ENHANCING EMPLOYABILITY OUTCOMES THROUGH AI TOOLS: A SEM-SPLS APPROACH WITH TAM AND SOFT SKILLS MEDIATION
Article Sidebar
Main Article Content
Abstract
There has been increased interest in understanding how AI is enhancing people’s ability to secure a good job lately, due to its rapid adoption in schools and workplaces. However, the relationships between how easy AI is to use and how valuable people think it is to its actual usefulness for getting a job are little studied. It examines the relationship between the usability of AI tools, their practical value, and their impact on employability, where soft skills act as a bridge between them. It studies the relationship between factors using Structural Equation Modeling and Partial Least Squares (SEM-PLS), exploring data from 429 users of learning environments. The study highlights significant relationships between constructs that are statistically significant, utilizing the Technology Acceptance Model (TAM). The findings show that the perceived usefulness of AI tools explains nearly a fifth of the changes in soft skills (18.1%) and close to a fifth of the improvements in employability outcomes (19%). In the same way, how easy a technology is to use (AI_EU_TAM) is essential for developing soft skills (β = 0.374, p = 0.000) and for getting a job (β = 0.246, p = 0.000). Having strong soft skills is very important for employment since it affects employability by 0.504 points (p = 0.000). Mediation confirms that soft skills help explain 56.1% of the relationship between AI_PU_TAM and EM and 76.8% of the relationship between AI_EU_TAM and EM. The results offer a unique perspective, demonstrating that the use of AI tools facilitates the development of new skills that support employability, which can inform future studies on online education and employment preparation.
JEL Classification Codes: I21, J24, O33.
Downloads
Article Details
Section
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
References
Abbas Khan, M., Khan, H., Omer, M. F., Ullah, I., & Yasir, M. (2025). Impact of artificial intelligence on the global economy and technology advancements. In S. El Hajjami, K. Kaushik, & I. U. Khan (Eds.), Artificial general intelligence (AGI) security (pp. 147–180). Springer Nature Singapore. https://doi.org/10.1007/978-981-97-3222-7_7
Abulail, R., Badran, O., Shkoukani, M., & Omeish, F. (2025). Exploring the factors influencing AI adoption intentions in higher education. Computer Science and Mathematics. https://doi.org/10.20944/preprints202503.1768.v1
Alazzawi, F. J. I., Shannaq, B., & Al-Zeidi, A. (2025). Predictive modeling for student specialization selection: Analyzing influences and developing tailored recommendations. In K. Arai (Ed.), Advances in information and communication (Vol. 1285, pp. 633–645). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-84460-7_40
Almeida, F., Junça Silva, A., Lopes, S. L., & Braz, I. (2025). Understanding recruiters’ acceptance of artificial intelligence: Insights from the technology acceptance model. Applied Sciences, 15(2), 746. https://doi.org/10.3390/app15020746
Asim, Z. (2022). Shaping healthcare system under industry 5.0: Trends and barriers. Sudan Journal of Medical Sciences, 17(3), 362–364. https://doi.org/10.18502/sjms.v17i3.12115
Asim, Z., & Sorooshian, S. (2022). Supporting management disciplines for research and development in public organizations. Processes, 10(12), 2542. https://doi.org/10.3390/pr10122542
Asim, Z., Sorooshain, S., Sami, W., Mahmood, M., Yusof, Y., Sarwat, S., & Vasudevan, A. (2024). Navigating the educational frontier and unveiling the role of virtual reality in faculty development: An emerging consideration in global accreditation. In Evaluating Global Accreditation Standards for Higher Education (pp. 226–240). IGI Global. https://doi.org/10.4018/979-8-3693-1698-6.ch015
