • Im, I., Hong, S. & Kang, M. S. (2011). An international comparison of technology adoption. Information & Management 48 (1): 1–8. DOI: 10.1016/j.im.2010.09.001.
  • Kalinic, Z. & Marinkovic, V. (2016). Determinants of users’ intention to adopt m-commerce: an empirical analysis. Information Systems and e-Business Management 14 (2): 367–387. DOI: 10.1007/s10257-015-0287-2.
  • Keramati, A., Taeb, R., Larijani, A. M. & Mojir, N. (2012). A combinative model of behavioural and technical factors affecting ‘Mobile’-payment services adoption: an empirical study. The Service Industries Journal 32 (9): 1489–1504. DOI: 10.1080/02642069.2011.552716.
  • Kerviler, G. de., Demoulin, N. T. M. & Zidda, P. (2016). Adoption of in-store mobile payment: Are perceived risk and convenience the only drivers? Journal of Retailing and Consumer Services 31: 334–344. DOI: 10.1016/j.jretconser.2016.04.011.
  • Kim, C., Mirusmonov, M. & Lee, I. (2010). An empirical examination of factors influencing the intention to use mobile payment. Computers in Human Behavior 26 (3): 310–322. DOI: 10.1016/j.chb.2009.10.013.
  • Koenig-Lewis, N., Marquet, M., Palmer, A. & Zhao, A. L. (2015). Enjoyment and social influence: predicting mobile payment adoption. The Service Industries Journal 35 (10): 537–554. DOI: 10.1080/02642069.2015.1043278.
  • Koenig-Lewis, N., Palmer, A. & Moll, A. (2010). Predicting young consumers' take up of mobile banking services. International Journal of Bank Marketing 28 (5): 410–432. DOI: 10.1108/02652321011064917.
  • Lee, J. M. (2019). Determinants of mobile payment usage and the moderating effect of gender: Extending the utaut model with privacy risk. International Journal of Electronic Commerce Studies 10 (1): 43–64. DOI: 10.7903/ijecs.1644.
  • Leong, L. Y., Hew, T. S., Tan, G. W. H. & Ooi, K. B. (2013). Predicting the determinants of the NFC-enabled mobile credit card acceptance: A neural networks approach. Expert Systems with Applications 40 (14): 5604–5620. DOI: 10.1016/j.eswa.2013.04.018.
  • Liébana-Cabanillas, F. & Lara-Rubio, J. (2017). Predictive and explanatory modeling regarding adoption of mobile payment systems. Technological Forecasting and Social Change 120: 32–40. DOI: 10.1016/j.techfore.2017.04.002.
  • Liébana-Cabanillas, F., Marinkovic, V., Ramos-de-Luna, I. & Kalinic, Z. (2018). Predicting the determinants of mobile payment acceptance: A hybrid SEM-neural network approach. Technological Forecasting and Social Change 129: 117–130. DOI: 10.1016/j.techfore.2017.12.015.
  • Liébana-Cabanillas, F., Muñoz-Leiva, F. & Sánchez-Fernández, J. (2015a). Behavioral model of younger users in m-payment systems. Journal of Organizational Computing and Electronic Commerce 25 (2): 169–190. DOI: 10.1080/10919392.2015.1033947.
  • Liébana-Cabanillas, F., Muñoz-Leiva, F. & Sánchez-Fernández, J. (2015c). Payment systems in new electronic environments: Consumer behavior in payment systems via SMS. International Journal of Information Technology & Decision Making, 14 (02): 421-449. DOI: 10.1142/S0219622015500078.
  • 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. Economic Research-Ekonomska Istraživanja 30 (1): 892–910. DOI: 10.1080/1331677X.2017.1305784.
  • Liébana-Cabanillas, F., Ramos-de-Luna, I. & Montoro-Ríos, F. J. (2015b). User behaviour in QR mobile payment system: the QR Payment Acceptance Model. Technology Analysis & Strategic Management 27 (9): 1031–1049. DOI: 10.1080/09537325.2015.1047757.
  • 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. Computers in Human Behavior 35: 464–478. DOI: 10.1016/j.chb.2014.03.022.
