Faktor- faktor yang mempengaruhi Aplikasi Mobile Banking di Kota Tegal: Pendekatan Empiris Memperluas UTAUT2 dengan Nilai dan Kepercayaan yang Dirasakan

Evan Fauzan Putra

doi:10.30595/jesh.v2i2.318

Authors

Evan Fauzan Putra Universitas Muhammadiyah Purwokerto

DOI:

https://doi.org/10.30595/jesh.v2i2.318

Keywords:

Performance Expectancy, Habit, Monetary Value, Trust, Behavioral Intention

Abstract

Tujuan penelitian ini adalah untuk mempelajari faktor-faktor yang memengaruhi penggunaan mobile banking (mBanking) oleh konsumen di Kota Tegal. Studi ini mengadopsi Teori Penerimaan dan Penggunaan Teknologi Terpadu yang Diperluas (UTAUT2) dengan Performance Expectancy, Habit, Monetary Value, Trust terhadap Behavioral Intention. Sasaran pada mini riset ini adalah masyarakat umum di Kota Tegal. Mini Riset ini menggunakan metode kuantitatif dengan pengambilan sampel secara purposive sampling dan diperoleh hasil 60 responden yang kemudian diolah menggunakan lima skala likert dengan analisis Structural Equation Model (SEM) yang berbasis pada komponen atau varian dengan pendekatan Partial Least Square (PLS). Hasil dari penelitian ini menunjukan bahwa Habit dan Trust berpengaruh posisitf terhadap Behavioral Intention. Sedangkan Performance Expectancy dan Monetary Value tidak berpengaruh terhadap Behavioral Intention.

References

ACI. (2018). Transactions 2025: The future of payments in India. Worldwide ACI. https://www.aciworldwide.com/-/media/ files/collateral/trends/transactions-2025-an-economic-times- report-on-the-future-of-payments-in-india.pdf

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.

Akturan, U., & Tezcan, N. (2012). Mobile banking adoption of the youth market: Perceptions and intentions. Marketing Intelligence & Planning, 30(4), 444–459.

Alalwan, A. A., Dwivedi, Y. K., & Rana, N. P. (2017). Factors influencing adoption of mobile banking by Jordanian bank customers: Extending UTAUT2 with trust. International Journal of Information Management, 37(3), 99–110.

Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423.

Armstrong, J. S., & Overton, T. S. (1977). Estimating nonresponse bias in mail surveys. Journal of Marketing Research, 14(3), 396. https://doi.org/10.2307/3150783

Aswani, R., Ilavarasan, P. V., Kar, A. K., & Vijayan, S. (2018). Adoption of public WiFi using UTAUT2: An exploration in an emerging economy. Procedia Computer Science, 132(2018), 297–306. Baabdullah, A. M., Alalwan, A. A., Rana, N. P., Kizgin, H., & Patil, P. (2019). Consumer use of mobile banking (M-Banking) in Saudi Arabia: Towards an integrated model. International Journal of Information Management, 44, 38–52.

Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94.

Baptista, G., & Oliveira, T. (2015). Understanding mobile banking: The unified theory of acceptance and use of technology combined with cultural moderators. Computers in Human Behavior, 50, 418–430.

Baptista, G., & Oliveira, T. (2017). Why so serious? Gamification impact in the acceptance of mobile banking services. Internet Research, 27(1), 118–139.

Barnes, S. J., & Corbitt, B. (2003). Mobile banking: Concept and potential. International Journal of Mobile Communications, 1(3), 273–288.

Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182.

Barrett, M., Davidson, E., Prabhu, J., & Vargo, S. L. (2015). Service innovation in the digital age: Key contributions and future directions. MIS Quarterly, 39(1), 135–154.

Bem, D. J., & Allen, A. (1974). On predicting some of the people some of the time: The search for cross-situational consistencies in behavior. Psychological Review, 81(6), 506–520.

