Evaluating the predictors of mobile health acceptance among Zimbabwean university students during the COVID-19 era: an integrated framework

References

• Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 1–17. https://doi.org/10.1016/0749-5978(91)90020-T
   Web of Science ®Google Scholar
• Ajzen, I. (2020). The theory of planned behavior: Frequently asked questions. Human Behavior and Emerging Technologies, 2(4), 314–324. https://doi.org/10.1002/hbe2.195
   Google Scholar
• Ajzen, I., & Fishbein, M. (1980). Understanding Attitudes And Predicting Social Behaviour. Prentice-Hall.
   Google Scholar
• Al-Gahtani, S. S. (2006). Information technology adoption, the roadmap to sustainable development : Examining three models. Saudi National Computer Conference, October 2016. https://www.researchgate.net/publication/255617584.
   Google Scholar
• Alaiad, A., Alsharo, M., & Alnsour, Y. (2019). The determinants of M-health adoption in developing countries: An empirical investigation. Applied Clinical Informatics, 10(5), 820–840. https://doi.org/10.1055/s-0039-1697906
   PubMed Web of Science ®Google Scholar
• Alam, M. Z., Hu, W., Kaium, M. A., Hoque, M. R., & Alam, M. M. D. (2020). Understanding the determinants of mHealth apps adoption in Bangladesh: A SEM-neural network approach. Technology in Society, 61(October 2019), 101255. https://doi.org/10.1016/j.techsoc.2020.101255
   Google Scholar
• Amin, M. (2016). Internet banking service quality and its implication on e-customer satisfaction and e-customer loyalty. International Journal of Bank Marketing, 34(3), 280–306. https://doi.org/10.1108/IJBM-10-2014-0139
   Web of Science ®Google Scholar
• Anderson, J. C., & Gerbin, D. W. (1988). Structural Equation Modelling in practise: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423. https://doi.org/10.1037/0033-2909.103.3.411
   Web of Science ®Google Scholar
• Arkorful, V. E., Lugu, B. K., & Shuliang, Z. (2023). Unearthing mask waste separation behavior in COVID-19 pandemic period: An empirical evidence from Ghana using an integrated theory of planned behavior and norm activation model. Current Psychology, 42(10), 8515–8530. https://doi.org/10.1007/s12144-021-02313-2
   PubMed Web of Science ®Google Scholar
• Arkorful, V. E., Shuliang, Z., Lugu, B. K., & Jianxun, C. (2022). Consumers’ mobile health adoption intention prediction utilizing an extended version of the theory of planned behavior. ACM SIGMIS Database: The DATABASE for Advances in Information Systems, 53(2), 96–114. https://doi.org/10.1145/3533692.3533699
   Google Scholar
• Byrne, B. M. (2013). Structural equation modelling with AMOS: Basic concepts, applications and programming. Routledge Taylor and Francis.
   Google Scholar
• Chen, K., & Chang, M. (2013). User acceptance of ‘near field communication’ mobile phone service : an investigation based on the ‘unified theory of acceptance and use of technology’ model. The Service Industries Journal, 33(6), 609–623. https://doi.org/10.1080/02642069.2011.622369
   Web of Science ®Google Scholar
• Cho, Y. M., Lee, S., Islam, S. M. S., & Kim, S. Y. (2018). Theories applied to m-health interventions for behavior change in low- and middle-income countries: A systematic review. Telemedicine Journal and e-Health, 24(10), 727–741. https://doi.org/10.1089/tmj.2017.0249
   PubMed Web of Science ®Google Scholar
• Chuang, L. M., Chen, P. C., & Chen, Y. Y. (2018). The determinant factors of travelers’ choices for pro-environment behavioral intention-integration theory of planned behavior, Unified theory of acceptance, and use of technology 2 and sustainability values. Sustainability, 10(6), 1869. https://doi.org/10.3390/su10061869
   Web of Science ®Google Scholar
• Dangaiso, P. (2023). Extending the Theory of Planned Behavior to predict organic food adoption behavior and perceived consumer longevity in subsistence markets: a post-peak COVID-19 perspective. Cogent Psychology, 10(1), 2258677. https://doi.org/10.1080/23311908.2023.2258677
   Web of Science ®Google Scholar
