Abdullah, F., Ward, R., & Ahmed, E. (2016). Investigating the influence of the most commonly used external variables of TAM on students’ Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) of e-portfolios. Computers in Human Behavior, 63, 75–90.
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, 1–20.
Al-Emran, M., & Salloum, S. A. (2017). Students’ Attitudes Towards the Use of Mobile Technologies in e-Evaluation. International Journal of Interactive Mobile Technologies (IJIM), 11(5), 195–202.
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.
Al-Maroof R.S., S. S. A. (2021). An Integrated Model of Continuous Intention to Use of Google Classroom. In: Al-Emran M., Shaalan K., Hassanien A. (Eds) Recent Advances in Intelligent Systems and Smart Applications. Studies in Systems, Decision and Control, Vol 295. Springer, Cham.
Al Damen, T. M. (2020). The Effectiveness of Teacher Electronic Feedback in Asynchronous Teaching: A Case Study of Foundation Students at Sultan Qaboos University. Arab World English Journal (AWEJ) Proceedings of 2nd MEC TESOL Conference.
Alarcon, G. M., Lyons, J. B., Christensen, J. C., Klosterman, S. L., Bowers, M. A., Ryan, T. J., Jessup, S. A., & Wynne, K. T. (2018). The effect of propensity to trust and perceptions of trustworthiness on trust behaviors in dyads. Behavior Research Methods, 50(5), 1906–1920.
Alfadda, H. A., & Mahdi, H. S. (n.d.). Measuring Students’ Use of Zoom Application in Language Course Based on the Technology Acceptance Model (TAM). Journal of Psycholinguistic Research, 1–18.
Alharbi, M. A., & Al-Hoorie, A. H. (2020). Turnitin peer feedback: controversial vs. non-controversial essays. International Journal of Educational Technology in Higher Education, 17, 1–17.
Ali, A. D. (2016). Effectiveness of Using Screencast Feedback on EFL Students’ Writing and Perception. English Language Teaching, 9(8), 106–121.
Bakhshi, S., Kanuparthy, P., & Shamma, D. A. (2014). If it is funny, it is mean: Understanding social perceptions of yelp online reviews. Proceedings of the 18th International Conference on Supporting Group Work, 46–52.
Balog, A., & Pribeanu, C. (2010). The role of perceived enjoyment in the students’ acceptance of an augmented reality teaching platform: A structural equation modelling approach. Studies in Informatics and Control, 19(3), 319–330.
Barclay, D., Higgins, C., & Thompson, R. (1995). The Partial Least Squares (pls) Approach to Casual Modeling: Personal Computer Adoption Ans Use as an Illustration.
Bei, L.-T., & Chiao, Y.-C. (2001). An integrated model for the effects of perceived product, perceived service quality, and perceived price fairness on consumer satisfaction and loyalty. Journal of Consumer Satisfaction, Dissatisfaction and Complaining Behavior, 14, 125.
Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88(3), 588.
Carless, D. (2006). Differing perceptions in the feedback process. Studies in Higher Education, 31(2), 219–233.
Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295(2), 295–336.
Cho, Y. C., & Sagynov, E. (2015). Exploring factors that affect usefulness, ease of use, trust, and purchase intention in the online environment. International Journal of Management & Information Systems (IJMIS), 19(1), 21–36.
Chuan, C. L., & Penyelidikan, J. (2006). Sample size estimation using Krejcie and Morgan and Cohen statistical power analysis: A comparison. Jurnal Penyelidikan IPBL, 7, 78–86.
Davis, C., & Ryder, A. (2012). Using an old technology in a new way or using a new technology in an old way? exploring the use of audio feedback post teaching observation. Middlesex Journal of Educational Technology, 2(1), 30–40.
Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results. In Massachusetts Institute of Technology: Vol. Ph.D. https://doi.org/oclc/56932490
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111–1132.
