Araştırma Makalesi
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Yapay Zekâ Tabanlı Karar Destek Sistemleri İle Personel Seçimi

Yıl 2025, Cilt: 6 Sayı: 2, 13 - 29, 11.12.2025

Öz

Dijitalleşmenin hız kazandığı günümüzde, insan kaynakları yönetiminde yapay zekâ (YZ) tabanlı karar destek sistemlerinin kullanımı giderek artmaktadır. Bu çalışma, bankacılık sektöründe personel seçim sürecini daha nesnel ve etkin hale getirmek amacıyla YZ destekli çok kriterli bir değerlendirme modeli önermektedir. Araştırmada, personel seçiminde dikkate alınması gereken dokuz temel kriter belirlenmiş ve bu kriterler TOPSIS yöntemiyle sıralanarak ideal aday profili oluşturulmuştur. Bulgular, problem çözme yeteneği, motivasyon ve iletişim becerisi gibi bireysel yeterliliklerin öncelikli olduğunu ortaya koymuştur. Ayrıca etik değerler ve teknolojik adaptasyon gibi kriterlerin göreli önem düzeyleri değerlendirilmiştir. Araştırma, karar verme süreçlerinde veri temelli ve şeffaf yaklaşımların benimsenmesini teşvik ederken, insan kaynakları uygulamalarında dijital dönüşümün yönünü belirlemeye katkı sunmaktadır. Çalışmanın sonuçları, literatürdeki benzer araştırmalarla karşılaştırılmış, teorik katkılar ve uygulamaya yönelik öneriler tartışılmıştır. Bu bağlamda araştırma, hem akademik literatüre katkı sağlamakta hem de sektörel uygulamalara rehberlik etmektedir.

Kaynakça

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PERSONNEL SELECTİON USİNG ARTİFİCİAL INTELLİGENCE-BASED DECİSİON SUPPORT SYSTEMS

Yıl 2025, Cilt: 6 Sayı: 2, 13 - 29, 11.12.2025

Öz

With the accelerating pace of digitalization, the use of artificial intelligence (AI)-based decision support systems in human resource management has become increasingly prevalent. This study proposes an AI-assisted multi-criteria evaluation model to enhance the objectivity and efficiency of personnel selection processes in the banking sector. Nine essential evaluation criteria were identified and prioritized using the TOPSIS method, resulting in the development of an ideal candidate profile. The findings indicate that individual competencies such as problem-solving ability, motivation, and communication skills are of primary importance. Additionally, the relative significance of criteria such as ethical values and technological adaptability was assessed. The study encourages the adoption of data-driven and transparent approaches in decision-making and contributes to shaping the digital transformation of human resources practices. The results are compared with similar studies in the existing literature, and both theoretical contributions and practical implications are discussed. Accordingly, this research offers valuable insights for both academia and industry by providing a structured framework for AI-integrated personnel selection.

