Journal of Research in Science, Mathematics and Technology Education

Exploring Factors Influencing the Acceptance of Dry Lab Technologies in Ghanaian Senior High Schools: IPMA insights on the influence of Technology Familiarity

Andrews Yirenkyi Kwame Agyemang 1 2 *
1 St Joseph College of Education, Bechem – Ghana2 Department of Teacher Education, Faculty of Educational Studies, College of Art and Built Environment, Kwame Nkrumah University of Science and Technology. Kumasi.* Corresponding Author
Download PDF
Journal of Research in Science, Mathematics and Technology Education, Volume 8, Issue SI, June 2025, pp. 103-134
OPEN ACCESS VIEWS: 124 DOWNLOADS: 88 Publication date: 15 Jun 2025
ABSTRACT
Dry labs are becoming increasingly vital for enhancing practical understanding in educational settings, particularly in resource-constrained environments. This study investigated the impact of dry labs on the conceptual understanding of practical chemistry among students in three (3) Category C Senior High Schools in Ghana. This study employed a mixed-methods approach. A total of 135 students participated and were selected through purposive and census sampling methods. The results indicate that dry labs significantly enhance students' understanding of practical chemistry concepts, with performance expectancy, computer familiarity, and social influence playing crucial roles in predicting behavioural intention and actual usage. Resistance to change was found to negatively impact acceptance, whereas gender had no significant effect. The Importance Performance Map Analysis (IPMA) further highlights the influential constructs guiding technology adoption. These findings underscore the importance of addressing resistance to change and leveraging social influence to improve the adoption of dry labs. These insights offer valuable implications for policymakers and educators seeking to integrate virtual labs into the chemistry curriculum in Ghana.
KEYWORDS
dry lab, technology acceptance, practical chemistry
CITATION (APA)
Agyemang, A. Y. K. (2025). Exploring Factors Influencing the Acceptance of Dry Lab Technologies in Ghanaian Senior High Schools: IPMA insights on the influence of Technology Familiarity. Journal of Research in Science, Mathematics and Technology Education, 8(SI), 103-134. https://doi.org/10.31756/jrsmte.415SI
REFERENCES
  1. Abbad, M. M. (2021). Using the UTAUT model to understand students’ usage of e-learning systems in developing countries. Education and Information Technologies, 26(6), 7205–7224. https://doi.org/10.1007/s10639-021-10573-5
  2. Abhishek, N., Kulal, A., Divyashree, M. S., & Dinesh, S. (2023). Effectiveness of MOOCs on learning efficiency of students: a perception study. Journal of Research in Innovative Teaching & Learning, (ahead-of-print). https://doi.org/10.1108/JRIT-12-2022-0091
  3. Alami, Y., & El Idrissi, I. (2022). Students' adoption of e-learning: evidence from a Moroccan business school in the COVID-19 era. Arab Gulf Journal of Scientific Research, 40(1), 54-78. https://doi.org/10.1108/AGJSR-05-2022-0052
  4. Almahasees, Z., & Jaccomard, H. (2020). Facebook translation service (FTS) usage among jordanians during COVID-19 lockdown. Advances in Science, Technology, Engineering Systems Journal, 5(6), 514-519. https://dx.doi.org/10.25046/aj050661
  5. Almaiah, M. A., & Alyoussef, I. Y. (2019). Analysis of the effect of course design, course content support, course assessment and instructor characteristics on the actual use of E-learning system. IEEE Access, 7, 171907-171922. https://doi.org/10.1109/ACCESS.2019.2956349
  6. Al‐Mamary, Y. H., & Al-Shammari, K. K. (2023). Determining factors that can influence the understanding and acceptance of advanced technologies in universities’ teaching and learning. International Journal of Advanced and Applied Sciences, 10(3), 87-95. https://doi.org/10.21833/ijaas.2023.03.012
