Exploring the nexus of artificial intelligence in talent acquisition: Unravelling cost-benefit dynamics, seizing opportunities, and mitigating risks
-
DOIhttp://dx.doi.org/10.21511/ppm.22(1).2024.37
-
Article InfoVolume 22 2024, Issue #1, pp. 462-476
- Cited by
- 325 Views
-
183 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
The rise in talent management complications led organizations to rely on the latest technologies to automate their routine HRM tasks through AI. This study proposed to examine fundamental aspects of AI in talent acquisition (cost-benefit, opportunities, and risk factors) from the context of strategic analysis and decision-making. 52 respondents from HRM and the information technology departments from fifteen large dairy enterprises, each with more than one thousand employees, were included in the focus group discussion. Both departments were included in the focus group discussion as they heavily employ AI in talent acquisition. The opinions were collected in multiple rounds based on the cost, benefit, opportunity, and risk criteria using the analytical hierarchy process, a multi-criteria decision-making framework. The findings demonstrated that most respondents opinioned AI supports talent acquisition with many opportunities (38.7%) that involve the identification of the best applicants (18.7%) and different benefits (33.2%) to the organization in the form of saving time and cost (16.1%) leading to higher efficacy. The study infers that the application of AI in HRM significantly contributes to talent acquisition, streamlining processes, improving efficiency, and enhancing decision-making. The study recommends that implementing AI in talent acquisition requires a strategic approach, and organizations need to consider factors such as data privacy, ethical use of AI, and ongoing training to ensure successful integration into their hiring processes. Additionally, regular monitoring and adjustments are essential to optimize the effectiveness of AI tools in talent acquisition.
Acknowledgment
The authors of this article would like to thank Prince Sultan University for its financial and academic support for this publication.
- Keywords
-
JEL Classification (Paper profile tab)O15, O31, O32
-
References82
-
Tables1
-
Figures5
-
- Figure 1. Theoretical framework for AI-based HRM applications
- Figure 2. Priority weight for criteria of AI-based application
- Figure 3. Priority weight for alternatives of AI-based application
- Figure 4. Performance sensitivity analysis
- Figure 5. Performance sensitivity analysis
-
- Table 1. Measurement scale used in the questionnaire
-
- Abbasi, M. S., Chandio, F. H., Soomro, A. F., & Shah, F. (2011). Social influence, voluntariness, experience and the internet acceptance: An extension of technology acceptance model within a South-Asian country context. Journal of Enterprise Information Management, 24(1), 30-52.
- Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.
- Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting behavior. Englewood Cliffs, NJ: Prentice Hall.
- Akash, B., & Anusha, D. K. (2018). Recruitment Chatbot. International Research Journal of Engineering and Technology, 5(8), 1248-1250.
- Albert, E. T. (2019). AI in talent acquisition: a review of AI-applications used in recruitment and selection. Strategic HR Review, 18(5), 215-221.
- AlMuhaideb, S., Alswailem, O., Alsubaie, N., Ferwana, I., & Alnajem, A. (2019). Prediction of hospital no-show appointments through artificial intelligence algorithms. Annals of Saudi Medicine, 39(6), 373-381.
- Arthur, J. B. (1992). The link between business strategy and industrial relations systems in American steel minimills. Industrial and Labor Relations Review, 45(3), 488-506.
- Arthur, J. B. (1994). Effects of human resource systems on manufacturing performance. Academy of Management Journal, 37(3), 670-687.
- Awa, H. O., Ojiabo, O. U., & Orokor, L. E. (2017a). Integrated technology-organization-environment (T-O-E) taxonomies for technology adoption. Journal of Enterprise Information Management, 30(6), 893-921.
- Awa, H. O., Ukoha, O., & Igwe, S. R. (2017b). Revisiting technology-organization-environment (T-O-E) theory for enriched applicability. The Bottom Line, 30(1), 2-22.
- Bersin, R. J. (2018). Talent trends technology disruptions for 2018. Deloitte.
- Bouchikhi, H., & Kimberly, J. R. (2008). The soul of the corporation: How to manage the identity of your company. New Delhi: Pearson Education Inc.
- 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.
- Bugg, K. (2015). Best practices for talent acquisition in 21st-century academic libraries. Library Leadership & Management, 29(4), 1-14.
- Bumblauskas, D., Gemmill, D., Igou, A., & Anzengruber, J. (2017). Smart maintenance decision support systems (SMDSS) based on corporate big data analytics. Expert Systems with Applications, 90, 303-317.
- Burkus, D., & Osula, B. (2011). Faulty Intel in the war for talent: Replacing the assumptions of talent management with evidence-based strategies. Journal of Business Studies Quarterly, 3(2), 1-9.
- Buzko, I., Dyachenko, Y., Petrova, M., Nenkov, N., Tuleninova, D., & Koeva, K. (2016). Artificial intelligence technologies in human resource development. Computer Modelling & New Technologies, 20(2), 26-29.
