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Financial determinants of governance scores in hospitality and tourism enterprises

Authors:
Hasan Evrim Arici at EU Business School Munich
Hasan Evrim Arici
  • EU Business School Munich
Hüseyin Arasli at Eastern Mediterranean University
Hüseyin Arasli
Mehmet Ali Köseoglu at Metropolitan State University
Mehmet Ali Köseoglu
Mehmet Bahri Saydam at Eastern Mediterranean University
Mehmet Bahri Saydam
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Abstract and Figures

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.
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Vol.:(0123456789)
Quality & Quantity
https://doi.org/10.1007/s11135-023-01820-7
1 3
Financial determinants ofgovernance scores inhospitality
andtourism enterprises
HasanEvrimArici1,5· HuseyinArasli2 · MehmetAliKoseoglu3·
MehmetBahriSaydam4 · VictorOluwafemiOlorunsola4
Accepted: 10 December 2023
© The Author(s), under exclusive licence to Springer Nature B.V. 2024
Abstract
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 identi-
fied 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.
Keywords Corporate governance score· Shareholder score· Financial indicators·
Machine learning· Hospitality and tourism
1 Introduction
Corporate governance aims to address interest conflicts between management and share-
holders, as well as between large and small shareholders thereby loweringagency issues
(Rani etal. 2014; Olorunsola etal. 2023). According to the agency hypothesis, organ-
izations with greater corporate governance standards outperform their rivals because
they have lower agency costs and more effective monitoring procedures (Bonazzi and
Islam 2007). Improved corporate governance systems, as (Ngobo and Fouda 2012),
decrease uncertainty between shareholders and executives. Several studies use primary
and survey-based data on corporate governance structure at the business level within a
certain industry. Several studies found a link between governance methods and financial
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