Kaynakça

  • AB Türkiye Raporu. (2024). Commıssıon Staff Workıng Document. https://enlargement.ec.europa.eu/document/download/8010c4db-6ef8-4c85-aa06-814408921c89_en?filename=T%C3%BCrkiye+Report+2024.pdf adresinden 11.05.2025 tarihinde erişilmiştir.
  • Ada, M., & Çakır, H. (2022). Topsıs ve ahp çok kriterli karar verme yöntemlerinin personel seçim sürecine uygulanması. International Journal of 3D Printing Technologies and Digital Industry, 6(2), 186-200. https://doi.org/10.46519/ij3dptdi.1018279
  • Andersen, B. & Fagerhaug, T. (2002). Performance measurement explained: designing and ımplementing your state-of-the-art system. The American Society for Quality.
  • Aycan, Z. (2001). Human resource management in Turkey‐Current issues and future challenges. International journal of manpower, 22(3), 252-260.
  • Balcıoğlu, Y., Artar, M., & Erdi̇l, P. (2022). Artificial ıntelligence in project management: an application in the banking sector. Akademik Araştırmalar ve Çalışmalar Dergisi (AKAD), 14(27), 323-334. https://doi.org/10.20990/kilisiibfakademik.1159862
  • Bogen, M., & Rieke, A. (2018). Help wanted: An examination of hiring algorithms, equity, and bias. Upturn.
  • Borgesius, F. Z. (2020). Price discrimination, algorithmic decision-making, and European non-discrimination law. European Business Law Review, 31(3), 401 – 422.
  • Boustani, N. (2021). Artificial intelligence impact on banks clients and employees in an Asian developing country. Journal of Asia Business Studies, 16(2), 267-278. https://doi.org/10.1108/jabs-09-2020-0376
  • Byars, L. L., & Rue, L. W. (1997). Human resources management (5. b.). Chicago: Irwin.
  • Chakraborty S., (2022) Topsıs and modified topsıs: A comparative analysis, Decision Analytics Journal, Vol 2.
  • Chen, J., Le, T., & Florence, D. (2021). Usability and responsiveness of artificial intelligence chatbot on online customer experience in e-retailing. International Journal of Retail & Distribution Management, ahead-of-print. https://doi.org/10.1108/IJRDM-08-2020-0312
  • Chen, Z. (2023). Ethics and discrimination in artificial intelligence-enabled recruitment practices. Humanities and social sciences communications, 10(1), 1-12. https://doi.org/10.1057/s41599-023-02079-x
  • Costello, K. (2019). Gartner Survey Shows 37 Percent of Organizations Have Implemented AI in Some Form Erişim tarihi: 14.06.2024, https://www.gartner.com/en/newsroom/press-releases/
  • Council of Europe. (2009). Ethics for the Prevention of Corruption in Turkey. https://rm.coe.int/16806eef3a adresinden 08.05.2025 tarihinde erişilmiştir.
  • Değermenci, A. & Ayvaz, B. (2016). Bulanık ortamda topsis yöntemi ile personel seçimi: katılım bankacılığı sektöründe bir uygulama. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, 15(30): 77-93.
  • Deloitte Türkiye. (2024) Bankacılık sektöründe dijital dönüşüm ve olgunluk analizi. https://www.deloitte.com/tr/tr/Industries/financial-services/research/digital-banking-maturity-2024.html adresinden 24.05.2025 tarihinde erişilmiştir.
  • Doğan, A. (2011). Elektronik insan kaynakları yönetimi ve fonksiyonları. İnternet Uygulamaları ve Yönetimi Dergisi, 2(2), 51-80. https://doi.org/10.5505/iuyd.2011.22932
  • Doğan, S., & KILIÇ, S. (2014). Algılanan örgütsel etik iklim ve üretkenlik karşıtı iş davranışları arasındaki ilişkilerin incelenmesi.Cumhuriyet Üniversitesi İktisadi ve İdari Bilimler Dergisi, 15(1).
  • Elden B. (2024). İşletmelerde Dijitalleşme Sürecinde Dijital Liderliğin Önemi. Yönetim ve Organizasyon Alanında Uluslararası Araştırma ve Değerlendirmeler, Serüven Yayınevi.
  • Elden, B. (2024). Üniversite Öğrencilerinin Girişimcilik Eğitiminin İşsizlik Kaygısına Etkisinde Çevresel Belirsizliğin Aracı Rolü. Kapadokya Akademik Bakış, 8(1), 74-85. https://doi.org/10.69851/car.1602733
  • Elden, B. (2025). Sustainability in the Banking Sector Determination of Success Factors with DEMATEL Method. In Multi-Criteria Decision Making Methods and Sustainable Applications in the Digital Age (pp. 181-210). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-8789-4.ch006
  • Fruchterman, J., & Mellea, J. (2018). Expanding employment success for people with disabilities. Benetech, November.
  • Geetha R & Bhanu Sree Reddy D (2018). Recruıtment through artıfıcıal Intellıgence: A conceptual study, International Journal of Mechanical Engineering and Technology (IJMET), 9(7), 63-70.
