ÇALIŞAN PERSPEKTİFİNDEN İŞLETMELERDE YAPAY ZEKÂ UYGULAMALARI: BİBLİYOMETRİK BİR ANALİZ

Öz

Organizasyonlarda çalışan faaliyetlerinde, yapay zekâ veya uygulamalarının etkinliği günden güne artmıştır. Bu durum çalışanlara birtakım kolaylıklar sağlamasının yanında birçok endişeyi de beraberinde getirmiştir. Araştırmada çalışanlar ve yapay zekâ işbirliği ve çatışmalarını belirlemeye yönelik bir ihtiyaç doğmuştur. Bu açıdan araştırmada işletmeler düzeyinde çalışanlar ve yapay zekâ etkileşimine yönelik çıkarımlarda bulunulması amaçlanmıştır. Yapay zeka uygulamaları ve çalışanlara ilişkin literatür bilgilerine yer verilmiştir. WoS kullanılarak bibliyometrik analiz yöntemi uygulanmıştır. Bu araştırma, 1996-2024 yılları arasında yayımlanmış toplam 497 çalışmayı içeren bir uygulamayı kapsamaktadır. Yapay zekâ uygulamalarında yazarların en fazla vurguladığı anahtar kelimeler, yapay zekâ, makine öğrenme, yönetim, yapay, bilgi, öğrenme, derin öğrenme, zekâ, büyük veri, veri madenciliği, çalışan, insan kaynakları, makine, teknoloji, iş, bilgi yönetimi, performans ve yapay sinir ağı olarak sıralanmıştır. Analizde yapay zekâ, makine öğrenme, yönetim, veri madenciliği, dijital dönüşüm, analiz, teknoloji, çalışan, insan kaynakları, işbirliği, verimlilik, insan kaynakları yönetimi, büyük veri, performans, dijital teknolojiler kavramları birlikte güçlü ilişkiler ortaya koymuştur. Sonuçlar işletmelerde yapay zekâ kullanımının çalışanlar açısından birçok kavram ile ilişkili olduğunu göstermiştir. Araştırma yapay zekâ uygulamalarının çalışanlarla olan etkileşiminde öne çıkan kavramlar üzerinden, insan kaynakları yönetimi, işbirliği, performans ve dijital dönüşüm gibi alanlarda stratejik kararlar alınmasına yönelik önemli çıkarımlar sunmaktadır.

Anahtar Kelimeler

• Çalışanlar
• Organizasyonlar
• Yapay Zekâ.

ARTIFICIAL INTELLIGENCE APPLICATIONS IN ORGANIZATIONS FROM AN EMPLOYEE PERSPECTIVE: A BIBLIOMETRIC ANALYSIS

Öz

In organizational settings, the effectiveness of artificial intelligence (AI) and AI-based applications in employees' activities continues to increase steadily over time. While this situation provides some conveniences to employees, it has also brought several concerns. Accordingly, a need has emerged to identify areas of collaboration and conflict between employees and AI. From this perspective, the present study aims to generate insights into employee–AI interactions at the organizational level. Literature on AI applications and their impact on employees is included. Bibliometric analysis was conducted using Web of Science (WoS) database. This research covers an application that includes a total of 497 studies published between 1996 and 2024. The keywords most emphasized by the authors in AI applications include artificial intelligence, machine learning, management, artificial, knowledge, learning, deep learning, intelligence, big data, data mining, employee, human resources, machine, technology, business, knowledge management, performance, and artificial neural networks. In the analysis, strong relationships between the concepts of artificial intelligence, machine learning, management, data mining, digital transformation, analysis, technology, employee, human resources, collaboration, productivity, human resource management, big data, performance, and digital technologies were revealed. Results have shown that the use of artificial intelligence in businesses is related to many concepts from the employees' perspective. The research offers important implications for strategic decision-making in areas such as human resources management, collaboration, performance, and digital transformation, based on the concepts that emerge in the interaction of artificial intelligence applications with employees.

