Implementation of the Unified Theory of Acceptance and Use of Technology (UTAUT) model during the pandemic era: A systematic literature review (SLR)
Abstract
The pandemic's unique situation has sparked interest for investigation, particularly in understanding ICT user behavior using the Unified Theory of Acceptance and Use of Technology (UTAUT) model. This article presents a systematic literature review (SLR) aiming to identify UTAUT model applications during the pandemic, explore contexts and methods used, assess global involvement, and understand factors influencing ICT adoption. Following the Reporting standards for Systematic Evidence Syntheses (ROSES) protocol, 70 articles were comprehensively analyzed out of 801 obtained from Scopus, ScienceDirect, and Google Scholar. The review revealed 249 researchers from 44 countries conducting empirical studies on ICT adoption with UTAUT during the pandemic. Dominant contexts were education, healthcare, and mobile technology. Notably, confirmed performance expectancy emerged as the main factor influencing ICT adoption intention, supported by 48 studies (68.57%). Additionally, facilitating conditions' effects were confirmed by 37 studies (52.86%), while effort expectancy and social influence effects were each confirmed by 35 studies (50%). The findings underscore the importance of education, healthcare, and mobile technology during crises, urging attention from governments, policymakers, technology managers, and academics. Individuals demonstrated strong motivation to utilize technology for work facilitation, regardless of resource availability, knowledge, comfort, or social influence from the newly adopted systems or technologies during the pandemic. Furthermore, countries affected by the pandemic could adopt successful systems or technologies from researchers' home countries to foster ICT adoption during future crises.
Keywords: ICT adoption, UTAUT model, Systematic Literature Review (SLR), ROSES protocol, COVID-19.
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Abbad, M. M. M. (2021). Using the UTAUT model to understand students’ usage of e-learning systems in developing countries. Education and Information Technologies, 26(6), 7205–7224. https://doi.org/10.1007/s10639-021-10573-5
Abdullah, F., & Ward, R. (2016). Developing a General Extended Technology Acceptance Model for E-Learning (GETAMEL) by analysing commonly used external factors. Computers in Human Behavior, 56, 238–256.
Acharjya, B., & Das, S. (2022). Adoption of e-Learning during the COVID-19 Pandemic: The moderating role of age and gender. International Journal of Web-Based Learning and Teaching Technologies, 17(2). https://doi.org/10.4018/IJWLTT.20220301.oa4
Ahmed, R. R., Štreimikienė, D., & Štreimikis, J. (2021). The extended UTAUT model and learning management system during Covid-19: Evidence From PLS-SEM and conditional process modeling. Journal of Business Economics and Management, 23(1), 82–104. https://doi.org/10.3846/jbem.2021.15664
Akande, O. N., Badmus, T. A., Akindele, A. T., & Arulogun, O. T. (2020). Dataset to support the adoption of social media and emerging technologies for students’ continuous engagement. Data in Brief, 31, 105926.
Akturan, U., & Tezcan, N. (2012). Mobile banking adoption of the youth market: Perceptions and intentions. Marketing Intelligence & Planning, 30(4), 444-459.
