Search results

The aim of this paper is to provide a narrative review of previous research on tourism demand modelling and forecasting and potential future developments.

A narrative approach is taken in this review of the current body of knowledge.

Significant methodological advancements in tourism demand modelling and forecasting over the past two decades are identified.

The distinct characteristics of the various methods applied in the field are summarised and a research agenda for future investigations is proposed.

本文旨在对先前关于旅游需求建模和预测的研究进行叙述性回顾并对未来潜在发展进行展望。

本文采用叙述性回顾方法对当前知识体系进行了评论。

本文确认了过去二十年旅游需求建模和预测方法论方面的重要进展。

本文总结了该领域应用的各种方法的独特特征, 并对未来研究提出了建议。

El objetivo de este documento es ofrecer una revisión narrativa de la investigación previa sobre modelización y previsión de la demanda turística y los posibles desarrollos futuros.

En esta revisión del marco actual de conocimientos sobre modelización y previsión de la demanda turística y los posibles desarrollos futuros,se adopta un enfoque narrativo.

Se identifican avances metodológicos significativos en la modelización y previsión de la demanda turística en las dos últimas décadas.

Se resumen las características propias de los diversos métodos aplicados en este campo y se propone una agenda de investigación para futuros trabajos.

This paper aims to solve the uncertainty problem of hypersonic vehicle tracking control; an adaptive terminal sliding mode control (TSMC) method based on extended state observer (ESO) is proposed. The combination of adaptive techniques, TSMC and ESO offers an effective approach for managing uncertain systems.

The dynamic model of a hypersonic vehicle is transformed into two control-oriented subsystems. The control system design incorporates an adaptive technique, an ESO and a TSMC. The ESO estimates the primary uncertainties, while the adaptive technique determines the upper limit of secondary uncertainties. These estimates are used for the design of the TSMC law. In addition, the filter is used to generate the reference trajectory to improve the dynamic performance of the system. The stability of the closed-loop system is proved by the Lyapunov stability theory.

A robust control system for hypersonic vehicles is developed with guaranteed stability and strong adaptability to various uncertainties such as parameter variations, external disturbances and actuator faults. Furthermore, the proposed system demonstrates enhanced dynamic performance compared to observer-based sliding mode control. Specifically, for the velocity and altitude tracking control, the settling time of the proposed sliding mode control is approximately 100 s and 70 s shorter than that of the observer-based sliding mode control, respectively.

Different from the single equivalent treatment, various uncertainties here are classified and treated with different strategies, which improves the disturbance rejection ability of the control system. This ability is of great significance for enhancing the autonomy, adaptability and reliability of hypersonic vehicles in extreme environments.

Two essential components, a robust information technology (IT) infrastructure and faculty training in student-centred pedagogies and technology usage, are necessary for effective blended learning designs. Many universities invest in IT infrastructure such as bandwidth, high-end subscriptions, servers, SMART boards, projectors, Wi-Fi enhancement, learning management systems, IT support, and other tools. Faculty training is crucial and includes instruction on using the new infrastructure and adopting pedagogical methods associated with blended learning. This study’s primary objective is to explore the challenges and pedagogical transformation towards blended learning designs in India. The research also investigates the impact of social context and emotional support on blended learning. It examines the mediating role of technostress among teachers between hybrid mode transformation and blended learning. The study’s results will provide critical insights for academic institutions’ higher management to encourage the adoption of learning designs and blended techniques by their employees during unforeseen events in the future, utilizing effective leadership and management skills. The study aims to assist academic institutions in meeting the demand for experiential learning in the classroom by incorporating blended learning. It acts as a bridge between industry expectations and academic outcomes. The study uniquely addresses the need for increased student engagement in the classroom.

This study aims to investigate low-carbon footprint travel choices, considering both destination attributes and climate change perceptions, and examine the impacts of nudging (a communication tool to alter individuals’ choices in a predictable way) on tourists’ preferences for carbon mitigation in destinations.

A discrete choice experiment questionnaire was administered to a sample of 958 Hong Kong respondents. Hybrid choice modeling was used to examine the respondents’ preferences for destination attributes and to explain preference heterogeneity using tourists’ climate change perceptions. The respondents’ willingness to pay for the destination attributes was also calculated to measure the monetary value of the attributes.

Destination type, carbon emissions and travel cost had significant effects on tourists’ choices of destination. Nudging increased tourists’ preference for low-carbon footprint choices. Tourists with higher climate change perceptions were more likely than others to select low-carbon destinations with carbon offset projects.

The findings of this study provide an impetus for destination management organizations to support local carbon offset projects, implement policies that mitigate carbon emissions and develop sustainable tourism to fulfill tourists’ demand for low-carbon footprint travel choices. Based on the findings, policymakers could promote sustainable tourism by publishing relevant climate change information on social media.

This study addressed a gap in the literature on tourist travel choice by considering carbon emission-related attributes and climate change perceptions and by confirming the role of nudging in increasing the choice of low-carbon destinations.

There is currently a gap in the research regarding the effect of corporate culture on corporate innovation capability. Based on cultural hierarchy theory, in this paper, we explore the interactions between cultural factors and innovation capability in emerging market firms (EMFs). We discuss the mechanisms by which incentive, institutional, and vibrant corporate cultures influence corporate innovation capability. Furthermore, we consider the transformation of artificial general intelligence (AGI) from a tool into a colleague and how this affects the relationship between corporate culture and innovation capability.