Benbasat, I., & Barki, H. (2007). Quo vadis, TAM? Journal of the Association for Information Systems, 8(4), 211–218.

Bentler, P. (1990). Quantitative methods in psychology: Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238–246.

Berraies, S., Yahia, K. Ben, & Hannachi, M. (2017). Identifying the effects of perceived values of mobile banking applications on customers: Comparative study between baby boomers, generation X and generation Y. International Journal of Bank Marketing, 35(6), 1018–1038.

Bolton, R. N., & Drew, J. H. (1991). A multistage model of customers’ assessments of service quality and value. Journal of Consumer Research, 17(4), 375–384.

Boonsiritomachai, W., & Pitchayadejanant, K. (2019). Determinants affecting mobile banking adoption by generation Y based on the unified theory of acceptance and use of technology model modified by the technology acceptance model concept. Kasetsart Journal of Social Sciences, 40(2), 349–358. https://doi.org/10.1016/j.kjss.2017.10.005

Browne, M. W., & Cudeck, R. (1992). Alternative ways of assessing model fit. Sage Focus Editions, 21(2), 230–258.

Burton-Jones, A., & Straub Jr., D. W. (2006). Reconceptualizing system usage: An approach and empirical test, informa- tion systems research. Information Systems Research, 17(3), 228–246.

Chaouali, W., & Souiden, N. (2019). The role of cognitive age in explaining mobile banking resistance among elderly people. Journal of Retailing and Consumer Services, 50(July 2018), 342–350.

Chawla, D., & Joshi, H. (2017). Consumer perspectives about mobile banking adoption in India: A cluster analysis. International Journal of Bank Marketing, 35(4), 616–636.

Chong, A. Y. L. (2013a). A two-staged SEM-neural network approach for understanding and predicting the determinants of m-commerce adoption. Expert Systems with Applications, 40(4), 1240–1247. https://doi.org/10.1016/j. eswa.2012.08.067

Chong, A. Y. L. (2013b). Predicting m-commerce adoption determinants: A neural network approach. Expert Systems with Applications, 40(2), 523–530.

Cruz, P., Neto, L. B. F., Munoz-Gallego, P., & Laukkanen, T. (2010). Mobile banking rollout in emerging markets: Evidence from Brazil. International Journal of Bank Marketing, 28(5), 342–371.

Dahlberg, T., Guo, J., & Ondrus, J. (2015). A critical review of mobile payment research. Electronic Commerce Research and Applications, 14(5), 265–284.

Davis, F. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.

Deb, M., & Agrawal, A. (2017). Factors impacting the adoption of m-banking: Understanding brand India’s potential for financial inclusion. Journal of Asia Business Studies, 11(1), 22–40. https://doi.org/10.1108/JABS-11-2015-0191

Deb, M., & Lomo-David, E. (2014). An empirical examination of customers adoption of m-banking in India. Marketing Intelligence & Planning, 32(4), 475–494.

Demirguc-Kunt, A., Leora Klapper, Singer, D., Ansar, S., & Hess, J. (2018). The Global Findex Database 2017: Measuring financial inclusion and the fintech revolution. https:// globalfindex.worldbank.org/

Doney, P. M., & Cannon, J. P. (1997). An examination of the nature of trust in buyer-seller relationships. Journal of Marketing, 61(2), 35–51.

Evans, J. R., & Mathur, A. (2018). The value of online surveys: A look back and a look ahead. Internet Research, 28(4), 854–887.

Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Reading MA: Addison-Wesley.

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

Garbarino, E., & Strahilevitz, M. (2004). Gender differences in the perceived risk of buying online and the effects of receiving a site recommendation. Journal of Business Research, 57(7), 768–775. https://doi.org/10.1016/S0148-2963(02)00363-6

Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51–90.

Giovanis, A., Assimakopoulos, C., & Sarmaniotis, C. (2018). Adoption of mobile self-service retail banking technologies: The role of technology, social, channel and personal factors. International Journal of Retail & Distribution Management, 47(9), 894–914.

