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This study aims to provide insights and guidance for future research on service robot acceptance in the tourism and hospitality (T&H) domain through a comprehensive literature review.

A total of 249 journal articles, retrieved from the Scopus and Web of Science databases, were reviewed using an inductive analysis method.

Since its emergence in 2008, the topic of robot acceptance has gained significant attention, especially after 2019. The research assesses the current state of research, methodologies used, theoretical foundations and antecedents influencing acceptance. Five categories of antecedents affecting acceptance were systematically identified: robot design factors, individual/organisational factors, situational factors, appraisal factors and affective factors.

This study provides a comprehensive overview of the current landscape of service robot acceptance research. It offers insights and theoretical directions to advance research in the T&H domain.

本研究旨在通过全面的文献综述, 为未来旅游与酒店(T&H)领域服务机器人接受的研究提供见解与指导。

本文采用归纳分析法对检索自Scopus和Web of Science数据库的249篇期刊文章进行分析。

自2008年出现第一篇研究机器人接受的文章以来, 机器人接受话题得到了极大关注, 尤其是在2019年之后。本研究评估了研究现状、方法、理论基础及影响接受的前因。系统性地归纳出影响机器人接受的五类前因：机器人设计、个体与组织、情境、评价及情感因素。

本研究全面概述了服务机器人接受研究的现状, 为推进旅游与酒店领域的研究进展提供了深刻见解与理论指导。

Este estudio tiene como objetivo proporcionar ideas y orientación para futuras investigaciones sobre la aceptación de robots de servicio en el ámbito del turismo y la hospitalidad (T&H) a través de una extensa revisión de la literatura.

Se revisaron un total de 249 artículos académicos, obtenidos de las bases de datos Scopus y Web of Science, utilizando un método de análisis inductivo.

Desde su aparición en 2008, el tema de la aceptación de robots ha recibido una atención significativa, especialmente después de 2019. La investigación evalúa el estado actual de la investigación, las metodologías empleadas, las bases teóricas y los factores que influyen en la aceptación. Se identificaron de manera sistemática cinco categorías de antecedentes que afectan la aceptación: factores de diseño del robot, factores individuales/organizacionales, factores situacionales, factores de evaluación y factores afectivos.

Este estudio proporciona una visión integral del panorama actual del panorama actual de la investigación sobre la aceptación de robots de servicio. Ofrece ideas y direcciones teóricas para avanzar en la investigación en el ámbito de T&H.

This study aims to extract knowledge of ancient Chinese scientific and technological documents bibliographic summaries (STDBS) and provide the knowledge graph (KG) comprehensively and systematically. By presenting the relationship among content, discipline, and author, this study focuses on providing services for knowledge discovery of ancient Chinese scientific and technological documents.

This study compiles ancient Chinese STDBS and designs a knowledge mining and graph visualization framework. The authors define the summaries' entities, attributes, and relationships for knowledge representation, use deep learning techniques such as BERT-BiLSTM-CRF models and rules for knowledge extraction, unify the representation of entities for knowledge fusion, and use Neo4j and other visualization techniques for KG construction and application. This study presents the generation, distribution, and evolution of ancient Chinese agricultural scientific and technological knowledge in visualization graphs.

The knowledge mining and graph visualization framework is feasible and effective. The BERT-BiLSTM-CRF model has domain adaptability and accuracy. The knowledge generation of ancient Chinese agricultural scientific and technological documents has distinctive time features. The knowledge distribution is uneven and concentrated, mainly concentrated on C1-Planting and cultivation, C2-Silkworm, and C3-Mulberry and water conservancy. The knowledge evolution is apparent, and differentiation and integration coexist.

This study is the first to visually present the knowledge connotation and association of ancient Chinese STDBS. It solves the problems of the lack of in-depth knowledge mining and connotation visualization of ancient Chinese STDBS.

Listing popularity indicates the public’s interest in a listing on peer-to-peer (P2P) accommodation platforms. Although listing popularity is crucial to the survival and development of the P2P accommodation platform, this issue has received limited attention in the tourism management discipline. Drawing upon the heuristic-systematic model and uncertainty reduction theory, this study aims to examine the impacts of host and property attributes on listing popularity.

The model was empirically validated using a data set of 6,828 listings on a popular P2P accommodation platform called Airbnb. This study chooses a hierarchical regression analysis to perform the model validation.

The findings reveal that host self-disclosure, host reputation and host identity verification are key host attributes in promoting listing popularity. Meanwhile, property visual description, property photo verification and property visual appeal are important property attributes in facilitating listing popularity.

The study adds useful insights on understanding on determinants of listing popularity. Future researchers are recommended to empirically verify the underlying psychological mechanism by which host attributes and property attributes influence listing popularity.

The P2P accommodation platform should promote the listing popularity by taking advantage of the host attributes and providing property attributes.

First, to the best of the authors’ knowledge, this study is one of the few studies to explore the formation of the listing popularity. Second, this study examines how the host and property attributes promote the listing popularity through the heuristic and systematic information processing modes.