Article In Press | Published on: April 14, 2026
Volume: 3, Issue: 1
1. Department of Foundation and Education Management Faculty of Education and Humanities, University of Guyana.
DOI: null
Corresponding Author: Colin A Ferreira, Department of Foundation and Education Management. Faculty of Education and Humanities University of Guyana.
Citation: Colin A Ferreira, (2026). Factors Influencing Guyanese Educators’ Intention to Use Technology, Proceedings of the International Academy of Sciences, RPC Publishes, 3(1); 1-20.
Copyright: © 2026 Colin A Ferreira, this is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
The purpose of this quantitative study was to explore and investigate empirically the relationship between five variables (perceived usefulness (PU), perceived ease of use (PEOU), subjective norms (SN), facilitating conditions (FC), and attitude towards use (ATU)) with behavioral intention (BI) to use technology. The sample consisted of 270 Guyanese educators from nursery, primary, secondary, and post-secondary institutions. Educators completed an online questionnaire. Structural equation modeling revealed statistically significant results for eight of the nine hypotheses. A statistically non-significant result was found for the relationship between facilitating conditions (FC) and educators’ behavioral intention (BI) to use technology. However, strong positive relationships were found between FC and PEOU, PEOU and PU, SN and PU, PU and ATU, PEOU and ATU, ATU and BI, SN and BI, and PU and BI, which are consistent with past studies and are supported by the three theoretical frameworks (Technology Acceptance Model, Theory of Planned Behavior, and Unified Theory of Acceptance and Use of Technology) that underpinned this study. In summary, the results indicated that Guyanese educators’ perceived usefulness (PU), subjective norms (SN), and attitude towards use (ATU) are positively correlated with their behavioral intention (BI) to use technology. Their perceived ease of use (PEOU) and subjective norms (SN) influence their perceived usefulness (PU). In addition, their PU and PEOU influence their ATU regarding technology use. Furthermore, their FC influence their PEOU regarding technology use. Implications for policymakers, administrators, educators, and curriculum designers are discussed.
1. Oke, J., Akinkunmi, W. B., Etebefia, S. O. (2019). Use of correlation, tolerance and variance inflation factor for multicollinearity test. GSJ,7(5):652-659.
2. Bervell, B., Arkorful, V. (2020). LMS-enabled blended learning utilization in distance tertiary education: establishing the relationships among facilitating conditions, voluntariness of use and use behaviour. International Journal of Educational Technology in Higher Education,17(1).
3. Bolarinwa, O. A. (2015). Principles and methods of validity and reliability testing of questionnaires used in social and health science researches. Nigerian Postgraduate Medical Journal,22(4):195-201.
4. Buraimoh, O. F., Boor, C. H. M.,Aladesusi, G. A. (2023). Examining facilitating condition and social influence as determinants of secondary school teachers’ behavioural intention to use mobile technologies for instruction. Indonesian Journal of Educational Research and Technology,3(1):25-34.
5. Nair, I.,Mukunda Das, V. (2012). Using technology acceptance model to assess teachers’ attitude towards use of technology as teaching tool: a SEM Approach. International Journal of Computer Applications,42(2):1-6.
6. Kang, H., Ahn, J.-W. (2021). Model setting and interpretation of results in research using structural equation modeling: A checklist with guiding questions for reporting. Asian Nursing Research,15(3):157-162.
7. Hess, T. J., McNab, A. L. Basoglu, K. A. (2014). Reliability generalization of perceived ease of use, perceived usefulness, and behavioral intentions. MIS quarterly,38(1):1-28.
8. Maydeu-Olivares, A. (2017). Maximum likelihood estimation of structural equation models for continuous data: Standard errors and goodness of fit. Structural Equation Modeling: A Multidisciplinary Journal, 24(3):383-394.
9. Sathyanarayana, S., Mohanasundaram, T. (2024). Fit indices in structural equation modeling and confirmatory factor analysis: reporting guidelines. Asian Journal of Economics, Business and Accounting,24(7):561-577.