Anahtar Kelimeler

• Employees
• Organizations
• Artificial Intelligence

Kaynakça

1. Abdullah, R., & Fakieh, B. (2020). Health care employees’ perceptions of the use of artificial intelligence applications: Survey study. Journal of Medical Internet Research, 22(5), 1-8. http://dx.doi.org/10.2196/17620
2. Akay, A. (2025). Environmental design in the age of AI: Bibliometric and thematic insights from a social sciences perspective. Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 34, 452-473. https://doi.org/10.35379/cusosbil.1696548
3. Al Husaeni, D.F., Widiaty, I., Mulyanti, B., Abdullah, A.G., Rıza, L.S., Suherman, A., & Al Husaeni, D.N. (2025). Trends and impacts of artificial intelligence application in the development of computational thinking skills. Informatics in Education, 24(2), 261–298. http://dx.doi.org/10.15388/infedu.2025.14
4. Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975. http://dx.doi.org/10.1016/j.joi.2017.08.007
5. Arslan, A., Cooper, C., Khan, Z., Golgeci, I., & Ali, I. (2022). Artificial intelligence and human workers interaction at team level: A conceptual assessment of the challenges and potential HRM strategies. International Journal of Manpower, 43(1), 75-88. http://dx.doi.org/10.1108/IJM-01-2021-0052
6. 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. http://dx.doi.org/10.1002/job.2735
7. Berigel, D.S., & Şılbır, L. (2024). A bibliometric analysis of AI literacy: Trends, topics and future directions. Near East University Journal of Education Faculty (NEUJE), 7(1), 42-53.
8. Bhargava, A., Bester, M., & Bolton, L. (2021). Employees’ perceptions of the implementation of robotics, artificial intelligence, and automation (RAIA) on job satisfaction, job security, and employability. Journal of Technology in Behavioral Science, 6, 106–113. https://doi.org/10.1007/s41347-020-00153-8