  7. Alshehri, A. A. M. (2021). The impact of usability, social and organisational factors on students' use of learning management systems in Saudi tertiary education (Doctoral dissertation). Edinburgh Napier University. http://researchrepository.napier.ac.uk/Output/2801449
  8. Altalhi, M. (2021). Toward a model for acceptance of MOOCs in higher education: the modified UTAUT model for Saudi Arabia. Education and Information Technologies, 26(2), 1589-1605. https://doi.org/10.1007/s10639-020-10317-x
  9. Amarantou, V., Kazakopoulou, S., Chatzoudes, D., & Chatzoglou, P. (2018). Resistance to change: an empirical investigation of its antecedents. Journal of Organizational Change Management, 31(2), 426-450. https://doi.org/10.1108/JOCM-05-2017-0196
  10. Ameen, N., Willis, R., Abdullah, M. N., & Shah, M. (2019). Towards the successful integration of e-learning systems in higher education in Iraq: A student perspective. British Journal of Educational Technology, 50(3), 1434–1446. https://doi.org/10.1111/bjet.12651
  11. Benadé, T., & Liebenberg, J. (2017). Investigating the acceptance of digital technologies in an Excel course. In 8th UNISA/ISTE Conference on Mathematics, Science and Technology Education, Mopani Camp, Kruger National Park, Limpopo, South Africa. http://hdl.handle.net/10500/23416
  12. Binyamin, S., Rutter, M., & Smith, S. (2019). Extending the Technology Acceptance Model to Understand Students’ Use of Learning Management Systems in Saudi Higher Education. International Journal of Emerging Technologies in Learning, 14(3), 4-21. https://doi.org/10.3991/ijet.v14i03.9732
  13. Bogusevschi, D., Muntean, C., & Muntean, G. M. (2020). Teaching and learning physics using 3D virtual learning environment: A case study of combined virtual reality and virtual laboratory in secondary school. Journal of Computers in Mathematics and Science Teaching, 39(1), 5-18. https://www.learntechlib.org/primary/p/210965/
  14. Brew, E. A., Nketiah, B., & Koranteng, R. (2021). A literature review of academic performance: An insight into factors and their influences on academic outcomes of students at senior high schools. Open Access Library Journal, 8(6), 1–14. https://doi.org/10.4236/oalib.1107423
  15. Byungura, J. C., Hansson, H., Muparasi, M., & Ruhinda, B. (2018). Familiarity with Technology among First‑Year Students in Rwandan Tertiary Education. Electronic Journal of e-Learning, 16(1), pp30-45.
  16. Chan, P., Van Gerven, T., Dubois, J. L., & Bernaerts, K. (2021). Virtual chemical laboratories: A systematic literature review of research, technologies and instructional design. Computers and Education Open, 2, 100053.. https://doi.org/10.1016/j.caeo.2021.100053
  17. Chatti H and Hadoussa S (2021). Factors affecting the adoption of e-learning technology by students during the COVID-19 quarantine period: The application of the UTAUT model. Engineering, Technology and Applied Science Research, 11(2): 6993-7000. https://doi.org/10.48084/etasr.3985
  18. Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In Modern methods for business research (pp. 295–336). Lawrence Erlbaum Associates. https://doi.org/10.4324/9781410604385-10
  19. Cho, G., Hwang, H., Sarstedt, M., & Ringle, C. M. (2020). Cutoff criteria for overall model fit indexes in generalized structured component analysis. Journal of Marketing Analytics, 8(4), 189–202. https://doi.org/10.1057/s41270-020-00089-1
  20. Cossu, R., Awidi, I., & Nagy, J. (2022). Can we use online technology to rejig the traditional laboratory experience to improve student engagement? Higher Education Pedagogies, 7(1), 1–19. https://doi.org/10.1080/23752696.2022.2068155
  21. Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches (4th ed.). Sage Publications.
  22. Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334. https://doi.org/10.1007/BF02310555
  23. 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.
