SavedAdd your journal

Journals / Nevşehir Hacı Bektaş Veli Üniversitesi SBE Dergisi / 2026 / Sayı: YZ (AI) ve Gelecek Perspektifler Özel Sayı

From rational to emotional: A human-centric operations research framework integrating emotion artifical intelligence

Rasyonelden duygusala: Duygu yapay zekåyı entegre eden insan-merkezli yöneylem araştırması çerçevesi

Can Mavruk(Niğde Ömer Halisdemir Üniversitesi)0000-0002-4084-7447
Pages
368–382
Publication Date
2025-12-07
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results indicate that in the call center scenario (n = 100 simulated cases), integrating emotional data resulted in 17% reduction in call escalations, average satisfaction increased from 3.4 to 4.1, and improved routing efficiency without increasing staff.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The rise of Emotion Artificial Intelligence (EAI) presents new opportunities for enhancing decision-making in Operations Research (OR) by incorporating human emotional states into analytical models.
📊
Key Findings & Data:Using simulation and synthetic datasets in Python programming language, the research demonstrates the value of emotion-aware optimization in a call center scenario, yielding improvements in satisfaction and operational efficiency.
💡
Conclusion & Impact:The results indicate that in the call center scenario (n = 100 simulated cases), integrating emotional data resulted in 17% reduction in call escalations, average satisfaction increased from 3.4 to 4.1, and improved routing efficiency without increasing staff.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Duygu yapay zeka · Yöneylem araştırması · İnsan merkezli optimizasyon

Abstract

The rise of Emotion Artificial Intelligence (EAI) presents new opportunities for enhancing decision-making in Operations Research (OR) by incorporating human emotional states into analytical models. This study explores how emotional data, such as customer sentiment, can be operationalized in OR models for improved outcomes in service systems. Using simulation and synthetic datasets in Python programming language, the research demonstrates the value of emotion-aware optimization in a call center scenario, yielding improvements in satisfaction and operational efficiency. A second simulation data is generated for emotion detection in call center scenario (neutral, frustrated, happy and angry) to explore how customer satisfaction varies within each detected emotion group and which emotion leads to longer calls. The paper proposes a three-layered conceptual framework, evaluates the implications through statistical analysis, and outlines ethical considerations for implementation. The results indicate that in the call center scenario (n = 100 simulated cases), integrating emotional data resulted in 17% reduction in call escalations, average satisfaction increased from 3.4 to 4.1, and improved routing efficiency without increasing staff. The findings support a new direction for OR that is adaptive, empathetic, and behaviorally informed, and for the nascent yet promising convergence.

Özet

Duygu Yapay Zeka'nın yükselişi, insan duygusal durumlarını analitik modellere dahil ederek Yöneylem Araştırması'nda karar vermeyi geliştirmek için yeni fırsatlar sunmuştur. Bu çalışma, müşteri duygusu ve çalışan kırılganlığı gibi duygusal verilerin, hizmet sistemlerinde ve işgücü yönetiminde iyileştirilmiş sonuçlar için yöneylem modellerinde nasıl işlevselleştirilebileceğini araştırmayı amaçlamaktadır. Python programlama dili kullanarak simülasyon ve sentetik veri kümeleri oluşturan bu araştırma, bir çağrı merkezi senaryosunda duyguya duyarlı optimizasyonun değerini göstererek memnuniyet ve operasyonel verimlilikte iyileştirmeler sağlamaktadır. Çağrı merkezi senaryosunda (nötr, sinirli, mutlu ve kızgın) duygu tespiti için ikinci bir simülasyon verisi üretilmiş ve müşteri memnuniyetinin her tespit edilen duygu grubu içinde nasıl değiştiği ve hangi duygunun daha uzun görüşmelere yol açtığı araştırılmıştır. Makale, üç katmanlı bir kavramsal çerçeve önermekte, istatistiksel analiz yoluyla çıkarımları değerlendirmekte ve uygulama için etik hususları belirtmektedir. Sonuçlar, çağrı merkezi senaryosunda (n = 100 simüle edilmiş vaka) duygusal verilerin entegre edilmesinin çağrı artışlarında %17'lik bir azalmaya, ortalama memnuniyetin 3,4'ten 4,1'e çıkmasına ve personel sayısını artırmadan yönlendirme verimliliğinin artmasına yol açtığını göstermektedir. Bulgular, uyarlanabilir, empatik ve davranışsal olarak bilgilendirilmiş yöneylem araştırması için ve yeni doğmakta olan ancak umut vadeden yakınsama için yeni bir yönü desteklemektedir

🔖

Cite This Article

Mavruk, C. (2025). From rational to emotional: A human-centric operations research framework integrating emotion artifical intelligence. Nevşehir Hacı Bektaş Veli Üniversitesi SBE Dergisi, Sayı: YZ (AI) ve Gelecek Perspektifler Özel Sayı, 368–382. https://doi.org/10.30783/nevsosbilen.1739466