  • Liu, Y., Wang, M., Huang, D., Huang, Q., Yang, H. & Li, Z. (2019b). The impact of mobility, risk, and cost on the users’ intention to adopt mobile payments. Information Systems and e-Business Management 17 (2): 319–342. DOI: 10.1007/s10257-019-00449-0.
  • Liu, Z., Ben, S. & Zhang, R. (2019a). Factors affecting consumers’ mobile payment behavior: a meta-analysis. Electronic Commerce Research 19 (3): 575–601. DOI: 10.1007/s10660-019-09349-4.
  • Lu, X. & Lu, H. (2020). Understanding chinese millennials' adoption intention towards third-party mobile payment. Information Resources Management Journal 33 (2): 40–63. DOI: 10.4018/IRMJ.2020040103.
  • Mallat N. (2007). Exploring consumer adoption of mobile payments – A qualitative study. The Journal of Strategic Information Systems 16 (4): 413–432. DOI: 10.1016/j.jsis.2007.08.001.
  • Martens, M., Roll, O. & Elliott, R. (2017). Testing the technology readiness and acceptance model for mobile payments across Germany and South Africa. International Journal of Innovation and Technology Management 14 (06): 1750033. DOI: 10.1142/S021987701750033X.
  • Moore, G. C. & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research 2 (3): 192–222. DOI: 10.1287/isre.2.3.192.
  • Musa, A., Khan, H. U. & Al-Share, K. A. (2015). Factors influence consumers' adoption of mobile payment devices in Qatar. International Journal of Mobile Communications 13 (6): 670. DOI: 10.1504/IJMC.2015.072100.
  • Nyaboga, A. B., Marwa, M. & Kabata, D. (2015). Motivational factors and use of mobile payment services in Kenya. Journal of business and economic studies 21 (1/2).
  • Oliveira, T., Thomas, M., Baptista, G. & Campos, F. (2016). Mobile payment: Understanding the determinants of customer adoption and intention to recommend the technology. Computers in Human Behavior 61: 404–414. DOI: 10.1016/j.chb.2016.03.030.
  • Ozturk, A. B., Bilgihan, A., Salehi-Esfahani, S. & Hua, N. (2017). Understanding the mobile payment technology acceptance based on valence theory. International Journal of Contemporary Hospitality Management 29 (8): 2027–2049. DOI: 10.1108/IJCHM-04-2016-0192.
  • Park, C. & Jun, J. K. (2003). A cross‐cultural comparison of Internet buying behavior. International Marketing Review 20 (5): 534–553. DOI: 10.1108/02651330310498771.
  • Park, J., Amendah, E., Lee, Y. & Hyun, H. (2018). M-payment service: Interplay of perceived risk, benefit, and trust in service adoption. Human Factors and Ergonomics in Manufacturing & Service Industries 29 (1): 31–43. DOI: 10.1002/hfm.20750.
  • Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce 7 (3): 101–134.
  • Qasim, H. & Abu-Shanab, E. (2016). Drivers of mobile payment acceptance: The impact of network externalities. Information Systems Frontiers 18 (5): 1021–1034. DOI: 10.1007/s10796-015-9598-6.
  • Ramos-de-Luna, I., Liébana-Cabanillas, F., Montoro-Ríos, F. & Luz, A. P. R. G. (2015). Nuevas perspectivas sobre el comportamiento del consumidor para la adopción del pago móvil sin contacto. Desenvolve Revista de Gestão do Unilasalle, 4(1): 65-86. DOI: http://dx.doi.org/10.18316/1649.
  • 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. Technological Forecasting and Social Change 146: 931–944. DOI: 10.1016/j.techfore.2018.09.018.
  • Ramos-de-Luna, I., Montoro-Ríos, F. & Liébana-Cabanillas, F. (2016). Determinants of the intention to use NFC technology as a payment system: an acceptance model approach. Information Systems and e-Business Management 14 (2): 293–314. DOI: 10.1007/s10257-015-0284-5.
