An Integrated Technology Acceptance Model for Smart City Mobile Applications: Identification of Key Factors and Extension of Technology Adoption

Authors

  • Musab Talha Akpınar Ankara Yildirim Beyazit University, Türkiye
  • Mehmet Atak Gazi University, Türkiye

DOI:

https://doi.org/10.32890/ijms2025.32.1.6

Keywords:

Mobile Application, Smart City, Smart Systems, Technology Adoption, TAM

Abstract

Smart cities have experienced significant improvements in service quality, resident satisfaction, sustainability, and economic development through the application of information and communication technology. With urban populations expected to grow, the adoption of smart city mobile applications (SCMA) can enhance the efficiency and impact of urban regions. This research explores the cognitive factors influencing user engagement and adoption of SCMA, by integrating identified factors with technological adaptation models. The decision of individual users to embrace such as SCMA has garnered attention from both information systems (IS) researchers and industry practitioners. Key factors affecting SCMA user acceptance include perceived enjoyment, innovation, trust, social influence, security, compliance, satisfaction, perceived benefit, ease of use, and intention to use. Data was analyzed using a structural equation model (SEM), with participants responding to questionnaire items on a five-point Likert scale. Out of an initial 1,142 responses, 1,062 valid samples were included in the final analysis after data filtering, achieving a response rate of 67 percent. Enhanced user satisfaction is crucial for the success of SCMA providers. The industry must allocate additional resources toward developing robust and reliable infrastructures and platforms that enhance mobility and service quality. To drive the future of Web 3.0, the industry must also address long-term challenges, such as creating new universal systems and environments. The conclusion of this study is discussed with respect to both theoretical and practical implications.

Downloads

Download data is not yet available.

References

Ismail, A., Abdin, F., Muhamad, N. S. A., & Nor, A. M. (2020). Effect of perceived fairness in pay system on work-related attitudes. International Journal of Management Studies, 27(2), 1-26. https://doi.org/10.32890/ijms.27.2.2020.7792

Abu-Dalbouh, H. M. (2013). A questionnaire approach based on the technology acceptance model for mobile tracking on patient progress applications. Journal Computer Science, 9(6), 763-770.

Adeel, A., Batool, S., & Madni, Z. ul-A. (2023). Intrinsic motivation and creativity: The role of digital technology and knowledge integration ability in facilitating creativity. International Journal of Management Studies, 30(1), 1-36. https://doi.org/10.32890/ijms2023.30.1.1

Agarwal, R., & Prasad, J. (1997). The role of innovation characteristics and perceived voluntariness in the acceptance of information technologies. Decision Sciences, 28(3), 557-582.

Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9(2), 204-215.

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

Amoako-Gyampah, K. (2007). Perceived usefulness, user involvement and behavioral intention: An empirical study of ERP implementation. Computers in Human Behavior, 23(3), 1232-1248.

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

Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88(3), 588.

Bhattacherjee, A. (2001). Understanding Information Systems Continuance: An Expectation-Confirmation Model. MIS Quarterly, 25(3), 351–370. https://doi.org/10.2307/3250921

Bhatti, A., & Ur Rahman, S. (2019). Perceived benefits and perceived risks effect on online shopping behavior with the mediating role of consumer purchase intention in Pakistan. International Journal of Management Studies, 26(1), 33-54.

Bibri, S. E., & Krogstie, J. (2017). Smart sustainable cities of the future: An extensive interdisciplinary literature review. Sustainable Cities and Society, 31, 183-212. https://doi.org/10.1016/ j.scs.2017.02.016

Caragliu, A., Del Bo, C., & Nijkamp, P. (2011). Smart cities in Europe. Journal of Urban Technology, 18(2), 65-82. https://doi.org/10.1080/10630732.2011.601117

Chandra, S., Srivastava, S. C., & 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(1), 29.

Chau, P. Y., & Hu, P. J. (2002). Examining a model of information technology acceptance by individual professionals: An exploratory study. Journal of Management Information Systems, 18(4), 191-229.

Chen, H., Rong, W., Ma, X., Qu, Y., & Xiong, Z. (2017). An extended technology acceptance model for mobile social gaming service popularity analysis. Mobile Information Systems, 2017.

Cheung, C. M., Lee, M. K., & Chen, Z. (2002). Using the Internet as a learning medium: An exploration of gender difference in the adoption of FaBWeb. In Proceedings of the 35th Annual Hawaii International Conference on System Sciences, Big Island, HI, 2002, pp. 475-483.

Ciupac-Ulici, M., Beju, D. G., Bresfelean, V. P., & Zanellato, G. (2023). Which factors contribute to the global expansion of M-commerce? Electronics, 12(1), 197.

Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results (Unpublished doctoral dissertation). Massachusetts Institute of Technology.

Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008

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.

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.

