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MODELING HUMAN-MACHINE INTERACTION WITH GAME THEORY IN THE CONTEXT OF INDUSTRY 5.0

Yıl 2025, Cilt: 8 Sayı: 2, 370 - 386, 02.01.2026

Öz

Industry 5.0 has emerged as a paradigm that redefines the interaction between humans and machines. However, in hybrid production environments, situations where humans and machines collaborate create new planning and optimization problems in terms of task allocation and scheduling. In this study, in line with Industry 5.0's human-centric production vision, within the framework of rational decision-making in human-machine interaction, the game theory framework is used to analyze which strategies human and machine players will employ, with what probabilities, and at what utility level the game will reach equilibrium. Three strategies representing different levels of interaction for human and machine players (25%, 50%, and 75% human and machine) were evaluated by three field experts based on nine dimensions derived from the literature. A gain/loss-based Human-Machine Collaboration Game (IMIPO) was created using utility values scored by the experts between -10 and +10 for each strategy intersection in the payoff matrix. The resulting matrix was modeled using linear programming techniques and solved using Excel Solver to calculate the equilibrium value and strategy probabilities for human and machine players. The solution found the equilibrium value of the game to be 3.245 under mixed strategies, with the human player most likely choosing the "High Human Involvement" strategy, while the machine player most likely chose the "Dominant Cobot" strategy.

