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This paper aims to analyze the research highlights, evolutionary process and future research directions in the field of tourism forecasting.

This study used CiteSpace to conduct a bibliometric analysis of 1,213 tourism forecasting articles.

The results show that tourism forecasting research has experienced three stages. The institutional collaboration includes transnational collaboration and domestic institutional collaboration. Collaboration between countries still needs to be strengthened. The authors’ collaboration is mainly based on on-campus collaboration. Articles with high co-citation are primarily published in core tourism journals and other relevant publications. The research content mainly pertains to tourism demand, revenue management, hotel demand and tourist volumes. Ex ante forecasting during the COVID-19 pandemic has broadened existing tourism forecasting research. The future forecasting research focuses on the rational use of big data, improving the accuracy of models and enhancing the credibility of forecasting results.

This paper uses CiteSpace to analyze tourism forecasting articles to obtain future research trends, which supplements existing research and provides directions for future research.

本文旨在分析旅游预测领域的研究重点、演化过程和未来的研究方向。

本研究使用 CiteSpace 软件对 1213 篇旅游预测文章进行了文 献计量学分析。

结果表明, 旅游预测研究经历三个阶段。机构合作包含国际机构合作和 国内机构合作, 需要持续加强国家之间的合作, 作者之间的合作多以校内合作为 主。高引用文章不仅发表在旅游领域的核心期刊还发表在其他专业的核心期刊上。 旅游预测研究的主要内容为旅游需求、收入管理、酒店需求和游客量。新冠疫情 期间的事前预测拓宽了现有的旅游预测研究。未来预测的研究重点在于合理利用 大数据, 提高模型的准确定以及提高预测结果的可信度。

本文使用 CiteSpace 分析旅游预测文章得到未来研究趋势, 既是对 现有研究的补充, 又为今后的研究提供方向。

Este artículo pretende analizar los aspectos más destacados de la investigación, el proceso evolutivo y las futuras orientaciones de la investigación en el campo de la previsión turística.

Este estudio utilizó CiteSpace para realizar un análisis bibliométrico de 1213 artículos sobre previsión turística.

Los resultados muestran que la investigación sobre previsión turística ha experimentado tres etapas. La colaboración institucional incluye la colaboración transnacional y la colaboración institucional nacional. La colaboración entre países aún debe reforzarse. La colaboración entre autores se basa principalmente en la colaboración dentro del campus. Los artículos con una alta cocitación se publican principalmente en las principales revistas de turismo y en otras publicaciones relevantes. El contenido de la investigación se refiere principalmente a la demanda turística, el revenue management, la demanda hotelera y los volúmenes turísticos. La previsión previa y durante la pandemia de la COVID-19 ha ampliado la investigación existente sobre previsión turística. La futura investigación sobre previsiones se centra en el uso racional de los big data, la mejora de la precisión de los modelos y el aumento de la credibilidad de los resultados de las previsiones.

Este artículo utiliza CiteSpace para analizar artículos de previsión turística con el fin de obtener futuras tendencias de investigación, lo que complementa la investigación existente y proporciona orientaciones para futuras investigaciones.

This paper aims to propose a dynamic parameter identification method based on sensitivity analysis for the 5-degree of freedom (DOF) hybrid robots, to solve the problems of too many identification parameters, complex model, difficult convergence of optimization algorithms and easy-to-fall into a locally optimal solution, and improve the efficiency and accuracy of dynamic parameter identification.

First, the dynamic parameter identification model of the 5-DOF hybrid robot was established based on the principle of virtual work. Then, the sensitivity of the parameters to be identified is analyzed by Sobol’s sensitivity method and verified by simulation. Finally, an identification strategy based on sensitivity analysis was designed, experiments were carried out on the real robot and the results were verified.

Compared with the traditional full-parameter identification method, the dynamic parameter identification method based on sensitivity analysis proposed in this paper converges faster when optimized using the genetic algorithm, and the identified dynamic model has higher prediction accuracy for joint drive forces and torques than the full-parameter identification models.

This work analyzes the sensitivity of the parameters to be identified in the dynamic parameter identification model for the first time. Then a parameter identification method is proposed based on the results of the sensitivity analysis, which can effectively reduce the parameters to be identified, simplify the identification model, accelerate the convergence of the optimization algorithm and improve the prediction accuracy of the identified model for the joint driving forces and torques.

The development of the digital age has made data and information more transparent, enhancing the strategic perspectives of both buyers (strategic waiting) and sellers (price fluctuations) in their decision-making. This research investigates the optimal dynamic pricing strategy of the content product developer in relation to their consideration of consumer fairness concerns to elucidate the impact of consumer fairness concerns on the dynamic pricing strategy of the developer.

This paper assumes that monopolistic content developers implement a dynamic pricing strategy for the content product. Through constructing a two-period dynamic pricing game model, this research investigates the optimal decisions of the content developer, contingent upon their consideration or disregard of consumer fairness concerns. In the extension section, the authors additionally account for the influence of myopic consumers on these optimal decisions.