10. Leys, C., Klein, O., Dominicy, Y., & Ley, C. (2018). Detecting multivariate outliers: Use a robust variant of the Mahalanobis distance. Journal of Experimental Social Psychology,74:150-156.
11. Schober, P., Boer, C., & Schwarte, L. A. (2018). Correlation coefficients: Appropriate use and interpretation. Anesthesia & Analgesia, 126(5):1763-1768.
12. Schreiber, J. B. (2017). Update to core reporting practices in structural equation modeling. Research in Social and Administrative Pharmacy,13(3):634-643.
13. Ainur, A. K, Sayang, M. D., Jannoo, Z., Yap, B. W. (2017). Sample size and non-normality effects on goodness of fit measures in structural equation models. Pertanika Journal of Science & Technology,25(2).
14. Karakaya-Ozyer, K., Aksu-Dunya, B. (2018). A Review of structural equation modeling applications in Turkish educational science literature, 2010-2015. International Journal of Research in Education and Science,4(1):279-291.
15. Momani, A. M. (2020). The unified theory of acceptance and use of technology: A new approach in technology acceptance. International Journal of Sociotechnology and Knowledge Development (IJSKD), 12(3): 79-98.
16. Gao, S., Mokhtarian, P. L. Johnston, R. A. (2008). Nonnormality of data in structural equation models. Transportation Research Record: Journal of the Transportation Research Board,2082(1):116-124.
17. Almahamid, S. O. U. D., Mcadams, A. C., Taher, A. K., Mo’taz, A. S. E. (2010). The relationship between perceived usefulness, perceived ease of use, perceived information quality, and intention to use e-government. Journal of Theoretical & Applied Information Technology,11.
18. Ajzen, I. (2011). The theory of planned behaviour: Reactions and reflections. Psychology & Health, 26(9):1113-1127.
19. Taherdoost, H. (2016). Validity and reliability of the research instrument; how to test the validation of a questionnaire/survey in research. International Journal of Academic Research in Management,5(3):28-36.
20. 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.
21. Owoc, M. L., Sawicka, A., Weichbroth, P. (2019). Artificial intelligence technologies in education: benefits, challenges and strategies of implementation. In IFIP international workshop on artificial intelligence for knowledge management. Cham: Springer International Publishing,37-58.
22. Kanchanatanee, K., Suwanno, N., Jarernvongrayab, A. (2014). Effects of attitude toward using, perceived usefulness, perceived ease of use and perceived compatibility on intention to use E-marketing. Journal of Management Research,6(3):1.
23. O’Connor, R. C., & Armitage, C. J. (2003). Theory of planned behaviour and parasuicide: An exploratory study. Current Psychology, 22(3):196-205.
24. Perry, J. L., Nicholls, A. R., Clough, P. J. Crust, L. (2015). Assessing model fit: Caveats and recommendations for confirmatory factor analysis and exploratory structural equation modeling. Measurement in Physical Education and Exercise Science,19(1):12-21.
25. Arif, S. (2025). Cross-cultural perspectives on AI in education: Case studies from global classrooms. AI EDIFY Journal,2(1):12-20.
26. Teo, T. (2009). Is there an attitude problem? Reconsidering the role of attitude in the TAM. British Journal of Educational Technology,40(6):1139-1141.
27. Shin, W. S. (2015). Teachers’ use of technology and its influencing factors in Korean elementary schools. Technology, Pedagogy and Education,24(4):461-476.
28. Lu, J., Yu, C. S., Liu, C. (2005). Facilitating conditions, wireless trust and adoption intention. Journal of Computer Information Systems,46(1):17-24.
29. Marisa, S., Gunawan, G.,Susilawati, E. (2024). Global education development plan to build sustainable education based on artificial intelligence. Qubahan Academic Journal, 4(2):38-46.
30. Van de Schoot, R.,Miočević, M. (2020). Small sample size solutions: A guide for applied researchers and practitioners. Taylor & Francis,284
31. Sain, Z. H. (2024). Exploring the Benefits of Artificial Intelligence in Enhancing Learning, Accessibility, and Teaching Efficiency. SSR Journal of Artificial Intelligence (SSRJAI),1(1):1-7.
