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The hospitality industry has witnessed numerous changes to enhance the stay experience of guests. To offer a memorable stay experience, the industry has started deploying intelligent robots. Therefore, this case study aims to examine and explore artificial intelligence (AI) enabled robots in hospitality industry in order to enhance guest experience in a smart city.

Semistructured interviews have been conducted at Novotel Ambassador Seoul Dongdaemun Hotels and Residences, Seoul, South Korea, to understand the stay experience of guests regarding services offered by AI enabled robots. The authors have selected employees for interviews since employees listen and witness the guest experience directly. Out of 214 employees in the hotel with varied experience and background, 26 interviews are conducted.

Through a systematic approach of coding, the authors have identified that deploying AI enabled robots facilitates the automation, information gathering, personalization and seamless service in the hospitality industry of a smart city. Further, with a back-and-forth mapping mechanism based on epistemological principles, the authors made four propositions that lead to the development of a research framework.

The practicing managers of hospitality industry can employ AI enabled robots within the scope of improving and automating the processes that can also offer increased personalization to enhance the stay experience, which is expected in a smart city.

The study offers a unique contribution to literature, since it is a live case study, and the information is from the practicing employees of a well-known organization in a hospitality sector from a smart city (Novotel Ambassador Seoul Dongdaemun Hotels and Residences, Seoul, South Korea).

To explore the impact of luxury hotel brands’ technologies on guests’ perceived value enhancement and its consequences, this study aims to examine how task–technology fit (TTF) and luxury–technology fit (LTF) affect luxury hotel guests’ perceived value enhancement, satisfaction and brand loyalty.

A scenario-based online survey was conducted with existing and potential luxury hotel guests. The relationships among TTF, LTF, value enhancement, satisfaction and loyalty were examined using partial least square structural equation modeling. Moreover, the respondents were asked their preferred service agent (human staff vs technology) in a luxury hotel to further understand luxury hotel guests’ perception toward technological services in a luxury hotel setting.

The results reveal that both TTF and LTF have significant impacts on the luxury hotel’s value enhancement, and the impacts of TTF and LTF on value enhancement are moderated by the guests’ technology optimism. The findings of this study suggest that the luxury hotel brands must consider the fit between the image of their brand and the technology to increase guests’ perceived value enhancement, satisfaction and loyalty.

Applying categorization theory, this study extended the concept of brand extension to technology implementation. The findings advance the current understanding of how technology affects luxury hotel consumers’ perceived value enhancement, and how consumers’ optimism influences the impacts of TTF and LTF on value enhancement.

By examining the effects of TTF and LTF on luxury hotel guests’ perceived value enhancement, this study would shed light on luxury hotel brands’ technology implementation strategies. By understanding how TTF and LTF affect consumers’ perceived value enhancement, luxury hotel brands will be able to better design their technology implementation plans.

Even though luxury hotel brands are well known for offering high-level and personalized services through interpersonal interactions between employees and consumers, they have been actively implementing a variety of technologies to enhance guest experience and satisfaction. This study investigates the role of technological innovations in a luxury hotel setting. Particularly, this study examines how technologies improve guests’ perceived value of a luxury hotel and lead to satisfaction and loyalty.

Purpose: This paper presents a review of the current state and potential capabilities for application of robots, artificial intelligence and automated services (RAISA) in hotel companies.

Design/methodology/approach: A two-step approach was applied in this study. First, the authors make a theoretical overview of the robots, artificial intelligence and service automation (RAISA) in hotels. Second, the authors make a detailed overview of various case studies from global hotel practice.

Findings: The application of RAISA in hotel companies is examined in connection with the impact that technology has on guest experience during each of the five stages of the guest cycle: pre-arrival, arrival, stay, departure, assessment.

Research implications: Its implications can be searched with respect to future research. It deals with topics such as how different generations (guests and employees) perceive RAISA in the hotel industry and what is the attitude of guests in different categories of hotels (luxury and economy) towards the use of RAISA. It also shows what is the attitude of different types of tourists (holiday, business, health, cultural, etc.) and what kinds of robots (androids or machines) are more appropriate for different types of hotel operations.

Practical implications: The implications are related to the improvement of operations and operational management, marketing and sales, enhancement of customer experience and service innovation, training and management.

Originality/value: This book chapter complements and expands research on the role of RAISA in the hotel industry and makes some projections about the use of technologies in the future.

This study aims to determine similarities and differences in business travellers' expectations and managers' perceptions of the service provided by guest houses.

Two questionnaires with seven‐point Likert scales were used to measure travellers' expectations of guest house service and managers' perceptions of these expectations. Data was analysed using Statistica Version 6.1 by employing factor analysis and ANOVA tests.

Business travellers deemed secure parking and the professionalism of staff as the most important attributes, while guest house managers thought it would be friendliness of front desk staff and efficient handling of complaints. Both groups indicated that cleanliness of rooms and services performed by staff adequately the first time, were of particular importance. The importance rating of the different service dimensions was not significantly influenced by the business travellers' gender, managerial position, nights of stay or by the guest house grading. Managers mostly overestimated guests' expectations.

The limitation of the study is the relatively small sample used (50 guest house managers and 300 business travellers within one city). Future research needs to examine a broader sample of guest house managers and could compare the expectations of different types of guests.

It is worth noting that guest house managers had overestimated the expectations of business travellers regarding the service provided. Management should focus on service dimensions important to guests and not those based on own opinions.

An understanding of business travellers' expectations could lead to their needs being more closely addressed and satisfaction levels being raised.

