How to cite this paper
Altarawneh, N & Hujran, O. (2026). Determinants of smart government continuous use: A two-staged structural equation modeling-artificial neural network approach.International Journal of Data and Network Science, 10(1), 351-368.
References
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Al-Debei, M. M., Hujran, O., & Al-Adwan, A. S. (2024). Net valence analysis of iris recognition technology-based FinTech. Financial Innovation, 10(1), 59.
Al-Debei, M. M., Dwivedi, Y. K., & Hujran, O. (2022). Why would telecom customers continue to use mobile value-added services? Journal of Innovation & Knowledge, 7(4), 100242.
Al-Hujran, O., Al-Debei, M. M., Chatfield, A., & Migdadi, M. (2015). The imperative of influencing citizen attitude toward e-government adoption and use. Computers in human Behavior, 53, 189-203.
Al-Hujran, O., & Migdadi, M. (2013). Public acceptance of m-government services in developing countries: The case of Jordan. In E-government implementation and practice in developing countries (pp. 242-263). IGI Global.
Al-Sharafi, M. A., Al-Emran, M., Arpaci, I., Marques, G., Namoun, A., & Iahad, N. A. (2023). Examining the impact of psychological, social, and quality factors on the continuous intention to use virtual meeting platforms during and beyond COVID-19 pandemic: A hybrid SEM-ANN approach. International Journal of Human–Computer Interaction, 39(13), 2673-2685.
Alalwan, A. A. (2020). Mobile food ordering apps: An empirical study of the factors affecting customer e-satisfaction and continued intention to reuse. International Journal of Information Management, 50, 28-44.
Alam, M. M. D., Alam, M. Z., Rahman, S. A., & Taghizadeh, S. K. (2021). Factors influencing mHealth adoption and its impact on mental well-being during COVID-19 pandemic: A SEM-ANN approach. Journal of biomedical informatics, 116, 103722.
Alarabiat, A., Hujran, O., Al-Fraihat, D., & Aljaafreh, A. (2024). Understanding Students' Resistance to Continue Using Online Learning. Education and Information Technologies, 29(5), 5421-5446.
Alarabiat, A., Hujran, O., Soares, D., & Tarhini, A. (2023). Examining students' continuous use of online learning in the post-COVID-19 era: an application of the process virtualization theory. Information Technology & People, 36(1), 21-47.
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Alkawsi, G. A., Ali, N., Mustafa, A. S., Baashar, Y., Alhussian, H., Alkahtani, A., Tiong, S. K., & Ekanayake, J. (2021). A hybrid SEM-neural network method for identifying acceptance factors of the smart meters in Malaysia: Challenges perspective. Alexandria Engineering Journal, 60(1), 227-240.
Almaiah, M., Al-Khasawneh, A., Althunibat, A., & Khawatreh, S. (2020). Mobile government adoption model based on combining GAM and UTAUT to explain factors according to adoption of mobile government services.
Almuraqab, N. (2017). Smart government services adoption in the UAE: A conceptual model. In Proceedings of Researchfora International Conference, At: Abu Dhabi.
Althunibat, A., Binsawad, M., Almaiah, M. A., Almomani, O., Alsaaidah, A., Al-Rahmi, W., & Seliaman, M. E. (2021). Sustainable applications of smart-government services: A model to understand smart-government adoption. Sustainability, 13(6), 3028.
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological bulletin, 103(3), 411.
Aoki, N. (2020). An experimental study of public trust in AI chatbots in the public sector. Government Information Quarterly, 37(4), 101490.
Arif, I., Aslam, W., & Hwang, Y. (2020). Barriers in adoption of internet banking: A structural equation modeling-Neural network approach. Technology in Society, 61, 101231.
Arifin, S. (2021). A Research on The Factors That Influence The Adoption of AI-Based Public Services in The Indonesian Government 서울대학교 대학원].
Ashfaq, M., Yun, J., Yu, S., & Loureiro, S. M. C. (2020). I, Chatbot: Modeling the determinants of users’ satisfaction and continuance intention of AI-powered service agents. Telematics and Informatics, 54, 101473.
Babullah, A., Dwivedi, Y., & Williams, M. (2015). Saudi citizens’ perceptions on mobile government (mGov) adoption factors.
Bagozzi, R. P., & Lee, K.-H. (2002). Multiple routes for social influence: The role of compliance, internalization, and social identity. Social psychology quarterly, 226-247.
Balakrishnan, J., & Dwivedi, Y. K. (2021). Role of cognitive absorption in building user trust and experience. Psychology & Marketing, 38(4), 643-668.
Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS quarterly, 351-370.
