Artificial Intelligence (AI), machine learning, and automation are rapidly advancing, significantly elevating the role of IT within business processes. From an HRM perspective, emerging AI-based solutions are increasingly relied upon in terms of processing time-consuming and complex tasks within the HRM functionalities. This study tackles the phenomenon of AI-based applications in HRM diffusion and adoption: specifically, the association between the HR roles emphasised within the organisation and the attitude of HR practitioners toward AI adoption, as well as the significance of performance expectancy, top management support, and competitive pressures as predictors of AI adoption in HRM. The study sample consisted of 186 senior HR professionals drawn from members of the Jordanian Human Resources Management Association. Results revealed that top management support and performance expectancy are significant predictors of the intention to adopt AI, while competitive pressure did not turn out to have a significant association with such an intention. For the HR roles emphasised, a significant positive influence on the intention to adopt AI has been found for the HR role of “change agent”, while the “employee champion” role possesses a significant negative influence in terms of AI adoption. Considering the noticeable research gap in AI diffusion and adoption within HRM, the study findings provide an important contribution to investigating and explaining this phenomenon. It reveals that HR leaders have a positive mindset toward the potential role of AI in enhancing HRM efficiency and quality.
Artificial Intelligence ; Human Resource Information Systems ; Information Technology Adoption
Bagozzi, R., Yi, Y. (1988), On the evaluation of structure equation models, Journal of the Academy of Marketing Science, 16, 74-94. https://doi.org/10.1007/BF02723327
Bhatiasevi, V., Naglis, M. (2018), Elucidating the determinants of business intelligence adoption and organizational performance, Information Development, 23(1), 78-96. https://doi.org/10.1177/0266666918811394
Boz, H., Kose, U. (2018), Emotion extraction from facial expressions by using Artificial Intelligence techniques, BRAIN– Broad Research in Artificial Intelligence and Neuroscience, 9(1), 5-16.
Buzko, I., Dyachenko, Y., Petrova, M., Nenkov, N., Tuleninova, D., Koeva, K. (2016), Artificial Intelligence technologies in human resource development, Computer Modelling and New Technologies, 20(2), 26-29.
Chang, N. (2010), The application of neural network to the allocation of enterprise human resources, 2010 2nd International Conference on E-Business and Information System Security, EBISS2010, 249-252. https://doi.org/10.1109/EBISS.2010.5473417
Chen, L.F., Chien, C.F. (2011), Manufacturing intelligence for class prediction and rule generation to support human capital decisions for high-tech industries, Flexible Services and Manufacturing Journal, 23(3), 263-289. https://doi.org/10.1007/s10696-010-9068-x
Chin, W.W. (1998), Issues and opinion on structural equation modeling, MIS Quarterly: Management Information Systems, 22(1).
Chiu, C.-Y., Chen, S., Chen, C.-L. (2017), An integrated perspective of TOE framework and innovation diffusion in broadband mobile applications adoption by enterprises, International Journal of Management, Economics and Social Sciences, 6(1), 14-39.
Chong, A.Y.L., Chan, F.T.S. (2012), Structural equation modeling for multi-stage analysis on Radio Frequency Identification (RFID) diffusion in the health care industry, Expert Systems with Applications, 39(10), 8645-8654. https://doi.org/10.1016/j.eswa.2012.01.201
Daramola, J.O., Oladipupo, O.O., Musa, A.G. (2010), A fuzzy expert system (FES) tool for online personnel recruitments, International Journal of Business Information Systems, 6(4), 444-462. https://doi.org/10.1504/IJBIS.2010.035741
Ellig, B.R. (1997), Is the human resource function neglecting the employees?, Human Resource Management, 36(1), 91-95. https://doi.org/10.1002/(sici)1099-050x(199721)36:1<91::aidhrm15>3.3.co;2-u
Fornell, C., Larcker, D.F. (1981), Structural Equation models with unobservable variables and measurement error: Algebra and statistics, Journal of Marketing Research, 18(3), 382. https://doi.org/10.2307/3150980
Gardner, S.D., Lepak, D.P., Bartol, K.M. (2003), Virtual HR: The impact of information technology on the human resource professional, Journal of Vocational Behavior, 63(2), 159-179. https://doi.org/10.1016/S0001-8791(03)00039-3
Hair, J.F., da Silva Gabriel, M.L.D., Patel, V.K. (2014), AMOS Covariance-based Structural Equation Modeling (CB-SEM): Guidelines on its application as a marketing research tool, Revista Brasileira de Marketing, 13(02), 44-55. https://doi.org/10.5585/remark.v13i2.2718
Hempel, P.S. (2004), Preparing the HR profession for technology and information work, Human Resource Management, 43(2-3), 163-177. https://doi.org/10.1002/hrm.20013
Huang, L.C., Huang, K.S., Huang, H.P., Jaw, B.S. (2004), Applying fuzzy neural network in human resource selection system, Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS, 1, 169-174.