32. Karahanna, E., Straub, D. W. (1999). The psychological origins of perceived usefulness and ease-of-use. Information & Management,35(4):237-250.
33. Weston, R., & Gore Jr, P. A. (2006). A brief guide to structural equation modeling. The Counseling Psychologist, 34(5):719-751.
34. Masrom, M. (2007). Technology acceptance model and e-learning. Technology, 21(24):81.
35. Akram, H., Abdelrady, A. H., Al-Adwan, A. S.,Ramzan, M. (2022). Teachers’ perceptions of technology integration in teaching-learning practices: A systematic review. Frontiers in Psychology, 13(1).
36. Ramorola, M. Z. (2013). Challenge of effective technology integration into teaching and learning. Africa Education Review,10(4):654-670.
37. Rhodes, R. E., Blanchard, C. M., Matheson, D. H. (2006). A multicomponent model of the theory of planned behaviour. British journal of health psychology, 11(1):119-137.
38. Hatem, G., Zeidan, J., Goossens, M.,Moreira, C. (2022). Normality testing methods and the importance of skewness and kurtosis in statistical analysis. BAU Journal-Science and Technology, 3(2):7.
39. Jeng, C. C. (2023). Why a variance inflation factor of 10 is not an ideal cutoff for multicollinearity diagnostics. Journal of Education Studies,57(2):67-93.
40. Marikyan, M.,Papagiannidis, P. (2021). Unified theory of acceptance and use of technology. TheoryHub book.
41. Williams, M. D., Rana, N. P. Dwivedi, Y. K. (2015). The unified theory of acceptance and use of technology (UTAUT): a literature review. Journal of Enterprise Information Management, 28(3): 443-488.
42. Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., Williams, M. D. (2019). Re-examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical model. Information Systems Frontiers,21(3):719-734.
43. Brown, T. A.,Moore, M. T. (2012). Confirmatory factor analysis. Handbook of structural equation modeling,361-379.
44. Dashdondov, K., Kim, M. H. (2023). Mahalanobis distance based multivariate outlier detection to improve performance of hypertension prediction. Neural Processing Letters,55(1):265-277.
45. Daniels, R. (2025), allocated for education infrastructure in 2025. Education.gov.gy.
46. Singh, A. S. (2017). Common procedures for development, validity and reliability of a questionnaire. International Journal of Economics, Commerce and Management, 5(5):790-801.
47. La Barbera, F. Ajzen, I. (2020). Control interactions in the theory of planned behavior: Rethinking the role of subjective norm. Europe’s Journal of Psychology,16(3): 401-417.
48. Jobst, L. J., Auerswald, M, Moshagen, M. (2022). The effect of latent and error non-normality on corrections to the test statistic in structural equation modeling. Behavior Research Methods, 54(5):2351-2363.
49. Bodinga, M. M. (2025). Tips for benefits of using AI in teaching and learning. Kashf Journal of Multidisciplinary Research,2(05):69-80.
50. Cheung, G. W., Cooper-Thomas, H. D., Lau, R. S.,Wang, L. C. (2024). Reporting reliability, convergent and discriminant validity with structural equation modeling: A review and best-practice recommendations. Asia pacific journal of management, 41(2):745-783.
51. 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. Applied System Innovation,1(3):36.
52. Kafyulilo, A., Fisser, P., Voogt, J. (2016). Factors affecting teachers’ continuation of technology use in teaching. Education and Information Technologies,21(6):1535-1554.
53. Romero-Rodríguez, J. M., Ramírez-Montoya, M. S., Buenestado-Fernández, M., & Lara-Lara, F. (2023). Use of ChatGPT at university as a tool for complex thinking: Students’ perceived usefulness. Journal of New Approaches in Educational Research,12(2)L:323-339.
54. Sarmah, H. K.,Hazarika, B. B. (2012). Determination of reliability and validity measures of a questionnaire. Indian Journal of Education and information management,1(11):508-517.
55. Bonett, D. G., Wright, T. A. (2015). Cronbach's alpha reliability: Interval estimation, hypothesis testing, and sample size planning. Journal of Organizational Behavior,36(1):3-15.