9. Brougham, D., & 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. http://dx.doi.org/10.1017/jmo.2016.55
10. Budhwar, P., Malik, A., Silva, M.T.T., & Thevisuthan, P. (2022). Artificial intelligence – challenges and opportunities for international HRM: a review and research agenda. The International Journal of Human Resource Management, 33(6), 1065-1097. https://doi.org/10.1080/09585192.2022.2035161
11. Chang, P-C., Zhang, W., Cai, Q., & Guo, H. (2024). Does AI-driven technostress promote or hinder employees’ artificial intelligence adoption ıntention? A moderated mediation model of affective reactions and technical self-efficacy. Psychology Research and Behavior Management, 17, 413-427. https://doi.org/10.2147/PRBM.S441444
12. Chiu, Y-T., Zhu, Y-Q., & Corbett, J. (2021). In the hearts and minds of employees: A model of pre-adoptive appraisal toward artificial intelligence in organizations. International Journal of Information Management, 60, 1-16. https://doi.org/10.1016/j.ijinfomgt.2021.102379
13. Cobo, M.J., López-Herrera, A.G., Herrera-Viedma, E., & Herrera F. (2011). An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field. Journal of Informetrics, 5, 146–166. http://dx.doi.org/10.1016/j.joi.2010.10.002
14. Cobo, M.J., Martinez, M.A., Gutierrez-Salcedo, M., Fujita, H., & Herrera-Viedma, E. (2015). 25 years at Knowledge-Based Systems: A bibliometric analysis. Knowledge-Based Systems, 80, 3–13. http://dx.doi.org/10.1016/j.knosys.2014.12.035
15. Cramarenco, R.E., Burcă-Voicu, M.I., & Dabija, D-C. (2023). The impact of artificial intelligence (AI) on employees’ skills and well-being in global labor markets: A systematic review. Oeconomia Copernicana, 14(3), 731–767. http://dx.doi.org/10.24136/oc.2023.022
16. Dabbous, A., Barakat, K.A., & Sayegh, M.M. (2022). Enabling organizational use of artificial intelligence: an employee perspective. Journal of Asia Business Studies, 16(2), 245-266. http://dx.doi.org/10.1108/JABS-09-2020-0372
17. Esen, D. (2023). Artificial intelligence and employee behaviour: A bibliometric analysis. Uluslararası Akademik Birikim Dergisi, 6, 109-124. http://dx.doi.org/10.5281/zenodo.10003975
18. Giering, O., Fedorets, A., Adriaans, J., & Kirchner, S. (2021) Artificial intelligence in Germany: Employees often unaware they are working with AI-based systems, DIW Weekly Report, ISSN 2568-7697, Deutsches Institut für Wirtschaftsforschung (DIW), Berlin, 11(48), 369-375. https://doi.org/10.18723/diw_dwr:2021-48-1
19. Guedes, T.A. (2023). Thematic analysis on ethical concepts of artificial intelligence in business: A systematic review and research agenda. International Journal of Business Marketing and Management (IJBMM), 8(6), 54-66.
20. Howard, J. (2019). Artificial intelligence: Implications for the future of work. American Journal of Industrial Medicine, 62, 917-926. https://doi.org/10.1002/ajim.23037
21. Huang, M-H., & Rust, R.T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155-172. https://doi.org/10.1177/1094670517752459
22. Hughes, C., Robert, L., Frady, K., & Arroyos, A., (2019), Artificial Intelligence, Employee Engagement, Fairness, and Job Outcomes, Managing Technology and Middle- and Low-skilled Employees (The Changing Context of Managing People), Emerald Publishing Limited, 61-68. https://doi.org/10.1108/978-1-78973-077-720191005
23. Jaiswal, A., Arun, C.J., & Varma, A. (2022). Rebooting employees: Upskilling for artificial intelligence in multinational corporations. The International Journal of Human Resource Management, 33(6), 1179-1208. https://doi.org/10.1080/09585192.2021.1891114
24. Jerdea, I-L. (2025). Artificial intelligence for product innovation: A bibliometric analysis. Proceedings of the International Conference on Business Excellence, 19(1), 3538-3552. https://doi.org/10.2478/picbe-2025-0270
25. Kądzielawski, G. (2023). The state of development of artificial intelligence in polish industry: opinions of employees. International Journal of Contemporary Management, 59(1), 12-25. https://doi.org/10.2478/ijcm-2022-0015
26. Kazim, S.I.H., Afzal, M.F., Gondal, S., Ashraf, M.U., & Umair, M. (2024). Evaluating the impact of artificial intelligence on employee engagement and performance in Pakistan. Journal of Excellence in Social Sciences, 3(1), 30-42.
