COVİD-19 PANDEMİSİNDE YÜKSEKÖĞRETİMDE TEKNOLOJİ KABULÜNE İLİŞKİN AMPİRİK BİR ÇALIŞMA

Yıl 2022, Cilt: 15 Sayı: 2, 481 - 504, 30.05.2022

Şafak Altay , Güven Gürkan İnan

Öz

Bilişim teknolojileri, küresel pazarlarda rekabet edebilmek için çeşitli avantajlar sağlamaktadır. Çoğu işletme hızlı pazar tepkisi, hızlı ve güvenilir tedarik zincirleri, büyük verilere dayalı hızlı karar gibi rekabet avantajları elde etmek için bu teknolojileri benimsemektedir. Yeni bir teknoloji edinmek zorlu bir süreçtir ve bir teknolojinin kabul edilme hızını etkileyen çeşitli faktörler vardır. Doğal afetler, ekonomik krizler, piyasa yapısı gibi çeşitli nedenlerle bazı faktörlerin etkileri değişkenlik gösterebilmektedir. 2020 yılının başından itibaren Covid-19 pandemisi birçok işletmenin ve tedarik zincirinin yeni koşullara uyum sağlamasına neden olmuştur. Yükseköğretim sektörü, pandemiden en çok etkilenen sektörlerden biridir ve geleneksel öğretimden çevrimiçi öğretime hızla geçmek zorunda kalmıştır. Bu çalışma, Covid-19 pandemi koşullarında uzaktan eğitim sistemlerinin kabulüne yönelik öz-yeterlik, kullanıcı deneyimi, yenilikçilik, kullanışlılık, kullanım kolaylığı ve niyetin olası etkilerini araştırmayı amaçlamaktadır. Araştırma, Türkiye’de Ardahan Üniversitesi›nde 598 öğrenci ile gerçekleştirilmiştir. Hipotezler, PLS-SEM (Kısmi En Küçük Kareler Yapısal Eşitlik Modellemesi) kullanılarak test edilmiştir. Bulgular, öz yeterlik ve yenilikçiliğin algılanan kullanım kolaylığı üzerinde etkisi olduğunu ve öz yeterlik ile kullanıcı deneyiminin algılanan kullanışlılık üzerinde olumlu bir etkisi olmadığını göstermektedir. Sonuçlarda algılanan kullanım kolaylığının algılanan kullanışlılık üzerinde olumlu etkileri olduğunu ve algılanan kullanışlılığın niyet üzerinde olumlu etkisi olduğu bulunmuştur.

Anahtar Kelimeler

Öz yeterlilik, Kullanıcı Deneyimi, Yenilikçilik, Kullanışlılık, Kullanım Kolaylığı, Niyet

AN EMPIRICAL STUDY OF TECHNOLOGY ACCEPTANCE IN HIGHER EDUCATION DURING COVID-19 PANDEMIC

Öz

Information technologies provide various advantages to compete in global markets. More businesses adopt these technologies to gain competitive advantages such as quick market response, fast and reliable supply chains, quick decision based on big data. It is a challenging process to acquire a new technology and there are different factors that affect the acceptance speed of a technology. The effects of some factors may vary due to various reasons such as natural disasters, economic crises, market structure. Since the beginning of 2020, Covid-19 pandemic caused many different businesses and supply chain to adapt new conditions. Higher education industry is one of the profoundly affected sectors from pandemic and it is forced to shift rapidly from traditional teaching to online teaching. This study aims to investigate the possible effects of self-efficacy, user experience, innovativeness, usefulness, ease of use and intention on acceptance of distance education systems under Covid-19 pandemic conditions. The study is conducted at Ardahan University, Turkey with 598 of students. The hypotheses were tested using PLS-SEM (Partial Least Squares Structural Equation Modelling). Findings reveal that self-efficacy and innovativeness have effects on perceived ease of use while self-efficacy and user experience do not have positive impact on perceived usefulness. Results also revealed that perceived ease of use has positive impacts on perceived usefulness, and perceived usefulness has positive impact on intention.

