21st Century Educational Technology Adoption in Accounting Education: Does Institutional Support Moderates Accounting Educators Acceptance Behaviour and Conscientiousness Trait towards Behavioural Intention?
Open accessAbstract
This study examines the interaction effects of institutional support between the educators' conscientiousness traits and acceptance behaviour towards their behavioural intention to adopt educational technology.Simple random sampling and questionnaire survey methods were used on university educators from several public universities in Malaysia, offering bachelor's degree programmes in accounting.Data were analysed using structural equation modelling to achieve the study's objectives.This study found that perceived usefulness, attitudes, and conscientiousness are significant, suggesting their important role as predictors of educational technology adoption among accounting educators.Meanwhile, institutional support is able to moderate the acceptance behaviour of accounting educators in the matter of usefulness, ease of use, and attitudes towards the behavioural intention to use educational technology; however, it has no interaction effect on conscientiousness.This study provides valuable insights into understanding the factors influencing accounting educators' intention to integrate technology in the teaching and learning activities of the 21 st century environment.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Faculty tend to accept and adopt technology if the institution they work at supports the technology and trains them on it.
“Faculty tend to accept and adopt technology if the institution they work at supports the technology and trains them on it (Dangi & Saat, 2021).”
From Into the Unknown: AI Adoption and Job Satisfaction of Christian Faculty (2026) · cites this paper as Dangi & Saat, 2021
In “Indirect Effects on ACPU, ACPEU, and ACAU” (page unavailable), the paper identifies training as a form of institutional support and reports that higher support can increase accounting educators’ intention to adopt educational technology. The claim extends that finding to faculty generally and to actual acceptance and adoption, whereas the study reports intentions; it also notes that support does not significantly increase intention when educators consider a technology impractical. (LLM, conf=0.84)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Scherer, R., Siddiq, F., & Tondeur, J. (2019) · Computers & Education · doi:10.1016/j.compedu.2018.09.009 · Cited by 1,923 · see citing works
Benitez, J., Henseler, J., Castillo, A., & Schuberth, F. (2020) · Information & Management · doi:10.1016/j.im.2019.05.003 · Cited by 1,788 · see citing works
Interpreting effect sizes of education interventions
Kraft, M. A. (2020) · Educational Researcher · doi:10.3102/0013189x20912798 · Cited by 1,281 · see citing works
Beyond small, medium, or large: points of consideration when interpreting effect sizes
Bakker, A., Cai, J., English, L., Kaiser, G., Mesa, V., & Van Dooren, W. (2019) · Educational Studies in Mathematics · doi:10.1007/s10649-019-09908-4 · Cited by 302 · see citing works
Flagged first, then by citations
Research works printed but not linked: 113
- 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, London.
- Jalil, N. (2018). Trend Pengangguran Dalam Kalangan Graduan Di Malaysia. [Unpublished degree's thesis], Universiti Tun Hussein Onn, Malaysia.
