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STRATEJİK KARAR ALMA VE YAPAY ZEKA: ÇALIŞANLARIN KARAR ALMA SÜREÇLERİNDE YENİ YAKLAŞIMLAR VE BİREYSEL ALGILAR

Yıl 2026, Cilt: 22 Sayı: 1, 291 - 307, 26.03.2026
https://doi.org/10.17130/ijmeb.1677954
https://izlik.org/JA92YY69TK

Öz

Yapay zeka, iş dünyasında stratejik karar alma süreçlerini önemli ölçüde dönüştürmüştür. Uzaktan çalışma ve dijitalleşmenin yaygınlaşmasıyla birlikte yapay zeka teknolojileri, çalışanların karar alma biçimlerini etkilemiş ve bu süreçlere yeni yaklaşımlar kazandırmıştır. Bu araştırmada, yapay zeka teknolojilerinin stratejik karar alma süreçlerine etkisi ve çalışanların bu süreçteki algılarının rolü incelenmiştir. Çalışma kapsamında İstanbul ilinde faaliyet gösteren gümrük müşavirliği çalışanlarına, Yapay Zeka Algısı Ölçeği, Yapay Zeka Tutum Ölçeği ve Karar Stratejileri Ölçeği uygulanmıştır. Veriler, SPSS 25 programı ile t-testi, tek yönlü varyans analizi ve Pearson korelasyon testi kullanılarak analiz edilmiştir. Cinsiyet, medeni durum, eğitim durumu ve çalışma süresine göre yapılan analizlerde bazı değişkenlerde anlamlı farklar saptanmıştır. Kadınların erkeklere göre daha yüksek puan aldığı, lisans mezunlarının ise Yapay Zekaya Yönelik Genel Tutum Ölçeği’nin pozitif boyutunda yüksek lisans mezunlarından daha yüksek puan aldığı belirlenmiştir. Ayrıca değişkenler arasında pozitif yönde anlamlı ilişkiler bulunmuştur. Bulgular, yapay zeka algısının ve karar stratejilerinin anlaşılmasına önemli katkılar sağlamaktadır.

