Yetenek Yönetiminde Yapay Zekâ Uygulamaları

Yıl 2023, Cilt: 4 Sayı: 1, 49 - 63, 30.06.2023

Coşkun Akça

Öz

Günümüz rekabetçi dünyasında, her örgütün kârlılık düzeyini büyük ölçüde artırarak istediği hedefe ulaşabilmesi için yetenekli işgücü sağlaması esastır. İnsan kaynakları yönetiminin çok önemli bir işlevi olan yetenek yönetimi örgütlerin doğru yeteneği elde etmesinde kilit rol oynamaktadır. Bu bağlamda, insan kaynakları yönetiminin özellikle yeteneği yönetmede yapay zekâ uygulamalarını benimsemeleri elzemdir. Yapay zekâ, makinelerin insan benzeri bilişsel görevleri yerine getirme yeteneği olarak değerlendirilmektedir. Yapay zekânın çalışanlar açısından önyargı ve belirsizlik riskleri vurgulansa da, bu sorunların çözülebilir nitelikte olduğu görülmektedir. Çalışmanın amacı, yapay zekâ uygulamalarının yetenek yönetimindeki rolünü incelemektir. Son derece rekabetçi ve küresel pazarda yetenek yönetimi, örgütsel etkinliğin kilit belirleyicisidir. Yetenek yönetimi işlevi, yeni yetenekleri çekme, seçme ve eğitme sürecinde büyük bir yatırım içerir. Yapay zekânın uygulanmaya başlanmasıyla birlikte yetenek kazanma rekabeti artmış ve teknolojiler potansiyel iş adaylarının insan kaynakları yöneticilerine kolay ulaşmasını sağlamıştır. Yapay zekâyı uygulayan örgütler yeteneği bulma ve işe alma, verileri analiz etme, verileri toplama, işyerindeki iş yükünü azaltma, işyeri verimliliğini zenginleştirme ve maliyetlerin azalması gibi pozitif bireysel ve örgütsel çıktılara ulaştıkları görülmektedir.

