Using mobile health apps during the Covid-19 pandemic in a developing country for business sustainability

References

• Abdelghani, M., Mahdy, R., & El-Gohari, H. (2021). Health anxiety to COVID-19 virus infection and its relationship to quality of life in a sample of health care workers in Egypt: A cross-sectional study. Archives of Psychiatry and Psychotherapy, 23(1), 19–20. https://doi.org/10.12740/APP/130304
   Web of Science ®Google Scholar
• Ahmed, M. Z., Ahmed, O., Aibao, Z., Hanbin, S., Siyu, L., & Ahmad, A. (2020). Epidemic of COVID-19 in China and associated psychological problems. Asian Journal of Psychiatry, 51, 102092. https://doi.org/10.1016/j.ajp.2020.102092
   PubMed Web of Science ®Google Scholar
• Ahorsu, D. K., Lin, C. Y., Imani, V., Saffari, M., Griffiths, M. D., & Pakpour, A. H. (2020). The fear of COVID-19 scale: Development and initial validation. International Journal of Mental Health and Addiction, 1–9. https://doi.org/10.1007/s11469-020-00270-8
   Web of Science ®Google Scholar
• Al Amin, M., Arefin, M. S., Sultana, N., Islam, M. R., Jahan, I., & Akhtar, A. (2021). Evaluating the customers’ dining attitudes, e-satisfaction and continuance intention toward mobile food ordering apps (MFOAs): Evidence from Bangladesh. European Journal of Management and Business Economics, 30(2), 85–103. https://doi.org/10.1108/EJMBE-04-2020-0066
   Web of Science ®Google Scholar
• Albashrawi, M., & Motiwalla, L. (2019). Privacy and personalization in continued usage intention of mobile banking: An integrative perspective. Information Systems Frontiers, 21(5), 1031–1043. https://doi.org/10.1007/s10796-017-9814-7
   Web of Science ®Google Scholar
• Al-Debei, M. M., Akroush, M. N., & Ashouri, M. I. (2015). Consumer attitudes towards online shopping. Internet Research: Electronic Networking Applications and Policy, 25(5), 707–733. https://doi.org/10.1108/IntR-05-2014-0146
   Web of Science ®Google Scholar
• Al-Emran, M., Arpaci, I., & Salloum, S. A. (2020). An empirical examination of continuous intention to use m-learning: An integrated model. Education and Information Technologies, 25(4), 2899–2918. https://doi.org/10.1007/s10639-019-10094-2
   Web of Science ®Google Scholar
• Baker-Eveleth, L., & Stone, R. W. (2020). User’s perceptions of perceived usefulness, satisfaction, and intentions of mobile application. International Journal of Mobile Communications, 18(1), 1–18. https://doi.org/10.1504/IJMC.2020.104431
   Web of Science ®Google Scholar
• Barbosa, R. A. P., Nery-da-Silva, G., Bidá, A. G., & Bajdiuk, C. U. (2020October). The impact of COVID-19 on the use of food delivery applications. CLAV 2020.‏ https://bibliotecadigital.fgv.br/ocs/index.php/clav/clav2020/paper/viewPaper/7590
   Google Scholar
• Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370. https://doi.org/10.2307/3250921
   Web of Science ®Google Scholar
• Chakraborty, D., Siddiqui, A., & Siddiqui, M. (2021). Factors associated with the adoption of health apps: Evidence from emerging economies. Journal of Electronic Commerce in Organizations (JECO), 19(4), 20–39. https://doi.org/10.4018/JECO.2021100102
   Web of Science ®Google Scholar
• Cheung, M. F., & To, W. M. (2017). The influence of the propensity to trust on mobile users’ attitudes toward in-app advertisements: An extension of the theory of planned behavior. Computers in Human Behavior, 76, 102–111. https://doi.org/10.1016/j.chb.2017.07.011
   Web of Science ®Google Scholar
• Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295, 295–336. 20805830936.
