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Web 2.0 applications enable travelers to evaluate several services and assessment attributes. Constructed websites in several languages trigger a new way of data collections resulting in data streams leading to the accumulation of vast amounts of data, called big data. The need for analysis is in high demand. This study aims to construct a model to investigate which single attribute or interrelated ones having an impact on the performances of hotels.

The total number of 1,137 observations collected from the website HolidayCheck.de are used from the hotels in the Bavaria region in 2016. Bavaria is a region where both domestic and foreign travelers mostly prefer to visit. Fuzzy rule-based systems, which is a combination of fuzzy set theory (FST) and fuzzy logic, are used. Although the FST is used to convert linguistically expressed perceptions by travelers into mathematically usable data, fuzzy logic is used to construct a model between service attributes and price-performance (PP) to attain the set of single and interrelated attributes on the assessment of PP.

No single attribute plays a key role in PP assessment. However, two or more interrelated combinations have different impacts on PP. For example, when “Food—Drink” and “Room” moves together from average to good level, PP reaches the highest level of assessment.

Accessibility to too much data is difficult.

A model can be continuously run so that any changes can be observed during the incoming of data.

As the consumer reviews and ratings are the crucial source of information for other travelers, hoteliers must monitor and respond them on time in order to deal with the complaints.

Travelers’ perceptions or evaluations are treated with a FST that measures the impression of human beings. New modeling enables researchers to observe not only any single attribute but also interrelated ones on the PP.

This theme issue aims to explore the depth and breadth of automation and artificial intelligence’s (AI’s) impact on the hospitality and tourism industry.

The thematic examination of the articles presented in this theme issue highlights the central results concerning the strategic inquiry, especially regarding their addition to knowledge, which is vital for fostering idea creation and theoretical research beneficial to various stakeholders in the tourism and hospitality sector. Given the swift evolution of these technologies, grasping their utilization, advantages and obstacles is essential for industry experts, decision-makers and scholars.

This theme issue encapsulates the transformative impact of automation and AI in the tourism and hospitality industry, highlighting the juxtaposition of enhanced operational efficiency against employment security concerns. It emphasizes the need for ethical governance to address AI’s ethical dilemmas and security vulnerabilities. The exploration of consumer attitudes towards AI, particularly in service robots and facial emotion recognition technologies, is deemed crucial for leveraging AI’s full potential in service delivery. Furthermore, AI’s entry into gastronomy prompts a reevaluation of traditional culinary practices, advocating for a balance between technological innovation and the preservation of cultural heritage.

This theme issue stands out for its focused exploration of the transformative effects of automation and AI within the hospitality and tourism industry. Unlike broader discussions on technology in business, it delves into specific AI-driven innovations, operational changes and the resultant shifts in consumer behavior and industry standards. By curating a collection of empirical research, theoretical analyses and content analyses, this issue is providing important insights into how automation and AI are reshaping service delivery, management practices and strategic decision-making in an industry characterized by its intense customer focus and reliance on human-to-human interaction.

The purpose of this study was to explore the relative importance of various attributes (i.e. the total cost of attending, conference organizer, publishing opportunities for an academic career and personal development, reputation as measured by the number of conferences held, location and size) in terms of their ability to impact the decision of attendees in their selection of which academic conference to attend.

Conjoint analysis was used to examine the relative importance of various attributes to impact an attendee’s decision to select an academic conference (i.e. the total cost of attending, conference organizer, publishing opportunities for an academic career and personal development, reputation as measured by the number of conferences held, location and size).

The choice-based conjoint analysis revealed that the most crucial attributes for attending an academic conference were the total cost of attending the conference (28% of relative importance) and the conference organizer (28%).

Since the total cost of attending a conference is one of the top two attributes, conference organizers should reduce the costs as much as possible. Recommendations include choosing a university venue instead of a hotel or conference center and offering multiple options for registration passes (i.e. one-day pass). However, organizers should be careful about meals provided during the conference program, as meals have a positive effect on attendees’ decisions.

This study introduces to the literature a new indicator for selecting an academic conference – the reputation of the conference as measured by the number of times it had been held. In previous studies, this variable was not considered in the context of selecting an academic conference.

Fuzzy rule-based system (FRBS), a soft computing method used for big data analysis, is used to determine which single hotel attribute or interrelated hotel attributes used in Travel 2.0 data play a role on price–performance (PP).

FRBS, based on fuzzy set theory, is used using the data set of four- and five-star hotels in the Alanya destination in Turkey collected from HolidayCheck.de website for the period between 2009 and 2016.

Single attributes do not have an impact on PP. At least two or more attributes are necessary to have an impact on PP. Compensations among attributes that are observed to be leading to PP not to change from their current level. Instead of assuming a linear relationship between hotel attributes and PP, non-linearity should often be assumed. In addition, some hotel attributes do not have an impact on PP until some other attribute reaches a certain level.

The limitations of this research can be grouped under two topics. While the first is related to data, which is German-speaking tourists staying at four- and five-star hotels between 2009 and 2016, the second is the limitation on generalizability. By implementing other types of data related to hotel attributes, new insights can be generated to shed light on different aspects of the relationship between hotel attributes and PP or other measures such as overall evaluation.

