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This study aims to build a comprehensive knowledge structure of smart hospitality and tourism research.

Content analysis is carried out on the themes of pertinent research from the perspectives of customers, suppliers, technology and policymakers.

Results show that over half of the reviewed articles are carried out from the perspective of customers, while those of suppliers, technology and policymakers are less discussed. The unbalanced number of articles reveals an evident mismatch between the supply and demand.

Findings provide theoretical and practical implications from different perspectives. Collaboration among various stakeholders is also advocated to achieve sustainable and balanced development of the smart hospitality and tourism industry.

This study contributes to smart hospitality and tourism research through an in-depth review of relevant literature. Based on the findings, the identified research gaps provide potential directions for future knowledge development.

Along with the development of the robotics industry, service robots have been gradually used in the hospitality industry. Nevertheless, service robot categorization and the fulfillment of the cognitive and emotional needs of consumers by hotel service robots have yet to be fully explored. Hence, the purpose of this study are to categorize hotel service robots, to explore consumers’ robot hotel experience, to identify the consumers’ preference of hotel service robot in general, to reveal consumers’ preference for hotel service robots based on their fulfillment of emotional needs and to examine the completion of cognitive–analytical and emotional–social tasks.

Through in-depth interviews with technology managers and questionnaire survey among consumers who have and have not had robot hotel stay experience to achieve the aforementioned research objectives.

Findings of in-depth interviews show that service robots can be categorized as check-in/out robots, artificial intelligence (AI) robots and service delivery robots. Results of questionnaire survey indicate that consumers prefer non-humanoid robots (n = 213, p = 47.87%) among check-in/out robots, the Xiaodu Smart Display (n = 163, p = 36. 63%) among the AI robots and the machine-shaped robot porter (I) (n = 178, p = 40.00%) among the service delivery robots.

This study provides implications, such as the adoption of robot-shaped AI with a screen display, to hotel managers to meet the needs of consumers regarding the completion of cognitive–analytical and emotional–social tasks of robots.

This study extends uncanny valley theory by identifying preference for the shape and functions of different categories of service robots and contributes to the limited literature on hotel robots.

The purpose of this study is to analyze state-of-the-art knowledge of artificial intelligence (AI) research in hospitality.

This study adopts the theory-context-methods framework to systematically review 100 AI-related articles recently published (i.e. from 2021 to April 2023) in three top-tier hospitality journals, namely, the International Journal of Contemporary Hospitality Management, International Journal of Hospitality Management and Journal of Hospitality Marketing and Management.

Findings suggest that studies of AI applications in hospitality are mostly theory-driven, whereas most AI methods research adopts a data-driven approach. State-of-the-art AI applications research exhibits the most interest in service robots. In AI methods research, little attention was paid to the amid-service/experience.

This study reveals inadequacies in theory, context and methods in contemporary AI research. More research from hospitality suppliers’ perspectives and research on generative AI applications are advocated in response to the unveiled research gaps and recent AI developments.

This study classifies the most recent AI research in hospitality into two main streams – AI applications research and AI methods research – and discusses the gaps in each research stream and latest AI developments. The paper then suggests future research directions to guide researchers in advancing AI research in hospitality.

Most previous studies have discussed the factors that drive the development of smart tourism or its benefits. Nevertheless, hindrances to smart tourism development have been largely ignored by previous studies. Hence, to bridge the aforementioned research gap, the present study identified the hindrances to smart tourism development in Hong Kong, which has been affected by the recent pandemic, based on the adjusted destination competitiveness conceptual models.

The purpose of this study is to investigate the hindrances to smart tourism development. A qualitative interview method using a semi-structured questionnaire with open-ended questions was adopted in the present study.

Findings of the present study indicated that the hindrances in developing smart tourism in Hong Kong are from four primary perspectives, namely, economic, sociocultural, technological and planning and management.

Seeking cooperation from popular tourist destinations can address the economic issues of market size and return on investment in technological infrastructure. Experiences, as the core product of travel, should be highlighted in smart tourism development and enterprises should be given support for the transformation.

Theoretically, based on the adjusted conceptual models for destination competitiveness, the present study identifies the macro factors that hinder the development of smart tourism in Hong Kong from different primary perspectives. Practically, factors such as policy support, which includes public funding or low-interest loans, are essential to supporting preliminary finances to attract entrepreneurs and young talents to participate in the development.