• Dangaiso, P., Makudza, F., & Hogo, H. (2022). Modelling perceived e-learning service quality, student satisfaction and loyalty. A higher education perspective. Cogent Education, 9(1), 2145805. https://doi.org/10.1080/2331186X.20222145805
   Web of Science ®Google Scholar
• Dangaiso, P., Makudza, F., Jaravaza, D. C., Kusvabadika, J., Makiwa, N., & Gwatinyanya, C. (2023). Evaluating the impact of quality antecedents on university students’ e-learning continuance intentions: A post COVID-19 perspective. Cogent Education, 10(1). https://doi.org/10.1080/2331186X.2023.2222654
   Web of Science ®Google Scholar
• Dangaiso, P., Makudza, F., Tshuma, S., Hogo, H., Masona, C., Sakarombe, U., Nedure, T., Nyathi, G., Jonasi, K., Towo, T., Manhando, T., Dangaiso, P., Makudza, F., Tshuma, S., Hogo, H., Mpondwe, N., Masona, C., Sakarombe, U., Nedure, T., & Muchowe, R. (2023). Can social marketing undo the COVID-19 infodemic ? Predicting consumer preventive health behavior in the marginalized communities in Zimbabwe. Cogent Public Health, 10(1). https://doi.org/10.1080/27707571.2023.2234599
   Google Scholar
• Davari, A., & Strutton, D. (2014). Marketing mix strategies for closing the gap between green consumers’ pro-environmental beliefs and behaviors. Journal of Strategic Marketing, 22(7), 563–586. https://doi.org/10.1080/0965254X.2014.914059
   Google Scholar
• Davies, F. D. (1989). Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Quarterly, 13(4), 313–339. https://doi.org/10.2307/249008
   Google Scholar
• Dilotsotlhe, N., & Akbari, M. (2021). Factors influencing the green purchase behaviour of millennials : An emerging country perspective. Cogent Business & Management, 8(1), 1908745. https://doi.org/10.1080/23311975.2021.1908745
   Web of Science ®Google Scholar
• Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019). Re-examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical model. Information Systems Frontiers, 21(3), 719–734. https://doi.org/10.1007/s10796-017-9774-y
   Web of Science ®Google Scholar
• Fornell, C., & Larcker, D. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3150980
   Web of Science ®Google Scholar
• Galura, J. C., Reyes, E. G. D., Pineda, J., L. S., Galura, J. C., Reyes, E. G. D., & Pineda, J. L. S. (2022). C5-LMS design using Google classroom : User acceptance based on extended unified theory of acceptance and use of technology. Interactive Learning Environments, 31(9), 6074–6083. https://doi.org/10.1080/10494820.2022.2028852
   Google Scholar
• Hair, J., J. F., Page, M., & Brunsveld, N. (2020). Essentials of business research methods (4th ed.). Routledge.
   Google Scholar
• Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage.
   Google Scholar
• Herington, C., & Weaven, S. (2009). E-retailing by banks: E-service quality and its importance to customer satisfaction. European Journal of Marketing, 43(9/10), 1220–1231. https://doi.org/10.1108/03090560910976456
   Web of Science ®Google Scholar
• Huang, Y. C. (2023). Integrated concepts of the UTAUT and TPB in virtual reality behavioral intention. Journal of Retailing and Consumer Services, 70(July 2022), 103127. https://doi.org/10.1016/j.jretconser.2022.103127
   Google Scholar
• Jaravaza, D. C., Risiro, J., & Mukucha, P. (2023). COVID-19 vaccination national radio advertising credibility assessment by rural consumers: The influence of indigenous medical knowledge systems and traditional beliefs. Cogent Public Health, 10(1). https://doi.org/10.1080/27707571.2023.2178052
   Google Scholar
• Jen, W., Lu, T., & Liu, P. (2009). An integrated analysis of technology acceptance behaviour models : Comparison of three major models. MIS Review, 15(1), 89–121.
   Google Scholar
• Johnstone, M.-L., & Tan, L. P. (2015). Exploring the gap between consumers’ green rhetoric and purchasing behaviour exploring the gap between consumer and purchasing behaviour. Journal of Business Ethics, 132(2), 311–328. https://doi.org/10.1007/s10551-014-2316-3
   Web of Science ®Google Scholar
• Kline, R. B. (2016). Principles and practice of structural equation modelling (3rd ed.). Guilford Publication.
   Google Scholar
• Kline, R. B. (2023). Principles and practice of structural equation modelling (5th ed.). The Guilford Press.