Alalwan, A. A., Dwivedi, Y. K., Rana, N. P. P., & Williams, M. D. (2016). Consumer adoption of mobile banking in Jordan: Examining the role of usefulness, ease of use, perceived risk and self-efficacy. Journal of Enterprise Information Management, 29(1), 118-139. https://doi.org/10.1108/JEIM-04-2015-0035
Alghamdi, A. M., Alsuhaymi, D. S., Alghamdi, F. A., Farhan, A. M., Shehata, S. M., & Sakoury, M. M. (2022). University students’ behavioral intention and gender differences toward the acceptance of shifting regular field training courses to e-training courses. Education and Information Technologies, 27(1), 451–468. https://doi.org/10.1007/s10639-021-10701-1
Alghatrifi, I., & Khalid, H. (2019). A systematic review of UTAUT and UTAUT2 as a baseline framework of information system research in adopting new technology: A case study of IPV6 adoption. Paper presented at 2019 6th International Conference on Research and Innovation in Information Systems (ICRIIS), Johor Bahru, Malaysia, 2019, pp. 1-6. https://doi.org/10.1109/ICRIIS48246.2019.9073292
Alhadid, I., Abu-Taieh, E., Alkhawaldeh, R. S., Khwaldeh, S., Masa’deh, R., Kaabneh, K., & Alrowwad, A. (2022). Predictors for e-government adoption of SANAD app services integrating UTAUT, TPB, TAM, trust, and perceived risk. International Journal of Environmental Research and Public Health, 19(14). https://doi.org/kv8z
Al-Harazneh, Y. M., Alobeytha, F. L., & Alodwan, T. A. A. (2022). Students’ perceptions of e-learning systems at the Jordanian universities through the lens of e-business booming during the Coronavirus pandemic. International Journal of Distance Education Technologies, 20(1). https://doi.org/10.4018/IJDET.295981
Alharbi, F. (2021). The use of digital healthcare platforms during the COVID-19 pandemic: The consumer perspective. Acta Informatica Medica, 29(1), 51–58. https://doi.org/ksn2
Al-Qaysi, N., Mohamad-Nordin, N., & Al-Emran, M. (2020). A systematic review of social media acceptance from the perspective of educational and information systems theories and models. Journal of Educational Computing Research, 57(8), 2085–2109. https://doi.org/10.1177/0735633118817879
Al-Saedi, K., Al-Emran, M., Abusham, E., & el Rahman, S. A. (2019). Mobile payment adoption: A systematic review of the UTAUT model. Paper presented at 2019 International Conference on Fourth Industrial Revolution (ICFIR), pp. 1–5.
Alshami, M., Abdulghafor, R., & Aborujilah, A. (2022). Extending the unified Theory of Acceptance and use of technology for COVID-19 contact tracing application by Malaysian users. Sustainability, 14(11). https://doi.org/10.3390/su14116811
Alvi, I. (2021). College students’ reception of social networking tools for learning in India: an extended UTAUT model. Smart Learning Environments, 8(1). https://doi.org/grxzm2
Anderson, R. M., Heesterbeek, H., Klinkenberg, D., & Hollingsworth, T. D. (2020). How will country-based mitigation measures influence the course of the COVID-19 epidemic? The Lancet, 395(10228), 931–934.
Apolinário-Hagen, J., Hennemann, S., Fritsche, L., Drüge, M., & Breil, B. (2019). Determinant factors of public acceptance of stress management apps: Survey study. JMIR Mental Health, 6(11). https://doi.org/10.2196/15373
Ardiansyahmiraja, B., Nadlifatin, R., Persada, S. F., Prasetyo, Y. T., Young, M. N., Redi, A. A. N. P., & Lin, S.-C. (2021). Learning from a distance during a pandemic outbreak: Factors affecting students’ acceptance of distance learning during school closures due to COVID-19. Journal of e-Learning and Knowledge Society, 17(2), 21–31.
Asvial, M., Mayangsari, J., & Yudistriansyah, A. (2021). Behavioral intention of e-learning: A case study of distance learning at a junior high school in Indonesia due to the COVID-19 pandemic. International Journal of Technology, 12(1), 54–64. https://doi.org/grxzd2
Azizan, S. N., Lee, A. S. H., Crosling, G., Atherton, G., Arulanandam, B. V., Lee, C. E., & Rahim, R. B. A. (2022). Online learning and COVID-19 in higher education: The value of IT models in assessing students’ satisfaction. International Journal of Emerging Technologies in Learning, 17(3), 245–278. https://doi.org/10.3991/ijet.v17i03.24871
Badwelan, A., & Bahaddad, A. A. (2021). Determine the main target audience characteristics in M-learning applications in Saudi Arabian university communities. International Journal of Advanced Computer Science and Applications, 12(8), 306–320. https://doi.org/kv82
Bamoallem, B., & Altarteer, S. (2022). Remote emergency learning during COVID-19 and its impact on university students perception of blended learning in KSA. Education and Information Technologies, 27(1), 157–179. https://doi.org/10.1007/s10639-021-10660-7
Baptista, G., & Oliveira, T. (2015). Understanding mobile banking: The unified theory of acceptance and use of technology combined with cultural moderators. Computers in Human Behavior, 50, 418–430.