  • Gounopoulos, D., Platanakis, E., Zopounidis, C., Doumpos, M., & Zhang, W. (2022). Operational research and artificial intelligence methods in banking. European Journal of Operational Research., 306(1), 1-16. https://doi.org/10.2139/ssrn.4085812
  • Heinrich K, Janiesch C, Krancher O, Stahmann P, Wanner J, Zschech P. (2025) Decision factors for the selection of AI-based decision support systems-The case of task delegation in prognostics. PLoS One., 20(7), e0328411. https://doi.org/10.1371/journal.pone.0328411
  • Hemalatha, P. Barani Kumari, Nishad Nawaz, Vijayakumar Gajenderan (2021). Impact of artificial ıntelligence on recruitment and selection of ınformation technology companies, Proceedings of the International Conference on Artificial Intelligence and Smart Systems (ICAIS-2021), 60-66.
  • Hmoud & Varallyai Laszlo, (2019). Will artifıcial ıntelligence take over human resources recruitment and selection?, Network Intelligence Studies 7(13), 21-30.
  • Hmoud, B. (2021). The adoption of artificial intelligence in human resource management. Forum Scientiae Oeconomia, 9(1), 105- 118. https://doi.org/10.23762/fso_Vol9_no1_7
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  • Kim, P. (2024). Artificial Intelligence, Big Data, Algorithmic management, and Labor Law, Oxford University Press
  • Koivunen, S., Ala-Luopa, S., Olsson, T., & Haapakorpi, A. (2022). The march of Chatbots into recruitment: recruiters’ experiences, expectations, and design opportunities. Computer Supported Cooperative Work (CSCW), 31(3), 487-516. https://doi.org/10.1007/s10606-022-09429-4
  • Kshetri, N. (2021). Evolving uses of artificial intelligence in human resource management in emerging economies in the Global South: Some preliminary evidence. Management Research Review, 44(7), 970-990. https://doi.org/10.1108/MRR-03-2020- 0168
  • Kulkarni, S. B., & Che, X. (2019). Intelligent software tools for recruiting. Journal of International Technology and Information Management, 28(2), 2-16. https://doi.org/10.58729/1941-6679.1398
  • Laurim, V., Arpaci, S., Prommegger, B., & Krcmar, H. (2021). Computer, whom should i hire?–acceptance criteria for artificial intelligence in the recruitment process, Proceedings of the 54th Hawaii International Conference on System Sciences.
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  • Mathew, S., Oswal, N. & Ateeq, K. (2021). Artificial intelligence (AI): Bringing a new revolution in human resource management (HRM). Grenze International Journal of Engineering and Technology, 1(1), 212-218.
  • Monjezi, M., Dehghani, H., Singh, T.N., Sayadi, A.R.. & Gholinejad, A., (2012). Application of TOPSIS method for selecting the most appropriate blast design, Arabian Journal of Geosciences, 5(1), 95-101. https://doi.org/10.1007/s12517-010-0133-2
  • Mujtaba, D. F., & Mahapatra, N. R. (2024). Fairness in AI-driven recruitment: Challenges, metrics, methods, and future directions. arXiv preprint arXiv:2405.19699. https://doi.org/10.48550/arXiv.2405.19699
  • Özcan, M. (2012). AHP ve TOPSIS Yöntemlerinin Personel Seçimi Sürecindeki Etkililiğinin Karşılaştırılması: Bir Üretim İsletmesinde Uygulama. (Yüksek Lisans Tezi, Hacettepe Üniversitesi) Ulusal Tez Merkezi.
  • Özdemir, N. (2019). Sağlık sektöründe çalışan ebelerin işkolikliklerinin iş stresleri üzerine etkileri. International Journal of Arts and Social Studies, 2(3), 60-69.
  • Özdemir, H. Ö., & İsmailçebi, Y. Z. (2020). Örgütsel bağlılığın örgütsel özdeşleşmeye etkisinde işten ayrılma niyetinin rolünün yapısal eşitlik modeli ile analizi. Anadolu Üniversitesi Sosyal Bilimler Dergisi, 20(1), 261-274.
  • Özdemir, N. (2021). Sağlık sektöründe insan kaynakları yönetiminde performans yönetimi ve personel verimliliği. Journal of Organizational Psychology and Behavior, 2(1), 28-36.
  • Özden, Ü. H. (2011). Topsis yöntemi ile avrupa birliğine üye ve aday ülkelerin ekonomik göstergelere göre siralanmasi. Trakya Üniversitesi Sosyal Bilimler Dergisi, 13(2), 215-236.
  • Öztürk, A., & Gökdeniz, İ. (2023). Örgütsel etik iklimin iş gören motivasyonuna etkisinde örgütsel özdeşleşmenin ve iş tatmininin aracı rolü. İşletme Araştırmaları Dergisi, 15(2), 907-924.
  • Pasa, S. F. (2000). Leadership influence in a high power distance and collectivist culture: The Turkish case. Leadership & Organization Development Journal, 21(8), 414–426.
  • Peng, A., Nushi, B., Kiciman, E., Inkpen, K., & Kamar, E. (2022). Investigations of performance and bias in human-AI teamwork in hiring. In Proceedings of the AAAI conference on artificial intelligence (36, No. 11, 12089-12097).