Jing, H. (2009), Application of fuzzy data mining algorithm in performance evaluation of human resource, IFCSTA 2009 Proceedings – 2009 International Forum on Computer Science-Technology and Applications, 1, 343-346. https://doi.org/10.1109/IFCSTA.2009.90
Kabak, M., Burmaoğlu, S., Kazançoğlu, Y. (2012), A fuzzy hybrid MCDM approach for professional selection, Expert Systems with Applications, 39(3), 3516-3525.https://doi.org/10.1016/j.eswa.2011.09.042
Lee, J. (2004), Discriminant analysis of technology adoption behavior: A case of internet technologies in small businesses, Journal of Computer Information Systems, 44(4), 57-66. https://doi. org/10.1080/08874417.2004.11647596
Lee, Y. T. (2010), Exploring high-performers’ required competencies, Expert Systems with Applications, 37(1), 434-439. https://doi.org/10.1016/j.eswa.2009.05.064
Lin, H.F. (2013), Understanding the determinants of electronic supply chain management system adoption: Using the technology-organization-environment framework, Technological Forecasting and Social Change, 80-92. https://doi.org/10.1016/j.techfore.2013.09.001
Lin, H.T. (2010), Personnel selection using analytic network process and fuzzy data envelopment analysis approaches, Computers and Industrial Engineering, 59(4), 937-944. https://doi.org/10.1016/j.cie.2010.09.004
Lin, A., Chen, N.C. (2012), Cloud computing as an innovation: Percepetion, attitude, and adoption, International Journal of Information Management, 32(6), 533-540. https://doi.org/10.1016/j.ijinfomgt.2012.04.001
Low, C., Chen, Y., Wu, M. (2011), Understanding the determinants of cloud computing adoption, Industrial Management and Data Systems, 111(7), 1006-1023. https://doi.org/10.1108/02635571111161262
Martins, R., Oliveira, T., Thomas, M.A. (2016), An empirical analysis to assess the determinants of SaaS diffusion in firms, Computers in Human Behavior, 62, 19-33. https://doi.org/10.1016/j.chb.2016.03.049
Mehrabad, S.M., Brojeny, F. (2007), The development of an expert system for effective selection and appointment of the jobs applicants in human resource management, Computers and Industrial Engineering, 53(2), 306-312. https://doi.org/10.1016/j.cie.2007.06.023
Obeidat, S.M. (2016), The link between e-HRM use and HRM effectiveness: An empirical study, Personnel Review, 45(6), 1281-1301. https://doi.org/10.1108/PR-04-2015-0111
Oliveira, T., Martins, M.F. (2010), Understanding e-business adoption across industries in European countries, Industrial Management and Data Systems, 110(9), 1337-1354. https://doi.org/10.1108/02635571011087428
Oliveira, T., Thomas, M., Espadanal, M. (2014), Assessing the determinants of cloud computing adoption: An analysis of the manufacturing and services sectors, Information and Management, 51(5), 497-510. https://doi.org/10.1016/j.im.2014.03.006
Panayotopoulou, L., Vakola, M., Galanaki, E. (2007), E-HR adoption and the role of HRM: Evidence from Greece, Personnel Review, 36(2), 277-294. https://doi.org/10.1108/00483480710726145
Premkumar, G., Ramamurthy, K. (1995), The Role of interorganizational and organizational factors on the decision mode for adoption of interorganizational systems, Decision Sciences, 26(3), 303-336. https://doi.org/10.1111/j.1540-5915.1995.tb01431.x
Premkumar, G., Roberts, M. (1999), Adoption of new information technologies in rural small businesses, Omega, 27(4), 467-484. https://doi.org/10.1016/S0305-0483(98)00071-1
Puklavec, B., Oliveira, T., Popovič, A. (2018), Understanding the determinants of business intelligence system adoption stages an empirical study of SMEs, Industrial Management and Data Systems, 118(1), 236-261. https://doi.org/10.1108/IMDS-05-2017-0170
Ramdani, B., Kawalek, P., Lorenzo, O. (2009), Predicting SMEs’ adoption of enterprise systems, Journal of Enterprise Information Management, 22, 10-24. https://doi.org/10.1108/17410390910922796
Rashid, T.A., Jabar, A.L. (2016), Improvement on predicting employee behaviour through intelligent techniques, IET Networks, 5(5), 136-142. https://doi.org/10.1049/iet-net.2015.0106
Sivaram, N., Ramar, K. (2010), Applicability of clustering and classification algorithms for recruitment data mining, International Journal of Computer Applications, 4(5),23-28. https://doi.org/10.5120/823-1165
