Research Article

Factors influencing technology integration among mathematics educators in South Africa: A modified UTAUT2 perspective

Antony Musasa 1 , Jameson Goto 1 * , Geoffrey Lautenbach 1
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1 University of Johannesburg, Johannesburg, SOUTH AFRICA* Corresponding Author
Contemporary Educational Technology, 17(2), April 2025, ep564, https://doi.org/10.30935/cedtech/15890
Published Online: 28 January 2025, Published: 01 April 2025
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ABSTRACT

Educators must effectively integrate technology into their teaching practices in today’s technology-driven world. This study investigated factors influencing technology integration into teaching among mathematics educators in Gauteng secondary schools in South Africa. The unified theory of acceptance and use of technology, extended by adding the technological pedagogical content knowledge (TPACK) framed the study. Data was collected using an online questionnaire from 309 mathematics educators. Exploratory and confirmatory factor analyses were used to validate and verify the measurement model. The structural equation modelling analyses indicated that hedonic motivation (HM), performance expectancy (PE) and TPACK influenced behavioral intention (BI) to integrate technology. TPACK, facilitating conditions (FC), effort expectancy (EE), social influence (SIN), descriptive norms (SID) and habit (HT) influenced the behavioral use (BU) of technology integration. The second-order structural modelling indicated that all the constructs contributed to technology integration. Still, TPACK was the most important, with the highest explained variance of 64.4%, followed by EE, FC, HM and HT, which all had explained variances above 50%. BI and BU, PE and social influence contributed less than 50% of the explained variance. Our findings could provide insights into future interventions for effective technology integration for in-service educator training.
Keywords: TPACK, facilitating conditions, technology integration, effort expectancy, mathematics

CITATION (APA)

Musasa, A., Goto, J., & Lautenbach, G. (2025). Factors influencing technology integration among mathematics educators in South Africa: A modified UTAUT2 perspective. Contemporary Educational Technology, 17(2), ep564. https://doi.org/10.30935/cedtech/15890

REFERENCES

  1. Aldekheel, A. Y., Khalil, O., & AlQenaei, Z. M. (2022). Factors impacting educators’ continued IT adoption in pre-college education. Journal of Information Technology Education: Research, 21, 465–500. https://doi.org/10.28945/5029
  2. Al-zboon, H. S., Gasaymeh, A. M., & Al-Rsa’i, M. S. (2021). The attitudes of science and mathematics educators toward the integration of information and communication technology (ICT) in their educational practice: The application of the unified theory of acceptance and use of technology (UTAUT). World Journal of Education, 11(1), 75–85. https://doi.org/10.5430/wje.v11n1p75
  3. Apuke, O. (2017). Quantitative research methods: A synopsis approach. Kuwait Chapter of the Arabian Journal of Business and Management Review, 6, 40–47. https://doi.org/10.12816/0040336
  4. Ardiç, M. A. (2021). Opinions and attitudes of secondary school mathematics educators towards technology. Participatory Educational Research, 8(3), 136–155. https://doi.org/10.17275/per.21.58.8.3
  5. Avci, S. (2022). Examining the factors affecting educators’ use of digital learning resources with UTAUT2. Malaysian Online Journal of Educational Technology, 10(3), 200–214. https://doi.org/10.52380/mojet.2022.10.3.399
  6. Backhaus, K., Erichson, B., Plinke, W., & Weiber, R. (2016). Multivariate analyse methoden [Multivariate analysis methods]. Springer. https://doi.org/10.1007/978-3-662-46076-4
  7. Bardakcı, S., & Alkan, M. F. (2019). Investigation of Turkish preservice educators’ intentions to use IWB in terms of technological and pedagogical aspects. Education and Information Technologies, 24, 2887–2907. https://doi.org/10.1007/s10639-019-09904-4
  8. Bećirović, S. (2023). Challenges and barriers for effective integration of technologies into teaching and learning. In S. Bećirović (Ed.), Digital pedagogy: The use of digital technologies in contemporary education (pp. 123–133). Springer. https://doi.org/10.1007/978-981-99-0444-0_10
  9. Brookes, E. (2023). The theory of planned behavior: Behavioral intention. Simply Psychology. https://www.simplypsychology.org/theory-of-planned-behavior.html
  10. Brown, T. (2006). Confirmatory factor analysis for applied research. In D. A. Kenny, & T. D. Little (Eds.), Methodology in the social sciences. The Guilford Press.