  • Ramos-de-Luna, I., Montoro-Ríos, F., Liébana-Cabanillas, F. & Luna, J. G.de. (2017). NFC technology acceptance for mobile payments: A Brazilian Perspective. Review of Business Management 19 (63): 82–103. DOI: 10.7819/rbgn.v0i0.2315.
  • Rodríguez-Entrena, M., Schuberth, F. & Gelhard, C. (2018). Assessing statistical differences between parameters estimates in Partial Least Squares path modeling. Quality & Quantity 52 (1): 57–69. DOI: 10.1007/s11135-016-0400-8.
  • Sanakulov, N. & Karjaluoto, H. (2017). A cultural comparison study of smartphone adoption in Uzbekistan, South Korea and Turkey. International Journal of Mobile Communications 15 (1): 85. DOI: 10.1504/IJMC.2017.10001206.
  • Sarstedt, M., Ringle, C. M. & Hair, J. F. (2017). Treating unobserved heterogeneity in PLS-SEM: A Multi-method approach. In Partial Least Squares Path Modeling: Basic Concepts, Methodological Issues and Applications, Latan H, Noonan R (eds). Springer International Publishing; Imprint: Springer: Cham; 197–217.
  • Schierz, P. G., Schilke, O. & Wirtz, B. W. (2010). Understanding consumer acceptance of mobile payment services: An empirical analysis. Electronic Commerce Research and Applications 9 (3): 209–216. DOI: 10.1016/j.elerap.2009.07.005.
  • Sellitto, C., Fong, M.W.L. & Phonthanukitithaworn, C. (2016). A comparative study of current and potential users of mobile payment services. SAGE Open 6 (4): 215824401667539. DOI: 10.1177/2158244016675397.
  • Shin, D. H. (2009). Towards an understanding of the consumer acceptance of mobile wallet. Computers in Human Behavior 25 (6): 1343–1354. DOI: 10.1016/j.chb.2009.06.001.
  • Shin, S. & Lee, W. (2014). The effects of technology readiness and technology acceptance on Nfc mobile payment services in Korea. Journal of Applied Business Research (JABR) 30 (6): 1615. DOI: 10.19030/jabr.v30i6.8873.
  • Slade, E., Williams, M., Dwivedi, Y. & Piercy, N. (2015). Exploring consumer adoption of proximity mobile payments. Journal of Strategic Marketing 23 (3): 209–223. DOI: 10.1080/0965254X.2014.914075.
  • Smart Card Alliance. (2011). The Mobile Payments and NFC Landscape: A U.S. Perspective: A Smart Card Alliance Payments Council White Paper, Smart Card Alliance Inc., Princeton Junction.
  • Statista. (2016). Mobile payment revenue worldwide 2015-2019. https://www.statista.com/statistics/226530/mobile-payment-transaction-volume-forecast/ [21 April 2020].
  • Statista. (2019). Smartphone-Nutzung in Deutschland bis 2019. https://de.statista.com/statistik/daten/studie/198959/umfrage/anzahl-der-smartphonenutzer-in-deutschland-seit-2010/ [21 April 2020].
  • Steenkamp, J. B., Hofstede, F. T. & Wedel, M. (1999). A cross-national investigation into the individual and national cultural antecedents of consumer innovativeness. Journal of Marketing 63 (2): 55. DOI: 10.2307/1251945.
  • Straub, D., Keil, M. & Brenner, W. (1997). Testing the technology acceptance model across cultures: A three country study. Information & Management 33 (1): 1–11. DOI: 10.1016/S0378-7206(97)00026-8.
  • Streukens, S. & Leroi-Werelds, S. (2016). Bootstrapping and PLS-SEM: A step-by-step guide to get more out of your bootstrap results. European Management Journal 34 (6): 618–632. DOI: 10.1016/j.emj.2016.06.003.
  • Su, P., Wang, L. & Yan J. (2018). How users’ Internet experience affects the adoption of mobile payment: a mediation model. Technology Analysis & Strategic Management 30 (2): 186–197. DOI: 10.1080/09537325.2017.1297788.