- Abedalaziz, N., Jamaluddin, S., & Leng, C. H. (2013). Measuring Attitudes toward Computer and Internet Usage among Postgraduate Students in Malaysia. The Turkish Online Journal of Educational Technology, 12(2), 200-216. https://files.eric.ed.gov/fulltext/EJ1015431.pdf
- 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:10.11648/j.edu.20180701. · DOI
- Ahmed, I., Nawaz, M. M., Ali, G., & Islam, T. (2015). Perceived organizational support and its outcomes. Management Research Review, 38(6), 627-639. https://doi:10.1108/mrr- Ajzen, I., & Fishbein, M. (1980). Understanding Attitudes and Predicting Social Behavior Englewood Cliff, NJ: Prentice-Hall. · DOI
- Akgunduz, Y., Alkan, C., & Gök, Ö. A. (2018). Perceived organizational support, employee creativity, and proactive personality: The mediating effect of meaning of work. Journal of Hospitality and Tourism Management, 34, 105-114. https://doi:10.1016/j.jhtm.2018.01.004 · DOI
- Akinde, T. A., & Adetimirin, A. A. (2017). Perceived usefulness as a correlate of the extent of information and communications technologies (ICTs) use for teaching by library educators in universities in Nigeria. International Journal of Library and Information Science, 9(3), 14-24. https://doi.org/10.5897/ijlis2016.0739 · DOI
- Al-Maroof, R. A. S., & Al-Emran, M. (2018). Students Acceptance of Google Classroom: An Exploratory Study using PLS-SEM Approach. International Journal of Emerging Technologies in Learning (iJET), 13(6), 112-123. https://doi:10.3991/ijet.v13i06.8275 · DOI · Paper page
- Alshehri, A., Rutter, M., & Smith, S. (2019). Assessing the Relative Importance of an E-learning system's Usability Design Characteristics Based on Students' Preferences. European Journal of Educational Research, 8(3), 839-855. https://doi:10.12973/eu-jer.8.3.839 · DOI
- Alshmrany, S., & Wilkinson, B. (2017). Factors Influencing the Adoption of ICT by Teachers in Primary Schools in Saudi Arabia: Teachers' Perspectives of the Integration of ICT in Primary Education. International Journal of Advanced Computer Science and Applications, 8(12), 143-156. https://doi.org/10.14569/ijacsa.2017.081218 · DOI
- Altawallbeh, M., Thiam, W., Alshourah, S., & Fong, S. F. (2015). The Role of Age and Gender in the Relationship between (Attitude, Subjective Norm and Perceived Behavioural Control) and Adoption of E-Learning at Jordanian Universities. Journal of Education and Practice, 6(15), 44-54. https://files.eric.ed.gov/fulltext/EJ1079974.pdf
- 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:10.1186/s40535-018-0048-7 · DOI
- Babalola, Y. A., & Tiamiyu, R. (2012). The Use of ICT in Teaching and Learning of Accounting Education in Nigeria. Paper presented at the 33rd Annual Convention and International Conference of Nigeria Association for Educational Medial and Technology (NAEMT), Emmanuel Alayande College of Education, Oyo State, Nigeria, October 8-12, 2012.
- Bajabaa, A. S. (2017). Influential factors and faculty members' practices in technology integration using ISTE standards for teacher preparation at Taibah University-Saudi Arabia. [Unpublished doctoral dissertation], Kansas State University Manhattan, Kansas.
- Baker, E. W., Al-Gahtani, S. S., & Hubona, G. S. (2007). The effects of gender and age on new technology implementation in a developing country. Information Technology & People, 20(4), 352-375. https://doi:10.1108/09593840710839798 · DOI
- Barnett, J. A. (2012). Why Consistency is Not Possible in Experienced Teacher Evaluations. The European Journal of Social & Behavioural Sciences, 2(2), 304-316. doi:10.15405/FutureAcademy/ejsbs(2301-2218).2012.2.13 · DOI
- 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:10.1057/ejis.2014. · DOI
- Bealing, W. E. J., Baker, R. L., & Russo, C. J. (2006). Personality: what it takes to be an accountant. The Accounting Educators' Journal, 16, 119-128. http://www.nabet.us/Archives/2003/pdf/bealing,_baker_and_russo.pdf
- Blau, P. (1964). Exchange and power in social life. New York: Wiley.
- Bohner, G., & Dickel, N. (2011). Attitudes and attitude change. Annual review of psychology, 62, 391-417. https://doi.org/10.4324/9781315784786 · DOI
- Breedt, M. M. (2015). Aspects influencing Accounting teachers' attitudes towards Computer- Aided Learning [Unpublished doctoral dissertation]. University of Pretoria, South Africa.