Kaynakça

  • Adel, A. (2022). Future of industry 5.0 in society: human-centric solutions, challenges and prospective research areas. Journal of Cloud Computing, 11(1), 40.
  • Ang, K. C., Sankaran, S., & Liu, D. (2025). Advancing sociotechnical systems theory: New principles for human-robot team design and development. Applied Ergonomics, 129, 104604.
  • Bouaziz, N., Bettayeb, B., Sahnoun, M. H., & Yassine, A. (2024). Incorporating uncertain human behavior in production scheduling for enhanced productivity in industry 5.0 context. International Journal of Production Economics, 274, 109311.
  • Breque, M., De Nul, L., & Petridis, A. (2021). Industry 5.0: towards a sustainable, human-centric and resilient European industry (No. KI-BD-20-021-EN-N). Directorate General for Research and Innovation (DG RTD) of the European Commission.
  • Carayannis, E. G., & Morawska-Jancelewicz, J. (2022). The Futures of Europe: Industry 5.0 as an enabler of human-centric, sustainable and resilient European industry. Journal of the Knowledge Economy, 13(2), 926–943.
  • Carayannis, E. G., & Morawska-Jancelewicz, J. (2022). The futures of Europe: Industry 5.0 as a renewal opportunity. Journal of the Knowledge Economy, 13(6), 3567–3584.
  • Ciccarelli, M., Forlini, M., Papetti, A., Palmieri, G., & Germani, M. (2024). Advancing human–robot collaboration in handcrafted manufacturing: cobot-assisted polishing design boosted by virtual reality and human-in-the-loop. The International Journal of Advanced Manufacturing Technology, 132(9), 4489-4504.
  • Destouet, C., Tlahig, H., Bettayeb, B., & Mazari, B. (2023). Flexible job shop scheduling problem under Industry 5.0: A survey on human reintegration, environmental consideration and resilience improvement. Journal of Manufacturing Systems, 67, 155-173.
  • Destouet, C., Tlahig, H., Bettayeb, B., & Mazari, B. (2024). Multi-objective sustainable flexible job shop scheduling problem: Balancing economic, ecological, and social criteria. Computers & Industrial Engineering, 195, 110419.
  • Dwivedi, A., Agrawal, D., Jha, A., & Mathiyazhagan, K. (2023). Studying the interactions among Industry 5.0 and circular supply chain: Towards attaining sustainable development. Computers & Industrial Engineering, 176, 108927.
  • Falerni, M. M., Pomponi, V., Karimi, H. R., Nicora, M. L., Dao, L. A., Malosio, M., & Roveda, L. (2024). A framework for human–robot collaboration enhanced by preference learning and ergonomics. Robotics and Computer-Integrated Manufacturing, 89, 102781.
  • Faraji, A., Ghasemi, E., Arya, S. H., & Mahabadi, H. A. (2024). Prediction of Effects of Industry 5.0 Concepts on Construction 5.0. In 3rd International Conference on recent advances in Engineering, Innovation & Technology.[online] https://www. researchgate. net/publication/385848718_Prediction_of_Effects_of_Industry_50_Concepts_on _Construction_50 [08.12. 2024].
  • Gao, Q., Liu, J., Liu, S., & Zhuang, C. (2025). From human-related to human-centric: A review of shop floor scheduling problem under Industry 5.0. Journal of Manufacturing Systems, 82, 531-546.
  • George, A. S., & George, A. H. (2023). Revolutionizing Manufacturing: Exploring the Promises and Challenges of Industry 5.0. Partners Universal International Innovation Journal, 1(2), 22-38.
  • Hwang, S. H., & Rey-Bellet, L. (2020). Strategic decompositions of normal form games: Zero-sum games and potential games. Games and Economic Behavior, 122, 370-390.
  • Kadir, B. A., Broberg, O., & Souza da Conceição, C. (2019). Current research and future perspectives on human–robot collaboration. International Journal of Advanced Manufacturing Technology, 105, 3899–3914.
  • Keshvarparast, A., Battini, D., Battaia, O., & Pirayesh, A. (2024). Collaborative robots in manufacturing and assembly systems: literature review and future research agenda. Journal of Intelligent Manufacturing, 35(5), 2065-2118.
  • Kovari, A. (2024). Industry 5.0: Generalized definition key applications opportunities and threats. Acta Polytechnica Hungarica, 21(3), 267-284.
  • Krajewski, L.J., Ritzman, L.P., & Malhotra, M.K. (2014). Operation Management Processes And Supply Chains, Pearson, İstanbul.
  • Lawless, W., & Moskowitz, I. S. (2025). An Entropy Approach to Interdependent Human–Machine Teams. Entropy, 27(2), 176.
  • Li, L., & Duan, L. (2025). Human centric innovation at the heart of industry 5.0–exploring research challenges and opportunities. International Journal of Production Research, 1-33.
  • Longo, F., Padovano, A., & Umbrello, S. (2020). Human–robot collaboration and the future of work: Towards a human-centered Industry 5.0. Computers & Industrial Engineering, 149, 106845.
  • Lou, S., Zhang, Y., Tan, R., & Lv, C. (2024). A human-cyber-physical system enabled sequential disassembly planning approach for a human-robot collaboration cell in Industry 5.0. Robotics and Computer-Integrated Manufacturing, 87, 102706.