Our findings reveal that the degree of consumer fairness concerns significantly influences the developer’s optimal dynamic pricing decision. When a developer offers content products with lower depth, there is a propensity for the developer to refrain from incorporating consumer fairness concerns into a dynamic pricing strategy. Conversely, in cases where the developer offers a high-depth content product, consumer fairness concerns benefit the developer. Furthermore, our analysis reveals a consistent benefit for the developer from the inclusion of myopic consumers.

Few studies have delved into the conjoined influence of consumer fairness concerns and strategic behavior on dynamic pricing strategy. Our findings indicate that consumer fairness concerns can enhance the efficiency of the value chain for content products under specific conditions. This paper not only enriches the existing literature on dynamic pricing by incorporating consumer fairness concerns theoretically but also offers practical insights. The outcomes of this research can guide content product developers in devising optimal dynamic pricing strategies.

To contribute to achieving the Sustainable Development Goals, this study aims to explore how to encourage innovative green behaviors among college students and the mechanisms behind the formation of green innovation behavior. Specifically, this study examines the influences of schools, mentors and college students themselves.

A multilevel, multisource study involving 261 students from 51 groups generally supported this study’s predictions.

Proenvironmental and responsible mentors significantly predicted innovative green behavior among college students. In addition, creative motivation mediated the logical chain among green intellectual capital, emotional intelligence and green innovation behavior.

The study findings offer new insights into the conditions required for college students to engage in green innovation. In addition, they provide practical implications for cultivating green innovation among college students.

The authors proposed and tested a multilevel theory based on the ability–motivation–opportunity framework. In this model, proenvironmental and responsible mentors, green intellectual capital and emotional intelligence triggered innovative green behavior among college students through creative motivation.

Prior research on green innovation has shown that institutional pressure stimulates enterprises to adopt green innovation. However, an institutional perspective does not explain why firms that face the same amount of institutional pressure execute different environmental practices and innovations. To address this research gap, the authors linked institutional theory with upper echelons theory and organization performance to build a comprehensive research model.

A total of 800 questionnaires were issued. The final usable questionnaires were 195, yielding a response rate of 24.38%. AMOS 23.0 was used to analyze the data and examine the relationships between the constructs in our model.

Institutional pressures affected both green innovation adoption (GIA) and the top management team's (TMT's) response. TMT's response influenced GIA. GIA was an important factor affecting firm performance. Furthermore, TMT's response mediated the relationship between institutional pressure and GIA. Institutional pressures indirectly affected green innovation performance but did not influence economic performance through GIA. Finally, TMT's response indirectly impacted firm performance through GIA.

The authors draw on institutional theory, upper echelons theory, and a performance-oriented perspective to explore the antecedents and consequences of GIA. This study has interesting implications for leaders and managers looking to implement green innovation and leverage it for firm performance to out compete with market rivals as well as to make the changes in collaboration with many other companies including market rivals to gain success in green innovation.

In this paper, we evaluated the impact of the US “Chip Act” on the participation of the Chinese electronics industry in the global value chain based on the dynamic CGE model. This is a meaningful attempt to use the GTAP-VA model to analyze the electronics industry in China.

We employ a Dynamic GTAP-VA Model to quantitatively evaluate the economic repercussions of the “Chip Act” on the Chinese electronic industries' GVC participation from 2023 to 2040.

The findings depict a discernible contraction in China’s electronic sector by 2040, marked by a −2.95% change in output, a −3.50% alteration in exports and a 0.45% increment in imports. Concurrently, the U.S., EU and certain Asian economies exhibit expansions within the electronic sector, indicating a GVC realignment. The “Chip Act” implementation precipitates a significant divergence in GVC participation across different countries and industries, notably impacting the electronics sector.

Through a meticulous temporal analysis, this manuscript unveils the nuanced economic shifts within the GVC, substantially bridging the empirical void in existing literature. This narrative accentuates the profound implications of policy regulations on global trade dynamics, contributing to the discourse on international economic policy and industry evolution.

We evaluated the impact of the US “Chip Act” on the participation of the Chinese electronics industry in the global value chain based on the dynamic CGE model. This is a meaningful attempt to use the GTAP-VA model to analyze the electronics industry in China.

The interaction between policy regulations and global value chain (GVC) dynamics is pivotal in understanding the contemporary global trade framework, especially within technology-driven sectors. The US “Chips Act” represents a significant regulatory milestone with potential ramifications on the Chinese electronic industries' engagement in the GVC.

The significance of this paper is that it quantifies for the first time the impact of the US Chip Act on the GVC participation index of East Asian countries in the context of US-China decoupling. With careful consideration of strategic aspects, this paper substantially fills the empirical gap in the existing literature by presenting subtle economic changes within GVCs, highlighting the profound implications of policy regulation on global trade dynamics.