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (20)

  1. Alia-Klein, N., Preston-Campbell, R. N., Moeller, S. J., Parvaz, M. A., Bachi, K., Gan, G., Zilverstand, A., Konova, A. B., & Goldstein, R. Z. (2018). Trait anger modulates neural activity in the fronto-parietal attention network. PloS one, 13(4), e0194444. https://doi.org/10.1371/journal.pone.0194444
  2. Altay, N., & Green III, W. G. (2006). OR/MS research in disaster operations management. European Journal of Operational Research, 175(1), 475-493. https://doi.org/10.1016/J.EJOR.2005.05.016
  3. Bartneck, C., Kulic´, D., Croft, E., & Zoghbi, S. (2009). Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots. Int J Soc Robot, 1, 71-81. https://doi.org/10.1007/s12369-008-0001-3
  4. Brailsford, S. C., Bolt, T. B., Bucci, G., Chaussalet, T. M., Connell, N. A., Harper, P. R., Klein, J. H., Pitt, M, & Taylor, M. (2013). Overcoming the barriers: a qualitative study of simulation adoption in the NHS. Journal of the Operational Research Society, 64(2), 157-168. https://doi.org/10.1057/jors.2011.130
  5. Calvo, R. A., & D’Mello, S. (2010). Affect detection: An interdisciplinary review of models, methods, and their applications. IEEE Transactions on Affective Computing, 1(1), 18-37. https://doi/10.1109/T-AFFC.2010.1
  6. Crawford, K., & Paglen, T. (2021). Excavating AI: The politics of images in machine learning training sets. AI & Society, 36(4), 1105–1116. https://doi.org/10.1007/s00146-021-01162-8
  7. Ekman, P., & Friesen, W. V. (1978). Facial action coding system: Investigator’s guide. Consulting Psychologists Press.
  8. Feidakis, M., Caballé, S., Daradoumis, T., Jiménez, D. G., & Conesa, J. (2014). Providing emotion awareness and affective feedback to virtualised collaborative learning scenarios. International Journal of Continuing Engineering Education and Life Long Learning, 24(2), 141-167. https://doi.org/10.1504/IJCEELL.2014.060154
  9. Grandey, A. A., Rupp, D., & Brice, W. N. (2015). Emotional labor threatens decent work: A proposal to eradicate emotional display rules. Journal of Organizational Behavior, 36(6), 770-785. https://doi.org/10.1002/job.2020
  10. Klein, G., Moon, B., & Hoffman, R. R. (2006). Making sense of sensemaking 1: Alternative perspectives. IEEE Intelligent Systems, 21(4), 70-73. https://doi.org/10.1109/MIS.2006.75
  11. Kunc, M., Malpass, J., & White, L. (Eds.). (2016). Behavioral operational research: Theory, Methodology and Practice. Springer. https://doi.org/10.1057/978-1-137-53551-1
  12. Mark, G., Iqbal, S., Czerwinski, M., & Johns, P. (2014, February). Capturing the mood: facebook and face-to-face encounters in the workplace. Proceedings of the 17th ACM conference on computer supported cooperative work & social computing, 1082-1094. https://doi.org/10.1145/2531602.2531673
  13. Narimisaei, J., Naeim, M., Imannezhad, S., Samian, P., & Sobhani, M. (2024). Exploring emotional intelligence in artificial intelligence systems: a comprehensive analysis of emotion recognition and response mechanisms. Annals of Medicine and Surgery, 86(8), 4657-4663. https://doi.org/10.1097/MS9.0000000000002315
  14. Picard, R. W. (1997). Affective Computing. The MIT Press.
  15. Picard, R. W., Vyzas, E., & Healey, J. (2001). Toward machine emotional intelligence: Analysis of affective physiological state. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(10), 1175-1191. https://doi.org/10.1109/34.954607
  16. Piispanen, J.R., & Rousi, R. (2024). Emotion AI in workplace environments: A case study. https://doi.org/10.48550/arXiv.2412.09251
  17. Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1), 83-98. https://doi.org/10.1504/IJSSCI.2008.017590
  18. Scherer, K. R. (2003). Vocal communication of emotion: A review of research paradigms. Speech Communication, 40(1-2), 227-256. https://doi.org/10.1016/S0167-6393(02)00084-5
  19. Tang, F., Zhou, Y., & Kato, N. (2020). Deep reinforcement learning for dynamic uplink/downlink resource allocation in high mobility 5G HetNet. IEEE Journal on Selected Areas in Communications, 38(12), 2773-2782. https://doi.org/10.1109/JSAC.2020.3005495
  20. Wewelwala, S. H., & Sumanathilaka, T. G. D. K. (2025). Hybrid emotion recognition: Enhancing customer interactions through acoustic and textual analysis. https://doi.org/10.48550/arXiv.2503.21927