EDUCATOR ACCEPTANCE, PERSONALITY TRAITS, TECHNOLOGICAL COMPETENCE AND 21ST-CENTURY EDUCATIONAL TECHNOLOGY ADOPTION: A PREDICTIVE RELEVANCE ANALYSIS USING PLSPREDICT
DOI:
https://doi.org/10.35631/IJEPC.11640016Keywords:
Acceptance, Educational Technology, Intention to Adopt, Personality Traits, Plspredict, Technological CompetenceAbstract
The study examines the intention to adopt 21st-century educational technology in higher education, focusing on the factors contributing to educators’ intention to adopt. The novel framework conceptualised in this study combines prominent theories and models, including acceptance, five-factor traits, and technological competence. The study uses partial least squares structural equation modelling (PLS-SEM) and predictive relevance analysis with PLSpredict to test the model’s predictive validity. The study found that the conceptualised model was reliable and consistent with sufficient discriminant validity, and the PLSpredict analysis concluded that the developed conceptual framework has high out-of-sample predictive power. The study’s implications for practice and policy include engaging with educators to understand their concerns about technology acceptance, designing personality mapping, aligning with future personality intervention, and ensuring that investment in technology is fully deployed in line with demands and new requirements.
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References
Abbasi, M. S. (2011). Culture, Demography and Individuals’ Technology Acceptance Behaviour: A PLS Based Structural Evaluation of an Extended Model of Technology Acceptance in South-Asian Country Context [Unpublished doctoral dissertation]. Brunel University. http://bura.brunel.ac.uk/handle/2438/6023
Abd Jalil, N. (2018). Trend Pengangguran Dalam Kalangan Graduan Di Malaysia [Unpublished doctoral dissertation]. Universiti Tun Hussein Onn.
Abdul Wahab, R., Mat Dangi, R. M., Abdul Latif, N. E., Mad, S., , & Mohd Noor, R. (2017). Technology-Based Accounting Education: Evidence on Acceptance and Usage. Advanced Science Letters, 23(8), 7737-7741. https://doi.org/10.1166/asl.2017.9565
Abu Karsh, S. M. (2018). New Technology Adoption by Business Faculty in Teaching: Analysing Faculty Technology Adoption Patterns. Education Journal, 7(1), 5-15. https://doi.org/10.11648/j.edu.20180701.12
Adam, B. (2020, 22 June). The 101 Hottest EdTech Tools According to Education Experts (Updated For 2020). Tutorful. https://tutorful.co.uk/blog/the-82-hottest-edtech-tools-of-2017-according-to-education-experts
Aftab, N., Rashid, S., Ali Shah, S. A., & Hackett, J. (2018). Direct effect of extraversion and conscientiousness with interactive effect of positive psychological capital on organizational citizenship behavior among university teachers. Cogent Psychology, 5(1), 1-11. https://doi.org/10.1080/23311908.2018.1514961
Agarwal, R., & Prasad, J. (1997). The Role of Innovation Characteristics and Perceived Voluntariness in the Acceptance of Information Technologies. Decision Sciences, 28(3), 557-582. https://doi.org/10.1111/j.1540-5915.1997.tb01322.x
Aguiar, G., & Gouveia, L. (2020). The Digital Transformation in Academic Accounting Research: Literature Review. Journal of Organizational Knowledge Management, 2020(2020), 1-9. https://doi.org/10.5171/2020.947901
Ahadiat, N. (2008). Technologies Used in Accounting Education: A Study of Frequency of Use Among Faculty. Journal of Education for Business, 83(3), 123-134. https://doi.org/10.3200/joeb.83.3.123-134
Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T
Al-Furaih, S. A., & Al-Awidi, H. M. (2020). Teachers’ change readiness for the adoption of smartphone technology: Personal concerns and technological competency. Technology, Knowledge and Learning, 25(2), 409-432. https://doi.org/10.1007/s10758-018-9396-6
Al-Htaybat, K., von Alberti-Alhtaybat, L., & Alhatabat, Z. (2018). Educating digital natives for the future: accounting educators’ evaluation of the accounting curriculum. Accounting Education, 27(4), 333-357. https://doi.org/10.1080/09639284.2018.1437758
Al Khateeb, A. A. M. (2017). Measuring Digital Competence and ICT Literacy: An Exploratory Study of In-Service English Language Teachers in the Context of Saudi Arabia. International Education Studies, 10(12), 38-51. https://doi.org/10.5539/ies.v10n12p38
Alshurafat, H., Al Shbail, M. O., Masadeh, W. M., Dahmash, F., & Al-Msiedeen, J. M. (2021). Factors affecting online accounting education during the COVID-19 pandemic: an integrated perspective of social capital theory, the theory of reasoned action and the technology acceptance model. Education and Information Technologies, 2021(26), 1-19. https://doi.org/10.1007/s10639-021-10550-y
Apostolou, B., Dorminey, J. W., & Hassell, J. M. (2020). Accounting education literature review (2019). Journal of Accounting Education, 51, 1-24. https://doi.org/10.1016/j.jaccedu.2020.100670
Asonitou, S. (2020). Technologies to Communicate Accounting Information in the Digital Era: Is Accounting Education Following the Evolutions? In A. Kavoura, E. Kefallonitis, & P. Theodoridis (Eds.), Strategic Innovative Marketing and Tourism (pp. 187-194). Springer https://doi.org/10.1007/978-3-030-36126-6_21
Atabek, O. (2020). Associations Between Emotional States, Self-Efficacy For and Attitude Towards Using Educational Technology. International Journal of Progressive Education, 16(2), 175-194. https://doi.org/10.29329/ijpe.2020.241.12
Avkiran, N., & Ringle, C. (2018). Partial least squares structural equation modeling. In C. C. Price, J. Zhu, & F. S. Hillier (Eds.), Handbook of Market Research (Vol. 267).