- Celik, D. (2016). Towards a semantic-based information extraction system for matching résumés to job openings. Turkish Journal of Electrical Engineering & Computer Sciences, 24(1), 141-159.
- Chang, N. (2010). The application of neural network to the allocation of enterprise human resources. 2nd International Conference on E-Business and Information System Security, EBISS2010 (pp. 249-252). Wuhan, China.
- Chatman, J. (1989). Improving interactional organizational research: A model of person-organization fit. The Academy of Management Review, 14(3), 333-349.
- 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.
- Chikhaoui, E., & Mehar, S. (2020). Artificial intelligence (AI) collides with patent law. Journal of Legal, Ethical and Regulatory Issues, 23(2).
- Cooke, F. L. (2018). Concepts, contexts, and mindsets: Putting human resource management research in perspectives. Human Resource Management Journal, 28(1), 1-3.
- Cooper, W., Leavitt, H. J., & Shelly, M. W. I. (1964). New perspectives in organization research. New York: John Wiley.
- Coyle, G. (2004). Practical strategy: Structured tools and techniques. Pearson Education.
- Crowe, T. J., Noble, J. S., & Machimada, J. S. (1998). Multi-attribute analysis of ISO 9000 registration using AHP. International Journal of Quality & Reliability Management, 15(2), 205-222.
- 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.
- Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(2), 319-340.
- Delery, J. E., & Doty, D. H. (1996). Modes of theorizing in strategic human resource management: Tests of universalistic, contingency, and configurational performance predictions. Academy of Management Journal, 39(4), 802-835.
- Depietro, R., Wiarda, E., & Fleischer, M. (1990). The context for change: organization, technology and environment. The processes of technological innovation. In The Processes of Technological Innovation, 199 (pp. 151-175). Lexington Books.
- Doty, D. H., Glick, W. H., & Huber, G. P. (1993). Fit, equifinality, and organizational effectiveness: A test of two configurational theories. Academy of Management Journal, 36(6), 1196-1250.
- Dursun, M., & Karsak, E. E. (2010). A fuzzy MCDM approach for personnel selection. Expert Systems with Applications, 37(6), 4324-4330.
- Faisal, S., & Naushad, M. (2020). An overview of green HRM practices among SMEs in Saudi Arabia. Entrepreneurship and Sustainability Issues, 8(2), 1228-1244.
- Fitz-Enz, J. (2000). The ROI human capital: Measuring the economic value of employee performance. New York, NY: American Management Association.
- Goodhue, D. L., & Thompson, R. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213-236.
- Huselid, M. A. (1995). The impact of human resource management practices on turnover, productivity, and corporate financial performance. Academy of Management Journal, 38(3), 635-672.
- Jani, M., & Saiyed, R. (2017). Person-organization fit: Gaining competitive advantage through successful talent acquisition. Apeejay-Journal of Management Sciences and Technology, 4(2), 54-60.
- Jia, Q., Guo, Y., Li, R., Li, Y., & Chen, Y. A. (2018). Conceptual artificial intelligence application framework in human resource management. Proceedings of the International Conference on Electronic Business (pp. 106-114). ICEB, Guilin, China.
- Jiang, F., Li, J., Du, M., & Wang, F. (2018). Research on the application of artificial intelligence technology in human resource management. 2nd International Conference on Systems, Computing, and Applications (SYSTCA 2018) (pp. 176-179).
- Jiang, K., & Messersmith, J. (2018). On the shoulders of giants: A meta-review of strategic human resource management. The International Journal of Human Resource Management, 29(1), 6-33.
- Jing, H. (2009). Application of fuzzy data mining algorithm in performance evaluation of human resource. 2009 International Forum on Computer Science-Technology and Applications (pp. 343-346). Chongqing, China.
- Kabak, M., Burmaoğlu, S., & Kazançoğlu, Y. (2012). A fuzzy hybrid MCDM approach for professional selection. Expert Systems with Applications, 39(3), 3516-3525.
- Kannan, G. (2009). Fuzzy approach for the selection of third party reverse logistics provider. Asia Pacific Journal of Marketing and Logistics, 21(3), 397-416.
- Kantardzic, M. (2011). Data mining: Concepts, models, methods, and algorithms. Hoboken, NJ: IEEE Press and John Wiley.
- Khan, S. (2021). Investment priorities of northeast Indian SMEs family business entrepreneurs based on BOCR aspects. Accounting, 7(2), 469-478.
- Khan, S., Honnutagi, A. Z., & Kumar, V. K. (2011). Prioritization of green IT parameters for Indian IT industry: Using analytical hierarchy process. World Journal of Social Sciences (WJSS), 1(4), 179-194.
- Khan, S., Khan, M. S. A., & Kumar, C. S. (2015). Multi-criteria decision in the adoption of cloud computing services for SME’s based on BOCR Analysis. Asian Journal of Management Research (AJMR), 5(4), 606-619.