  24. de Oliveira, L. C., Pinochet, L. H. C., Bueno, R. L. P., & de Oliveira, M. A. (2019). Effect of gaming on behavioral intention to use online training: An adjustment of the UTAUT model applied to TRT-2. Revista de Administração da Universidade Federal de Santa Maria, 12(3), 472-491.
  25. Drueke, B., Mainz, V., Lemos, M., Wirtz, M. A., & Boecker, M. (2021). An evaluation of forced distance learning and teaching under pandemic conditions using the technology acceptance model. Frontiers in Psychology, 12, 4533. https://doi.org/10.3389/fpsyg.2021.701347
  26. Du, L., & Lv, B. (2024). Factors influencing students’ acceptance and use generative artificial intelligence in elementary education: an expansion of the UTAUT model. Education and Information Technologies, 1-20. https://doi.org/10.1007/s10639-024-12835-4
  27. Dubey, P., Pradhan, R.L. and Sahu, K.K. (2023), "Underlying factors of student engagement to E-learning", Journal of Research in Innovative Teaching & Learning, Vol. 16 No. 1, pp. 17-36. https://doi.org/10.1108/JRIT-09-2022-0058
  28. Dwivedi, Y. K., Rana, N. P., Tamilmani, K., & Raman, R. (2020). A meta-analysis based modified unified theory of acceptance and use of technology (meta-UTAUT): A review of emerging literature. Current Opinion in Psychology, 36, 13–18. https://doi.org/10.1016/j.copsyc.2020.03.008
  29. Estriegana, R., Medina-Merodio, J. A., & Barchino, R. (2019). Student acceptance of virtual laboratory and practical work: An extension of the technology acceptance model. Computers & Education, 135, 1–14. https://doi.org/10.1016/j.compedu.2019.02.010
  30. Field, A. (2024). Discovering statistics using IBM SPSS statistics (6th ed.). Sage Publications Limited.
  31. 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
  32. Hair Jr, J. F., Howard, M. C., & Nitzl, C. (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of business research, 109, 101-110. https://doi.org/10.1016/j.jbusres.2019.11.069
  33. Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial least squares structural equation modelling (PLS-SEM) using R: A workbook (p. 197). Springer Nature. https://doi.org/10.1007/978-3-030-80519-7
  34. Hair Jr, J. F., Matthews, L. M., Matthews, R. L., & Sarstedt, M. (2017). PLS-SEM or CB-SEM: Updated guidelines on which method to use. International Journal of Multivariate Data Analysis, 1(2), 107-123. https://doi.org/10.1504/IJMDA.2017.087624
  35. Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24. https://doi.org/10.1108/EBR-11-2018-0203
  36. 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 and Data Systems, 117(3), 442–458. https://doi.org/10.1108/IMDS-04-2016-0130
  37. Haverila, M., Twyford, J. C., & Haverila, K. (2020). Identification of key variables and constructs in the context of wine tasting room: importance-performance analysis. International Journal of Wine Business Research, 33(1), 80–101. https://doi.org/10.1108/ijwbr-02-2020-0006
  38. Hendrajaya, C. T., Brahmasari, I. A., & Ratih, I. A. B. (2024). The influence of effort expectancy, performance expectancy, and social influence on perceived risk, behavioral intention, and actual use moderated by user trust in social commerce in Indonesia. Edelweiss Applied Science and Technology, 8(6), 4683-4699.