Ayele, A. A., & Birhanie, W. K. (2018). Acceptance and use of e-learning systems: the case of teachers in technology institutes of Ethiopian Universities. Applied Informatics, 5(1), 1-11. https://doi.org/10.1186/s40535-018-0048-7
Azucar, D., Marengo, D., & Settanni, M. (2018). Predicting the Big 5 personality traits from digital footprints on social media: A meta-analysis. Personality and Individual Differences, 124, 150-159. https://doi.org/10.1016/j.paid.2017.12.018
Barnett, T., Pearson, A. W., Pearson, R., & Kellermanns, F. W. (2015). Five-factor model personality traits as predictors of perceived and actual usage of technology. European Journal of Information Systems, 24(4), 374-390. https://doi.org/10.1057/ejis.2014.10
Becker, J.-M., Ringle, C. M., Sarstedt, M., & Völckner, F. (2015). How collinearity affects mixture regression results. Marketing Letters, 26(4), 643-659. https://doi.org/10.1007/s11002-014-9299-9
Bendersky, C., & Shah, N. P. (2013). The downfall of extraverts and rise of neurotics: The dynamic process of status allocation in task groups. Academy of Management Journal, 56(2), 387-406. https://www.jstor.org/stable/23412595
Benitez, J., Henseler, J., Castillo, A., & Schuberth, F. (2020). How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research. Information & management, 57(2). https://doi.org/10.1016/j.im.2019.05.003
Bere, A., & Rambe, P. (2016). An empirical analysis of the determinants of mobile instant messaging appropriation in university learning. Journal of Computing in Higher Education, 28(2), 172-198. https://doi.org/10.1007/s12528-016-9112-2
Berninzoni, D. (2020). The Neuroscience link between Neuroticism and Social Media Addiction. Lake Forest College. https://www.lakeforest.edu/news/the-neuroscience-link-between-neuroticism-and-social-media-addiction
Biggins, D., Holley, D., Evangelinos, G., & Zezulkova, M. (2017). Digital competence and capability frameworks in the context of learning, self-development and HE pedagogy. In E-learning, e-education, and online training (pp. 46-53). Springer. https://doi.org/10.1007/978-3-319-49625-2_6
Blankley, A. I., Kerr, D. S., & Wiggins, C. E. (2018). An examination and analysis of technologies employed by accounting educators. The Accounting Educators’ Journal, XXVIII, 75-98. https://www.aejournal.com/ojs/index.php/aej/article/view/390
Buabeng-Andoh, C. (2012). Factors influencing teachers’ adoption and integration of information and communication technology into teaching: A review of the literature. International Journal of Education and Development using Information and Communication Technology, 8(1), 136-155.