- Buckenmeyer, J. A. (2010). Beyond Computers In The Classroom: Factors Related To Technology Adoption to Enhance Teaching and Learning. Contemporary Issues in Education Research, 3(4), 27-36. https://core.ac.uk/download/pdf/268108376.pdf
- Cheng, J.-C., Chen, C.-Y., Teng, H.-Y., & Yen, C.-H. (2016). Tour leaders' job crafting and job outcomes: The moderating role of perceived organizational support. Tourism Management Perspectives, 20, 19-29. https://doi:10.1016/j.tmp.2016.06.001 · DOI
- Cheng, J.-C., & O-Yang, Y. (2018). Hotel employee job crafting, burnout, and satisfaction: The moderating role of perceived organizational support. International Journal of Hospitality Management, 72, 78-85. https://doi:10.1016/j.ijhm.2018.01.005 · DOI
- Chin, W. W. (2010). How to write up and report PLS analyses. In Handbook of partial least squares (pp. 655-690). Berlin, Heidelberg: Springer.
- Cobb-Clark, D. A., & Schurer, S. (2012). The stability of big-five personality traits. Economics Letters, 115(1), 11-15. https://doi:10.1016/j.econlet.2011.11.015 · DOI
- Compeau, D. R., & Higgins, C. A. (1995). Application of Social Cognitive Theory to Training for Computer Skills. Information Systems Research, 6(2), 118-143. https://doi.org/10.1287/isre.6.2.118 · DOI
- 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 · DOI · Paper page
- Dijkstra, T. K., & Henseler, J. (2015). Consistent partial least squares path modeling. Mis Quarterly, 39(2), 297-316. https://www.researchgate.net/publication/265316206_Consistent_Partial_ Least_Squares_Path_Modeling
- Dougherty, K. (2015). Factors That Influence College Faculty to Adopt Digital Technologies in Their Practice. Paper presented at the Higher Education in Transformation Conference, Dublin, Ireland. pp.307-318.
- Eady, M. J., & Lockyer, L. (2013). Tools for learning: technology and teaching strategies. In Learning to Teach in the Primary School (pp. 71-94). Australia: Queensland University of Technology.
- Eisenberger, R., Huntington, R., Hutchison, S., & Sowa, D. (1986). Perceived organizational support. Journal of applied psychology, 71(3), 500-507. shorturl.at/ejDWX
- Elkaseh, A. M., Wong, K. W., & Fung, C. C. (2016). Perceived Ease of Use and Perceived Usefulness of Social Media for e-Learning in Libyan Higher Education: A Structural Equation Modeling Analysis. International Journal of Information and Education Technology, 6(3), 192-199. https://doi:10.7763/IJIET.2016.V6.683 · DOI
- 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. http://seminar.utmspace.edu.my/jisri/
- Fathema, N., Shannon, D., & Ross, M. (2015). Expanding The Technology Acceptance Model (TAM) to Examine Faculty Use of Learning Management Systems (LMSs) In Higher Education Institutions. MERLOT Journal of Online Learning and Teaching, 11(2), 210- 232. https://jolt.merlot.org/Vol11no2/Fathema_0615.pdf
- Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Ontario: Addison-Wesley Pub. Co.
