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This study aimed to identify distinctive characteristics of hotel guests and understand their perception of guestroom technologies (GRTs) in hotels by utilizing the theory of consumer innovativeness and the social influence theory.

The data were collected from 268 hotel guests. The K-means clustering algorithm was employed to identify participants based on their views on the significance of technologies provided in guest rooms. A multivariate analysis of variance was applied to investigate if there were significant differences among clusters regarding social influence on hotel bookings, technology innovativeness and technology expertise and knowledge.

Two clusters were identified: technology compassionates and casual users of technology. Findings revealed that technology compassionates are more influenced by their friends when booking a hotel compared to casual users of technology. The ability to link up multiple wireless mobile devices, accessible outlets and mobile websites were the most critical GRTs for technology compassionates.

Technology should be considered a crucial part of the hotel guest experience. Hoteliers ought to continue investing in smart technologies to improve their guests' experiences. GRTs can reduce overhead staff costs while giving guests more control over their stay by utilizing everyday items like smartphones and offering them more power over their lodging experience.

This study advances the existing literature on GRTs by identifying which GRTs produce the most customer satisfaction. Moreover, this study explores the impact of social influence, innovativeness as a personality trait and having expert knowledge of technologies on preferences for GRTs.

This paper addresses a crucial research need by examining the influence of compatibility, a pivotal design element for hotel concierge apps, on the socio-psychological dynamics of digital hotel guests. While prior research has examined the constructs, their application to digital concierge apps introduces a unique context. We posit that compatibility significantly influences central variables rooted in theory of planned behaviors (TPBs) and technology acceptance model (TAM), fostering positive usage intentions.

Analyzing data from 668 four-star hotel guests through PLS-SEM substantiates compatibility’s role, endorsing the theoretical amalgamation of affordance, TPB, and TAM frameworks.

Compatibility positively affected perceived ease of use, perceived usefulness, and attitude toward behavior. Besides, usage intention positively affected willingness to pay a price premium and revisit intention.

This paper adopts compatibility as a unifying force for integrating TPB and TAM; the predictive ability of digital concierges' usage intentions on revisit intentions to upscale hotels. Further, this paper is the first attempt to highlight employing compatibility as a pivotal design factor for digital concierge apps in the hospitality setting.

Green hospitality studies have not adequately focused on the diffusion of eco-innovative hotels amongst visitors. This study aims to fill this gap by identifying green hotel attributes that influence visitors’ adoption of eco-friendly hotel and their intentions to partake in green initiatives.

The paper uses a mixed-method approach to explore the drivers of customers’ green hotel adoption and consumption. In the qualitative phase, data were collected via 20 open-ended interviews and analyzed to derive a measurement scale. The scale was then tested through a survey comprising 500 respondents using structural equation modelling.

The study results elucidate how guests’ visit intentions and green consumption behavior is built through their perception of newness and uniqueness of eco-innovative attributes. Findings shed light on how green hotel’s sustainable communication and corporate social responsibility outreach efforts positively influence guest visit intentions.

Study results reveal perceived eco-innovativeness as an important antecedent of visit intentions. Based on guest’s preferences, green hotels striving to increase its visitors’ base could begin by expanding their eco-innovative attributes.

Contrasting previous studies that have exclusively used the theory of planned behavior constructs, this study argues that diffusion of innovation constructs also offer valuable insights into guests’ visit intentions. While existing studies have covered limited number of eco-innovative attributes, this study adds to the literature by presenting a comprehensive set of attributes including trustworthiness of communication and observability of its social impacts.

This study aims to analyze the importance and performance of customer-facing technologies in luxury hotels. The study also assessed differences between and within the four generations in the importance-performance analysis (IPA).

Data were collected using a Qualtrics panel of recent luxury hotel customers in the USA belonging to all four generations. The cross-generational IPA was conducted using t-tests and (ANAOVA).

The IPA matrix concentrated most technology items in either low importance – low performance or high importance – high performance quadrants. One-way ANOVA revealed significant differences between generations on the importance ratings of all technology items except wireless charging power solutions and on the performance ratings of all technology items. Furthermore, post hoc tests indicated that millennials rated luxury technology most favorably among the four cohorts, followed by generations Z, X and Baby Boomers. In addition, significant differences between the importance and performance of many technology items within each generational cohort were observed. Overall, Wi-Fi was unanimously ranked across generations as the most important technology among luxury guests, but it was the only one that scored lower in performance than importance.