Butt, S., Mahmood, A., & Saleem, S. (2022). The role of institutional factors and cognitive absorption on students’ satisfaction and performance in online learning during COVID 19. PLoS ONE, 17(6), e0269609. https://doi.org/10.1371/journal.pone.0269609
Cao, J., Yang, T., Lai, I. K. W., & Wu, J. (2021). Is online education more welcomed during COVID-19? An empirical study of social impact theory on online tutoring platforms. International Journal of Electrical Engineering Education. https://doi.org/10.1177/0020720920984001
Cao, T. M., & Nguyen, N. P. (2022). Factors affecting students in Vietnam’s intention on using smartphones for learning on the mobile learning platforms. Journal of Educational and Social Research, 12(2), 113–125. https://doi.org/10.36941/jesr-2022-0038
Carcary, M., Maccani, G., Doherty, E., & Conway, G. (2018). Exploring the determinants of IoT adoption: Findings from a systematic literature review. In Zdravkovic, J., Grabis, J., Nurcan, S., & Stirna, J. (Eds.), Perspectives in business informatics research (BIR 2018) (Vol. 330, pp. 113-125). Lecture Notes in Business Information Processing. Springer International Publishing. https://doi.org/10.1007/978-3-319-99951-7_8
Chaouali, W., & Souiden, N. (2019). The role of cognitive age in explaining mobile banking resistance among elderly people. Journal of Retailing and Consumer Services, 50, 342–350.
Chaveesuk, S., Khalid, B., Bsoul-Kopowska, M., Rostanska, E., & Chaiyasoonthorn, W. (2022). Comparative analysis of variables that influence behavioral intention to use MOOCs. PLoS ONE, 17(4), e0262037. https://doi.org/10.1371/journal.pone.0262037
Chaveesuk, S., Khalid, B., & Chaiyasoonthorn, W. (2021). Digital payment system innovations: A marketing perspective on intention and actual use in the retail sector. Innovative Marketing, 17(3), 109–123. https://doi.org/10.21511/im.17(3).2021.09
Chotigo, J., & Kadono, Y. (2021). Comparative analysis of key factors encouraging food delivery app adoption before and during the Covid-19 pandemic in Thailand. Sustainability, 13(8). https://doi.org/10.3390/su13084088
Cruz, A. M., Lopez Portillo, H. P., Daum, C., Rutledge, E., King, S., & Liu, L. (2022). Technology Acceptance and usability of a mobile app to support the workflow of health care aides who provide services to older adults: Pilot mixed methods study. JMIR Aging, 5(2). https://doi.org/10.2196/37521
Dindar, M., Suorsa, A., Hermes, J., Karppinen, P., & Näykki, P. (2021). Comparing technology acceptance of K-12 teachers with and without prior experience of learning management systems: A Covid-19 pandemic study. Journal of Computer Assisted Learning, 37(6), 1553–1565. https://doi.org/10.1111/jcal.12552
Dwivedi, Y. K., Hughes, D. L., Coombs, C., Constantiou, I., Duan, Y., Edwards, J. S., Gupta, B., Lal, B., Misra, S., Prashant, P., Raman, R., Rana, N. P., Sharma, S. K., & Upadhyay, N. (2020). Impact of COVID-19 pandemic on information management research and practice: Transforming education, work and life. International Journal of Information Management, 55, 102211. https://doi.org/10.1016/j.ijinfomgt.2020.102211
El-Sofany, H. F., & El-Seoud, S. A. (2022). Implementing effective learning with ubiquitous learning technology during coronavirus pandemic. Computer Systems Science and Engineering, 40(1), 389–404. https://doi.org/10.32604/CSSE.2022.018619
Eneizan, B., Alshare, F., Enaizan, O., Al-Salaymeh, M., Almestarihi, R., & Saleh, A. (2022). Older adult’s acceptance of online shopping (Digital marketing): Extended UTAUT model with Covid 19 fear. Journal of Theoretical and Applied Information Technology, 100(7), 2334–2342. http://www.jatit.org/volumes/Vol100No7/29Vol100No7.pdf
Fisch, C., & Block, J. (2018). Six tips for your (systematic) literature review in business and management research. Management Review Quarterly, 68(2), 103–106.
Garavand, A., Samadbeik, M., Nadri, H., Rahimi, B., & Asadi, H. (2019). Effective factors in adoption of mobile health applications between medical sciences students using the UTAUT model. Methods of Information in Medicine, 58(04/05), 131–139.