Buabeng-Andoh, C. (2018). Predicting students’ intention to adopt mobile learning: A combination of theory of reasoned action and technology acceptance model. Journal of Research in Innovative Teaching & Learning, 11(2), 178-191. https://doi.org/10.1108/jrit-03-2017-0004
Burritt, R., & Christ, K. (2016). Industry 4.0 and environmental accounting: a new revolution? Asian Journal of Sustainability and Social Responsibility, 1(1), 23-38. https://doi.org/10.1186/s41180-016-0007-y
Camadan, F., Reisoglu, I., Ursavas, Ö. F., & McIlroy, D. (2018). How teachers’ personality affect on their behavioral intention to use tablet PC. International Journal of Information and Learning Technology, 35(1), 12-28. https://doi.org/10.1108/ijilt-06-2017-0055
Candolfi Arballo, N., Chan Nuñez, M. E., & Rodriguez Tapia, B. (2019). Technological Competences: A Systematic Review of the Literature in 22 Years of Study. International Journal of Emerging Technologies in Learning (iJET), 14(4), 4-33. https://doi.org/10.3991/ijet.v14i04.9118
Cappelleri, J. C., Darlington, R. B., & Trochim, W. M. (1994). Power analysis of cutoff-based randomized clinical trials. Evaluation Review, 18(2), 141-152. https://doi.org/10.1177/0193841X940180020
Chao, C. M. (2019). Factors Determining the Behavioral Intention to Use Mobile Learning: An Application and Extension of the UTAUT Model. Front Psychol, 10, 1652. https://doi.org/10.3389/fpsyg.2019.01652
Chin, W., Cheah, J.-H., Liu, Y., Ting, H., Lim, X.-J., & Cham, T. H. (2020). Demystifying the role of causal-predictive modeling using partial least squares structural equation modeling in information systems research. Industrial management & data systems, 120(12), 2161-2209. https://doi.org/10.1108/IMDS-10-2019-0529
Chin, W. W. (2010). How to write up and report PLS analyses. In V. V. Esposito, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares (pp. 655-690). Springer.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Lawrence Erlbaum.
Dalpé, J., Demers, M., Verner-Filion, & Valleranda, J. R. J. (2019). From personality to passion: The role of the Big Five factors. Personality and Individual Differences, 138, 280-285. https://doi.org/10.1016/j.paid.2018.10.021
Dauda, A., & Olawale, B. V. (2020). Integration of educational technology in accounting education: evidence from selected tertiary institutions in North West region of Nigeria. International Journal of Scientific Research and Engineering Development, 3(2), 191-198.
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
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.
Delgado, A. J., Wardlow, L., McKnight, K., & O’Malley, K. (2015). Educational technology: A review of the integration, resources, and effectiveness of technology in K-12 classrooms. Journal of Information Technology Education: Research, 14(397-416). https://eric.ed.gov/?id=EJ1084456
Dijkstra, T. K., & Henseler, J. (2015). Consistent partial least squares path modeling. Mis Quarterly, 39(2), 297-316. https://www.jstor.org/stable/26628355
Dolce, P., Esposito Vinzi, V., & Lauro, C. (2017). Predictive path modeling through PLS and other component-based approaches: methodological issues and performance evaluation. In Latan H. & Noonan R. (Eds.), Partial Least Squares Path Modeling: Basic Concepts, Methodological Issues and Applications (pp. 153-172). Springer International Publishing. https://doi.org/10.1007/978-3-319-64069-3_7
Dua, S., Wadhawan, S., & Gupta, S. (2016). Issues, Trends & Challenges of Digital Education: An Empowering Innovative Classroom Model for Learning. International Journal of Science technology and Management, 5(5), 142-149.
Evermann, J., & Tate, M. (2016). Assessing the predictive performance of structural equation model estimators. Journal of Business Research, 69(10), 4565-4582. https://doi.org/10.1016/j.jbusres.2016.03.050
Faqih, K. M. S. (2016, 14-15 March). Which is more important in e-learning adoption, perceived value or perceived usefulness? Examining the moderating influence of perceived compatibility. e-Proceeding of the 4th Global Summit on Education GSE 2016 Kuala Lumpur, Malaysia. https://myjurnal.mohe.gov.my/public/article-view.php?id=112755
Farhad Khan, M. R., Iahad, N. A., & Miskon, S. (2014). Exploring the Influence of Big Five Personality Traits towards Computer Based Learning (CBL) Adoption. Journal of Information Systems Research and Innovation, 8, 1-8.
Ferguson, C. J. (2009). An effect size primer: A guide for clinicians and researchers. Professional Psychology: Research and Practice, 40(5), 532-538. https://doi.org/10.1037/a0015808
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley Pub. Co.
Fornell, C., & Cha, J. (1994). Partial Least Squares. Advanced Methods of Marketing Research, 407, 52-78.
Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models With Unobservable Variables and Measurement Error. Journal of marketing Research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104
Foued, H. A. (2021). The Adoption Determinants of Mobile Technologies in the Accounting Profession. Academy of Accounting and Financial Studies Journal, 25(3), 1-11. https://www.abacademies.org/abstract/the-adoption-determinants-of-mobile-technologies-in-the-accounting-profession-12978.html
Foulger, T. S., Graziano, K. J., Schmidt-Crawford, D., & Slykhuis, D. (2017). Editor-Invited Article Teacher Educator Technology Competencies. Jl. of Technology and Teacher Education, 25(4), 413-448.
