Navigating Transparency in AI-Powered Luxury Hospitality: A Dynamic Guest-Centric Approach

PurposeThe purpose of this study is to assess the comparative influence of two empathy dimensions (cognitive and emotional attributes) on emotional service experience and behavioral intention among business and leisure hotel guests. Studies relevant to empathy dimensions are relatively scarce in tourism and hospitality.Design/methodology/approachThe current study reviewed the concepts of empathy, and empirically compared perceptions of empathy attributes between the two groups. The survey was intended to examine how well the hotel employees emotionally handle hotel guests’ incidents or inquiries related to any discomforts through personalized attention. The data were collected from 330 hotel guests who had actually complained about service failures while staying at the hotel during the record-breaking summer of 2013 in terms of number of visitors to Jeju. An active empathetic listening (AEL) tool has been taken to measure the hotel guest’s cognitive views and behavioral intentions, as well as emotional empathy measures under the empathic concern and emotional contagion.FindingsThe results revealed that empathetic dimensions strongly influenced the service experiences of hotel guests. While the emotional service experience of business guests was almost completely determined by the cognitive empathy, the emotional service experience of the leisure guests was mainly governed by the emotional empathy.Practical implicationsThese outcomes suggest that the empathetic services through a “purpose of visit”-oriented manner might enhance the guest’s overall emotion positively.Originality/valueAccording to the prior literatures and empirical findings in hospitality and tourism, empathy can be seen as subscale in SERVQUAL instrument. This paper focus on insights of empathy dimensions, and it was revealed that the interaction of both the cognitive and emotional dimensions of empathy conjointly determines the overall emotional service experience and intention of hotel guests.

The use of robotics and artificial intelligence have created a shift in the ways the service-based hospitality and tourism industry can fulfill the needs and wants of consumers that were earlier fulfilled only by humans. Robots have added the automation and self-service experience that play a vital role in the improvements of efficiency, speed, and the overall experience for the guests using technology. While there are many benefits of using robots in the industry, there are also risks associated with the excessive usage of robots on guest experience. As a result of the pros and cons on the topic, it is very important to gather data and analyze the results to further investigate and understand what the outcomes will be for the industry, its employees, and its customers. The purpose of this study is to examine the perceptions of the use of robots in the hotels as perceived by hotel guests who used a service robot and who did not. A self-administered survey was developed, and 939 usable responses were collected from hotel guests. Factor analysis showed that five factors emerged in the study: Advantages, Attitudes, Disadvantages, Pandemic Related, and Fear. Guests recognize the opportunities that service robots are bringing to their experience while voicing their concerns and fears about the use of them. Findings also showed that there are significant differences between users and non-users.

论客房科技的应用：顾客特点的双重作用摘要研究目的本论文主要研究酒店顾客对客房科技（GRTs）的态度以及检验顾客特点和背景情况对GRTs的态度是否有不同的影响。研究设计/方法/途径研究样本包括508位在过去12个月内消费过的酒店顾客, 样本通过在线自助式问卷来采集。本论文的分析步骤分为两步：（1）通过K-means聚类分析和判别分析以确立群组, 以及（2）一系列Chi-square分析以判定酒店顾客特点和态度是否根据获得的顾客背景情况而有差别。研究结果研究结果表明在获得的酒店顾客背景情况中, 态度型因子（比如网络支付喜好）和人口统计类型因子（比如年龄）对于酒店顾客GRTs态度有显著差异。研究实践意义研究结果对酒店经营者针对不同细分市场对GRTs的喜好来制定个性化营销战略有着珍贵指导和实践的启示意义。研究原创性/价值本论文主要通过一系列高级统计操作来研究GRTs以及市场细分方法。研究结果对相关文献有着显著价值, 对以计划行为理论 （Theory of Planned Behavior）为基础, 检验新型GRTs对顾客决策行为的影响提供了实践研究。行业实践者能够借鉴研究结论来制定与科技需求和细分市场需求相关的营销和定价战略。关键词酒店技术；客房技术；房间内部技术；市场划分；付款意愿；决策纸张类型–研究论文