智慧旅游发展的障碍

之前的大多数研究都讨论了推动智慧旅游发展的因素或其好处。然而, 以往的研究在很大程度上忽略了智慧旅游发展的障碍。因此, 为弥合上述研究空白, 本研究基于调整后的目的地竞争力概念模型, 确定了受近期疫情影响的香港智慧旅游发展的障碍。

本研究采用了带有开放式问题的半结构化问卷的定性访谈方法来采集数据。

本研究结果表明, 香港发展智慧旅游的障碍主要来自四个方面, 即经济、社会文化、技术以及规划和管理。

寻求热门旅游目的地的合作可以解决市场规模和技术基础设施投资回报等经济问题。智慧旅游发展应突出体验作为旅游核心产品, 支持企业转型。

理论上, 本研究基于调整后的目的地竞争力概念模型, 从不同的首要视角识别阻碍香港智慧旅游发展的宏观因素。从实际意义上来讲, 公共资金或低息贷款在内的政策支持等因素对于支持初步资金, 以及吸引企业家和青年人才来参与发展至关重要。

With the disruptive evolution of artificial intelligence (AI), the roles of robotics in hospitality and tourism are shifting rapidly from automatic to emotional works. Although academics have focused on examining robotic applications in hospitality and tourism, concrete images of the different roles of robots are unclear. Thus, this study aims to systematically aggregate and evaluate existing robotic technology-related studies published in Social Science Citation Index-listed hospitality and tourism journals to link the fragmented knowledge and provide an up-to-date overview of robotic technology in hospitality and tourism.

This study retrieved 134 robotic technology-related articles and used descriptive and content analyses to analyze the retrieved papers thoroughly.

The top keyword identified was service robot. Robotic technologies are categorized into AI-supplemented, AI-generated, robotic technology anthropomorphism (RTA)-facilitated and RTA-mediated.

Future studies can consider exploring service robots further from the perspectives of suppliers in tourism. A more comprehensive categorization of robotic technologies is also recommended.

This study contributes to the robotic research realm by providing a holistic view of robotic applications in hospitality and tourism research. This study also attempts to pin down the potential research directions to guide researchers in expanding future studies.

随着人工智能（AI）的颠覆性发展, 机器人在酒店和旅游业中的角色正迅速从自动化工作转变为情感性工作。尽管学术界已开始研究机器人在酒店和旅游业中的应用, 但对于机器人不同角色的具体图像仍不清晰。本研究系统性地汇总并评估了发表在社会科学引文索引（SSCI）列出的酒店与旅游期刊中的现有机器人技术相关研究, 以连接零散的知识, 并提供关于酒店与旅游业机器人技术的最新概览。

本研究检索了134篇与机器人技术相关的文章, 并采用描述性分析和内容分析方法对检索到的文章进行了深入分析。

最常见的关键词是服务机器人。机器人技术分为AI辅助、AI生成、机器人技术拟人化(RTA)促进和RTA中介。

未来的研究可以考虑从旅游供应商的角度进一步探索服务机器人。还建议对机器人技术进行更全面的分类。

本研究通过提供酒店与旅游研究中机器人应用的整体视角, 丰富了机器人研究领域。本研究还尝试明确潜在的研究方向, 为未来的研究扩展提供指导。

This study aims to delve into the ethical challenges in artificial intelligence (AI) technologies to underscore the necessity of establishing principles for ethical AI utilization in hospitality and tourism.

A narrative review of research on ethical AI across diverse realms was conducted to reflect current research progress and examine whether sufficient measures have been taken to address issues pertinent to AI utilization in hospitality and tourism.

Ethical issues including privacy concerns, detrimental stereotypes, manipulation and brutalization pertinent to AI utilization are elaborated. How AI should be properly used and managed ethically, responsibly and sustainably is suggested.

Five fine-tuned principles for regulating AI use in hospitality and tourism are proposed.

A resilient mindset, enhancement of AI context adaptability, equilibrium between development and regulation and collaborative effort of multiple stakeholders are paramount.

Through applying the AI evolution trajectory model, this study contributes to the current discourse of managing AI by proposing a framework that addresses the specific characteristics of hospitality and tourism.

The purpose of this study is to explore the impact of online reviews on answer adoption in virtual Q&A communities, with an eye toward extending knowledge exchange and community management.

Online reviews contain rich cognitive and emotional information about community members regarding the provided answers. As feedback information on answers, it is crucial to explore how online reviews affect answer adoption. Based on signaling theory, a research model reflecting the influence of online reviews on answer adoption is established and empirically examined by using secondary data with 69,597 Q&A data and user data collected from Zhihu. Meanwhile, the moderating effects of the informational and emotional consistency of reviews and answers are examined.