Anahtar Kelimeler

Self-efficacy, user experience, innovativeness, usefulness, ease of use, intention

Kaynakça

• Abdullah, A., Al Enazi, S., & Damaj, I. (2016). Agrisys: A smart and ubiquitous controlled-environment agriculture system. 2016 3rd MEC International Conference on Big Data and Smart City (ICBDSC), 1–6.
• Agarwal, R., & Karahanna, E. (2000). Time flies when you’re having fun: Cognitive absorption and beliefs about information technology usage. MIS Quarterly, 665–694.
• Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9(2), 204–215.
• Aguilera-Hermida, A. P., Quiroga-Garza, A., Gómez-Mendoza, S., del Río Villanueva, C. A., Avolio Alecchi, B., & Avci, D. (2021). Comparison of students’ use and acceptance of emergency online learning due to COVID-19 in the USA, Mexico, Peru, and Turkey. Education and Information Technologies, 26(6), 6823–6845. https://doi.org/10.1007/s10639-021-10473-8
• Ahmad, T. B. T., Madarsha, K. B., Zainuddin, A. M. H., Ismail, N. A. H., & Nordin, M. S. (2010). Faculty’s acceptance of computer based technology: Cross-validation of an extended model. Australasian Journal of Educational Technology, 26(2).
• Albelbisi, N. A., & Yusop, F. D. (2019). Factors influencing learners’ self –regulated learning skills in a massive open online course (MOOC) environment. Turkish Online Journal of Distance Education, 1–16. https://doi.org/10.17718/tojde.598191
• Al-Emran, M., Mezhuyev, V., & Kamaludin, A. (2018). Technology acceptance model in m-learning context: A systematic review. Computers & Education, 125, 389–412. https://doi.org/10.1016/j.compedu.2018.06.008
• Al-Maroof, R. S., Alhumaid, K., Alhamad, A. Q., Aburayya, A., & Salloum, S. (2021). User acceptance of smart watch for medical purposes: An empirical study. Future Internet, 13(5), 127. https://doi.org/10.3390/fi13050127
• Al-Maroof, R. S., Salloum, S. A., Hassanien, A. E., & Shaalan, K. (2020). Fear from COVID-19 and technology adoption: The impact of Google Meet during Coronavirus pandemic. Interactive Learning Environments, 1–16. https://doi.org/10.1080/10494820.2020.1830121
• Al-Okaily, M., M Alqudah, H., Matar, A., Lutfi, A., & Taamneh, A. (2020). Impact of Covid-19 pandemic on acceptance of e-learning system in Jordan: A Case of transforming the traditional education systems. Humanities & Social Sciences Reviews, 8(4), 840–851. https://doi.org/10.18510/hssr.2020.8483
• Al-Qaysi, N., Mohamad-Nordin, N., & Al-Emran, M. (2020). Employing the technology acceptance model in social media: A systematic review. Education and Information Technologies, 25(6), 4961–5002. https://doi.org/10.1007/s10639-020-10197-1
• Alshurideh, M. T., al Kurdi, B., Masa’deh, R., & Salloum, S. A. (2021). The moderation effect of gender on accepting electronic payment technology: a study on United Arab Emirates consumers. Review of International Business and Strategy, 31(3), 375–396. https://doi.org/10.1108/RIBS-08-2020-0102
• Arıkan, R. (2004). Araştırma teknikleri ve rapor hazırlama, Asil Yayın Dağıtım. Ankara.
• Arora, S., Chaudhary, P., & Singh, R. K. (2021). Impact of coronavirus and online exam anxiety on self-efficacy: the moderating role of coping strategy. Interactive Technology and Smart Education, 18(3), 475–492. https://doi.org/10.1108/ITSE-08-2020-0158
• Baber, H. (2021). Modelling the acceptance of e-learning during the pandemic of COVID-19-A study of South Korea. The International Journal of Management Education, 19(2), 100503. https://doi.org/10.1016/j.ijme.2021.100503
• Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94. https://doi.org/10.1007/BF02723327
• Barclay, D., Higgins, C., & Thompson, R. (1995). The Partial Least Squares (PLS) approach to causal modelling: Personal computer adoption and use as an illustration, Walter de Guyter. Ontairo.
• Bandura, A. (2010). Self‐efficacy. The Corsini Encyclopedia of Psychology, 1–3.
• Burton-Jones, A., & Hubona, G. S. (2006). The mediation of external variables in the technology acceptance model. Information & Management, 43(6), 706–717. https://doi.org/10.1016/j.im.2006.03.007