Bhattacherjee, A. (2001a). An empirical analysis of the antecedents of electronic commerce service continuance. Decision support systems, 32(2), 201-214.
Bhattacherjee, A. (2001b). Understanding information systems continuance: An expectation-confirmation model. MIS quarterly, 351-370.
Bhattacherjee, A., Perols, J., & Sanford, C. (2008). Information technology continuance: A theoretic extension and empirical test. Journal of Computer Information Systems, 49(1), 17-26.
Boustani, N. M., Xu, Q., & Xu, Y. (2022). Getting Smarter: Blockchain and IOT Mixture in China Smart Public Services. Smart Cities, 5(4), 1811-1828.
Cai, J., Zhao, Y., & Sun, J. (2022). Factors influencing fitness app users’ behavior in China. International Journal of Human–Computer Interaction, 38(1), 53-63.
Chan, T., Cheung, C., Shi, N., Lee, M., & Lee, Z. (2016). An empirical examination of continuance intention of social network sites. Pacific Asia Journal of the Association for Information Systems, 8(4), 69-90.
Chen, C.-C., Hsiao, K.-L., & Li, W.-C. (2020). Exploring the determinants of usage continuance willingness for location-based apps: A case study of bicycle-based exercise apps. Journal of Retailing and Consumer Services, 55, 102097.
Chen, L. (2018). Mobile work continuance of knowledge workers: An empirical study. Journal of Computer Information Systems, 58(2), 131-141.
Cheng, Y., & Jiang, H. (2020). How do AI-driven chatbots impact user experience? Examining gratifications, perceived privacy risk, satisfaction, loyalty, and continued use. Journal of Broadcasting & Electronic Media, 64(4), 592-614.
Chohan, S. R., & Hu, G. (2020). Success factors influencing citizens’ adoption of IoT service orchestration for public value creation in smart government. IEEE Access, 8, 208427-208448.
Chong, A. Y.-L. (2013). Understanding mobile commerce continuance intentions: an empirical analysis of Chinese consumers. Journal of Computer Information Systems, 53(4), 22-30.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
Edo, O. C., Ang, D., Etu, E. E., Tenebe, I., Edo, S., & Diekola, O. A. (2023). Why do healthcare workers adopt digital health technologies-A cross-sectional study integrating the TAM and UTAUT model in a developing economy. International Journal of Information Management Data Insights, 3(2), 100186.
Elareshi, M., Habes, M., Youssef, E., Salloum, S. A., Alfaisal, R., & Ziani, A. (2022). SEM-ANN-based approach to understanding students’ academic-performance adoption of YouTube for learning during Covid. Heliyon, 8(4).
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Fitriani, W. R., Hidayanto, A. N., Sandhyaduhita, P. I., Purwandari, B., & Kosandi, M. (2019). Determinants of continuance intention to use open data website: An insight from Indonesia. Pacific Asia Journal of the Association for Information Systems, 11(2), 96-120.
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Huang, S. Y., Lee, C.-J., & Lee, S.-C. (2021). Toward a Unified Theory of Customer Continuance Model for Financial Technology Chatbots. Sensors, 21(17), 5687.
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Hujran, O. (2024). Evaluating Smart Government Maturity: Insights From Abu Dhabi Government. In CS & IT Conference Proceedings (Vol. 14, No. 11). CS & IT Conference Proceedings.
Hujran, O., Al-Debei, M. M., Al-Adwan, A. S., Alarabiat, A., & Altarawneh, N. (2023a). Examining the antecedents and outcomes of smart government usage: An integrated model. Government Information Quarterly, 101783.
Hujran, O., Alarabiat, A., Al-Adwan, A. S., & Al-Debei, M. (2023b). Digitally transforming electronic governments into smart governments: SMARTGOV, an extended maturity model. Information Development, 39(4), 811-834.
Hujran, O., Abu-Shanab, E., & Aljaafreh, A. (2020). Predictors for the adoption of e-democracy: an empirical evaluation based on a citizen-centric approach. Transforming Government: People, Process and Policy, 14(3), 523-544.
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Al-Debei, M. M., Dwivedi, Y. K., & Hujran, O. (2022). Why would telecom customers continue to use mobile value-added services? Journal of Innovation & Knowledge, 7(4), 100242.
Al-Hujran, O., Al-Debei, M. M., Chatfield, A., & Migdadi, M. (2015). The imperative of influencing citizen attitude toward e-government adoption and use. Computers in human Behavior, 53, 189-203.
Al-Hujran, O., & Migdadi, M. (2013). Public acceptance of m-government services in developing countries: The case of Jordan. In E-government implementation and practice in developing countries (pp. 242-263). IGI Global.