Anahtar Kelimeler

yapay zeka,, Yetenek yönetimi, İnsan Kaynakları Yönetimi

Kaynakça

• Abou-Zahra, S., Brewer, J. and Cooper, M. (2018). Artificial intelligence (AI) for web accessibility: Is conformance evaluation a way forward? [Paper presentation]. Proceedings of the 15th International Web for All Conference, 1–4, Lyon, France. https://doi.org/10.1145/3192714.3192834
• Acemoglu, D. and Restrepo, P. (2020). Robots and jobs: Evidence from US labor markets. Journal of Political Economy, 128(6), 2188–2244.
• Alola, U.V. and Atsa’am, D.D. (2020), “Measuring employees’ psychological capital using data mining approach”, Journal of Public Affairs, 20(2), 1-10. doi: 10.1002/pa.2050. Arrieta, A.B., Dıaz-Rodrıguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcıa, S., Gil-Lopez, S., Molina, D. and Benjamins, R. (2020), “Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI”. Information Fusion, 58, 82-115.
• Atack, J., Margo, R. A. and Rhode, P. W. (2019). “Automation” of manufacturing in the late nineteenth century: The hand and machine labor study. Journal of Economic Perspectives, 33(2), 51–70.
• Azadeh, A., Rouzbahmana, M. and Saberi, M. (2009). Utilization of an artificial intelligence approach for assessment of job satisfaction. International Journal of Intelligent Information Technology Application, 2(6). 250-255.
• Benbya, H., Davenport, T. H. and Pachidi, S. (2020). Artificial intelligence in organizations: Current state and future opportunities. MIS Quarterly Executive, 19(4). Article 4.
• Bersin, J. (2018). “Talent trends technology disruptions for 2018”, available at: https://www.isaconnection.org/assets/documents/2018BersinHRTechDisruptionsReport.pdf.
• Blass, E. (2007). Talent management maximizing talent for business performance, executive summary. London: Chartered Management Institute and Ashridge Consulting.
• Brougham, D. and Haar, J. (2018). Smart technology, artificial intelligence, robotics, and algorithms (STARA): Employees’ perceptions of our future workplace. Journal of Management & Organization, 24(2), 239–257.
• Caplan, J. (2014). Thinking differently about talent development. Human Resource Management International Digest, 22(2), 42-45.
• Charlier, R. and Kloppenburg, S. (2017). “Artificial intelligence in HR: A No-brainer”, available at:www.pwc.nl.
• Choi, D. Y. and Kang, J. H. (2019). Net job creation in an increasingly autonomous economy: The challenge of a generation. Journal of Management Inquiry, 28(3), 300–305.
• Claus, L. (2019). HR disruption—Time already to reinvent talent management. BRQ Business Research Quarterly, 22(3), 207-215.
• Collings, D. and Mellahi, K. (2013). “Commentary on: ‘Talent—innate or acquired? Theoretical considerations and their implications for talent management’”, Human Resource Management Review, Elsevier Inc., 23, 305-321.
• David, H. (2015). Why are there still so many jobs? The history and future of workplace automation. Journal of Economic Perspectives, 29(3), 3–30. https://doi.org/ 10.1257/jep.29.3.3.
• Devanna, M.A., Fombrun, C., Tichy, N. and Warren, L. (1982). “Strategic planning and human resource management”, Human Resource Management, 21(11), 11-17.
• Dhamija, P. and Bag, S. (2020). “Role of artificial intelligence in operations environment: A review and bibliometric analysis”, The TQM Journal, 32(4), 869-896. doi: 10.1108/TQM-10-2019-0243.
• DiClaudio, M. (2019). People analytics and the rise of HR: How data, analytics and emerging technology can transform human resources (HR) into a profit center. Strategic HR Review, 18(2), 42–46. https://doi.org/10.1108/SHR-11-2018-0096.
• Doğan, S. ve Demiral, A.G.Ö. (2008). İnsan kaynakları yönetiminde çalışanların kendilerine doğru yolculuk yöntemi: yetenek yönetimi. Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 17(3), 145-166.
• Dries, N. (2013). “The psychology of talent management: a review and research agenda”, Human Resource Management Review, 23(4), 286-289.
• Fiest, G. (2013). Scientific talent: Nature shaped by nurture. S. B. Kaufmann (Ed.), The Complexity of Greatness: Beyond Talent or Practice, in (pp. 257–274). Oxford: Oxford University Press.
• Fountaine, T., McCarthy, B. and Saleh, T. (2019). “Building the AI-powered organization”, Harvard Business Review, 97(4), 62-73.