De Figueiredo, J. M. (2000). Finding sustainable profitability in electronic commerce. MIT Sloan Management Review, 41(4), 41.
Dijkstra, T. K., & Henseler, J. (2015). Consistent and asymptotically normal PLS estimators for linear structural equations. Computational Statistics & Data Analysis, 81, 10–23.
Edeiken-Cooperman, N., & Berenato, C. L. (2014). Students’ Perceptions of Electronic Feedback as an Alternative to Handwritten Feedback: One University’s Inquiry. Journal of Education and Learning, 3(1), 79–85.
ElShaer, A., Casanova, D., Freestone, N. S., & Calabrese, G. (2020). Students’ perceptions of the value of electronic feedback—Does disciplinary background really matter? British Journal of Educational Technology, 51(2), 590–606.
Engel, F. L., Goossens, P., & Haakma, R. (1994). Improved efficiency through I-and E-feedback: A trackball with contextual force feedback. International Journal of Human-Computer Studies, 41(6), 949–974.
Esteban-Millat, I., Martínez-López, F. J., Pujol-Jover, M., Gázquez-Abad, J. C., & Alegret, A. (2018). An extension of the technology acceptance model for online learning environments. Interactive Learning Environments, 26(7), 895–910.
Ferrin, D. L., & Dirks, K. T. (2003). The use of rewards to increase and decrease trust: Mediating processes and differential effects. Organization Science, 14(1), 18–31.
Goodhue, D. L., Lewis, W., & Thompson, R. (2012). Does PLS have adavantages for small sample size or non-normal data? MIS Quaterly.
Govender, I., & Sihlali, W. (2014). A study of mobile banking adoption among university students using an extended TAM. Mediterranean Journal of Social Sciences, 5(7), 451.
Grabowski, P., & Callier, F. M. (2011). Lur’e feedback systems with both unbounded control and observation: well-posedness and stability using nonlinear semigroups. Nonlinear Analysis: Theory, Methods & Applications, 74(10), 3065–3085.
Gupta, A., Villegas, C. V, Watkins, A. C., Foglia, C., Rucinski, J., Winchell, R. J., Barie, P. S., & Narayan, M. (2020). General Surgery Residents’ Perception of Feedback: We Can Do Better. Journal of Surgical Education, 77(3), 527–533.
Hair, J., Hult, G. T. M., Ringle, C., Sarstedt, M., Hair, J. F. F., Hult, G. T. M., … Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications.
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–152.
Hair, J., Hollingsworth, C. L., Randolph, A. B., & Chong, A. Y. L. (2017). An updated and expanded assessment of PLS-SEM in information systems research. Industrial Management & Data Systems, 117(3), 442–458. https://doi.org/10.1108/IMDS-04-2016-0130
Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W., Ketchen Jr, D. J., Hair, J. F., Hult, G. T. M., & Calantone, R. J. (2014). Common beliefs and reality about PLS: Comments on Rönkkö and Evermann (2013). Organizational Research Methods, 17(2), 182–209.
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.
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 (pp. 277–319). Emerald Group Publishing Limited.
Hernandez, R. M. (2021). Employing Technology Acceptance Model (TAM): An Analysis on Students’ Reception on Online Learning Platforms During Covid-19 Pandemic. 2021 IEEE International Conference on Automatic Control & Intelligent Systems (I2CACIS), 58–63.
Hu, L., & Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3(4), 424.
Khan, M. R., Rana, S., & Hosen, M. I. (2021). Impact of trustworthiness on the usage of m-banking apps: A study on Bangladeshi consumers. Business Perspectives and Research, 22785337211001970.
Kline, R. B. (2015). Principles and practice of structural equation modeling. Guilford publications.
Krejcie, R. V, & Morgan, D. W. (1970). Determining sample size for research activities. Educational and Psychological Measurement, 30(3), 607–610.