An online questionnaire was distributed to corporate employees to explore their attitudes towards AGI and corporate culture. In total, 523 valid questionnaires were empirically analysed using partial least squares structural equation modelling and multigroup analysis (MGA).

The results showed that incentive culture, institutional culture, and vibrant culture had a positive impact on corporate innovation capability. MGA revealed significant differences between employees who considered AGI a tool and those who considered it a colleague. Employees who treated AGI as a colleague were likely to be influenced by a vibrant culture, whereas employees who treated AGI as a tool were likely to be influenced by an incentive or institutional culture.

Building on cultural hierarchy theory, our study provides a new theoretical framework to enrich current research on the relationship between corporate culture and AGI. The study can help EMF managers adjust incentive and institutional cultures before AGI shifts from being a tool to a colleague and negatively impacts innovation capacity.

This paper explores optimal service resource management strategy, a continuous challenge for health information service to enhance service performance, optimise service resource utilisation and deliver interactive health information service.

An adaptive optimal service resource management strategy was developed considering a value co-creation model in health information service with a focus on collaborative and interactive with users. The deep reinforcement learning algorithm was embedded in the Internet of Things (IoT)-based health information service system (I-HISS) to allocate service resources by controlling service provision and service adaptation based on user engagement behaviour. The simulation experiments were conducted to evaluate the significance of the proposed algorithm under different user reactions to the health information service.

The results indicate that the proposed service resource management strategy, considering user co-creation in the service delivery, process improved both the service provider’s business revenue and users' individual benefits.

The findings may facilitate the design and implementation of health information services that can achieve a high user service experience with low service operation costs.

This study is amongst the first to propose a service resource management model in I-HISS, considering the value co-creation of the user in the service-dominant logic. The novel artificial intelligence algorithm is developed using the deep reinforcement learning method to learn the adaptive service resource management strategy. The results emphasise user engagement in the health information service process.

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 present a kinematics performance analysis and control for a continuum robot based on a dynamic model to achieve control of the robot.

To analyze the motion characteristics of the robot, its kinematics model is derived by the geometric analysis method, and the influence of the configuration parameters of the robot on workspace is investigated. Moreover, the dynamic model is established by the principle of virtual work to analyze the mapping relationship among the bending shape, the forces/torques applied to the robot. To achieve better control of the robot, a control strategy for continuum robot based on the dynamic model is put forward.

Results of the simulations and experiments verify the proposed continuum structure and motion model, the maximum position error is 5.36 mm when the robot performs planar bending motion and the average position error of the robot in spatial circular motion is 5.84 mm. The proposed model can accurately describe the deformation movement of the robot and realize its motion control with a few position errors.

The kinematics analysis and control model proposed in this paper can achieve precise control of the robot, which can be used as a reference for the motion planning and shape reconstruction of continuum robot.

The main cause of boiler tube failure in high-temperature thermal power plants is molten sulphate-vanadate-induced hot corrosion. Applying thermal spray coatings on alloy steels can reduce hot corrosion. This study aims to examine the impact of nano yttria stabilized zirconia (YSZ) reinforcement on Ni-20Cr composite coatings on hot corrosion behaviour of T22 steel in a corrosive environment of Na₂SO₄-60%V₂O₅ at 650 °C over 50 cycles.

The coatings were deposited using a high velocity oxyfuel technique. The samples were subjected to hot corrosion in a Silicon tube furnace at 650 °C for 50 cycles. Weight gain data after each cycle were used to analyse the kinetics of corrosion behaviour. Corrosion products were examined using X-ray diffraction, scanning electron microscopy, energy dispersive and cross-sectional analytical methods.

During investigation, nano YSZ-reinforced Ni-20Cr composite coatings on T22 steel were discovered to give superior corrosion resistance in a molten salt environment at 650 °C. Throughout the experiment, the coatings gained less weight and formed protective oxide scales. Increased YSZ concentration in the coating matrix resulted in better protection against hot corrosion.

The inclusion of nano YSZ reduced porosity in Ni-20Cr coatings by filling voids and interlocking particles, as well as blocking the penetration of corroding species, hence improving the corrosion resistance of composite coatings. The corrosion rate decreased with increasing YSZ content in the coating matrix.

It should be noted that the high temperature corrosion behaviour of thermally sprayed nano YSZ-Ni-20Cr composite coatings has never been investigated and is not documented in the literature. As a result, the current study can provide useful information for the application of nano YSZ-reinforced coatings in high-temperature fuel combustion conditions.

The authors revisit the notion of co-production, highlight more critical and re-politicized forms of co-production and introduce three principles for its operationalization. The paper’s viewpoint aims to find entry points for enabling more equitable disaster research and actions via co-production.

The authors draw insights from the authors’ reflections as climate and disaster researchers and literature on knowledge politics in the context of disaster and climate change, especially within critical disaster studies and feminist political ecology.

Disaster studies can better contribute to disaster risk reduction via political co-production and situating local and Indigenous knowledge at the center through three principles, i.e. ensuring knowledge plurality, surfacing norms and assumptions in knowledge production and driving actions that tackle existing knowledge (and broader sociopolitical) structures.

The authors draw out three principles to enable the political function of co-production based on firsthand experiences of working with local and Indigenous peoples and insights from a diverse set of co-production, feminist political ecology and critical disaster studies literature. Future research can observe how it can utilize these principles in its respective contexts.