  11. Cirneanu, A. L., & Moldoveanu, C. E. (2024). Use of digital technology in integrated mathematics education. Applied System Innovation, 7(4), Article 66. https://doi.org/10.3390/asi7040066
  12. De Jesus, N. M., Castro, E. T., & Buyan, C. A. (2023). Acceptance analysis of IWB systems in education using UTAUT2 model: The moderating role of perceived technological innovations. In Proceedings of the 2023 IEEE International Conference on Automatic Control and Intelligent Systems (pp. 269–274). IEEE. https://doi.org/10.1109/I2CACIS57635.2023.10193662
  13. De Leeuw, A., Valois, P., Ajzen, I., & Schmidt, P. (2015). Using the theory of planned behavior to identify key beliefs underlying pro-environmental behavior in high-school students: Implications for educational interventions. Journal of Environmental Psychology, 42, 128–138. https://doi.org/10.1016/j.jenvp.2015.03.005
  14. Diseko, R., & Mashiteng, E. (2020). The experiences of mathematics educators in the use of smartboards. In Proceedings of the SITE Interactive Conference (pp. 456–462). Association for the Advancement of Computing in Education. https://hdl.handle.net/10210/446429
  15. Dlamini, R. (2022). Factors constraining teacher integration of ICT in Gauteng schools. The Independent Journal of Teaching and Learning, 17(2), 28–43.
  16. El-Masri, M., & Tarhini, A. (2017). Factors affecting the adoption of e-learning systems in Qatar and USA: Extending the unified theory of acceptance and use of technology 2 (UTAUT2). Educational Technology Research and Development, 65(3), 743–763. https://doi.org/10.1007/s11423-016-9508-8
  17. Ertmer, P. A., Ottenbreit-Leftwich, A. T., Sadik, O., Sendurur, E., & Sendurur, P. (2012). Educator beliefs and technology integration practices: A critical relationship. Computers & Education, 59(2), 423–435. https://doi.org/10.1016/j.compedu.2012.02.001
  18. Field, A. (2009). Discovering statistics using SPSS (3rd ed.). SAGE.
  19. Field, A. (2018). Review of discovering statistics using IBM SPSS statistics (5th ed.). SAGE.
  20. 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
  21. Gaillard, C. (2019). Finding the missing variables: A systematic review of mathematics improvement strategies for South African public schools. South African Journal of Education, 39(3). https://doi.org/10.15700/saje.v39n3a1582
  22. Gkrimpizi, T., Peristeras, V., & Magnisalis, I. (2023). Classification of barriers to digital transformation in higher education institutions: Systematic literature review. Education Sciences, 13(7), Article 746. https://doi.org/10.3390/educsci13070746
  23. Graham, M. A., Mokgwathi, M. S., & De Villiers, J. J. R. (2021). Safety factors associated with mathematics achievement in South African schools. Eurasia Journal of Mathematics, Science and Technology Education, 17(12), Article em2052. https://doi.org/10.29333/ejmste/11416
  24. Graham, M. A., Stols, G., & Kapp, R. (2020). Educator practice and integration of ICT: Why are or aren’t South African educators using ICTs in their classrooms. International Journal of Instruction, 13(2), 749–766. https://doi.org/10.29333/iji.2020.13251a
  25. Grujicic, S., & Nikolic, A. (2021). Cross-section studies: Advantages and disadvantages. Zdravstvena Zaštita, 50, Article 44. https://doi.org/10.5937/zdravzast50-35574
  26. Hair, Jr, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006.) Multivariate data analysis (6th ed.). Pearson-Prentice Hall.