Gumasing, M. J. J., Prasetyo, Y. T., Persada, S. F., Ong, A. K. S., Young, M. N., Nadlifatin, R., & Redi, A. A. N. P. (2022). Using online grocery applications during the COVID-19 pandemic: Their relationship with open innovation. Journal of Open Innovation: Technology, Market, and Complexity, 8(2). https://doi.org/10.3390/joitmc8020093
Habib, S., & Hamadneh, N. N. (2021). Impact of perceived risk on consumers technology acceptance in online grocery adoption amid Covid-19 pandemic. Sustainability, 13(18). https://doi.org/10.3390/su131810221
Haddaway, N. R., Macura, B., Whaley, P., & Pullin, A. S. (2018). ROSES reporting standards for systematic evidence syntheses: Pro forma, flow-diagram and descriptive summary of the plan and conduct of environmental systematic reviews and systematic maps. Environmental Evidence, 7. https://doi.org/10.1186/s13750-018-0121-7
Hayrol Azril Mohamed Shaffril, Asnarulkhadi Abu Samah, & Syafila Kamarudin. (2021a). Speaking of the devil: A systematic literature review on community preparedness for earthquakes. Natural Hazards, 108, 2393–2419. https://doi.org/10.1007/s11069-021-04797-4
Hayrol Azril Mohamed Shaffril, Asnarulkhadi Abu Samah, & Samsul Farid Samsuddin. (2021b). Guidelines for developing a systematic literature review for studies related to climate change adaptation. Environmental Science and Pollution Research, 28, 22265–22277.
Hayrol Azril Mohamed Shaffril, Samsul Farid Samsuddin, & Asnarulkhadi Abu Samah. (2021c). The ABC of systematic literature review: The basic methodological guidance for beginners. Quality & Quantity, 55(4), 1319–1346. https://doi.org/10.1007/s11135-020-01059-6
Hidayat, D., Anisti, P., & Wibawa, D. (2020). Crisis management and communication experience in education during the Covid–19 pandemic in Indonesia. Jurnal Komunikasi: Malaysian Journal of Communication, 36(3), 67–82.
Hong, Q. N., Fàbregues, S., Bartlett, G., Boardman, F., Cargo, M., Dagenais, P., Gagnon, M.-P., Griffiths, F., Nicolau, B., & O’Cathain, A. (2018). The Mixed Methods Appraisal Tool (MMAT) version 2018 for information professionals and researchers. Education for Information, 34(4), 285–291.
Ivenicki, A. (2021). Digital lifelong learning and higher education: Multicultural strengths and challenges in pandemic times. Ensaio: Avaliação e Políticas Públicas Em Educação, 29, 360–377.
Jevsikova, T., Stupuriene, G., Stumbriene, D., Juškevičiene, A., & Dagiene, V. (2021). Acceptance of distance learning technologies by teachers: Determining factors and emergency state influence. Informatica, 32(3), 517–542. https://doi.org/10.15388/21-INFOR459
Jones, C., Miguel-Cruz, A., & Brémault-Phillips, S. (2021). Technology acceptance and usability of the BrainFx SCREEN in Canadian military members and veterans with posttraumatic stress disorder and mild traumatic brain injury: Mixed methods UTAUT study. JMIR Rehabilitation and Assistive Technologies, 8(2), e26078.
Joshi, M., & Pande, B. P. (2022). Role, impact, and scope of ICT tools and knowledge during pandemic emergencies and beyond. In Mittal, M., & Battineni, G. (Eds.), Information and Communication Technology (ICT) frameworks in telehealth (pp. 99–114). Springer. https://doi.org/10.1007/978-3-031-05049-7_6
Kader, M. A. R. A., Aziz, N. N. A., Zaki, S. M., Ishak, M., & Hazudin, S. F. (2022). The effect of technostress on online learning behaviour among undergraduates. Malaysian Journal of Learning and Instruction, 19(1), 183–211. https://doi.org/10.32890/mjli2022.19.1.7
Khalid, B., Lis, M., Chaiyasoonthorn, W., & Chaveesuk, S. (2021). Factors influencing behavioural intention to use MOOCs. Engineering Management in Production and Services, 13(2), 83–95. https://doi.org/10.2478/emj-2021-0014
Khan, M. H., Mustaffa, N. A., & Habib, M. M. (2021). Users acceptance of mobile finance service in Bangladesh and the impact of COVID-19: Extended UTAUT2. AIUB Journal of Science and Engineering, 20(3), 87–96. https://doi.org/10.53799/AJSE.V20I3.193
Kharma, Q., Nairoukh, K., Hussein, A., Abualhaj, M., & Shambour, Q. (2021). Online Learning Acceptance model during Covid-19: An integrated conceptual model. International Journal of Advanced Computer Science and Applications, 12(5), 499–505. https://doi.org/10.14569/IJACSA.2021.0120561
Koenig-Lewis, N., Marquet, M., Palmer, A., & Zhao, A. L. (2015). Enjoyment and social influence: Predicting mobile payment adoption. The Service Industries Journal, 35(10), 537–554. https://doi.org/10.1080/02642069.2015.1043278
Latip, M. S. A., Tamrin, M., Noh, I., Rahim, F. A., & Latip, S. N. N. A. (2022). Factors affecting e-learning acceptance among students: The moderating effect of self-efficacy. International Journal of Information and Education Technology, 12(2), 116–122. https://doi.org/kv9h
Liu, L., Miguel Cruz, A., Ruptash, T., Barnard, S., & Juzwishin, D. (2017). Acceptance of Global Positioning System (GPS) technology among dementia clients and family caregivers. Journal of Technology in Human Services, 35(2), 99–119.
