Determinants of user acceptance of digital insurance platform service on InsurTech: an empirical study in South Korea

References

• Agag, G., & El-Masry, A. A. (2016). Understanding consumer intention to participate in online travel community and effects on consumer intention to purchase travel online and WOM: An integration of innovation diffusion theory and TAM with trust. Computers in Human Behavior, 60, 97–111. https://doi.org/10.1016/j.chb.2016.02.038
   Web of Science ®Google Scholar
• Agarwal, R., & Karahanna, E. (2000). Time files when you’re having fun: Cognitive absorption and beliefs about information technology usage. MIS Quarterly, 24(4), 665–694. https://doi.org/10.2307/3250951
   Web of Science ®Google Scholar
• Bagozzi, R. P., & Yi, Y. (2012). Specification, evaluation, and interpretation of structural equation models. Journal of the Academy of Marketing Science, 40(1), 8–34. https://doi.org/10.1007/s11747-011-0278-x
   Web of Science ®Google Scholar
• Bailey, J. E., & Pearson, S. W. (1983). Development of tool for measuring and analyzing computer user satisfaction. Management Science, 29(5), 530–545. https://doi.org/10.1287/mnsc.29.5.530
   Web of Science ®Google Scholar
• Balapour, A., Reychav, I., Sabherwal, R., & Azuri, J. (2019). Mobile technology identity and self-efficacy: Implications for the adoption of clinically supported mobile health apps. International Journal of Information Management, 49, 58–68. https://doi.org/10.1016/j.ijinfomgt.2019.03.005
   Web of Science ®Google Scholar
• Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215. https://doi.org/10.1037/0033-295X.84.2.191
   PubMed Web of Science ®Google Scholar
• Bauer, R. A. (1960). Consumer behavior as risk taking. In Proceedings of the 43rd National Conference of the American Marketing Association (pp. 389–398). American Marketing Association.
   Google Scholar
• Bharati, P., & Berg, D. (2005). Service quality from the other side: Information systems management at Duquesne light. International Journal of Information Management, 25(4), 367–380. https://doi.org/10.1016/j.ijinfomgt.2005.04.008
   Web of Science ®Google Scholar
• Bollen, K. A. (1989). Structural equations with latent variables. Wiley.
   Google Scholar
• Bose, A., & Bastid, V. (2018). World insurance report 2018. Capgemini, Efma. https://worldinsurancereport.com/resources/world-insurance-report-2018/
   Google Scholar
• Braun, A., & Schreiber, F. (2017). The current InsurTech landscape: Business models and disruptive potential. Institute of Insurance Economics I.VW-HSG, University of St. Gallen.
   Google Scholar
• Cappiello, A. (2018). Technology and the insurance industry: Digital disruptions and InsurTech start-ups. Springer International Publishing. https://doi.org/10.1007/978-3-319-74712-5_3
   Google Scholar
• CB Insights. (2019). Quarterly InsurTech briefing Q2 2019 (pp. 1–43). Willis Towers Watson & CB Insights. https://www.cbinsights.com/reports/CB-Insights_Insurance-Tech-Q2-2019.pdf
   Google Scholar
• Chang, S. C., & Tung, F. C. (2008). An empirical investigation of students’ behavioural intentions to use the online learning course websites. British Journal of Educational Technology, 39(1), 71–83. https://doi.org/10.1111/j.1467-8535.2007.00742.x
   Web of Science ®Google Scholar
• Chen, C. C., & Tsai, J. L. (2019). Determinants of behavioral intention to use the personalized location-based mobile tourism application: An empirical study by integrating TAM with ISSM. Future Generation Computer Systems, 96, 628–638. https://doi.org/10.1016/j.future.2017.02.028
   Web of Science ®Google Scholar
• Chen, F., Curran, P. J., Bollen, K. A., Kirby, J., & Paxton, P. (2008). An empirical evaluation of the use of fixed cutoff points in RMSEA test statistic in structural equation models. Sociological Methods & Research, 36(4), 462–494. https://doi.org/10.1177/0049124108314720
   PubMed Web of Science ®Google Scholar
• Choe, J. Y., Kim, J. J., & Hwang, J. (2022). Innovative robotic restaurants in Korea: Merging a technology acceptance model and theory of planned behavior. Asian Journal of Technology Innovation, 30(2), 466–489. https://doi.org/10.1080/19761597.2021.2005466
   Web of Science ®Google Scholar