The findings of this study contribute to hospitality scholarship in two primary ways: the importance and performance of technology and generational differences. The results advance the understanding of the impact of generational factors on customer-facing technological adoptions in the luxury hotel sector.

Technologies that are pervasive in the home also become vital offerings for hotels. The more pervasive technology, the more a luxury hotel must work to ensure that it performs at optimal levels. Additionally, which technologies are most important to targeted generations are provided so practitioners may budget for their implementation.

This research is a pivotal step forward in unraveling the intricate interplay between generational factors and technological evaluations, providing a foundation for future research and practical applications in a rapidly evolving technological landscape in the hospitality industry.

Sustainable tourism is becoming more popular all over the world. Eco-friendly (green) hotels are properties that are friendly to the environment and are becoming increasingly popular among green travellers. Electronic word-of-mouth is a technique of communicating with consumers in order to share their experiences, and it is a significant marketing tool for hotels. This paper aims to identify the main themes shared in online reviews by tourists visiting eco-friendly hotels, and which of these themes were associated with satisfaction and dissatisfaction ratings.

The research used qualitative content analyses to analyse 1,202 user-generated content of the top 10 hotels in UK shared by guests on an online platform.

The analyses revealed nine themes in descriptions of airline travel experiences. These are “hotel amenities”, “services”, “location”, “staff”, “eco” (eco-friendly activities), “value” and “recommend/revisit” (intentions). Negative comments are associated with the “bathroom”, “mattress”, “water”, “bed”, “price”, “shower”, “Wi-Fi” and “restaurant” concepts.

This study differs from previous research in which it aims to address a void in the literature on the shortcomings of research focused on finding the dominant themes expressed in online reviews by tourists visiting eco-friendly hotels, and it does so using data mining approach.

This paper reports the results of research that examines the interrelationships between efficacy of sustainability values (SV) and pro-sustainable behaviors of potential tourists. A partially mediated model is postulated and tested to help explain additional error variance in predicting consumers’ destination choice decisions in tourism, hence voiding a critical research gap. Coined as the “environmentally intellectualist behavior,” a new mediator variable is tested to explain additional error variance in human-value models.

The study is based on data collected from two representative samples of potential tourists from the USA and Canada. Data analyses include exploratory and confirmatory factor analyses that were used to examine the underlying domain structures of SV, followed by a predictive model using structural equation modeling.

The study findings suggest that values are salient factors that underlie pro-sustainable tourism and travel behavior. Moreover, the results confirm the existence of a higher-order sustainability construct. The study contributes original insights to the field by demonstrating that there are direct and indirect positive relationships between SV, environmental behaviors and decisions of consumers who take a pro-sustainable stance when traveling.

By modeling values as antecedents to attitudes and testing interrelationships between SV and the mediator variables coined as the environmentally intellectual behavior, the authors developed and tested a predictive model to explain destination- and product choice decisions. The model tested herein advances the value theory in two fundamental ways: first, this study demonstrates that SV can be modeled as higher-order factors. Second, values are antecedents to attitude and other variables, therefore must be included in consumer behavior models. Finally, the culture or origin of tourists matters when examining the impact of values on tourists’ choice decisions. Political actions and environmental attitudes can be modeled as mediators to explain additional error variance.

The explosion of data due to the sophistication of information and communication technology makes it simple for prospective tourists to learn about previous hotel guests' experiences. They prioritize the rating score when selecting a hotel. However, rating scores are less reliable for suggesting a personalized preference for each aspect, especially when they are in a limited number. This study aims to recommend ratings and personalized preference hotels using cross-domain and aspect-based features.

We propose an aspect-based cross-domain personalized recommendation (AsCDPR), a novel framework for rating prediction and personalized customer preference recommendations. We incorporate a cross-domain personalized approach and aspect-based features of items from the review text. We extracted aspect-based feature vectors from two domains using bidirectional long short-term memory and then mapped them by a multilayer perceptron (MLP). The cross-domain recommendation module trains MLP to analyze sentiment and predict item ratings and the polarities of the aspect based on user preferences.

Expanded by its synonyms, aspect-based features significantly improve the performance of sentiment analysis on accuracy and the F1-score matrix. With relatively low mean absolute error and root mean square error values, AsCDPR outperforms matrix factorization, collaborative matrix factorization, EMCDPR and Personalized transfer of user preferences for cross-domain recommendation. These values are 1.3657 and 1.6682, respectively.

