You are here:

Unpacking teachers’ intentions to integrate technology: A meta-analysis
ARTICLE

, Department of Teacher Education and School Research (ILS), Norway ; , School of Education, Australia

Educational Research Review Volume 27, Number 1, ISSN 1747-938X Publisher: Elsevier Ltd

Abstract

The Technology Acceptance Model (TAM) is a key model describing teachers' intentions to use technology. This meta-analysis clarifies some of the contradictory findings surrounding the relations within the TAM for a sample of 45 studies comprising 300 correlations. We evaluate the overall fit of the TAM and its structural parameters, and quantify the between-sample variation through meta-analytic structural equation modeling. The TAM fitted the data well, and all structural parameters were statistically significant. On average, the TAM variables explained 39.2% of the variance in teachers' intentions to use technology. Several sample, measurement, and publication characteristics, including teachers’ experience and the representation of the TAM variables, moderated the relations within the TAM. Overall, the TAM represents a valid model explaining technology acceptance—however, the degree of explanation and the relative importance of predictors vary across samples. Implications for further research, in particular the generalizability of the TAM, are discussed.

Citation

Scherer, R. & Teo, T. (2019). Unpacking teachers’ intentions to integrate technology: A meta-analysis. Educational Research Review, 27(1), 90-109. Elsevier Ltd. Retrieved February 4, 2023 from .

This record was imported from Educational Research Review on March 15, 2019. Educational Research Review is a publication of Elsevier.

Full text is availabe on Science Direct: http://dx.doi.org/10.1016/j.edurev.2019.03.001

