## 6 Illustrative Case Study and Discussion
### Discussion  
This study evaluated the KPC performance quantitatively using the F1-score and confirmed the KPE performance qualitatively through two case studies. The results showed that the presented process and methods are viable in identifying which conflict drivers are issues and monitoring them in order to manage them in practice. Recent NLP research in the computer science domain has been progressing toward developing a large language model (LLM) that comprehensively understands human language and can be used for general purposes. Compared to previous text-based studies in the construction domain, the recent generative LLM showed usefulness in reducing the burden of labor-intensive annotation tasks while resulting in higher performance [28]. It can help researchers efficiently extract latent information and meaningful insights from text data by omitting the process of training a text model from scratch or fine-tuning a pre-trained language model. When utilizing an interactive LLM like ChatGPT, the prompt has a significant effect on outputs. The authors finalized the prompts used in this study after much trial and error. A lesson learned through trial and error is that prompts should be as specific as possible about the output the user expects. Although this study performed prompt engineering manually, there were recent attempts to apply ML technology to improve prompt engineering [47]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**ID** & **Major Event** & **Count** \\ \hline \multirow{8}{*}{D-10} & “A new airport in the southeastern region must find its meaning in terms of balanced national development”, “Co-existence and balanced national development between the metropolitan area and local areas”, “The construction of a hub airport linked to the Busan Port can no longer be delayed”, “In order to host the 2030 Busan World Expo, Gadeok Island New Airport must be built as soon as possible”, “Strategy for building a mega city in the southeastern region” \\ \hline \multirow{8}{*}{D-07} & “Voices of concern that the location of the new airport may be determined by political logic”, “Advertisement about the justification for the construction of a new airport”, “Joint statement in support of a new airport”, “Rally to pray for a new airport”, “Protest calling for withdrawal of support for the Gadeok Island New airport” \\ \hline \multirow{8}{*}{D-03} & “Destruction of animal and plant habitats”, “Land reclamation”, “Damage to migratory bird habitats”, “Bird strike”, “Destruction of nearby forests” \\ \hline \multirow{8}{*}{D-01} & “Communication and cooperation”, “Lack of communication between politicians and residents”, “There was no prior talk with local residents”, “The Busan mayor’s unilateral decision-making and communication structure”, “Strengthening citizen communication functions” \\ \hline \multirow{8}{*}{D-06} & “Development of transportation-industrial infrastructure connected to Gadeok New Airport”, “Airport open 24 h a day”, “A complex logistic system linking ports and airports is essential”, “Development of airport complex city”, “Hub airport where large airplanes can land and take-off” \\ \hline \multirow{8}{*}{D-08} & “Negative opinions about the new airport”, “Regional selfishness”, “Disappointment of Yeongnam residents”, “Request for Gadeok New Airport instead of Gimhae New Airport”, “Positive perception of Busan-Ulsan-Gyeongnam residents regarding the passage of the Gadeok Island New Airport Special Law” \\ \hline \multirow{2}{*}{D-14} & “Gadeok Island New Airport Special Law” & 159 \\  & (5.7\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: KPE results of the Gadeok Island New Airport Project. Because of the difficulty of fine-tuning an LLM due to its enormous model size, utilizing ML-based prompt engineering on existing LLMs can be an efficient approach to solve domain-specific problems. Most previous studies of conflict in construction have been retrospective, based on case studies and literature reviews.