  • Suh, B. & Han, I. (2002). Effect of trust on customer acceptance of Internet banking. Electronic Commerce Research and Applications 1 (3-4): 247–263. DOI: 10.1016/S1567-4223(02)00017-0.
  • Sun, H. & Zhang, P. (2006). The role of moderating factors in user technology acceptance. International Journal of Human-Computer Studies 64 (2): 53–78. DOI: 10.1016/j.ijhcs.2005.04.013.
  • Tan, K.L., Memon, M.A., Sim, P.L., Leong, C. M., Soetrisno, F.K. & Hussain, K. (2019). Intention to use mobile payment system by ethnicity: A partial least squares multi-group approach. Asian Journal of Business Research 9 (1). DOI: 10.14707/ajbr.190055.
  • Taylor, E. (2016). Mobile payment technologies in retail: a review of potential benefits and risks. International Journal of Retail & Distribution Management 44 (2): 159–177. DOI: 10.1108/IJRDM-05-2015-0065.
  • Teo, A.C., Tan, GW.H., Ooi, K.B., Hew, T.S. & Yew, K. T. (2015). The effects of convenience and speed in m-payment. Industrial Management & Data Systems 115 (2): 311–331. DOI: 10.1108/IMDS-08-2014-0231.
  • Thakur, R. & Srivastava, M. (2014). Adoption readiness, personal innovativeness, perceived risk and usage intention across customer groups for mobile payment services in India. Internet Research 24 (3): 369–392. DOI: 10.1108/IntR-12-2012-0244.
  • Thatcher, J. B., Srite, M., Stepina, L.P. & Yongmei, L. I. U. (2003). Culture, overload and personal innovativeness with information technology: Extending the nomological net. Journal of Computer Information Systems 44 (1): 74–81.
  • van der Heijden, H., Verhagen, T. & Creemers, M. (2003). Understanding online purchase intentions: contributions from technology and trust perspectives. European Journal of Information Systems 12 (1): 41–48. DOI: 10.1057/palgrave.ejis.3000445.
  • van Herk, H., Poortinga, Y. H. & Verhallen, T.M.M. (2005). Equivalence of survey data: relevance for international marketing. European Journal of Marketing 39 (3/4): 351–364. DOI: 10.1108/03090560510581818.
  • Venkatesh V., Morris, M. G., Davis, G. B. & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly 27 (3): 425. DOI: 10.2307/30036540.
  • Venkatesh, V., Thong, J. & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. Management Information Systems Quarterly 36 (1): 157–178.
  • Verkijika, S. F. (2020). An affective response model for understanding the acceptance of mobile payment systems. Electronic Commerce Research and Applications 39: 100905. DOI: 10.1016/j.elerap.2019.100905.
  • Webster, J. & Watson, R. T. (2002). Analyzing the past to prepare for the future: Writing a literature review. MIS Quarterly 26 (2): 8–23.
  • Weiber, R. & Mühlhaus, D. (2014). Strukturgleichungsmodellierung: Eine anwendungsorientierte Einführung in die Kausalanalyse mit Hilfe von AMOS, SmartPLS und SPSS. 2, extended and revised. Springer Gabler: Berlin.
  • Wu, J., Liu, L. & Huang, L. (2017). Consumer acceptance of mobile payment across time. Industrial Management & Data Systems 117 (8): 1761–1776. DOI: 10.1108/IMDS-08-2016-0312.
  • Xin, H., Techatassanasoontorn, A. A. & Tan F. B. (2015). Antecedents of Consumer Trust in Mobile Payment Adoption. Journal of Computer Information Systems 55 (4): 1–10. DOI: 10.1080/08874417.2015.11645781.
  • Ya, P. C., Li, Y. L.& Dong, H. Z. (2017). Understanding the intention to continue use of a mobile payment provider: An examination of Alipay wallet in China. International Journal of Business and Information 12 (4): 369–390.
  • 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. Computers in Human Behavior 28 (1): 129–142. DOI: 10.1016/j.chb.2011.08.019.