The current study positions guest experience as the focal variable in understanding guest satisfaction. Two sets of hotel studies are reviewed. In the first set, the term experience is used interchangeably with overall satisfaction. For example, Manhas and Tukamushaba (2015) defines the experience quality as the overall level of customer satisfaction with service. In the second set, Ren, Qiu, Wang, and Lin (2016) demonstrates a link between experience and customer satisfaction in the budget hotel context. The Ren et al. (2016) is perhaps the most relevant to the current paper and uses three dimensions of experience: tangible-sensory experience (e.g. cleanliness, quietness); staff interactional experience; and aesthetic experience (visual appeal). However, service quality is not included in their model. Hence, the missing gap in the literature is a comprehensive study that specifies service quality and experience as separate constructs, demonstrates that they are separate discriminate constructs, and examines the differential effect of each on guest satisfaction.Data was gathered via survey targeting hotels guests who had stayed at 3-star to 5-star hotels in Indonesia, in the past 12 months (n = 324). Ro (2012) three-steps regression was applied to analyse mediating role of experience in service quality-satisfaction relationship.The results show that both service quality and experience influence customer satisfaction. A major finding of the study is that experience mediates the relationship between service quality and customer satisfaction. The mediation is partial, with a smaller direct role still played by the service quality attribute of ambience. Nonetheless, service quality per se is not as strategically important for its own sake; rather only if it is leveraged to provide a better hotel guest experience. The new vital emphasis must now be on the overall guest experience, not simply on the more operational variables of service quality. Service quality must still be managed by necessity, but managers must give at least equal attention to the overall guest experience. If the hotel management’s Key Performance Indicators (KPIs) do not include experience aspects, then they are missing attention to half of their strategic assets. The first contribution of this paper is to evaluate the differential role of service quality and experience in directly influencing guest satisfaction. Secondly, this study reveals that experience mediates the relationship between service quality and guest satisfaction. It can also provide hotel managers with more understandings about hotel guest’s experience to improve guest’s satisfaction level.KeywordsExperienceService qualitySatisfactionHotel guest

The luxury hospitality sector is being transformed by AI, driven by the need to enhance guest experience, improve operational efficiency, and address labour shortages. However, this transformation challenges the traditional, human-centric model of service in luxury hospitality. Technologies such as chatbots, virtual concierges, and predictive analytics allow for the customisation of services by analysing guest preferences, creating a highly personalised experience [1]. This efficiency, combined with being able to operate 24/7, strengthens the case for widespread adoption in the industry, but what does this mean for the human-centric service that luxury hospitality is built on?

Digital transformation in the hospitality industry has become critical in improving operational efficiency and guest experience. This study aims to analyze the impact of implementing digital technologies, such as Artificial Intelligence (AI), the Internet of Things (IoT), and big data analytics, on operational performance and guest satisfaction in star-rated hotels in Indonesia. Using a qualitative descriptive approach, this study was conducted through in-depth interviews with operational and IT managers from five-star-rated hotels in Jakarta and Bali, which have adopted digital technologies in their operational activities. The results showed that implementing a cloud-based property management system can increase operational efficiency by up to 30%. In comparison, using AI, such as chatbots, can reduce staff workload by up to 70%. The use of big data analytics also allows hotels to provide more personalized services, increasing guest satisfaction by up to 15%. The study also found that contactless digital technologies, which have become increasingly important during the COVID-19 pandemic, improve guests’ perceptions of safety and comfort. However, system integration, staff training, and initial investment costs are the main challenges faced. The study concluded that digital transformation significantly improves hotel efficiency, guest satisfaction, and competitiveness in an increasingly competitive market. Continuous adoption of digital technologies is highly recommended to maintain the relevance of the hospitality industry in the future.

Purpose The increasingly competitive hotel industry and emerging customer trends where guests are more discerning and want a personalized experience has led to the need of innovative applications. Personalization is much more important for hotels, especially now in the post-COVID lockdown era, as it challenges their business model. However, personalization is difficult to design and realize due to the variety of factors and requirements to be considered. Differences are both in the offer (hotels and their rooms) and demand (customers’ profiles and needs) in the accommodation domain. As for the implementation, critical issues are in hardware-dependent and vendor-specific Internet of Things devices which are difficult to program. Additionally, there is complexity in realizing applications that consider varying customer needs and context via existing personalization options. This paper aims to propose an ontological framework to enhance the capabilities of hotels in offering their accommodation and personalization options based on a guest’s characteristics, activities and needs. Design/methodology/approach A research approach combining both quantitative and qualitative methods was used to develop a hotel room personalization framework. The core of the framework is a hotel room ontology (HoROnt) that supports well-defined machine-readable descriptions of hotel rooms and guest profiles. Hotel guest profiles are modeled via logical rules into an inference engine exploiting reasoning functionalities used to recommend hotel room services and features. Findings Both the ontology and the inference engine module have been validated with promising results which demonstrate high accuracy. The framework leverages user characteristics, and dynamic contextual data to satisfy guests’ needs for personalized service provision. The semantic rules provide recommendations to both new and returning guests, thereby also addressing the cold start issue. Originality/value This paper extends HoROnt in two ways, to be able to add: instances of the concepts (room characteristics and services; guest profiles), i.e. to create a knowledge base, and logical rules into an inference engine, to model guests’ profiles and to be used to offer personalized hotel rooms. Thanks to the standards adopted to implement personalization, this framework can be integrated into existing reservation systems. It can also be adapted for any type of accommodation since it is broad-based and personalizes varying features and amenities in the rooms.

