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Lookup NU author(s): Dr Weizheng ZhangORCiD
This is the authors' accepted manuscript of a conference proceedings (inc. abstract) that has been published in its final definitive form by Global Alliance of Marketing & Management Association, 2025.
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Generative artificial intelligence (GAI) is revolutionising every industry and impacting society, gradually replacing the human touch and transforming every walk of life. The tourism industry is in the early stages of incorporating GAI, with increasing investments in this technology, as exemplified by Expedia - a leading online travel agency (OTA) (Gursoy et al., 2023; Expedia Group, 2023). Given the prevalence of GAI-enabled services and applications in the tourism industry, significant concerns regarding societal impacts such as unfair, biased, stereotypical, and discriminatory information conveyed by GAI are a prominent concern (Kim et al., 2023). Emerging research suggests that people trust algorithmic recommendations from GAI less than human recommendations (Castelo et al., 2019), while some perceive that it provides fairer recommendations (less bias) than human (Qin et al., 2023). However, different communities, particularly from different ethnic or cultural backgrounds, may have different levels of trust and perceptions of algorithmic recommendations from GAI. Recent literature has highlighted the related research lacks the foundations of equality, diversity, and inclusivity (EDI) in our society (Tsung-Yu et al., 2024). Despite the implementation of GAI in the tourism industry, previous research has not considered the fairness of algorithms concerning ethnic group users. However, this remains important because the cultural representation of ethnic groups shapes both tourists and organisations. (Yang et al., 2016). In this research, we also investigate how varied communication styles of GAI can affect the user perception when they receive recommendations from the GAI. By exploring whether and how warm-oriented (vs. competent-oriented) messages from GAI can shape user perception of algorithmic fairness and trust in GAI, our work enriches existing literature that has defined competence and warmth as two universal perceptions that form human impressions of another person or service (Belanche et al., 2021). Little is known about how users react when they receive an algorithmic message from GAI and how they respond when confronted with messages containing biased information, hindering their trust to adopt GAI recommendations. We recruited a randomised and balanced pool of minority versus non-minority participants living in the UK who have experience with GAI. We adopt an explanatory sequential design (Creswell et al., 2003) comprising four distinct studies. In Study 1, we examine the baseline model between communication styles and recommendation acceptance. In Study 2, we expand the research model by including sequential mediators (perceived algometric fairness and perceived trust in GAI) and personalisation as a moderator. In Study 3, we will design a scenario-based experiment for a 2 (competence-oriented vs. warmth-oriented) x 2 (minority vs. non-minority) between-subject factorial experiment to examine how the communication types used by GAI influence users’ perceived algorithmic fairness and trust in GAI, subsequently their response to the GAI (accept or not to accept the recommendations). In Study 4, we will conduct qualitative semi-structured interviews with 15 GAI end-users using purposive and snowball sampling strategies to confirm the results of Study 1-3 and offer more comprehensive findings of the ethical issues (EDI perspective) on GAI. By exploring the implications for social norms and identity construction, this study contributes to global sustainable initiatives in the digital age, including the 2030 Agenda for Sustainable Development, particularly SDG 10 (i.e., reduce inequality), leading to greater social sustainability in tourism consumption, production, and development (Gössling, 2021). Finally, this research will contribute to the literature on human experiences of GAI and provide insights for increasing user acceptance of GAI’s recommendations in the tourism industry.
Author(s): Vernes W, Chang Y, Zhang W
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: 2025 Global Marketing Conference
Year of Conference: 2025
Pages: 238-240
Online publication date: 24/07/2025
Acceptance date: 01/06/2025
Date deposited: 25/07/2025
ISSN: 1976-8699
Publisher: Global Alliance of Marketing & Management Association
URL: https://doi.org/10.15444/GMC2025.03.06.03
DOI: 10.15444/GMC2025.03.06.03
ePrints DOI: 10.57711/cs9w-x743