• Choi, C. (2018). A study on a business model based on the InsurTech cases. The e-Business Studies, 19(4), 155–168. https://doi.org/10.20462/TeBS.2018.8.19.4.155
   Google Scholar
• Claudy, M. C., Garcia, R., & O'Driscoll, A. (2014). Consumer resistance to innovation – a behavioral reasoning perspective. Journal of the Academy of Marketing Science, 43(4), 528–544. https://doi.org/10.1007/s11747-014-0399-0
   Web of Science ®Google Scholar
• Cortis, D., Debattista, J., Debono, J., & Farrel, M. (2019). Disrupting finance: InsurTech. Palgrave Pivot. https://doi.org/10.1007/978-3-030-02330-0_5
   Google Scholar
• Crespo, A. H., & del Bosque, I. R. (2008). The effect of innovativeness on the adoption of B2C e-commerce: A model based on the theory of planned behaviour. Computers in Human Behavior, 24(6), 2830–2847. https://doi.org/10.1016/j.chb.2008.04.008
   Web of Science ®Google Scholar
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–339. https://doi.org/10.2307/249008
   Web of Science ®Google Scholar
• 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. https://doi.org/10.1287/mnsc.35.8.982
   Web of Science ®Google Scholar
• Deloitte. (2019). Accelerating insurance innovation in the age of InsurTech (pp. 1–13). Deloitte Center for Financial Services. https://www2.deloitte.com/content/dam/Deloitte/us/Documents/financial-services/us-accelerating-insurance-innovation.pdf
   Google Scholar
• DeLone, W. H., & McLean, E. R. (1992). Information systems success: The quest for the dependent variable. Information Systems Research, 3(1), 60–95. https://doi.org/10.1287/isre.3.1.60
   Web of Science ®Google Scholar
• DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9–30. https://doi.org/10.1080/07421222.2003.11045748
   Web of Science ®Google Scholar
• Efron, B., & Tibshirani, R. J. (1994). An introduction to the bootstrap. Chapman & Hall/CRC.
   Google Scholar
• Emanuel, S., Dremel, C., & Uebernickel, F. (2018). Exploring characteristics and transformational capabilities of InsurTech innovations to understand insurance value creation in a digital world. Electronic Markets, 28(3), 287–305. https://doi.org/10.1007/s12525-018-0304-7
   Web of Science ®Google Scholar
• Emma, L. S., Yogesh, K. D., Niall, C. P., & Michael, D. W. (2015). Modeling consumers’ adoption intentions of remote mobile payments in The United Kingdom: Extending UTAUT with innovativeness, risk, and trust. Psychology & Marketing, 32(8), 860–873. https://doi.org/10.1002/mar.20823
   Web of Science ®Google Scholar
• 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. https://doi.org/10.1016/S1071-5819(03)00111-3
   Web of Science ®Google Scholar
• Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
   Web of Science ®Google Scholar
• Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51–90. https://doi.org/10.2307/30036519
   Web of Science ®Google Scholar
• Giordana, M. (2018). Fintech sector: Business model analysis in the mobile payments area [Master thesis, Politecnico di Torino]. http://webthesis.biblio.polito.it/id/eprint/7269
   Google Scholar
• Gorla, H., Somers, T. M., & Wong, B. (2010). Organizational impact of system quality, information quality, and service quality. The Journal of Strategic Information Systems, 19(3), 207–228. https://doi.org/10.1016/j.jsis.2010.05.001
   Web of Science ®Google Scholar
• Gupta, A., Dhiman, N., Yousaf, A., & Arora, N. (2020). Social comparison and continuance intention of smart fitness wearables: An extended expectation confirmation theory perspective. Behaviour & Information Technology, 40(13), 1341–1354. https://doi.org/10.1080/0144929X.2020.1748715
   Web of Science ®Google Scholar
• Gursoy, D., Chi, O. H., Lu, L., & Nunkoo, R. (2019). Consumers acceptance of artificially intelligent (AI) device use in service delivery. International Journal of Information Management, 49, 157–169. https://doi.org/10.1016/j.ijinfomgt.2019.03.008
   Web of Science ®Google Scholar
• Hagiu, A., & Wright, J. (2015). Multi-sided platforms. International Journal of Industrial Organization, 43, 162–174. https://doi.org/10.1016/j.ijindorg.2015.03.003
   Web of Science ®Google Scholar
• Hair, J. F., Tatham, R. L., Anderson, R. E., & Black, W. (2010). Multivariate data analysis (5th ed.). Prentice Hall.