- 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:10.1108/IntR-12-2017-0515 · DOI
- Gaiziuniene, L., & Janiunaite, B. (2018, 19-20 April). Adaptation Of E-Learning Tools as Innovation: Overcoming Barriers Using Educational Factors. Paper presented at the 28th International Scientific Conference on Economic and Social Development, Paris. shorturl.at/djGT5
- Ghavifekr, S., Abd Razak, A. Z., A. Ghani, M. F., Ran, N. Y., Meixi, Y., & Tengyue, Z. (2016). ICT Integration In Education: Incorporation for Teaching & Learning Improvement. The Malaysian Online Journal of Educational Technology, 2(2), 24-45. https://eric.ed.gov/?id=EJ1086419
- 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:10.14429/djlit.38.4.12676 · DOI
- Gilakjani, A. P., Leong, L.-M., & Ismail, H. N. (2013). Teachers' Use of Technology and Constructivism. I.J.Modern Education and Computer Science, 4, 49-63. https://doi:10.5815/ijmecs.2013.04.07 · DOI
- Godoe, P., & Johansen, T. S. (2012). . (2012). Understanding adoption of new technologies: Technology readiness and technology acceptance as an integrated concept. Journal of European Psychology Students, 3(1), 38-52. https://doi: 10.5334/jeps.aq · DOI
- 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:10.1080/23265507.2017.1339572 · DOI
- Gordon, L. C., Gratz, E., Kung, D. S., Moore, L., & Urbizagastegui, S. G. (2018). Utilization of Information Technology as Instructional Support in Higher Education -A Case Study. Communications of the IIMA, 16(1), 1-23. https://scholarworks.lib.csusb.edu/ciima/vol16/iss1/3/
- Gouldner, A. W. (1960). The norm of reciprocity: A preliminary statement. American sociological review, 25(2), 161-178. https://doi.org/10.2307/2092623 · DOI · Paper page
- Govender, D. W., & Maistry, S. M. (2017). Exploring Teachers' Propensity for Technology Adoption in Business Education. Journal of Social Sciences, 31(2), 193-202. https://doi:10.1080/09718923.2012.11893028 · DOI
- 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 · DOI
- 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:10.1108/IMDS-04-2016- 0130 · DOI · Paper page
- Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis (7 ed.). Upper Saddle River, New Jersey: Prentice-Hall.
- Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). United States of America: Sage Publications, Inc.
- 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:10.1108/EBR-11-2018-0203 · DOI · Paper page
- 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 · DOI · Paper page
- 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 · DOI
- 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:10.3390/su12125171 · DOI
- 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 · DOI · Paper page
- Hussain, A., Mkpojiogu, E. O. C., & Yusof, M. M. (2016). Perceived usefulness, perceived ease of use, and perceived enjoyment as drivers for the user acceptance of interactive mobile maps. Paper presented at the Proceedings of the International Conference on Applied Science and Technology 2016 (ICAST'16), Kedah, Malaysia.
- Jain, A. K., Giga, S. I., & Cooper, C. L. (2013). Perceived organizational support as a moderator in the relationship between organisational stressors and organizational citizenship behaviors. International Journal of Organizational Analysis, 21(3), 313-334. https://doi:10.1108/IJOA-Mar-2012-0574 · DOI
- John, S. P. (2015). The integration of information technology in higher education: A study of faculty's attitude towards IT adoption in the teaching process. Contaduría y Administración, 60, 230-252. https://doi:10.1016/j.cya.2015.08.004 · DOI
- Johnson, A. M., Jacovina, M. E., Russell, D. G., & Soto, C. M. (2016). Challenges and solutions when using technologies in the classroom. In S. A. Crossley & D. S. McNamara (Eds.), Adaptive educational technologies for literacy instruction (pp. 13-29). New York: Taylor & Francis.
- 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:10.1109/access.2017.2714379 · DOI
- Karahanna, E., & Straub, D. W. (1999). The psychological origins of perceived usefulness and ease-of-use. Information & management, 35(4), 237-250. https://doi.org/10.1016/s0378-7206(98)00096-2 · DOI
- 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:10.1080/13664530.2017.1363083 · DOI
- Khan, S. H., Hasan, M., & Clement, C. K. (2012). Barriers to the Introduction of ICT into Education in Developing Countries: The Example of Bangladesh. International Journal of Instruction, 5(2), 61-80. https://eric.ed.gov/?id=ED533790
- 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:10.1080/07380569.2018.1428001 · DOI
- Kim, Y. J., Chun, J. U., & Song, J. (2009). Investigating the Role of Attitude in Technology Acceptance from an Attitude Strength Perspective. International Journal of Information Management, 29(1), 67-77. https://doi.org/10.1016/j.ijinfomgt.2008.01.011 · DOI
- Kirkland, J. E. (2017). The Roles of Perceived Organizational Support and Anticipated Change in Organizational Support in Predicting Employee Affective Commitment and Well- Being. [Unpublished Master's Thesis], University of Houston, Texas, USA.