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
Fussell, S. G., & Truong, D. (2022). Using virtual reality for dynamic learning: an extended technology acceptance model. Virtual Reality, 2022(26), 249-267. https://doi.org/10.1007/s10055-021-00554-x
Geisser, S. (1975). A predictive approach to the random effect model. Biometrika, 61(1), 101-107. https://doi.org/10.1093/biomet/61.1.101
Gholami, Z., Abdekhoda, M., & Gavgani, V. Z. (2018). Determinant Factors in Adopting Mobile Technology-based Services by Academic Librarians. DESIDOC Journal of Library & Information Technology, 38(4). https://doi.org/10.14429/djlit.38.4.12676
Glaesser, J. (2018). Competence in educational theory and practice: a critical discussion. Oxford Review of Education, 45(1), 70-85. https://doi.org/10.1080/03054985.2018.1493987
Göncz, L. (2017). Teacher personality: a review of psychological research and guidelines for a more comprehensive theory in educational psychology. Open Review of Educational Research, 4(1), 75-95. https://doi.org/10.1080/23265507.2017.1339572
Grabinski, K., Kedzior, M., & Krasodomska, J. (2015). Blended learning in tertiary accounting education in the CEE region-A Polish perspective. Accounting and Management Information Systems, 14(2), 378.
Granic, A. (2022). Educational Technology Adoption: A systematic review. Education and Information Technologies, 27(7), 9725-9744. https://doi.org/10.1007/s10639-022-10951-7
Graziano, K. J., Foulger, T. S., Schmidt-Crawford, D. A., & Slykhuis, D. (2017, March 5-9). Technology Integration and Teacher Preparation: The Development of Teacher Educator Technology Competencies. Society for Information Technology & Teacher Education International Conference, Austin, TX, United States.
Guia, S. B. (2017, September 17). The best careers for your personality: How to decide your next career move. Retrieved October 21 from https://startupbeat.com/best-careers-per-personality-type/26304/
Guillén-Gámez, F. D., & Mayorga-Fernández, M. J. (2020). Identification of variables that predict teachers’ attitudes toward ICT in higher education for teaching and research: A study with regression. Sustainability, 12(4), 1312. https://doi.org/10.3390/su12041312
Gurjar, N., & Sivo, S. (2022). Predicting and explaining pre-service teachers’ social networking technology adoption. Italian Journal of Educational Technology. https://doi.org/10.17471/2499-4324/1245
Hair, J. F., Babin, B. J., & Krey, N. (2017). Covariance-based structural equation modeling in the journal of advertising: review and recommendations. Journal of Advertising, 46(1), 163-177. https://doi.org/10.1080/00913367.2017.1281777
Hair, J. F., Black, W. C., Babin, B., & Anderson, R. (2018). Multivariate Data Analysis. Cengage.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis (7 ed.). Prentice Hall.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2014). A Primer of Partial Least Squares Structural Equation Modelling (PLS-SEM). Sage Publications, Inc.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM).
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19, 139-151. https://doi.org/10.2753/MTP1069-6679190202
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
Hair, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2018). Advanced issues in partial least squares structural equation modeling (PLS-SEM). Sage.
Hasan, A. R. (2022). Artificial Intelligence (AI) in Accounting & Auditing: A Literature Review. Open Journal of Business and Management, 10(01), 440-465. https://doi.org/10.4236/ojbm.2022.101026
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
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. Advances in International Marketing, 20, 277-320. https://doi.org/10.1108/S1474-7979(2009)0000020014
Herrador-Alcaide, T. C., Hernández-Solís, M., & Hontoria, J. F. (2020). Online Learning Tools in the Era of m-Learning: Utility and Attitudes in Accounting College Students. Sustainability, 12(12), 1-23. https://doi.org/10.3390/su12125171
Hizam, S. M., Akter, H., Sentosa, I., & Ahmed, W. (2021). Digital competency of educators in the virtual learning environment: a structural equation modeling analysis. IOP Conference Series: Earth and Environmental Science, 704(1), 12-23. https://doi.org/10.1088/1755-1315/704/1/012023
Huang, H.-M., & Liaw, S.-S. (2018). An analysis of learners’ intentions toward virtual reality learning based on constructivist and technology acceptance approaches. International Review of Research in Open and Distributed Learning, 19(1). https://doi.org/10.19173/irrodl.v19i1.2503
Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic Management Journal, 20, 195-204. https://doi.org/10.1002/(SICI)1097-0266(199902)20:2<195::AID-SMJ13>3.0.CO;2-7
Hussain, A., Mkpojiogu, E. O. C., & Yusof, M. M. (2016, 12 August). Perceived usefulness, perceived ease of use, and perceived enjoyment as drivers for the user acceptance of interactive mobile maps Proceedings of the International Conference on Applied Science and Technology 2016 (ICAST’16), Kedah, Malaysia. https://doi.org/10.1063/1.4960891
Instefjord, E. J. (2018). Professional Digital Competence in Teacher Education: A mixed methods study of the emphasis on and integration of Professional Digital Competence in Teacher Education Programmes in Norway (Publication Number UiS 396) University of Stavanger]. Norway.