Lazim, C. S. L. M., Ismail, N. D. B., & Tazilah, M. D. A. K. (n.d.). APPLICATION OF TECHNOLOGY ACCEPTANCE MODEL (TAM) TOWARDS ONLINE LEARNING DURING COVID-19 PANDEMIC: ACCOUNTING STUDENTS PERSPECTIVE.
Lee, Y., Kozar, K. A., & Larsen, K. R. T. (2003). The technology acceptance model: Past, present, and future. Communications of the Association for Information Systems, 12(1), 50.
Leminen, A., Tykkyläinen, M., & Laatikainen, T. (2018). Self-monitoring induced savings on type 2 diabetes patients’ travel and healthcare costs. International Journal of Medical Informatics, 115, 120–127.
Liu, S.-H., Liao, H.-L., & Pratt, J. A. (2009). Impact of media richness and flow on e-learning technology acceptance. Computers & Education, 52(3), 599–607.
Lohmöller, J. B. (1989). Latent variable path modeling with partial least squares. Heidelberg, Germany: Physica-Verlag.
Ma, L. (2021). Understanding non-adopters’ intention to use internet pharmacy: Revisiting the roles of trustworthiness, perceived risk and consumer traits. Journal of Engineering and Technology Management, 59, 101613.
Mansoori, E., & Baradaran-Kazem-Zadeh, R. (2007). Determining the factors affecting e-customer satisfaction. Proceedings of the 5th International Conference (T) FT| Industrial Engineering, 1–17.
Martin, S. (2021). The Effects of Virtual Feedback and Virtual Environment on Productivity.
Masrom, M. (2007). Technology acceptance model and e-learning. Technology, 21(24), 81.
Mayer, R. C., & Davis, J. H. (1999). The effect of the performance appraisal system on trust for management: A field quasi-experiment. Journal of Applied Psychology, 84(1), 123.
Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An integrative model of organizational trust. Academy of Management Review, 20(3), 709–734.
McGrath, A. L., Taylor, A., & Pychyl, T. A. (2011). Writing Helpful Feedback: The Influence of Feedback Type on Students’ Perceptions and Writing Performance. Canadian Journal for the Scholarship of Teaching and Learning, 2(2), 5.
Mo, C.-Y., Hsieh, T.-H., Lin, C.-L., Jin, Y. Q., & Su, Y.-S. (2021). Exploring the Critical Factors, the Online Learning Continuance Usage during COVID-19 Pandemic. Sustainability, 13(10), 5471.
Neumann, K. L., & Kopcha, T. J. (2019). Designing a Revision Tool to Distinguish Surface-Level and Text-Based Writing Feedback. International Journal of Designs for Learning, 10(1), 64–77.
Nunnally, J. C., & Bernstein, I. H. (1978). Psychometric theory.
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory. In McGraw-Hill, New York. https://doi.org/10.1037/018882
O AbuSa’aleek, A., & Shariq, M. (2021). Innovative Practices in Instructor E-feedback: A Case Study of E-feedback Given in Three Linguistic Courses during the COVID-19 Pandemic. Arab World English Journal (AWEJ) Special Issue on Covid, 19.
Parkin, H. J., Hepplestone, S., Holden, G., Irwin, B., & Thorpe, L. (2012). A role for technology in enhancing students’ engagement with feedback. Assessment & Evaluation in Higher Education, 37(8), 963–973.
Rizun, M., & Strzelecki, A. (2020). Students’ acceptance of the Covid-19 impact on shifting higher education to distance learning in Poland. International Journal of Environmental Research and Public Health, 17(18), 6468.
Roter, D. L., Larson, S., Shinitzky, H., Chernoff, R., Serwint, J. R., Adamo, G., & Wissow, L. (2004). Use of an innovative video feedback technique to enhance communication skills training. Medical Education, 38(2), 145–157.