  39. Henseler, J., & Fassott, G. (2010). Testing moderating effects in PLS path models: An illustration of available procedures. In V. E. Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares: Concepts, methods and applications (pp. 713-735). Springer. https://doi.org/10.1007/978-3-540-32827-8_31
  40. Hesse, F. W., Kobsda, C., Schemmann, C., GLC, G. L. C., & Austauschdienst eV, D. A. (2022). Digital transformation of higher education: Global learning report 2022. https://doi.org/10.21241/ssoar.79627
  41. Huang, H. H., Chang, M. H., Chen, P. T., Lin, C. L., Sung, P. S., Chen, C. H., & Fan, S. Y. (2024). Exploring factors affecting the acceptance of fall detection technology among older adults and their families: a content analysis. BMC geriatrics, 24(1), 694. https://doi.org/10.1186/s12877-024-05262-0
  42. Jambulingam, M. (2013). Behavioural intention to adopt mobile technology among tertiary students. World Applied Sciences Journal, 22(9), 1262–1271. https://doi.org/10.5829/idosi.wasj.2013.22.09.2748
  43. Kim, J., & Lee, K. S. S. (2022). Conceptual model to predict Filipino teachers’ adoption of ICT-based instruction in class: Using the UTAUT model. Asia Pacific Journal of Education, 42(4), 699–713. https://doi.org/10.1080/02188791.2020.1776213
  44. Kline, R. B. (2023). Response to Leslie Hayduk’s review of Principles and practice of structural equation modelling (4th ed.). Canadian Studies in Population, 45(3–4), 188–195. https://doi.org/10.25336/csp29418
  45. Kock, N. (2017). Common method bias: A full collinearity assessment method for PLS-SEM. In H. Latan & R. Noonan (Eds.), Partial least squares path modeling (pp. 245-257). Springer. https://doi.org/10.1007/978-3-319-64069-3_11
  46. Kolil, V. K., & Achuthan, K. (2024). Virtual labs in chemistry education: A novel approach for increasing student’s laboratory educational consciousness and skills. Education and Information Technologies, 1-25. https://doi.org/10.1007/s10639-024-12858-x
  47. Kumar, R. (2018). Research methodology: A step-by-step guide for beginners (5th ed.). SAGE.
  48. Kumsura, A., Sresteesang, W., & Tongnuch, T. (2024). Cognitive, Affective, and Normative Drivers of Pro-Environmental Intentions Among Urban Forest Visitors–The IPMA Approach. ABAC Journal, 44(4), 76-90. https://doi.org/10.59865/abacj.2024.43
  49. Lapan, S. D., Quartaroli, M. T., & Riemer, F. J. (Eds.). (2011). Qualitative research: An introduction to methods and designs. John Wiley & Sons.
  50. Lazar, I. M., Panisoara, G., & Panisoara, I. O. (2020). Digital technology adoption scale in the blended learning context in higher education: Development, validation and testing of a specific tool. PloSone, 15(7),e0235957. https://doi.org/10.1371/journal.pone.0235957
  51. Manyilizu, M. C. (2023). Effectiveness of virtual laboratory vs. paper-based experiences to the hands-on chemistry practical in Tanzanian secondary schools. Education and Information Technologies, 28(5), 4831-4848. https://doi.org/10.1007/s10639-022-11327-7
  52. Marchewka, J. T., & Kostiwa, K. (2007). An application of the UTAUT model for understanding student perceptions using course management software. Communications of the IIMA, 7(2), Article 10. https://doi.org/10.58729/1941-6687.1038
  53. Merino-Campos, C., del-Castillo, H., & Medina-Merodio, J. A. (2023). Factors affecting the Acceptance of Video Games as a Tool to improve students’ academic performance in Physical Education. Education and Information Technologies, 28(5), 5717-5737. https://doi.org/10.1007/s10639-022-11295-y
  54. Merriam, S. B., & Tisdell, E. J. (2015). Qualitative research: A guide to design and implementation (4th ed.). John Wiley & Sons.