  27. Hodges, C. B., & Prater, A. H. (2014). Technologies on the horizon: Educators respond to the horizon report. TechTrends, 58(3), 71–77. https://doi.org/10.1007/s11528-014-0754-5
  28. Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
  29. Jarrah, A. M., Fayez, K., Almarashdi, H., & Fidalgo, P. (2024). Enhancing mathematics education in the UAE: Elementary educators’ views on distance education methods. Contemporary Educational Technology, 16(3), Article ep516. https://doi.org/10.30935/cedtech/14714
  30. Johnson, A. M., Jacovina, M. E., Russell, D. G., & Soto, C. M. (2016). Adaptive educational technologies for literacy instruction (1st ed.). Routledge. https://doi.org/10.4324/9781315647500-2
  31. Joo, Y. J., Park, S., & Lim, E. (2018). Factors influencing preservice educators’ intention to use technology: TPACK, educator self-efficacy, and technology acceptance model. Journal of Educational Technology & Society, 21(3), 48–59.
  32. Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31–36. https://doi.org/10.1007/BF02291575
  33. Kankaraš, M., & Capecchi, S. (2024). Neither agree nor disagree: Use and misuse of the neutral response category in Likert-type scales. METRON. https://doi.org/10.1007/s40300-024-00276-5
  34. Kim, J. H. (2023). Cross-sectional study: The role of observation in epidemiological studies. In A. K. Mitra (Ed.), Statistical approaches for epidemiology: From concept to application (pp. 19–42). Springer. https://doi.org/10.1007/978-3-031-41784-9_2
  35. Limayem, M., & Hirt, S. G. (2003). Force of habit and information systems usage: Theory and initial validation. Journal of the Association for Information Systems, 4(1), Article 3. https://doi.org/10.17705/1jais.00030
  36. Liu, J., Dai, Q., & Chen, J. (2024). Factors affecting teachers’ use of digital resources for teaching mathematical cultures: An extended UTAUT-2 model. Education and Information Technologies. https://doi.org/10.1007/s10639-024-13105-z
  37. Mailizar, M., Almanthari, A., & Maulina, S. (2021). Examining educators’ behavioral intention to use e-learning in teaching of mathematics: An extended TAM model. Contemporary Educational Technology, 13(2), Article ep298. https://doi.org/10.30935/cedtech/9709
  38. Masango, M. M., Van Ryneveld, L., & Graham, M. A. (2022). A paperless classroom: Importance of training and support in the implementation of electronic textbooks in Gauteng public schools. EJEL, 20(3). https://doi.org/10.34190/ejel.20.3.2320
  39. Matemera, S. (2020). Smart board use and pedagogic practices among educators: A case of a South African township school [PhD dissertation, University of Johannesburg].
  40. Mihai, M. A. (2020). The use of interactive whiteboards in urban Gauteng classrooms. Perspectives in Education, 38(2), 318–336. https://doi.org/10.18820/2519593X/pie.v38.i2.21
  41. Minty, R., & Moll, I. (2020). Gauteng paperless classrooms: Fantasy, fiction, or reality? Mousaion: South African Journal of Information Studies, 38(1). https://doi.org/10.25159/2663-659X/7323
  42. Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for educator knowledge. The Educators College Record, 108(6), 1017–1054. https://doi.org/10.1111/j.1467-9620.2006.00684.x
  43. Mlambo, S., Rambe, P., & Schlebusch, L. (2020). Effects of Gauteng province’s educators’ ICT self-efficacy on their pedagogical use of ICTS in classrooms. Heliyon, 6(4), Article e03730. https://doi.org/10.1016/j.heliyon.2020.e03730
  44. Mokoena, M. M., Simelane-Mnisi, S., & Mji, A. (2022). Challenges and solutions for educators’ use of interactive whiteboards in high schools. Universal Journal of Educational Research, 10(1), 36–47. https://doi.org/10.13189/ujer.2022.100104
  45. Ndlovu, M., & Meyer, D. (2019). Readiness of educators to teach mathematics with technology: A case study of a school in Gauteng. In Proceedings from the 63rd International Council on Education and Teaching Conference (pp. 188–197).
