İşletmelerde yapay zekâ tabanlı insan kaynakları uygulamaları

Ayşe Asiltürk; Ersin Ersoy

doi:10.47243/jos.2690

Yazarlar

Ayşe Asiltürk Trabzon Üniversitesi / Türkiye https://orcid.org/0000-0002-6221-6208
Ersin Ersoy Tokat Gaziosmanpaşa Üniversitesi / Türkiye https://orcid.org/0000-0002-9510-8828

DOI:

https://doi.org/10.47243/jos.2690

Anahtar Kelimeler:

Yapay Zekâ Teknikleri- Yapay Zekâ Tabanlı İnsan Kaynakları Uygulamaları- İşletmeler- Makine Öğrenimi

Özet

Yapay zekânın (YZ) gelişimi iş hayatının hemen her alanını etkilerken; işletmelerde YZ tabanlı insan kaynakları (İK) uygulamalarının giderek yaygınlaştığı görülmektedir. Post modern insan kaynakları yönetimi (İKY) bağlamında YZ tabanlı İK uygulamalarını konu alan teorik ve amprik çalışmaların sayısı gün geçtikçe artmaktadır. Ancak henüz Türkçe literatürde YZ tabanlı İK uygulamalarını bütünsel olarak inceleyen çalışma sayısı oldukça yetersizdir. Bu çalışma YZ’nin başlıca alt çalışma konuları olan, “makine öğrenimi, doğal dil işleme, bilgisayarla görü, robotik ve otonom sistemler, konuşma tanıma ve üretimi, bilgi temsili ve muhakeme, veri madenciliği ve büyük veri (BV)analitiği, yapay sinir ağları ve derin sinir ağlarına” dayalı İK uygulamalarını ele almayı amaçlamaktadır. Doküman analizi ve betimsel analiz ile son dönemde yayınlanan YZ temalı İK araştırmalarının bulguları incelenmiş, YZ’nin başlıca alt çalışma konuları bağlamında bir değerlendirme yapılmıştır. Çalışma bulguları, YZ tabanlı İK uygulamalarının işletmeler açısından umut verici bir biçimde geliştirildiğini göstermektedir. Bununla birlikte YZ ve İK uzmanlarının işbirliğinin artırılması ile geliştirilecek yeni YZ tabanlı İK uygulamalarının işletmeler açısından daha etkili sonuçlar doğurması beklenmektedir.

İndirmeler

İndirme verileri henüz mevcut değil.

Referanslar

ABDUL-RAHMAN, F. (2023). The effect of applying neural network information systems in achieving parallel processing of decisions and streamlining smart solutions for human resources: An applied study of a sample of educational leaders at Al-Mustansiriyah University. International Journal of Research in Social Sciences and Humanities, 13(3), 340-350.

ATHANASOPOULOU, K., DANEVA, G. N., ADAMOPOULOS, P. G., & SCORILAS, A. (2022). Artificial intelligence: The milestone in modern biomedical research. Biomedinformatics, 2(4), 727-744.

BAHJA, M. (2020). Natural language processing applications in business. E-Business - Higher Education and Intelligence Applications. IntechOpen: London, UK.

BHARATHI, A. (2022). Natural language processing for enterprise applications. Ushus Journal of Business Management, 21(4), 29-39.

BINGOL, M. C., & AYDOGMUS, O. (2020). Performing predefined tasks using the human–robot interaction on speech recognition for an industrial robot. Engineering Applications of Artificial Intelligence, 95, 103903, 1-13.

BONCI, A., CEN CHENG, P. D., INDRI, M., NABISSI, G., & SIBONA, F. (2021). Human-robot perception in industrial environments: A survey. Sensors, 21(5), 1571, 1-29.

BOWEN, G. A. (2009). Document analysis as a qualitative research method. Qualitative Research Journal, 9(2), 27-40.

BRETZ JR, R. D., & JUDGE, T. A. (1994). The role of human resource systems in job applicant decision processes. Journal of Management, 20(3), 531-551.

CELEBI, M. E., & AYDIN, K. (Eds.). (2016). Unsupervised learning algorithms. Springer, Cham.

CHEN, Y., YANG, C., GU, Y., & HU, B. (2022). Influence of mobile robots on human safety perception and system productivity in wholesale and retail trade environments: A pilot study. IEEE Transactions on Human-Machine Systems, 52, 624-635.

CHIEN, C. F., & CHEN, L. F. (2008). Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry. Expert Systems with Applications, 34(1), 280-290.

CHOWDHARY, K. R., & CHOWDHARY, K. R. (2020). Automatic speech recognition. Fundamentals of Artificial Intelligence, 651-668.

COLEMAN, J. P. (2021). AI and our understanding of intelligence. Intelligent Systems and Applications: Proceedings of the 2020 Intelligent Systems Conference, 1, 183-190. Springer International Publishing.

CUI, J., & GU, Y. (2023). Application of machine learning in digital human resource management. SHS Web of Conferences, 170, 01002. EDP Sciences.

DANDE, A. A., & PUND, D. M. (2023). A review study on applications of natural language processing. International Journal of Scientific Research in Science, Engineering and Technology, 122, 122-126.

