Mobil Ödeme Kullanımına Yönelik Niyet ve Algıların SosyoDemografik Özelliklere Göre Farklılıklarının İncelenmesi*

Yıl 2019, Sayı: 87, 111 - 139, 23.12.2019

Cansu Türker , Abdullah Okumuş

https://doi.org/10.26650/imj.2019.87.0005

Öz

Araştırmanın temel amacı, bireylerin QR kodlu mobil ödeme sistemlerinin kullanımına yönelik niyet ve algılarının
sosyo-demografik özelliklere göre nasıl farklılaştığının belirlenmesidir. Bu doğrultuda, mobil ödeme sistemlerinin
kullanımının yaygınlaşması için hangi hususlara dikkat edilmesi gerektiğini açıklamaya çalışmak bu çalışmanın bir diğer
amacını oluşturmaktadır. Türkiye’de yaygınlaşmaya başlayan QR kodlu mobil ödeme sistemlerine yönelik pazarlama
alanında yapılmış çalışmaların azlığı dikkat çekmekte ve bu durum araştırmanın temel motivasyonunu oluşturmaktadır.
Araştırmada, çevrimiçi anket yöntemiyle 485 katılımcıdan oluşan bir örneklemden veri elde edilmiştir. Kullanılan mobil
ödeme ölçeğinde yer alan niyet ve algılara yönelik geçerlilik ve güvenilirlik analizleri yapılmıştır. Keşfedici faktör analizleri
sonucunda elde edilen kullanma niyeti ve algılardan oluşan mobil ödeme ölçeği boyutlarının sosyo-demografik özelliklere
göre nasıl bir farklılaşma gösterdiğini belirlemek amacıyla bağımsız örneklem t testi ve tek yönlü ANOVA uygulanmıştır.
Analizler sonucunda, kullanma niyeti, algılanan fayda, algılanan kullanım kolaylığı, algılanan güven, algılanan uyumluluk
ve algılanan güvenlik değişkenlerinin, sosyo-demografik özelliklere göre önemli farklılıklar gösterdiği belirlenmiştir.
Katılımcıların daha önceden mobil ödeme kullanmış veya kullanmamış olmalarının, kullanma niyeti ve mobil ödemeye
yönelik algıları açısından farklılıklara neden olduğu tespit edilmiştir. Bu doğrultuda, bireylerin mobil ödemelere yönelik
kullanma niyeti ve algılarının, sosyo-demografik faktörlere göre farklılıklarının belirlenmiş olması, uygulayıcılar için QR
kodlu mobil ödeme sistemlerinin yaygınlaştırılmasının sağlanması adına çeşitli kullanıcı segmentlerine ilişkin pazarlama
faaliyetlerini ve stratejilerini yönetmeleri açısından önemli veriler içermektedir.

Anahtar Kelimeler

Teknoloji Kabul Modeli, Teknoloji Kabulü, Mobil Ödeme, Mobil Ödeme Ölçeği, Online Tüketici Davranışı

Destekleyen Kurum

Yazarlar bu çalışma için finansal destek almamışlardır.

An Investigation of the Differences of Intention and Perceptions Toward Mobile Payment Usage in Terms of Socio-Demographic Characteristics

Öz

The aim of this study is to determine how users’ intention regarding the use of technology, and how their perceptions
toward technology, differ according to socio-demographics characteristics. In this respect, another aim of this study is to
try to explain what might be taken into consideration in order to expand the use of mobile payment systems. The essential motivation of this study is that the number of studies in the marketing field on mobile payment systems
using QR code, which has become common in Turkey, is limited. In this study, a sample of 485 participants
was included using an online survey method. Validity and reliability analyses were conducted for intention
and perceptions in the mobile payment scale. Independent sample t test and one-way ANOVA were applied
to determine how the dimensions of the mobile payment scale, which is composed of intention to use and
perceptions obtained from exploratory factor analyses, differ according to socio-demographic characteristics.
As a result of these analyses, it was found that the intention to use of mobile payments, perceived usefulness,
perceived ease of use, perceived trust, perceived compatibility and perceived security variables showed
significant differences according to socio-demographic characteristics. It was determined that participants
differ in terms of intention to use and all the perceptions toward mobile payment addressed in this research
regardless of whether they had made mobile payments before or not. Accordingly, specifying the differences
in perceptions and intention to use of mobile payments according to socio-demographic factors, this study
provides valuable data for practitioners in terms of managing marketing activities and strategies for various
user segments in order to ensure that mobile payment systems using QR code become widespread in Turkey.

