International conference on construction engineering and project management (국제학술발표논문집)

  • 2024.07a
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  • Pages.407-414
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  • 2024
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  • 2508-9048(eISSN)
Korea Institute of Construction Engineering and Management (한국건설관리학회)
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Identification of Social Conflict Stakeholders in Public Infrastructure Projects using ChatGPT

  • Do Namgoong (Department of Civil and Environmental Engineering, Yonsei University) ;
  • Seung H. Han (Department of Civil and Environmental Engineering, Yonsei University)
  • Published : 2024.07.29
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Abstract

Social conflict surrounding public infrastructure projects has grown because of increasing project complexity and accelerated conflict propagation. This social conflict stems from various concerns ranging from environmental issues to regulatory and compliance requirements resulting in the intervention of various stakeholders with different interests. Against this backdrop, understanding the stakeholders involved and their dynamics is crucial for effective project management and smooth implementation of the project. Therefore, this paper introduces an analytical process utilizing ChatGPT to automatically identify stakeholders involved in the social conflict surrounding the public infrastructure project from news articles. As a result, a stakeholder network is constructed to delve into the complex stakeholder interrelationships and identify key stakeholders of the specific period. To explore the potential of the proposed process, an experimental case study of the Jeju 2nd Airport project, which suffered from a high level of social conflict, was conducted. The proposed process enables timely analysis of the conflict situation which is crucial for successful conflict management. This study highlights the significance of a systemic approach to timely stakeholder analysis, setting the groundwork for a quantitative and up-to-date investigation of social conflicts around public infrastructure projects.

Keywords

Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2022R1A2C1012018).

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