- 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:10.4301/s1807-17752017000100002 · DOI
- 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:10.1080/09523987.2018.1439712 · DOI
- Lee, J., Cerreto, F. A., & Lee, J. (2010). Theory of Planned Behavior and Teachers' Decisions Regarding Use of Educational Technology. Educational Technology & Society, 13(1), 152-164. https://www.jstor.org/stable/jeductechsoci.13.1.152
- Lee, Y., Kozar, K. A., & Larsen, K. R. (2003). The technology acceptance model: Past, present, and future. Communications of the Association for information systems, 12(1), 50. https://doi.org/10.17705/1cais.01250 · DOI · Paper page
- Lipsey, M. W. (1998). Design sensitivity: Statistical power for applied experimental research. In L. Bickman & D. J. Rog (Eds.), Handbook of applied social research methods (pp. 39 -68). Thousand Oaks, CA: Sage.
- Luu, M. (2017). The Moderating Role of Transformational Leadership and Perceived Organizational Support in the Relationship Between Openness to Experience and Creativity. [Unpublished Master's Thesis], San José State University, San Jose, United States. (4809)
- Markovikj, M., & Serafimovska, E. (2018). Personality dimensions and student's participatory behavior in student organization. ANNUAL of Institute for Sociological, Political and Juridical Resarch, XLII(Special issue), 83-94. http://hdl.handle.net/20.500.12188/7602
- McKenney, S., & Visscher, A. J. (2019). Technology for teacher learning and performance. Technology, Pedagogy and Education, 28(2), 129-132. https://www.tandfonline.com/doi/full/10.1080/1475939X.2019.1600859 · DOI
- McKinney, E., Yoos, C. J., & Snead, K. (2017). The need for 'skeptical' accountants in the era of Big Data. Journal of Accounting Education, 38, 63-80. https://doi:10.1016/j.jaccedu.2016.12.007 · DOI
- Mohd Yusof, M. N., & Tahir, Z. (2017). Kepentingan Penggunaan Media Sosial Teknologi Maklumat Dalam Pendidikan IPTA (Importance of Information Technology-Driven Social Media in Public Higher Education Institutions). Journal of Social Sciences and Humanities, 12(3), 1-10. https://ejournal.ukm.my/ebangi/article/view/22483
- Momani, A. M., & Jamous, M. M. (2017). The Evolution of Technology Acceptance Theories. International Journal of Contemporary Computer Research (IJCCR), 1(1), 51-58. https://ssrn.com/abstract=2971454
- Nartey, L. T., Annan, A., & Nunoo, T. E. (2018). Organisational Support and Its Influence on Teacher Commitment: Evidence from Colleges of Education in Ghana. Advances in Sciences and Humanities, 4(3), 45-55. https://doi:10.11648/j.ash.20180403. · DOI
- Naujokaitiene, J., Tereseviciene, M., & Zydziunaite, V. (2015). Organizational Support for Employee Engagement in Technology-Enhanced Learning. SAGE Open, 5(4), 1-9. https://doi:10.1177/2158244015607585 · DOI
- Nikian, S., Nor, F. M., & Aziz, M. A. (2013). Malaysian Teachers' Perception of Applying Technology in the Classroom. Procedia -Social and Behavioral Sciences, 103, 621-627. https://doi:10.1016/j.sbspro.2013.10.380 · DOI
- Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric Theory (3rd ed.). New York: McGraw- Hill, Inc.