To remain competitive in an ever-changing marketplace, many accommodation managers are constantly upgrading their portfolio of service offerings for guests. Luxury suites with butler service were created as an extra component of luxury to the accommodation model. This inclusion was a welcomed innovation for guests who were willing to pay top dollar for top-of-the-line services and the ultimate in exclusivity. Butlers act as the luxury service delivery mechanisms in accommodations, going above and beyond to meet and exceed guest expectations. This chapter makes a unique contribution to the compendium of knowledge on luxury hospitality and tourism by providing insights from the supply side of luxury accommodation brands in the Caribbean and Pacific regions. A comprehensive review of the literature on the topic coupled with semi-structured interviews were conducted in the Maldives, Jamaica, St. Barts, The Bahamas, St. Lucia, Antigua, Cayman Islands, Barbados and Mexico. With an expressed need to examine the operationalisation (Miller & Mills, 2012a, 2012b) and subjectivity constructs (Godey et al., 2012a, 2012b) on Luxury Management in Hospitality and Tourism, this chapter aims to provide an excellent addition to the discourse. It also aims to address a major gap in the literature as there is a lacuna of recent research on Luxury Management in Tourism in Small Island Developing States (SIDS) from two major tourism dependent regions of the world – the Caribbean and the Pacific.

The hotel industry has witnessed an increasing number of service automation through service robots such as robot concierges. However, few studies have documented how to identify how hotel guests perceive a robot concierge for their service encounter. Therefore, the purpose of this study is to examine the effects of robot concierges on hotel guests’ attitudes and adoption intentions of robot concierges.

This study investigated the effects of robot concierges’ morphology and their level of interactivity with guests at different levels of hotel service on guests’ attitudes and their intentions to adopt robot concierges. To achieve the study’s objectives, this study conducted a 3 × 2 × 3 between-subjects factorial design experiment. Moreover, the survey asked questions about subjects’ preferences of their service encounters (e.g. human employees, robot concierges and/or no preference) and reasons for their selected preference.

The results demonstrated that the robot’s morphology significantly influenced guests’ attitudes toward robot concierges. In particular, the caricatured robot was the most preferred morphology of robot concierges. The findings showed that even if guests had favorable attitudes toward robot concierges, they preferred human employees to robot concierges because of humans’ sincere and genuine interactions.

This study contributes to the literature by investigating the causal impacts of the morphology of robot concierges, level of interactivity and level of hotel service on guests’ attitudes toward robot concierges. The thematic analysis of service encounter preference provides an overview of the factors that guests expect for their service encounters in a hotel setting.

This paper attempts to identify the emerging themes that can shed light on the sales and marketing issues and challenges being faced by global hospitality organizations.

The paper utilizes a multi‐method approach for data collection. A thorough literature review, a focus group and personal interviews were conducted to explore the themes and construct a tentative thematic web. Publicly accessible secondary data in the form of customer reviews were drawn from world's leading web site – Tripadvisor.com. A total of 702 reviews of the customers of luxury hotels from the world's top two tourist destinations – France and USA – were analyzed, using thematic analysis. Customers' perspective is juxtaposed with industry's perspective to offer insights on the sales and marketing issues and challenges being faced by hospitality organizations globally.

Marketing challenges, namely personalization/customization of services, service management, creating a strong parent brand, under‐utilization of the social media and diverting tourists from heritage properties, are identified as some of the key emerging sales and marketing issues and challenges faced by the global hospitality industry.

Analytically identified themes in this research paper provide valuable insights on issues and challenges related to sales and marketing for the policy makers and practitioners from the hospitality industry. Findings are based on the analyses of real customer data from the world's leading tourist destinations. This makes the paper very valuable for both practitioners in the hospitality industry around the globe as well as for academic researchers working in this area.

This work is unique in nature because the authors did find any scholarly work addressing such an important issue. Use of real customer data and thematic analysis – a widely used qualitative analytic method for a scholarly study – in the hospitality field, enhances its value and contributes to the field by opening up avenues for further scholarly explorations and investigations.

This research proposes to organise and distil this massive amount of data, making it easier to understand. Using data mining, machine learning techniques and visual approaches, researchers and managers can extract valuable insights (on guests' preferences) and convert them into strategic thinking based on exploration and predictive analysis. Consequently, this research aims to assist hotel managers in making informed decisions, thus improving the overall guest experience and increasing competitiveness.

This research employs natural language processing techniques, data visualisation proposals and machine learning methodologies to analyse unstructured guest service experience content. In particular, this research (1) applies data mining to evaluate the role and significance of critical terms and semantic structures in hotel assessments; (2) identifies salient tokens to depict guests' narratives based on term frequency and the information quantity they convey; and (3) tackles the challenge of managing extensive document repositories through automated identification of latent topics in reviews by using machine learning methods for semantic grouping and pattern visualisation.

This study’s findings (1) aim to identify critical features and topics that guests highlight during their hotel stays, (2) visually explore the relationships between these features and differences among diverse types of travellers through online hotel reviews and (3) determine predictive power. Their implications are crucial for the hospitality domain, as they provide real-time insights into guests' perceptions and business performance and are essential for making informed decisions and staying competitive.

This research seeks to minimise the cognitive processing costs of the enormous amount of content published by the user through a better organisation of hotel service reviews and their visualisation. Likewise, this research aims to propose a methodology and method available to tourism organisations to obtain truly useable knowledge in the design of the hotel offer and its value propositions.

Hospitality services are among the most robust experiences enjoyed by consumers. To deliver them successfully, careful attention to detail is first needed for the design and development of those experiences. This chapter begins by exploring the array of elements that holistically construct an experience, from process to people to physical environments. Then, the customer experience management framework is used to outline a recommended technique for approaching experience design. Finally, a brief survey of visualization techniques shows how experiences can be modeled for assessment and improvement.

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.

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.

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.

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.