• Carter, L., & Bélanger, F. (2005). The utilization of e‐government services: Citizen trust, innovation and acceptance factors. Information Systems Journal, 15(1), 5–25.
• Castiblanco Jimenez, I. A., Cepeda García, L. C., Violante, M. G., Marcolin, F., & Vezzetti, E. (2020). Commonly used external TAM variables in e-learning, agriculture and virtual reality applications. Future Internet, 13(1), 7. https://doi.org/10.3390/fi13010007
• Celuch, K., Taylor, S. A., & Goodwin, S. (2004). Understanding insurance salesperson internet information management intentions: A test of competing models. Journal of Insurance Issues, 22–40.
• Chang, C.-T., 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 & Education, 111, 128–143. https://doi.org/10.1016/j.compedu.2017.04.010
• Chen, N.-H. (2019). Extending a TAM–TTF model with perceptions toward telematics adoption. Asia Pacific Journal of Marketing and Logistics, 31(1), 37–54. https://doi.org/10.1108/APJML-02-2018-0074
• Cheng, Y.-M. (2011). Antecedents and consequences of e-learning acceptance. Information Systems Journal, 21(3), 269–299. https://doi.org/10.1111/j.1365-2575.2010.00356.x
• Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295(2), 295–336.
• Chintalapati, N., & Daruri, V. S. K. (2017). Examining the use of YouTube as a learning resource in higher education: Scale development and validation of TAM model. Telematics and Informatics, 34(6), 853–860. https://doi.org/10.1016/j.tele.2016.08.008
• Chow, M., Herold, D. K., Choo, T.-M., & Chan, K. (2012). Extending the technology acceptance model to explore the intention to use Second Life for enhancing healthcare education. Computers & Education, 59(4), 1136–1144. https://doi.org/10.1016/j.compedu.2012.05.011
• Cicha, K., Rizun, M., Rutecka, P., & Strzelecki, A. (2021). COVID-19 and higher education: First-year students’ expectations toward distance learning. Sustainability, 13(4), 1889. https://doi.org/10.3390/su13041889
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319. https://doi.org/10.2307/249008
• Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User Acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982
• Deloitte (2020). Yeni nesil teknolojilerin COVID-19 mücadelesindeki önemi-ülke örnekleri. https://www2.deloitte.com/content/dam/Deloitte/tr/Documents/consulting/yeni-nesil-teknolojilerin-covid-19-mucadelesindeki-onemi.pdf
• Dünya Sağlık Örgütü [WHO]. (2020). WHO Coronavirus (COVID-19) dashboard. https://covid19.who.int/
• Elnagar, A., Afyouni, I., Shahin, I., Nassif, A. B., & Salloum, S. A. (2021). The empirical study of e-learning post-acceptance after the spread of COVID-19: A multi-analytical approach based hybrid SEM-ANN. ArXiv E-Prints, arXiv-2112.
• Ernst &Young. (2020). Dünya, yıkıcı etkiye sahip değişimin kaçınılmaz olduğunun farkına vardı. https://assets.ey.com/content/dam/ey-sites/ey-com/tr_tr/pdf/2020/08/ey-turkiye-covid-19-sonrasi-bilgi-teknolojilerinin-dijital-dunyada-yeni-rolu--rapor.pdf
• Fauzi, A., Wandira, R., Sepri, D., & Hafid, A. (2021). Exploring students’ acceptance of Google Classroom during the Covid-19 pandemic by using the technology acceptance model in West Sumatera Universities. Electronic Journal of E-Learning, 19(4), pp233-240.
• Fishbein, M., & Ajzen, I. (1977). Belief, attitude, intention, and behavior: An introduction to theory and research. Philosophy and Rhetoric, 10(2).
• 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/10.2307/3151312
• Franke, G., & Sarstedt, M. (2019). Heuristics versus statistics in discriminant validity testing: A comparison of four procedures. Internet Research, 29(3), 430–447. https://doi.org/10.1108/IntR-12-2017-0515
• Gefen, D., Karahanna, E., & Straub, D. W. (2003). Inexperience and experience with online stores: The importance of TAM and trust. IEEE Transactions on Engineering Management, 50(3), 307–321.
• Goh, E., & Wen, J. (2021). Applying the technology acceptance model to understand hospitality management students’ intentions to use electronic discussion boards as a learning tool. Journal of Teaching in Travel & Tourism, 21(2), 142–154. https://doi.org/10.1080/15313220.2020.1768621