  • Mao, Z., Sun, Y., Fang, K., Huang, D., & Zhang, J. (2024). Model and metaheuristic for human–robot collaboration assembly line worker assignment and balancing problem. Computers & Operations Research, 165, 106605.
  • Matoušek, J., & Gärtner, B. (2007). Understanding and using linear programming (Vol. 1). Berlin: Springer.
  • Mukul, E., & Güler, M. (2024, December). An Integrated Hesitant Fuzzy-based SWOT Analysis for Industry 5.0 Technology Evaluation. In 2024 8th International Symposium on Innovative Approaches in Smart Technologies (ISAS) (pp. 1-6). IEEE.
  • Murthy, P. R. (2005). Operations research (linear programming). bohem press.
  • Nahavandi, S. (2019). Industry 5.0—A human-centric solution. Sustainability, 11(16), 4371.
  • Nourmohammadi, A., Fathi, M., & Ng, A. H. (2022). Balancing and scheduling assembly lines with human-robot collaboration tasks. Computers & Operations Research, 140, 105674.
  • Ojstersek, R., Javernik, A., & Buchmeister, B. (2021). The impact of the collaborative workplace on the production system capacity: Simulation modelling vs. real-world application approach. Advances in Production Engineering & Management, 16(4), 431-442.
  • Orso, V., Ziviani, R., Bacchiega, G., Bondani, G., Spagnolli, A., & Gamberini, L. (2022). Employee-centric innovation: Integrating participatory design and video-analysis to foster the transition to Industry 5.0. Computers & Industrial Engineering, 173, 108661.
  • Osborne, M. J., & Rubinstein, A. (1994). A course in game theory. MIT press.
  • Proia, S., Carli, R., Cavone, G., & Dotoli, M. (2021). Control techniques for safe, ergonomic, and efficient human-robot collaboration in the digital industry: A survey. IEEE Transactions on Automation Science and Engineering, 19(3), 1798-1819.
  • Rega, A., Di Marino, C., Pasquariello, A., Vitolo, F., Patalano, S., Zanella, A., & Lanzotti, A. (2021). Collaborative workplace design: A knowledge-based approach to promote human–robot collaboration and multi-objective layout optimization. Applied Sciences, 11(24), 12147.
  • Romero, D., Stahre, J., & Taisch, M. (2020). The Operator 4.0: Towards socially sustainable factories of the future. Computers & industrial engineering, 139, 106128.
  • Sanogo, K., Benhafssa, A. M., & Sahnoun, M. H. (2025). A game theory approach for optimizing job shop scheduling problems with transportation in common shared human-robot environments. Computers & Industrial Engineering, 111366.
  • Shabur, M. A., Shahriar, A., & Ara, M. A. (2025). From automation to collaboration: exploring the impact of industry 5.0 on sustainable manufacturing. Discover sustainability, 6(1), 341.
  • Tallat, R., Hawbani, A., Wang, X., Al-Dubai, A., Zhao, L., Liu, Z., ... & Alsamhi, S. H. (2023). Navigating industry 5.0: A survey of key enabling technologies, trends, challenges, and opportunities. IEEE Communications Surveys & Tutorials, 26(2), 1080-1126.
  • Von Neumann, J., & Morgenstern, O. (1953). Theory of games and economic behavior. Princeton University Press. Wang, J., Hong, Y., Wang, J., Xu, J., Tang, Y., Han, Q. L., & Kurths, J. (2022). Cooperative and competitive multi-agent systems: From optimization to games. IEEE/CAA Journal of Automatica Sinica, 9(5), 763-783.
  • Wang, S., & Jiao, R. J. (2025). Cognitive intelligent task allocation for human-automation symbiosis in Industry 5.0 manufacturing systems via non-cooperative game theory: a bi-level optimization approach. The International Journal of Advanced Manufacturing Technology, 136(3), 1717-1739.
  • Wolniak, R. (2023). Industry 5.0–characteristic, main principles, advantages and disadvantages. Zeszyty Naukowe. Organizacja i Zarządzanie/Politechnika Śląska, (170), 663-678.
  • Yang, Y., & Zhang, Y. (2024). Design of Human-Machine Integration System to Meet Diverse Interactive Tasks. International Journal of Human–Computer Interaction, 1-14.
  • Zhang, X., Fathollahi-Fard, A. M., Tian, G., Truong Pham, D., Zhao, Q., & Aljuaid, M. (2025b). A multi-objective bi-population evolutionary algorithm for human-robot collaborative disassembly sequence planning with interval type-2 fuzzy modelling. Journal of Engineering Design, 1-57.
  • Zhang, X., Fathollahi-Fard, A. M., Tian, G., Yaseen, Z. M., Pham, D. T., Zhao, Q., & Wu, J. (2024). Human-robot collaboration in mixed-flow assembly line balancing under uncertainty: an efficient discrete bees algorithm. Journal of Industrial Information Integration, 41, 100676.
  • Zhang, X., Zhao, Q., Tian, G., Fathollahi-Fard, A. M., Yaseen, Z. M., & Pham, D. T. (2025a). Multi-objective heterogeneous interactive human-robot collaborative disassembly line balancing with partial destructive mode in type-2 fuzzy environment. Journal of Industrial Information Integration, 100963.
  • Zou, R., Liu, S., Luo, Y., Liu, Y., Feng, J., Wei, M., & Sun, J. (2024, March). Balancing humans and machines: A study on integration scale and its impact on collaborative performance. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 38, No. 16, pp. 17628-17636).