PurposeThis paper aims to bridge the extended reality framework and the luxury hospitality literature by providing insights into how immersive technologies using artificial intelligence (AI) can shape luxury value and consumer differentiation.Design/methodology/approachThe authors conducted three experimental studies comparing immersive AI versus traditional hospitality across luxury contexts (hotels, restaurants and spas). Study 1 investigates the effect of immersive AI (vs traditional hospitality) on customers’ behavioral intentions and the need for differentiation using virtual-assisted reality. Study 2 tests the underlying mechanism of the need for differentiation and luxury value in an augmented reality context. Study 3 provides additional support for the proposed underlying mechanism using virtual-assisted reality in luxury hospitality.FindingsThe findings reveal that immersive AI (vs traditional) luxury hospitality reduces customers’ behavioral intentions of using such services and perceived luxury value. Moreover, the findings indicate that the intention to use immersive AI (vs traditional) luxury hospitality services is contingent upon customers’ need for differentiation.Originality/valueThe findings have important theoretical and managerial implications for immersive technologies in luxury hospitality. They shed light on the dynamics between integrating immersive AI into luxury hospitality and its impact on customers’ differentiation motives and perceived luxury value. The findings reveal the detrimental effect of using immersive AI (vs traditional hospitality) within this context.

What can hotels learn from the last recovery? Examining hotel occupancy rate and the guest experience

This study aimed at investigating the guests’ experience dimensions and their effects on destination marketing bottom lines of satisfaction and revisit intention. This is borne of the overarching importance of customer experience (CX) which has been acknowledged by scholars and practitioners and understanding of the concept been sought from various sectoral perspectives. Admittedly, experience-based investigations in the hotel service sector have been scarce, particularly from a non-western context. The study utilised an intercept approach to collect data from 400 hotel guests in a city in Southern Nigeria. The research model and the formulated hypotheses were tested using partial least square structural equation model (PLS-SEM) with the aid of SmartPLS software. The study findings reveal that guest experience dimensions (utilitarian, hedonic and relational) have a positive and significant influence on guests’ revisit intention and satisfaction with the exception of hedonic experience whose relationship with satisfaction was statistically insignificant. Overall, the research model explained 56.5 per cent of the variance in guest satisfaction and 68.7 per cent on guest revisit intention. This study adds an empirically supported strategic direction for proper guest experience management in hotels from a non-western context.

This study examines the factors influencing the likelihood of luxury hotel guests returning for future visits. Drawing from existing literature, the author identified five underlying factors and 18 observable factors, leading to the formulation of nine conceptual hypotheses. The sample consisted of 317 guests from ten 5-star luxury hotels in Thailand. Utilizing structural equation modeling (SEM) with LISREL 9.10, it was determined that all variables in the model positively impacted the intention of luxury hotel guests to revisit, explaining 81% of the variance collectively (R2). Among these factors, hotel image emerged as the most significant predictor, followed by hotel service quality, guest loyalty, and guest experience. Notably, it was observed that there were strong connections between guest experience and service quality, as well as hotel image and service quality. Additionally, eight out of nine hypotheses received empirical support. This study underscores the importance of luxury hotels fostering a workforce aligned with the brand's values, as staff play a pivotal role in influencing guest revisit intentions. These findings hold particular relevance as the tourism and hospitality industry navigates the post-COVID-19 recovery phase.