   Google Scholar
• Leong, L. Y., Hew, T. S., Ooi, K. B., Metri, B., & Dwivedi, Y. K. (2022). Extending the theory of planned behavior in the social commerce context: A meta-analytic SEM (MASEM) approach. Information Systems Frontiers, 25(5), 1847–1879. https://doi.org/10.1007/s10796-022-10337-7
   Web of Science ®Google Scholar
• Malhotra, N. K., Nunan, D., & Birks, D. F. (2017). Marketing research. An applied approach (5th ed.). Pearson.
   Google Scholar
• Matanhire, E., Vingirayi, I., & Manyanga, W. (2022, July). The impact of digital marketing in financial organizations. International Journal of Information, Business and Management, 13(2). https://www.researchgate.net/publication/361740407
   Google Scholar
• Manyanga, W., Ruzvidzo, P., & Chinokopota, T. (2022). Effect of the ubiquity of mobile banking on customer satisfaction in the banking sector in Zimbabwe. Cleaned International Journal of Information, Business and Management, 14(2), 2022.
   Google Scholar
• Mathai, N., McGill, T., & Toohey, D. (2022). Factors influencing consumer adoption of electronic health records. Journal of Computer Information Systems, 62(2), 267–277. https://doi.org/10.1080/08874417.2020.1802788
   Web of Science ®Google Scholar
• Melzner, J., Heinze, J., & Fritsch, T. (2014). Mobile health applications in workplace health promotion: An integrated conceptual adoption framework. Procedia Technology, 16, 1374–1382. https://doi.org/10.1016/j.protcy.2014.10.155
   Google Scholar
• Moták, L., Neuville, E., Chambres, P., Marmoiton, F., Monéger, F., Coutarel, F., & Izaute, M. (2017). Les variables prédictives des intentions d’utilisation d’une navette autonome : aller au-delà du MAT et de la TCP ? European Review of Applied Psychology, 67(5), 269–278. https://doi.org/10.1016/j.erap.2017.06.001
   Web of Science ®Google Scholar
• Muposhi, A., Dhurup, M., & Surujlal, J. (2015). The green dilemma: Reflections of a generation Y consumer cohort on green purchase behaviour. The Journal for Transdisciplinary Research in Southern Africa, 11(4), 31–40. https://doi.org/10.4102/td.v11i3.64
   Google Scholar
• Ndayizigamiye, P., & Maharaj, M. (2016, October 13-16). Mobile health adoption in Burundi: A UTAUT perspective. IEEE Xplore: IEEE 2016 Global Humanitarian Technology Conference, Seattle, WA. https://doi.org/10.1109/GHTC.2016.7857342
   Google Scholar
• Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory. McGraw-Hill.
   Google Scholar
• Nyagadza, B., Mazuruse, G., Muposhi, A., Chuchu, T., Makoni, T., & Kusotera, B. (2022). Emotions’ influence on customers’ e-banking satisfaction evaluation in e-service failure and e-service recovery circumstances. Social Sciences & Humanities Open, 6(1), 100292. https://doi.org/10.1016/j.ssaho.2022.100292
   Google Scholar
• Pennycook, G., McPhetres, J., Zhang, Y., Lu, J. G., & Rand, D. G. (2020). Fighting COVID-19 misinformation on social media: Experimental evidence for a scalable accuracy-nudge intervention. Association for. Psychological Science, 31(7), 770–780. https://doi.org/10.1177/0956797620939054
   PubMed Web of Science ®Google Scholar
• Postal and Telecommunications Regulatory Authority of Zimbabwe (POTRAZ). (2023). Postal and telecommunications regulatory authority of abridged postal and telecommunications. 0–20. Retrieved July 2, 2023, https://www.potraz.gov.zw/wp-content/uploads/2022/10/2022-Second-Quarter-Abridged-Sector-Performance-Report.pdf
   Google Scholar
• Puri, N., Coomes, E. A., Haghbayan, H., & Gunaratne, K. (2020). Social media and vaccine hesitancy: New updates for the era of COVID-19 and globalized infectious diseases. Human Vaccines & Immunotherapeutics, 16(11), 2586–2593. https://doi.org/10.1080/21645515.2020.1780846
   PubMed Web of Science ®Google Scholar
• Purwanto, P., Abdullah, I., Ghofur, A., Abdullah, S., & Elizabeth, M. Z. (2022). Adoption of Islamic microfinance in Indonesia an empirical investigation: An extension of the theory of planned behaviour. Cogent Business & Management, 9(1). https://doi.org/10.1080/23311975.2022.2087466
   Web of Science ®Google Scholar
• Rahman, M. M., Lesch, M. F., Horrey, W. J., & Strawderman, L. (2017). Assessing the utility of TAM, TPB, and UTAUT for advanced driver assistance systems. Accident; Analysis and Prevention, 108(June), 361–373. https://doi.org/10.1016/j.aap.2017.09.011
   PubMedGoogle Scholar
• Raycov, T., & Malcoulides, G. A. (2008). An introduction to applied multivariate analysis. Routledge Taylor and Francis Group.