Dinev, T., & Hu, Q. (2007). The centrality of awareness in the formation of user behavioral intention toward protective information technologies. Journal of the Association for Information Systems, 8(7), 23.

Dinh, H. T., Lee, C., Niyato, D., & Wang, P. (2013). A survey of mobile cloud computing: Architecture, applications, and approaches. Wireless Communications and Mobile Computing, 13(18), 1587-1611.

Eighmey, J., & McCord, L. (1998). Adding value in the information age: Uses and gratifications of sites on the World Wide Web. Journal of Business Research, 41(3), 187-194.

Fang, Y. H., Chiu, C. M., & Wang, E. T. (2011). Understanding customers' satisfaction and repurchase intentions: An integration of IS success model, trust, and justice. Internet Research, 21(4), 479-503.

Fathema, N., Shannon, D., & Ross, M. (2015). Expanding the technology acceptance model (TAM) to examine faculty use of learning management systems (LMSs) in higher education institutions. Journal of Online Learning & Teaching, 11(2).

Fishbein, M., & Ajzen, I. (1977). Belief, attitude, intention, and behavior: An introduction to theory and research. Philosophy and Rhetoric, 10(2), 130-132.

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

Gefen, D. (2000). E-commerce: The role of familiarity and trust. Omega, 28(6), 725-737.

Ghazal, T. M., Hasan, M. K., Alzoubi, H. M., Al Hmmadi, M., Al-Dmour, N. A., Islam, S.,... & Mago, B. (2022, May). Securing smart cities using blockchain technology. In 2022 1st International Conference on AI in Cybersecurity (ICAIC) (pp. 1-4). IEEE.

Godwin-Jones, R. (2001). Language testing tools and technologies. Language Learning & Technology, 5(2), 8-12.

Gumussoy, Ç. A., & Yeterel, A. C. (2016). Fırsat sitelerinden tekrar satın Alma kararını etkileyen faktörlerin araştırılması. Bilişim Teknolojileri Dergisi, 9(3), 275-292.

Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis. Prentice Hall.

Hamid, A. A., Razak, F. Z. A., Bakar, A. A., & Abdullah, W. S. W. (2016). The effects of perceived usefulness and perceived ease of use on continuance intention to use e-government. Procedia Economics and Finance, 35, 644-649.

Hargittai, E. (2007). Whose space? Differences among users and non-users of social network sites. Journal of Computer-Mediated Communication, 13(1), 276-297.

Huang, J. H., Lin, Y. R., & Chuang, S. T. (2007). Elucidating user behavior of mobile learning: A perspective of the extended technology acceptance model. The Electronic Library, 25(5), 585-598.

Hubert, M., Blut, M., Brock, C., Backhaus, C., & Eberhardt, T. (2017). Acceptance of smartphone‐ based mobile shopping: Mobile benefits, customer characteristics, perceived risks, and the impact of application context. Psychology & Marketing, 34(2), 175-194.

Huizingh, E. K. (2000). The content and design of web sites: An empirical study. Information & Management, 37(3), 123-134.

Hussain, M., & Mubarik, S. (2021). Measuring human resource attitude using organisational theory of relationship: The way forward. International Journal of Management Studies, 28(1), 57-88. https://doi.org/10.32890/ijms.28.1.2021.9409

Ibrahim, N. A., Kura, K. M., Dasuki, S. I., & Abubakar Alkali, A. M. (2020). Problematic internet use and health outcomes: Does trait self-control matter? International Journal of Management Studies, 27(2) July, 77-96. https://doi.org/10.32890/ijms.27.2.2020.10569

Jamal, A., & Sharifuddin, J. (2015). Perceived value and perceived usefulness of halal labeling: The role of religion and culture. Journal of Business Research, 68(5), 933-941.

Jarvenpaa, S. L., Tractinsky, N., & Saarinen, L. (1999). Consumer trust in an Internet store: A cross-cultural validation. Journal of Computer-Mediated Communication, 5(2), JCMC526.

Javed, A. R., Shahzad, F., ur Rehman, S., Zikria, Y. B., Razzak, I., Jalil, Z., & Xu, G. (2022). Future smart cities requirements, emerging technologies, applications, challenges, and future aspects. Cities, 129, 103794.

Jeong, H. (2011). An investigation of user perceptions and behavioral intentions towards the e-library. Library Collections, Acquisitions, and Technical Services, 35(2-3), 45-60.

Joo, J., & Sang, Y. (2013). Exploring Koreans’ smartphone usage: An integrated model of the technology acceptance model and uses and gratifications theory. Computers in Human Behavior, 29(6), 2512-2518.

Jöreskog, K. G., & Sörbom, D. (1996). LISREL 8: User's reference guide. Scientific Software International.

Kalakota, R., & Whinston, A. B. (1997). Electronic commerce: A manager's guide. Addison-Wesley Professional.