   Google Scholar
• Chiu, W., Cho, H., & Chi, C. G. (2020). Consumers’ continuance intention to use fitness and health apps: An integration of the expectation–confirmation model and investment model. Information Technology & People, 34(3), 978–998. https://doi.org/10.1108/ITP-09-2019-0463
   Web of Science ®Google Scholar
• Cho, J. (2016). The impact of post-adoption beliefs on the continued use of health apps. International Journal of Medical Informatics, 87, 75–83. https://doi.org/10.1016/j.ijmedinf.2015.12.016
   PubMed Web of Science ®Google Scholar
• Chou, C. H., Chiu, C. H., Ho, C. Y., & Lee, J. C. (2013June). Understanding mobile apps continuance usage behavior and habit: an expectance-confirmation theory. PACIS, 132. https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=44f16ae97b36c00d0071c3efc0093d1fc5bf2ad8
   Google Scholar
• Cobelli, N., & Chiarini, A. (2020). Improving customer satisfaction and loyalty through mHealth service digitalization: New challenges for Italian pharmacists. The TQM Journal, 32(6), 1541–1560. https://doi.org/10.1108/TQM-10-2019-0252
   Google Scholar
• 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
   Web of Science ®Google Scholar
• Delgado-Ballester, E. (2004). Applicability of a brand trust scale across product categories. European Journal of Marketing, 38(5/6), 573–592. https://doi.org/10.1108/03090560410529222
   Google Scholar
• Dillon, A. (2001). User acceptance of information technology. In W. Karwowski (ed.) Encyclopedia of Human Factors and Ergonomics Vol. 1, (pp. 1105–1109). London: Taylor and Francis.
   Google Scholar
• Elsafty, A., Elbouseery, I. M., & Shaarawy, A. (2020). Factors affecting the behavioral intention to use standalone electronic personal health record applications by adults in Egypt. Business and Management Studies, 6(4), 14–36. https://doi.org/10.11114/bms.v6i4.5066
   Google Scholar
• Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Addison-Wesley.
   Google Scholar
• Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: algebra and statistics. Journal of Marketing Research (JMR), 18(3), 382–388. https://doi.org/10.1177/002224378101800313
   Web of Science ®Google Scholar
• Foroughi, B., Iranmanesh, M., & Hyun, S. S. (2019). Understanding the determinants of mobile banking continuance usage intention. Journal Of Enterprise Information Management, 32(6), 1015–1033. https://doi.org/10.1108/JEIM-10-2018-0237
   Web of Science ®Google Scholar
• Gaber, H. R., & Elsamadicy, A. M. (2021). What drives customers to continue using ride-sharing apps during the COVID-19 pandemic? The case of Uber in Egypt. Cogent Business & Management, 8(1), 1–21. https://doi.org/10.1080/23311975.2021.1944009
   Web of Science ®Google Scholar
• Gefen, D., Benbasat, I., & Pavlou, P. (2008). A research agenda for trust in online environments. Journal of Management Information Systems, 24(4), 275–286. https://doi.org/10.2753/MIS0742-1222240411
   Web of Science ®Google Scholar
• Gupta, P., Prashar, S., Vijay, T. S., & Parsad, C. (2021). Examining the influence of antecedents of continuous intention to use an informational app: The role of perceived usefulness and perceived ease of use. International Journal of Business Information Systems, 36(2), 270–287. https://doi.org/10.1504/IJBIS.2021.112829
   Google Scholar
• 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. https://doi.org/10.1108/EBR-11-2018-0203
   Web of Science ®Google Scholar
• 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. https://doi.org/10.1016/S2212-5671(16)00079-4
   Google Scholar
• Hanjaya, M., Kenny, K., & Gunawan, F. (2019). Understanding factors influencing consumers online purchase intention via mobile app: Perceived ease of use, perceived usefulness, system quality, information quality, and service quality. Marketing Instytucji Naukowych i Badawczych, 2(32), 175–206. https://bibliotekanauki.pl/articles/1341812
   Google Scholar
• Han, M., & Lee, E. (2018). Effectiveness of mobile health application use to improve health behavior changes: A systematic review of randomized controlled trials. Healthcare Informatics Research, 24(3), 207–226. https://doi.org/10.4258/hir.2018.24.3.207
   PubMed Web of Science ®Google Scholar
• 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.