   Google Scholar
• Heidenreich, S., & Handrich, M. (2015). What about passive innovation resistance? Investigating adoption-related behavior from a resistance perspective. Journal of Product Innovation Management, 32(6), 878–903. https://doi.org/10.1111/jpim.12161
   Web of Science ®Google Scholar
• Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modelling in international marketing. Advances in International Marketing, 20, 277–319. https://doi.org/10.1108/S1474-7979(2009)0000020014
   Web of Science ®Google Scholar
• Hu, G., Chohan, S. R., & Liu, J. (2022). Does IoT service orchestration in public services enrich the citizens’ perceived value of digital society? Asian Journal of Technology Innovation, 30(1), 217–243. https://doi.org/10.1080/19761597.2020.1865824
   Web of Science ®Google Scholar
• Hu, L., & Bentler, P. (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
   Web of Science ®Google Scholar
• Jackson, J. D., Mun, Y. Y., & Park, J. S. (2013). An empirical test of three mediation models for the relationship between personal innovativeness and user acceptance of technology. Information & Management, 50(4), 154–161. https://doi.org/10.1016/j.im.2013.02.006
   Web of Science ®Google Scholar
• Jan, A. L. (2017). End-consumer trust and adoption of smart contracts in life insurance in South Africa (pp. 1–88). The University of Pretoria's Gordon Institute of Business Science. http://hdl.handle.net/2263/64865
   Google Scholar
• Kai-Ying, C., & Meng-Lin, C. (2013). User acceptance of NFC mobile phone service: An investigation based on the UTAUT model. The Service Industries Journal, 33(6), 609–623. https://doi.org/10.1080/02642069.2011.622369
   Web of Science ®Google Scholar
• Kaur, P. (2016). Underpinnings of user participation in innovation on online communication platforms [Doctoral dissertation, Aalto University, Helsinki]. https://aaltodoc.aalto.fi/bitstream/handle/123456789/20583/isbn9789526068312.pdf?sequence = 1&isAllowed = y
   Google Scholar
• KB Financial Group. (2018). KB knowledge vitamins: Growth and influence of insurance aggregation services. KB Financial Group Management Research Institute, 18(47), 1–10. https://www.kbfg.com/kbresearch/vitamin/reportView.do?vitaminId = 1003681
   Google Scholar
• Keaveney, S. M. (1995). Customer switching behavior in service industries: An exploratory study. Journal of Marketing, 59(2), 71–82. https://doi.org/10.1177/002224299505900206
   Web of Science ®Google Scholar
• Kim, H. (2015). A study of use intention of NFC service using UTAUT model. Korea Trade Exhibition Review, 10(2), 25–48. https://doi.org/10.16938/ijtfs.2015.10.2.025
   Google Scholar
• Kim, K. I. (2014). An exploratory study on IMS performance modeling using information system success model. Journal of Digital Convergence, 12(3), 127–140. https://doi.org/10.14400/JDC.2014.12.3.127
   Google Scholar
• Korea Capital Market Institute. (2019). Current status of domestic and international InsurTech markets and implications. KCMI Capital Market Focus, 20, 1–5. https://www.kcmi.re.kr/publications/pub_detail_view.php?syear = 2019&zcd = 002001016&zno = 1486&cno = 5302.
   Google Scholar
• Korea Development Bank. (2019). Recent trends of global investment in Fintech startups, issue brief. Future Strategy Research Institute, 832, 4–6.
