653
Views
0
CrossRef citations to date
0
Altmetric
SOCIAL PSYCHOLOGY

Technological-personal factors of university students’ behavioral intention to continue using online services after the pandemic

, , &
Article: 2251810 | Received 31 Dec 2022, Accepted 10 Aug 2023, Published online: 23 Nov 2023

References

  • Abbas, T. M., Jones, E., & Hussien, F. M. (2016). Technological factors influencing university tourism and hospitality students’ intention to use e-learning: A comparative analysis of Egypt and the United Kingdom. Journal of Hospitality & Tourism Education, 28(4), 189–18. https://doi.org/10.1080/10963758.2016.1226845
  • Abdullah, M. S., & Toycan, M. (2017). Analysis of the factors for the successful E-Learning services adoption from Education providers’ and students’ perspectives: A case study of Private universities in northern Iraq. Eurasia Journal of Mathematics, Science and Technology Education, 14(3), 1097–1109. https://doi.org/10.12973/ejmste/81554
  • Ajzen, I. (1991). The theory of planned Behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
  • Ajzen, I. (2011). The theory of planned behaviour: Reactions and reflections. Psychology & Health, 26(9), 1113–1127. https://doi.org/10.1080/08870446.2011.613995
  • Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social Behavior. Prentice- Hall.
  • AL-Alawi, A. N. S. (2017). Holistic approach to the factors affecting individual investor’s decision making in the GCC markets: Evidence from Oman and Saudi Arabia. University of Plymouth. https://pearl.plymouth.ac.uk/handle/10026.1/8609
  • Alarabiat, A., Hujran, O., Soares, D., & Tarhini, A. (2021). 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. https://doi.org/10.1108/ITP-02-2021-0142
  • Al-Busaidy, M., & Weerakkody, V. (2009). E-government diffusion in Oman: A public sector employees’ perspective. Transforming Government: People, Process and Policy, 3(4), 375–393. https://doi.org/10.1108/17506160910997883
  • Al-Debei, M. M., Al-Lozi, E. M., & Papazafeiropoulou, A. (2013). Why people keep coming back to Facebook: Explaining and predicting continuance participation from an extended theory of planned behaviour perspective. Decision Support Systems, 55(1), 43–54. https://doi.org/10.1016/j.dss.2012.12.032
  • Al-Hadidi, A. (2010). Exploratory study on adoption and diffusion of m-government services in the sultanate of Oman. Cardiff University (United Kingdom).
  • Al-Mamari, Q., Corbitt, B., & Gekara, V. O. (2013). E-government adoption in Oman: Motivating factors from a government perspective. Transforming Government: People, Process and Policy, 7(2), 199–224. https://doi.org/10.1108/17506161311325369
  • Alraja, M. (2022). Frontline healthcare providers’ behavioural intention to internet of things (IoT)-enabled healthcare applications: A gender-based, cross-generational study. Technological Forecasting and Social Change, 174, 121256. https://doi.org/10.1016/j.techfore.2021.121256
  • Alraja, M. N., Butt, U. J., & Abbod, M. (2023). Information security policies compliance in a global setting: An employee’s perspective. Computers & Security, 129, 103208–103208. https://doi.org/10.1016/J.COSE.2023.103208
  • Alraja, M. N., Imran, R., Khashab, B. M., & Shah, M. (2022). Technological innovation, sustainable green practices and SMEs sustainable performance in times of crisis (COVID-19 pandemic). Information Systems Frontiers, 24(4), 1081–1105. https://doi.org/10.1007/s10796-022-10250-z
  • Alraja, M. N., Khan, S. F., Khashab, B., & Aldaas, R. (2020). Does Facebook Commerce Enhance SMEs performance? A structural equation analysis of Omani SMEs. SAGE Open, 10(1), 1–14. https://doi.org/10.1177/2158244019900186
  • Alruwaie, M., El-Haddadeh, R., & Weerakkody, V. (2020). Citizens’ continuous use of eGovernment services: The role of self-efficacy, outcome expectations and satisfaction. Government Information Quarterly, 37(3), 101485. https://doi.org/10.1016/j.giq.2020.101485
  • Baudier, P., Ammi, C., & Hikkerova, L. (2022). Impact of advertising on users’ perceptions regarding the internet of things. Journal of Business Research, 141, 355–366. https://doi.org/10.1016/j.jbusres.2021.11.038
  • Bwalya, K. J. (2012). E-government adoption and synthesis in Zambia: Context, issues and challenges.
