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    How Does CEO Duality Influence ESG Scores in Hospitality and Tourism Companies? Confounding Roles of Governance Mechanisms and Financial Indicators
    (Sage Publications Inc, 2025) Arici, Hasan Evrim; Aladag, Omer Faruk; Koseoglu, Mehmet Ali
    Previous studies have yielded inconsistent results about the impact of CEO duality on corporate performance in the hospitality and tourism (H&T) industry. To further delve into this relationship, we investigated the causal relationship between CEO duality and environmental, social, and governance (ESG) performance under various board characteristics and financial indicators. The data from the Thomson Reuters Eikon database were evaluated using a machine learning technique that included targeted maximum likelihood estimation (TMLE), augmented inverse probability weighting (AIPW), and neural network analysis, all of which are doubly robust estimators with cross-fitting. The findings suggest that CEO duality negatively impacts environmental pillar scores but not other outcomes (i.e., governance and social pillar scores). Among the governance practices and financial indicators, policy executive compensation performance, policy executive compensation ESG performance, and return on invested capital (ROIC) have positive relations with total ESG scores. The results have important ramifications for helping H&T companies develop effective boards of directors and governance systems, as well as achieve targeted ESG performance objectives.
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    Machine Learning Analysis of Global Innovation Index Enablers: Regional Variations and Covid-19 Effects
    (Springer, 2026) Koseoglu, Mehmet Ali; Arici, Hasan Evrim; Aladag, Omer Faruk
    Drawing on the national innovation systems (NIS) perspective, this study uses the Global Innovation Index (GII) as an integrated measure of countries’ innovation capabilities and outcomes to examine how different innovation enablers jointly shape innovation performance. Using GII data for 2013–2022, we apply ensemble machine learning algorithms to analyze how the GII input and output dimensions, treated as NIS elements, predict overall GII scores. Countries are grouped into five subsamples (all countries, G20, European countries, all countries during the COVID-19 period, and low-income countries) to capture contextual variation in innovation systems. The results show that innovation performance is driven not by single factors but by context-specific combinations of enablers. Research and development and creative/intangible outputs form a powerful configuration for most countries, whereas information and communication technologies, credit availability and innovation linkages become particularly salient during COVID-19. In low-income countries, tertiary education and creative outputs emerge as a distinctive configuration associated with higher innovation performance. These findings demonstrate that the relative importance and effective combinations of NIS elements vary systematically across country groups and over time. By mapping these patterns, the study advances NIS research methodologically and substantively, showing how machine learning can uncover non-linear, context-dependent configurations of innovation enablers and informing tailored policy interventions for different country groups. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026.