   Google Scholar
• Rejali, S., Aghabayk, K., Esmaeli, S., & Shiwakoti, N. (2023). Comparison of technology acceptance model, theory of planned behavior, and unified theory of acceptance and use of technology to assess a priori acceptance of fully automated vehicles. Transportation Research Part A: Policy and Practice, 168(December 2022), 103565. https://doi.org/10.1016/j.tra.2022.103565
   Google Scholar
• Rosenstock, I. M. (2000). Health belief model. In A. E. Kazdin (Ed.), Encyclopedia of psychology (Vol. 4, pp. 78–80). Oxford University Press.
   Google Scholar
• Said, G. R. E. (2022). Factors affecting mHealth technology adoption in developing countries: The case of Egypt. Computers, 12(1), 9. https://doi.org/10.3390/computers12010009
   Web of Science ®Google Scholar
• Saunders, M. N. K., Lewis, P., & Thornhill, A. (2018). Research methods for business students (8th ed.). Pearson.
   Google Scholar
• Schiffman, L. G., & Kanuk, L. L. (1994). Consumer behavior (4th ed.). Prentice-Hall.
   Google Scholar
• Schumacker, R. E., & Lomax, R. G. (2010). A beginner’s guide to structural equation modeling. Routledge.
   Google Scholar
• Shekhar, S. K. (2022). Social marketing plan to decrease the COVID-19 vaccine hesitancy among senior citizens in rural India. Sustainability, 14(13), 7561. https://doi.org/10.3390/su14137561
   Web of Science ®Google Scholar
• Siripipatthanakul, S., Limna, P., Sriboonruang, P., & Kaewpuang, P, Manipal GlobalNxt University, Malaysia. (2023). Applying the TPB and the UTAUT models predicting intentions to use telemedicine among Thai people during the COVID-19 pandemic. International Journal of Computing Sciences Research, 7(June), 1362–1384. https://doi.org/10.25147/ijcsr.2017.001.1.107
   Google Scholar
• Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Pearson.
   Google Scholar
• Umar, U., Mas’ud, A., & Matazu, S. (2021). Direct and indirect effects of customer financial condition in the acceptance of Islamic microfinance in a frontier market. Journal of Islamic Marketing, 13(9), 1940–1957. https://doi.org/10.1108/JIMA-12-2019-0267
   Web of Science ®Google Scholar
• Vasli, P., Shekarian-Asl, Z., Zarmehrparirouy, M., & Hosseini, M. (2022). The predictors of COVID-19 preventive health behaviors among adolescents: The role of health belief model and health literacy. Journal of Public Health. https://doi.org/10.1007/s10389-022-01808-x
   Google Scholar
• Venkatesh, M., & Davis, D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425. https://doi.org/10.2307/30036540
   Web of Science ®Google Scholar
• Winkle, C. M., Van, Bueddefeld, J. N. H., Halpenny, E. A., Mackay, J., Winkle, C. M., Van, Bueddefeld, J. N. H., & Halpenny, E. A. (2019). The unified theory of acceptance and use of technology 2: Understanding mobile device use at festivals. Leisure Studies, 38(5), 634–650. https://doi.org/10.1080/02614367.2019.1618895
   Web of Science ®Google Scholar
• Zhang, X., Liu, S., Wang, L., Zhang, Y., & Wang, J. (2020). Mobile health service adoption in China: Integration of theory of planned behavior, protection motivation theory and personal health differences. Online Information Review, 44(1), 1–23. https://doi.org/10.1108/OIR-11-2016-0339
   Web of Science ®Google Scholar