Babashahi, L., Barbosa, C. E., Lima, Y., Lyra, A., Salazar, H., Argôlo, M., Almeida, M. A. D., & Souza, J. M. D. (2024). AI in the workplace: A systematic review of skill transformation in the industry. Administrative Sciences, 14(6), 127. https://doi.org/10.3390/admsci14060127
Badulescu, D., Simut, R., Bodog, S. A., Badulescu, A., Simut, C., & Zapodeanu, D. (2025). Shaping AI-related competencies for the labor market and business: A PLS-SEM approach. International Journal of Computers Communications & Control, 20(1), 1-19. https://doi.org/10.15837/ijccc.2025.1.6894
Bankins, S., Ocampo, A. C., Marrone, M., Restubog, S. L. D., & Woo, S. E. (2024). A multilevel review of artificial intelligence in organizations: Implications for organizational behavior research and practice. Journal of Organizational Behavior, 45(2), 159–182. https://doi.org/10.1002/job.2735
Bobitan, N., Dumitrescu, D., Popa, A. F., Sahlian, D. N., & Turlea, I. C. (2024). Shaping tomorrow: Anticipating skills requirements based on the integration of artificial intelligence in business organizations—A foresight analysis using the scenario method. Electronics, 13(11), 2198. https://doi.org/10.3390/electronics13112198
Buckley, I. A. (2020). The impact of teaching operating systems using two different teaching modalities synchronous online versus traditional face-to-face course delivery. International Journal of Advanced Computer Science and Applications, 11(9), 9-15. https://doi.org/10.14569/IJACSA.2020.0111202
Caggiano, V., Schleutker, K., Petrone, L., & González-Bernal, J. (2020). Towards identifying the soft skills needed in curricula: Finnish and Italian students’ self-evaluations indicate differences between groups. Sustainability, 12(10), 4031. https://doi.org/10.3390/su12104031
Cai, R., Cain, L. N., & Jeon, H. (2022). Customers’ perceptions of hotel AI-enabled voice assistants: Does brand matter? International Journal of Contemporary Hospitality Management, 34(8), 2807–2831. https://doi.org/10.1108/IJCHM-10-2021-1313
Cascella, M., Montomoli, J., Bellini, V., & Bignami, E. (2023). Evaluating the feasibility of ChatGPT in healthcare: An analysis of multiple clinical and research scenarios. Journal of Medical Systems, 47(1), 33. https://doi.org/10.1007/s10916-023-01925-4
Catal, C., Ozcan, A., Donmez, E., & Kasif, A. (2023). Analysis of cybersecurity knowledge gaps based on cyber security body of knowledge. Education and Information Technologies, 28, 1809–1831. https://doi.org/10.1007/s10639-022-11261-8
Ciaschi, M., & Barone, M. (2024, September). Exploring the role of Artificial Intelligence in assessing soft skills. In 2024 19th Conference on Computer Science and Intelligence Systems (FedCSIS) (pp. 573-578). IEEE. https://doi.org/10.15439/2024F2063
Chatterjee, S., Chaudhuri, R., Vrontis, D., Thrassou, A., & Ghosh, S. K. (2021). Adoption of artificial intelligence-integrated CRM systems in agile organizations in India. Technological Forecasting and Social Change, 168, 120783. https://doi.org/10.1016/j.techfore.2021.120783
Chessa, M., Delzanno, G., Ferrando, A., Gelati, L., Guerrini, G., Mascardi, V., Noceti, N., Odone, F., & Vitali, F. (2022). Smart rogaining for computer science orientation. Frontiers in Education, 7, 971027. https://doi.org/10.3389/feduc.2022.971027
Cottafava, D., Cavaglià, G., & Corazza, L. (2019). Education of sustainable development goals through students’ active engagement: A transformative learning experience. Sustainability Accounting, Management and Policy Journal, 10(3), 521–544. https://doi.org/10.1108/SAMPJ-05-2018-0152