   Google Scholar
• Hensher, M., Cooper, P., Dona, S. W. A., Angeles, M. R., Nguyen, D., Heynsbergh, N., Peeters, A., & Peeters, A. (2021). Scoping review: Development and assessment of evaluation frameworks of mobile health apps for recommendations to consumers. Journal of the American Medical Informatics Association, 28(6), 1318–1329. https://doi.org/10.1093/jamia/ocab041
   PubMed Web of Science ®Google Scholar
• Horváth, M., & Michalkova, A. (2012). Monitoring customer satisfaction in service industry: A cluster analysis approach. Quality Innovation Prosperity, 16(1), 49–54. https://doi.org/10.12776/qip.v16i1.61
   Google Scholar
• Hsu, C. L., & Lin, J. C. C. (2015). What drives purchase intention for paid mobile apps?–An expectation confirmation model with perceived value. Electronic Commerce Research and Applications, 14(1), 46–57. https://doi.org/10.1016/j.elerap.2014.11.003
   Web of Science ®Google Scholar
• Humbani, M., & Wiese, M. (2019). An integrated framework for the adoption and continuance intention to use mobile payment apps. Marketing, 37(2), 646–664. https://doi.org/10.1108/IJBM-03-2018-0072
   Web of Science ®Google Scholar
• Jenkins, T. (2022). Wearable medical sensor devices, machine and deep learning algorithms, and internet of things-based healthcare systems in COVID-19 patient screening, diagnosis, monitoring, and treatment. American Journal of Medical Research, 9(1), 49–64. https://www.proquest.com/openview/5bab3f8b28a44b9b657bc87e310fc8a9/1?pq-origsite=gscholar&cbl=2044467v
   Google Scholar
• Kapoor, A., Guha, S., Das, M. K., Goswami, K. C., & Yadav, R. (2020). Digital healthcare: The only solution for better healthcare during COVID-19 pandemic? Indian Heart Journal, 72(2), 61–64. https://doi.org/10.1016/j.ihj.2020.04.001
   PubMedGoogle Scholar
• Kasperbauer, T. J., & Wright, D. E. (2020). Expanded FDA regulation of health and wellness apps. Bioethics, 34(3), 235–241. https://doi.org/10.1111/bioe.12674
   PubMed Web of Science ®Google Scholar
• Keiningham, T. L., Perkins-Munn, T., & Evans, H. (2003). The impact of customer satisfaction on share-of-wallet in a business-to-business environment. Journal of Service Research, 6(1), 37–50. https://doi.org/10.1177/1094670503254275
   Google Scholar
• Kim, S. H., Bae, J. H., & Jeon, H. M. (2019). Continuous intention on accommodation apps: Integrated value-based adoption and expectation–confirmation model analysis. Sustainability, 11(6), 1578. https://doi.org/10.3390/su11061578
   Web of Science ®Google Scholar
• Kock, N. (2018). Should bootstrapping be used in pls-sem? Toward stable p-value calculation methods. Journal of Applied Structural Equation Modeling, 2(1), 1–12. https://doi.org/10.47263/JASEM.2(1)02
   Google Scholar
• Lăzăroiu, G., Neguriţă, O., Grecu, I., Grecu, G., & Mitran, P. C. (2020). Consumers’ decision-making process on social commerce platforms: Online trust, perceived risk, and purchase intentions. Frontiers in Psychology, 11, 890. https://doi.org/10.3389/fpsyg.2020.00890
   PubMed Web of Science ®Google Scholar
• Lee, S., & Kim, B. G. (2021). User, system, and social related factors affecting perceived usefulness for continuance usage intention of mobile apps. International Journal of Mobile Communications, 19(2), 190–217. https://doi.org/10.1504/IJMC.2021.113457
   Web of Science ®Google Scholar