Gronroos, C. (1984). A service quality model and its mMarketing implications. European Journal of Marketing, 18(4), 36–44.

Guadagnoli, E., & Velicer, W. F. (1988). Relation of sample size to the stability of component patterns. Psychological Bulletin, 103(2), 265–275. https://doi.org/10.1037/0033-2909.103.2.265

Gupta, A., & Arora, N. (2017). Consumer adoption of m-banking: A behavioral reasoning theory perspective. International Journal of Bank Marketing, 35(4), 733–747.

Gupta, B., Dasgupta, S., & Gupta, A. (2008). Adoption of ICT in a government organization in a developing country: An empirical study. Journal of Strategic Information Systems, 17(2), 140–154.

Gupta, K. P., Manrai, R., & Goel, U. (2019). Factors influencing adoption of payments banks by Indian customers: Extending UTAUT with perceived credibility. Journal of Asia Business Studies, 13(2), 173–195.

Gupta, Sumeet, Yun, H., Xu, H., & Kim, H. W. (2017). An exploratory study on mobile banking adoption in Indian metropolitan and urban areas: A scenario-based experiment. Information Technology for Development, 23(1), 127–152.

Gupta, Sunil. (2013). The mobile banking and payment revolution. European Financial Review, 2(36), 3–6.

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Upper Saddle River, NJ: Pearson Prentice Hall.

Hanafizadeh, P., Behboudi, M., Koshksaray, A. A., & Tabar, M. J.S. (2014). Mobile-banking adoption by Iranian bank clients.

Telematics and Informatics, 31(1), 62–78. Hayes, A. F. (2017). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (2nd ed.). Guilford Publications.

Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55. https://doi.org/10.1080/10705519909540118

IMAI. (2017). Mobile internet report. https://cms.iamai.in/ Content/ResearchPapers/dc232cb2-22d2-46da-919e- e7a536887291.pdf

Jackson, L. A., Zhao, Y., Kolenic, A., Fitzgerald, H. E., Harold, R., & Von Eye, A. (2008). Race, gender, and information technology use: The new digital divide. CyberPsychology & Behavior, 11(4), 437–442. https://doi.org/10.1089/ cpb.2007.0157

Joreskog, K. G., & Sorbom, D. (1993). LISREL 8: Structural equation modeling with the SIMPLIS command language. Scientific Software International.

Kaiser, H. F., & Rice, J. (1974). Little Jiffy, Mark IV. Educational and Psychological Measurement, 34(1), 111–117.

Kim, C., Tao, W., Shin, N., & Kim, K. S. (2010). An empirical study of customers perceptions of security and trust in e-payment systems. Electronic Commerce Research and Applications, 9(1), 84–95.

Kim, G., Shin, B., & Lee, H. G. (2009). Understanding dynamics between initial trust and usage intentions of mobile banking. Information Systems Journal, 19(3), 283–311.

Kim, S. S., & Malhotra, N. K. (2005). A longitudinal model of continued IS use: An integrative view of four mechanisms underlying postadoption phenomena. Management Science, 51(5), 741–755.

Kline, R. B. (2015). Principles and practice of structural equation modeling. Guilford publications.

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. https://doi.org/10.1108/02652321011064917

Kumar, V. V. R., Lall, A., & Mane, T. (2017). Extending the TAM model: Intention of management students to use mobile banking: Evidence from India. Global Business Review. https://doi.org/10.1177/0972150916666991

Kwateng, K. O., Atiemo, K. A. O., & Appiah, C. (2018). Acceptance and use of mobile banking: An application of UTAUT2. Journal of Enterprise Information Management, 32(1), 118–151.