56. 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.
57. Jobst, L. J., Bader, M., Moshagen, M. (2023). A tutorial on assessing statistical power and determining sample size for structural equation models. Psychological Methods,28(1):207.
58. Iacobucci, D. (2010). Structural equations modeling: Fit Indices, sample size, and advanced topics. Journal of Consumer Psychology, 20(1):90-98.
59. Demir, S. (2022). Comparison of normality tests in terms of sample sizes under different skewness and kurtosis coefficients. International Journal of Assessment Tools in Education,9(2):397-409.
60. Daniels, R. (2026,). education budget for 2026 boosts access and equity. Education.gov.gy.
61. Davies, R. S.,West, R. E. (2013). Technology integration in schools. In Handbook of research on educational communications and technology). New York, NY: Springer New York,841-853.
62. Brandford, B. B., Kumar, J. A., Arkorful, V., Agyapong, E. M.,Osman, S. (2021). Remodelling the role of facilitating conditions for Google Classroom acceptance: A revision of UTAUT2. Australasian Journal of Educational Technology,38(1):115-135.
63. Chuttur, M. (2009). Overview of the technology acceptance model: Origins, developments and future directions.
64. Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., et;al.(2021). An introduction to structural equation modeling. In Partial least squares structural equation modeling (PLS-SEM) using R: a workbook Cham: Springer International Publishing, (pp. 1-29).
65. Ham, M., Jeger, M.,Frajman Ivković, A. (2015). The role of subjective norms in forming the intention to purchase green food. Economic Research-Ekonomska Istraživanja,28(1):738-748.
66. 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.
67. Peñarroja, V., Sánchez, J., Gamero, N., Orengo, V., Zornoza, A. M. (2019). The influence of organisational facilitating conditions and technology acceptance factors on the effectiveness of virtual communities of practice. Behaviour & Information Technology,38(8):845-857.
68. Venkatesh, V., Thong, J. Y., Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Quarterly,36(1): 157-178.
69. Teo, T. (2011). Factors influencing teachers’ intention to use technology: Model development and test. Computers & Education,57(4):2432-2440.
70. Spiteri, M., & Chang Rundgren, S. N. (2020). Literature review on the factors affecting primary teachers’ use of digital technology. Technology, Knowledge and Learning,25(1):115-128.
71. Wangdi, T., Dhendup, S. Gyelmo, T. (2023). Factors influencing teachers’ intention to use technology: Role of TPACK and facilitating conditions. International Journal of Instruction, 16(2):1017-1036.
72. Li, Y., Tolosa, L., Rivas-Echeverria, F., Marquez, R. (2025). Integrating AI in education: Navigating UNESCO global guidelines, emerging trends, and its intersection with sustainable development goals.
73. Mustafa, M. B., Nordin, M. B., Razzaq, A. B. A. (2020). Structural equation modelling using AMOS: Confirmatory factor analysis for taskload of special education integration program teachers. Universal Journal of Educational Research, 8(1) :127-133.
74. Dinc, M. S., Budic, S. (2016). The impact of personal attitude, subjective norm, and perceived behavioural control on entrepreneurial intentions of women. Eurasian Journal of Business and Economics,9(17):23-35.
75. Sideridis, G., Simos, P., Papanicolaou, A., Fletcher, J. (2014). Using structural equation modeling to assess functional connectivity in the brain: Power and sample size considerations. Educational and Psychological Measurement, 74(5):733-758.
76. Christmann, A.,Van Aelst, S. (2006). Robust estimation of Cronbach’s alpha. Journal of Multivariate Analysis,97(7):1660-1674.
77. King, W. R., He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43(6):740-755.
78. Schreiber, J. B., Nora, A., Stage, F. K., Barlow, E. A.,King, J. (2006). Reporting structural equation modeling and confirmatory factor analysis results: A review. The Journal of educational research, 99(6):323-338.
79. Holtzman, S., Vezzu, S. (2011). Confirmatory factor analysis and structural equation modeling of noncognitive assessments using PROC CALIS. NorthEast SAS Users Group (NESUG), 2011 proceedings 11-14.