• Gold, A. H., Malhotra, A., & Segars, A. H. (2001). Knowledge management: An organizational capabilities perspective. Journal of Management Information Systems, 18(1), 185–214. https://doi.org/10.1080/07421222.2001.11045669
• Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications.
• 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.
• Hart, J., & Sutcliffe, A. (2019). Is it all about the Apps or the Device?: User experience and technology acceptance among iPad users. International Journal of Human-Computer Studies, 130, 93–112. https://doi.org/10.1016/j.ijhcs.2019.05.002
• Hassenzahl, M. (2008). User experience (UX). Proceedings of the 20th International Conference of the Association Francophone d’Interaction Homme-Machine on - IHM ’08, 11–15. https://doi.org/10.1145/1512714.1512717
• Hassenzahl, M. (2018). The Thing and I: Understanding the Relationship Between User and Product (pp. 301–313). https://doi.org/10.1007/978-3-319-68213-6_19
• He, T., & Zhu, C. (2017). Digital informal learning among Chinese university students: The effects of digital competence and personal factors. International Journal of Educational Technology in Higher Education, 14(1), 44. https://doi.org/10.1186/s41239-017-0082-x
• 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
• Heslin, P. A., & Klehe, U.-C. (2006). Self-efficacy. Encyclopedia Of Industrial/Organizational Psychology, SG Rogelberg, Ed, 2, 705–708.
• Hester, A. J., Hutchins, H. M., & Burke-Smalley, L. A. (2016). Web 2.0 and transfer: Trainers’ use of technology to support employees’ learning transfer on the job. Performance Improvement Quarterly, 29(3), 231–255. https://doi.org/10.1002/piq.21225
• Holden, H., & Rada, R. (2011). Understanding the influence of perceived usability and technology self-efficacy on teachers’ technology acceptance. Journal of Research on Technology in Education, 43(4), 343–367.
• Hornbæk, K., & Hertzum, M. (2017). Technology acceptance and user experience. ACM Transactions on Computer-Human Interaction, 24(5), 1–30. https://doi.org/10.1145/3127358
• Horst, M., Kuttschreuter, M., & Gutteling, J. M. (2007). Perceived usefulness, personal experiences, risk perception and trust as determinants of adoption of e-government services in The Netherlands. Computers in Human Behavior, 23(4), 1838–1852. https://doi.org/10.1016/j.chb.2005.11.003
• Hurt, H. T., Joseph, K., & Cook, C. D. (1977). Scales for the measurement of innovativeness. Human Communication Research, 4(1), 58–65.
• Hwang, Y. (2014). User experience and personal innovativeness: An empirical study on the Enterprise Resource Planning systems. Computers in Human Behavior, 34, 227–234. https://doi.org/10.1016/j.chb.2014.02.002
• İKÇÜ. (2021). İKÇÜ Üniversite Bilgi Yönetim Sistemi. https://ikcuubys.ikcu.edu.tr/
• Jackson, J. D., Yi, M. Y., & Park, J. S. (2013). An empirical test of three mediation models for the relationship between personal innovativeness and user acceptance of technology. Information & Management, 50(4), 154–161. https://doi.org/10.1016/j.im.2013.02.006
• Jang, J., Ko, Y., Shin, W. S., & Han, I. (2021). Augmented reality and virtual reality for learning: An examination using an extended technology acceptance model. IEEE Access, 9, 6798–6809. https://doi.org/10.1109/ACCESS.2020.3048708
• Jang, S., & Lee, C. (2018). The impact of location-based service factors on usage intentions for technology acceptance: The moderating effect of innovativeness. Sustainability, 10(6), 1876. https://doi.org/10.3390/su10061876
• Joo, Y. J., Lee, H. W., & Ham, Y. (2014). Integrating user interface and personal innovativeness into the TAM for mobile learning in Cyber University. Journal of Computing in Higher Education, 26(2), 143–158. https://doi.org/10.1007/s12528-014-9081-2