Lutfi, A., Saad, M., Almaiah, M. A., Alsaad, A., Al-Khasawneh, A., Alrawad, M., Alsyouf, A., & Al-Khasawneh, A. L. (2022). Actual use of mobile learning technologies during social distancing circumstances: Case study of King Faisal University students. Sustainability, 14(12). https://doi.org/10.3390/su14127323
Maison, D., Jaworska, D., Adamczyk, D., & Affeltowicz, D. (2021). The challenges arising from the COVID-19 pandemic and the way people deal with them. A qualitative longitudinal study. PloS ONE, 16(10), e0258133.
Maphosa, V., Dube, B., & Jita, T. (2020). A UTAUT evaluation of WhatsApp as a tool for lecture delivery during the COVID-19 lockdown at a Zimbabwean University. International Journal of Higher Education, 9(5), 84–93. https://doi.org/10.5430/ijhe.v9n5p84
Martín-Martín, A., Orduna-Malea, E., Thelwall, M., & López-Cózar, E. D. (2018). Google Scholar, Web of Science, and Scopus: A systematic comparison of citations in 252 subject categories. Journal of Informetrics, 12(4), 1160–1177.
Mingers, J., & Lipitakis, E. A. E. C. G. (2010). Counting the citations: A comparison of Web of Science and Google Scholar in the field of business and management. Scientometrics, 85(2), 613–625. https://doi.org/10.1007/s11192-010-0270-0
Muangmee, C., Kot, S., Meekaewkunchorn, N., Kassakorn, N., Tiranawatananun, S., & Khalid, B. (2021). Students’ use behavior towards e-learning tools during COVID-19 pandemics: Case study of higher educational institutions of Thailand. International Journal of Evaluation and Research in Education, 10(4), 1166–1175. https://doi.org/kv9k
Mussa, I. H., & Sazalli, N. A. H. (2021). The readiness of Iraqi EFL teachers to use mobile learning in teaching English in schools. Indonesian Journal of Electrical Engineering and Computer Science, 24(2), 880–887. https://doi.org/10.11591/ijeecs.v24.i2.pp880-887
Musyaffi, A. M., Johari, R. J., Rosnidah, I., Sari, D. A. P., Amal, M. I., Tasyrifania, I., Pertiwia, S. A., & Sutanti, F. D. (2021). Digital payment during pandemic: An extension of the unified model of QR Code. Academic Journal of Interdisciplinary Studies, 10(6), 213–223. https://doi.org/10.36941/ajis-2021-0166
Muthuraman, A., & Abdullah, S. I. N. W. (2022). Perceived satisfaction towards emergency remote teaching amidst Covid-19 crisis: A case among undergraduate students in Penang, Malaysia. Journal of Institutional Research South East Asia, 20(1), 76–103.
Nguyen, V. T. (2022). The perceptions of social media users of digital detox apps considering personality traits. Education and Information Technologies, 27, 9293–9316. https://doi.org/10.1007/s10639-022-11022-7
Nguyen, V. T., & Nguyen, C. T. H. (2022). Factors influencing intention to use the COVID-19 contact tracing application. Journal of Computer Science, 18(6), 453–462. https://doi.org/10.3844/jcssp.2022.453.462
Nikou, S., & Aavakare, M. (2021). An assessment of the interplay between literacy and digital Technology in Higher Education. Education and Information Technologies, 26(4), 3893–3915. https://doi.org/10.1007/s10639-021-10451-0
Okour, K. S., Alharbi, M. A., & Alazzam, M. B. (2019). Identify factors that influence healthcare quality by adoption mobile health application in KSA e-health. Indian Journal of Public Health Research and Development, 10(11), 2409–2413.
