Work Engagement: Global Trends, Bibliometric Analysis (2020-2025)
DOI:
https://doi.org/10.37134/ejoss.vol12.Sp.14.2026Keywords:
Work engagement, Bibliometric analysis, Global trendsAbstract
This study examines global trends in work engagement research from 2020 to 2025 through a bibliometric analysis, with particular attention to Pakistan’s contribution. Using Scopus data, 1,616 publications authored by 5,767 scholars were analyzed. Bibliometric indicators, including citations, h-index, g-index, and m-index, were generated using BiblioMagika and visualized through Microsoft Excel. The findings indicate steady growth in work engagement research, with dominant contributions from Business, Management, Psychology, and Social Sciences. The United States, Spain, and the United Kingdom emerged as leading countries in publication output, while authors such as Bartelmus and Markandya demonstrated high citation impact. Frontiers in Psychology and the International Journal of Environmental Research and Public Health were identified as highly influential journals. Despite the expanding global literature, research output remains concentrated in developed countries, with limited contributions from Pakistan. The study highlights the need for greater geographic diversity, methodological expansion, and integration of related constructs to guide future work engagement research.
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Ahmi, A., & Mohd Herry Mohd Nasir. (2019). Examining the Trend of the Research on eXtensible Business Reporting Language (XBRL): A Bibliometric Review. International Journal of Innovation, Creativity and Change. 5(2), 1145–1167. https://ssrn.com/abstract=3839843
Boubker, O. (2024). From chatting to self-educating: Can AI tools boost student learning outcomes? Expert Systems with Applications, 238. https://doi.org/10.1016/j.eswa.2023.121820
Cascajares, M., Alcayde, A., Salmerón-Manzano, E., & Manzano-Agugliaro, F. (2021). The bibliometric literature on scopus and wos: The medicine and environmental sciences categories as case of study. International Journal of Environmental Research and Public Health, 18(11). https://doi.org/10.3390/ijerph18115851
Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20(1). https://doi.org/10.1186/s41239-023-00411-8
Chiu, T. K. F. (2024). Future research recommendations for transforming higher education with generative AI. Computers and Education: Artificial Intelligence, 6. https://doi.org/10.1016/j.caeai.2023.100197
Dai, W., Lin, J., Jin, F., Li, T., Tsai, Y.-S., Gaševi´gaševi´c, D., & Chen, G. (2023). Can Large Language Models Provide Feedback to Students? A Case Study on ChatGPT. IEEE International Conference on Advanced Learning Technologies (ICALT), Orem, UT, USA, 323–325. https://doi.org/10.1109/ICALT58122.2023.00100.
Faqih, K. M. S. (2022). Investigating the adoption of an innovation using an extended UTAUT model: The case of mobile learning technology. Journal of Theoretical and Applied Information Technology, 100(17), 5600–5625.
Fernández-Batanero, J. M., Montenegro-Rueda, M., Fernández-Cerero, J., & García-Martínez, I. (2022). Assistive technology for the inclusion of students with disabilities: a systematic review. Educational Technology Research and Development, 70(5), 1911–1930. https://doi.org/10.1007/s11423-022-10127-7
Gayed, J. M., Carlon, M. K. J., Oriola, A. M., & Cross, J. S. (2022). Exploring an AI-based writing Assistant’s impact on English language learners. Computers and Education: Artificial Intelligence, 3. https://doi.org/10.1016/j.caeai.2022.100055
Harzing, A.-W, & Alankangas, S. (2015). Google Scholar, Scopus and the Web of Science: A longitudinal and cross-disciplinary comparison. 1–19. https://harzing.com/download/gsscowos.pdf
Ismail, F., Tan, E., Rudolph, J., Crawford, J., & Tan, S. (2023). Artificial intelligence in higher education. A protocol paper for a systematic literature review. Journal of Applied Learning and Teaching, 6(2), 56–63. https://doi.org/10.37074/jalt.2023.6.2.34
Izhar, N. A., Ishak, N. A., & Baharudin, S. M. (2023). A Bibliometric Analysis of 21st Century Learning Using Scopus Database. International Journal of Learning, Teaching and Educational Research, 22(3), 225–240. https://doi.org/10.26803/ijlter.22.3.14
