| Yazarlar |
Hasan Evrim Arici
Kastamonu University, Turkey |
|
Huseyin Arasli
Universitetet i Stavanger, Norway |
|
Mehmet Ali Koseoglu
Metropolitan State University, United States |
|
Mehmet Bahri Saydam
Eastern Mediterranean University, Turkey |
|
Victor Oluwafemi Olorunsola
Eastern Mediterranean University, Turkey |
| Özet |
| While the importance of robust corporate governance in hospitality and tourism (H&T) enterprises is widely acknowledged among scholars in the fields of H&T and general management, existing literature reveals several gaps, inconclusive findings, and disparate conclusions. In addressing these gaps and advancing scientific understanding, this study delves into the estimation of governance scores in H&T enterprises through an exploration of financial indicators. Leveraging the Thomson Reuters Eikon database and employing machine learning methodologies such as bagging, random forest, and boosted regression algorithms, our research identifies the most influential predictors of governance scores. Our results highlight that assets/equity emerges as the most potent predictor of shareholder score in H&T businesses, while the composition of independent board members is identified as the paramount predictor of both governance score and management score. Beyond contributing to the scholarly discourse, our study holds significant practical implications. By elucidating the nexus between financial metrics and governance ratings, our findings empower H&T businesses to strategically align their governance mechanisms, fostering enhanced corporate governance practices. |
| Anahtar Kelimeler |
| Corporate governance score | Financial indicators | Hospitality and tourism | Machine learning | Shareholder score |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SCOPUS dergilerinde yayımlanan tam makale |
| Dergi Adı | Quality and Quantity |
| Dergi ISSN | 0033-5177 |
| Makale Dili | İngilizce |
| Basım Tarihi | 01-2024 |
| Sayı | 1 |
| Doi Numarası | 10.1007/s11135-023-01820-7 |
| Atıf Sayıları |