Anahtar Kelimeler

Technology Acceptance Model, Technology Adoption, Mobile Payment Scale, Online Consumer Behavior

Kaynakça

• Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes,50(2), 179–211.
• Amoroso, D. L. ve Magnier-Watanabe R. (2012). Building a research model for mobile wallet consumer adoption: the case of mobile Suica in Japan. Journal of Theoretical and Applied Electronic Commerce Research, 7(1), 94–110.
• Aslam, W., Ham M., & Arif, I. (2017). Consumer behavioral intentions towards mobile payment services: An empirical analysis in Pakistan. Market-Tržište, 29(2), 161–176.
• Awad, N. F., & Ragowsky, A. (2008). Establishing trust in electronic commerce through online word of mouth: An examination across genders. Journal of Management Information Systems, 24(4), 101–121.
• Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist, 37(2), 122–147.
• Bhattacherjee, A. (2001). “Understanding information systems continuance: an expectationconfirmation model”. MIS quarterly, 25(3), 351-370.
• BTK (2015). “Mobil Ödeme Hizmetleri”. [Erişim: 20 Ocak 2019: https://www.btk.gov.tr/uploads/pages/slug/mobil-odeme-hizmetleri.pdf]
• Chandra, S, Srivastava C. S., & Theng Y. L. (2010). Evaluating the role of trust in consumer adoption of mobile payment systems: An empirical analysis. CAIS, 27(29), 561–588.
• Chellappa, R. K. (2008). Consumers’ trust in electronic commerce transactions: the role of perceived privacy and perceived security. [Erişim:7 Ocak 2019 https://www.bus.emory.edu/ram/Papers/sec-priv.pdf]
• Chung, J. E., Park, N., Wang, H., Fulk, J., & McLaughlin, M. (2010). Age differences in perceptions of online community participation among non-users: An extension of the Technology Acceptance Model. Computers in Human Behavior, 26(6), 1674–1684.
• Coursaris, C., & Hassanein K. (2002). Understanding m-commerce: a consumer-centric model. Quarterly journal of electronic commerce, 3(3), 247–272.
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 13(3), 319–340.
• Davis, F. D. (1993). User acceptance of information technology: system characteristics, user perceptions and behavioral impacts”. International Journal of Man-Machine Studies, 38(3), 475–487.
• Davis, F. D., Bagozzi R. P., & Warshaw P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management Science, 35(8), 982–1003.
• Devaraj, S. Fan M., & Kohli R. (2002). Antecedents of B2C channel satisfaction and preference: validating e-commerce metrics. Information Systems Research, 13(3), 316–333.
• Durmuş, B., Yurtkoru E. S. ve Çinko M. (2011). Sosyal Bilimlerde SPSS’le Veri Analizi. İstanbul: Beta Yayınları.
• Fishbein, M., & Ajzen I. (1975). Belief, attitude, and behavior: An introduction to theory and research. Reading, Mass.: Addison Wessley.
• Gefen, D. (2000). E-commerce: the role of familiarity and trust. Omega, 28(6), 725–737.
• Gefen, D., Karahanna E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS quarterly, 27(1), 51–90.
• Gefen, D., & Straub, D. W. (1997). Gender differences in the perception and use of e-mail: An extension to the technology acceptance model. MIS quarterly, 21(4), 389–400.
• Gefen, D., & Straub, D. W. (2004). Consumer trust in B2C e-Commerce and the importance of social presence: experiments in e-Products and e-Services. Omega, 32(6), 407–424.
• Hair J. F., Black W. C., Babin, B. J., & Anderson, R. E. (2016). Multivariate Data Analysis. Edinbugh: Pearson.
• Jarvenpaa S.L., Tractinsky, N., & Saarinen, L. (1999). Consumer trust in an ınternet store: A crosscultural validation. Journal of Computer-Mediated Communication, 5(2).
• Karahanna, E., Straub, D. W., & Chervany, N. L. (1999). Information technology adoption across time: a cross- sectional comparison of pre-adoption and post-adoption beliefs. MIS quarterly, 23(2), 183–213.
• Khalilzadeh, J., Ozturk, A. B., & Bilgihan, A. (2017). Security-related factors in extended UTAUT model for NFC based mobile payment in the restaurant industry. Computers in Human Behavior, 70, 460–474.
• 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.
• López-Nicolás, C., Molina-Castillo, F. C., & Bouwman, H. (2008). An assessment of advanced mobile services acceptance: Contributions from TAM and diffusion theory models. Information & Management, 45(6), 359–364.
• Luarn, P., & Lin, H. (2005). Toward an understanding of the behavioral intention to use mobile banking. Computers in Human Behavior, 21(6), 873–891.