Oktavia, T., Adli, A. N., Pramoedito, F. A., & Khan, A. H. I. (2022). The influence factors to use video conferencing applications during Work From Home (WFH). ICIC Express Letters, 16(2), 109–115. https://doi.org/10.24507/icicel.16.02.109
Oliveira, T., Thomas, M., Baptista, G., & Campos, F. (2016). Mobile payment: Understanding the determinants of customer adoption and intention to recommend the technology. Computers in Human Behavior, 61, 404–414.
Oluyinka, S., Endozo, A. N., & Cusipag, M. N. (2021). Integrating trialability and compatibility with UTAUT to assess canvas usage during Covid-19 quarantine period. Asia-Pacific Social Science Review, 21(2), 31–47.
Osei, H. V, Kwateng, K. O., & Boateng, K. A. (2022). Integration of personality trait, motivation and UTAUT 2 to understand e-learning adoption in the era of COVID-19 pandemic. Education and Information Technologies, 27, 10705–10730. https://doi.org/grxzcq
Pagaling, G. T., Espiritu, A. I., Dellosa, M. A. A., Leochico, C. F. D., & Pasco, P. M. D. (2022). The practice of teleneurology in the Philippines during the COVID-19 pandemic. Neurological Sciences, 43(2), 811–819. https://doi.org/10.1007/s10072-021-05705-1
Pandey, N., & Pal, A. (2020). Impact of digital surge during Covid-19 pandemic: A viewpoint on research and practice. International Journal of Information Management, 55, 102171.
Park, Y.-J., & Ahn, S.-S. (2021). Retail distribution strategies for train tickets: The extended UTAUT model. Journal of Distribution Science, 19(9), 5–17. https://doi.org/kv9q
Prasetyo, Y. T., Roque, R. A. C., Chuenyindee, T., Young, M. N., Diaz, J. F. T., Persada, S. F., Miraja, B. A., & Perwira Redi, A. A. N. (2021). Determining factors affecting the acceptance of medical education elearning platforms during the Covid-19 pandemic in the Philippines: UTAUT2 approach. Healthcare, 9(7). https://doi.org/10.3390/healthcare9070780
Prins, A. A. M., Costas, R., van Leeuwen, T. N., & Wouters, P. F. (2016). Using Google Scholar in research evaluation of humanities and social science programs: A comparison with Web of Science data. Research Evaluation, 25(3), 264–270.
Puriwat, W., & Tripopsakul, S. (2021). Understanding food delivery mobile application technology adoption: A UTAUT model integrating perceived fear of Covid-19. Emerging Science Journal, 5, 94–104.
Rahi, S., Khan, M. M., & Alghizzawi, M. (2021). Factors influencing the adoption of telemedicine health services during COVID-19 pandemic crisis: An integrative research model. Enterprise Information Systems, 15(6), 769–793. https://doi.org/gmd5nm
Raza, S. A., Qazi, W., Khan, K. A., & Salam, J. (2021). Social isolation and acceptance of the Learning Management System (LMS) in the time of COVID-19 pandemic: An expansion of the UTAUT model. Journal of Educational Computing Research, 59(2), 183–208. https://doi.org/10.1177/0735633120960421
Razif, M., Miraja, B. A., Persada, S. F., Nadlifatin, R., Belgiawan, P. F., Perwira Redi, A. A. N., & Lin, S.-C. (2020). Investigating the role of environmental concern and the unified theory of acceptance and use of technology on working from home technologies adoption during Covid-19. Entrepreneurship and Sustainability Issues, 8(1), 795–808. https://doi.org/kv9s
Robinson, P., & Lowe, J. (2015). Literature reviews vs systematic reviews. Australian and New Zealand Journal of Public Health, 39(2), 103. https://doi.org/10.1111/1753-6405.12393
Rouidi, M., Elouadi, A. E., Hamdoune, A., Choujtani, K., & Chati, A. (2022). TAM-UTAUT and the acceptance of remote healthcare technologies by healthcare professionals: A systematic review. Informatics in Medicine Unlocked, 32, 101008. https://doi.org/gsdft7
Sakka, Y. M. H., & Namaziandost, E. (2022). Students’ acceptance of distance learning as a result of COVID-19 impact on higher education in Jordan. Education Research International, 7697947. https://doi.org/10.1155/2022/7697947
Salloum, S. A., & Al-Emran, M. (2018). Factors affecting the adoption of e-payment systems by university students: Extending the TAM with trust. International Journal of Electronic Business, 14(4), 371–390.