Al-Sharafi, M. A., Al-Emran, M., Arpaci, I., Marques, G., Namoun, A., & Iahad, N. A. (2023). Examining the impact of psychological, social, and quality factors on the continuous intention to use virtual meeting platforms during and beyond COVID-19 pandemic: A hybrid SEM-ANN approach. International Journal of Human–Computer Interaction, 39(13), 2673-2685.
Alalwan, A. A. (2020). Mobile food ordering apps: An empirical study of the factors affecting customer e-satisfaction and continued intention to reuse. International Journal of Information Management, 50, 28-44.
Alam, M. M. D., Alam, M. Z., Rahman, S. A., & Taghizadeh, S. K. (2021). Factors influencing mHealth adoption and its impact on mental well-being during COVID-19 pandemic: A SEM-ANN approach. Journal of biomedical informatics, 116, 103722.
Alarabiat, A., Hujran, O., Al-Fraihat, D., & Aljaafreh, A. (2024). Understanding Students' Resistance to Continue Using Online Learning. Education and Information Technologies, 29(5), 5421-5446.
Alarabiat, A., Hujran, O., Soares, D., & Tarhini, A. (2023). Examining students' continuous use of online learning in the post-COVID-19 era: an application of the process virtualization theory. Information Technology & People, 36(1), 21-47.
Alghamdi, A., Elbeltagi, I., Elsetouhi, A., & Yacine Haddoud, M. (2018). Antecedents of continuance intention of using Internet banking in Saudi Arabia: A new integrated model. Strategic Change, 27(3), 231-243.
Alkawsi, G. A., Ali, N., Mustafa, A. S., Baashar, Y., Alhussian, H., Alkahtani, A., Tiong, S. K., & Ekanayake, J. (2021). A hybrid SEM-neural network method for identifying acceptance factors of the smart meters in Malaysia: Challenges perspective. Alexandria Engineering Journal, 60(1), 227-240.
Almaiah, M., Al-Khasawneh, A., Althunibat, A., & Khawatreh, S. (2020). Mobile government adoption model based on combining GAM and UTAUT to explain factors according to adoption of mobile government services.
Almuraqab, N. (2017). Smart government services adoption in the UAE: A conceptual model. In Proceedings of Researchfora International Conference, At: Abu Dhabi.
Althunibat, A., Binsawad, M., Almaiah, M. A., Almomani, O., Alsaaidah, A., Al-Rahmi, W., & Seliaman, M. E. (2021). Sustainable applications of smart-government services: A model to understand smart-government adoption. Sustainability, 13(6), 3028.
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological bulletin, 103(3), 411.
Aoki, N. (2020). An experimental study of public trust in AI chatbots in the public sector. Government Information Quarterly, 37(4), 101490.
Arif, I., Aslam, W., & Hwang, Y. (2020). Barriers in adoption of internet banking: A structural equation modeling-Neural network approach. Technology in Society, 61, 101231.
Arifin, S. (2021). A Research on The Factors That Influence The Adoption of AI-Based Public Services in The Indonesian Government 서울대학교 대학원].
Ashfaq, M., Yun, J., Yu, S., & Loureiro, S. M. C. (2020). I, Chatbot: Modeling the determinants of users’ satisfaction and continuance intention of AI-powered service agents. Telematics and Informatics, 54, 101473.
Babullah, A., Dwivedi, Y., & Williams, M. (2015). Saudi citizens’ perceptions on mobile government (mGov) adoption factors.
Bagozzi, R. P., & Lee, K.-H. (2002). Multiple routes for social influence: The role of compliance, internalization, and social identity. Social psychology quarterly, 226-247.
Balakrishnan, J., & Dwivedi, Y. K. (2021). Role of cognitive absorption in building user trust and experience. Psychology & Marketing, 38(4), 643-668.
Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS quarterly, 351-370.
Bhattacherjee, A. (2001a). An empirical analysis of the antecedents of electronic commerce service continuance. Decision support systems, 32(2), 201-214.
Bhattacherjee, A. (2001b). Understanding information systems continuance: An expectation-confirmation model. MIS quarterly, 351-370.
Bhattacherjee, A., Perols, J., & Sanford, C. (2008). Information technology continuance: A theoretic extension and empirical test. Journal of Computer Information Systems, 49(1), 17-26.
Boustani, N. M., Xu, Q., & Xu, Y. (2022). Getting Smarter: Blockchain and IOT Mixture in China Smart Public Services. Smart Cities, 5(4), 1811-1828.