ENDÜSTRİ 5.0 BAĞLAMINDA İNSAN-MAKİNE ETKİLEŞİMİNİN OYUN TEORİSİYLE MODELLENMESİ

Yıl 2025, Cilt: 8 Sayı: 2, 370 - 386, 02.01.2026

Öz

Endüstri 5.0, insan ve makine arasındaki etkileşimi yeniden tanımlayan bir paradigma olarak ortaya çıkmıştır. Ancak hibrit üretim ortamlarında insan ve makinenin ortak faaliyet yürüttüğü durumlar, görev paylaşımı ve zamanlama açısından yeni planlama ve optimizasyon problemlerini beraberinde getirmektedir. Bu çalışmada Endüstri 5.0’ın insan merkezli üretim vizyonuna uygun olarak, insan-makine etkileşiminin rasyonel karar verme yaklaşımı çerçevesinde, insan ve makine oyuncularının hangi oyun stratejilerini hangi olasılıklarla oynayacakları ve oyunun hangi fayda düzeyinde dengeye varacağı oyun teorisi çerçevesinde iki kişilik, sıfır toplamlı karma stratejili oyun olarak ele alınmaktadır. Çalışmada, insan ve makine oyuncuları için farklı etkileşim düzeylerini temsil eden üçer strateji (%25, %50, %75 insan ve makine) üç alan uzmanı tarafından literatürden elde edilen dokuz boyut temel alınarak değerlendirilmiştir. Uzmanların ödemeler matrisindeki her strateji kesişimini -10 ile +10 arasında puanladığı fayda değerleri kullanılarak kazanç/kayıp temelli İnsan-Makine İşbirliği Oyunu (İMİPO) oluşturulmuştur. Elde edilen matris, doğrusal programlama tekniğiyle modellenmiş ve Excel Solver ile çözülerek insan ve makine oyuncuları için oyunun denge değeri ve strateji olasılıkları hesaplanmıştır. Çözüm sonucunda, oyunun denge değeri karma stratejiler altında 3.245 olarak bulunmuş ve insan oyuncusunun “Yüksek İnsan Katılımı”, makine oyuncusunun ise “Baskın Cobot” stratejisini en yüksek olasılıkla tercih ettiği belirlenmiştir.