Sivathanu, B., Pillai, R. (2018), Smart HR 4.0 – how industry 4.0 is disrupting HR, Human Resource Management International Digest, 26(4), 7-11. https://doi.org/10.1108/HRMID-04-2018-0059
Straub, D.W. (1989), Validating instruments in MIS research, MIS Quarterly: Management Information Systems, 13(2), 147--165. https://doi.org/10.2307/248922
Strohmeier, S., Franca, P. (2015), Artificial Intelligence techniques in human resource management – a conceptual exploration, In: C. Kahraman, S., Çevik Onar (Eds.), Intelligent techniques in engineering management (pp. 149-172), Cham: Springer. https://doi.org/10.1007/978-3-319-17906-3
Strohmeier, S., Piazza, F. (2013), Domain driven data mining in human resource management: A review of current research, Expert Systems with Applications, 40(7), 2410-2420. https://doi.org/10.1016/j.eswa.2012.10.059
Sun, S., Cegielski, C.G., Jia, L., Hall, D.J. (2018), Understanding the factors affecting the organizational adoption of big data, Journal of Computer Information Systems, 58(3), 193-203. https://doi.org/10.1080/08874417.2016.1222891
Tai, W.S., Hsu, C.C. (2006) A realistic personel selection tool based on fuzzy data mining method, Proceedings of the 9th Joint Conference on Information Sciences, JCIS 2006, 2006. https://doi.org/10.2991/jcis.2006.46
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), 3-18. https://doi.org/10.1080/10494821003714632
Tung, K.Y., Huang, I.C., Chen, S.L., Shih, C.T. (2005), Mining the generation Xers’ job attitudes by artificial neural network and decision tree – Empirical evidence in Taiwan, Expert Systems with Applications, 29(4), 783-794. https://doi.org/10.1016/j.eswa.2005.06.012
Ulrich, D. (1997), HR of the future, Human Resource Management, 36(1), 175-179. https://doi.org/10.1002/(SICI)10 9 9 - 0 50X(19 9721)36:1<175::AIDHRM28>3.0.CO;2-9
Ulrich, D. (1998), A new mandate for human resources, Harvard Business Review, 76(1), 124-134.
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.
Venkatesh, V., Thong, J.Y.L.T., Xu, X. (2012), Consumer acceptance and use of IT, MIS Quarterly, 36(1), 157-178.
Voermans, M., Van Veldhoven, M. (2007), Attitude towards E-HRM: An empirical study at Philips, Personnel Review, 36(6), 887-902. https://doi.org/10.1108/00483480710822418
Wang, Y.M., Wang, Y.S., Yang, Y.F. (2010), Understanding the determinants of RFID adoption in the manufacturing industry, Technological Forecasting and Social Change, 77(5), 803-815. https://doi.org/10.1016/j.techfore.2010.03.006
Wang, Y.S., Li, H.T., Li, C.R., Zhang, D.Z. (2016), Factors affecting hotels’ adoption of mobile reservation systems: A technology-organization-environment framework, Tourism Management, 53, 163-172. https://doi.org/10.1016/j.tourman.2015.09.021
Warshaw, P.R., Davis, F.D. (1985), Disentangling behavioral intention and behavioral expectation, Journal of Experimental Social Psychology, 21(3), 213-228. https://doi.org/10.1016/0022-1031(85)90017-4
Wu, W.W. (2009), Exploring core competencies for RandD technical professionals, Expert Systems with Applications, 36(5), 9574-9579. https://doi.org/10.1016/j.eswa.2008.07.052
Yang, Z., Sun, J., Zhang, Y., Wang, Y. (2015), Understanding SaaS adoption from the perspective of organizational users: A tripod readiness model, Computers in Human Behavior, 45, 254-264. https://doi.org/10.1016/j.chb.2014.12.022
Zhao, X. (2008), A study of performance evaluation of HRM: Based on data mining, Proceedings – 2008 International Seminar on Future Information Technology and Management Engineering, FITME 2008, 45-48. https://doi.org/10.1109/FITME.2008.133
AffiliationUniversity of Debrecen, Debrecen, Hungary Hungary
Bio Statement (e.g., department and rank)
Bilal Hmoud is a Ph.D. student at the Department of Business Informatics, Faculty of Economics and Business, University of Debrecen, Debrecen, Hungary. He received his master’s degree in Human Resources Counselling from the University of Pécs, Pécs, Hungary. His current research interests pertain to the application of artificial intelligence in human resource management and the adoption thereof. He has over seven years of professional work experience in the human resource management field at regional level in the Middle East, specialising in the area of human resource management development.