- Ogundana, O. M., Ibidunni, A. S., & Jinadu, O. (2015). ICT Integration in Accounting Education: Evidence from Two Private Higher Institutions in Nigeria. Acta Universitatis Danubius, 9(2), 114-126. http://journals.univ-danubius.ro/index.php/communicatio/article/view/3171/3189
- Palmer, J. C., Komarraju, M., Carter, M. Z., & Karau, S. J. (2017). Angel on one shoulder: Can perceived organizational support moderate the relationship between the Dark Triad traits and counterproductive work behavior? Personality and Individual Differences, 110, 31-37. https://doi:10.1016/j.paid.2017.01.018 · DOI
- Park, S. Y. (2009). An Analysis of the Technology Acceptance Model in Understanding University Students' Behavioral Intention to Use e-Learning. Educational Technology & Society, 12(3), 150-162. https://www.jstor.org/stable/jeductechsoci.12.3.150
- Pincus, K. V., Stout, D. E., Sorensen, J. E., Stocks, K. D., & Lawson, R. A. (2017). Forces for change in higher education and implications for the accounting academy. Journal of Accounting Education, 40, 1-18. https://doi:10.1016/j.jaccedu.2017.06.001 · DOI
- Pornsakulvanich, V., Dumrongsiri, N., Sajampun, P., Sornsri, S., John, S. P., Sriyabhand, T., . . . Jiradilok, S. (2015). An Analysis of Personality Traits and Learning Styles as Predictors of Academic Performance. ABAC Journal, 32(3), 1-19. https://www.academia.edu/3477290/
- Porter, W. W., & Graham, C. R. (2016). Institutional drivers and barriers to faculty adoption of blended learning in higher education. British Journal of Educational Technology, 47(4), 748-762. https://doi:10.1111/bjet.12269 · DOI
- Prasad, C. V. V. S. N. V., Lalitha, P., & Srikar, P. V. N. (2015). Barriers to the Use of Information and Communication Technology (ICT) in Secondary Schools: Teacher's Perspective. Journal of Management Research, 7(2). https://doi:10.5296/jmr.v7i2.6935 · DOI
- Quazi, A., & Talukder, M. (2011). Demographic Determinants of Adoption of Technological. Innovation, Journal of Computer Information Systems, 51(3), 38-46. https://doi:10.1080/08874417.2011.11645484 · DOI
- 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.). Kuala Lumpur, Malaysia: Pearson.
- Rana, K. B. (2017). Use of Educational Technologies in Teaching and Learning Activities: Strategies and Challenges-A Nepalese case. [Unpublished Master's Thesis]. Univeristy of Oslo, Norway.
- Rhoades, L., & Eisenberger, R. (2002). Perceived organizational support: A review of the literature. Journal of applied psychology, 87(4), 698-714. https://doi:10.1037/0021- 9010.87.4.698 · DOI · Paper page
- Ringle, C. M., Sarstedt, M., Mitchell, R., & Gudergan, S. P. (2018). Partial least squares structural equation modeling in HRM research. The International Journal of Human Resource Management, 31(12), 1617-1643. https://doi:10.1080/09585192.2017.1416655 · DOI · Paper page
- Roberts, F. D., Kelley, C. L., & Medlin, B. D. (2007). Factors influencing accounting faculty members' decision to adopt technology in the classroom. College Student Journal, 41(2), 423-435. https://eric.ed.gov/?id=EJ777948
- Roldán, J. L., & Sánchez-Franc, M. J. (2012). Variance-based structural equation modeling: Guidelines for using partial least squares in information systems research. Research Methodologies, Innovations and Philosophies in Software Systems Engineering and Information Systems, 193-221. doi:10.4018/978-1-4666-0179-6. · DOI
- Sabzian, F., & Gilakjani, A. P. (2013). Teachers' attitudes about computer technology training, professional development, integration, experience, anxiety, and literacy in English language teaching and learning. International Journal of Applied Science and Technology, 3(1), 67-75. http://ijastnet.com/journals/Vol_3_No_1_January_2013/9.pdf
- 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:10.1109/access.2019.2939467 · DOI · Paper page
- Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: investigating subjective norm and moderation effects. Information & management, 44(1), 90-103. https://doi.org/10.1016/j.im.2006.10.007 · DOI
- Senik, R., & Broad, M. (2011). Information technology skills development for accounting graduates: Intervening conditions. International Education Studies, 4(2), 105-110. https://doi.org/10.5539/ies.v4n2p105 · DOI
- Shropshire, J., Warkentin, M., & Sharma, S. (2015). Personality, attitudes, and intentions: Predicting initial adoption of information security behavior. Computers & Security, 49, 177-191. https://doi:10.1016/j.cose.2015.01.002 · DOI
- 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). Thousand Oaks, CA: Sage.