Santosa, A. D., Taufik, N., Prabowo, F. H. E., & Rahmawati, M. (2021). Continuance intention of baby boomer and X generation as new users of digital payment during COVID-19 pandemic using UTAUT2. Journal of Financial Services Marketing, 26(4), 259–273.
Shao, D., & Lee, I.-J. (2020). Acceptance and influencing factors of social virtual reality in the urban elderly. Sustainability, 12(22), 9345. https://doi.org/10.3390/su12229345
Shiferaw, K. B., Mengiste, S. A., Gullslett, M. K., Zeleke, A. A., Tilahun, B., Tebeje, T., Wondimu, R., Desalegn, S., & Mehari, E. A. (2021). Healthcare providers’ acceptance of telemedicine and preference of modalities during COVID-19 pandemics in a low-resource setting: An extended UTAUT model. PLoS ONE, 16(4), e0250220. https://doi.org/gk4rxm
Shoheib, Z., & Abu-Shanab, E. A. (2022). Adapting the UTAUT2 model for social commerce context. International Journal of e-Business Research, 18(1). https://doi.org/kv9v
Sitar-Taut, D.-A., & Mican, D. (2021). Mobile learning acceptance and use in higher education during social distancing circumstances: An expansion and customization of UTAUT2. Online Information Review, 45(5), 1000–1019. https://doi.org/grh7vs
Sora, B., Nieto, R., del Campo, A. M., & Armayones, M. (2021). Acceptance and use of telepsychology from the clients perspective: Questionnaire study to document perceived advantages and barriers. JMIR Mental Health, 8(10). https://doi.org/10.2196/22199
Souiden, N., Ladhari, R., & Chaouali, W. (2021). Mobile banking adoption: A systematic review. International Journal of Bank Marketing, 39(2), 214–241. https://doi.org/kwcp
Taiwo, A. A., & Downe, A. G. (2013). The Theory of User Acceptance and Use of Technology (UTAUT): A meta-analytic review of empirical findings. Journal of Theoretical & Applied Information Technology, 49(1).
Tamilmani, K., Rana, N. P., & Dwivedi, Y. K. (2018). Mobile application adoption predictors: Systematic review of UTAUT2 studies using weight analysis. In S. A. Al-Sharhan, A. C. Simintiras, Y. K. Dwivedi, M. Janssen, M. Mäntymäki, L. Tahat, I. Moughrabi, T. M. Ali, & N. P. Rana (Eds.), Challenges and opportunities in the digital era (Vol. 11195, pp. 1–12). Lecture Notes in Computer Science. Springer. https://doi.org/kwcq
Tamilmani, K., Rana, N. P., Wamba, S. F., & Dwivedi, R. (2021). The extended Unified Theory of Acceptance and Use of Technology (UTAUT2): A systematic literature review and theory evaluation. International Journal of Information Management, 57. https://doi.org/ghrkpq
Teo, E., Fraunholz, B., & Unnithan, C. (2005). Inhibitors and facilitators for mobile payment adoption in Australia: A preliminary study. Paper presented at International Conference on Mobile Business (ICMB'05), Sydney, NSW, Australia, 2005, pp. 663-666.
Tomczyk, S., Barth, S., Schmidt, S., & Muehlan, H. (2021). Utilizing health behavior change and technology acceptance models to predict the adoption of COVID-19 contact tracing apps: Cross-sectional survey study. Journal of Medical Internet Research, 23(5), e25447.
Vassli, L. T., & Farshchian, B. A. (2018). Acceptance of health-related ICT among elderly people living in the community: A systematic review of qualitative evidence. International Journal of Human-Computer Interaction, 34(2), 99–116. https://doi.org/gjxz25
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.