   Google Scholar
• KPMG. (2016). General insurance industry review: With the top 10 emerging trends impacting the sector (pp. 1–32). KPMG Australia. https://assets.kpmg/content/dam/kpmg/au/pdf/2016/general-insurance-industry-review-2016.pdf
   Google Scholar
• KPMG. (2019). 2019 Fintech 100: Leading global Fintech innovators (pp. 1–111). H2 Ventures & KPMG. https://home.kpmg/xx/en/home/insights/2019/11/2019-fintech100-leading-global-fintech-innovators-fs.html
   Google Scholar
• Kuisma, T., Laukkanen, T., & Hiltunen, M. (2007). Mapping the reasons for resistance to internet banking: A means-end approach. International Journal of Information Management, 27(2), 75–85. https://doi.org/10.1016/j.ijinfomgt.2006.08.006
   Web of Science ®Google Scholar
• Latikka, R., Turja, T., & Oksanen, A. (2019). Self-efficacy and acceptance of robots. Computers in Human Behavior, 93, 157–163. https://doi.org/10.1016/j.chb.2018.12.017
   Web of Science ®Google Scholar
• Lee, Y. H., Hsieh, Y. C., & Hsu, C. N. (2011). Adding innovation diffusion theory to the technology acceptance model: Supporting employees’ intentions to use e-learning systems. Journal of Educational Technology & Society, 14(4), 124–137. https://www.jstor.org/stable/10.2307jeductechsoci.14.4.124
   Web of Science ®Google Scholar
• Lu, J., Yao, J. E., & Yu, C. S. (2005). Personal innovativeness, social influences and adoption of wireless internet services via mobile technology. The Journal of Strategic Information Systems, 14(3), 245–268. https://doi.org/10.1016/j.jsis.2005.07.003
   Web of Science ®Google Scholar
• Luarn, P., & Lin, H. H. (2005). Toward an understanding of the behavioural intention to use mobile banking. Computers in Human Behavior, 21(6), 873–891. https://doi.org/10.1016/j.chb.2004.03.003
   Web of Science ®Google Scholar
• Maican, C. I., Cazan, A. M., Lixandroiu, R. C., & Dovleac, L. (2019). A study on academic staff personality and technology acceptance: The case of communication and collaboration applications. Computers & Education, 128, 113–131. https://doi.org/10.1016/j.compedu.2018.09.010
   Web of Science ®Google Scholar
• Mallat, N. (2007). Exploring consumer adoption of mobile payments – a qualitative study. Journal of Strategic Information Systems, 16(4), 413–432. https://doi.org/10.1016/j.jsis.2007.08.001
   Web of Science ®Google Scholar
• Marafie, Z., Lin, K. J., Zhai, Y., & Li, J. (2018). Proactive Fintech: Using intelligent IoT to deliver positive InsurTech feedback. In IEEE 20th Conference on Business Informatics (pp. 72–81). IEEE. https://doi.org/10.1109/CBI.2018.10048
   Google Scholar
• Marano, P. (2019). Navigating InsurTech: The digital intermediaries of insurance products and customer protection in the EU. Maastricht Journal of European and Comparative Law, 26(2), 294–315. https://doi.org/10.1177/1023263X19830345
   Google Scholar
• Martins, C., Oliveira, T., & Popovič, A. (2014). Understanding the internet banking adoption: A unified theory of acceptance and use of technology and perceived risk application. International Journal of Information Management, 34(1), 1–13. https://doi.org/10.1016/j.ijinfomgt.2013.06.002
   Web of Science ®Google Scholar
• McFall, L., & Moor, S. (2018). Who, or what, is InsurTech personalizing?: Persons, process and historical classifications of risk. Distinktion: Journal of Social Theory, 19(2), 193–213. https://doi.org/10.1080/1600910X.2018.1503609
   Web of Science ®Google Scholar
• McKinsey. (2017). Digital disruption in insurance: Cutting through the noise. McKinsey & Company. https://www.mckinsey.com/~/media/mckinsey/industries/financial%20services/our%20insights/time%20for%20insurance%20companies%20to%20face%20digital%20reality/digital-disruption-in-insurance.ashx
   Google Scholar
• McKinsey. (2018). Insurance 2030: The impact of AI on the future of insurance. McKinsey & Company. https://www.mckinsey.com/industries/financial-services/our-insights/insurance-2030-the-impact-of-ai-on-the-future-of-insurance#
   Google Scholar
• Nelson, R. R., Todd, P. A., & Wixom, B. H. (2005). Antecedents of information and system quality: An empirical examination within the context of data warehousing. Journal of Management Information Systems, 21(4), 199–235. https://doi.org/10.1080/07421222.2005.11045823
   Web of Science ®Google Scholar
• Nicoletti, B. (2017). The future of Fintech: A business model for InsurTech initiatives. Palgrave Macmillan. https://doi.org/10.1007/978-3-319-51415-4_8
   Google Scholar
• Nunnally, J., & Bernstein, I. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
   Google Scholar
• Oliveira, T., Faria, M., Thomas, M. A., & Popovič, A. (2014). Extending the understanding of mobile banking adoption: When UTAUT meets TTF and ITM. International Journal of Information Management, 34(5), 689–703. https://doi.org/10.1016/j.ijinfomgt.2014.06.004
   Web of Science ®Google Scholar
• 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. https://doi.org/10.1016/j.chb.2016.03.030
   Web of Science ®Google Scholar
• Park, S., & Park, J. (2017a). InsurTech innovation: The current and the future. Korea Insurance Research Institute, 2017(11), 1–170. Retrieved May 3, 2020, from http://www.riss.kr/search/detail/DetailView.do?p_mat_type = 1a0202e37d52c72d&control_no = f6662e218162908247de9c1710b0298d
   Google Scholar
• Park, Y., & Park, H. (2017b). Testing for use and acceptance of internet banking based on UTAUT model. Korea Society of Industrial Information Systems, 22(1), 11–21. https://doi.org/10.9723/jksiis.2017.22.1.011
   Google Scholar
• Parker, G. G., Alstyne, V. M., & Choudary, S. (2016). Platform revolution. W.W. Norton & Company.