- Soto, C. J., & Jackson, J. J. (2013). Five-factor model of personality Oxford University Press.
- Svendsen, G. B., Johnsen, J.-A. K., Almås-Sørensen, L., & Vittersø, J. (2013). Personality and technology acceptance: the influence of personality factors on the core constructs of the Technology Acceptance Model. Behaviour & Information Technology, 32(4), 323- 334. https://doi.org/10.1080/0144929x.2011.553740 · DOI
- Tanduklangi, A. (2017). Determinants of User Intention in Using e-Learning Technology in Indonesian Context: An Empirical Study. Mediterranean Journal of Social Sciences, 8(3), 69-77. https://doi:10.5901/mjss.2017.v8n3p69 · DOI
- 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:10.1016/j.tate.2017.11.005 · DOI
- 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://aisel.aisnet.org/jitta/vol11/iss2/2/
- 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 · DOI · Paper page
- 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 · DOI · Paper page
- Wang, Z., Zhang, J., Thomas, C. L., Yu, J., & Spitzmueller, C. (2017). Explaining benefits of employee proactive personality: The role of engagement, team proactivity composition and perceived organizational support. Journal of Vocational Behavior, 101, 90-103. https://doi:10.1016/j.jvb.2017.04.002 · DOI
- Wells, P. K. (2018). How well do our introductory accounting textbooks reflect current accounting practice? Journal of Accounting Education, 42, 40-48. https://doi:10.1016/j.jaccedu.2017.12.003 · DOI
- 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 · DOI
- Zaidi, N. R., Abdul Wajid, R., Zaidi, F. B., Zaidi, G. B., & Zaidi, M. T. (2013). The big five personality traits and their relationship with work engagement among public sector university teachers of Lahore. African Journal of Business Management, 7(15), 1344- 1353. https://doi:10.5897/AJBM12.290 · DOI
- Zheng, Y., Wang, J., Doll, W., Deng, X., & Williams, M. (2018). The impact of organisational support, technical support, and self-efficacy on faculty perceived benefits of using learning management system. Behaviour & Information Technology, 37(4), 311-319. https://doi:10.1080/0144929x.2018.1436590 · DOI
Cited by
Sorted by citations
Into the Unknown: AI Adoption and Job Satisfaction of Christian Faculty
Manicone, H. A., B, R., FelixOtuorimuo, I. E., Graf, L. M., Joseph, R. A., Moore, E. D., & Wilson, J. E. (2026) · Journal for the Scholarship of Teaching & Learning · doi:10.70623/ohtr6058
- Authors
- Mat Dangi, M. R., & Mohamed Saat, M.
- Venue
- International Journal of Academic Research in Business and Social Sciences
- Published
- 2021-01-20
- Volume
- 11
- Issue
- 1
- ISSN
- 2222-6990 (ISSN-L)
- OpenAlex
- W3113655515
- Cited by
- 11
- References
- 4
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 2 · 2023: 4 · 2022: 2
- FWCI
- 1.71
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- International Journal of Academic Research in Business and Social Sciences (publishedVersion, cc-by)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Social Sciences › Business, Management and Accounting › Accounting › Accounting Education and Careers
- Social Sciences › Education › Technology-Enhanced Education Studies
- Keywords
- educational technology adoption, accounting teachers, institutional support, technology acceptance, behavioral intention, perceived usefulness, perceived ease of use, attitudes toward technology, accounting education, moderating effect
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review