Walrave, M., Waeterloos, C., & Ponnet, K. (2021). Ready or not for contact tracing? Investigating the adoption intention of COVID-19 Contact-tracing technology using an extended Unified Theory of Acceptance and Use of Technology model. Cyberpsychology, Behavior, and Social Networking, 24(6), 377–383. https://doi.org/10.1089/cyber.2020.0483
Williams, M. D., Rana, N. P., & Dwivedi, Y. K. (2015a). The unified theory of acceptance and use of technology (UTAUT): A literature review. Journal of Enterprise Information Management, 28(3), 443-488.
Wu, R.-Z., Lee, J.-H., & Tian, X.-F. (2021). Determinants of the intention to use cross-border mobile payments in Korea among Chinese tourists: An integrated perspective of UTAUT2 with TTF and ITM. Journal of Theoretical and Applied Electronic Commerce Research, 16(5), 1537–1556. https://doi.org/10.3390/jtaer16050086
Xiao, Y., & Watson, M. (2017). Guidance on conducting a systematic literature review. Journal of Planning Education and Research, 39(1), 93–112. https://doi.org/gcskzk
Yan, C., Siddik, A. B., Akter, N., & Dong, Q. (2021). Factors influencing the adoption intention of using mobile financial service during the COVID-19 pandemic: The role of FinTech. Environmental Science and Pollution Research, 30, 61271–61289. https://doi.org/kwc5
Yoelianto, F., & Tjhin, V. U. (2022). Social isolation, a new variable affecting behavioral intention to use subscription video on demand. Journal of Theoretical and Applied Information Technology, 100(11), 3788–3799.
Yuduang, N., Ong, A. K. S., Prasetyo, Y. T., Chuenyindee, T., Kusonwattana, P., Limpasart, W., Sittiwatethanasiri, T., Gumasing, M. J. J., German, J. D., & Nadlifatin, R. (2022). Factors influencing the perceived effectiveness of COVID-19 risk assessment mobile application “MorChana” in Thailand: UTAUT2 approach. International Journal of Environmental Research and Public Health, 19(9). https://doi.org/10.3390/ijerph19095643
Yunus, M. M., Ang, W. S., & Hashim, H. (2021). Factors affecting Teaching English as a Second Language (TESL) postgraduate students’ behavioural intention for online learning during the COVID-19 pandemic. Sustainability, 13(6). https://doi.org/10.3390/su13063524
Zacharis, G., & Nikolopoulou, K. (2022). Factors predicting university students’ behavioral intention to use eLearning platforms in the post-pandemic normal: An UTAUT2 approach with ‘Learning Value.’ Education and Information Technologies, 27, 12065–12082. https://doi.org/10.1007/s10639-022-11116-2
Zanetta, L. D., Hakim, M. P., Gastaldi, G. B., Seabra, L. M. J., Rolim, P. M., Nascimento, L. G. P., Medeiros, C. O., & da Cunha, D. T. (2021). The use of food delivery apps during the COVID-19 pandemic in Brazil: The role of solidarity, perceived risk, and regional aspects. Food Research International, 149, 110671. https://doi.org/10.1016/j.foodres.2021.110671
Zhao, Y., & Bacao, F. (2021). How does the pandemic facilitate mobile payment? An investigation on users’ perspective under the COVID-19 pandemic. International Journal of Environmental Research and Public Health, 18(3), 1–22. https://doi.org/gk73g5
Zhou, L. L., Owusu-Marfo, J., Asante Antwi, H., Antwi, M. O., Kachie, A. D. T., & Ampon-Wireko, S. (2019). Assessment of the social influence and facilitating conditions that support nurses’ adoption of Hospital Electronic Information Management systems (HEIMS) in Ghana using the Unified Theory of Acceptance and Use of Technology (UTAUT) model. BMC Medical Informatics and Decision Making, 19(1). https://doi.org/gk99d9
Zhou, M., Dzingirai, C., Hove, K., Chitata, T., & Mugandani, R. (2022). Adoption, use and enhancement of virtual learning during COVID-19. Education and Information Technologies, 27, 8939–8959. https://doi.org/10.1007/s10639-022-10985-x
Zulherman, Nuryana, Z., Pangarso, A., & Zain, F. M. (2021). Factor of zoom cloud meetings: Technology adoption in the pandemic of COVID-19. International Journal of Evaluation and Research in Education, 10(3), 816–825. https://doi.org/10.11591/ijere.v10i3.21726
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