Keywords

References

View References & Citations Map
  1. Adams, R.J., Smart, P., & Huff, A.S. (2017). Shades of grey: Guidelines for working with the grey literature in systematic reviews for management and organizational studies. International Journal of Management Reviews, 19(4), pp. 432-454. Available online: https://doi.org/10.1111/ijmr.12102.
  2. Aguinis, H., Gottfredson, R.K., & Wright, T.A. (2011). Best-practice recommendations for estimating interaction effects using meta-analysis. Journal of Organizational Behavior, 32, pp. 1033-1043. Available online: https://doi.org/10.1002/job.719.
  3. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), pp. 179-211. Available online: https://doi.org/10.1016/0749-5978(91)90020-T.
  4. Atif, A., Richards, D., Busch, P., & Bilgin, A. (2015). Assuring graduate competency: A technology acceptance model for course guide tools. Journal of Computing in Higher Education, 27(2), pp. 94-113. Available online: https://doi.org/10.1007/s12528-015-9095-4.
  5. Aypay, A., Celik, H.C., Aypay, A., & Sever, M. (2012). Technology acceptance in education: A study of pre-service teachers in Turkey. TOJET - Turkish Online Journal of Educational Technology, 11(4), pp. 264-272.
  6. Ball, D.M. (2008). An empirical investigation of the contribution of computer self-efficacy, computer anxiety, and instructors' experience with the use of technology to their intention to use emerging educational technology in traditional classrooms. Dissertation Abstracts International Section A: Humanities and Social Sciences, 69(2-A), p. 576.
  7. Baydas, O., & Goktas, Y. (2017). A model for preservice teachers' intentions to use ICT in future lessons. Interactive Learning Environments, 25(7), pp. 930-945. Available online: https://doi.org/10.1080/10494820.2016.1232277.
  8. Borenstein, M., Hedges, L.V., Higgins, J.P.T., & Rothstein, H.R. (2009). Introduction to meta-analysis. Chichester, West Sussex: John Wiley & Sons, Ltd.
  9. Borenstein, M., Higgins, J.P.T., Hedges, L.V., & Rothstein, H.R. (2017). Basics of meta-analysis: I2 is not an absolute measure of heterogeneity. Research Synthesis Methods, 8(1), pp. 5-18. Available online: https://doi.org/10.1002/jrsm.1230.
  10. Brenner, A.M., & Brill, J.M. (2016). Investigating practices in teacher education that promote and inhibit technology integration transfer in early career teachers. TechTrends, 60, pp. 136-144. Available online: https://doi.org/10.1007/s11528-016-0025-8.
  11. Cai, Z., Fan, X., & Du, J. (2017). Gender and attitudes toward technology use: A meta-analysis. Computers & Education, 105, pp. 1-13. Available online: https://doi.org/10.1016/j.compedu.2016.11.003.
  12. Chen, H.-R., & Tseng, H.-F. (2012). Factors that influence acceptance of web-based e-learning systems for the in-service education of junior high school teachers in Taiwan. Evaluation and Program Planning, 35(3), pp. 398-406. Available online: https://doi.org/10.1016/j.evalprogplan.2011.11.007.
  13. Cheung, M.W.-L. (2015). Meta-analysis: A structural equation modeling approach. Chichester, West Sussex: John Wiley & Sons Ltd.
  14. Cheung, M.W.-L. (2018). metaSEM: Meta-analysis using structural equation modeling. Available online: https://cran.r-project.org/web/packages/metaSEM/index.html.
  15. Cheung, M.W.-L., & Chan, W. (2005). Meta-analytic structural equation modeling: A two-stage approach. Psychological Methods, 10(1), pp. 40-64. Available online: https://doi.org/10.1037/1082-989X.10.1.40.
  16. Cheung, M.W.-L., & Cheung, S.F. (2016). Random-effects models for meta-analytic structural equation modeling: Review, issues, and illustrations. Research Synthesis Methods, 7(2), pp. 140-155. Available online: https://doi.org/10.1002/jrsm.1166.
  17. Cheung, M.W.-L., & Hong, R.Y. (2017). Applications of meta-analytic structural equation modeling in health psychology: Examples, issues, and recommendations. Health Psychology Review, 11(3), pp. 265-279. Available online: https://doi.org/10.1080/17437199.2017.1343678.
  18. Cheung, E.Y.M., & Sachs, J. (2006). Test of the technology acceptance model for a web-based information system in a hong-kong Chinese sample. Psychological Reports, 99(3), pp. 691-703. Available online: https://doi.org/10.2466/PR0.99.7.691-703.
  19. Chiu, T.K.F. (2017). Introducing electronic textbooks as daily-use technology in schools: A top-down adoption process. British Journal of Educational Technology, 48(2), pp. 524-537. Available online: https://doi.org/10.1111/bjet.12432.
