Lóránt Dénes Dávid

This study investigates the perceptions of employees in the hotel industry of the Republic of Serbia regarding the acceptance and importance of artificial intelligence (AI). Through a modified UTAUT model and the application of structural equation analysis (SEM), we investigated the key factors shaping AI acceptance. Research results show that behavioral intention and habit show a significant positive impact on AI usage behavior, while facilitating conditions have a limited but measurable impact on behavioral intention. Other factors, including social influence, hedonic motivation, performance expectancy, and effort expectancy, have minimal influence on the examined variables. The analysis reveals the crucial mediating role of behavioral intention, effectively bridging the gap between various predictors and AI usage behavior, thereby highlighting its significance in the broader context of technology adoption in the hotel industry. The primary goal of the study, which closes significant research gaps, as well as the manner in which it uses a specific model and statistical analysis to accomplish this goal, shows how innovative the work is. This method not only broadens the field’s understanding but also offers valuable insights for shaping sustainable development practices in the hospitality sector in the Republic of Serbia.

Environmental practices have become an important matter in all aspects of life and industries, especially in the post-COVID-19 era. However, these practices continue to face many criticisms about their seriousness and effectiveness. In this context, this study aims to analyze the relationship between adopting green practices in hotels on one side and hotel image, customer satisfaction, and customer loyalty on the other side, considering the star-level rating system of the hotels and the hotel operating categories (chain or independent). This study depended on a sample of 235 hotels in the Hungarian capital of Budapest. Several analytical methods were used to achieve the study aim, including descriptive statistics, t-test, arithmetic averages comparison, text mining, NLP, and sentiment analysis. This study revealed that: (I) The higher the hotel star rank, the better the reviews and valuation factors. (II) Hotels that operate in chains show more attention to environmental practices. (III) Customers are more loyal to and satisfied with green hotels, and this increases as the hotel’s star rating increases.