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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.

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 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.

The purpose of this study is to examine hotel guests’ attitudes toward guest room technologies (GRTs) and determine whether hotel guests’ characteristics and attitudes regarding GRTs vary according to hotel guest typologies.

The data were gathered from a sample of 508 hotel guests who had stayed in a hotel in the past 12 months via a self-administered survey on Qualtrics survey software. The analysis of the study consisted of two main research steps: identification of cluster groups via the K-means cluster analysis algorithm and discriminant analysis; and performing a series of chi-square analyses to determine whether hotel guests’ characteristics and attitudes vary according to obtained hotel guest typologies.

Results indicated significant attitudinal (e.g. internet payment preference) and demographic (e.g. age) differences among the obtained hotel guest typologies regarding their attitudes toward GRTs.

The results provide valuable guidance and a pragmatic approach for those hotel managers that aim to generate tailored marketing strategies for guest segments that are interested in GRTs.

This study concentrates on GRTs with a market segmentation approach by using advanced statistical procedures. It contributes to the body of related research literature by offering empirical evidence where the study evaluates the impact of the availability of new GRTs on guest decision-making based on the principles of the theory of planned behavior. Practitioners will be able to use the presented findings to generate marketing and pricing strategies with respect to the technological needs and wants of each typology.

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。

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。

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

This paper presents an analysis of the impact of current technologies on customer experiences in upscale hotels and assesses the potential of the latest technologies for enhancing customers’ stay.

A two-step approach was applied in this study. The qualitative phase included an examination of upscale hotel websites, interviews with hotel managers and an internet search regarding the latest technological innovations in hotels. In the quantitative stage, a questionnaire was developed for hotel guests, generating a sample of 310 valid completed questionnaires.

The results reveal that hotel guests value digital involvement in their hotel experience. Moreover, business travellers and younger generations give greater importance to latest technologies.

This study analyses the most innovative technologies, providing guidance for hoteliers wishing to upgrade or implement new technologies. Based on the findings, hoteliers can achieve greater differentiation by offering the most important and latest technology to guests, enhancing their experience and attracting new customers, which can potentially lead to increased revenues. The study’s results are also important because they include the perceptions of both managers and customers.

This research examines the lodging experience in the context of environmentally friendly hotels by discovering the underlying guest segments. A mixed-method approach is deployed, which first reveals three lodging experience dimensions entailing, functionality, hedonism and social responsibility via in-depth interviews. Subsequently, a questionnaire survey is conducted which gathers responses from 326 guests staying at seven certified green hotels. A cluster analysis based on green lodging experiences is performed that evokes three distinct guest segments labeled as (1) spontaneous guests, (2) active guests, and (3) devoted guests. The study notes that social responsibility is the most important lodging experience across the three resultant segments. The study also finds about 31% of respondents tend not to pay much attention to green lodging operations. It leads to a suggestion that the implementation of green operations may be accomplished in a way not notably compromising certain service expectations by those not profusely aspiring of the notion of green operations. Even though meeting the needs of core customers is a vital task.

Guest experience and satisfaction have been central constructs in the hospitality management literature for decades. In recent years, the use of big data as an increasing trending practice in hospitality research has been characterised as a modern approach that offers valuable insights into understanding and enhancing guest experience and satisfaction. Recognising such potential, both researchers and practitioners need to better understand big data’s application and contribution in the hospitality landscape. The purpose of this paper is to critically review and synthesise the literature to shed light on trends and extant patterns in the application of big data in hospitality, particularly in research focusing on hotel guest experience and satisfaction.

This research is based on a Preferred Reporting Items for Systematic Reviews and Meta-analysis literature review of academic journal articles in Google Scholar published up to the end of 2020.

By data types, user-generated content, especially online reviews and ratings, was at the centre of attention for hospitality-related big data research. By variables, the hospitality-related big data fell into two crucial factor categories: physical environment and guest-to-staff interactions.

This paper shows that big data research can create new insights into attributes that have been extensively researched in the hospitality field. It facilitates a thorough understanding of big data studies and provides valuable insights into future prospects for both researchers and practitioners.

This research intends to look at the regional characteristics through an analysis of crowd preference and confidence, and investigates how regional characteristics are going to affect human beings at all aspects in a scenario of sharing economy. The purpose of this paper is to introduce an approach to provide an understandable rating score. Furthermore, the paper aims to find the relationships between different features classified in this study by using machine learning methods. Furthermore, due to the importance of performance of methods, the performance of the features is also improved.

The Rating Matching Rate (RMRate) approach is proposed to provide score in terms of simplicity and understandability for all features. The relationships between features can be extracted from accommodation data set using decision tree (DT) algorithms (J48, HoeffdingTree, and REPTree). Usability of these methods was evaluated using different metrics. Two techniques, “ClassBalancer” and “SpreadSubsample,” are applied to improve the performance of algorithms.

Experimental outcomes using the RMRate approach show that the scores are very easy to understand. Three property types are very popular almost in all of selected countries in this study (“apartment”, “house”, and “bed and breakfast”). The findings also indicate that “Entire home/apt” is the most common room-type and 4.5 and 5 star-rating are the most given star-rating by users. The proposed DT algorithms can find the relationships between features significantly. In addition, applied CB and SS techniques could improve the performance of algorithms efficiently.

This study gives precise details about the guests’ preferences and hosts’ preferences. The proposed techniques can effectively improve the performance in predicting the behavior of users in sharing economy. The findings can also help group decision making in P2P platforms efficiently.

This study aims to determine whether the branded luxury guestroom amenities provided in five-star hotels of Delhi influence the guest's hotel purchase decisions. It also identified the amenities which are found to be most and least influential in affecting the guest's hotel selection and purchase behaviour. The study was conducted in the three upscale five-star hotels of Delhi. The selection of amenities and brands to be considered as luxury was based upon three focus group interviews with the room's division manager of the hotels. A structured questionnaire was drafted to identify the most and least useful hotel amenities among the respondents, influencing their hotel selection and purchase behaviours. Wi-Fi in the guestroom was found to be the most valuable amenity, with stationery items regarded as least valuable. It was also found that the guest's hotel selection and purchase decisions were significantly influenced when luxury branded amenities were placed in the guestroom. Guests were even found to pay extra when the hotel provided access to luxury branded amenities in the guestrooms. This is a novel attempt to find how the guests' hotel selection and purchase intentions are influenced by the placement of branded luxury amenities in guestrooms.

This paper aims to explore the role of six sigma in the assembly of service components, during the service exchange. Specifically, it seeks to examine whether applying six sigma principles to customer‐facing processes enhances customer satisfaction.

The paper uses a field study to examine the relationship between conformance with a standardized service assembly process and customer satisfaction. Data were collected over a two‐month period using participant observation at the front desk of a leading hotel, and were analyzed using logistic regression.

The results of the study provide a basis for discussion of the issues associated with applying six sigma to the service assembly process. The results suggest that the application of six sigma to customer‐facing processes does not improve customer satisfaction.

Much of the documented success of six sigma has focused on its application to processes with minimum or no customer involvement. In contrast, this paper focuses on the application of six sigma in the service exchange, which typically encompasses a high degree of customer involvement. The paper also adopts a customer‐centric view and differentiates two different processes in a service exchange. This novel view suggests fresh theorizing and research directions. The intent is to encourage discourse among academics and practitioners regarding the applicability of six sigma in a service setting, and identify avenues for future research in this domain.