  46. Netsianda, A., & Ramaila, S. (2021). The use of ICT in mathematics teaching and learning in South African secondary schools. In Proceedings of the EDULEARN21 (pp. 623–632). IATED. https://doi.org/10.21125/edulearn.2021.0173
  47. Njiku, J., Mutarutinya, V., & Maniraho, J. F. (2021). Building mathematics educators’ TPACK through collaborative lesson design activities. Contemporary Educational Technology, 13(2), Article ep297. https://doi.org/10.30935/cedtech/9686
  48. Ozudogru, M., & Ozudogru, F. (2019). Technological pedagogical content knowledge of mathematics educators and the effect of demographic variables. Contemporary Educational Technology, 10(1), 1–24. https://doi.org/10.30935/cet.512515
  49. Padayachee, K. (2017). A snapshot survey of ICT integration in South African schools. South African Computer Journal, 29(2), 36–65. https://doi.org/10.18489/sacj.v29i2.463
  50. Perienen, A. (2020). Frameworks for ICT integration in mathematics education-A educator’s perspective. Eurasia Journal of Mathematics, Science and Technology Education, 16(6), Article em1845. https://doi.org/10.29333/ejmste/7803
  51. Plageras, A., Kalovrektis, K., Xenakis, A., & Vavougios, D. (2023). The application of TPACK in the methodology of the flipped classroom and with the evaluation of the UTAUT to measure the impact of stem activities in improving the understanding of concepts of applied sciences. In Proceedings from the 15th International Conference on Education and New Learning Technologies (pp. 569–575). https://doi.org/10.21125/edulearn.2023.0243
  52. Pultoo, A., Bullee, A., Meunier, J. N., Sheoraj, K., Panchoo, S., Naseeven, P., Ujoodha, M., Roocha, V., Rajcoomar, H., & Oojorah, A. (2020). Classe21: Educators’ acceptance of technology-enhanced classroom using the UTAUT model. Journal of Education and Social Sciences, 14(1), 39–48.
  53. Reddy, V., Juan, A., Isdale, K., & Fongwa, S. (2019). Mathematics achievement and the inequality gap: TIMSS 1995 to 2015. In N. Spaull, & J. Jansen (Eds.), South African schooling: The enigma of inequality. Policy Implications of Research in Education, vol 10 (pp. 169–187). Springer. https://doi.org/10.1007/978-3-030-18811-5_9
  54. Saal, P. E., & Graham, M. A. (2023). Comparing the use of educational technology in mathematics education between South African and German schools. Sustainability, 15(6), Article 4798. https://doi.org/10.3390/su15064798
  55. Schmidt, D. A., Baran, E., Thompson, A. D., Mishra, P., Koehler, M. J., & Shin, T. S. (2009). Technological pedagogical content knowledge (TPACK): The development and validation of an assessment instrument for preservice educators. Journal of Research on Technology in Education, 42(2), 123–149. https://doi.org/10.1080/15391523.2009.10782544
  56. Spangenberg, E. D., & De Freitas, G. (2019). Mathematics teachers’ levels of technological pedagogical content knowledge and information and communication technology integration barriers. Pythagoras, 40(1), Article a431. https://doi.org/10.4102/pythagoras.v40i1.431
  57. Stols, G., Ferreira, R., Van der Merwe, A., De Villiers, C., Venter, S., Pelser, A., & Olivier, W. A. (2015). Perceptions and needs of South African mathematics teachers concerning their use of technology for instruction. South African Journal of Education, 35(4), 1–13. https://doi.org/10.15700/saje.v35n4a1209
  58. Streukens, S., & Leroi-Werelds, S. (2023). Multicollinearity: An overview and introduction of ridge PLS-SEM estimation. In H. Latan, F. F. Hair Jr, & R. Noonan (Eds.), Partial least squares path modeling: Basic concepts, methodological issues and applications (pp. 183–207). Springer. https://doi.org/10.1007/978-3-031-37772-3_7