The negative binomial regression results show that both answer-related signals (informational support and emotional support) and answerers-related signals (answerers’ reputations and expertise) positively impact answer adoption. The informational consistency of reviews and answers negatively moderates the relationships among information support, emotional support and answer adoption but positively moderates the effect of answerers’ expertise on answer adoption. Furthermore, the emotional consistency of reviews and answers positively moderates the effect of information support and answerers’ reputations on answer adoption.

Although previous studies have investigated the impacts of answer content, answer source credibility and personal characteristics of knowledge seekers on answer adoption in virtual Q&A communities, few have examined the impact of online reviews on answer adoption. This study explores the impacts of informational and emotional feedback in online reviews on answer adoption from a signaling theory perspective. The results not only provide unique ideas for community managers to optimize community design and operation but also inspire community users to provide or utilize knowledge, thereby reducing knowledge search costs and improving knowledge exchange efficiency.

Knowledge adoption is the key to effective knowledge exchange in virtual question-and-answer (Q&A) communities. Although previous studies have examined the effects of knowledge content, knowledge source credibility and the personal characteristics of knowledge seekers on knowledge adoption in virtual Q&A communities from a static perspective, the impact of answer deviation on knowledge adoption has rarely been explored from a context-based perspective. The purpose of this study is to explore the impact of two-way deviation on knowledge adoption in virtual Q&A communities, with the aim of expanding the understanding of knowledge exchange and community management.

The same question and the same answerer often yield multiple answers. Knowledge seekers usually read multiple answers to make adoption decisions. The impact of deviations among answers on knowledge seekers' knowledge adoption is critical. From a context-based perspective, a research model of the impact of the deviation of horizontal and vertical answers on knowledge adoption is established based on the heuristic-systematic model (HSM) and empirically examined with 88,287 Q&A data points and answerer data collected from Zhihu. Additionally, the moderation effects of static factors such as answerer reputation and answer length are examined.

The negative binomial regression results show that the content and emotion deviation of horizontal answers negatively affect knowledge seekers' knowledge adoption. The content deviation of vertical answers is negatively associated with knowledge adoption, while the emotion deviation of vertical answers is positively related to knowledge adoption. Moreover, answerer reputation positively moderates the negative effect of the emotion deviation of horizontal answers on knowledge adoption. Answer length weakens the negative correlation between the content deviation of horizontal and vertical answers and knowledge adoption.

This study extends previous research on knowledge adoption from a static perspective to a context-based perspective. Moreover, information deviation is expanded from a one-way variable to a two-way variable. The combined effects of static and contextual factors on knowledge adoption are further uncovered. This study can not only help knowledge seekers identify the best answers but also help virtual Q&A community managers optimize community design and operation to reduce the cost of knowledge search and improve the efficiency of knowledge exchange.

Hotels provide high-quality guest experiences to generate perceived value for money (PVM), positively influencing word-of-mouth (WOM) and electronic word-of-mouth (eWOM) communication. This study aims to (1) verify the impacts of the perceived quality by the guests about their experiences in hotels on their PVM; (2) inspect the influence of guests' perception of hotel prices on PVM; (3) examine the impacts of guest PVM on their hotel experiences on WOM and eWOM and (4) investigate the consequences of the hotel guests' behavioural engagement on social networking sites (HGBE-SNS) on eWOM.

This quantitative and descriptive study consists of a survey with 371 guests who evaluated their experiences at three hotels in Brazil. PLS-SEM tested the hypothetical model that resorted to the stimulus-organism-response theory (S-O-R), proposed by Mehrabian and Russell (1974). Cluster Analysis compared the PVM, WOM and eWOM of groups of hotel guests with different levels of social media engagement.

Perceived quality by hotel guests positively impacts PVM. Perceived price negatively influences PVM. PVM had a positive and robust impact on WOM. PVM impacts and explains weakly eWOM. In contrast, HGBE-SNS affects and better explains eWOM than PVM.

This unprecedented investigation concomitantly exhibits the relationships between perceived quality, price, PVM, WOM, eWOM and HGBE-SNS. Hotels must offer high perceived quality experiences to influence PVM and WOM positively. PVM is unable to stimulate eWOM strongly. HGBE-SNS is pivotal for guests to share their hotel experiences through eWOM. This study suggests marketing strategies for hospitality companies to amplify customer engagement on SNS.