Exploring corporate governance research in accounting journals through latent semantic and topic analyses

Zengul F. D., Byrd J. D., Oner N., Edmonds M., Savage A.

INTELLIGENT SYSTEMS IN ACCOUNTING FINANCE & MANAGEMENT, cilt.26, sa.4, ss.175-192, 2019 (ESCI) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 26 Sayı: 4
  • Basım Tarihi: 2019
  • Doi Numarası: 10.1002/isaf.1461
  • Dergi Adı: INTELLIGENT SYSTEMS IN ACCOUNTING FINANCE & MANAGEMENT
  • Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus
  • Sayfa Sayıları: ss.175-192
  • Anahtar Kelimeler: corporate governance, latent semantic analysis, natural language processing, topic analysis, SARBANES-OXLEY ACT, EARNINGS MANAGEMENT, BIG DATA, ACCOUNTABILITY, RESPONSIBILITY, DISCLOSURE, OWNERSHIP, PAY, COMPETITION, EXPERTISE
  • Ankara Üniversitesi Adresli: Hayır

Özet

The literature on corporate governance (CG) has been expanding at an unprecedented rate since major corporate scandals surfaced, such as Enron, WorldCom, and HealthSouth. Corresponding with accounting's important role in CG, accounting scholars increasingly have investigated CG in recent years, so the body of literature is growing. Although previous attempts have been made to summarize extant literature on CG via reviews, none of these attempts has utilized recent developments in text analyses and natural language processing. This study uses latent semantic and topic analyses to address this research gap by analysing abstracts from 1,399 articles in all accounting journals that the Australian Business Deans Council (ABDC) has rated A and A*. The ABDC journal list is widely recognized as a journal-quality indicator across many universities worldwide. The analyses revealed 10 distinct research topics on CG in the ABDC's top accounting journals. The results presented include the five most representative articles for each topic, as distinguished by topic scores. This study carries important practice and policy implications, as it reveals major research streams and exhibits how researchers respond to various CG problems.