• Kim, E.-J., Kim, J. J., & Han, S.-H. (2021). Understanding student acceptance of online learning systems in higher education: Application of social psychology theories with consideration of user innovativeness. Sustainability, 13(2), 896. https://doi.org/10.3390/su13020896
• Kim, M., Cho, W. D., Lee, J., Park, R. W., Mukhtar, H., & Kim, K.-H. (2010). Ubiquitous Korea Project. In Handbook of Ambient Intelligence and Smart Environments (pp. 1257–1283). Springer US. https://doi.org/10.1007/978-0-387-93808-0_46
• Kim, S. H. (2008). Moderating effects of job relevance and experience on mobile wireless technology acceptance: Adoption of a smartphone by individuals. Information & Management, 45(6), 387–393. https://doi.org/10.1016/j.im.2008.05.002
• Kobul, M.G. (2022) Socioeconomic status influences Turkish digital natives’ internet use habitus, Behaviour & Information Technology, https://doi.org/10.1080/0144929X.2022.2034970
• Kwon, O., Choi, K., & Kim, M. (2007). User acceptance of context-aware services: Self-efficacy, user innovativeness and perceived sensitivity on contextual pressure. Behaviour & Information Technology, 26(6), 483–498. https://doi.org/10.1080/01449290600709111
• Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & Management, 40(3), 191–204. https://doi.org/10.1016/S0378-7206(01)00143-4
• Li, Y., Qi, J., & Shu, H. (2008). Review of relationships among variables in TAM. Tsinghua Science & Technology, 13(3), 273–278.
• Marangunić, N., & Granić, A. (2015). Technology acceptance model: A literature review from 1986 to 2013. Universal Access in the Information Society, 14(1), 81–95. https://doi.org/10.1007/s10209-014-0348-1
• Midgley, D. F., & Dowling, G. R. (1978). Innovativeness: The concept and its measurement. Journal of Consumer Research, 4(4), 229-242.
• Mokhtar, S. A., Katan, H., & Hidayat-ur-Rehman, I. (2018). Instructors’ behavioural intention to use learning management system: an integrated TAM perspective. TEM Journal, 7(3), 513.
• Moon, J.-W., & Kim, Y.-G. (2001). Extending the TAM for a World-Wide-Web context. Information & Management, 38(4), 217–230.
• Ngafeeson, M. N., & Sun, J. (2015). The effects of technology innovativeness and system exposure on student acceptance of e-textbooks. Journal of Information Technology Education: Research, 14, 55.
• Noh, N. H. M., Raju, R., Eri, Z. D., & Ishak, S. N. H. (2021). Extending technology acceptance model (TAM) to measure the students’ acceptance of using digital tools during open and distance learning (ODL). IOP Conference Series: Materials Science and Engineering, 1176(1), 012037. https://doi.org/10.1088/1757-899X/1176/1/012037
• Ong, C.-S., Lai, J.-Y., & Wang, Y.-S. (2004). Factors affecting engineers’ acceptance of asynchronous e-learning systems in high-tech companies. Information & Management, 41(6), 795–804.
• Park, Y., Son, H., & Kim, C. (2012). Investigating the determinants of construction professionals’ acceptance of web-based training: An extension of the technology acceptance model. Automation in Construction, 22, 377–386. https://doi.org/10.1016/j.autcon.2011.09.016
• Patricia Aguilera-Hermida, A. (2020). College students’ use and acceptance of emergency online learning due to COVID-19. International Journal of Educational Research Open, 1, 100011. https://doi.org/10.1016/j.ijedro.2020.100011
• Portz, J. D., Bayliss, E. A., Bull, S., Boxer, R. S., Bekelman, D. B., Gleason, K., & Czaja, S. (2019). Using the technology acceptance model to explore user experience, intent to use, and use behavior of a patient portal among older adults with multiple chronic conditions: Descriptive qualitative study. J Med Internet Res, 21(4), e11604. https://doi.org/10.2196/11604
• Pressley, T., & Ha, C. (2021). Teaching during a Pandemic: United States teachers’ Self-Efficacy during COVID-19. Teaching and Teacher Education, 106, 103465. https://doi.org/10.1016/j.tate.2021.103465
• Rauniar, R., Rawski, G., Yang, J., & Johnson, B. (2014). Technology acceptance model (TAM) and social media usage: an empirical study on Facebook. Journal of Enterprise Information Management, 27(1), 6–30. https://doi.org/10.1108/JEIM-04-2012-0011