  59. Taimalu, M., & Luik, P. (2019). The impact of beliefs and knowledge on the integration of technology among educator educators: A path analysis. Teaching and Educator Education, 79, 101–110. https://doi.org/10.1016/j.tate.2018.12.012
  60. Tang, X., Yuan, Z., Deng, X., & Xiang, L. (2023). Predicting secondary school mathematics educators’ digital teaching behavior using partial least squares structural equation modeling. Electronic Research Archive, 31(10), 6274–6302. https://doi.org/10.3934/era.2023318
  61. Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach’s alpha. International Journal of Medical Education, 2, 53–55. https://doi.org/10.5116/ijme.4dfb.8dfd
  62. Tosuntaş, Ş. B., Çubukçu, Z., & Beauchamp, G. (2021). A new model for the factors that affect interactive whiteboard usage of educators and its effect on performance. Education and Information Technologies, 26(3), 3575–3592. https://doi.org/10.1007/s10639-021-10428-z
  63. 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
  64. 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. https://doi.org/10.2307/41410412
  65. Wang, X., & Cheng, Z. (2020). Cross-sectional studies: Strengths, weaknesses, and recommendations. Chest, 158(1), S65–S71. https://doi.org/10.1016/j.chest.2020.03.012
  66. Wieber, F., Thürmer, J. L., & Gollwitzer, P. M. (2015). Promoting the translation of intentions into action by implementation intentions: Behavioral effects and physiological correlates. Frontiers in Human Neuroscience, 9. https://doi.org/10.3389/fnhum.2015.00395
  67. Wijaya, T. T., & Weinhandl, R. (2022). Factors influencing students’ continuous intentions for using micro-lectures in the post-COVID-19 period: A modification of the UTAUT-2 approach. Electronics, 11(13), Article 1924. https://doi.org/10.3390/electronics11131924
  68. Wijaya, T. T., Cao, Y., Bernard, M., Rahmadi, I. F., Lavicza, Z., & Surjono, H. D. (2022a). Factors influencing microgame adoption among secondary school mathematics educators supported by structural equation modelling-based research. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.952549
  69. Wijaya, T. T., Cao, Y., Weinhandl, R., Yusron, E., & Lavicza, Z. (2022b). Applying the UTAUT model to understand factors affecting micro-lecture usage by mathematics educators in China. Mathematics, 10(7), Article 1008. https://doi.org/10.3390/math10071008
  70. Wijaya, T. T., Zhou, Y., Houghton, T., Weinhandl, R., Lavicza, Z., & Yusop, F. D. (2022c). Factors affecting the use of digital mathematics textbooks in Indonesia. Mathematics, 10(11), Article 1808. https://doi.org/10.3390/math10111808
  71. Yildiz, E., & Arpaci, I. (2024). Understanding pre-service mathematics teachers’ intentions to use GeoGebra: The role of technological pedagogical content knowledge. Education and Information Technologies, 29, 18817–18838. https://doi.org/10.1007/s10639-024-12614-1
  72. Yuan, Z., Deng, X., Ding, T., Liu, J., & Tan, Q. (2023a). Factors influencing secondary school educators’ usage behavior of dynamic mathematics software: A partial least squares structural equation modeling (PLS-SEM) method. Electronic Research Archive, 31(9), 5649–5684. https://doi.org/10.3934/era.2023287
  73. Yuan, Z., Liu, J., Deng, X., Ding, T., & Wijaya, T. T. (2023b). Facilitating conditions as the biggest factor influencing elementary school educators’ usage behavior of dynamic mathematics software in China. Mathematics, 11(6), Article 1536. https://doi.org/10.3390/math11061536
  74. Zhou, Y., Li, X., & Wijaya, T. T. (2022). Determinants of behavioral intention and use of interactive whiteboard by K-12 educators in remote and rural areas. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.934423
  75. Zulu, M. W. (2020). An exploration of the integration of technology by mathematics educators: The case of 10 schools in KwaZulu-Natal under Umlazi District [PhD dissertation, University of KwaZulu-Natal].