  • Chang, V., Wang, Y., & Wills, G. (2020). Research investigations on the use or non-use of hearing aids in the smart cities. Technological Forecasting and Social Change, 153, 119231. https://doi.org/10.1016/J.TECHFORE.2018.03.002
  • Ching-Ter, C., Hajiyev, J., & Su, C. R. (2017). Examining the students’ behavioral intention to use e-learning in Azerbaijan? The general extended technology acceptance model for E-learning approach. Computers and Education, 111, 128–143. https://doi.org/10.1016/j.compedu.2017.04.010
  • Cho, V., Cheng, T. C. E., & Lai, W. M. J. (2009). The role of perceived user-interface design in continued usage intention of self-paced e-learning tools. Computers and Education, 53(2), 216–227. https://doi.org/10.1016/j.compedu.2009.01.014
  • Cohen, J. (1988). Statistical power analysis for the behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. https://www.questia.com/library/98533078/statistical-power-analysis-for-the-behavioral-sciences
  • D’Ambra, J., Wilson, C. S., & Akter, S. (2013). Application of the task-technology fit model to structure and evaluate the adoption of E-books by academics. Journal of the American Society for Information Science and Technology, 64(1), 48–64. https://doi.org/10.1002/asi.22757
  • Davis, F. (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
  • Eom, S. B., & Estelami, H. (2012). Effects of LMS, self-efficacy, and self-regulated learning on LMS effectiveness in business education. Journal of International Education in Business, 5(2), 129–144. https://doi.org/10.1108/18363261211281744
  • Escobar-Rodriguez, T., & Monge-Lozano, P. (2012). The acceptance of Moodle technology by business administration students. Computers and Education, 58(4), 1085–1093. https://doi.org/10.1016/j.compedu.2011.11.012
  • Fathema, N., Shannon, D., & Ross, M. (2015). Interaction matters: Strategies to promote engaged learning in an online introductory nutrition course. MERLOT Journal of Online Learning and Teaching, 11(2), 249–261.
  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39. https://doi.org/10.1177/002224378101800104
  • García Botero, G., Questier, F., Cincinnato, S., He, T., & Zhu, C. (2018). Acceptance and usage of mobile assisted language learning by higher education students. Journal of Computing in Higher Education, 30(3), 426–451. https://doi.org/10.1007/s12528-018-9177-1
  • Geisser, S. (1974). A predictive approach to the random effect model. Biometrika, 61(1), 101. https://doi.org/10.1093/biomet/61.1.101
  • Goodhue, D. L., Lewis, W., & Thompson, R. (2012). Does pls have advantages for small sample size or non-normal data? MIS Quarterly: Management Information Systems, 36(3), 981–1001. https://doi.org/10.2307/41703490
  • Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly: Management Information Systems, 19(2), 213–233. https://doi.org/10.2307/249689
  • Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Sage Publishing. https://doi.org/10.1007/s10995-012-1023-x
  • Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a Silver Bullet. Journal of Marketing Theory & Practice, 19(2), 139–152. https://doi.org/10.2753/mtp1069-6679190202
  • 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(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
  • Hu, Y., & Liu, D. (2022). Government as a non-financial participant in innovation: How standardization led by government promotes regional innovation performance in China. Technovation, 114, 102524. https://doi.org/10.1016/J.TECHNOVATION.2022.102524
  • Hung, S. Y., Chang, C. M., & Yu, T. J. (2006). Determinants of user acceptance of the e-government services: The case of online tax filing and payment system. Government Information Quarterly, 23(1), 97–122. https://doi.org/10.1016/J.GIQ.2005.11.005
  • Hung, S. Y., Tang, K. Z., Chang, C. M., & De Ke, C. (2009). User acceptance of intergovernmental services: An example of electronic document management system. Government Information Quarterly, 26(2), 387–397. https://doi.org/10.1016/j.giq.2008.07.003
  • Imran, R., Alraja, M. N., & Khashab, B. (2022). Sustainable performance and green innovation: Green human resources management and big data as antecedents. IEEE Transactions on Engineering Management, 1–16. https://doi.org/10.1109/tem.2021.3114256