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. https://doi.org/10.1287/mnsc.35.8.982
Falebita, O. S., & Kok, P. J. (2024). Artificial intelligence tools usage: A structural equation modeling of undergraduates’ technological readiness, self-efficacy and attitudes. Journal for STEM Education Research, 1-26. https://doi.org/10.1007/s41979-024-00132-1
Fitriani, H., & Ajayi, S. (2022). Preparing Indonesian civil engineering graduates for the world of work. Industry and Higher Education, 36, 471–487. https://doi.org/10.1177/09504222211046187
Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial least squares structural equation modeling (PLS-SEM) using R: A workbook (p. 197). Springer Nature. https://doi.org/10.1007/978-3-030-80519-7
Hair, J. F., L.D.S. Gabriel, M., Da Silva, D., & Braga Junior, S. (2019a). Development and validation of attitudes measurement scales: Fundamental and practical aspects. RAUSP Management Journal, 54(4), 490-507. https://doi.org/10.1108/RAUSP-05-2019-0098
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019b). 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
Jackson, D., Lambert, C., Sibson, R., Bridgstock, R., & Tofa, M. (2024). Student employability-building activities: participation and contribution to graduate outcomes. Higher Education Research & Development, 43(6), 1308–1324. https://doi.org/10.1080/07294360.2024.2325154
Jawarneh, M., Alshar′ e, M., Dewi, D. A., Al Nasar, M., Almajed, R., & Ibrahim, A. (2023). The impact of virtual reality technology on Jordan’s learning environment and medical informatics among physicians. International Journal of Computer Games Technology, 2023(1), 1678226. https://doi.org/10.1155/2023/8464298
Jędrych, E., & Rzepka, A. (Eds.). (2024). Organizational Development, Innovation, and Economy 5.0: Challenges in the Digital Era. Taylor & Francis. https://doi.org/10.4324/9781003502272
Khatri, B., Arora, S., & Khan, S. A. (2024). Analysis of servant leadership and organizational citizenship behaviour among social service sector employees. In Organizational Behavior and Human Resource Management for Complex Work Environments (pp. 443–465). IGI Global. https://doi.org/10.4018/979-8-3693-3466-9.ch021
Khatri, B., Sharma, N., Khan, S. A., & Nandini, G. (2024). Navigating employer branding in the digital age: Exploring the impact of social media networking during the COVID-19 pandemic. In Global Practices on Effective Talent Acquisition and Retention (pp. 313–332). IGI Global. https://doi.org/10.4018/979-8-3693-1938-3.ch017
Koc, E., Hatipoglu, S., Kivrak, O., Celik, C., & Koc, K. (2023). Houston, we have a problem!: The use of ChatGPT in responding to customer complaints. Technology in Society, 74, 102333. https://doi.org/10.1016/j.techsoc.2023.102333
Kuang, Y.-R., Zou, M.-X., Niu, H.-Q., Zheng, B.-Y., Zhang, T.-L., & Zheng, B.-W. (2023). ChatGPT encounters multiple opportunities and challenges in neurosurgery. International Journal of Surgery (London, England), 109(10), 2886–2891. https://doi.org/10.1097/JS9.0000000000000571
Kumar, M. N., Khan, S. A., & Suresh, N. (2025). Accreditation's influence on student career choices and employability: The Indian experience. In Global Perspectives on Quality Management and Accreditation in Higher Education (pp. 251–282). IGI Global. https://doi.org/10.4018/979-8-3693-9481-6.ch011
Liu, G., & Ma, C. (2024). Measuring EFL learners’ use of ChatGPT in informal digital learning of English based on the technology acceptance model. Innovation in Language Learning and Teaching, 18(2), 125-138. https://doi.org/10.1080/17501229.2023.2240316
