Asia-Pacific Journal of Business (아태비즈니스연구)

KNU The Institute of Management & Economy Research (강원대학교 경영경제연구소)
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Firm Classification based on MBTI Organizational Character Type: Using Firm Review Big Data

MBTI 조직성격유형화에 따른 기업분류: 기업리뷰 빅데이터를 활용하여

  • Lee, Hanjun (Department of Management Information Systems, Myongji University) ;
  • Shin, Dongwon (Industry Academic Cooperation Foundation, Myongji University) ;
  • An, Byungdae (Institute for Business Research and Education (IBRE), Korea University)
  • 이한준 (명지대학교 경영정보학과) ;
  • 신동원 (명지대학교 산학협력단) ;
  • 안병대 (고려대학교 경영대학)
  • Received : 2021.08.31
  • Accepted : 2021.09.21
  • Published : 2021.09.30
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Abstract

Purpose - The purpose of this study is to classify KOSPI listed companies according to their organizational character type based on MBTI. Design/methodology/approach - This study collected 109,989 reviews from an online firm review website, Jobplanet. Using these reviews and the descriptions about organizational character, we conducted document similarity analysis. Doc2Vec technique was hired for the analysis. Findings - First, there are more companies belonging to Extraversion(E), Intuition(N), Feeling(F), and Judging(J) than Introversion(I), Sensing(S), Thinking(T), and Perceiving(P) as organizational character types of MBTI. Second, more companies have EJ and EP as the behavior type and NT and NF as the decision-making type. Third, the top-3 organizational character type of which firms have among 16 types are ENTJ, ENFP, and ENFJ. Finally, companies belonging to the same industry group were found to have similar organizational character. Research implications or Originality - This study provides a noble way to measure organizational character type using firm review big data and document similarity analysis technique. The research results can be practically used for firms in their organizational diagnosis and organizational management, and are meaningful as a basic study for various future studies to empirically analyze the impact of organizational character.

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Acknowledgement

이 논문은 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임 (No. NRF-2019R1G1A1084863)

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