Jain, K., & Raghuram, J. N. V. (2024). Unlocking potential: The impact of AI on education technology. In Multidisciplinary Reviews (Vol. 7, Issue 3). Malque Publishing. https://doi.org/10.31893/multirev.2024049
Knight, S., Dickson-Deane, C., Heggart, K., Kozanoğlu, D. C., Maher, D., Narayan, B., & Zarrabi, F. (2023). Generative AI in the Australian education system: An open data set of stakeholder recommendations and emerging analysis from a public inquiry. Australasian Journal of Educational Technology, 39(5), 101–124. https://doi.org/https://doi.org/10.14742/ajet.8922
Li, H.-F. (2023). Effects of a ChatGPT-based flipped learning guiding approach on learners’ courseware project performances and perceptions. Australasian Journal of Educational Technology, 39(5), 40–58. https://doi.org/https://doi.org/10.14742/ajet.8923
Marzuki, Widiati, U., Rusdin, D., Darwin, & Indrawati, I. (2023). The impact of AI writing tools on the content and organization of students’ writing: EFL teachers’ perspective. Cogent Education, 10(2). https://doi.org/10.1080/2331186X.2023.2236469
Matthews, J., & Volpe, C. R. (2023). Academics’ perceptions of ChatGPT-generated written outputs: A practical application of Turing’s Imitation Game. Australasian Journal of Educational Technology, 39(5), 82–100. https://doi.org/https://doi.org/10.14742/ajet.8896
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & Group, P. (2009). Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. Annals of Internal Medicine, 151(4), 264–269. https://doi.org/10.7326/0003-4819-151-4-200908180-00135
Nguyen Thanh, B., Thi Hong Vo, D., Nguyen Nhat, M., Thu Tra Pham, T., Thai Trung, H., & Ha Xuan, S. (2023). Race with the machines: Assessing the capability of generative AI in solving authentic assessments. Australasian Journal of Educational Technology, 39(5), 59–81. https://doi.org/https://doi.org/10.14742/ajet.8902
Pham, T., Nguyen, B., Ha, S., & Nguyen Ngoc, T. (2023). Digital transformation in engineering education: Exploring the potential of AI-assisted learning. Australasian Journal of Educational Technology, 2023(5), 1–19. https://doi.org/https://doi.org/10.14742/ajet.8825
Pillai, R., Sivathanu, B., Metri, B., & Kaushik, N. (2024). Students’ adoption of AI-based teacher-bots (T-bots) for learning in higher education. Information Technology and People, 37(1), 328–355. https://doi.org/10.1108/ITP-02-2021-0152
Ray, S. S., Peddinti, P. R. T., Verma, R. K., Puppala, H., Kim, B., Singh, A., & Kwon, Y. N. (2024). Leveraging ChatGPT and Bard: What does it convey for water treatment/desalination and harvesting sectors? Desalination, 570. https://doi.org/10.1016/j.desal.2023.117085
Razack, H. I. A., Mathew, S. T., Saad, F. F. A., & Alqahtani, S. A. (2021). Artificial intelligence-assisted tools for redefining the communication landscape of the scholarly world. Science Editing, 8(2), 134–144. https://doi.org/10.6087/kcse.244
Santiago, C. S., Embang, S. I., Acanto, R. B., Ambojia, K. W. P., Aperocho, M. D. B., Balilo, B. B., Cahapin, E. L., Conlu, M. T. N., Lausa, S. M., Laput, E. Y., Malabag, B. A., Paderes, J. J., & Romasanta, J. K. N. (2023). Utilization of Writing Assistance Tools in Research in Selected Higher Learning Institutions in the Philippines: A Text Mining Analysis. International Journal of Learning, Teaching and Educational Research, 20(11), 259–284. https://doi.org/10.26803/ijlter.22.11.14
Tang, A., Li, K. K., Kwok, K. O., Cao, L., Luong, S., & Tam, W. (2023). The importance of transparency: Declaring the use of generative artificial intelligence (AI) in academic writing. In Journal of Nursing Scholarship. John Wiley and Sons Inc. https://doi.org/10.1111/jnu.12938
Thompson, K., Corrin, L., & Lodge, J. M. (2023). AI in tertiary education: progress on research and practice. Australasian Journal of Educational Technology, 39(5), 1–7. https://doi.org/https://doi.org/10.14742/ajet.9251
Van Eck, N. J., & Waltman, L. (2014). Visualizing Bibliometric Networks. In Measuring Scholarly Impact (pp. 285–320). Springer International Publishing. https://doi.org/10.1007/978-3-319-10377-8_13
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