This study assists users in recommending hotels based on their priority preferences. Users do not need to read other people's reviews to capture the key aspects of items. This model could enhance system reliability in the hospitality industry by providing personalized recommendations.

This study introduces a new approach that embeds aspect-based features of items in a cross-domain personalized recommendation. AsCDPR predicts ratings and provides recommendations based on priority aspects of each user's preferences.

This study aims to progress scholarly understanding of the staycation phenomenon by examining customer segments and documenting local customers’ attribute preferences.

A stated choice experiment is used to examine customer preferences for staycation package attributes. Latent class discrete choice modeling is deployed to classify customers into market segments based on their preferences. The profile of each segment is enhanced by documenting customer characteristics and consumption styles.

Six prominent market segments are identified using a combination of sociodemographics, consumption styles and staycation attribute preferences. The findings draw on consumer experiences during the COVID-19 pandemic to generate theoretical insights into preferred staycation packages. Empirically, the estimation results from the research framework and choice experimental method demonstrate that staycation market segments exhibit distinct preference structures.

Practitioners and policymakers can incorporate the findings of this study in designing and/or assessing staycation packages. This can ensure differentiated products for defined segments that resonate within local communities through positive word of mouth, thus offering prospective spillovers to visiting friends and relatives.

This is a pioneering study on preference heterogeneity from the customer perspective, with a focus on staycation markets. The findings can encourage and assist hotel sector leaders to capitalize on local market developments to achieve a more resilient hospitality business model.

This study aims to examine hotel guests’ acceptance of technology agency – the extent to which they would let artificial intelligence (AI)-based systems make decisions for them when staying in hotels. The examination was conducted through the prism of several antecedents of acceptance of technology agency, including perceived ethics, benefits, risks and convenience orientation.

A thorough literature review provided the foundation of the structural model, which was tested using confirmatory factor analysis, followed by structural equation modeling. Data were collected from 400 US hotel guests.

The most important determinant of acceptance of technology agency was perceived ethics, followed by benefits. Risks of using AI-based systems to make decisions for consumers had a negative impact on acceptance of technology agency. In addition, perceived loss of competence and unpredictability had relatively strong impacts on risks.

The results provide a conceptual foundation for research on systems that make decisions for consumers. As AI is increasingly incorporated in the business models of hotel companies to make decisions, ensuring that the decisions are perceived as ethical and beneficial for consumers is critical to increase the utilization of such systems.

Most research on AI in hospitality is either conceptual or focuses on consumers’ intentions to stay in hotels that may be equipped with AI technologies. Occupying a unique position within the literature, this study discusses the first time AI-based systems that make decisions for consumers. The value of this study stems from the examination of the main concept of technology agency, which was never examined in hospitality.

This study aims to examine the perceived values of the metaverse when adopting it in the luxury hospitality business. Based on the cost–benefit perspective, this research provides solid theoretical contributions and actionable managerial recommendations.

An exploratory sequential mixed-method design was used. For the qualitative phase, 21 hotel managers and 24 hotel guests (who often stay in four-star and five-star hotels and resorts) were interviewed after showing them a series of videos about using the metaverse in the hotel business. Based on the results of the qualitative phase, the analytic hierarchy process method was used, and 476 valid questionnaires were analyzed.

The results highlight the perceived benefits (personalized services, immersive experience and positive brand image) and costs (lack of human touch, time and effort and security and privacy) of metaverse adoption for hotel managers and their guests. In addition, the study determines the weight of each value attribute of metaverse adoption for each travel stage (pre-travel, during travel and post-travel).

Regarding metaverse adoption, the research offers practical suggestions for luxury hotels. For instance, the cost of equipment and the time and effort required are perceived costs of metaverse adoption. To address these challenges, hotels may offer free equipment (e.g. VR headsets) and training to their guests to stimulate the use of the metaverse.

This study addresses a gap in the literature by presenting a conceptual framework for examining metaverse adoption in the luxury hotel scenario. Unlike using conventional models like the technology acceptance model or the unified theory of acceptance and use of technology to investigate a technology’s adoption, this study stands out by unraveling the topic through the lens of value proposition. The latter often comes from an efficient value co-creation process, which is indeed shaped by an adequate appreciation of the congruence of perceived values (i.e. perceived benefits and costs) of metaverse from hotel manager and guest perspectives.