Iqbal, M., Chairil Furqan, A., Mapparessa, N., & Tenriwaru, D. (2019). Ethics Inclusion in Accounting Learning: Establishing a Convergence Model of Education Stakeholders According to International Federation of Accountants. Universal Journal of Educational Research, 7(10), 2068-2081. https://doi.org/10.13189/ujer.2019.071004
Kanwal, F., & Rehman, M. (2017). Factors Affecting E-Learning Adoption in Developing Countries–Empirical Evidence From Pakistan’s Higher Education Sector. IEEE Access, 5, 10968-10978. https://doi.org/10.1109/access.2017.2714379
Kavirayani, K. (2018). Historical perspectives on personality – The past and current concept: The search is not yet over. Archives of Medicine and Health Sciences, 6(1), 180-186. https://doi.org/10.4103/amhs.amhs_63_18
Kearney, M., Schuck, S., Aubusson, P., & Burke, P. F. (2017). Teachers’ technology adoption and practices: lessons learned from the IWB phenomenon. Teacher Development, 22(4), 481-496. https://doi.org/10.1080/13664530.2017.1363083
Khan, A. K., Aboud, O. A. A., & Faisal, S. M. (2018). An Empirical Study of Technological Innovations in the Field of Accounting - Boon or Bane. Business and Management Studies, 4(1), 51-58. https://doi.org/10.11114/bms.v4i1.3057
Khlaif, Z. (2018). Teachers' Perceptions of Factors Affecting Their Adoption and Acceptance of Mobile Technology in K-12 Settings. Computers in the Schools, 35(1), 49-67. https://doi.org/10.1080/07380569.2018.1428001
Kim, L. E., Jorg, V., & Klassen, R. M. (2019). A Meta-Analysis of the Effects of Teacher Personality on Teacher Effectiveness and Burnout. Educational Psychology Review, 31(1), 163-195. https://doi.org/10.1007/s10648-018-9458-2
Kim, Y., Park, Y., & Choi, J. (2017). A study on the adoption of IoT smart home service: using Value-based Adoption Model. Total Quality Management & Business Excellence, 28(9-10), 1149-1165. https://doi.org/10.1080/14783363.2017.1310708
Kraft, M. A. (2018). Interpreting effect sizes of education interventions. Brown University Working Paper.
Krieger, F., Drews, P., & Velte, P. (2021). Explaining the (non-) adoption of advanced data analytics in auditing: A process theory. International Journal of Accounting Information Systems, 41(2021), 100511. https://doi.org/10.1016/j.accinf.2021.100511
Kumar, A., & Mantri, A. (2021). Evaluating the attitude towards the intention to use ARITE system for improving laboratory skills by engineering educators. Education and Information Technologies, 27(1), 671-700. https://doi.org/10.1007/s10639-020-10420-z
Kwarteng, J. T. (2018). Preservice Accounting Teachers' Anxiety About Teaching Practicum. International Journal of Research in Teacher Education, 9(4), 71-80.
Lai, H., Pitafi, A. H., Hasany, N., & Islam, T. (2021). Enhancing Employee Agility Through Information Technology Competency: An Empirical Study of China. SAGE Open, 11(2), 1-18. https://doi.org/10.1177/21582440211006687
Lai, P. C. (2017). The Literature Review of Technology Adoption Models and Theories for the Novelty Technology. Journal of Information Systems and Technology Management, 14(1). https://doi.org/10.4301/s1807-17752017000100002
Landers, R. N., & Lounsbury, J. W. (2006). An investigation of Big Five and narrow personality traits in relation to Internet usage. Computers in Human Behavior, 22(2), 283-293. https://doi.org/10.1016/j.chb.2004.06.001
Lane, W., & Manner, C. (2011). The Impact of Personality Traits on Smartphone Ownership and Use. International Journal of Business and Social Science, 2(17), 22-28.
