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This paper aims to examine the open innovation engagement process in terms of how online community members create and share knowledge for open innovation and the consequences of engagement.

A mixed methods approach based on netnography (Study 1), qualitative interviews (Study 2) and surveys (Study 3) was adopted in three studies.

The results of Study 1 show that hotel brand community members actively create and share their knowledge by evaluating hotel policies, providing service suggestions and creating new service ideas. The results of Study 2 identified enjoyment and empowerment as major antecedents of the engagement and brand loyalty and a sense of brand community as major consequences. In Study 3, the relationships among them were quantitatively examined.

This research provides empirical knowledge on online engagement and identifies the innovation value of online platforms. The research also provides knowledge on the engagement process for open innovation by online community members in terms of its antecedents and consequences. As a main limitation of the study, this research only focuses on a single online brand community.

While most existing tourism research analyzes the role of user-generated content in customer decision-making, this research provides a fresh insight into the innovation value of customer knowledge.

Given the recent growth of service robot research in hospitality and tourism management (HTM), the purpose of this study is to identify a research agenda by conducting a systematic and holistic review of service robot research published in both HTM and broader business management (BM) journals.

Adopting a service ecosystem perspective, 38 HTM articles and 13 highly cocited BM articles out of 126 BM articles were qualitatively reviewed to analyze the intellectual structures and foundations of robotics research.

The relationships between service robots and the four multilevel actors of the service ecosystem were analyzed: the consumer, employee, management and society. Twenty-eight specific research questions were proposed for the robotics-customer relationship, robotics-employee relationship, robotics-management relationship and robotics-society relationship.

This study contributes to understanding the intellectual structures and evolution of rapidly growing HTM robotics research in terms of the holistic relationships among the four service ecosystem actors of robotics. Future research needs to identify other actors and their activities to examine the service ecosystem of robotics.

This study provides a pathway for future hospitality and tourism research by helping to focus on important robotics issues and further develop the theoretical and empirical knowledge of robotics. This work informs practitioners of key issues associated with the industrial adoption of robots.

To provide a dynamic view on accommodation choice behaviors during the pandemic, this study aims to examine the impact of recent trends on prospective travelers’ preferences for hotels and Airbnb.

The paper adopts a mixed methods approach that incorporates three independent studies (experimental analysis, online search pattern analysis and an econometric event study) to understand customer decision-making behaviors.

The findings indicate that travelers prefer Airbnb entire flats/apartments to hotels when the pandemic is trending upward. This result externally validates travelers’ preference toward Airbnb during periods of high risk. Interestingly, when the trends go downward, however, the same behavioral pattern was not identified.

This study provides important empirical insights into how the evolution of health crises influence customer decision-making for hotels and Airbnb. Future research needs to consider the role of socio-demographic factors in accommodation selection behaviors and examine how travelers react to cleanliness levels between Airbnb and hotels.

As one of initial studies that empirically examine Airbnb customers’ decision-making behaviors in the context of the COVID-19 pandemic’s trends, this study provides a dynamic view on how the evolution of the pandemic influences accommodation choice behaviors.

This study aims to examine the impact of artificial intelligence (AI) adoption on job insecurity and its subsequent effect on turnover intentions within the hotel industry. It investigated how AI-induced job insecurity affects the likelihood of employees considering leaving their current hotel jobs for other hotels or for opportunities outside the hotel sector, mediated by feelings of job stress and insecurity.

Quantitative data analysis used 259 responses from frontline hotel employees. Confirmatory factor analysis was used to explore the factor structure and assess model fit indices. Structural equation modeling was then applied to test the hypotheses.

Findings reveal that AI awareness has a positive impact on job stress and insecurity. Moreover, job insecurity is found to positively affect turnover intentions, with a notably stronger effect observed for turnover intentions toward non-hotel companies. Additionally, the influence of social capital as a moderator on the relationship between job insecurity and turnover intention varies depending on the specific dimensions of turnover intention.

This study contributes to enhancing both theoretical frameworks and empirical insights into turnover dynamics within the hotel sector. However, future research should take into account employees’ positions, roles, organizations and career levels by examining these factors in relation to technology awareness, job stress, job insecurity and turnover intention.

This study initially focuses on the phenomenon of dynamic turnover issues within the hospitality sector, offering empirical and practical perspectives on effectively integrating new technologies and managing human resources amidst the automation and AI era.

本研究探讨了人工智能（AI）应用对酒店业工作不安全感的影响, 以及其对员工流失意向的后续影响。研究调查了AI引发的工作不安全感如何通过工作压力和不安全感的感受影响员工考虑离开当前酒店工作、转投其他酒店或者寻求酒店行业外的机会。

本研究采用了259名一线酒店员工的定量数据分析。采用验证性因子分析（CFA）探索因子结构并评估模型拟合指标。随后, 应用结构方程模型（SEM）来检验假设。

研究结果显示, AI意识对工作压力和不安全感有积极影响。此外, 工作不安全感被发现对员工流失意向产生正向影响, 尤其是对转投非酒店公司的流失意向影响更为显著。此外, 社会资本作为调节变量对工作不安全感与流失意向之间的关系的影响取决于流失意向的具体维度。

本研究有助于加强酒店业人才流失动态的理论框架和实证见解。然而, 未来研究应考虑员工的职位、角色、组织和职业水平, 通过研究这些因素与技术意识、工作压力、工作不安全感和流失意向之间的关系。

本研究首次聚焦于酒店业中动态人才流失问题的现象, 提供了在自动化和人工智能时代有效整合新技术并管理人力资源的实证和实践观点。