  20. Cohen, J., Cohen, P., West, S.G., & Aiken, L.S. (2015). Applied multiple regression/correlation analysis for the behavioral Sciences. New York, NY: Routledge.
  21. Cote, T., & Milliner, B. (2015). Training ELF teachers to create a blended learning environment: Encouraging CMS adoption and implementation. Paper presented at the EUROCALL conference 2015, padova, Italy.
  22. Davis, F.D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results. Available online: https://dspace.mit.edu/handle/1721.1/15192.
  23. Davis, F.D., & Venkatesh, V. (1996). A critical assessment of potential measurement biases in the technology acceptance model: Three experiments. International Journal of Human-Computer Studies, 45(1), pp. 19-45. Available online: https://doi.org/10.1006/ijhc.1996.0040.
  24. European Commission (2013). Survey of schools: ICT in education. Benchmarking access, use and attitudes to technology in europe's schools—final study report. Luxembourg: Publications Office of the European Union. Available online: https://doi.org/10.2759/94499.
  25. Evidence Partners (2018). DistillerSR: Systematic review and literature software.
  26. Evidence Partners (2018). DistillerAI: Automated reference screening software.
  27. 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), pp. 210-232.
  28. Fishbein, M. (1979). A theory of reasoned action: Some applications and implications. Nebraska Symposium on Motivation, 27, pp. 65-116.
  29. Fraillon, J., Ainley, J., Schulz, W., Friedman, T., & Gebhardt, E. (2014). Preparing for life in a digital age. The IEA international computer and information literacy study international report. Amsterdam: International Association for the Evaluation of Educational Achievement (IEA). Available online: https://doi.org/10.1007/978-3-319-14222-7.
  30. Gamage, S., & Gamage, T. (2017). Strategies for training or supporting teachers to integrate technology into the classroom. Ottawa, Canada: International Development Research Centre. Available online: https://eppi.ioe.ac.uk/CMS/Portals/0/PDF%20reviews%20and%20summaries/Teacher%20technology%202017.pdf.
  31. Haddaway, N.R., Collins, A.M., Coughlin, D., & Kirk, S. (2015). The role of Google scholar in evidence reviews and its applicability to grey literature searching. PLoS One, 10(9) Available online: https://doi.org/10.1371/journal.pone.0138237.
  32. Hauk, N., Hüffmeier, J., & Krumm, S. (2018). Ready to be a silver surfer? A meta-analysis on the relationship between chronological age and technology acceptance. Computers in Human Behavior, 84, pp. 304-319. Available online: https://doi.org/10.1016/j.chb.2018.01.020.
  33. Hedges, L.V., & Pigott, T.D. (2004). The power of statistical tests for moderators in meta-analysis. Psychological Methods, 9(4), pp. 426-445. Available online: https://doi.org/10.1037/1082-989X.9.4.426.
  34. Hong, R.Y., & Cheung, M.W.-L. (2015). The structure of cognitive vulnerabilities to depression and anxiety: Evidence for a common core etiologic process based on a meta-analytic review. Clinical Psychological Science, 3(6), pp. 892-912. Available online: https://doi.org/10.1177/2167702614553789.
  35. Howard, S.K., Chan, A., Mozejko, A., & Caputi, P. (2015). Technology practices: Confirmatory factor analysis and exploration of teachers' technology integration in subject areas. Computers & Education, 90, pp. 24-35. Available online: https://doi.org/10.1016/j.compedu.2015.09.008.
  36. Hsiao, C.H., & Yang, C. (2011). The intellectual development of the technology acceptance model: A co-citation analysis. International Journal of Information Management, 31(2), pp. 128-136. Available online: https://doi.org(10.1016/j.ijinfomgt.2010.07.003.
  37. Hu, P.J.-H., Clark, T.H.K., & Ma, W.W. (2003). Examining technology acceptance by school teachers: A longitduinal study. Information & Management, 41, pp. 227-241. Available online: https://doi.org/10.1016/S0378-7206(03)00050-8.
  38. Ibili, E., & Sahin, S. (2016). The use of cartoons in elementary classrooms: An analysis of teachers' behavioral intention in terms of gender. Educational Research and Reviews, 11(8), pp. 508-516.
  39. Imtiaz, M.A., & Maarop, N. (2014). A review of technology acceptance studies in the field of education. Jurnal Teknologi, 69(2), pp. 27-32.
  40. Kabakci-Yurdakul, I., Ursavaş, O.F., & Becit-Isciturk, G. (2014). an integrated approach for preservice teachers' acceptance and use of technology: UTAUT-PST scale. European Journal of Educational Research, 55, pp. 21-36. Available online: https://doi.org/10.14689/ejer.2014.55.2.