  • Insani, A. H., Soewarno, N., & Isnalita, I. (2018, July 1). Online shop customers behaviour: E-Service quality, attitude and actual use. International Conference of Communication Science Research (ICCSR 2018) . https://doi.org/10.2991/iccsr-18.2018.25
  • Kim, T. G., Lee, J. H., & Law, R. (2008). An empirical examination of the acceptance behaviour of hotel front office systems: An extended technology acceptance model. Tourism Management, 29(3), 500–513. https://doi.org/10.1016/j.tourman.2007.05.016
  • Klopping, I. M., & Mckinney, E. (2004). Extending the technology acceptance model extending the technology acceptance model and the task and the task-technology fit model to technology fit model to consumer E consumer E-Commerce Commerce. Information Technology, Learning & Performance Journal, 22(1), 35–48. https://web.p.ebscohost.com/ehost/pdfviewer/pdfviewer?vid=3&sid=179add22-4be8-4d23-8707-02fe5a6de8b7%40redis
  • Kock, N. (2014). Advanced mediating effects tests, multi-group analyses, and measurement model assessments in PLS-Based SEM. International Journal of E-Collaboration, 10(1), 1–13. https://doi.org/10.4018/ijec.2014010101
  • Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of E-Collaboration, 11(4), 1–10. https://doi.org/10.4018/ijec.2015100101
  • Kock, N. (2019a). WarpPLS user manual: Version 6.0 (6th ed.). ScriptWarp Systems. www.scriptwarp.com
  • Kock, N. (2019b). Factor-based structural equation modeling with WarpPLS. Australasian Marketing Journal (AMJ), 27(1), 57–63. https://doi.org/10.1016/j.ausmj.2019.02.002
  • Kock, N., & Hadaya, P. (2018). Minimum sample size estimation in PLS-SEM: The inverse square root and gamma-exponential methods. Information Systems Journal, 28(1), 227–261. https://doi.org/10.1111/ISJ.12131
  • Kock, N., & Verville, J. (2012). Exploring free questionnaire data with anchor variables: An illustration based on a study of it in healthcare. International Journal of Healthcare Information Systems and Informatics, 7(1), 46–63. https://doi.org/10.4018/jhisi.2012010104
  • Kumi, R., Reychav, I., & Sabherwal, R. (2012, January 1). The impact of facilitating conditions on anxiety, attitude, self-efficacy, and performance: Insights from an empirical study of iPad adoption. Proceedings of the 2012 AIS SIGED: IAIM International Conference on Information Systems Education and Research. https://aisel.aisnet.org/siged2012/4
  • Kurfalı, M., Arifoğlua, A., Tokdemir, G., & Paçina, Y. (2017). Adoption of e-government services in Turkey. Computers in Human Behavior, 66, 168–178. https://doi.org/10.1016/j.chb.2016.09.041
  • Lau, A. S. M. (2004). To encourage the adoption of G2C E-Government services in Hong Kong. Electronic Government, 1(3), 273–292. https://doi.org/10.1504/EG.2004.005552
  • Lee-Partridge, J. E., & Ho, P. S. (2003). A retail investor’s perspective on the acceptance of internet stock trading. Proceedings of the 36th Annual Hawaii International Conference on System Sciences, HICSS 2003. https://doi.org/10.1109/HICSS.2003.1174437
  • Lemay, D. J., Morin, M. M., Bazelais, P., & Doleck, T. (2018). Modeling students’ perceptions of simulation-based learning using the technology acceptance model. Clinical Simulation in Nursing, 20, 28–37. https://doi.org/10.1016/j.ecns.2018.04.004
  • Lin, W. S. (2012). Perceived fit and satisfaction on web learning performance: IS continuance intention and task-technology fit perspectives. International Journal of Human Computer Studies, 70(7), 498–507. https://doi.org/10.1016/j.ijhcs.2012.01.006
  • Lin, T. C., & Huang, C. C. (2008). Understanding knowledge management system usage antecedents: An integration of social cognitive theory and task technology fit. Information & Management, 45(6), 410–417. https://doi.org/10.1016/j.im.2008.06.004
  • Lu, X., Yu, Z., Guo, B., & Zhou, X. (2014). Predicting the content dissemination trends by repost behavior modeling in mobile social networks. Journal of Network and Computer Applications, 42, 197–207. https://doi.org/10.1016/j.jnca.2014.01.015
