Generative artificial intelligence (GAI) offers important opportunities for the hospitality and tourism (HT) industry in the context of operations, design, marketing, destination management, human resources, revenue management, accounting and finance, strategic management, and beyond. However, the implementation of GAI in HT contexts comes with ethical, legal, social, and economic considerations that require careful reflection by HT firms. The purpose of this study is to offer a critical examination of the effects of GAI applications across a broad spectrum of stakeholders in the HT industry, in an effort to integrate practical and academic insights and foresights and drive academic research forward. Through the contributions of a purposeful selection of scholars, educators, and industry-practitioners, along the tenets of the stakeholder theory of the firm, this study highlights the potential challenges and opportunities of GAI and considers how academics can navigate the (research) complexities of this rapidly evolving technological phenomenon.

Purpose: This paper aims to explore how emotional expressions embedded in online hotel reviews influence consumers’ helpfulness perceptions. In particular, this study develops and tests hypotheses analyzing empirical data with a text-mining method in the context of hotels to investigate how review valence influences the perceived helpfulness of online hotel reviews and to examine the role of negative emotional expressions embedded in online consumer reviews with respect to perceived helpfulness.Design/methodology/approach: This study collected 520,668 online reviews involving 488 hotels in New York City (NYC) on Tripadvisor.com. Of these reviews, 69,202 reviews (13.29 per cent) that had received helpfulness votes were analyzed by a text mining method and negative binomial regression.Findings: This study demonstrates that negative reviews are considered more helpful than positive reviews when potential customers read online hotel reviews for their future stay. However, when intensively negative emotions were expressed, the degree of helpfulness regarding negative reviews was diminished.Originality/value: While emotional expressions prevail in online consumer reviews, surprisingly little attention has been devoted to the consequences of emotional expressions in consumers’ information processing and decision-making. Due to the nature of service, given the inseparability of production and consumption, which often hinders the execution of flawless service, consumers tend to be more dependent on reviews to minimize any potential failures they may encounter later on. Therefore, this study fills a gap by demonstrating that negative reviews and emotional expressions play a more crucial role in consumers’ information processing and decision-making.

Purpose: This study aims to investigate the effect of customers’ multisensory service experience on customer satisfaction with cognitive effort and affective evaluations using big data and business intelligence techniques. Design/methodology/approach: Online customer reviews for all New York City hotels were collected from Tripadvisor.com and analyzed through business intelligence and big data analytics techniques including data mining, text analytics, sentiment analysis and regression analysis. Findings: The current study identifies the relationship between affective evaluations (i.e. positive affect and negative affect) and customer satisfaction. Research findings also find the negative effect of reviewer’s cognitive effort on satisfaction rating. More importantly, this study demonstrates the moderating role of multisensory experience as an innovative marketing tool on the relationship between affect/cognitive evaluation and customer satisfaction in the hospitality setting. Originality/value: This study is the first study to explore the critical role of sensory marketing on hotel guest experience in the context of hotel customer experience and service innovation, based on big data and business intelligence techniques.

Purpose: Electronic word of mouth in the form of user-generated content (UGC) in social media plays an important role in influencing customer decision-making and enhancing service providers’ brand images, sales and service innovations. While few research studies have explored real content generated by hotel guests in social media, business analytics techniques are still not widely seen in the literature and how such techniques can be deployed to benefit hoteliers has not been fully explored. Thus, this study aims to explore the significant factors that affect hotel guest satisfaction via UGC and business analytics and also to showcase the use of business analytics tools for both the hospitality industry and the academic world. Design/methodology/approach: This study uses big data and business analytics techniques. Big data and business analytics enable hoteliers to develop effective and efficient strategies improving products and services for guest satisfaction. Therefore, this study analyzes 200,431 hotel reviews on Tripadvisor.com through business analytics to explore and assess the significant factors affecting guest satisfaction. Findings: The findings show that service, room and value evaluations are the top-three factors affecting overall guests’ satisfaction. While brand type and negative emotions are negatively associated with guests’ satisfaction, all other factors considered were positively associated with guests’ satisfaction. Originality/value: The current study serves as a great starting point to further explore the relationship between specific evaluation factors and guests’ overall satisfaction by analyzing user-generated online reviews through business analytics so as to assist hoteliers to resolve performance-related problems by analyzing service gaps that exist in these influential factors.

This study examines how destination marketing organization’s (DMO’s) social media posts and comments increase customer engagement and explores the moderating role of information richness in the smart tourism context. To test the proposed hypotheses, the current study employed social media analytics by analyzing unique panel data from 72 DMOs’ Facebook event pages. The research findings indicate that a DMO’s social media efforts (quantitative aspect) are positively associated with customer engagement and DMOs having a higher level of information richness (qualitative aspect) in their messages have greater customer engagement on DMOs’ Facebook event pages. The current study also sheds light on the scant research stream of measuring DMOs’ social media success empirically and presents the possibility of how social media analytics can be applied to hypothesis testing in tourism research instead of traditional research methods.