  41. King, W.R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43(6), pp. 740-755. Available online: https://doi.org/10.1016/j.im.2006.05.003.
  42. Kirmizi, O. (2014). Measuring technology acceptance level of Turkish pre-service English teachers by using technology acceptance model. Educational Research and Reviews, 9(23), pp. 1323-1333.
  43. Kline, R.B. (2016). Principles and practice of structural equation modeling. New York, London: Guilford Press.
  44. Koutromanos, G., Styliaras, G., & Christodoulou, S. (2015). Student and in-service teachers' acceptance of spatial hypermedia in their teaching: The case of hypersea. Education and Information Technologies, 20(3), pp. 559-578. Available online: https://doi.org/10.1007/s10639-013-9302-8.
  45. Lai, H.-C., Chang, C.-Y., Wen-Shiane, L., Fan, Y.-L., & Wu, Y.-T. (2013). The implementation of mobile learning in outdoor education: Application of QR codes. British Journal of Educational Technology, 44(2), pp. E57-E62. Available online: https://doi.org/10.1111/j.1467-8535.2012.01343.x.
  46. Lee, Y., Kozar, K.A., & Larsen, K.R.T. (2003). The technology acceptance model: Past, present, and future. Communications of the Association for Information Systems, 12, p. 50.
  47. Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & Management, 40, pp. 191-204. Available online: https://doi.org/10.1016/S0378-7206(01)00143-4.
  48. Luan, W.S., & Teo, T. (2009). Investigating the technology acceptance among student teachers in Malaysia: An application of the Technology Acceptance Model (TAM). Asia-Pacific Education Researcher, 18(2), pp. 261-272.
  49. Luan, W.S., & Teo, T. (2011). Student teachers' acceptance of computer technology. Technology acceptance in education: Research and issues, pp. 43-61. Rotterdam: Sense Publishers.
  50. Mac Callum, K., Jeffrey, L., & Kinshuk (2014). Factors impacting teachers' adoption of mobile learning. Journal of Information Technology Education: Research, 13, pp. 141-162.
  51. Marangunić, N., & Granić, A. (2015). Technology acceptance model: A literature review from 1986 to 2013. Universal Access in the Information Society, 14(1), pp. 81-95. Available online: https://doi.org/10.1007/s10209-014-0348-1.
  52. Marsh, H.W., Hau, K.-T., & Grayson, D. (2005). Goodness of fit evaluation in structural equation modeling. Contemporary psychometrics, pp. 275-340. Mahwah, NJ: Lawrence Erlbaum.
  53. Mathieson, K. (1991). Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2(3), pp. 173-191. Available online: https://doi.org/10.1287/isre.2.3.173.
  54. McGill, T., Klobas, J., & Renzi, S. (2011). LMS use and instructor performance: The role of task-technology fit. International Journal on E-Learning, 10(1), pp. 43-62.
  55. Moher, D., Liberati, A., Tetzlaff, J., Altman, D.G., & The PRISMA Group (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Medicine, 6(7), pp. 1-6. Available online: https://doi.org/10.1371/journal.pmed.1000097.
  56. Mortensen, M.J., & Vidgen, R. (2016). A computational literature review of the technology acceptance model. International Journal of Information Management, 26, pp. 1248-1259. Available online: https://doi.org/10.1016/j.ijinfomgt.2016.07.007.
  57. Muthén, B., & Muthén, L. (n.d.). Mplus (Version 7.3). Los Angeles, CA: Muthén & Muthén.
  58. Nistor, N. (2014). When technology acceptance models won't work: Non-significant intention-behavior effects. Computers in Human Behavior, 34, pp. 299-300. Available online: https://doi.org/10.1016/j.chb.2014.02.052.
  59. Nistor, N., & Heymann, J.O. (2010). Reconsidering the role of attitude in the TAM: An answer to Teo (2009). British Journal of Educational Technology, 41(6), pp. E142-E145. Available online: https://doi.org/10.1111/j.1467-8535.2010.01109.x.
  60. OECD (2016). Teachers' ICT and problem-solving skills: Competencies and needs. Education Indicators in Focus, 40, pp. 1-4. Available online: https://doi.org/10.1787/5jm0q1mvzqmq-en.
  61. Okazaki, S., & dos Santos, L.M.R. (2012). Understanding E-learning adoption in Brazil: Major determinants and gender effects. International Review of Research in Open and Distance Learning, 13(4), pp. 91-106. Available online: https://doi.org/10.19173/irrodl.v13i4.1266.
  62. Park, N., Lee, K.M., & Cheong, P.H. (2007). University instructors' acceptance of electronic courseware: An application of the Technology Acceptance Model. Journal of Computer-Mediated Communication, 13(1), pp. 163-186. Available online: https://doi.org/10.1111/j.1083-6101.2007.00391.x.
