Earticle

초록

영어

This study explores a novel approach to assessing cultural fit during the early stages of mergers and acquisitions (M&A) by leveraging publicly available employee review data and large language models (LLMs). Recognizing the limitations of traditional due diligence in accessing internal cultural data, the proposed framework utilizes fine-tuned models and chain-of-thought (CoT) reasoning strategies to infer corporate cultural characteristics based on the Denison and Ko [2016] framework. The model evaluates four key traits-Mission, Consistency, Involvement, and Adaptability-across twelve dimensions, using Low-Rank Adaptation (LoRA) for efficient fine-tuning. Experimental results demonstrate that LoRA-tuned models consistently outperform few-shot prompting across both proprietary (e.g., GPT-4o) and open-source (e.g., Llama 3.2-3B) models, with significant improvements in both text summarization and numerical prediction accuracy. Additionally, CoT reasoning-particularly Multi-step and Hybrid strategies-yields substantial performance gains, especially in smaller models, enabling them to approximate the results of large-scale systems at reduced cost. These findings highlight the practical utility of combining PEFT and CoT methods for scalable, objective, and early-stage cultural assessments in M&A decision-making.

목차

Abstract
1. Introduction
2. Literature Review
2.1 The Importance of Corporate Culture Fit in Mergers and Acquisitions
2.2 Frameworks for Corporate Culture Analysis
2.3 Recent trend of train and test time computing
3. Methods for Predicting Corporate Culture Characteristics and Cultural Similarities
3.1 Using Public Information for Predicting Corporate Culture
3.2 Predicting Corporate Culture Similarities
4. Experiments
4.1 Data collection
4.2 Evaluation Metrics
5. Results
5.1 Comparing LoRA Fine-tuning and Few-shot Prompting
5.2 Comparing Chain-of-Thought strategies
6. Discussion
7. Conclusion and Implications
References

키워드

저자

• Ivan Ivanov [ Master Candidate, Department of MIS, Hannam University ] First Author
• Hee Seok Song [ Professor, Department of MIS, Hannam University ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  JITAM [Journal of Information Technology Applications and Management]

• 간기

  격월간

• pISSN

  1598-6284

• eISSN

  2508-1209

• 수록기간

  1999~2026

• 십진분류

  KDC 005 DDC 005