Lawrence, J. E., & Tar, U. A. (2018). Factors that influence teachers’ adoption and integration of ICT in teaching/learning process. Educational Media International, 55(1), 79-105. https://doi.org/10.1080/09523987.2018.1439712
Mason, C. H., & Perreault, W. D. (1991). Collinearity, power, and interpretation of multiple regression analysis. Journal of marketing Research, 28(3), 268-280. https://doi.org/10.1177/002224379102800
Mayes, R., Natividad, G., & Spector, J. (2015). Challenges for Educational Technologists in the 21st Century. Education Sciences, 5(3), 221-237. https://doi.org/10.3390/educsci5030221
Milutinovic, V. (2022). Examining the influence of pre-service teachers' digital native traits on their technology acceptance: A Serbian perspective. Educ Inf Technol (Dordr), 1-29. https://doi.org/10.1007/s10639-022-10887-y
Mirzajani, H., Mahmud, R., Ahmad Fauzi, M. A., & Wong, S. L. (2016). Teachers’ acceptance of ICT and its integration in the classroom. Quality Assurance in Education, 24(1), 26-40. https://doi.org/10.1108/qae-06-2014-0025
Mohd Muzi, N. A. f., Mohd Nadzir, N. A., Abdul Mutalib, S. F., Mohamed Zukri, S., & Mohd Fauzi, N. Z. (2021). Factors Affecting Academicians’ Acceptance On E-Learning Application. Journal of Social Sciences and Humanities, 18(4), 78-91. https://ir.uitm.edu.my/id/eprint/45550
Moore, G. C., & Benbasat, I. (1996). Integrating diffusion of innovations and theory of reasoned action models to predict utilization of information technology by end-users. In K. Kautz & J. Pries-Heje (Eds.), Diffusion and adoption of information technology, (pp. 132-146). Chapman and Hall. https://doi.org/10.1007/978-0-387-34982-4_10
Mourão, L. (2018). The role of leadership in the professional development of subordinates. IntechOpen Limited.
Namoco, S. O. (2022). Determinants in the Use of Web 2.0 Tools in Teaching among the Philippine Public University Educators: A PLS-SEM Analysis of UTAUT. Asia Pacific Journal of Educators and Education, 36(2), 77-98. https://doi.org/10.21315/apjee2021.36.2.5
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric Theory (3rd ed.). McGraw-Hill, Inc.
Oshio, A., Taku, K., Hirano, M., & Saeed, G. (2018). Resilience and Big Five personality traits: A meta-analysis. Personality and Individual Differences, 127(2018), 54-60. https://doi.org/10.1016/j.paid.2018.01.048
Palta, A. (2019). Examining the Attitudes and the Opinions of Teachers about Altruism. Universal Journal of Educational Research, 7(2), 490-493. https://doi.org/10.13189/ujer.2019.070222
Pornsakulvanich, V., Dumrongsiri, N., Sajampun, P., Sornsri, S., John, S. P., Sriyabhand, T., . . . Jiradilok, S. (2012). An Analysis of Personality Traits and Learning Styles as Predictors of Academic Performance. ABAC Journal, 32(3), 1-19. http://www.assumptionjournal.au.edu/index.php/abacjournal/article/view/117
Purnamasari, I., Handayanna, F., & Faisal, A. (2021). Implementation Technology Acceptance Model (Tam) on Acceptance of the Zoom Application in Online Learning. Jurnal Riset Informatika, 3(2), 85-92. https://doi.org/10.34288/jri.v3i2.195
Ramayah, T., Cheah, J., Chuah, F., Ting, H., & Memon, M. (2018). Partial least squares structural equation modeling (PLS-SEM) using smartPLS 3.0: An Updated Guide and Practical Guide to Statistical Analysis (2nd ed.). Pearson.
Ramírez-Correa, P., Grandón, E. E., Alfaro-Pérez, J., & Painén-Aravena, G. (2019). Personality Types as Moderators of the Acceptance of Information Technologies in Organizations: A Multi-Group Analysis in PLS-SEM. Sustainability, 11(14). https://doi.org/10.3390/su11143987
Raza, S. A., & Shah, N. (2017). Influence of the Big Five personality traits on academic motivation among higher education students: Evidence from developing nation (87136) https://mpra.ub.uni-muenchen.de/87136/
Rigdon, E. E. (2014). Rethinking partial least squares path modeling: Breaking chains and forging ahead. Long Range Planning, 47(3), 161-167. https://doi.org/10.1016/j.lrp.2014.02.003
Ringle, C. M., Wende, S., & Becker, J.-M. (2015). SmartPLS 3. SmartPLS.
Rogers, E. M. (1995). Diffusion of Innovations (4th ed.). The Free Press.