   Google Scholar
• Paul, G. S., Oliver, S., & Bernd, W. W. (2009). Understanding consumer acceptance of mobile payment services: An empirical analysis. Electronic Commerce Research and Applications, 9(3), 209–216. https://doi.org/10.1016/j.elerap.2009.07.005
   Web of Science ®Google Scholar
• Pavlou, P. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 101–134. https://doi.org/10.1080/10864415.2003.11044275
   Web of Science ®Google Scholar
• Pitt, L., Watson, R., & Kavan, C. B. (1995). Service quality: A measure of information systems effectiveness. MIS Quarterly, 19(2), 173–187. https://doi.org/10.2307/249687
   Web of Science ®Google Scholar
• Preacher, K. J., Zyphur, M. J., & Zhang, Z. (2010). A general multilevel SEM framework for assessing multilevel mediation. Psychological Methods, 15(3), 209–233. https://doi.org/10.1037/a0020141
   PubMed Web of Science ®Google Scholar
• Puschmann, T. (2017). Fintech. Business & Information Systems Engineering, 59(1), 69–76. https://doi.org/10.1007/s12599-017-0464-6
   Web of Science ®Google Scholar
• PwC. (2016). InsurTech: A golden opportunity for insurers to innovate. PwC Consulting. https://www.pwc.nl/en/publicaties/insurtech-a-golden-opportunity-for-insurers-to-innovate.html
   Google Scholar
• Ram, S. (1987). A model of innovation resistance. Advances in Consumer Research, 14, 208–212.
   Google Scholar
• Ratnasingam, P. (2005). Trust in inter-organizational exchanges: A cases study in business to business electronic commerce. Decision Support System, 39(3), 525–544. https://doi.org/10.1016/j.dss.2003.12.005
   Web of Science ®Google Scholar
• Reuver, D. M., Sørensen, C., & Basole, C. R. (2018). The digital platform: A research agenda. Journal of Information Technology, 33(2), 124–135. https://doi.org/10.1057/s41265-016-0033-3
   Web of Science ®Google Scholar
• Rogers, E. M. (1983). Diffusion of innovations (3rd ed.). The Free Press.
   Google Scholar
• Rogers, E. M. (2003). Diffusion of innovations (5th ed.). The Free Press.
   Google Scholar
• Seddon, P. B. (1997). A respecification and extension of the DeLone and McLean model of IS success. Information System Research, 8(3), 240–253. https://doi.org/10.1287/isre.8.3.240
   Web of Science ®Google Scholar
• Seddon, P. B., & Kiew, M. Y. (1996). A partial test and development of the Delone & McLean model of IS success. Australasian Journal of Information Systems, 4(1), 90–109. https://doi.org/10.3127/ajis.v4i1.379
   Google Scholar
• Sharma, R., Singh, G., & Sharma, S. (2020). Modelling internet banking adoption in Fiji: A developing country perspective. International Journal of Information Management, 53, Article 102116. https://doi.org/10.1016/j.ijinfomgt.2020.102116
   Web of Science ®Google Scholar
• Sheth, J. N. (1981). Psychology of innovation resistance: The less developed concept in diffusion research. Research in Marketing, 4, 273–282.
