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The influence of generative AI on workforce dynamics in tourism and hospitality

dc.contributor.advisorTrigo-Guedes,Rui
dc.contributor.authorGoodluck, Amakiri Promise
dc.contributor.institutionLisbon School of Economics & Management
dc.date.accessioned2026-05-04T08:50:16Z
dc.date.available2026-05-04T08:50:16Z
dc.date.issued2025
dc.descriptionTrabalho Final de Mestrado, Management Information Systems, ISEG, 2025.
dc.description.abstractRapid advancements in recent years in computer science, particularly in generative artificial intelligence (AI) have enabled the development of intelligent systems increasingly sophisticatedly capable of performing tasks traditionally associated with human cognition. Within the tourism and hospitality sector, new technologies are reshaping operational processes, service delivery models, and the fundamental nature of workforce interactions. Although existing literature highlights AI’s potential to enhance productivity and optimize workflows, the psychosocial dimensions of AI integration remain insufficiently examined. Specifically, limited research explores how individual perceptions of performance shape emotional adaptation and expectations regarding broader industry transformation. Addressing this gap, the present study investigates the mediating role of emotional responses in the relationship between perceived individual impact and perceived sector-wide change associated with generative AI adoption. A quantitative research design was employed using a cross-sectional survey administered to 66 stakeholders, including managers, front-line employees, and students, within the Portuguese tourism and hospitality context. The survey instrument assessed three core constructs: Perceived Impact on Workforce Performance (PEWP), Emotional Responses (ER), and Perceived Impact on Tourism and Hospitality (PETH). Data was analyzed using IBM SPSS Statistics, incorporating descriptive statistics, correlation analysis, and mediation modeling via the process macro. The findings reveal a significant positive relationship between the perceived impact of AI on workforce performance and employees’ emotional responses (p < .001). This suggests that individuals who perceive AI as beneficial to their daily tasks exhibit heightened emotional engagement, characterized by both enthusiasm regarding improved efficiency and apprehension related to adaptation demands. However, no statistically significant relationship emerged between emotional responses and perceived industry-level transformation, nor did emotional responses mediate the relationship between individual perceptions and sector-wide assessments. These results indicate that employees tend to cognitively separate their personal emotional reactions to AI from their strategic evaluations of the tourism and hospitality industry’s future. While generative AI introduces substantial pressures for individual adaptation, these emotional dynamics do not appear to distort perceptions of industry-wide competitiveness or technological transformation. This distinction underscores a nuanced workforce perspective in which emotional adaptation and strategic judgment operate independently. Overall, this study contributes to the growing body of literature on AI adoption in service-oriented industries by demonstrating the importance of treating emotional adaptation and strategic assessments as distinct yet parallel processes. For organizational leaders, these findings highlight the need to develop management strategies that support emotional well-being alongside clear, transparent communication to ensure the responsible and effective integration of generative AI technologies.en
dc.formatapplication/pdf
dc.identifier.urihttp://hdl.handle.net/10400.5/118325
dc.identifier.urlhttp://hdl.handle.net/10400.5/118325
dc.language.isoeng
dc.subjectTourism and Hospitality
dc.subjectWorkforce Dynamics
dc.subjectEmotional Response
dc.subjectMediation Analysis
dc.subjectGenerative AI
dc.subjectTechnology Acceptance
dc.titleThe influence of generative AI on workforce dynamics in tourism and hospitalityen
dc.typemaster thesis
dspace.entity.typePublication
rcaap.rightsopenAccess

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