27. Khanfar, A.A., Mavi, R.K., Iranmanesh, M., & Gengatharen, D. (2024). Determinants of artificial intelligence adoption: Research themes and future directions. Information Technology and Management, 1-22. https://doi.org/10.1007/s10799-024-00435-0
28. Kılınç, E. (2023). Bibliometrics analysis of studies carried out in Scopus database in the field of artificial intelligence in businesses. Uluslararası Yönetim akademisi Dergisi, 6(4), 1185-1198. https://doi.org/10.33712/mana.1380858
29. Kong, H., Yuan, Y., Baruch, Y., Bu, N., Jiang, X., & Wang, K. (2021). Influences of artificial intelligence (AI) awareness on career competency and job burnout. International Journal of Contemporary Hospitality Management, 33(2), 717-734. https://doi.org/10.1108/IJCHM-07-2020-0789
30. Kong, H., Jiang, X., Zhou, X., Baumi T., Li, J., & Yu, J. (2024). Influence of artificial intelligence (AI) perception on career resilience and informal learning. Tourism Review, 79(1), 219-233. https://doi.org/10.1108/TR-10-2022-0521
31. Łapi´nska, J., Escher, I., Górka, J., Sudolska, A., & Brzustewicz, P. (2021). Employees’ trust in artificial intelligence in companies: The case of energy and chemical industries in Poland. Energies, 14, 1-20. https://doi.org/10.3390/en14071942 Li, W., Qin, X., Yam, K.C., Deng, H., Chen, C., Dong, X., Jiang, L., & Tang, W. (2024a). Embracing artificial intelligence (AI) with job crafting: Exploring trickle-down effect and employees’ outcomes. Tourism Management, 104, 1-18. https://doi.org/10.1016/j.tourman.2024.104935
32. Li, Y., Song, Y., Sun, Y., & Zeng, M. (2024b). When do employees learn from artificial intelligence? The moderating effects of perceived enjoyment and task-related complexity. Technology in Society, 77, 1-15. https://doi.org/10.1016/j.techsoc.2024.102518
33. Lichtenthaler, U. (2020). Extremes of acceptance: employee attitudes toward artificial intelligence. Journal of Business Strategy, 41(5), 39-45. https://doi.org/10.1108/JBS-12-2018-0204
34. Moral-Muñoz, Jose A., Herrera-Viedma, E., Santisteban-Espejo, A., & Cobo, M.J. (2020). Software tools for conducting bibliometric analysis in science: An up-to-date review. El Profesional de la Información, 29(1), 1-20. https://doi.org/10.3145/epi.2020.ene.03
35. Nawaz, N., Arunachalam, H., Pathi, B.K., & Gajenderan, V. (2024). The adoption of artificial intelligence in human resources management practices. International Journal of Information Management Data Insights, 4, 1-11. https://doi.org/10.1016/j.jjimei.2023.100208
36. Öztırak, M. (2023). A study on the impact of artificial intelligence anxiety on the innovation-oriented behaviours of employees. Optimum Journal of Economics and Management Sciences, 10(2), 267-286. https://doi.org/10.17541/optimum.1255576
37. Pektaş, T. (2024). The impact of artificial intelligence anxiety on employees: A comprehensive review of psychological and organizational dynamics. International Journal of Education Technology and Scientific Researches, 9(26), 194-206. http://dx.doi.org/10.35826/ijetsar.733
38. Pereira, V., Hadjielias, E., Christofi, M., & Vrontis, D. (2023). A systematic literature review on the impact of artificial intelligence on workplace outcomes: A multi-process perspective. Human Resource Management Review, 33, 1-22. https://doi.org/10.1016/j.hrmr.2021.100857
39. Pillai, R., Ghanghorkar, Y., Sivathabu, B., Algharabat, R., & Rana, N.P. (2024). Adoption of artificial intelligence (AI) based employee experience (EEX) chatbots. Information Technology & People, 37(1), 449-478. https://doi.org/10.1108/ITP-04-2022-0287
40. Prentice, C., Lopes, S.D., & Wang, X. (2020a). Emotional intelligence or artificial intelligence– an employee perspective. Journal of Hospitality Marketing & Management, 29(4), 377-403. https://doi.org/10.1080/19368623.2019.1647124
41. Prentice, C., Lopes, S.D., & Wang, X. (2020b). The impact of artificial intelligence and employee service quality on customer satisfaction and loyalty. Journal of Hospitality Marketing & Management, 29(7), 739-756. https://doi.org/10.1080/19368623.2020.1722304
