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This study aims to evaluate the behavioral predictors that affect tourists’ intentions to visit silver screen destinations.

The survey questionnaire was composed of multi-item measures, and a total of 432 questionnaires were collected by purposive sampling technique. Participants were asked about sensory stimuli, social stimuli, naturalistic stimuli, cultural stimuli, hospitality culture stimuli, cognitive responses, affective responses and behavior approach, which were evaluated using a seven-point Likert scale.

The authors discovered that cognitive and affective responses positively influence the tourists’ intention toward destinations with film-induced tourism, because tourists highly value unique and refreshing symbolic connotations of silver screen destinations, which are commonly predisposed to nostalgia and poetic on the scene.

The originality of this study and the theoretical value of the present research lies with revealing specific relations within the film-induced experienscape constructs that are based on the multistakeholder and multidisciplinary approach. Moreover, this study puts forward constructive suggestions for destination stakeholders in regard to how to market film-induced tourism that uses a multidisciplinary approach that is encompassed by experienscape constructs, which thereby reinforces the film-induced tourists’ experience and their behavior approach.

This study illustrates the conceptual framework that expands the knowledge of the fundamental components that describe how AI-driven servant leadership (SEL) influences the job resources (JR), work engagement (WE) and job performance (JP) of tourism and hospitality employees.

The empirical study was conducted on a sample of 953 international tourism and hospitality employees who were selected via a purposive and snowball sampling approach in a cross-sectional survey. The analysis was performed using a partial least square-structural equation modeling.

The results of this study confirmed the positive impact of AI-driven SEL on employee JR with the boundary conditions of AI-driven SEL.

This study finding assists tourism and hospitality practitioners in understanding that in the near future, AI will have a major effect on the nature of work, including the impact on leadership styles. Hence, AI-driven SEL holds both positive (through direct impact on JR) and negative (via boundary conditions) impacts on employees’ JP and ultimately organizational success. Accordingly, managers should employ AI-driven SEL to increase employees’ JR, and once employees achieve high WE, they should constrict AI-driven SEL boundary conditions and their influence between JR and WE and WE and JP.

This study offers a novel and original conceptual model that advances AI-driven social theory, SEL theory and job demands-resources (JD-R) theory by synthesizing, applying and generalizing gained knowledge in a methodical way.

For the past two decades, researchers have been exploring knowledge of work engagement in the hospitality and tourism industry (HTI). This study aims to provide a comprehensive analysis of global research trends in the field through a bibliometric analysis.

A total of 335 papers from 2005 to 2021 were searched using the Scopus database. The analysis focused on the temporal distribution, most productive journals, countries/territories, institutions, authors, international and authors' collaboration, the most cited publications and author keywords. The VOS viewer and R software were used to visualise the scientific landscapes.

The results indicated a rapidly increasing trend of studies on work engagement in the HTI. The journal with the most published articles was the International Journal of Hospitality Management. The USA ranked first in production, and Karatepe, O.M., from Turkey was the leading author. The first and most cited article was from Salanova et al. (2005). Other themes were identified, mainly related to job resources and outcomes associated with work engagement.

The study provides a more comprehensive understanding of the progress of work engagement in HTI studies and offers clues for further investigation in this field.

This is the first paper to explore work engagement in the HTI using quantitative and visualised bibliometric analysis.