80. Ministry of Education. (2022). Information and communication technology in education: Policy and master plan.
81. Davis, F. D. Davis, F. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly,13(3):319-340.
82. Lee, C.,Wan, G. (2010). Including subjective norm and technology trust in the technology acceptance model: a case of e-ticketing in China. ACM SIGMIS Database: The DATABASE for Advances in Information Systems,41(4):40-51.
83. Ramayah, T. Ignatius, J. (2005). Impact of perceived usefulness, perceived ease of use and perceived enjoyment on intention to shop online. ICFAI Journal of systems management (IJSM),3(3):36-51.
84. Rintaningrum, R. (2023). Technology integration in English language teaching and learning: Benefits and challenges. Cogent Education,10(1):2164-690.
85. Al-Swidi, A., Mohammed Rafiul Huque, S., Haroon Hafeez, M.,Noor Mohd Shariff, M. (2014). The role of subjective norms in theory of planned behavior in the context of organic food consumption. British Food Journal,116(10):1561-1580.
86. Ritzhaupt, A. D., Dawson, K.,Cavanaugh, C. (2012). An investigation of factors influencing student use of technology in K-12 classrooms using path analysis. Journal of Educational Computing Research, 46(3):229-254.
87. Sommer, L. (2011). The theory of planned behaviour and the impact of past behaviour. International Business & Economics Research Journal (IBER),10(1):91-110.
88. Del Greco, L., Walop, W.,McCarthy, R. H. (1987). Questionnaire development: 2. Validity and reliability. CMAJ: Canadian Medical Association Journal,136(7):699.
89. Wolf, E. J., Harrington, K. M., Clark, S. L.,Miller, M. W. (2013). Sample size requirements for structural equation models: An evaluation of power, bias, and solution propriety. Educational and Psychological Measurement,73(6):913-934.
90. McInerney, D. M., Dowson, M., Yeung, A. S. (2005). Facilitating conditions for school motivation: Construct validity and applicability. Educational and Psychological Measurement,65(6):1046-1066.
91. Tavakol, M.,Dennick, R. (2011). Making sense of Cronbach’s alpha. International Journal of Medical Education,2(2):53-55.
92. Kautonen, T., Van Gelderen, M., Tornikoski, E. T. (2013). Predicting entrepreneurial behaviour: A test of the theory of planned behaviour. Applied economics,45(6):697-707.
93. Ambarwati, R., Harja, Y. D., Thamrin, S. (2020). The role of facilitating conditions and user habits: A case of Indonesian online learning platform. The Journal of Asian Finance, Economics and Business,7(10):481-489.
94. Holstein, K., McLaren, B. M., Aleven, V. (2018, June). Student learning benefits of a mixed-reality teacher awareness tool in AI-enhanced classrooms. In International conference on artificial intelligence in education Cham: Springer International Publishing,15-168.
95. Ebadi, S., & Raygan, A. (2023). Investigating the facilitating conditions, perceived ease of use and usefulness of mobile-assisted language learning. Smart Learning Environments,10(1):30.
96. Martí-Parreño, J., Seguí-Mas, D.,Seguí-Mas, E. (2016). Teachers’ attitude towards and actual use of gamification. Procedia-Social and Behavioral Sciences,228(228):682-688.
97. O’Brien, R. M. (2007). A caution regarding rules of thumb for variance inflation factors. Quality & Quantity, 41(5):673-690.
98. Raykov, T., Tomer, A., Nesselroade, J. R. (1991). Reporting structural equation modeling results in Psychology and Aging: some proposed guidelines. Psychology and Aging, 6(4):499-503.
99. Blackwell, C. K., Lauricella, A. R.,Wartella, E. (2014). Factors influencing digital technology use in early childhood education. Computers & Education, 77(77), 82–90.
100. John Jr, G. A. (2025). AI in education: A systematic literature review of emerging trends, benefits, and challenges. In Seminars in Medical Writing and Education AG Editor (Argentina).4:795-795.