Ladhari, R., & Leclerc, A. (2013). Building loyalty with online financial services customers: Is there a gender difference? Journal of Retailing and Consumer Services, 20(6), 560–569. https://doi.org/10.1016/j.jretconser.2013.07.005

Lance, C. E., Butts, M. M., & Michels, L. C. (2006). The sources of four commonly reported cutoff criteria: What did they really say? Organizational Research Methods, 9(2), 202–220. https://doi.org/10.1177/1094428105284919

Laukkanen, T., & Kiviniemi, V. (2010). The role of information in mobile banking resistance. International Journal of Bank Marketing, 28(5), 372–388.

Laukkanen, T., & Pasanen, M. (2008). Mobile banking innovators and early adopters: How they differ from other online users? Journal of Financial Services Marketing, 13(2), 86–94. https://doi.org/10.1057/palgrave.fsm.4760077

Leech, N. L., Barrett, K. C., & Morgan, G. A. (2015). IBM SPSS for intermediate statistics: Use and interpretation (5th ed.). Routledge.

Lewicki, R. J. (2006). The handbook of conflict resolution: Theory and practice (M. Deutsch, P. T. Coleman, & E. C. Marcus (Eds.); 2nd ed.). John Wiley & Sons.

Liebana-Cabanillas, F., Japutra, A., Molinillo, S., Singh, N., & Sinha, N. (2020). Assessment of mobile technology use in the emerging market: Analyzing intention to use m-payment services in India. Telecommunications Policy, 44(9). https:// doi.org/10.1016/j.telpol.2020.102009

Limayem, M., Hirt, S. G., & Cheung, C. M. K. (2007). How habit limits the predictive power of intention: The case of information systems continuance. MIS Quarterly, 31(4), 705–737.

Link, M. (2018). New data strategies: Nonprobability sampling, mobile, big data. Quality Assurance in Education, 26(2), 303–314.

MacKenzie, S. B., & Podsakoff, P. M. (2012). Common method bias in marketing: Causes, mechanisms, and procedural remedies. Journal of Retailing, 88(4), 542–555.

Makanyeza, C. (2017). Determinants of consumers’ intention to adopt mobile banking services in Zimbabwe. International Journal of Bank Marketing, 35(6), 997–1017.

Malaquias, F., Malaquias, R., & Hwang, Y. (2018). Understanding the determinants of mobile banking adoption: A longitudinal study in Brazil. Electronic Commerce Research and Applications, 30(May), 1–7. https://doi.org/10.1016/j. elerap.2018.05.002

Malaquias, R. F., & Hwang, Y. (2019). Mobile banking use: A comparative study with Brazilian and U.S. participants. International Journal of Information Management, 44(October 2018), 132–140. https://doi.org/10.1016/j. ijinfomgt.2018.10.004

Minton, H. L., & Schneider, F. W. (1980). Differential psychology. Waveland Press.

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.

Natarajan, T., Balasubramanian, S. A., & Kasilingam, D. L. (2017). Understanding the intention to use mobile shopping applications and its influence on price sensitivity. Journal of Retailing and Consumer Services, 37, 8–22.

Neuman, L. W. (2011). Social research methods: Qualitative and quantitative approaches (7th ed.). Pearson Education.

Nunnally, J. (1978). Psychometric theory (2nd ed.). McGraw-Hill. Nysveen, H., & Pedersen, P. E. (2005). Explaining intention to use mobile chat services: Moderating effects of gender. Journal of Consumer Marketing, 22(5), 247–256. https://doi. org/10.1108/07363760510611671

Oliveira, T., Faria, M., Thomas, M. A., & Popovic, A. (2014). Extending the understanding of mobile banking adoption: When UTAUT meets TTF and ITM. International Journal of Information Management, 34(5), 689–703.

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.

Oliver. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing, 17(4), 460–469.

Pal, A., Herath, T., De, R., & Rao, H. R. (2020). Contextual facilitators and barriers influencing the continued use of mobile payment services in a developing country: Insights from adopters in India. Information Technology for Development, 26(2), 394–420. https://doi.org/10.1080/0268 1102.2019.1701969

Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64(1), 12–40.