Cai, J., Zhao, Y., & Sun, J. (2022). Factors influencing fitness app users’ behavior in China. International Journal of Human–Computer Interaction, 38(1), 53-63.
Chan, T., Cheung, C., Shi, N., Lee, M., & Lee, Z. (2016). An empirical examination of continuance intention of social network sites. Pacific Asia Journal of the Association for Information Systems, 8(4), 69-90.
Chen, C.-C., Hsiao, K.-L., & Li, W.-C. (2020). Exploring the determinants of usage continuance willingness for location-based apps: A case study of bicycle-based exercise apps. Journal of Retailing and Consumer Services, 55, 102097.
Chen, L. (2018). Mobile work continuance of knowledge workers: An empirical study. Journal of Computer Information Systems, 58(2), 131-141.
Cheng, Y., & Jiang, H. (2020). How do AI-driven chatbots impact user experience? Examining gratifications, perceived privacy risk, satisfaction, loyalty, and continued use. Journal of Broadcasting & Electronic Media, 64(4), 592-614.
Chohan, S. R., & Hu, G. (2020). Success factors influencing citizens’ adoption of IoT service orchestration for public value creation in smart government. IEEE Access, 8, 208427-208448.
Chong, A. Y.-L. (2013). Understanding mobile commerce continuance intentions: an empirical analysis of Chinese consumers. Journal of Computer Information Systems, 53(4), 22-30.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
Edo, O. C., Ang, D., Etu, E. E., Tenebe, I., Edo, S., & Diekola, O. A. (2023). Why do healthcare workers adopt digital health technologies-A cross-sectional study integrating the TAM and UTAUT model in a developing economy. International Journal of Information Management Data Insights, 3(2), 100186.
Elareshi, M., Habes, M., Youssef, E., Salloum, S. A., Alfaisal, R., & Ziani, A. (2022). SEM-ANN-based approach to understanding students’ academic-performance adoption of YouTube for learning during Covid. Heliyon, 8(4).
Eren, B. A. (2021). Determinants of customer satisfaction in chatbot use: evidence from a banking application in Turkey. International Journal of Bank Marketing.
Fitriani, W. R., Hidayanto, A. N., Sandhyaduhita, P. I., Purwandari, B., & Kosandi, M. (2019). Determinants of continuance intention to use open data website: An insight from Indonesia. Pacific Asia Journal of the Association for Information Systems, 11(2), 96-120.
Flavián, C., Guinalíu, M., & Gurrea, R. (2006). The role played by perceived usability, satisfaction and consumer trust on website loyalty. Information & Management, 43(1), 1-14.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50.
Gong, X., Lee, M. K., Liu, Z., & Zheng, X. (2020). Examining the role of tie strength in users’ continuance intention of second-generation mobile instant messaging services. Information Systems Frontiers, 22, 149-170.
Hair, J., Hair Jr, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2023). Advanced issues in partial least squares structural equation modeling. saGe publications.
Hair , J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial least squares structural equation modeling (PLS-SEM) using R: A workbook. Springer Nature.
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.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the academy of marketing science, 43, 115-135.
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. In New challenges to international marketing (Vol. 20, pp. 277-319). Emerald Group Publishing Limited.
Hope, P., & Zhang, X. (2015). Examining user satisfaction with single sign-on and computer application roaming within emergency departments. Health informatics journal, 21(2), 107-119.
Huang, S. Y., Lee, C.-J., & Lee, S.-C. (2021). Toward a Unified Theory of Customer Continuance Model for Financial Technology Chatbots. Sensors, 21(17), 5687.
Hujran, O., & Altarawneh, N. (2025). Why Would Citizens Continue Using Chatbot Services?. In CS & IT Conference Proceedings (Vol. 15, No. 11). CS & IT Conference Proceedings.
Hujran, O. (2024). Evaluating Smart Government Maturity: Insights From Abu Dhabi Government. In CS & IT Conference Proceedings (Vol. 14, No. 11). CS & IT Conference Proceedings.
Hujran, O., Al-Debei, M. M., Al-Adwan, A. S., Alarabiat, A., & Altarawneh, N. (2023a). Examining the antecedents and outcomes of smart government usage: An integrated model. Government Information Quarterly, 101783.
Hujran, O., Alarabiat, A., Al-Adwan, A. S., & Al-Debei, M. (2023b). Digitally transforming electronic governments into smart governments: SMARTGOV, an extended maturity model. Information Development, 39(4), 811-834.
Hujran, O., Abu-Shanab, E., & Aljaafreh, A. (2020). Predictors for the adoption of e-democracy: an empirical evaluation based on a citizen-centric approach. Transforming Government: People, Process and Policy, 14(3), 523-544.
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