Kaynakça

  • Adel, A. (2022). Future of industry 5.0 in society: human-centric solutions, challenges and prospective research areas. Journal of Cloud Computing, 11(1), 40.
  • Ang, K. C., Sankaran, S., & Liu, D. (2025). Advancing sociotechnical systems theory: New principles for human-robot team design and development. Applied Ergonomics, 129, 104604.
  • Bouaziz, N., Bettayeb, B., Sahnoun, M. H., & Yassine, A. (2024). Incorporating uncertain human behavior in production scheduling for enhanced productivity in industry 5.0 context. International Journal of Production Economics, 274, 109311.
  • Breque, M., De Nul, L., & Petridis, A. (2021). Industry 5.0: towards a sustainable, human-centric and resilient European industry (No. KI-BD-20-021-EN-N). Directorate General for Research and Innovation (DG RTD) of the European Commission.
  • Carayannis, E. G., & Morawska-Jancelewicz, J. (2022). The Futures of Europe: Industry 5.0 as an enabler of human-centric, sustainable and resilient European industry. Journal of the Knowledge Economy, 13(2), 926–943.
  • Carayannis, E. G., & Morawska-Jancelewicz, J. (2022). The futures of Europe: Industry 5.0 as a renewal opportunity. Journal of the Knowledge Economy, 13(6), 3567–3584.
  • Ciccarelli, M., Forlini, M., Papetti, A., Palmieri, G., & Germani, M. (2024). Advancing human–robot collaboration in handcrafted manufacturing: cobot-assisted polishing design boosted by virtual reality and human-in-the-loop. The International Journal of Advanced Manufacturing Technology, 132(9), 4489-4504.
  • Destouet, C., Tlahig, H., Bettayeb, B., & Mazari, B. (2023). Flexible job shop scheduling problem under Industry 5.0: A survey on human reintegration, environmental consideration and resilience improvement. Journal of Manufacturing Systems, 67, 155-173.
  • Destouet, C., Tlahig, H., Bettayeb, B., & Mazari, B. (2024). Multi-objective sustainable flexible job shop scheduling problem: Balancing economic, ecological, and social criteria. Computers & Industrial Engineering, 195, 110419.
  • Dwivedi, A., Agrawal, D., Jha, A., & Mathiyazhagan, K. (2023). Studying the interactions among Industry 5.0 and circular supply chain: Towards attaining sustainable development. Computers & Industrial Engineering, 176, 108927.
  • Falerni, M. M., Pomponi, V., Karimi, H. R., Nicora, M. L., Dao, L. A., Malosio, M., & Roveda, L. (2024). A framework for human–robot collaboration enhanced by preference learning and ergonomics. Robotics and Computer-Integrated Manufacturing, 89, 102781.
  • Faraji, A., Ghasemi, E., Arya, S. H., & Mahabadi, H. A. (2024). Prediction of Effects of Industry 5.0 Concepts on Construction 5.0. In 3rd International Conference on recent advances in Engineering, Innovation & Technology.[online] https://www. researchgate. net/publication/385848718_Prediction_of_Effects_of_Industry_50_Concepts_on _Construction_50 [08.12. 2024].
  • Gao, Q., Liu, J., Liu, S., & Zhuang, C. (2025). From human-related to human-centric: A review of shop floor scheduling problem under Industry 5.0. Journal of Manufacturing Systems, 82, 531-546.
  • George, A. S., & George, A. H. (2023). Revolutionizing Manufacturing: Exploring the Promises and Challenges of Industry 5.0. Partners Universal International Innovation Journal, 1(2), 22-38.
  • Hwang, S. H., & Rey-Bellet, L. (2020). Strategic decompositions of normal form games: Zero-sum games and potential games. Games and Economic Behavior, 122, 370-390.
  • Kadir, B. A., Broberg, O., & Souza da Conceição, C. (2019). Current research and future perspectives on human–robot collaboration. International Journal of Advanced Manufacturing Technology, 105, 3899–3914.
  • Keshvarparast, A., Battini, D., Battaia, O., & Pirayesh, A. (2024). Collaborative robots in manufacturing and assembly systems: literature review and future research agenda. Journal of Intelligent Manufacturing, 35(5), 2065-2118.
  • Kovari, A. (2024). Industry 5.0: Generalized definition key applications opportunities and threats. Acta Polytechnica Hungarica, 21(3), 267-284.
  • Krajewski, L.J., Ritzman, L.P., & Malhotra, M.K. (2014). Operation Management Processes And Supply Chains, Pearson, İstanbul.
  • Lawless, W., & Moskowitz, I. S. (2025). An Entropy Approach to Interdependent Human–Machine Teams. Entropy, 27(2), 176.
  • Li, L., & Duan, L. (2025). Human centric innovation at the heart of industry 5.0–exploring research challenges and opportunities. International Journal of Production Research, 1-33.
  • Longo, F., Padovano, A., & Umbrello, S. (2020). Human–robot collaboration and the future of work: Towards a human-centered Industry 5.0. Computers & Industrial Engineering, 149, 106845.
  • Lou, S., Zhang, Y., Tan, R., & Lv, C. (2024). A human-cyber-physical system enabled sequential disassembly planning approach for a human-robot collaboration cell in Industry 5.0. Robotics and Computer-Integrated Manufacturing, 87, 102706.
  • Mao, Z., Sun, Y., Fang, K., Huang, D., & Zhang, J. (2024). Model and metaheuristic for human–robot collaboration assembly line worker assignment and balancing problem. Computers & Operations Research, 165, 106605.
  • Matoušek, J., & Gärtner, B. (2007). Understanding and using linear programming (Vol. 1). Berlin: Springer.