DENG, C., JI, X., RAINEY, C., ZHANG, J., & LU, W. (2020). Integrating machine learning with human knowledge. iScience, 23(11).

DEVARAJ, R. R., & KAUSHIK, K. (2023, August). Machine learning driven skill prioritisation for human resource planning. Proceedings of the 2023 International Conference on Research in Adaptive and Convergent Systems, 1-3.

DIETTERICH, T. G. (1990). Machine learning. Annual Review of Computer Science, 4(1), 255-306.

DWIVEDI, Y. K., HUGHES, L., ISMAGILOVA, E., AARTS, G., COOMBS, C., CRICK, T., ... & WILLIAMS, M. D. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994, 1-47.

GARG, S., SINHA, S., KAR, A. K., & MANI, M. (2022). A review of machine learning applications in human resource management. International Journal of Productivity and Performance Management, 71(5), 1590-1610.

GELBARD, R., RAMON‐GONEN, R., CARMELI, A., BITTMANN, R. M., & TALYANSKY, R. (2018). Sentiment analysis in organizational work: Towards an ontology of people analytics. Expert Systems, 35(5), e12289, 1-15.

GUOHAO, Q., BIN, W., BAI, W., & BAOLI, Z. (2019, June). Competency analysis in human resources using text classification based on deep neural network. 2019 IEEE Fourth International Conference on Data Science in Cyberspace, 322-329.

HAINES, V. Y., & LAFLEUR, G. (2008). Information technology usage and human resource roles and effectiveness. Human Resource Management, 47(3), 525-540.

HAN, S., & LEE, G. (2016). A preliminary study on text mining-based human resource allocation in a construction project. Proceedings of the International Symposium on Automation and Robotics in Construction, 33(1), IAARC Publications.

HOPKO, S., WANG, J., & MEHTA, R. (2022). Human factors considerations and metrics in shared space human-robot collaboration: A systematic review. Frontiers in Robotics and AI, 9, 799522, 1-15.

HUSSAIN, M. T., & BAIG, M. E. (2022). Human resource management by machine learning algorithms. International Journal for Research in Applied Science and Engineering Technology, 10(XI), 629-632.

JANIESCH, C., ZSCHECH, P., & HEINRICH, K. (2021). Machine learning and deep learning. Electronic Markets, 31(3), 685-695.

JIANG, Y. (2022, July). Application of data mining technology in enterprise human resource management informatization. 2022 International Conference on Artificial Intelligence and Autonomous Robot Systems, 228-232.

JORDAN, M. I., & MITCHELL, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260.

KANOJIA, D., & JOSHI, A. (2023). Applications and challenges of SA in real-life scenarios. Computational Intelligence Applications for Text and Sentiment Data Analysis, 49-80.

KHURANA, D., KOLI, A., KHATTER, K., & SINGH, S. (2023). Natural language processing: State of the art, current trends, and challenges. Multimedia Tools and Applications, 82(3), 3713-3744.

KIRAL, B. (2020). Nitel bir veri analizi yöntemi olarak doküman analizi. Siirt Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 8(15), 170-189.

KRIKKE, J. (2006). Machine translation inching toward human quality. IEEE Intelligent Systems, 21(2), 4-6.

KRIPPENDORFF, K. (2018). Content analysis: An introduction to its methodology. Sage Publications.

KUMAR, M. R., SHARMA, A., BHARGAVI, Y. K., & RAMESH, G. (2022, August). Human resource management using machine learning-based solutions. 2022 3rd International Conference on Electronics and Sustainable Communication Systems, 801-806.

KUNZE, L., HAWES, N., DUCKETT, T., HANHEIDE, M., & KRAJNÍK, T. (2018). Artificial intelligence for long-term robot autonomy: A survey. IEEE Robotics and Automation Letters, 3, 4023-4030.

KÜHL, N., GOUTIER, M., BAIER, L., WOLFF, C., & MARTIN, D. (2022). Human vs. supervised machine learning: Who learns patterns faster? Cognitive Systems Research, 76, 78-92.

LAKSANA, E. A. (2021). Machine learning design on human resource system. Turkish Journal of Computer and Mathematics Education, 12(8), 1057-1061.

LI, J., & ZHOU, Z. (2022). Design of human resource management system based on deep learning. Computational Intelligence and Neuroscience, (1), 9122881, 1-9.

LIU, Q., WAN, H., & YU, H. (2023). The application of deep learning in human resource management: A new perspective on employee recruitment and performance evaluation. Academic Journal of Management and Social Sciences, 3(1), 101-104.

MAHADEVKAR, S. V., KHEMANI, B., PATIL, S., KOTECHA, K., VORA, D. R., ABRAHAM, A., & GABRALLA, L. A. (2022). A review on machine learning styles in computer vision—Techniques and future directions. IEEE Access, 10, 107293-107329.

MARTINSONS, M. G. (1997). Human resource management applications of knowledge-based systems. International Journal of Information Management, 17(1), 35-53.