• Lee, S. M., & Lee, D. (2020). “Untact”: A new customer service strategy in the digital age. Service Business, 14(1), 1–22. https://doi.org/10.1007/s11628-019-00408-2
   Web of Science ®Google Scholar
• Lee, S. W., Sung, H. J., & Jeon, H. M. (2019). Determinants of continuous intention on food delivery apps: Extending UTAUT2 with information quality. Sustainability, 11(11), 3141. https://doi.org/10.3390/su11113141
   Web of Science ®Google Scholar
• Lew, S. L., Lau, S. H., Leow, M. C., & Leow, M.-C. (2019). Usability factors predicting continuance of intention to use cloud e-learning application. Heliyon, 5(6), e01788. https://doi.org/10.1016/j.heliyon.2019.e01788
   PubMed Web of Science ®Google Scholar
• Li, C. Y., & Fang, Y. H. (2019). Predicting continuance intention toward mobile branded apps through satisfaction and attachment. Telematics and Informatics, 43, 101248. https://doi.org/10.1016/j.tele.2019.101248
   Web of Science ®Google Scholar
• Ling, S. W., Koo, A. C., & Ong, C. C. (2010). The reasons for encouraging or inhibiting students’ active participation in asynchronous online discussion: three cases from Malaysia. International Journal of Interdisciplinary Social Sciences, 5(1).
   Google Scholar
• Liu, H., Shao, M., Liu, X., & Zhao, L. (2021). Exploring the influential factors on readers’ continuance intentions of E-Book APPs: Personalization, usefulness, playfulness, and satisfaction. Frontiers in Psychology, 12, 262. https://doi.org/10.3389/fpsyg.2021.640110
   Web of Science ®Google Scholar
• Mansour, E. (2021). A survey of Egyptian physicians’ awareness and use of coronavirus-related mHealth applications. Information Development, 02666669211049494. https://doi.org/10.1177/02666669211049494
   Google Scholar
• Mathieson, K. (1991). Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2(3), 173–191. https://doi.org/10.1287/isre.2.3.173
   Web of Science ®Google Scholar
• McLean, G., Osei-Frimpong, K., Al-Nabhani, K., & Marriott, H. (2020). Examining consumer attitudes towards retailers’m-commerce mobile applications–An initial adoption vs. continuous use perspective. Journal of Business Research, 106, 139–157. https://doi.org/10.1016/j.jbusres.2019.08.032
   Web of Science ®Google Scholar
• Ming, L. C., Untong, N., Aliudin, N. A., Osili, N., Kifli, N., Tan, C. S., Goh, H. P., Ng, P. W., Al-Worafi, Y. M., Lee, K. S., & Goh, H. P. (2020). Mobile health apps on COVID-19 launched in the early days of the pandemic: Content analysis and review. JMIR mHealth and uHealth, 8(9), e19796. https://doi.org/10.2196/19796
   PubMed Web of Science ®Google Scholar
• Munoz-Leiva, F., Climent-Climent, S., & Liébana-Cabanillas, F. (2017). Determinants of intention to use the mobile banking apps: An extension of the classic TAM model. Spanish Journal of marketing-ESIC, 21(1), 25–38. https://doi.org/10.1016/j.sjme.2016.12.001
   Google Scholar
• Nikhashemi, S. R., Knight, H. H., Nusair, K., & Liat, C. B. (2021). Augmented reality in smart retailing: A (n)(A) symmetric approach to continuous intention to use retail brands’ mobile AR apps. Journal of Retailing and Consumer Services, 60, 102464. https://doi.org/10.1016/j.jretconser.2021.102464
   Web of Science ®Google Scholar
• Nobar, H. B. K., & Rostamzadeh, R. (2018). The impact of customer satisfaction, customer experience and customer loyalty on brand power: Empirical evidence from hotel industry. Journal of Business Economics and Management, 19(2), 417–430. https://doi.org/10.3846/jbem.2018.5678