Roldán, J. L., & Sánchez-Franc, M. J. (2012). Variance-based structural equation modeling: Guidelines for using partial least squares in information systems research. In Research Methodologies, Innovations and Philosophies in Software Systems Engineering and Information Systems (pp. 193-221). https://doi.org/10.4018/978-1-4666-0179-6.ch010
Salloum, S. A., Al-Emran, M., Habes, M., Alghizzawi, M., Ghani, M. A., & Shaalan, K. (2021). What Impacts the Acceptance of E-learning Through Social Media? An Empirical Study. In M. Alghizzawi (Ed.), Recent Advances in Technology Acceptance Models and Theories (pp. 419-431). Springer https://doi.org/10.1007/978-3-030-64987-6_24
Salloum, S. A., Mohammad Alhamad, A. Q., 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
Santi, I. H. (2022). Google Classroom Learning Media Acceptance And Use Analysis Using Technology Acceptance Model (TAM). International Journal of Economics, Social Science, Entrepreneurship and Technology (IJESET), 1(1), 78-87. http://journal.sinergicendikia.com/index.php/ijeset
Santoso, A., & Lestari, S. (2019). The Roles of Technology Literacy and Technology Integration to Improve Students’ Teaching Competencies. KnE Social Sciences, 3(11), 243-256. https://doi.org/10.18502/kss.v3i11.4010
Sarstedt, M., Ringle, C. M., & Hair Jr., J. F. (2014). PLS-SEM: Looking Back and Moving Forward. Long Range Planning, 47, 132–137. https://doi.org/10.1016/j.lrp.2014.02.008
Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers & Education, 128, 13-35. https://doi.org/10.1016/j.compedu.2018.09.009
Scherer, R., & Teo, T. (2019). Unpacking teachers’ intentions to integrate technology: A meta-analysis. Educational research review, 27, 90-109. https://doi.org/10.1016/j.edurev.2019.03.001
Schmidt, P. J., Riley, J., & Church, K. S. (2020). Investigating Accountants’ Resistance to the Adoption of Data Analytics Technology. Accounting Horizons, 34(4), 165-180. https://doi.org/10.2308/HORIZONS-19-154
Sekaran, U. (2003). Research Methods for Business: A Skill-Building Approach (4th ed.). John Wiley & Sons, Inc.
Sekaran, U., & Bougie, R. (2016). Research methods for business: A skill building approach. John Wiley & Sons.
Setiyawan, J., & Santoso, H. B. (2022). Factors Affecting User Acceptance of e-Learning Implementation in the Context of Higher Education: A Case Study of Health Science. Journal of Educators Online, 19(1), 119-137. https://doi.org/10.9743/JEO.2022.19.1.9
Shmueli, G., & Koppius, O. R. (2011). Predictive analytics in information systems research. Mis Quarterly, 35(3), 553-572. https://doi.org/10.2139/ssrn.1606674
Shmueli, G., Ray, S., Estrada, J. M. V., & Chatla, S. B. (2016). The elephant in the room: Predictive performance of PLS models. Journal of Business Research, 69(10), 4552-4564. https://doi.org/10.1016/j.jbusres.2016.03.049
Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J.-H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322-2347. https://doi.org/10.1108/EJM-02-2019-0189
Siddiquei, N. L., & Khalid, D. R. (2018). The relationship between Personality Traits, Learning Styles and Academic Performance of E-Learners. Open Praxis, 10(3), 249-263. https://doi.org/10.5944/openpraxis.10.3.870
Solano, L., Cabrera, P., Ulehlova, E., & Espinoza, V. (2017). Exploring the Use of Educational Technology in EFL Teaching: A Case Study of Primary Education in the South Region of Ecuador. Teaching English with Technology, 17(2), 77-86. https://eric.ed.gov/?id=EJ1140683
Soto, C. J. (2018). Big Five personality traits. In M. H. Bornstein, M. E. Arterberry, K. L. Fingerman, & J. E. Lansford (Eds.), The SAGE encyclopedia of lifespan human development (pp. 240-241). Sage.
Sriyabhand, T., & John, S. P. (2014). An Empirical Study about the Role of Personality Traits in Information Technology Adoption. Silpakorn University Journal of Social Sciences, Humanities, and Arts, 14(2), 67-90. https://so02.tci-thaijo.org/index.php/hasss/article/view/20001
Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society, 36, 111-147. https://doi.org/10.1111/j.2517-6161.1974.tb00994.x
Stout, D. E., & Ruble, T. L. (1995). Assessing the practical significance of empirical results in accounting education research: the use of effect size information. Journal of Accounting Education, 13(3), 281-298. https://doi.org/10.1016/0748-5751(95)00010-J
Sultan, W. H., Woods, P. C., & Koo, A. C. (2011). A constructivist approach for digital learning: Malaysian schools case study. Educational Technology & Society, 14(4), 149-163.
Tang, J.-H., Chen, M.-C., Yang, C.-Y., Chung, T.-Y., & Lee, Y.-A. (2016). Personality traits, interpersonal relationships, online social support, and Facebook addiction. Telematics and Informatics, 33(1), 102-108. https://doi.org/10.1016/j.tele.2015.06.003
Tas, Z., Onal, A., Donmez, A., Herguner, G., & Yaman, Ç. (2021). The Predictive of Social Networks-Based Learning in Physical Education and Sports Teachers: The Big Five Personality. The Turkish Online Journal of Educational Technology, 20(3), 92-100. http://www.tojet.net/articles/v20i3/2037.pdf
Taylor, S., & Todd, P. A. (1995). Understanding Information Technology Usage: A Test of Competing Models. Information Systems Research, 6(2), 144-176. https://www.jstor.org/stable/23011007
Teeroovengadum, V., Heeraman, N., & Jugurnath, B. (2017). Examining the antecedents of ICT adoption in education using an extended technology acceptance model (TAM). International Journal of Education and Development using Information and Communication Technology, 13(3), 4-23.
