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Departing from previous studies, this paper aims to explore the predictive roles of financial indicators on diversity.

Data on all companies that are publicly traded was acquired from the Refinitiv Eikon database. The final list, which comprises 873 worldwide business data from 2021, composed the dataset. We used fundamental forward selection techniques, multiple regression and best subset regression in R programming to look at the data and find the most critical factors.

We found support for the predictive roles of financial indicators on total diversity score and its three components in global companies. In addition, bagging and random forest algorithms were able to find a predictor role of total liability on the diversity pillar score and inclusion score. In contrast, the people development score was best estimated by R. The boosted regression algorithm was also able to find evidence of the predictor role of total liability for people development and inclusion score but not for diversity pillar score.

This study is one of the first to examine financial predictors of firms’ diversity scores using machine learning algorithms. The discussion section offers theoretical and practical implications and directions for further research.

Current research aims to explore the dominant themes shared in online reviews by customers visiting chain coffee shops as well as which of these themes were linked with satisfaction and dissatisfaction.

The data for the current research was gathered from TripAdvisor using a Python-based web crawler. In total, 20,499 online reviews were scrapped and analyzed using Leximancer software.

The analyses revealed six themes describing chain coffee shop experiences. These are “product quality,” “ambiance and atmosphere,” “customer service,” “menu variety,” “location and convenience” and “price and value.” Dissatisfaction factors are sourced from issues such as “price and value,” “ambiance and atmosphere as well as “menu variety.”

This study fills a research lacuna by using a large dataset of online reviews to comprehensively analyze customer experiences in chain coffee shops, identifying critical themes and sentiment trends. This novel approach offers valuable insights for improving service strategies within the competitive coffee chain industry.

Horse-based tourism stands at the intersection of cultural heritage, leisure activities, and eco-friendly travel, captivating enthusiasts and researchers alike with its diverse facets and impacts. This study examines the horse-based tourism literature to provide an overview of horse-based tourism publications.

Using a systematic literature review (SLR) method, pertinent journal articles published over the past 3 decades were retrieved and analyzed. Based on the review process, 44 papers were identified and analyzed by publication year, journal distribution, research method, and lead author. Using Leximancer software, a thematic analysis was undertaken to determine the major themes of horse-based tourism.

The findings revealed a rising trend of horse-based tourism articles and the appearance of an increasing number of studies in tourism-oriented journals. In addition, it was discovered that the majority of available studies are qualitative, whereas quantitative research is few and limited.

Our research establishes a foundational resource for future studies and scholarly discourse on the multifaceted contributions of horse-based tourism.

This study can assist decision-makers in understanding the potential of horse-based tourism in the sustainable development of destinations. Moreover, it provides clear direction on implementing appropriate strategies to manage horse-based tourism.

This study distinguishes itself as the inaugural comprehensive literature review encompassing the breadth of horse-based tourism publications and research domains. By pioneering this endeavor, we not only contribute a unique perspective to the existing body of knowledge in the field but also emphasize the vital role of horse-based tourism in fostering economic and social sustainability for the countries involved.

Environmental, social and governance (ESG) scores are compelling for firm strategy and performance. Thus, this study aims to explore ESG scores’ predictive roles on global firms’ diversity scores.

A total of 1,114 global firm-year data from the Thomson Reuters Eikon database was analyzed using machine learning algorithms like rpart, support vector machine, partykit and evtree.

The results reveal a positive association between diversity, resulting in greater comprehensiveness and relevance. Broadly speaking, the two factors with the most significant values for calculating the overall diversity scores of businesses are ESG scores and social scores. ESG scores and environmental scores are the most effective predictors for the diversity pillar and people development scores. In contrast, community and social scores are the most important predictor factors for the inclusion scores.

The research is particularly pertinent to managers and investors considering ESG issues while making decisions. The results indicate that leaders and practitioners should prioritize ESG elements and diversity problems to enhance performance.