  • Yang, Y., Liu, Y., Li, H. & Yu, B. (2015). Understanding perceived risks in mobile payment acceptance. Industrial Management & Data Systems 115 (2): 253–269. DOI: 10.1108/IMDS-08-2014-0243.
  • Zhang, J. & Mao, E. (2020). Cash, credit, or phone? An empirical study on the adoption of mobile payments in the United States. Psychology & Marketing 37 (1): 87–98. DOI: 10.1002/mar.21282.
  • Zhou, T. (2011). The effect of initial trust on user adoption of mobile payment. Information Development 27 (4): 290–300. DOI: 10.1177/0266666911424075.
  • Zhou, T. (2013). An empirical examination of continuance intention of mobile payment services. Decision Support Systems 54 (2): 1085–1091. DOI: 10.1016/j.dss.2012.10.034.
  • Zhou, T. (2014). Understanding the determinants of mobile payment continuance usage. Industrial Management & Data Systems 114 (6): 936–948. DOI: 10.1108/IMDS-02-2014-0068.
  • Zhu, K., Dong, S., Xu, S. X. & Kraemer, K. L. (2006). Innovation diffusion in global contexts: determinants of post-adoption digital transformation of European companies. European Journal of Information Systems 15 (6): 601–616. DOI: 10.1057/palgrave.ejis.3000650.
Toplam 107 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular İşletme
Bölüm Araştırma Makaleleri
Yazarlar

Michael Fügl Bu kişi benim 0000-0001-5071-187X

Elif Kocagöz 0000-0001-9575-1664

Yayımlanma Tarihi 27 Şubat 2022
Gönderilme Tarihi 8 Ocak 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 7 Sayı: 17

Kaynak Göster

APA Fügl, M., & Kocagöz, E. (2022). Consumer Adoption of Proximity Mobile Payment: An Empirical Cross-Cultural Study. İktisadi İdari Ve Siyasal Araştırmalar Dergisi, 7(17), 173-201. https://doi.org/10.25204/iktisad.1055044
AMA Fügl M, Kocagöz E. Consumer Adoption of Proximity Mobile Payment: An Empirical Cross-Cultural Study. İKTİSAD. Şubat 2022;7(17):173-201. doi:10.25204/iktisad.1055044
Chicago Fügl, Michael, ve Elif Kocagöz. “Consumer Adoption of Proximity Mobile Payment: An Empirical Cross-Cultural Study”. İktisadi İdari Ve Siyasal Araştırmalar Dergisi 7, sy. 17 (Şubat 2022): 173-201. https://doi.org/10.25204/iktisad.1055044.
EndNote Fügl M, Kocagöz E (01 Şubat 2022) Consumer Adoption of Proximity Mobile Payment: An Empirical Cross-Cultural Study. İktisadi İdari ve Siyasal Araştırmalar Dergisi 7 17 173–201.
IEEE M. Fügl ve E. Kocagöz, “Consumer Adoption of Proximity Mobile Payment: An Empirical Cross-Cultural Study”, İKTİSAD, c. 7, sy. 17, ss. 173–201, 2022, doi: 10.25204/iktisad.1055044.
ISNAD Fügl, Michael - Kocagöz, Elif. “Consumer Adoption of Proximity Mobile Payment: An Empirical Cross-Cultural Study”. İktisadi İdari ve Siyasal Araştırmalar Dergisi 7/17 (Şubat 2022), 173-201. https://doi.org/10.25204/iktisad.1055044.
JAMA Fügl M, Kocagöz E. Consumer Adoption of Proximity Mobile Payment: An Empirical Cross-Cultural Study. İKTİSAD. 2022;7:173–201.
MLA Fügl, Michael ve Elif Kocagöz. “Consumer Adoption of Proximity Mobile Payment: An Empirical Cross-Cultural Study”. İktisadi İdari Ve Siyasal Araştırmalar Dergisi, c. 7, sy. 17, 2022, ss. 173-01, doi:10.25204/iktisad.1055044.
Vancouver Fügl M, Kocagöz E. Consumer Adoption of Proximity Mobile Payment: An Empirical Cross-Cultural Study. İKTİSAD. 2022;7(17):173-201.
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