  55. Ministry of Education - Ghana. (2019). Education sector performance report (ESPR) 2019. Planning, Budgeting, Monitoring and Evaluation Unit (PBME). https://ndpc.gov.gh/media/Ministry_of_Education_APR_2019.pdf
  56. Mouli, D. C., Pibulcharoensit, S., & Varghese, M. M. (2023). The application of UTAUT on e-learning usage among physics students of international schools in Bangkok, Thailand. Scholar: Human Sciences, 15(1), 20-29. https://doi.org/10.14456/shserj.2023.3
  57. Mundy, M. A., Kupczynski, L., & Kee, R. (2012). Teacher’s perceptions of technology use in the schools. SAGE Open, 2(1), 2158244012440813. https://doi.org/10.1177/2158244012440813
  58. Munn, M., Knuth, R., Van Horne, K., Shouse, A. W., & Levias, S. (2017). How do you like your science, wet or dry? How two lab experiences influence student understanding of science concepts and perceptions of authentic scientific practice. CBE—Life Sciences Education, 16(2), ar39. https://doi.org/10.1187/cbe.16-04-0158
  59. Ndubisi, N. O., Jantan, M., & Richardson, S. (2001). Is the technology acceptance model valid for entrepreneurs? Model testing and examining usage determinants. Asian Academy of Management Journal, 6(2), 31-54.
  60. Onwuegbuzie, A. J., & Collins, K. M. (2007). A typology of mixed methods sampling designs in social science research. Qualitative Report, 12(2), 281-316. http://www.nova.edu/ssss/QR/QR12-2/onwuegbuzie2.pdf
  61. Ozdem-Yilmaz, Y., & Bilican, K. (2020). Discovery Learning—Jerome Bruner. Science education in theory and practice: An introductory guide to learning theory, 177-190. https://doi.org/10.1007/978-3-030-43620-9_13
  62. Penn, M., & Ramnarain, U. (2019). South African university students’ attitudes towards chemistry learning in a virtually simulated learning environment. Chemistry education research and practice, 20(4), 699-709. https://doi.org/10.1039/C9RP00014C
  63. Radhamani, R., Kumar, D., Nizar, N., Achuthan, K., Nair, B., & Diwakar, S. (2021). What virtual laboratory usage tells us about laboratory skill education pre-and post-COVID-19: Focus on usage, behavior, intention and adoption. Education and information technologies, 26(6), 7477-7495. https://doi.org/10.1007/s10639-021-10583-3
  64. Rahman, H., Wahid, S. A., Ahmad, F., & Ali, N. (2024). Game-based learning in metaverse: Virtual chemistry classroom for chemical bonding for remote education. Education and Information Technologies, 1-25.https://doi.org/10.1007/s10639-024-12575-5
  65. Ringle, C. M., & Sarstedt, M. (2016). Gain more insight from your PLS-SEM results: The importance-performance map analysis. Industrial management & data systems, 116(9), 1865-1886. https://doi.org/10.1108/IMDS-10-2015-0449
  66. Sadykov, T., & Čtrnáctová, H. (2019). Application of interactive methods and technologies of teaching chemistry. Chemistry Teacher International, 1(2), 20180031. https://doi.org/10.1515/cti-2018-0031
  67. Salloum, S. A. S. (2018). Investigating students' acceptance of e-learning system in higher educational environments in the UAE: Applying the extended technology acceptance model (TAM) (Master’s thesis, The British University in Dubai).
  68. Salloum, S. A., & Shaalan, K. (2019). Factors affecting students’ acceptance of e-learning systems in higher education using UTAUT and structural equation modeling approaches. In A. Hassanien, M. Tolba, K. Shaalan, & A. Azar (Eds.), Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2018 (Vol. 845, pp. 469-480). Springer. https://doi.org/10.1007/978-3-319-99010-1_43
  69. Sánchez‐Prieto, J. C., Huang, F., Olmos‐Migueláñez, S., García‐Peñalvo, F. J., & Teo, T. (2019). Exploring the unknown: The effect of resistance to change and attachment on mobile adoption among secondary pre‐service teachers. British Journal of Educational Technology, 50(5), 2433-2449. https://doi.org/10.1111/bjet.12822
  70. Sanghvi, P. (2020). Piaget’s theory of cognitive development: a review. Indian Journal of Mental Health, 7(2), 90-96.