42. Presbitero, A., & Teng-Calleja, M. (2023). Job attitudes and career behaviors relating to employees’ perceived incorporation of artificial intelligence in the workplace: A career self-management perspective. Personnel Review, 52(4), 1169-1187. https://doi.org/10.1108/PR-02-2021-0103
43. Ramachandran, K.K., Mary, A.A.S., Hawladar, S., Asokk, D., Bhaskar, B., & Pitroda, J.R. (2022). Machine learning and role of artificial intelligence in optimizing work performance and employee behavior. Materials Today: Proceedings, 51, 2327-2331. https://doi.org/10.1016/j.matpr.2021.11.544
44. Rao, S., Chitranshi, J., & Punjabi, N. (2020). Role of artificial intelligence in employee engagement and retention. Journal of Applied Management-Jidnyasa, 12(2), 42-60.
45. Rožman, M., Oreški, D., & Tominc, P. (2023). Artificial-intelligence-supported reduction of employees’ workload to increase the company’s performance in today’s VUCA environment. Sustainability, 15, 1-21. https://doi.org/10.3390/su15065019
46. Sağbaş, M., & Kılınç, S. (2024). Artificial intelligence in business management: A bibliometric analysis. Sinop Üniversitesi Sosyal Bilimler Dergisi, 8, 504-531. https://doi.org/10.30561/sinopusd.1561011
47. Sekaki, Y., Ziane, H., & Khazzar, A. (2025). Artificial intelligence in management studies (2021–2025): A bibliometric mapping of themes, trends, and global contributions. International Journal of Accounting, Finance, Auditing, Management and Economics, 6(9), 62–80.
48. Sharma, S., & Saxena, P. (2024). Role of emotional and artificial intelligence in employee performance: A perspective from the Indian Service Industry. Abhigyan, 42(1) 43–56. https://doi.org/10.1177/09702385241233078
49. Shi, Y., Blainey, S., Sun, C., & Jing, P. (2020). A literature review on accessibility using bibliometric analysis techniques. Journal of Transport Geography, 87, 1-12. https://doi.org/10.1016/j.jtrangeo.2020.102810
50. Strich, F., Mayer, A-S., & Fiedler, M. (2021). What do I do in a world of artificial intelligence? Investigating the impact of substitutive decision-making AI systems on employees’ professional role identity. Journal of the Association for Information Systems, 22(2), 304-324. https://doi.org/10.17705/1jais.00663
51. Sunman, G. (2025). Artificial intelligence in management: Bibliometric analysis. Politik Ekonomik Kuram, 9(2), 723-739. https://doi.org/10.30586/pek.1637141
52. Tong, S., Jia, N., Luo, X., & Fang, Z. (2021). The Janus face of artificial intelligence feedback: Deployment versus disclosure effects on employee performance. Strategic Management Journal, 42, 1600-1631. https://doi.org/10.1002/smj.3322
53. Wassan, S., Gulati, K., Pallathadka, H., Suhail, B., Kuhar, P., & Gupta, A. (2021). How artificial intelligence transforms the experience of employees. Turkish Journal of Computer and Mathematics Education, 12(10), 7116-7135.
54. Vergara, D., Bosque, A., Lampropoulos, G., & Fernández-Arias, p. (2025). Trends and applications of artificial intelligence in project management. Electronics, 14, 1-18. https://doi.org/10.3390/electronics14040800
55. Verma, S., & Singh, V. (2022). Impact of artificial intelligence-enabled job characteristics and perceived substitution crisis on innovative work behavior of employees from high-tech firms. Computers in Human Behavior, 131, 1-9. https://doi.org/10.1016/j.chb.2022.107215
56. Virontis, D., Christofi, M., Pereira, V., Tarba, S., Makrides, A., & Trichina, E. (2022). Artificial intelligence, robotics, advanced technologies and human resource management: A systematic review. The International Journal of Human Resource Management, 33(6), 1237-1266. https://doi.org/10.1080/09585192.2020.1871398
57. Yawalkar, M.V.V. (2019). A study of artificial intelligence and its role in human resource management. International Journal of Research and Analytical Reviews (IJRAR), 6(1), 20-24.
58. Yu, L., & Li, Y. (2022). Artificial intelligence decision-making transparency and employees’ trust: The parallel multiple mediating effect of effectiveness and discomfort. Behavioral Sciences, 12, 1-17. https://doi.org/10.3390/bs12050127
59. Zhu, Y-Q., Corbett, J.U., & Chiu, Y-T. (2021). Understanding employees’ responses to artificial intelligence. Organizational Dynamics, 50, 1-10. https://doi.org/10.1016/j.orgdyn.2020.100786