  • Pwc. (2024) ON Dijital – PwC / Strategy& Dijital Bankacılık Raporu. https://www.strategyand.pwc.com/tr/tr/medya/dijital-bankacilik-raporu.html adresinden 24.05.2025 tarihinde erişilmiştir.
  • Raghavan, M., Barocas, S., Kleinberg, J., & Levy, K. (2020). Mitigating bias in algorithmic hiring: Evaluating claims and practices. In Proceedings of the 2020 conference on fairness, accountability, and transparency (469-481) ACM. https://dl.acm.org/doi/10.1145/3351095.3372828
  • Riikkinen, M., Saarijärvi, H., Sarlin, P., & Lähteenmäki, I. (2018). Using artificial intelligence to create value in insurance. International Journal of Bank Marketing, 36(6), 1145-1168. https://doi.org/10.1108/IJBM-01-2017-0015
  • Saaty, T. (2000). Fundamentals of Decision Making and Priority Theory with the Analytical Hierarchy Process, RWS publications.
  • Saaty, T.L., Niemira, M.P., (2006). A framework for making a better decision, Research Review, 13(1).
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  • Secinaro, S., Calandra, D., Secinaro, A., Muthurangu, V., & Biancone, P. (2021). The role of artificial intelligence in healthcare: a structured literature review. BMC Medical Informatics and Decision Making, 21(1), 125. https://doi.org/10.1186/s12911-021-01488-9
  • Seppälä, P., & Małecka, M. (2024). AI and discriminative decisions in recruitment: Challenging the core assumptions. Big Data & Society, 11(1). https://doi.org/10.1177/20539517241235872
  • Sharma, S., Islam, N., Singh, G., & Dhir, A. (2022). Why do retail customers adopt artificial ıntelligence (aı) based autonomous decision-making systems?. IEEE Transactions on Engineering Management, 71, 1846-1861. https://doi.org/10.1109/tem.2022.3157976
  • Shields, J. (2018). Over 98% of Fortune 500 companies use applicant tracking systems (ATS).
  • Shyjith, K., Ilangkumaran, M., Kumanan, S., (2008). Multi-criteria decision-making approach to evaluate optimum maintenance strategy in textile industry, Journal of Quality in Maintenance Engineering, 14(4), 375-386. https://doi.org/10.1108/13552510810909975
  • Tambe, P., Cappelli, P., & Yakubovich, V. (2019). Artificial ıntelligence in human resources management: challenges and a path forward. California Management Review, 61(4), 15-42.
  • Tanke, M. L. (1990). Human Resource Management For The Hospitality Industry (5. b.). DelmarLearning.
  • Taş, A., & Karataş, P. Ç. (2021). Yazılım sektöründe nitelikli personel seçiminin Nötrosofik AHP ve TOPSİS yöntemleri ile incelenmesi. İşletme Araştırmaları Dergisi, 13(1), 969-979. https://doi.org/10.20491/isarder.2021.1178
  • TBB, (2024) Türkiye Bankalar Birliği verileri, Erişim tarihi: 14.05.2025 https://www.tbb.org.tr/sites/default/files/raporlar/51410/ekler/Dijital-Internet-Mobil_Bankacilik_Istatistikleri-Eylul_2024_0.pdf
  • TBB - Müşteri Şikayetleri. (2024). Müşteri Şikayetleri Hakem Heyeti Yıllık Faaliyet Raporu. https://www.tbb.org.tr/faaliyetler/bireysel-musteri-hakem-heyeti/yillik-raporlar/pdf/5980 adresinden 23.05.2025 tarihinde erişilmiştir.
  • Tiutiu, M., & Dabija, D. (2023). Improving Customer Experience Using Artificial Intelligence in Online Retail. Proceedings of the International Conference on Business Excellence, 17, 1139 - 1147. https://doi.org/10.2478/picbe-2023-0102.
  • TÜSİAD. (2025). Üretken Yapay Zeka Devrimi: Küresel Etkiler ve Türkiye'nin Konumu. https://tusiad.org/tr/yayinlar/raporlar/item/11785-uretken-yapay-zeka-devrimi-kuresel-etkiler-ve-turkiye-nin-konumu adresinden 17.06.2025 tarihinde erişilmiştir.
  • Upadhyay, A. K., & 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
  • Van Esch, P., Black, J. S., & Ferolie, J. (2019). Marketing AI recruitment: The next phase in job application and selection,Computers in Human Behavior, 90, 215-222. https://doi.org/10.1016/j.chb.2018.09.009
  • Wilkens, U., Lutzeyer, I., Zheng, C., Beser, A., & Prilla, M. (2025). Augmenting diversity in hiring decisions with artificial intelligence tools. The International Journal of Human Resource Management, 1–38. https://doi.org/10.1080/09585192.2025.2492867
Toplam 73 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Strateji, Yönetim ve Örgütsel Davranış (Diğer)
Bölüm Araştırma Makalesi
Yazarlar

Bedirhan Elden 0000-0001-5955-1606

Gönderilme Tarihi 26 Eylül 2025
Kabul Tarihi 8 Ekim 2025
Yayımlanma Tarihi 11 Aralık 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 6 Sayı: 2

Kaynak Göster

APA Elden, B. (2025). Yapay Zekâ Tabanlı Karar Destek Sistemleri İle Personel Seçimi. Ahi Evran Akademi, 6(2), 13-29.