   Web of Science ®Google Scholar
• Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17(4), 460–469. https://doi.org/10.1177/002224378001700405
   Web of Science ®Google Scholar
• Ozturk, A. B., Bilgihan, A., Nusair, K., & Okumus, F. (2016). What keeps the mobile hotel booking users loyal? Investigating the roles of self-efficacy, compatibility, perceived ease of use, and perceived convenience. International Journal of Information Management, 36(6), 1350–1359. https://doi.org/10.1016/j.ijinfomgt.2016.04.005
   Web of Science ®Google Scholar
• Park, E. (2020). User acceptance of smart wearable devices: An expectation-confirmation model approach. Telematics and Informatics, 47, 101318. https://doi.org/10.1016/j.tele.2019.101318
   Web of Science ®Google Scholar
• Peterson, R. A. (1994). A meta-analysis of Cronbach’s coefficient alpha. Journal of Consumer Research, 21(2), 381–391. https://doi.org/10.1086/209405
   Web of Science ®Google Scholar
• Qalati, S. A., Vela, E. G., Li, W., Dakhan, S. A., Hong Thuy, T. T., Merani, S. H., & Foroudi, P. (2021). Effects of perceived service quality, website quality, and reputation on purchase intention: The mediating and moderating roles of trust and perceived risk in online shopping. Cogent Business & Management, 8(1), 1869363. https://doi.org/10.1080/23311975.2020.1869363
   Web of Science ®Google Scholar
• Rakshit, S., Islam, N., Mondal, S., & Paul, T. (2021). Mobile apps for SME business sustainability during COVID-19 and onwards. Journal of Business Research, 135, 28–39. https://doi.org/10.1016/j.jbusres.2021.06.005
   PubMed Web of Science ®Google Scholar
• Rasmussen, J. (1989). Analysis of Likert-Scale data: A reinterpretation of Gregoire and Driver. Psychol Bull, 105(1), 167–170. https://doi.org/10.1037/0033-2909.105.1.167
   Web of Science ®Google Scholar
• Rhea, C. K., Felsberg, D. T., & Maher, J. P. (2018). Toward evidence-based smartphone apps to enhance human health: Adoption of behavior change techniques. American Journal of Health Education, 49(4), 210–213. https://doi.org/10.1080/19325037.2018.1473177
   Web of Science ®Google Scholar
• Satici, S. A., Saricali, M., Satici, S. A., Griffiths, M. D., & Can, G. (2020). Intolerance of uncertainty and mental wellbeing: Serial mediation by rumination and fear of COVID-19. International Journal of Mental Health and Addiction, 1–12. https://doi.org/10.1007/s11469-020-00443-5
   Web of Science ®Google Scholar
• Schomakers, E. M., Lidynia, C., Vervier, L. S., Valdez, A. C., & Ziefle, M. (2022). Applying an Extended UTAUT2 Model to Explain user acceptance of lifestyle and therapy mobile health apps: survey study. JMIR mHealth and uHealth, 10(1), e27095. https://doi.org/10.2196/27095
   PubMed Web of Science ®Google Scholar
• Sheth, J. (2020). Impact of Covid-19 on consumer behavior: Will the old habits return or die? Journal of Business Research, 117, 280–283. https://doi.org/10.1016/j.jbusres.2020.05.059
   PubMed Web of Science ®Google Scholar
• Tam, C., Santos, D., & Oliveira, T. (2020). Exploring the influential factors of continuance intention to use mobile Apps: Extending the expectation confirmation model. Information Systems Frontiers, 22(1), 243–257. https://doi.org/10.1007/s10796-018-9864-5
   Web of Science ®Google Scholar
• Tolman, E. C. (1932). Purposive behavior in animals and men. Univ of California Press.‏.