Teo, T. (2019). Students and Teachers' Intention to Use Technology: Assessing Their Measurement Equivalence and Structural Invariance. Journal of Educational Computing Research, 57(1), 201-225. https://doi.org/10.1177/0735633117749430
Thohir, M. A., Yuliati, L., Ahdhianto, E., Untari, E., & Yanti, F. A. (2021). Exploring the Relationship Between Personality Traits and TPACK-Web of Pre-service Teacher. Contemporary Educational Technology, 13(4), 322-338. https://doi.org/10.30935/cedtech/11128
Thomas, M., & Chukhlomin, V. (2020). Introducing TCA-TPACK: A competency based conceptual framework for faculty development in technology-enhanced accounting and business education Society for Information Technology & Teacher Education International Conference, Waynesville, NC USA.
Uerz, D., Volman, M., & Kral, M. (2018). Teacher educators' competences in fostering student teachers’ proficiency in teaching and learning with technology: An overview of relevant research literature. Teaching and Teacher Education, 70, 12-23. https://doi.org/10.1016/j.tate.2017.11.005
Vallerand, R. J. (1997). Toward a hierarchical model of intrinsic and extrinsic motivation. In M. P. Zanna (Ed.), Advances in experimental social psychology (Vol. 29, pp. 271-360). Academic Press.
Venkatesh, V., & Bala, H. (2008). Technology Acceptance Model 3 and a Research Agenda on Interventions. Decision Science, 39(2), 273-312. 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. https://doi.org/10.1287/mnsc.46.2.186.11926
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. https://doi.org/10.2307/30036540
Vlachogianni, P., & Tselios, N. (2022). The relationship between perceived usability, personality traits and learning gain in an e-learning context International Journal of Information and Learning Technology, 39(1), 70-81. https://doi.org/10.1108/IJILT-08-2021-0116
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
Watty, K., McKay, J., & Ngo, L. (2014). Embracing Digital Technologies in Accounting Education. Deakin University.
Weerasinghe, S., & Hindagolla, M. (2017). Technology Acceptance Model in the Domains of LIS and Education: A Review of Selected Literature. Library Philosophy and Practice (e-journal)(1582). http://digitalcommons.unl.edu/libphilprac/1582
Weng, F., Yang, R.-J., Ho, H.-J., & Su, H.-M. (2018). A TAM-Based Study of the Attitude towards Use Intention of Multimedia among School Teachers. Appl. Syst. Innov., 1(36). https://doi.org/10.3390/asi1030036
Wolugbom, K. R., Nwosu, F. C., & Ibitoroko, B.-G. (2020). Perceived impact of e-learning technology utilization in accounting education. Nigerian Journal of Business Education, 7(1), 495-506. http://www.nigjbed.com.ng/index.php/nigjbed/article/view/417
Wong, H., & Wong, R. (2017). Students’ Perceptions on Studying Accounting Information System Course. International Journal of Business Administration, 8(2), 1-9. https://doi.org/10.5430/ijba.v8n2p1
Wu, S. P. W., Corr, J., & Rau, M. A. (2019). How instructors frame students' interactions with educational technologies can enhance or reduce learning with multiple representations. Computers & Education, 128, 199-213. https://doi.org/10.1016/j.compedu.2018.09.012
Wülferth, H. (2013). Validity and Reliability of Empirical Discretion Model. In Managerial Discretion and Performance in China (pp. 257-368). Springer. https://doi.org/10.1007/978-3-642-35837-1_5
Xu, R., Frey, R. M., Fleisch, E., & Ilic, A. (2016). Understanding the impact of personality traits on mobile app adoption – Insights from a large-scale field study. Computers in Human Behavior, 62, 244-256. https://doi.org/10.1016/j.chb.2016.04.011
Yang, H.-H., & Su, C.-H. (2017). Learner Behaviour in a MOOC Practice-oriented Course: In Empirical Study Integrating TAM and TPB. The International Review of Research in Open and Distributed Learning, 18(5), 35–63. https://doi.org/10.19173/irrodl.v18i5.2991
Yoon, S. (2020). A Study on the Transformation of Accounting Based on New Technologies: Evidence from Korea. Sustainability, 12(20), 1-22. https://doi.org/10.3390/su12208669
Yulisman, H., Widodo, A., Riandi, R., & Nurina, C. I. E. (2019). Moderated effect of teachers’ attitudes to the contribution of technology competencies on TPACK. Jurnal Pendidikan Biologi Indonesia, 5(2), 185-196. https://doi.org/10.22219/jpbi.v5i2.7818