  63. Parkman, S. (2015). Factors affecting pre-service teachers' Acceptance of technology-rich learning Environments in abu dhabi. Paper presented at the society for information technology & teacher education international conference 2015, las vegas, NV, United States Available online: https://www.learntechlib.org/p/150468.
  64. Pittalis, M., & Christou, C. (2011). Extending the Technology Acceptance Model to assess secondary school teachers ‘intention to use CABRI in Geometry Teaching‘. Paper presented at the seventh congress of the european society for research in mathematics education Poland: University of Rzeszów.
  65. Pynoo, B., Tondeur, J., van Braak, J., Duyck, W., Sijnave, B., & Duyck, P. (2012). Teachers’ acceptance and use of an educational portal. Computers & Education, 58(4), pp. 1308-1317. Available online: https://doi.org/10.1016/j.compedu.2011.12.026.
  66. Rosseel, Y. (2018). laavan: Latent Variable Analysis . R package version 0.6-1. Available online: https://cran.r-project.org/web/packages/lavaan/index.html.
  67. Sadeghi, K., Saribagloo, J.A., Aghdam, S.H., & Mahmoudi, H. (2014). The impact of iranian teachers cultural values on computer technology acceptance. TOJET - Turkish Online Journal of Educational Technology, 13(4), pp. 124-136.
  68. Ritter, N.L. (2017). Technology acceptance model of online learning management systems in higher education: A meta-analytic structural equation model. International Journal of Learning Management Systems, 5(1), pp. 1-15. Available online: https://doi.org/10.18576/ijlms/050101.
  69. Savalei, V. (2018). A comparison of several approaches for controlling measurement error in small samples. Psychological Methods Available online: https://doi.org/10.1037/met0000181.
  70. Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information & Management, 44(1), pp. 90-103. Available online: https://doi.org/10.1016/j.im.2006.10.007.
  71. Scherer, R., & Siddiq, F. (2015). Revisiting teachers' computer self-efficacy: A differentiated view on gender differences. Computers in Human Behavior, 53, pp. 48-57. Available online: https://doi.org/10.1016/j.chb.2015.06.038.
  72. 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, pp. 13-35. Available online: https://doi.org/10.1016/j.compedu.2018.09.009.
  73. Schmidt, F.L., & Hunter, J.E. (2015). Methods of meta-analysis: Correcting error and bias in research findings. Thousand Oaks, CA: Sage Publications.
  74. Sharp, J.H. (2006). Development, extension, and application: A review of the technology acceptance model. Information Systems Electronic Journal, 5(9), pp. 1-11. Available online: http://isedj.org/5/9/.
  75. Shashaani, L. (1993). Gender-based differences in attitudes toward computers. Computers & Education, 20(2), pp. 169-181. Available online: https://doi.org/10.1016/0360-1315(93)90085-W.
  76. Sheng, Z., Kong, W., Cortina, J.M., & Hou, S. (2016). Analyzing matrices of meta-analytic correlations: Current practices and recommendations. Research Synthesis Methods, 7, pp. 187-208. Available online: https://doi.org/10.1002/jrsm.1206.
  77. Shiue, Y.-M. (2007). Investigating the sources of teachers' instructional technology use through the decomposed theory of planned behavior. Journal of Educational Computing Research, 36(4), pp. 425-453. Available online: https://doi.org/10.2190/A407-22RR-50X6-2830.
  78. Siddiq, F., & Scherer, R. (2016). The relation between teachers' emphasis on the development of students' digital information and communication skills and computer self-efficacy: The moderating roles of age and gender. Large-scale Assessments in Education, 4(1), pp. 1-21. Available online: https://doi.org/10.1186/s40536-016-0032-4.
  79. Siddiq, F., Scherer, R., & Tondeur, J. (2016). Teachers' emphasis on developing students' digital information and communication skills (TEDDICS): A new construct in 21st century education. Computers & Education, 92–93, pp. 1-14. Available online: https://doi.org/10.1016/j.compedu.2015.10.006.
  80. Simonsohn, U., Nelson, L.D., & Simmons, J.P. (2014). P-curve: A key to the file-drawer. Journal of Experimental Psychology: General, 143(2), pp. 534-547. Available online: https://doi.org/10.1037/a0033242.
  81. Snijders, T.A.B., & Bosker, R.J. (2012). Multilevel analysis: An introduction to basic and advanced multilevel modeling. Thousand Oaks, CA: SAGE Publications.
  82. Straub, E.T. (2009). Understanding technology adoption: Theory and future directions for informal learning. Review of Educational Research, 79(2), pp. 625-649. Available online: https://doi.org/10.3102/0034654308325896.