• Frey, C. B. and Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254–280.
• Gagne, F. (2000). “Understanding the complex choreography of talent development through DGMT-Based Analysis.” K. A. Heller, F. J. Moncks, R. J. Sternberg, and R. F. Sabotnik (Eds.), International Handbook of Giftedness and Talent, in (pp. 67–79). Oxford: Elsevier.
• Gallardo-Gallardo, E., Thunnissen, M. and Scullion, H. (2020). Talent management: context matters. The International Journal of Human Resource Management, 31(4), 457-473.
• Gandossy, R. and Kao, T. (2004). Talent wars: Out of mind, out of practice. Human Resource Planning, 27(4), 15−19.
• Horvathova, P. and Davidova, M. (2011). Application of talent management with the of information and communications technology in the Czech Republic and other developing, emerging and transitions economies. World Academy of Science, Engineering and Technology, 80(56), 56-75.
• Huang, G.Z.D., Roy, M.H., Ahmed, Z.U., Heng, J.S.T. and Lim, J.H.M. (2002). “Benchmarking the human capital strategies of MNCs in Singapore”, Benchmarking: An International Journal, 9(4), 357-373.
• Huang, M. H. and Rust, R. T. (2018). Artificial Intelligence in service. Journal of Service Research, 21(2), 155–172.
• Jha, A.K., Awasthi, S. and Paul S. (2014). Talent DNA- A mechanism to make accurate decision on talent needs. Asian Journal of Management, 5(2), 218-220.
• Jia, Q., Guo, Y., Li, R., Li, Y. and Chen, Y. (2018). “A Conceptual Artificial Intelligence Application Framework in Human Resource Management”, International Consortium for Electronic Business, Scopus, in Chang, F.-K., Li, E.Y. and Li, E.Y. (Eds), Proceedings of the International Conference on Electronic Business (ICEB), 106-114.
• Kabwe, C. and Okorie, C. (2019). The efficacy of talent management in international business: The case of European multinationals. Thunderbird International Business Review, 61(6), 857-872.
• Kakhki, F. D., Freeman, S. A. and Mosher, G. A. (2019). Evaluating machine learning performance in predicting injury severity in agribusiness industries. Safety Science, 117, 257–262.
• Kane, G. C. (2017). The evolutionary implications of social media for organizational knowledge management. Information and Organization, 27(1), 37–46.
• Kaplan, A. and Haenlein, M. (2019). “Siri, Siri, in my hand: who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence”, Business Horizons, 62(1), 15-25.
• Kaufman, S. B. (2013). The Complexity of greatness: Beyond talent or practice. Oxford: Oxford University Press.
• Kehinde, J.S. (2012). Talent management: Effect on organizational performance. Journal of management research, 4(2), 178.
• Liu, S., Li, G. and Xia, H. (2020 February). "Analysis of Talent Management in the Artificial Intelligence Era," in Proceedings of the 5th Asia-Pacific Conference on Economic Research and Management Innovation, 38–42. doi: 10.2991/AEBMR.K.210218.007.
• Lu, H., Li, Y., Chen, M., Kim, H. and Serikawa, S. (2018). Brain intelligence: Go beyond artificial intelligence. Mobile Networks and Applications, 23(2), 368–375.
• Ludger, G. F. (2009). Artificial Intelligence - Structures and strategies for complex problem solving (5th Edition). London: Pearson.
• Makarius, E. E., Mukherjee, D., Fox, J. D. and Fox, A. K. (2020). Rising with the machines: A sociotechnical framework for bringing artificial intelligence into the organization. Journal of Business Research, 120, 262–273.
• Masum, A.-K., Beh, L.-S., Azad, A.-K. and Hoque, K. (2018). “Intelligent human resource information system (i-HRIS): a holistic decision support framework for HR excellence”, The International Arab Journal of Information Technology, Scopus, 15(1), 121-130.
• Meisels, A. and Schaerf, A. (2003). Modelling and solving employee timetabling problems. Annals of Mathematics and Artificial Intelligence, 39(1–2), 41–59.
• Merlin, R. and Jayam, R. (2018). Artificial intelligence in human resource management. International Journal of Pure and Applied Mathematics, 119(17), 1891-1895.
• Michaels, E., Handfield-Jones, H. and Axelrod, B. (2001). The War for Talent: Brighton, MA: Harvard Business Press.
• Michell, K. (2022). The 6 Bs of talent management: How to protect your competitive edge. Retrieved from https://www.bdo.com.au/en-au/insights/advisory/articles/the-6-bs-of-talent-management-how-to-protect-your-competitive-edge. In 25.01.2023.