MATARIĆ, M. (2023). Socially assistive robotics: Methods and implications for the future of work and care. Social Robots in Social Institutions, 14-15, IOS Press.

MING, L. (2022). A deep learning-based framework for human resource recommendation. Wireless Communications and Mobile Computing, 2022, 1-12.

NEDELCU, A., NEDELCU, B., SGARCIU, A. I., & SGARCIU, V. (2019, June). Mining data for human resources. 2019 11th International Conference on Electronics, Computers and Artificial Intelligence, 1-4.

NOYES, J., & STARR, A. (1996). Use of automatic speech recognition: Current and potential applications. Computing & Control Engineering Journal, 7(5), 203-208.

O’LEARY, Z., & HUNT, J. (2014). Primary data: Surveys, interviews, and observation. The Essential Guide to Doing Your Research Project, 201-216.

PATEL, N., TRIVEDI, S., & FARUQUI, N. (2023, February). An innovative deep neural network for stress classification in the workplace. 2023 International Conference on Smart Computing and Application, 1-5.

PICCIALLI, F., CASOLLA, G., CUOMO, S., GIAMPAOLO, F., & DI COLA, V. S. (2019). Decision making in the IoT environment through unsupervised learning. IEEE Intelligent Systems, 35(1), 27-35.

PLATANOU, K., MÄKELÄ, K., BELETSKIY, A., & COLICEV, A. (2018). Using online data and network-based text analysis in HRM research. Journal of Organizational Effectiveness: People and Performance, 5(1), 81-97.

POMPERADA, J. R. (2022). Human resource information system with machine learning integration. Qubahan Academic Journal, 2(2), 5-8.

PREMA, M., RAJU, V., & RAMYA, M. (2022). Natural language processing for data science workforce analysis. J Wirel Mob Netw Ubiquitous Comput Depend Appl, 13(4), 225-232.

PRIOR, L. (2016). Using documents in social research. Qualitative Research, 171-185.

RANJAN, J., GOYAL, D. P., & AHSON, S. I. (2008). Data mining techniques for better decisions in human resource management systems. International Journal of Business Information Systems, 3(5), 464-481.

SELVAGGIO, M., COGNETTI, M., NIKOLAIDIS, S., IVALDI, S., & SICILIANO, B. (2021). Autonomy in physical human-robot interaction: A brief survey. IEEE Robotics and Automation Letters, 6(4), 7989-7996.

SENFT, E., LEMAIGNAN, S., BAXTER, P. E., BARTLETT, M., & BELPAEME, T. (2019). Teaching robots social autonomy from in situ human guidance. Science Robotics, 4(35), eaat1186.

SINDHURA, K., SABARIRAJAN, A., NARANG, P., BHANUSHALI, M. M., & TURAI, A. K. (2022, April). Human resource management-based economic analysis using data mining. 2022 3rd International Conference on Intelligent Engineering and Management, 872-876.

THORAT, S. G., BHAGAT, A. P., & DONGRE, K. A. (2018, August). Neural network-based psychometric analysis for employability. 2018 International Conference on Research in Intelligent and Computing in Engineering, 1-5.

TORFI, A., SHIRVANI, R. A., KENESHLOO, Y., TAVAF, N., & FOX, E. A. (2020). Natural language processing advancements by deep learning: A survey. arXiv preprint arXiv:2003.01200.

YE, Y. (2021, December). Human resource performance management evaluation based on big data mining. 2021 IEEE International Conference on Industrial Application of Artificial Intelligence, 79-85.

YUAN, S., QI, Q., DAI, E., & LIANG, Y. (2022). Human resource planning and configuration based on machine learning. Computational Intelligence and Neuroscience, (1), 3605722, 1-6.

YUGANTHINI, P., VIGNESWARI, A., JANCY, S., & ANTOPRAVEENA, M. D. (2021, June). Activity tracking of employees in industries using computer vision. 2021 5th International Conference on Trends in Electronics and Informatics, 1321-1329.

WAN, J., LI, K., HU, M., YANG, D., & CAO, J. (2023, August). Research on daily behavior recognition method based on computer vision. Third International Conference on Computer Vision and Pattern Analysis, 12754, 701-706.

ZENG, J. (2021, April). Application of big data processing technology in human resource management information system. Journal of Physics: Conference Series, 1881(3), 032029. IOP Publishing.

ZHAO, Y., HRYNIEWICKI, M. K., CHENG, F., FU, B., & ZHU, X. (2019). Employee turnover prediction with machine learning: A reliable approach. Intelligent Systems and Applications: Proceedings of the 2018 Intelligent Systems Conference, 2, 737-758. Springer International Publishing.

ZHUKOVA, I., KULTSOVA, M., NAVROTSKY, M., & DVORYANKİN, A. (2014). Intelligent support of decision making in human resource management using case-based reasoning and ontology. Knowledge-Based Software Engineering: 11th Joint Conference, Volgograd, Russia, September 17-20, 2014, 172-184. Springer International Publishing.

ZHOU, D. (2022). Application of data mining technology in enterprise digital human resource management. Security and Communication Networks, (1), 7611623, 1-9.