  • Mahat, J., Ayub, A. F. M., Luan, S., & Wong, J. (2012). An assessment of students’ mobile self-efficacy, readiness and personal innovativeness towards mobile learning in higher Education in Malaysia. Procedia - Social & Behavioral Sciences, 64, 284–290. https://doi.org/10.1016/j.sbspro.2012.11.033
  • Mensah, I. K., Luo, C., & Thani, X. C. (2021). The moderating impact of technical support and internet self-efficacy on the adoption of Electronic government services. International Journal of Public Administration, 45(14), 1039–1052. https://doi.org/10.1080/01900692.2021.1961150
  • Mohammed, F., Ibrahim, O., Nilashi, M., & Alzurqa, E. (2017). Cloud computing adoption model for e-government implementation. Information Development, 33(3), 303–323. https://doi.org/10.1177/0266666916656033
  • Mou, J., & Benyoucef, M. (2021). Consumer behavior in social commerce: Results from a meta-analysis. Technological Forecasting and Social Change, 167, 120734. https://doi.org/10.1016/J.TECHFORE.2021.120734
  • Ndou, V. (2004). E-Government for developing countries: Opportunities and challenges. Electronic Journal on Information Systems in Developing Countries, 18(1), 1–24. https://doi.org/10.1002/j.1681-4835.2004.tb00117.x
  • Ozen, A. O., Pourmousa, H., & Alıpourc, N. (2018). Investigation of the critical factors affecting e-government acceptance: A systematic review and a conceptual model. Innovative Journal of Business and Management, 7(3), 77–84.
  • 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. https://doi.org/10.1111/j.1467-8535.2011.01229.x
  • Peng, D. X., & Lai, F. (2012). Using partial least squares in operations management research: A practical guideline and summary of past research. Journal of Operations Management, 30(6), 467–480. https://doi.org/10.1016/j.jom.2012.06.002
  • Ping, R. A. (2004). On assuring valid measures for theoretical models using survey data. Journal of Business Research, 57(2), 125–141. https://doi.org/10.1016/S0148-2963(01)00297-1
  • Pinzone, M., Guerci, M., Lettieri, E., & Huisingh, D. (2019). Effects of ‘green’ training on pro-environmental behaviors and job satisfaction: Evidence from the Italian healthcare sector. Journal of Cleaner Production, 226, 221–232. https://doi.org/10.1016/j.jclepro.2019.04.048
  • 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. https://doi.org/10.1037/0021-9010.88.5.879
  • Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012). Sources of method bias in social Science research and recommendations on how to control it. Annual Review of Psychology, 63(1), 539–569. https://doi.org/10.1146/annurev-psych-120710-100452
  • Preacher, K. J. (2015). Advances in mediation analysis: A survey and synthesis of new developments. Annual Review of Psychology, 66(1), 825–852. https://doi.org/10.1146/annurev-psych-010814-015258
  • Rogers, E. M. (1962). Diffusion of innovations. Free Press of Glencoe.
  • Roth, T., Stohr, A., Amend, J., Fridgen, G., & Rieger, A. (2022). Blockchain as a driving force for federalism: A theory of cross-organizational task-technology fit. International Journal of Information Management, 68, 102476. https://doi.org/10.1016/j.ijinfomgt.2022.102476
  • Sánchez-Prieto, J. C., Olmos-Migueláñez, S., & García-Peñalvo, F. J. (2017). Mlearning and pre-service teachers: An assessment of the behavioral intention using an expanded TAM model. Computers in Human Behavior, 72, 644–654. https://doi.org/10.1016/j.chb.2016.09.061
  • Scuotto, V., Del Giudice, M., Garcia-Perez, A., Orlando, B., & Ciampi, F. (2020). A spill over effect of entrepreneurial orientation on technological innovativeness: An outlook of universities and research based spin offs. The Journal of Technology Transfer, 45(6), 1634–1654. https://doi.org/10.1007/s10961-019-09760-x
  • Sharma, S. K., Gaur, A., Saddikuti, V., & Rastogi, A. (2017). Structural equation model (SEM)-neural network (NN) model for predicting quality determinants of e-learning management systems. Behaviour and Information Technology, 36(10), 1053–1066. https://doi.org/10.1080/0144929X.2017.1340973
  • Shin, D. H. (2017). Conceptualizing and measuring quality of experience of the internet of things: Exploring how quality is perceived by users. Information & Management, 54(8), 998–1011. https://doi.org/10.1016/j.im.2017.02.006
  • Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society, 36(2), 111–147. https://doi.org/10.1111/j.2517-6161.1974.tb00994.x
  • Suki, N. M., & Ramayah, T. (2010). User acceptance of the e-government services in Malaysia: Structural equation modelling approach. Interdisciplinary Journal of Information, Knowledge, and Management, 5, 395–413. https://doi.org/10.28945/1308
  • Susanto, T. D., & Aljoza, M. (2015). Individual acceptance of e-government services in a developing country: Dimensions of perceived usefulness and perceived ease of use and the importance of trust and social influence. Procedia Computer Science, 72, 622–629. https://doi.org/10.1016/j.procs.2015.12.171
  • Susanto, T. D., & Goodwin, R. (2011). User acceptance of SMS-based eGovernment services. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6846, 75–87. https://doi.org/10.1007/978-3-642-22878-0_7
  • Tarhini, A., Hone, K., & Liu, X. (2015). A cross-cultural examination of the impact of social, organisational and individual factors on educational technology acceptance between British and Lebanese university students. British Journal of Educational Technology, 46(4), 739–755. https://doi.org/10.1111/bjet.12169
  • Tejedo-Romero, F., Araujo, J. F. F. E., Tejada, Á., & Ramírez, Y. (2022). E-government mechanisms to enhance the participation of citizens and society: Exploratory analysis through the dimension of municipalities. Technology in Society, 70, 101978. https://doi.org/10.1016/j.techsoc.2022.101978
  • 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. https://doi.org/10.1007/s12564-009-9066-4
  • Terry, D. J., & O’Leary, J. E. (1995). The theory of planned behaviour: The effects of perceived behavioural control and self‐efficacy. British Journal of Social Psychology, 34(2), 199–220. https://doi.org/10.1111/J.2044-8309.1995.TB01058.X
  • Tripathi, S., & Jigeesh, N. (2015). Task-Technology Fit (TTF) Model To Evaluate Adoption of Cloud Computing: A Multi-Case Study. International Journal of Applied Engineering Research, 10(3), 9185–9200. http://www.ripublication.com
  • Ullah, A., Pinglu, C., Ullah, S., Qaisar, Z. H., & Qian, N. (2022). The dynamic nexus of E-Government, and sustainable development: Moderating role of multi-dimensional regional integration index in Belt and road partner countries. Technology in Society, 68, 101903. https://doi.org/10.1016/j.techsoc.2022.101903
  • Usoro, A., Echeng, R., & Majewski, G. (2014). A model of acceptance of web 2.0 in learning in higher Education: A case study of two cultures. E-Learning & Digital Media, 11(6), 644–653. https://doi.org/10.2304/elea.2014.11.6.644
  • Van Teijlingen, E., & Hundley, V. (2002). The importance of pilot studies. Nursing Standard (Royal College of Nursing (Great Britain): 1987), 16(40), 33–36. https://doi.org/10.7748/NS2002.06.16.40.33.C3214
  • 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. https://doi.org/10.2307/30036540
  • Verkijika, S. F., & De Wet, L. (2018). Quality assessment of e‐government websites in S ub‐S aharan a frica: A public values perspective. Electronic Journal of Information Systems in Developing Countries, 84(2), e12015. https://doi.org/10.1002/isd2.12015
  • 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, 102076. https://doi.org/10.1016/j.ijinfomgt.2020.102076
  • Wirtz, B. W., & Piehler, R. (2015). eGovernment applications and public personnel acceptance: An empirical analysis of the public servant perspective. International Journal of Public Administration, 39(3), 238–247. https://doi.org/10.1080/01900692.2014.1003384
  • Wu, B., & Chen, X. (2017). Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in Human Behavior, 67, 221–232. https://doi.org/10.1016/j.chb.2016.10.028
  • Zahid, H., & Din, B. H. (2019). Determinants of intention to adopt e-government services in Pakistan: An imperative for sustainable development. Resources, 8(3), 128. https://doi.org/10.3390/resources8030128
  • Zhao, F., & Khan, M. S. (2013). An empirical study of E-Government service adoption: Culture and behavioral intention. International Journal of Public Administration, 36(10), 710–722. https://doi.org/10.1080/01900692.2013.791314