Karahanna, E., Ahuja, M., Srite, M., & Galvin, J. (2002). Individual differences and relative advantage: The case of GSS. Decision Support Systems, 32(4), 327-341.

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.

Koivisto, K., Makkonen, M., Frank, L., & Riekkinen, J. (2016). Extending the technology acceptance model with personal innovativeness and technology readiness: A comparison of three models. BLED 2016: Proceedings of the 29th Bled e-Conference" Digital Economy", ISBN 978-961-232-287-8.

Komninos, N. (2011). Intelligent cities: Variable geometries of spatial intelligence. Intelligent Buildings International, 3(3), 172–188. https://doi.org/10.1080/17508975.2011.579339

Kourtit, K., Nijkamp, P., & Arribas, D. (2012). Smart cities in perspective – a comparative European study by means of self-organizing maps. Innovation: The European Journal of Social Science Research, 25(2), 229–246. https://doi.org/10.1080/13511610.2012.660330

Lau, A. S., Yen, J., & Chau, P. Y. (2001). Adoption of on-line trading in the Hong Kong financial market. Journal of Electronic Commerce Research, 2(2), 58-65.

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.

Liang, H., Xue, Y., & Byrd, T. A. (2003). PDA usage in healthcare professionals: Testing an extended technology acceptance model. International Journal of Mobile Communications, 1(4), 372-389.

Lin, C. Y., Fang, K., & Tu, C. C. (2010). Predicting consumer repurchase intentions to shop online. Journal of Computer, 5(10), 1527-1533.

Lu, H. P., & Su, P. Y. J. (2009). Factors affecting purchase intention on mobile shopping web sites. Internet Research, 19(4), 442-458.

Luarn, P., & Lin, H. H. (2005). Toward an understanding of the behavioral intention to use mobile banking. Computers in Human Behavior, 21(6), 873-891.

Ma, R., Lam, P. T., & Leung, C. K. (2018). Potential pitfalls of smart city development: A study on parking mobile applications (apps) in Hong Kong. Telematics and Informatics, 35(6), 1580-1592.

Moan, I., & Rise, J. (2006). Predicting smoking reduction among adolescents using an extended version of the theory of planned behavior. Psychology and Health, 21, 717–738.

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.

Mora, L., Deakin, M., & Reid, A. (2018). Smart-city development paths: Insights from the first two decades of research. Smart and Sustainable Planning for Cities and Regions: Results of SSPCR 2017 2, 403-427.

Moslehpour, M., Pham, V. K., Wong, W. K., & Bilgiçli, İ. (2018). E-purchase intention of Taiwanese consumers: Sustainable mediation of perceived usefulness and perceived ease of use. Sustainability, 10(1), 234.

Mugo, D. G., Njagi, K., Chemwei, B., & Motanya, J. O. (2017). The technology acceptance model (TAM) and its application to the utilization of mobile learning technologies. Journal of Education and Practice, 8(7), 155-166.

Mutahar, A. M., Daud, N. M., Ramayah, T., Isaac, O., & Aldholay, A. H. (2018). The effect of awareness and perceived risk on the technology acceptance model (TAM): Mobile banking in Yemen. International Journal of Services and Standards, 12(2), 180-204.

Nash J. (2019). Exploring how social media platforms influence fashion consumer decisions in the UK retail sector. Journal of Fashion Marketing and Management, 23(1), 82–103.

Nor, K. M., Pearson, J. M., & Ahmad, A. (2010). Adoption of internet banking: Theory of the diffusion of innovation. International Journal of Management Studies, 17(1), 69-85.

Nov, O., & Ye, C. (2008). Personality and technology acceptance: Personal innovativeness in IT, openness and resistance to change. In Proceedings of the 41st Annual Hawaii International Conference on System Sciences, 448-448.

Oghuma, A. P., Park, M. C., & Rho, J. J. (2012). Adoption of government service initiative in developing countries: A citizen-centric public service delivery perspective. Government Information Quarterly, 29(1), 123-134.

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.

Park, E., & del Pobil, A. P. (2013). Modeling the user acceptance of long-term evolution (LTE) services. Annals of Telecommunications, 68(5), 307-315.

Park, E., & Kim, K. J. (2013). User acceptance of long‐term evolution (LTE) services: An application of extended technology acceptance model. Program.

Park, E., & Kim, K. J. (2014). An integrated adoption model of mobile cloud services: Exploration of key determinants and extension of technology acceptance model. Telematics and Informatics, 31(3), 376-385.

Park, E., Baek, S., Ohm, J., & Chang, H. J. (2014). Determinants of player acceptance of mobile social network games: An application of extended technology acceptance model. Telematics and Informatics, 31(1), 3-15.