  • Mukul, E., & Güler, M. (2024, December). An Integrated Hesitant Fuzzy-based SWOT Analysis for Industry 5.0 Technology Evaluation. In 2024 8th International Symposium on Innovative Approaches in Smart Technologies (ISAS) (pp. 1-6). IEEE.
  • Murthy, P. R. (2005). Operations research (linear programming). bohem press.
  • Nahavandi, S. (2019). Industry 5.0—A human-centric solution. Sustainability, 11(16), 4371.
  • Nourmohammadi, A., Fathi, M., & Ng, A. H. (2022). Balancing and scheduling assembly lines with human-robot collaboration tasks. Computers & Operations Research, 140, 105674.
  • Ojstersek, R., Javernik, A., & Buchmeister, B. (2021). The impact of the collaborative workplace on the production system capacity: Simulation modelling vs. real-world application approach. Advances in Production Engineering & Management, 16(4), 431-442.
  • Orso, V., Ziviani, R., Bacchiega, G., Bondani, G., Spagnolli, A., & Gamberini, L. (2022). Employee-centric innovation: Integrating participatory design and video-analysis to foster the transition to Industry 5.0. Computers & Industrial Engineering, 173, 108661.
  • Osborne, M. J., & Rubinstein, A. (1994). A course in game theory. MIT press.
  • Proia, S., Carli, R., Cavone, G., & Dotoli, M. (2021). Control techniques for safe, ergonomic, and efficient human-robot collaboration in the digital industry: A survey. IEEE Transactions on Automation Science and Engineering, 19(3), 1798-1819.
  • Rega, A., Di Marino, C., Pasquariello, A., Vitolo, F., Patalano, S., Zanella, A., & Lanzotti, A. (2021). Collaborative workplace design: A knowledge-based approach to promote human–robot collaboration and multi-objective layout optimization. Applied Sciences, 11(24), 12147.
  • Romero, D., Stahre, J., & Taisch, M. (2020). The Operator 4.0: Towards socially sustainable factories of the future. Computers & industrial engineering, 139, 106128.
  • Sanogo, K., Benhafssa, A. M., & Sahnoun, M. H. (2025). A game theory approach for optimizing job shop scheduling problems with transportation in common shared human-robot environments. Computers & Industrial Engineering, 111366.
  • Shabur, M. A., Shahriar, A., & Ara, M. A. (2025). From automation to collaboration: exploring the impact of industry 5.0 on sustainable manufacturing. Discover sustainability, 6(1), 341.
  • Tallat, R., Hawbani, A., Wang, X., Al-Dubai, A., Zhao, L., Liu, Z., ... & Alsamhi, S. H. (2023). Navigating industry 5.0: A survey of key enabling technologies, trends, challenges, and opportunities. IEEE Communications Surveys & Tutorials, 26(2), 1080-1126.
  • Von Neumann, J., & Morgenstern, O. (1953). Theory of games and economic behavior. Princeton University Press. Wang, J., Hong, Y., Wang, J., Xu, J., Tang, Y., Han, Q. L., & Kurths, J. (2022). Cooperative and competitive multi-agent systems: From optimization to games. IEEE/CAA Journal of Automatica Sinica, 9(5), 763-783.
  • Wang, S., & Jiao, R. J. (2025). Cognitive intelligent task allocation for human-automation symbiosis in Industry 5.0 manufacturing systems via non-cooperative game theory: a bi-level optimization approach. The International Journal of Advanced Manufacturing Technology, 136(3), 1717-1739.
  • Wolniak, R. (2023). Industry 5.0–characteristic, main principles, advantages and disadvantages. Zeszyty Naukowe. Organizacja i Zarządzanie/Politechnika Śląska, (170), 663-678.
  • Yang, Y., & Zhang, Y. (2024). Design of Human-Machine Integration System to Meet Diverse Interactive Tasks. International Journal of Human–Computer Interaction, 1-14.
  • Zhang, X., Fathollahi-Fard, A. M., Tian, G., Truong Pham, D., Zhao, Q., & Aljuaid, M. (2025b). A multi-objective bi-population evolutionary algorithm for human-robot collaborative disassembly sequence planning with interval type-2 fuzzy modelling. Journal of Engineering Design, 1-57.
  • Zhang, X., Fathollahi-Fard, A. M., Tian, G., Yaseen, Z. M., Pham, D. T., Zhao, Q., & Wu, J. (2024). Human-robot collaboration in mixed-flow assembly line balancing under uncertainty: an efficient discrete bees algorithm. Journal of Industrial Information Integration, 41, 100676.
  • Zhang, X., Zhao, Q., Tian, G., Fathollahi-Fard, A. M., Yaseen, Z. M., & Pham, D. T. (2025a). Multi-objective heterogeneous interactive human-robot collaborative disassembly line balancing with partial destructive mode in type-2 fuzzy environment. Journal of Industrial Information Integration, 100963.
  • Zou, R., Liu, S., Luo, Y., Liu, Y., Feng, J., Wei, M., & Sun, J. (2024, March). Balancing humans and machines: A study on integration scale and its impact on collaborative performance. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 38, No. 16, pp. 17628-17636).
Toplam 46 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular İşletme
Bölüm Araştırma Makalesi
Yazarlar

Serkan Genç 0000-0002-9520-8466

Gülper Basmacı Aktuna 0000-0002-8038-9639

Emre Bilgin Sarı 0000-0001-5110-1918

Gönderilme Tarihi 24 Kasım 2025
Kabul Tarihi 7 Aralık 2025
Yayımlanma Tarihi 2 Ocak 2026
Yayımlandığı Sayı Yıl 2025 Cilt: 8 Sayı: 2

Kaynak Göster

APA Genç, S., Basmacı Aktuna, G., & Bilgin Sarı, E. (2026). ENDÜSTRİ 5.0 BAĞLAMINDA İNSAN-MAKİNE ETKİLEŞİMİNİN OYUN TEORİSİYLE MODELLENMESİ. Journal of Business in The Digital Age, 8(2), 370-386. https://doi.org/10.46238/jobda.1829532

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