Kaynakça

  • References
  • Abrokwah-Larbi, K., & Awuku-Larbi, Y. (2024). The impact of artificial intelligence in marketing on the performance of business organizations: Evidence from SMEs in an emerging economy. Journal of Entrepreneurship in Emerging Economies, 16(4), 1090–1117. https://doi.org/10.1108/JEEE-07-2022-0207.
  • Rahman, A., & Muktadir, M. G. (2021). SPSS: An imperative quantitative data analysis tool for social science research. International Journal of Research and Innovation in Social Science, 5(10), 300–302.
  • Acciarini, C., Brunetta, F., & Boccardelli, P. (2021). Cognitive biases and decision-making strategies in times of change: A systematic literature review. Management Decision, 59(3), 638–652. https://doi.org/10.1108/MD-07-2019-1006.
  • Ahmad, S. F., Han, H., Alam, M. M., Rehmat, M., Irshad, M., Arraño-Muñoz, M., … Ariza-Montes, A. (2023). Impact of artificial intelligence on human loss in decision making, laziness and safety in education. Humanities and Social Sciences Communications, 10(1), 1–14. https://doi.org/10.1057/s41599-023-01787-8.
  • Ahmed, R. R., Streimikiene, D., Streimikis, J., & Siksnelyte-Butkiene, I. (2024). A comparative analysis of multivariate approaches for data analysis in management sciences. E+M Ekonomie a Management, 27(1), 192–210. https://doi.org/10.15240/tul/001/2024-5-001.
  • Alzoubi, H. M., & Aziz, R. (2021). Does emotional intelligence contribute to quality of strategic decisions? The mediating role of open innovation. Journal of Open Innovation: Technology, Market, and Complexity, 7(2), 130. https://doi.org/10.3390/joitmc7020130.
  • Antes, A. L., Burrous, S., Sisk, B. A., Schuelke, M. J., Keune, J. D., DuBois, J. M. (2021). Exploring perceptions of healthcare technologies enabled by artificial intelligence: An online, scenario-based survey. BMC Medical Informatics and Decision Making, 21, 1–15. https://doi.org/10.1186/s12911-021-01586-8.
  • Apalı, A., Köse, E., & Aldemir, M. E. (2022). Denetçilerin yapay zekâ’ya yönelik algılarının denetim kalitesine etkisi üzerine bir araştırma. Denetişim, 26(2), 32–44.
  • Bag, S., Gupta, S., Kumar, A., & Sivarajah, U. (2021). An integrated artificial intelligence framework for knowledge creation and B2B marketing rational decision making for improving firm performance. Industrial Marketing Management, 92(1), 178–189. https://doi.org/10.1016/j.indmarman.2020.12.001.
  • Baltacı, A. (2018). Nitel araştırmalarda örnekleme yöntemleri ve örnek hacmi sorunsalı üzerine kavramsal bir inceleme. Bitlis Eren Üniversitesi Sosyal Bilimler Dergisi, 7(1), 231–274.
  • 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. https://doi.org/10.1002/job.2735.
  • Belhadi, A., Kamble, S., Fosso Wamba, S., & Queiroz, M. M. (2022). Building supply-chain resilience: An artificial intelligence-based technique and decision-making framework. International Journal of Production Research, 60(14), 4487–4507. https://doi.org/10.1080/00207543.2021.1950935.
  • Bhargava, A., Bester, M., & Bolton, L. (2021). Employees’ perceptions of the implementation of robotics, artificial intelligence, and automation on job satisfaction, job security, and employability. Journal of Technology in Behavioral Science, 6(1), 106–113. https://doi.org/10.1007/s41347-020-00153-8.
  • Cansız, Ö. F., & Ünsalan, K. (2020). Yapay zekâ ve istatistiksel yöntemler ile küresel ticarette rekabet ölçütü olan lojistik performans indeksine etken parametrelerin ülke bazlı incelenmesi ve tahmin modellerinin geliştirilmesi. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 32(2), 571–582. https://doi.org/10.35234/fumbd.706406.
  • Cao, D. M., Sayed, M. A., Islam, M. T., Mia, M. T., Ayon, E. H., Ghosh, B. P., … Raihan, A. (2024). Advanced cybercrime detection: A comprehensive study on supervised and unsupervised machine learning approaches using real-world datasets. Journal of Computer Science and Technology Studies, 6(1), 40–48. https://doi.org/10.32996/jcsts.2024.6.1.5.
  • De Bruijn, H., Warnier, M., & Janssen, M. (2022). The perils and pitfalls of explainable AI: Strategies for explaining algorithmic decision-making. Government Information Quarterly, 39(2), 101–108. https://doi.org/10.1016/j.giq.2021.101666.
  • Dersan Tonbil, D., & Yavuz Aksakal, N. (2024). İşe alım, temin-seçim süreçlerinde yapay zekâ ve teknolojilerinin kullanımı: Nitel bir araştırma. İstanbul Ticaret Üniversitesi Girişimcilik Dergisi, 7(15), 38–56.
  • Doshi, A. R., Bell, J. J., Mirzayev, E., & Vanneste, B. S. (2025). Generative artificial intelligence and evaluating strategic decisions. Strategic Management Journal, 46(3), 583–610. https://doi.org/10.1002/smj.3677.
  • Feng, J., Han, P., Zheng, W., & Kamran, A. (2022). Identifying the factors affecting strategic decision-making ability to boost the entrepreneurial performance: A hybrid structural equation modeling–artificial neural network approach. Frontiers in Psychology, 13(2), 1–13. https://doi.org/10.3389/fpsyg.2022.1038604.
  • Fındıkçı, A., & Kavacık, M. (2024). Yapay zekâ ve gümrük işlemleri: Bir literatür incelemesi, avantaj ve dezavantajları. Gümrük ve Ticaret Dergisi, 11(36), 80–95. https://doi.org/10.70490/.1459211.
  • Goldfarb, A., & Lindsay, J. R. (2021). Prediction and judgment: Why artificial intelligence increases the importance of humans in war. International Security, 46(3), 7–50. https://doi.org/10.1162/isec_a_00425.
  • Gupta, S., Modgil, S., Bhattacharyya, S., & Bose, I. (2022). Artificial intelligence for decision support systems in the field of operations research: Review and future scope of research. Annals of Operations Research, 308(1), 215–274. https://doi.org/10.1007/s10479-020-03856-6.
  • İnce, H., İmamoğlu, S. E., & İmamoğlu, S. Z. (2021). Yapay zeka uygulamalarının karar verme üzerine etkileri: Kavramsal bir çalışma. International Review of Economics and Management, 9(1), 50–63. https://doi.org/10.18825/iremjournal.866432.
  • Kaya, F., Aydin, F., Schepman, A., Rodway, P., Yetişensoy, O., & Demir-Kaya, M. (2022). The roles of personality traits, AI anxiety, and demographic factors in attitudes toward artificial intelligence. International Journal of Human–Computer Interaction, 40(2), 1–18. https://doi.org/10.1080/10447318.2022.2151730.
  • Kaygusuz, N. A. (2023). Nöropazarlama ve yapay zekâ ilişkisinin tüketici davranışları üzerindeki etkisine yönelik kavramsal bir model önerisi. Journal of Academic Social Science Studies, 16(95), 21–42. https://doi.org/10.29228/JASSS.67916.
  • Keding, C. (2021). Understanding the interplay of artificial intelligence and strategic management: Four decades of research in review. Management Review Quarterly, 71(1), 91–134. https://doi.org/10.1007/s11301-020-00181-x.
  • Kocaman, O. (2024). Yapay zekâ uygulamalarının kamu yönetiminde karar almaya etkisi. Yasama Dergisi, 49, 153–192.
  • Kurtboğan, H., & Ak, M. (2024). İnsanlığın pi noktası: Yapay zekâ. Ankara: Nobel Bilimsel Eserler. Kurz, H. D. (2022). On machine ages: Causes, forms and effects of technological change. In The Routledge Handbook of Smart Technologies (pp. 56–76). London: Routledge.
  • Leyer, M., & Schneider, S. (2021). Decision augmentation and automation with artificial intelligence: Threat or opportunity for managers? Business Horizons, 64(5), 711–724. https://doi.org/10.1016/j.bushor.2021.02.026.
  • Menzies, J., Sabert, B., Hassan, R., & Mensah, P. K. (2024). Artificial intelligence for international business: Its use, challenges, and suggestions for future research and practice. Thunderbird International Business Review, 66(2), 185–200. https://doi.org/10.1002/tie.22370.
  • Mogaji, E., & Nguyen, N. P. (2022). Managers’ understanding of artificial intelligence in relation to marketing financial services: Insights from a cross-country study. International Journal of Bank Marketing, 40(6), 1272–1298. https://doi.org/10.1108/IJBM-09-2021-0440.
  • Morandini, S., Fraboni, F., De Angelis, M., Puzzo, G., Giusino, D., & Pietrantoni, L. (2023). The impact of artificial intelligence on workers’ skills: Upskilling and reskilling in organisations. Informing Science, 26, 39–68. https://doi.org/10.28945/5078.
  • Pallant, J. (2020). SPSS survival manual: A step by step guide to data analysis using IBM SPSS (7th ed.). London & New York: Routledge.
  • Pavlova, K. S., & Knyazeva, N. V. (2022). Artificial intelligence technologies in tax consulting and forensic tax expertise. Digital Technologies in the New Socio-Economic Reality, 291–300. https://doi.org/10.1007/978-3-030-83175-2_38.
  • Racine, E., Boehlen, W., & Sample, M. (2019). Healthcare uses of artificial intelligence: Challenges and opportunities for growth. In Healthcare Management Forum, 32(5), 272–275.
  • Rajagopal, N. K., Qureshi, N. I., Durga, S., Ramirez Asis, E. H., Huerta Soto, R. M., Gupta, S. K., … Deepak, S. (2022). Future of business culture: An artificial intelligence-driven digital framework for organization decision-making process. Complexity, 22(1), 779–807. https://doi.org/10.1155/2022/7796507.
  • Rodgers, W., Murray, J. M., Stefanidis, A., Degbey, W. Y., & Tarba, S. Y. (2023). An artificial intelligence algorithmic approach to ethical decision-making in human resource management processes. Human Resource Management Review, 33(1), 100–125. https://doi.org/10.1016/j.hrmr.2022.100925.
  • Shaw, S., Rowland, Z., & Machova, V. (2021). Internet of things smart devices, sustainable industrial big data, and artificial intelligence-based decision-making algorithms in cyber-physical system-based manufacturing. Economics, Management and Financial Markets, 16(2), 106–116. https://doi.org/10.22381/16220217.
  • 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), 1–9. https://doi.org/10.17705/1jais.00663.
  • Şeker, E. Z., Geçici, E., & Taşkın, A. (2024). Gümrük kontrol noktalarında riskli geçişlerin belirlenmesine yönelik yapay zekâ temelli bir yaklaşım. Karadeniz Fen Bilimleri Dergisi, 14(2), 476–492. https://doi.org/10.31466/kfbd.1367857.
  • Wamba, S. F. (2022). Impact of artificial intelligence assimilation on firm performance: The mediating effects of organizational agility and customer agility. International Journal of Information Management, 67(1), 102–144. https://doi.org/10.1016/j.ijinfomgt.2022.102544.
  • Yin, J., Ngiam, K. Y., & Teo, H. H. (2021). Role of artificial intelligence applications in real-life clinical practice: Systematic review. Journal of Medical Internet Research, 23(4), e257–269. https://doi.org/10.2196/25759.
  • Zhang, J., Qin, Q., Li, G., & Tseng, C. H. (2021). Sustainable municipal waste management strategies through life cycle assessment method: A review. Journal of Environmental Management, 287, 112–138. https://doi.org/10.1016/j.jenvman.2021.112238.

STRATEGIC DECISION MAKING AND ARTIFICIAL INTELLIGENCE: NEW APPROACHES AND INDIVIDUAL PERCEPTIONS IN EMPLOYEE DECISION MAKING PROCESSES

Yıl 2026, Cilt: 22 Sayı: 1, 291 - 307, 26.03.2026
https://doi.org/10.17130/ijmeb.1677954
https://izlik.org/JA92YY69TK

Öz

Artificial intelligence (AI) has significantly transformed strategic decision-making processes in the business world. With the rise of remote work and digitalization, AI technologies have influenced influenced the ways employees make decisions and have introduced new approaches to these processes. This study examined the impact of AI technologies on strategic decision-making processes and the role of employees’ perceptions in this interaction in these processess. A survey was conducted among customs brokerage employees working in Istanbul using the Artificial Intelligence Perception Scale, the Artificial Intelligence Attitude Scale, and the Decision Strategies Scale. The data were analyzed using t-tests, one-way ANOVA, and Pearson correlation analysis with SPSS 25 software. Analyses based on gender, marital status, educational level, and length of work experience experience revealed significant differences in some variables. Female participants scored higher than males, and undergraduate participants had higher scores than postgraduate participants on the positive dimension of the General Attitude Toward Artificial Intelligence Scale. In addition, positive and significant correlations were found between the variables. These findings provide important insights into understanding the effects of AI perception and decision-making strategies.

Kaynakça

  • References
  • Abrokwah-Larbi, K., & Awuku-Larbi, Y. (2024). The impact of artificial intelligence in marketing on the performance of business organizations: Evidence from SMEs in an emerging economy. Journal of Entrepreneurship in Emerging Economies, 16(4), 1090–1117. https://doi.org/10.1108/JEEE-07-2022-0207.
  • Rahman, A., & Muktadir, M. G. (2021). SPSS: An imperative quantitative data analysis tool for social science research. International Journal of Research and Innovation in Social Science, 5(10), 300–302.
  • Acciarini, C., Brunetta, F., & Boccardelli, P. (2021). Cognitive biases and decision-making strategies in times of change: A systematic literature review. Management Decision, 59(3), 638–652. https://doi.org/10.1108/MD-07-2019-1006.
  • Ahmad, S. F., Han, H., Alam, M. M., Rehmat, M., Irshad, M., Arraño-Muñoz, M., … Ariza-Montes, A. (2023). Impact of artificial intelligence on human loss in decision making, laziness and safety in education. Humanities and Social Sciences Communications, 10(1), 1–14. https://doi.org/10.1057/s41599-023-01787-8.
  • Ahmed, R. R., Streimikiene, D., Streimikis, J., & Siksnelyte-Butkiene, I. (2024). A comparative analysis of multivariate approaches for data analysis in management sciences. E+M Ekonomie a Management, 27(1), 192–210. https://doi.org/10.15240/tul/001/2024-5-001.
  • Alzoubi, H. M., & Aziz, R. (2021). Does emotional intelligence contribute to quality of strategic decisions? The mediating role of open innovation. Journal of Open Innovation: Technology, Market, and Complexity, 7(2), 130. https://doi.org/10.3390/joitmc7020130.
  • Antes, A. L., Burrous, S., Sisk, B. A., Schuelke, M. J., Keune, J. D., DuBois, J. M. (2021). Exploring perceptions of healthcare technologies enabled by artificial intelligence: An online, scenario-based survey. BMC Medical Informatics and Decision Making, 21, 1–15. https://doi.org/10.1186/s12911-021-01586-8.
  • Apalı, A., Köse, E., & Aldemir, M. E. (2022). Denetçilerin yapay zekâ’ya yönelik algılarının denetim kalitesine etkisi üzerine bir araştırma. Denetişim, 26(2), 32–44.
  • Bag, S., Gupta, S., Kumar, A., & Sivarajah, U. (2021). An integrated artificial intelligence framework for knowledge creation and B2B marketing rational decision making for improving firm performance. Industrial Marketing Management, 92(1), 178–189. https://doi.org/10.1016/j.indmarman.2020.12.001.
  • Baltacı, A. (2018). Nitel araştırmalarda örnekleme yöntemleri ve örnek hacmi sorunsalı üzerine kavramsal bir inceleme. Bitlis Eren Üniversitesi Sosyal Bilimler Dergisi, 7(1), 231–274.
  • 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. https://doi.org/10.1002/job.2735.
  • Belhadi, A., Kamble, S., Fosso Wamba, S., & Queiroz, M. M. (2022). Building supply-chain resilience: An artificial intelligence-based technique and decision-making framework. International Journal of Production Research, 60(14), 4487–4507. https://doi.org/10.1080/00207543.2021.1950935.
  • Bhargava, A., Bester, M., & Bolton, L. (2021). Employees’ perceptions of the implementation of robotics, artificial intelligence, and automation on job satisfaction, job security, and employability. Journal of Technology in Behavioral Science, 6(1), 106–113. https://doi.org/10.1007/s41347-020-00153-8.
  • Cansız, Ö. F., & Ünsalan, K. (2020). Yapay zekâ ve istatistiksel yöntemler ile küresel ticarette rekabet ölçütü olan lojistik performans indeksine etken parametrelerin ülke bazlı incelenmesi ve tahmin modellerinin geliştirilmesi. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 32(2), 571–582. https://doi.org/10.35234/fumbd.706406.
  • Cao, D. M., Sayed, M. A., Islam, M. T., Mia, M. T., Ayon, E. H., Ghosh, B. P., … Raihan, A. (2024). Advanced cybercrime detection: A comprehensive study on supervised and unsupervised machine learning approaches using real-world datasets. Journal of Computer Science and Technology Studies, 6(1), 40–48. https://doi.org/10.32996/jcsts.2024.6.1.5.
  • De Bruijn, H., Warnier, M., & Janssen, M. (2022). The perils and pitfalls of explainable AI: Strategies for explaining algorithmic decision-making. Government Information Quarterly, 39(2), 101–108. https://doi.org/10.1016/j.giq.2021.101666.
  • Dersan Tonbil, D., & Yavuz Aksakal, N. (2024). İşe alım, temin-seçim süreçlerinde yapay zekâ ve teknolojilerinin kullanımı: Nitel bir araştırma. İstanbul Ticaret Üniversitesi Girişimcilik Dergisi, 7(15), 38–56.
  • Doshi, A. R., Bell, J. J., Mirzayev, E., & Vanneste, B. S. (2025). Generative artificial intelligence and evaluating strategic decisions. Strategic Management Journal, 46(3), 583–610. https://doi.org/10.1002/smj.3677.
  • Feng, J., Han, P., Zheng, W., & Kamran, A. (2022). Identifying the factors affecting strategic decision-making ability to boost the entrepreneurial performance: A hybrid structural equation modeling–artificial neural network approach. Frontiers in Psychology, 13(2), 1–13. https://doi.org/10.3389/fpsyg.2022.1038604.
  • Fındıkçı, A., & Kavacık, M. (2024). Yapay zekâ ve gümrük işlemleri: Bir literatür incelemesi, avantaj ve dezavantajları. Gümrük ve Ticaret Dergisi, 11(36), 80–95. https://doi.org/10.70490/.1459211.
  • Goldfarb, A., & Lindsay, J. R. (2021). Prediction and judgment: Why artificial intelligence increases the importance of humans in war. International Security, 46(3), 7–50. https://doi.org/10.1162/isec_a_00425.
  • Gupta, S., Modgil, S., Bhattacharyya, S., & Bose, I. (2022). Artificial intelligence for decision support systems in the field of operations research: Review and future scope of research. Annals of Operations Research, 308(1), 215–274. https://doi.org/10.1007/s10479-020-03856-6.
  • İnce, H., İmamoğlu, S. E., & İmamoğlu, S. Z. (2021). Yapay zeka uygulamalarının karar verme üzerine etkileri: Kavramsal bir çalışma. International Review of Economics and Management, 9(1), 50–63. https://doi.org/10.18825/iremjournal.866432.
  • Kaya, F., Aydin, F., Schepman, A., Rodway, P., Yetişensoy, O., & Demir-Kaya, M. (2022). The roles of personality traits, AI anxiety, and demographic factors in attitudes toward artificial intelligence. International Journal of Human–Computer Interaction, 40(2), 1–18. https://doi.org/10.1080/10447318.2022.2151730.
  • Kaygusuz, N. A. (2023). Nöropazarlama ve yapay zekâ ilişkisinin tüketici davranışları üzerindeki etkisine yönelik kavramsal bir model önerisi. Journal of Academic Social Science Studies, 16(95), 21–42. https://doi.org/10.29228/JASSS.67916.
  • Keding, C. (2021). Understanding the interplay of artificial intelligence and strategic management: Four decades of research in review. Management Review Quarterly, 71(1), 91–134. https://doi.org/10.1007/s11301-020-00181-x.
  • Kocaman, O. (2024). Yapay zekâ uygulamalarının kamu yönetiminde karar almaya etkisi. Yasama Dergisi, 49, 153–192.
  • Kurtboğan, H., & Ak, M. (2024). İnsanlığın pi noktası: Yapay zekâ. Ankara: Nobel Bilimsel Eserler. Kurz, H. D. (2022). On machine ages: Causes, forms and effects of technological change. In The Routledge Handbook of Smart Technologies (pp. 56–76). London: Routledge.
  • Leyer, M., & Schneider, S. (2021). Decision augmentation and automation with artificial intelligence: Threat or opportunity for managers? Business Horizons, 64(5), 711–724. https://doi.org/10.1016/j.bushor.2021.02.026.
  • Menzies, J., Sabert, B., Hassan, R., & Mensah, P. K. (2024). Artificial intelligence for international business: Its use, challenges, and suggestions for future research and practice. Thunderbird International Business Review, 66(2), 185–200. https://doi.org/10.1002/tie.22370.
  • Mogaji, E., & Nguyen, N. P. (2022). Managers’ understanding of artificial intelligence in relation to marketing financial services: Insights from a cross-country study. International Journal of Bank Marketing, 40(6), 1272–1298. https://doi.org/10.1108/IJBM-09-2021-0440.
  • Morandini, S., Fraboni, F., De Angelis, M., Puzzo, G., Giusino, D., & Pietrantoni, L. (2023). The impact of artificial intelligence on workers’ skills: Upskilling and reskilling in organisations. Informing Science, 26, 39–68. https://doi.org/10.28945/5078.
  • Pallant, J. (2020). SPSS survival manual: A step by step guide to data analysis using IBM SPSS (7th ed.). London & New York: Routledge.
  • Pavlova, K. S., & Knyazeva, N. V. (2022). Artificial intelligence technologies in tax consulting and forensic tax expertise. Digital Technologies in the New Socio-Economic Reality, 291–300. https://doi.org/10.1007/978-3-030-83175-2_38.
  • Racine, E., Boehlen, W., & Sample, M. (2019). Healthcare uses of artificial intelligence: Challenges and opportunities for growth. In Healthcare Management Forum, 32(5), 272–275.
  • Rajagopal, N. K., Qureshi, N. I., Durga, S., Ramirez Asis, E. H., Huerta Soto, R. M., Gupta, S. K., … Deepak, S. (2022). Future of business culture: An artificial intelligence-driven digital framework for organization decision-making process. Complexity, 22(1), 779–807. https://doi.org/10.1155/2022/7796507.
  • Rodgers, W., Murray, J. M., Stefanidis, A., Degbey, W. Y., & Tarba, S. Y. (2023). An artificial intelligence algorithmic approach to ethical decision-making in human resource management processes. Human Resource Management Review, 33(1), 100–125. https://doi.org/10.1016/j.hrmr.2022.100925.
  • Shaw, S., Rowland, Z., & Machova, V. (2021). Internet of things smart devices, sustainable industrial big data, and artificial intelligence-based decision-making algorithms in cyber-physical system-based manufacturing. Economics, Management and Financial Markets, 16(2), 106–116. https://doi.org/10.22381/16220217.
  • 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), 1–9. https://doi.org/10.17705/1jais.00663.
  • Şeker, E. Z., Geçici, E., & Taşkın, A. (2024). Gümrük kontrol noktalarında riskli geçişlerin belirlenmesine yönelik yapay zekâ temelli bir yaklaşım. Karadeniz Fen Bilimleri Dergisi, 14(2), 476–492. https://doi.org/10.31466/kfbd.1367857.
  • Wamba, S. F. (2022). Impact of artificial intelligence assimilation on firm performance: The mediating effects of organizational agility and customer agility. International Journal of Information Management, 67(1), 102–144. https://doi.org/10.1016/j.ijinfomgt.2022.102544.
  • Yin, J., Ngiam, K. Y., & Teo, H. H. (2021). Role of artificial intelligence applications in real-life clinical practice: Systematic review. Journal of Medical Internet Research, 23(4), e257–269. https://doi.org/10.2196/25759.
  • Zhang, J., Qin, Q., Li, G., & Tseng, C. H. (2021). Sustainable municipal waste management strategies through life cycle assessment method: A review. Journal of Environmental Management, 287, 112–138. https://doi.org/10.1016/j.jenvman.2021.112238.
Toplam 44 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Politika ve Yönetim (Diğer), İşletme
Bölüm Araştırma Makalesi
Yazarlar

Zekeriya Şahin 0000-0002-2811-3264

Gönderilme Tarihi 16 Nisan 2025
Kabul Tarihi 9 Ekim 2025
Yayımlanma Tarihi 26 Mart 2026
DOI https://doi.org/10.17130/ijmeb.1677954
IZ https://izlik.org/JA92YY69TK
Yayımlandığı Sayı Yıl 2026 Cilt: 22 Sayı: 1

Kaynak Göster

APA Şahin, Z. (2026). STRATEGIC DECISION MAKING AND ARTIFICIAL INTELLIGENCE: NEW APPROACHES AND INDIVIDUAL PERCEPTIONS IN EMPLOYEE DECISION MAKING PROCESSES. Uluslararası Yönetim İktisat ve İşletme Dergisi, 22(1), 291-307. https://doi.org/10.17130/ijmeb.1677954


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