Park, S. Y., Nam, M. W., & Cha, S. B. (2012). University students' behavioral intention to use mobile learning: Evaluating the technology acceptance model. British Journal of Educational Technology, 43(4), 592-605.

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.

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.

Rafique, H., Anwer, F., Shamim, A., Minaei-Bidgoli, B., Qureshi, M. A., & Shamshirband, S. (2018). Factors affecting acceptance of mobile library applications: Structural equation model. Libri, 68(2), 99-112.

Ramayah, T., & Ignatius, J. (2005). Impact of perceived usefulness, perceived ease of use and perceived enjoyment on intention to shop online. ICFAI Journal of Systems Management, 3(3), 36-51.

Rogers, E. M. (1995). Diffusion of innovations: Modifications of a model for telecommunications. In Die diffusion von innovationen in der telekommunikation (pp. 25-38).

Rosen, L. D., Whaling, K., Carrier, L. M., Cheever, N. A., & Rokkum, J. (2013). The media and technology usage and attitudes scale: An empirical investigation. Computers in Human Behavior, 29(6), 2501-2511.

Sağlam, H. (2014). Re-thinking the concept of “ornament” in architectural design. Procedia-Social and Behavioral Sciences, 122, 126-133.

Salisbury, W. D., Pearson, R. A., & Pearson, A. W. (2001). Perceived security and World Wide Web purchase intention. Industrial Management & Data Systems, 101(4), 165-177

Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information & Management, 44(1), 90-103.

Schneider, F. B., & National Research Council. (1999). Trust in cyberspace (p. 214). National Academy Press.

Shahjehan, A., Shah, S. I., Qureshi, J. A., & Wajid, A. (2021). A meta-analysis of smartphone addiction and behavioral outcomes. International Journal of Management Studies, 28(2), 103-125.

Shin, D. H., & Shin, Y. J. (2011). Why do people play social network games? Computers in Human Behavior, 27(2), 852-861.

Suki, N. M., & Suki, N. M. (2011). Exploring the relationship between perceived usefulness, perceived ease of use, perceived enjoyment, attitude and subscribers’ intention towards using 3G mobile services. Journal of Information Technology Management, 22(1), 1-7.

Taherdoost, H. (2018). A review of technology acceptance and adoption models and theories. Procedia Manufacturing, 22, 960-967.

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

Teo, T. (2010). Examining the influence of subjective norm and facilitating conditions on the intention to use technology among pre-service teachers: A structural equation modeling of an extended technology acceptance model. Asia Pacific Education Review, 11(2), 253-262.

Thatcher, J. B., Stepina, L. P., Srite, M., & Liu, Y. (2003). Culture, overload and personal innovativeness with information technology: Extending the nomological net. Journal of Computer Information Systems, 44(1), 74-81.

Tok, Y. C., & Chattopadhyay, S. (2023). Identifying threats, cybercrime and digital forensic opportunities in Smart City Infrastructure via threat modeling. Forensic Science International: Digital Investigation, 45, 301540.

Tornatzky, L. G., & Klein, K. J. (1982). Innovation characteristics and innovation adoption-implementation: A meta-analysis of findings. IEEE Transactions on Engineering Management, (1), 28-45.

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., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27(3), 451-481.

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., Davis, G. 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

Widaman, K. F., & Thompson, J. S. (2003). On specifying the null model for incremental fit indices in structural equation modeling. Psychological Methods, 8(1), 16.

Wixom, B. H., & Todd, P. A. (2005). A theoretical integration of user satisfaction and technology acceptance. Information Systems Research, 16(1), 85-102.

Yfantis, V., Vassilopoulou, K., Pateli, A., & Usoro, A. (2013, August). The influential factors of m-government’s adoption in the developing countries (pp. 157-171). In International Conference on Mobile Web and Information Systems.

Yoon, H. Y. (2016). User acceptance of mobile library applications in academic libraries: An application of the technology acceptance model. The Journal of Academic Librarianship, 42(6), 687-693.

Zaki, H. O., Kamarulzaman, Y., & Mohtar, M. (2019). Does need for cognition, need for affect and perceived humour influence consumers’ brand attitude? International Journal of Management Studies, 26(2), 1-20.

Zha, X., Zhang, J., Yan, Y., & Wang, W. (2015). Comparing flow experience in using digital libraries: Web and mobile context. Library Hi Tech.

Zhao, Y., Deng, S., & Zhou, R. (2015). Understanding mobile library apps continuance usage in China: A theoretical framework and empirical study. Libri, 65(3), 161-173.

Downloads

Published

08-01-2025

Research impact

Harvested 2026-09-07
5 citations, from OpenCitations — the highest of the sources checked

Counts differ between services because each indexes a different body of literature. None of them is the whole picture.

Identifiers DOI 10.32890/ijms2025.32.1.6 OpenAlex W4406134072