Liu, K., & Tao, D. (2022). The roles of trust, personalization, loss of privacy, and anthropomorphism in public acceptance of smart healthcare services. Computers in Human Behavior, 127, 107026. https://doi.org/10.1016/j.chb.2021.107026
Mäkelä, E., & Stephany, F. (2024). Complement or substitute? How AI increases the demand for human skills (Version 3). arXiv. https://doi.org/10.48550/ARXIV.2412.19754
Mitchell, A., & Vaughan, A. G. (2022). Implementing team-based learning: Findings from a database. Journal of Information Technology Education: Innovations in Practice, 21, 1–23. https://doi.org/10.28945/4903
Moundy, K., Chafiq, N., & Talbi, M. (2022). Digital textbook and flipped classroom: Experimentation of the self-learning method based on the development of soft skills and disciplinary knowledge. International Journal of Emerging Technologies in Learning, 17(7), 240–259. https://doi.org/10.3991/ijet.v17i07.28933
Pillai, R., & Sivathanu, B. (2020). Adoption of AI-based chatbots for hospitality and tourism. International Journal of Contemporary Hospitality Management, 32(10), 3199–3226. https://doi.org/10.1108/IJCHM-04-2020-0259
Pillai, R., Sivathanu, B., Metri, B., & Kaushik, N. (2023). Students’ adoption of AI-based teacher-bots (T-bots) for learning in higher education. Information Technology & People, 37(1), 328–355. https://doi.org/10.1108/ITP-02-2021-0152
Rahimzadeh, V., Kostick-Quenet, K., Blumenthal Barby, J., & McGuire, A. L. (2023). Ethics education for healthcare professionals in the era of ChatGPT and other large language models: Do we still need it? American Journal of Bioethics, 23(10), 17–27. https://doi.org/10.1080/15265161.2023.2233358
Sahin, Y. G., & Celikkan, U. (2020). Information technology asymmetry and gaps between higher education institutions and industry. Journal of Information Technology Education: Research, 19, 339–365. https://doi.org/10.28945/4553
Satpathy, S., Dash, K. K., & Mohapatra, M. (2020). A study on the new design thinking for industrial revolution 4.0, requirements, and graduate readiness. Rupkatha Journal on Interdisciplinary Studies in Humanities, 12(4). https://doi.org/10.21659/rupkatha.v12n4.09
Scott, F. J., & Willison, D. (2021). Students’ reflections on an employability skills provision. Journal of Further and Higher Education, 45, 1118–1133. https://doi.org/10.1080/0309877X.2021.1928025
Shaikh, A. A., & Karjaluoto, H. (2015). Mobile banking adoption: A literature review. Telematics and Informatics, 32(1), 129–142. https://doi.org/10.1016/j.tele.2014.05.003
Siddoo, V., Sawattawee, J., Janchai, W., & Thinnukool, O. (2019). An exploratory study of digital workforce competency in Thailand. Heliyon, 5(5), e01723. https://doi.org/10.1016/j.heliyon.2019.e01723
Szilárd, S., Benedek, A., & Ionel-Cioca, L. (2018). Soft skills development needs and methods in micro-companies of ICT sector. Procedia Social and Behavioral Sciences, 238, 94–103. https://doi.org/10.1016/j.sbspro.2018.03.012
Subramanian, Y. R., & R, R. (2024). The transformative role of artificial intelligence in human resource. International Journal on Recent Trends in Business and Tourism, 08(02), 14–25. https://doi.org/10.31674/ijrtbt.2024.v08i02.002
Survey: TAM and Soft Skills. (2025). Google Docs. https://docs.google.com/forms/d/e/1FAIpQLScv8fw0c8-49mPJGWNF3W7oVYy87RN_DS-8iQ6rjdAhEGV4xA/viewform?usp=dialog
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
Wang, C., Ahmad, S. F., Bani Ahmad Ayassrah, A. Y. A., Awwad, E. M., Irshad, M., Ali, Y. A., Al-Razgan, M., Khan, Y., & Han, H. (2023). An empirical evaluation of technology acceptance model for artificial intelligence in e-commerce. Heliyon, 9(8), e18349. https://doi.org/10.1016/j.heliyon.2023.e18349