• Rigdon, Edward E. (2012). Rethinking partial least squares path modeling: In praise of simple methods, Long Range Planning, Volume 45, Issues 5–6, 2012
• Rini, G. P., & Khasanah, I. (2021). Intention to use online meeting applications during Covid-19 pandemic: A technology acceptance model perspective. Jurnal Manajemen Dan Pemasaran Jasa, 14(1), 77–94.
• Salloum, S. A., Qasim Mohammad Alhamad, A., Al-Emran, M., Abdel Monem, A., & Shaalan, K. (2019). Exploring students’ acceptance of e-learning through the development of a comprehensive technology acceptance model. IEEE Access, 7, 128445–128462. https://doi.org/10.1109/ACCESS.2019.2939467
• Sidi Mohamed, D. (2021). The Digital Educational Innovation In The Weather Of Covid-19 In Morocco. MENACIS2021,19.
• Soper, D. S. (2021). A-priori sample size calculator for structural equation models [Software]. https://www.danielsoper.com/statcalc
• Sprenger, D. A., & Schwaninger, A. (2021). Technology acceptance of four digital learning technologies (classroom response system, classroom chat, e-lectures, and mobile virtual reality) after three months’ usage. International Journal of Educational Technology in Higher Education, 18(1), 8. https://doi.org/10.1186/s41239-021-00243-4
• Stoel, L., & Hye Lee, K. (2003). Modeling the effect of experience on student acceptance of Web‐based courseware. Internet Research, 13(5), 364–374. https://doi.org/10.1108/10662240310501649
• Sukendro, S., Habibi, A., Khaeruddin, K., Indrayana, B., Syahruddin, S., Makadada, F. A., & Hakim, H. (2020). Using an extended technology acceptance model to understand students’ use of e-learning during Covid-19: Indonesian sport science education context. Heliyon, 6(11), e05410. https://doi.org/10.1016/j.heliyon.2020.e05410
• Syahruddin, S., Mohd Yaakob, M. F., Rasyad, A., Widodo, A. W., Sukendro, S., Suwardi, S., Lani, A., Sari, L. P., Mansur, M., Razali, R., & Syam, A. (2021). Students’ acceptance to distance learning during Covid-19: The role of geographical areas among Indonesian sports science students. Heliyon, 7(9), e08043. https://doi.org/10.1016/j.heliyon.2021.e08043
• Talsma, K., Robertson, K., Thomas, C., & Norris, K. (2021). COVID-19 beliefs, self-efficacy and academic performance in first-year university students: Cohort comparison and mediation analysis. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.643408
• Tsai, W.-H., Wu, Y.-S., Cheng, C.-S., Kuo, M.-H., Chang, Y.-T., Hu, F.-K., Sun, C.-A., Chang, C.-W., Chan, T.-C., Chen, C.-W., Lee, C.-C., & Chu, C.-M. (2021). A technology acceptance model for deploying masks to combat the COVID-19 Pandemic in Taiwan (My Health Bank): Web-based cross-sectional survey study. Journal of Medical Internet Research, 23(4), e27069. https://doi.org/10.2196/27069
• Utami, T. L. W. (2021). Technology adoption on online learning during Covid-19 pandemic: Implementation of technology acceptance model (TAM). Diponegoro International Journal of Business, 4(1), 8–19. https://doi.org/10.14710/dijb.4.1.2021.8-19
• Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on I<nterventions. Decision Sciences, 39(2), 273–315. https://doi.org/10.1111/j.1540-5915.2008.00192.x
• Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204.
• Venkatesh, V., & Morris, M. G. (2000). Why don’t men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS Quarterly, 115–139.
• Voorhees, C. M., Brady, M. K., Calantone, R., & Ramirez, E. (2016). Discriminant validity testing in marketing: An analysis, causes for concern, and proposed remedies. Journal of the Academy of Marketing Science, 44(1), 119–134. https://doi.org/10.1007/s11747-015-0455-4
• Yemez, İ. (2021). Etik tüketimin hedonik tüketim ve kompulsif satın alma davranışı üzerindeki etkisinin PLS-SEM ile incelenmesi. Turkish Journal of Marketing, 6(2), 104-124.
• Yoon, C., Jeong, C., & Rolland, E. (2015). Understanding individual adoption of mobile instant messaging: A multiple perspectives approach. Information Technology and Management, 16(2), 139–151. https://doi.org/10.1007/s10799-014-0202-4