A data-driven model called FRBS is constructed using original verbal statements. Novel insights pertinent to relations between hotel attributes and PP have been extracted.

乏晰规则为本系统（FRBS）作为大数据分析的软计算方法, 接用旅游2.0数据来分析出哪个酒店属性或者相关酒店属性对于价格-效益（PP）有直接作用。

本论文采用FRBS以Fuzzy Set理论为基础, 采集2009年至2016年之间的HolidayCheck.de网站上的土耳其阿拉尼亚目的地的四星和五星酒店数据作为样本, 进行分析计算。

酒店个别属性并不对PP具有直接影响。至少两个或者以上属性加和对PP有直接影响。酒店属性与PP的关系并不是预想的那样线性关系, 而是非线性的。此外, 一些酒店属性起初并不对PP有影响, 而是直到其他属性达到一定的水平后才起反应。

本论文使用原始文本数据来构建以数据导向的FRBS模型。本论文给出了对于酒店属性和PP的关系相关的新型建议。

This article introduces the Responsible AI for Service Excellence (RAISE) framework. RAISE is a strategic framework for responsibly integrating AI into service industries. It emphasizes collaborative AI design and deployment that aligns with the evolving global standards and societal well-being while promoting business success and sustainable development.

This multidisciplinary conceptual article draws upon the United Nations' Sustainable Development Goals (SDGs) and AI ethics guidelines to lay out three principles for practicing RAISE: (1) Embrace AI to serve the greater good, (2) Design and deploy responsible AI and (3) Practice transformative collaboration with different service organizations to implement responsible AI.

By acknowledging the potential risks and challenges associated with AI usage, this article provides practical recommendations for service entities (i.e. service organizations, policymakers, AI developers, customers and researchers) to strengthen their commitment to responsible and sustainable service practices.

This is the first service research article to discuss and provide specific practices for leveraging responsible AI for service excellence.

The purpose of this paper is to critically review and synthesize the scale development practices in hospitality and tourism and propose updated guidelines applicable for both reflective and composite scales.

Using a critical review of a sample of scale development papers published in six top-tier tourism and hospitality journals, the current guidelines' weaknesses and strengths and their applications are identified. Updated procedures are proposed to overcome these weaknesses.

This review showed several issues in applying existing guidelines, including incomplete and unclear data collection processes, inconsistent data analysis processes including validity/reliability assessment and lack of criteria to identify and assess composite scales. As such, this paper proposes updated guidelines by incorporating the confirmatory composite analysis to assess composite scales.

This paper offers unique theoretical and methodological contributions to scale development literature, by identifying the shortcomings of current practices of scale development and propose guidelines for both reflective and composite scales. The current scale development procedures cannot be applied for composite scales, and so the results of composite scale development using the current procedures are questionable. The updated guidelines by the current study improve new scales' accuracy, reliability and validity.

Voice command and voice search are becoming increasingly popular in the hospitality and tourism industry, with many hotels and travel companies investing in voice-enabled technology to improve guest experiences and streamline operations. This technology allows travellers to manage their travel plans, request services and get information through natural voice commands on any voice-enabled device. Voice assistants are also multilingual, allowing hotels to customise responses to guests who do not speak the local language. Angie, a multilingual, in-room voice assistant, is an example of this technology. It can fulfil guest requests, answer common questions about the property and create streamlined access to a wide range of hotel amenities, such as ordering room service or requesting extra towels. Hotels can control questions and responses to assist stretched staff and provide upsell and advertising revenue through digital promotions or recommended onsite amenities or discounts. In addition, voice command technology can be used to book travel and find things to do at a destination. Google Assistant can help with travel plans like booking a hotel, checking flight status and finding things to do at a destination. In conclusion, voice command and voice search technology are transforming the hospitality and tourism industry by improving guest experiences, reducing operational costs and increasing revenue.

Artificial Intelligence (AI) has revolutionised the tourism industry, offering personalised experiences and streamlining operations. AI provides customised recommendations for travellers through data analysis and machine learning, making their journeys more meaningful. It has also improved efficiency through automated processes, chatbots and enhanced security measures. AI's ability to analyse large volumes of data enables tourism organisations to make data-driven decisions and target their marketing strategies effectively. One of the most notable contributions of AI in tourism is its ability to offer personalised recommendations. By analysing vast travel history, preferences and online behaviour, AI systems can provide tailored suggestions for destinations, accommodations, activities and dining options. This level of customisation enhances the overall travel experience, making it more relevant and satisfying for individual travellers. AI has also greatly improved operational efficiency within the tourism sector. Chatbots, powered by natural language processing, are increasingly being deployed by hotels, airlines and travel agencies to provide instant customer support and assistance. These chatbots can answer queries, offer recommendations and handle booking processes, reducing waiting times and enhancing customer satisfaction. In addition, facial recognition technology allows for quick and accurate identity verification at airports, hotels and other travel-related facilities. This improves security and provides travellers with a seamless and efficient experience. As technology advances, we expect AI to play a more prominent role in augmented reality, voice recognition and virtual assistants, further enhancing the travel experience and facilitating seamless interactions. In conclusion, AI has transformed the tourism industry by providing personalised recommendations, improving operational efficiency, enhancing security measures and enabling data-driven destination management.