Patil, P., Tamilmani, K., Rana, N. P., & Raghavan, V. (2020). Understanding consumer adoption of mobile payment in India: Extending Meta-UTAUT model with personal innovativeness, anxiety, trust, and grievance redressal. International Journal of Information Management, 54(May). https://doi.org/10.1016/j.ijinfomgt.2020.102144

Pavlou, P. A., & Fygenson, M. (2006). Understanding and predicting electronic commerce adoption: An extension of the theory of planned behavior. MIS Quarterly, 30(1), 115–143.

Peng, L., Zhang, W., Wang, X., & Liang, S. (2019). Moderating effects of time pressure on the relationship between perceived value and purchase intention in social E-commerce sales promotion: Considering the impact of product involvement. Information and Management, 56(2), 317–328.

Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N.P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903.

Priya, R., Gandhi, A. V., & Shaikh, A. (2018). Mobile banking adoption in an emerging economy: An empirical analysis of young Indian consumers. Benchmarking, 25(2), 743–762. https://doi.org/10.1108/BIJ-01-2016-0009

Ramadan, R., & Aita, J. (2018). A model of mobile payment usage among Arab consumers. International Journal of Bank Marketing, 36(7), 1213–1234.

RBI. (2019a). List of banks permitted to provide mobile banking service in India. https://www.rbi.org.in/scripts/bs_ viewcontent.aspx?Id=2463

RBI. (2019b). NEFT and RTGS waiver of charges. https://www. rbi.org.in/Scripts/NotificationUser.aspx?Id=11586&Mode=0 RBI. (2019c). Payment systems vision 2019-2021. https://www. rbi.org.in/Scripts/PublicationVisionDocuments.aspx?Id=921 Sahoo, D., & Pillai, S. (2017). Role of mobile banking servicescape on customer attitude and engagement: An empirical investigation in India. International Journal of Bank Marketing, 35(7), 1113–1130.

Sampaio, C. H., Ladeira, W. J., & Santini, F. D. O. (2017). Apps for mobile banking and customer satisfaction: A cross- cultural study. International Journal of Bank Marketing, 35(7), 1131–1151.

Sankaran, R., & Chakraborty, S. (2020). Why customers make mobile payments? Applying a means-end chain approach. Marketing Intelligence & Planning, ahead-of-p(ahead-of- print). https://doi.org/10.1108/MIP-12-2019-0622

Schreiber, J. B., Stage, F. K., King, J., Nora, A., & Barlow, E. (2006). Reporting structural equation modeling and confirmatory factor analysis results: A review. Journal of Educational Research, 99(6), 323–337.

Shaikh, A. A., & Karjaluoto, H. (2015). Mobile banking adoption: A literature review. Telematics and Informatics, 32(1), 129– 142.

Shankar, A., & Datta, B. (2018). Factors affecting mobile payment adoption intention: An Indian perspective. Global Business Review, 19(3; suppl), S72–S89.

Shankar, A., & Rishi, B. (2020). Convenience matter in mobile banking adoption intention? Australasian Marketing Journal (In Press). https://doi.org/10.1016/j.ausmj.2020.06.008

Shareef, M. A., Baabdullah, A., Dutta, S., Kumar, V., & Dwivedi,Y. K. (2018). Consumer adoption of mobile banking services: An empirical examination of factors according to adoption stages. Journal of Retailing and Consumer Services, 43, 54–67.

Shaw, N., & Sergueeva, K. (2019). The non-monetary benefits of mobile commerce: Extending UTAUT2 with perceived value. International Journal of Information Management, 45(November), 44–55.

Sheppard, B. H., Hartwick, J., Warshaw, P. R., & Sheppard, B.H. (1988). The theory of reasoned action : A meta-analysis of past research with recommendations for modifications and future research. Journal of Consumer Research, 15(3), 325–343.