This study investigates the relationship between sustainability practices, guest experience, and tourism recovery in the hospitality industry of San Jose Del Monte, Bulacan, Philippines. The primary objective is to explore how sustainable operations influence guest satisfaction and contribute to the sector’s resilience and recovery in the post-pandemic context. A mixed-method research design was employed to provide a comprehensive analysis. The quantitative component utilized structured questionnaires distributed through printed copies, Google Forms, and social media, targeting stakeholders such as hotel guests, business owners, and procurement managers. Slovin’s formula was applied to determine a statistically relevant sample size, ensuring a 95% confidence level and a ±5% margin of error. The qualitative component involved thematic analysis of stakeholder perspectives to gain deeper insights into motivations and challenges surrounding sustainability. The study’s main findings reveal that integrating sustainable practices—such as eco-friendly operations, energy efficiency, and waste reduction—positively impacts guest satisfaction and loyalty. Moreover, these practices enhance business reputation and contribute significantly to long-term recovery and resilience. Technology adoption and supportive government policies emerged as critical enablers of sustainable transformation within the industry. The analysis also highlights the importance of guest-centric strategies, emphasizing that positive guest experiences and environmentally responsible practices go hand-in-hand. The research underscores the vital role of sustainability in driving recovery and competitiveness in the hospitality sector. Businesses that embed sustainability into their operations not only minimize environmental impact but also improve service quality, customer satisfaction, and operational efficiency. The study offers practical insights for industry stakeholders and policymakers, advocating for a balanced approach that aligns economic goals with environmental and social responsibility. Embracing sustainable practices and prioritizing guest experience will be crucial in ensuring the continued growth and resilience of the hospitality industry in a post-pandemic world.

This study examines AI's role in enhancing guest satisfaction and efficiency in the hotel industry. Employing a mixedmethods approach, it analyzes guest feedback and interviews staff at AI-integrated hotels. The findings aim to identify key AI applications that boost satisfaction and efficiency, and outline challenges and best practices for AI implementation. This research offers a holistic view of AI's influence on hospitality, enriching understanding and guiding industry practices. As the hotel industry continues to evolve, the integration of artificial intelligence (AI) technologies has become increasingly prevalent, aiming to enhance guest experience. This research investigates the impact of AI integration on guest experience enhancement within the hotel industry. The purpose of this study is to comprehensively explore how AI technologies influence various aspects of guest satisfaction in hotels. A mixed-methods approach is employed, combining quantitative analysis of guest feedback data with qualitative methods by interviewing the guests staying in the hotel. Data is collected from a diverse range of hotels that have implemented AI technologies, allowing for a nuanced understanding of the impacts across their establishments. This research is expected to provide valuable insights into the multifaceted effects of AI integration in the hotel industry. Specifically, it aims to identify the specific AI applications that most significantly contribute to guest satisfaction levels. Additionally, the study seeks to uncover potential challenges and limitations associated with AI implementation, as well as best practices for successful integration. This topic lies in its comprehensive examination of AI's impact on both guest experience within the hotel industry. While previous research has explored AI's role in hospitality, few studies have undertaken such a holistic analysis, considering its implications for guests. By addressing this gap, this research contributes to a deeper understanding of the transformative effects of AI in the hotel sector, providing practical insights for industry practitioners and stakeholders.

Purpose: The aim of the study was to assess the relationship between cultural intelligence of hotel employees and guest satisfaction, a cross-cultural study in Kenya. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study investigated the correlation between the cultural intelligence (CQ) of hotel employees and guest satisfaction across different cultural contexts. Through a cross-cultural analysis, researchers found a significant positive relationship between the cultural intelligence of hotel employees and guest satisfaction levels. Specifically, higher levels of cultural intelligence among employees were associated with increased guest satisfaction, suggesting that employees who possess a better understanding and appreciation of diverse cultures are better equipped to meet the needs and preferences of guests from various backgrounds. This highlights the importance of fostering cultural intelligence among hotel staff to enhance overall guest experiences and satisfaction in the hospitality industry. Implications to Theory, Practice and Policy: Social exchange theory, service climate theory and resource-based view theory may be used to anchor future studies on assessing the relationship between cultural intelligence of hotel employees and guest satisfaction, a cross-cultural study in Kenya. Hospitality organizations should prioritize the integration of cultural intelligence training programs into their employee development initiatives. These programs should not only focus on enhancing employees' cultural knowledge but also emphasize the development of cultural mindfulness and adaptability skills. Government bodies and industry associations should advocate for the inclusion of cultural intelligence training requirements within the accreditation and certification standards for hospitality professionals.

New technologies represent an important challenge for all sectors across the world, becoming a relevant opportunity for the luxury hospitality industry as well. This study empirically investigates the effects of openness to change and status consumption orientation in influencing luxury hotel guests' perceived usefulness of voice assistants when integrated with their staying. Specifically, we shed light on the potential interplay between these two constructs in shaping guests' perceived usefulness of these devices. Finding suggests that openness to changes and status consumption represents two potentially alternative aspects that managers could leverage to encourage hotel guests' perceived usefulness and vocal assistant devices. This implies that openness to change on the part of luxury hotel guests may be a necessary but not sufficient condition for these devices to be accepted. Rather, luxury hotel managers should also consider the status consumption orientation of their guests.