  83. Šumak, B., Hericko, M., & Pušnik, M. (2011). A meta-analysis of e-learning technology acceptance: The role of user types and e-learning technology types. Computers in Human Behavior, 27, pp. 2067-2077. Available online: https://doi.org/10.1016/j.chb.2011.08.005.
  84. Šumak, B., & Sorgo, A. (2016). The acceptance and use of interactive whiteboards among teachers: Differences in UTAUT determinants between pre- and post-adopters. Computers in Human Behavior, 64, pp. 602-620. Available online: https://doi.org/10.1016/j.chb.2016.07.037.
  85. Sun, H., & Zhang, P. (2006). The role of moderating factors in user technology acceptance. International Journal of Human-Computer Studies, 64, pp. 53-78. Available online: https://doi.org/10.1016/j.ijhcs.2005.04.013.
  86. Tang, D., & Chen, L. (2011). A review of the evolution of research on information technology acceptance model. Paper presented at the international conference on business management and electronic information, guangzhou, China Available online: https://doi.org/10.1109/ICBMEI.2011.5917980.
  87. Teo, T. (2008). Pre-service teachers' attitudes towards computer use: A Singapore survey. Australasian Journal of Educational Technology, 24(4), pp. 413-424.
  88. Teo, T. (2009). The impact of subjective norm and facilitating conditions on pre-service teachers' attitude toward computer use: A structural equation modeling of an extended technology acceptance model. Journal of Educational Computing Research, 40(1), pp. 89-109. Available online: https://doi.org/10.2190/EC.40.1.d.
  89. Teo, T. (2009). Modelling technology acceptance in education: A study of pre-service teachers. Computers & Education, 52(2), pp. 302-312. Available online: https://doi.org/10.1016/j.compedu.2008.08.006.
  90. Teo, T. (2011). Factors influencing teachers' intention to use technology: Model development and test. Computers & Education, 57(4), pp. 2432-2440. Available online: https://doi.org/10.1016/j.compedu.2011.06.008.
  91. Teo, T. (2012). Examining the intention to use technology among pre-service teachers: An integration of the technology acceptance model and theory of planned behavior. Interactive Learning Environments, 20(1), pp. 3-18.
  92. Teo, T. (2013). Influences of contextual variables on the intention to use technology in education: A latent variable modeling approach. Campus-Wide Information Systems, 30(2), pp. 95-105. Available online: https://doi.org/10.1108/10650741311306282.
  93. Teo, T. (2015). Comparing pre-service and in-service teachers' acceptance of technology: Assessment of measurement invariance and latent mean differences. Computers & Education, 83, pp. 22-31. Available online: https://doi.org/10.1016/j.compedu.2014.11.015.
  94. Teo, T., Fan, X., & Du, J. (2015). Technology acceptance among pre-service teachers: Does gender matter?. Australasian Journal of Educational Technology, 31(3), pp. 235-251. Available online: https://doi.org/10.14742/ajet.1672.
  95. Teo, T., Lee, C.B., Chai, C.S., & Wong, S.L. (2009). Assessing the intention to use technology among pre-service teachers in Singapore and Malaysia: A multigroup invariance analysis of the technology acceptance model (TAM). Computers & Education, 53(3), pp. 1000-1009. Available online: https://doi.org/10.1016/j.compedu.2009.05.017.
  96. Teo, T., & Milutinović, V. (2015). Modelling the intention to use technology for teaching mathematics among pre-service teachers in Serbia. Australasian Journal of Educational Technology, 31(4), pp. 363-380. Available online: https://doi.org/10.14742/ajet.1668.
  97. Teo, T., Milutinović, V., Zhou, M., & Banković, D. (2017). Traditional vs. innovative uses of computers among mathematics pre-service teachers in Serbia. Interactive Learning Environments, 25(7), pp. 811-827. Available online: https://doi.org/10.1080/10494820.2016.1189943.
  98. Teo, T., & Noyes, J. (2011). An assessment of the influence of perceived enjoyment and attitude on the intention to use technology among pre-service teachers: A structural equation modeling approach. Computers & Education, 57(2), pp. 1645-1653. Available online: https://doi.org/10.1016/j.compedu.2011.03.002.
  99. Teo, T., & Noyes (2014). Explaining the intention to use technology among pre-service teachers: A multi-group analysis of the unified theory of acceptance and use of technology. Interactive Learning Environments, 22(1), pp. 51-66. Available online: https://doi.org/10.1080/10494820.2011.641674.

These references have been extracted automatically and may have some errors. Signed in users can suggest corrections to these mistakes.

Suggest Corrections to References