• Phillips-Wren, G., Doran, R. and Merrill, K. (2016), “Creating a value proposition with a social media strategy for talent acquisition”, Journal of Decision Systems, 25, 450-462.
• Pillai, R. and Sivathanu, B. (2020). "Adoption of artificial intelligence (AI) for talent acquisition in IT/ITeS organizations", Benchmarking: An International Journal, 27(9), 2599-2629. https://doi.org/10.1108/BIJ-04-2020-0186.
• Poquet, O. and De Laat, M. (2021). Developing capabilities: Lifelong learning in the age of AI. British Journal of Educational Technology. 52(4), 1695-1708.
• Premnath, E. and Chully, A.A. (2020). Artificial Intelligence in Human Resource Management: A Qualitative Study in the Indian Context, 11(12), 1193-1205.
• Queiroz, M.M., Pereira, S.C.F., Telles, R. and Machado, M.C. (2019). “Industry 4.0 and digital supply chain capabilities: A framework for understanding digitalisation challenges and opportunities”, Benchmarking: An International Journal, 28(5). 1761-1782 doi: 10.1108/BIJ-12-2018-0435.
• Rampersad, G. (2020). Robot will take your job: Innovation for an era of artificial intelligence. Journal of Business Research, 116, 68–74.
• Reiche, B. S., Lee, Y. T. and Allen, D. G. (2019). Actors, structure, and processes: A review and conceptualization of global work integrating IB and HRM research. Journal of Management, 45(2), 359-383.
• Samarasinghe, K. R. and Medis, A. (2020). Artificial intelligence based strategic human resource management (AISHRM) for industry 4.0. Global Journal of Management and Business Research, 20(2), 1–5.
• Saradhi, V. V. and Palshikar, G. K. (2011). Employee churn prediction. Expert Systems with Applications, 38(3), 1999–2006.
• Schoorman, F. D. (1988). Escalation bias in performance appraisals: An unintended consequence of supervisor participation in hiring decisions. Journal of Applied Psychology, 73(1), 58-62.
• Sekhri, A. and Cheema, D. J. (2019). The new era of HRM: AI reinventing HRM functions. International Journal of Scientific Research and Review, 7(3), 3073–3077.
• Simeunovi´c, N., Kamenko, I., Bugarski, V., Jovanovi´c, M. and Lali´c, B. (2017). Improving workforce scheduling using artificial neural networks model. Advances in Production Engineering & Management, 12(4), 337–352.
• Singh, K. (2004). Impact of HR practices on perceived firm performance in India. Asia Pacific Journal of Human Resources, 42(3), 301-317.
• Singh, N. (2018). “Strategic human resource practices for innovation performance: an empirical investigation”, Benchmarking: An International Journal, 25(9), 3459-3478.
• Su, Z. X., Wang, Z. and Chen, S. (2020). The impact of CEO transformational leadership on organizational voluntary turnover and employee innovative behaviour: The mediating role of collaborative HRM. Asia Pacific Journal of Human Resources, 58(2), 197–219.
• Tripathi, P., Ranjan, J. and Pandeya, T. (2012). Human Resource Management Through AI Approach: An Experimental Study of an Expert System. In National conference on communication technologies & its impact on next generation computing CTNGC. Proceedings published by International Journal of Computer Application.
• Trost, G. (2000). Prediction of Excellence in School, Higher Education and Work. Heller, F. J. Moncks, R. J. Sternberg, and R. F. Sabotnik (Eds.), In International Handbook of Giftedness and Talent, in (pp. 317–327). Oxford: Elsevier.
• Upadhyay, A. K. and Khandelwal, K. (2018). Applying artificial intelligence: Implications for recruitment. Strategic HR Review, 17(5), 255–258. https://doi.org/ 10.1108/SHR-07-2018-0051.
• Vaiman, V., Cascio, W. F., Collings, D. G. and Swider, B. W. (2021). The shifting boundaries of talent management. Human Resource Management, 60(2), 253-257.
• Vaiman, V., Collings, D.G. and Scullion, H. (2017). "Contextualising talent management", Journal of Organizational Effectiveness: People and Performance, 4(4), 294-297.
• Williams, M. R. (2000). The War for Talent: Getting the best from the best: CIPD Publishing.
• Yuldoshev, N., Tursunov, B. and Qozoqov, S. (2018). Use of artificial intelligence methods in operational planning of textile production. Journal of Process Management New Technologies, 6(2), 41–51.