   Google Scholar
• Shin, H., Kang, S.-E., & Lee, C.-K. (2023). Impact of innovation characteristics of airport self-bag-drop service on attitude, trust, and behavioural intention: Using trust transfer theory. Asian Journal of Technology Innovation, 31(3), 604–624. https://doi.org/10.1080/19761597.2022.2146595
   Web of Science ®Google Scholar
• Song, C. H., Kim, S. W., & Sohn, Y. W. (2020). Acceptance of public cloud storage services in South Korea: A multi-group analysis. International Journal of Information Management, 51, Article 102035. https://doi.org/10.1016/j.ijinfomgt.2019.11.003
   Web of Science ®Google Scholar
• Suh, C., & Seong, S. (2004). Individual characteristics affecting user`s intention to use internet shopping mall. Asia Pacific Journal of Information Systems, 14(3), 1–22. http://www.koreascience.or.kr/article/JAKO200416638000163
   Google Scholar
• Takagi, C., Purnomo, S. H., & Kim, M.-K. (2021). Adopting smart agriculture among organic farmers in Taiwan. Asian Journal of Technology Innovation, 29(2), 180–195. https://doi.org/10.1080/19761597.2020.1797514
   Web of Science ®Google Scholar
• Talke, K., & Heidenreich, S. (2014). How to overcome pro-change bias: Incorporating passive and active innovation resistance in innovation decision models. Journal of Product Innovation Management, 31(5), 894–907. https://doi.org/10.1111/jpim.12130
   Web of Science ®Google Scholar
• Taylor, S., & Todd, P. A. (1995). Assessing IT usage: The role of prior experience. MIS Quarterly, 19(2), 561–570. https://doi.org/10.2307/249633
   Web of Science ®Google Scholar
• 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. https://doi.org/10.1108/IntR-12-2012-0244
   Web of Science ®Google Scholar
• Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: Toward a conceptual model of utilization. MIS Quarterly, 15(1), 125–143. https://doi.org/10.2307/249443
   Web of Science ®Google Scholar
• Torres, R., & Sidorova, A. (2019). Reconceptualizing information quality as effective use in the context of business intelligence and analytics. International Journal of Information Management, 49, 316–329. https://doi.org/10.1016/j.ijinfomgt.2019.05.028
   Web of Science ®Google Scholar
• Velayutham, S., Aldridge, J. M., & Fraser, B. (2012). Gender differences in student motivation and self-regulation in science learning: A multi-group structural equation modeling analysis. International Journal of Science and Mathematics Education, 10(6), 1347–1368. https://doi.org/10.1007/s10763-012-9339-y
   Web of Science ®Google Scholar
• Venkatesh, V., & Davis, F. D. (2000). Theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
   Web of Science ®Google Scholar
• Venkatesh, V., Morris, M. G., Davis, B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
   Web of Science ®Google Scholar
• Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412
   Web of Science ®Google Scholar
• Wang, C., & Teo, T. S. H. (2020). Online service quality and perceived value in mobile government success: An empirical study of mobile police in China. International Journal of Information Management, 52, Article 102076. https://doi.org/10.1016/j.ijinfomgt.2020.102076
   Web of Science ®Google Scholar
• Wang, Y. S., & Shih, Y. W. (2009). Why do people use information kiosks? A validation of the unified theory of acceptance and use of technology. Government Information Quarterly, 26(1), 158–165. https://doi.org/10.1016/j.giq.2008.07.001
   Web of Science ®Google Scholar
• Williams, M. D., & Dwivedi, Y. K. (2015). The unified theory of acceptance and use of technology (UTAUT): A literature review. Journal of Enterprise Information Management, 28(3), 443–488. https://doi.org/10.1108/JEIM-09-2014-0088
   Web of Science ®Google Scholar
• Wonglimpiyarat, J., & Yuberk, N. (2005). In support of innovation management and Roger's innovation diffusion theory. Government Information Quarterly, 22(3), 411–422. https://doi.org/10.1016/j.giq.2005.05.005
   Web of Science ®Google Scholar
• Wu, J. H., & Wang, S. C. (2005). What drives mobile commerce? An empirical evaluation of the revised technology acceptance model. Information & Management, 42(5), 719–729. https://doi.org/10.1016/j.im.2004.07.001
   Web of Science ®Google Scholar
• Yang, S. H., Hwang, Y. S., & Park, J. K. (2016). A study on the use of Fintech payment services based on the UTAUT model. Journal of Management & Economics, 38(1), 183–209.
   Google Scholar
• Yiu, C. S., Grant, K., & Edgar, D. (2007). Factors affecting the adoption of internet banking in Hong Kong – implications for the banking sector. International Journal of Information Management, 27(5), 336–351. https://doi.org/10.1016/j.ijinfomgt.2007.03.002
   Web of Science ®Google Scholar
• Zheng, Y., Zhao, K., & Stylianou, A. (2013). The impacts of information quality and system quality on users’ continuance intention in information-exchange virtual communities: An empirical investigation. Decision Support Systems, 56, 513–524. https://doi.org/10.1016/j.dss.2012.11.008
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar