روش‌شناسی دلفی مبتنی بر هوش مصنوعی: رویکردی نوین در زاویه‌بندی پژوهش‌های مدیریتی

Aggarwal, A., Tam, C. C., Wu, D., Li, X. & Qiao, S. (2023). Artificial intelligence–based chatbots for promoting health behavioral changes: systematic review. Journal of medical Internet research, 25, e40789.

Aithal, P.S. & Aithal, S. (2023). Use of AI-based GPTs in experimental, empirical, and exploratory research methods. International Journal of Case Studies in Business, IT, and Education (IJCSBE), 7(3), 411-425.

Bertolotti, F. & Mari, L. (2025). An LLM-based Delphi Study to Predict GenAI Evolution. arXiv preprint arXiv:2502.21092.

Bertolotti, F. & Mari, L. (2025). An LLM-based Delphi Study to Predict GenAI Evolution. arXiv preprint arXiv:2502.21092.

Bughin, J., Manyika, J. & Woetzel, J. (2017). A Future That Works: Automation, Employment, and Productivity. McKinsey Global Institute.

Cabero, J. & Barroso, J. (2013). La utilizacion del juicio de experto para la evaluacion de tic: el coeficiente de competencia expert. Bordon, 65(2), 25–38. ISSN: 0210- 5934.

Di Zio, S., Calleo, Y. & Bolzan, M. (2023). Delphi-based visual scenarios: an innovative use of generative adversarial networks. Futures, 154, 103280.

Ecoffet, A. (2023). GPT-4 Technical Report. arXiv 2023, arXiv: 2303.08774.

Fathullah, M.A., Subbarao, A. & Muthaiyah, S. (2023). Methodological investigation: traditional and systematic reviews as preliminary findings for Delphi technique. International Journal of Qualitative Methods, 22, 16094069231190747.

Gamboa, A. J. P. & Díaz-Guerra, D. D. (2023). Artificial Intelligence for the development of qualitative studies. LatIA, 1, 4-4.

Hasson, F. & Keeney, S. (2011). Enhancing rigour in the Delphi technique research. Technological forecasting and social change, 78(9), 1695-1704.

Hudoud, A. (2025). Integrating Artificial Intelligence into Research Methodology: Examining Potential Bias and Mitigation Strategies. The Arab Journal for Quality Assurance in Higher Education, 18(64).

Janiesch, C., Zschech, P. & Heinrich, K. (2021). Machine learning and deep learning. Electronic Markets, 31(3), 685-695. https: //www.ibm.com/topics/chain-of-thoughts

Kelly, J. (2023). Goldman Sachs predicts 300 million jobs will Be lost or degraded by artificial intelligence. Forbes. Web Link https://tinyurl.com/3xb437rb. Accessed on 04 May 2023.

Knoth, N., Tolzin, A., Janson, A. & Leimeister, J. M. (2024). AI literacy and its implications for prompt engineering strategies. Computers and Education: Artificial Intelligence, 6, 100225.

Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M. & Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical image analysis, 42, 60-88.

Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H. & Neubig, G. (2023). Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM Computing Surveys, 55(9), 1–35.

Mai, V., Neef, C. & Richert, A. (2022). “Clicking vs. writing”—The impact of a chatbot’s interaction method on the working alliance in AI-based coaching. Coaching| Theorie & Praxis, 8(1), 15-31.

Miao, S. Y., Liang, C. C. & Su, K. Y. (2024). A diverse corpus for evaluating and developing English math word problem solvers. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020. doi: 10.18653/v1/2020.acl-main.92. URL https: //aclanthology.org/2020.acl-main.92.

Misawa, T., Koizumi, A., Tamura, R. & Yoshimi, K. (2025). Exploring utilization of generative AI for research and education in data-driven materials science. arXiv preprint arXiv:2504.08817.

Mueller, R. M., Thoring, K., Klöckner, H. W. & Larsen, K. (2024). Crafting Future Scenarios with the Help of AI: Potentials of a Hybrid Delphi Expert Panel.

Nasa, P., Jain, R. & Juneja, D. (2021). Delphi methodology in healthcare research: How to decide its appropriateness. World Journal of Methodology, 11(4), 116-129. https://doi.org/10. 5662/wjm.v11.i4.116

Newman-Griffis, D. (2024). AI Thinking: A framework for rethinking artificial intelligence in practice. arXiv preprint arXiv: 2409.12922.

Pathak, A. R., Pandey, M. & Rautaray, S. (2018). Application of deep learning for object detection. Procedia computer science, 132, 1706-1717.

Rahimi, S. (2024). Saturation in qualitative research: An evolutionary concept analysis. International Journal of Nursing Studies Advances, 100174.

Russell, S. J. & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.

Saunders, B., Sim, J., Kingstone, T., Baker, S., Waterfield, J., Bartlam, B., ... & Jinks, C. (2018). Saturation in qualitative research: exploring its conceptualization and operationalization. Quality & quantity, 52(4), 1893-1907. https://doi.org/10.1007/s11135-017-0574-8

Schmalz, U., Ringbeck, J. & Spinler, S. (2020). Door-to-door travel in 2035 – a Delphi study. Technological Forecasting and Social Change, 157, 120096. https://doi.org/10.1016/j.techfore.2020.120096

Speed, C. & Metwally, A. A. (2025). The Human-AI Hybrid Delphi Model: A Structured Framework for Context-Rich, Expert Consensus in Complex Domains. arXiv preprint arXiv:2508.09349.

Von Krogh, G. (2018). Artificial intelligence in organizations: New opportunities for phenomenon-based theorizing. Academy of Management Discoveries, 4(4), 404-409. https://doi.org/10.5465/amd.2018.0084

Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E. & Zhou, D. (2022). Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, 35, 24824-24837.

White, J. (2023). A prompt pattern catalog to enhance prompt engineering with ChatGPT. http://arxiv.org/pdf/2302.11382v1.

Wolff, T. (2023). How to Craft Prompts for Maximum Effectiveness. Available online: https: //medium.com/mlearning-ai/from-zero-shot-to-chain-of-thought-prompt-engineering-choosing-the-right-prompt-types-88800f242137 (accessed on 14 November 2023).