Singh, N., Sinha, N., & Liebana-cabanillas, F. J. (2020). Determining factors in the adoption and recommendation of mobile wallet services in India: Analysis of the effect of innovativeness, stress to use and social influence. International Journal of Information Management, 50, 191–205.

Singh, S., Srivastava, R. K., & Sindhu Singh, R. . S. (2018). Predicting the intention to use mobile banking in India. International Journal of Bank Marketing, 36(2), 357–378. https://doi.org/10.1108/IJBM-12-2016-0186

Sivathanu, B. (2018). Adoption of digital payment systems in the era of demonetization in India: An empirical study. Journal of Science and Technology Policy Management, 10(1), 07-2017–0033.

Strasheim, A. (2014). Testing main and interaction effects in structural equation models with a categorical moderator variable. Management Dynamics, 23(4), 31–69.

Sweeney, J. C., & Soutar, G. N. (2001). Consumer perceived value: The development of a multiple item scale. Journal of Retailing, 77(2), 203–220.

Tam, C., & Oliveira, T. (2016). Understanding the impact of m-banking on individual performance: DeLone & McLean and TTF perspective. Computers in Human Behavior, 61, 233–244.

Tanaka, J. S., & Huba, G. J. (1985). A fit index for covariance structure models under arbitrary GLS estimation. British Journal of Mathematical and Statistical Psychology, 38(2), 197–201.

Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144–176.

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.

TRAI. (2019). Press Release No. 13/2019. https://main.trai.gov. in/release-publication/reports/telecom-subscriptions-reports

Venkatesh, V. (2015). Technology acceptance model and the unified theory of acceptance and use of technology. Wiley Encyclopedia of Management, 1–9.

Venkatesh, V., & Morris, M. G. (2000). Why don’t men ever stop to ask for directions? Gender, social influence, and their role in technology 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: Management Information Systems, 27(3), 425–478.

Venkatesh, V., Thong, J., & Xu, X. (2012). Consumer acceptance and user of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178.

Venkatesh, V., Thong, J. Y. L., & Xu, X. (2016). Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the Association for Information Systems, 17(5), 328–376.

Verkijika, S. F. (2018). Factors influencing the adoption of mobile commerce applications in Cameroon. Telematics and Informatics, 35(6), 1665–1674.

Walsh, G., Evanschitzky, H., & Wunderlich, M. (2008). Identification and analysis of moderator variables: Investigating the customer satisfaction-loyalty link. European Journal of Marketing, 42(9–10), 977–1004.

Walsh, G., Shiu, E., & Hassan, L. M. (2014). Replicating, validating, and reducing the length of the consumer perceived value scale. Journal of Business Research, 67(3), 260–267.

Wang, W. (2012). Factors influencing mobile services adoption: A brand-equity perspective. Internet Research, 22(2), 142–179. Webster, J., & Watson, R. T. (2002). Analyzing the past to prepare for the future: Writing a literature review. MIS Quarterly, 26(2), 13–23.

Wolin, L. D., & Korgaonkar, P. (2003). Web advertising: Gender differences in beliefs, attitudes and behavior. Internet Research, 13(5), 375–385. https://doi.org/10.1108/10662240310501658

Wu, K., Zhao, Y., Zhu, Q., Tan, X., & Zheng, H. (2011). A meta- analysis of the impact of trust on technology acceptance model: Investigation of moderating influence of subject and context type. International Journal of Information Management, 31(6), 572–581.

Zeithaml, V. A. (1988). Consumer perceptions of price, quality, and value. Journal of Marketing, 52(3), 2–22.

Zhou, T., Lu, Y., & Wang, B. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in Human Behavior, 26(4), 760–767. https://doi.org/10.1016/j. chb.2010.01.013

Zikmund, W. G., Babin, B. J., Carr, J. C., Adhikari, A., & Griffin, M. (2016). Business Research Methods (8th ed.). Cengage Learning.