- Kristof, A. L. (1996). Person-organization fit: An integrative review of its conceptualizations, measurement and implications. Personnel Psychology, 49(1), 1-49.
- Kwon, H. K., & Seo, K. K. (2014). A fuzzy AHP based multi-criteria decision-making model to select a cloud service. International Journal of Smart Home, 8(3), 175-180.
- Lee, D. Y., & Lehto, M. R. (2013). User acceptance of YouTube for procedural learning: An extension of the technology acceptance model. Computers & Education, 61, 193-208.
- Lee, S. G., Trimi, S., & Kim, C. (2013). The impact of cultural differences on technology adoption. Journal of World Business, 48(1), 20-29.
- Lee, Y. T. (2010). Exploring high-performers’ required competencies. Expert Systems with Applications, 37(1), 434-439.
- Miles, R. E., & Snow, C. C. (1978). Organizational strategy, structure and process. New York: McGraw-Hill.
- Millet, I., & Saaty, T. L. (2000). On the relativity of relative measures–accommodating both rank preservation and rank reversals in the AHP. European Journal of Operational Research, 121(1), 205-212.
- Nadler, D. A., & Tushman, M. L. (1980). A model for diagnosing organizational behavior. Organizational Dynamics, 9(2), 35-51.
- Nair, P. (2017). The rise of the AI recruiter: Is HR tech the next to challenge human intuition.
- Nicastro, D. (2020, May 18). 7 ways artificial intelligence is reinventing human resources. CMSwire.
- Oliveira, T., & Martins, M. F. (2011). Literature review of information technology adoption models at firm level. The Electronic Journal Information Systems Evaluation, 14(1), 110-121.
- Oliveira, T., Martins, R., Sarker, S., Thomas, M., & Popovič, A. (2019). Understanding SaaS adoption: The moderating impact of the environment context. International Journal of Information Management, 49, 1-12.
- Pearson J. (2009, February 17). People – Process – Technology – The eternal triangle. Deconstructing ITSM.
- Petrovic-Lazerevic, S. (2001). Personnel selection fuzzy model. International Transactions in Operational Research, 8(1), 89-105.
- Pillai, R., & Sivathanu, B. (2020). Adoption of artificial intelligence (AI) for talent acquisition in IT/ITeS organizations. Benchmarking: An International Journal, 27(9), 2599-2629.
- Rana, G., & Sharma, R. (2019). Emerging human resource management practices in Industry 4.0. Strategic HR Review, 18(4), 176-181.
- Rashid, T. A., & Jabar, A. L. (2016). Improvement on predicting employee behaviour through intelligent techniques. IET Networks, 5(5), 136-142.
- Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.
- Saaty, T. (1980). The analytic hierarchy process. New York, NY: McGraw-Hill.
- Saaty, T. L. (1990). How to make a decision: The analytic hierarchy process. European Journal of Operational Research, 48(1), 9-26.
- Saaty, T. L. (2009). Applications of analytic network process in entertainment. Iranian Journal of Operations Research, 1(2), 41-55.
- Saaty, T. L., & Vargas, L. G. (1996). Decision making with the analytic network process. Springer, USA.
- Saba, T., & Leader, A. I. D. A. (2021). About the Artificial Intelligence and Data Analytics (AIDA) Lab. 2021 1st International Conference on Artificial Intelligence and Data Analytics (CAIDA). Riyadh, Saudi Arabia.
- Sahay, P. (2014). Design thinking in talent acquisition: A practitioner’s perspective. Strategic HR Review, 13(4/5), 170-180.
- Shih, K. H., Hung, H. F., & Lin, B. (2010). Assessing user experiences and usage intentions of m-banking service. International Journal of Mobile Communications, 8(3), 257-277.
- 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.
- 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.
- Tai, W. S., & Hsu, C. C. (2006). A realistic personnel selection tool based on fuzzy data mining method. Proceedings of the 9th Joint Conference on Information Sciences (JCIS). Kaohsiung, Taiwan.
- Tanveer, M., Hassan, S., & Bhaumik, A. (2020). Academic policy regarding sustainability and artificial intelligence (AI). Sustainability, 12(22), 9435.
- Thong, J. (1991). An integrated model of information system adoption in small business. Journal of Management Information System, 15(4), 187-214.
- 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.
- Walford-Wright, G., & Scott-Jackson, W. (2018). Talent rising; people analytics and technology driving talent acquisition strategy. Strategic HR Review, 17(5), 226-233.
- Wang, L. (2017). Using Artificial intelligence to improve the level of human resources management. Sinopec in China, 7, 53-54.
- Wu, W. W. (2009). Exploring core competencies for R&D technical professionals. Expert Systems with Applications, 36(5), 9574-9579.
- Zhao, X. (2008). A study of performance evaluation of HRM: Based on data mining. Proceedings of the 2008 International Seminar on Future Information Technology and Management Engineering (pp. 45-48). Leicestershire, UK.