• Malhotra, N. K. (2010). Marketing Research An Applied Orientation. New Jersey: Pearson. 6th Edition.
• Mallat, N. (2007). Exploring consumer adoption of mobile payments–A qualitative study. The Journal of Strategic Information Systems, 16(4), 413–432.
• Matemba, E., D., & Li, G. (2018). Consumers’ willingness to adopt and use WeChat wallet: An empirical study in South Africa. Technology in Society, 53, 55–68.
• 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.
• Morris, M. G., & Venkatesh, V. (2000). Age differences in technology adoption decisions: Implications for a changing work force. Personnel psychology, 53(2), 375–403.
• Parasuraman, A., Zeithaml, V. A., & Malhotra, A. (2005). ES-QUAL: a multiple-item scale for assessing electronic service quality. Journal of Service Research, 7(3), 213–233.
• 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.
• Pavlou, P. A. (2002). Institution-based trust in interorganizational exchange relationships: the role of online B2B marketplaces on trust formation. The Journal of Strategic Information Systems, 11(3-4), 215–243.
• Plouffe, C. R., Hulland, J. S., & Vandenbosch, M. (2001). Richness versus parsimony in modeling technology adoption decisions—understanding merchant adoption of a smart card-based payment system. Information Systems Research, 12(2), 208–222.
• Pousttchi, K. (2003). Conditions for acceptance and usage of mobile payment procedures. The Second International Conference on Mobile Business, Vienna: 201–210.
• Robey, D. (1979). User attitudes and management information system use. Academy of Management Journal. 22(3), 527–538.
• Rogers, E. M. (1995). Lessons for guidelines from the diffusion of innovations. Joint Commission Journal on Quality and Patient Safety, 21(7), 324–328.
• Rogers, E. M. (1983). Diffusion of lnnovations. Third Edition. New York: Free Press.
• Schneider, F. (1998). Trust in Cyberspace. Washington, DC: National Academy Press.
• 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.
• Shin, D. (2009). Towards an understanding of the consumer acceptance of mobile wallet. Computers in Human Behavior, 25(6), 1343–1354.
• Statista (2018). Share of internet users who are likely to use mobile payments on their smartphone in the next year as of March 2018, by country. [Erişim: 20 Ocak 2019: https://www.statista.com/ statistics/934055/likelihood-of-online-users-mobile-payment-system-usage-country/].
• Sun, H., & Zhang P. (2006). Causal relationships between perceived enjoyment and perceived ease of use: An alternative approach. Journal of the Association for Information Systems, 7(1), 618–645.
• Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144–176.
• Tsai, C., Wang, C., & Lu, M. (2011). Using the technology acceptance model to analyze ease of use of a mobile communication system. Social Behavior and Personality: an international journal, 39(1), 65–69.
• Turan, A. H. (2008). İnternet alışverişi tüketici davranışını belirleyen etmenler: geliştirilmiş Teknoloji Kabul Modeli (E-TAM) ile bir model önerisi. Akademik Bilişim, 8.
• Tüfekci, Ö. K. (2014). Karekodların pazarlama iletişimi rolünü teknoloji kabul modeli ile açıklamaya yönelik bir araştırma. Pamukkale İşletme ve Bilişim Yönetimi Dergisi, 1(1), 36–52.
• TÜİK (2018). Hanehalkı Bilişim Teknolojileri Kullanım Araştırması. [Erişim: 20 Ocak 2019: http:// www.tuik.gov.tr/PreTablo.do?alt_id=1028].
• Vallerand, R.J. (1997). “Toward a hierarchical model of intrinsic and extrinsic motivation”. In Advances in Experimental Social Psyhology. 29: 271-360.
• Van der Heijden, H. (2003). Factors influencing the usage of websites: the case of a generic portal in The Netherlands. Information & management, 40(6), 541–549.
• Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342–365.
• Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204.
• Venkatesh, V., & Morris, M. G. (2000). Why don’t men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS quarterly, 24(1), 115–139.
• Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 23(3), 425–478.
• Visa (2016). “Digital Payments Study”. [Erişim: 5 Ocak 2019: https://www.visaeurope.com/media/ images/v2%20visa%20digital%20payments%20european%20fact%20sheet%2010.10.16-7340177.pdf ].
• 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.
• Yuen, Y. Y. (2013). Gender and age effect on acceptance of internet banking: cultural comparison between United States and Malaysia. International Journal of Business and Management, 8(18), 1.