Document Type : Research Paper
Authors
1 Ph.D Candidate on Sport Management, Department of Physical Education and Sports Science, Najafabad Branch, Islamic Azad University, Najafabad, Iran
2 Sport medicine research center, Najafabad Branch, Islamic Azad University, Najafabad, Iran
3 Assistant professor on sport management, Sport Medicine Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran
4 Assistant Professor on sport management, Department of Physical Education and Sports Sciences, Esfahan (khorasgan) Branch, Islamic Azad University, Esfahan, Iran
Abstract
Introduction: Large language models (LLMs) are a class of artificial intelligence (AI) models built on deep learning and natural language processing (NLP). This study aims to develop a comprehensive site selection model for sports facilities in Iran using LLMs.
Methods: This qualitative study employs grounded theory methodology with a constructivist approach. Data were collected through in-depth interviews with three LLMs: ChatGPT, Claude, and Google Gemini, which are proficient in understanding Persian. A purposive sampling strategy was employed.
Results: Out of approximately 250 transcribed sentences from the interviews, 175 initial categories were identified and coded by the researcher in the initial coding phase after removing duplicates and irrelevant items. Subsequently, 27 codes were extracted in focused coding and finally organized into five core codes: operations management, influencing factors, organizing elements, requirements, and feedback.
Conclusion: The comprehensive constructivist model for the site selection of sports facilities in Iran, utilizing a feedback system, particularly during key technological review stages and expert verbal judgments, demonstrates high efficiency and effectiveness. This feedback system allows the model to dynamically respond to new needs and implement corrective actions and strategy updates with speed and accuracy. The conclusion from this model indicates that an effective feedback system contributes to the flexibility and continuous optimization of the site selection processes, enabling decision-makers to make better-informed decisions based on precise and up-to-date data.
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