Saeed, M. A., & Al Qunayeer, H. S. (2020). Exploring teacher interactive e-feedback on students’ writing through Google Docs: factors promoting interactivity and potential for learning. The Language Learning Journal, 1–18.
Sáiz-Manzanares, M. C., Marticorena-Sánchez, R., Rodríguez-Díez, J. J., Rodríguez-Arribas, S., Díez-Pastor, J. F., & Ji, Y. P. (2021). Improve teaching with modalities and collaborative groups in an LMS: an analysis of monitoring using visualisation techniques. Journal of Computing in Higher Education, 1–32.
Salloum, S. A., Alhamad, A. Q. M., Al-Emran, M., Monem, A. A., & Shaalan, K. (2019). Exploring Students’ Acceptance of E-Learning Through the Development of a Comprehensive Technology Acceptance Model. IEEE Access, 7, 128445–128462.
Schicker, L.-M., Watzenig, D., Hartavi, A. E., & Troglia, M. (2021). How Should TrustVehicles Behave? Trustworthiness, User Acceptance and Expectations. In Enhanced Trustworthiness and End User Acceptance of Conditionally Automated Vehicles in the Transition Period (pp. 1–23). Springer.
Suki, N. M., & Suki, N. M. (2011). Exploring the relationship between perceived usefulness, perceived ease of use, perceived enjoyment, attitude and subscribers’ intention towards using 3G mobile services. Journal of Information Technology Management, 22(1), 1–7.
Tatsanajamsuk, P., & Saengboon, S. (2021). The Roles of Indirect Asynchronous Electronic Feedback and Direct Corrective Feedback in Improving Students’ L2 Writing: A Multiple Case Study of Thai Undergraduate Students. LEARN Journal: Language Education and Acquisition Research Network, 14(2), 397–426.
Ter Mors, E., Weenig, M., Ellemers, N., & Daamen, D. (2006). The influence of (in) congruence of communicator expertise and trustworthiness on acceptance of CCS technologies. International Conference on Greenhouse Gas Control Technologies, 18–22.
Trial, D. (n.d.). Model Fit.
Urbach, N., & Ahlemann, F. (2010). Structural equation modeling in information systems research using partial least squares. Journal of Information Technology Theory and Application, 11(2), 5–40. https://doi.org/10.1037/0021-9010.90.4.710
Van den Bulck, S., Spitaels, D., Vaes, B., Goderis, G., Hermens, R., & Vankrunkelsven, P. (2020). The effect of electronic audits and feedback in primary care and factors that contribute to their effectiveness: a systematic review. International Journal for Quality in Health Care, 32(10), 708–720.
Van der Heijden, H. (2004). User acceptance of hedonic information systems. MIS Quarterly, 28(4), 695–704.
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.
Walter, N., Ortbach, K., & Niehaves, B. (2015). Designing electronic feedback–analyzing the effects of social presence on perceived feedback usefulness. International Journal of Human-Computer Studies, 76, 1–11.
Watkins, D., Dummer, P., Hawthorne, K., Cousins, J., Emmett, C., & Johnson, M. (2014). Healthcare Students’ Perceptions of Electronic Feedback through GradeMark®. Journal of Information Technology Education, 13.
Wiener, J. L., & Mowen, J. C. (1986). Source credibility: On the independent effects of trust and expertise. ACR North American Advances.
Zareekbatani, A. (2015). Technology and L2 writing: EFL student perspectives on electronic feedback using online learning logs.
Zhou, R., & Feng, C. (2017). Difference between leisure and work contexts: The roles of perceived enjoyment and perceived usefulness in predicting mobile video calling use acceptance. Frontiers in Psychology, 8, 350.
Zieve, G. G., Richardson, L. P., Katzman, K., Spielvogle, H., Whitehouse, S., & McCarty, C. A. (2017). Adolescents’ perspectives on personalized e-feedback in the context of health risk behavior screening for primary care: qualitative study. Journal of Medical Internet Research, 19(7), e261.