   Google Scholar
• Tseng, C. H., Chen, R. J., Tsai, S. Y., Wu, T. R., Tsaur, W. J., Chiu, H. W., Lo, Y. S., & Lo, Y.-S. (2022). Exploring the COVID-19 pandemic as a catalyst for behavior change among patient health record app users in Taiwan: development and usability study. Journal of Medical Internet Research, 24(1), e33399. https://doi.org/10.2196/33399
   PubMed Web of Science ®Google Scholar
• Vaghefi, I., & Tulu, B. (2019). The continued use of mobile health apps: Insights from a longitudinal study. JMIR mHealth and uHealth, 7(8), e12983. https://doi.org/10.2196/12983
   PubMed Web of Science ®Google Scholar
• Van der Heijden, H., Verhagen, T., & Creemers, M. (2003). Understanding online purchase intentions: Contributions from technology and trust perspectives. European Journal of Information Systems, 12(1), 41–48. https://doi.org/10.1057/palgrave.ejis.3000445
   Web of Science ®Google Scholar
• 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. https://doi.org/10.1111/j.1540-5915.1996.tb01822.x
   Web of Science ®Google Scholar
• Venkatesh, V., & Davis, F. D. (2000). A 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
• Wang, C., & Qi, H. (2021). influencing factors of acceptance and use behavior of mobile health application users: systematic review. Healthcare, 9(3), 357. https://doi.org/10.3390/healthcare9030357
   Web of Science ®Google Scholar
• Wani, M., Raghavan, V., Abraham, D., & Kleist, V. (2017). Beyond utilitarian factors: User experience and travel company website successes. Information Systems Frontiers, 19(4), 769–785. https://doi.org/10.1007/s10796-017-9747-1
   Web of Science ®Google Scholar
• Weng, G. S., Zailani, S., Iranmanesh, M., & Hyun, S. S. (2017). Mobile taxi booking application service’s continuance usage intention by users. Transportation Research Part D: Transport and Environment, 57, 207–216. https://doi.org/10.1016/j.trd.2017.07.023
   Web of Science ®Google Scholar
• Wu, L., Chiu, M. L., & Chen, K. W. (2020). Defining the determinants of online impulse buying through a shopping process of integrating perceived risk, expectation-confirmation model, and flow theory issues. International Journal of Information Management, 52, 102099. https://doi.org/10.1016/j.ijinfomgt.2020.102099
   Web of Science ®Google Scholar
• Wu, J., Xie, X., Yang, L., Xu, X., Cai, Y., Wang, T., & Xie, X. (2021). Mobile health technology combats COVID-19 in China. Journal of Infection, 82(1), 159–198. https://doi.org/10.1016/j.jinf.2020.07.024
   Web of Science ®Google Scholar
• 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. https://doi.org/10.1016/j.acalib.2016.08.003
   Web of Science ®Google Scholar
• Zhao, J., Fang, S., & Jin, P. (2018). Modeling and quantifying user acceptance of personalized business modes based on TAM, trust and attitude. Sustainability, 10(2), 356. https://doi.org/10.3390/su10020356
   Web of Science ®Google Scholar
• Zolkepli, I. A., Mukhiar, S. N. S., & Tan, C. (2021). Mobile consumer behaviour on apps usage: The effects of perceived values, rating, and cost. Journal of Marketing Communications, 27(6), 571–593. https://doi.org/10.1080/13527266.2020.1749108
   Google Scholar
• Zwanka, R. J., & Buff, C. (2021). COVID-19 generation: A conceptual framework of the consumer behavioral shifts to be caused by the COVID-19 pandemic. Journal of International Consumer Marketing, 33(1), 58–67. https://doi.org/10.1080/08961530.2020.1771646
   Web of Science ®Google Scholar