  71. Santos, J., Figueiredo, A. S., & Vieira, M. (2019). Innovative pedagogical practices in higher education: An integrative literature review. Nurse education today, 72, 12-17. https://doi.org/10.1016/j.nedt.2018.10.003
  72. 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
  73. Schuberth, F., Rademaker, M. E., & Henseler, J. (2023). Assessing the overall fit of composite models estimated by partial least squares path modelling. European Journal of Marketing, 57(6), 1678-1702. https://doi.org/10.1108/EJM-08-2020-0586
  74. Shahzad, M. F., Xu, S., & Baheer, R. (2024). Assessing the factors influencing the intention to use information and communication technology implementation and acceptance in China’s education sector. Humanities and Social Sciences Communications, 11(1), 1-15. https://doi.org/10.1057/s41599-024-02777-0
  75. Shana, Z., & Abulibdeh, E. S. (2020). Science practical work and its impact on high students' academic achievement. Journal of Technology and Science Education (JOTSE), 10(2), 199-215. https://doi.org/10.3926/jotse.888
  76. Srijamdee, K., & Pholphirul, P. (2020). Does ICT familiarity always help promote educational outcomes? Empirical evidence from PISA-Thailand. Education and Information Technologies, 25(4), 2933-2970. https://doi.org/10.1007/s10639-019-10089-z
  77. Sypsas, A., & Kalles, D. (2018, November). Virtual laboratories in biology, biotechnology and chemistry education: a literature review. In Proceedings of the 22nd Pan-Hellenic Conference on Informatics (pp. 70-75). https://doi.org/10.1145/3291533.3291560
  78. Trybou, J. (2017). Performance Expectancy, Effort Expectancy and Social Influence as Factors Predicting The Acceptance of (Non-) Fluoroscopy- Guided Positioning For Radiographs, and The Relationship With Leadership, 2016–2017.
  79. 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
  80. Wijenayaka, L. A., & Iqbal, S. S. (2021). Going virtual with practical chemistry amidst the COVID-19 pandemic lockdown: Significance, constraints and implications for future. Asian Association of Open Universities Journal, 16(3), 255-270. https://doi.org/10.1108/AAOUJ-09-2021-0102
  81. Winkelmann, K., Keeney-Kennicutt, W., Fowler, D., & Macik, M. (2017). Development, implementation, and assessment of general chemistry lab experiments performed in the virtual world of second life. Journal of Chemical Education, 94(7), 849-858. https://doi.org/10.1021/acs.jchemed.6b00733
  82. Winkelmann, K., Keeney-Kennicutt, W., Fowler, D., Macik, M. L., Guarda, P. P., & Ahlborn, C. J. (2023). Learning gains and attitudes of students performing chemistry experiments in an immersive virtual world. In Cross Reality (XR) and Immersive Learning Environments (ILEs) in Education (pp. 82-96). Routledge.
  83. Yang, C., Zhang, J., Hu, Y., Yang, X., Chen, M., Shan, M., & Li, L. (2024). The impact of virtual reality on practical skills for students in science and engineering education: a meta-analysis. International Journal of STEM Education, 11(1), 28. https://doi.org/10.1186/s40594-024-00487-2
  84. Yee, M. L. S., & Abdullah, M. S. (2021). A review of UTAUT and extended model as a conceptual framework in education research. Jurnal Pendidikan Sains Dan Matematik Malaysia, 11, 1-20. https://doi.org/10.37134/jpsmm.vol11.sp.1.2021
  85. Yu, X., & Khazanchi, D. (2017). Using embedded mixed methods in studying IS phenomena: Risks and practical remedies with an illustration. Communications of the Association for Information Systems, 41, 23-36. https://doi.org/10.17705/1CAIS.04102
LICENSE
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.