Task To Tech: An Exploration of Generative Ai in Tourism Marketing through Student Experiments and Practitioner Interviews
DOI:
https://doi.org/10.36276/mws.v24i1.945Keywords:
Generative AI, Tourism, MarketingAbstract
This study investigates the adoption and impact of Generative Artificial Intelligence (GenAI) in tourism marketing, exploring the intersection of technology performance, user adoption, and task suitability through the dual perspectives of emerging talent and established professionals. A multi-method research design was employed, combining a quasi-experiment with higher education tourism students and semi-structured interviews with industry practitioners. The experiment compared the performance of a control group against an AI-prompted group on standardized marketing tasks. The interviews explored real-world adoption drivers, guided by an integrated framework of Task-Technology Fit (TTF) and the Unified Theory of Acceptance and Use of Technology (UTAUT). Experimental results demonstrate that GenAI significantly enhances efficiency across all tasks. However, its impact on quality is task-dependent: it modestly improves long-form text, has a neutral effect on short-form content, and markedly decreases the quality of visual design outputs. The qualitative findings reveal that practitioner adoption is primarily driven by high TTF and strong Performance Expectancy. The study also identifies Effort Expectancy, Social Influence, and Facilitating Conditions as key factors, while highlighting Digital Literacy as a critical, overarching moderator that governs the effective utilization of AI tools. The study advocates for a fundamental shift in tourism education and professional development toward fostering critical AI literacy to ensure technology is leveraged as a collaborative tool that augments, rather than compromises, strategic and creative quality.
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