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This study aims to provide an overview, the state-of-the-art “research fronts”, the emerging themes of investigation and a research agenda of crisis communication for destinations’ image.

This research is conducted with a bibliographic coupling study, complemented with an H-Classic classification and a thematic analysis of the articles included in the four clusters provided by the bibliometric methodology (papers dating from 2017 to 2021, both years included).

Based on the bibliometric analysis, four thematic clusters were identified. Two of these clusters supply the “research fronts”, the most current themes in a scientific field: Cluster 1 addresses communication related to tourists’ safety, and cluster 2 enhances the role of stakeholders’ collaboration to create destinations resilience in crisis communication. The other two clusters highlight emerging themes for future investigation: Cluster 3 focuses on recovery marketing communication strategies for a post-crisis era, and cluster 4 analyses how crisis communication strategies contribute to reduce tourists’ risk perception and boosting travel intention. Finally, a future research agenda is proposed, based on the emerging themes from this study.

To the best of the authors’ knowledge, this is the first bibliometric study to analyse crisis communication for destinations’ image (pre-crisis, crisis and post-crisis). This study, which covers the most recent academic literature in this field, provides insights of communication strategies from recent crises and disasters within the “research fronts”. Besides, a research agenda useful for future scholar investigation is proposed with its emerging themes. These rising topics and learnings from past events could be used by destination marketing organisations in crisis communication for destination image recovery in the current post-pandemic scenario or in upcoming crises or disasters.

本研究提供了目的地形象危机沟通的概述、最先进的“研究前沿”、新兴的研究主题以及研究议程。

本文进行了书目耦合研究, 辅以 H-经典分类和对文献计量方法提供的四个集群中包含的文章的主题分析 (论文日期为 2017–2021 年间)。

根据文献计量分析, 确定了四个主题集群。 其中两个集群提供“研究前沿”, 这是科学领域的最新主题：集群 一 解决与游客安全相关的沟通, 集群 二 加强利益相关者合作的作用, 以在危机沟通中创造目的地恢复力。 其他两个集群突出了未来调查的新兴主题：集群 三 侧重于后危机时代的复苏营销传播策略, 集群 四分析危机传播策略如何有助于降低游客的风险感知和提高旅行意愿。最后, 本文提出了基于新兴主题的未来研究议程。

据我们所知, 这是第一个分析了目的地形象的危机传播（危机前、危机和危机后）的, 独特的文献计量研究。该研究涵盖了该领域最新的学术文献, 通过其“研究前沿”提供了有关近期危机和灾难的沟通策略的见解。此外, 本文还提出了具有新兴主题的研究议程。这些新兴话题以及从过去事件中吸取的教训, 可以被目的地营销组织 (DMO) 用来进行灾难沟通, 以便在当前的大流行后情景或未来的危机或灾难中恢复目的地的形象。

Este estudio proporciona una perspectiva general, los “research fronts”- los temas más actuales de una disciplina científica-, los temas emergentes y una agenda de investigación sobre comunicación de crisis de la imagen de los destinos turísticos.

La investigación está basada en un análisis bibliográfico coupling, complementado con una clasificación h-Classics y un análisis temático de todos los artículos examinados con esta metodología bibliométrica (artículos fechados entre 2017 y 2021, ambos años incluidos).

Con este análisis bibliométrico, se identifican cuatro clusters temáticos. Dos de ellos, presentan los “research fronts”, los temas más vigentes de un campo científico: el cluster 1 se refiere a la comunicación realizada para transmitir el concepto de seguridad a los turistas, y el cluster 2 destaca la relevancia de la colaboración de todos los agentes turísticos para crear resiliencia en los destinos en la comunicación de crisis. Los otros dos clusters recogen los temas emergentes de investigación futura: el cluster 3 se centra en las estrategias de marketing para la época de postcrisis y el cluster 4 analiza cómo la comunicación contribuye a reducir la percepción de riesgo de los turistas y, por tanto, a potenciar su intención de viaje. Por último, el artículo propone una agenda de investigación basada en estos temas emergentes.

Hasta donde tenemos conocimiento, éste es el primer estudio bibliométrico especialmente enfocado a la comunicación de crisis para la imagen de los destinos turísticos (con sus tres etapas, precrisis, crisis y poscrisis). Esta investigación, que analiza la literatura más reciente en este campo, proporciona conocimiento sobre la comunicación de las crisis y desastres más recientes, a través de sus “research fronts”. Asimismo, propone una agenda con nuevos temas que están surgiendo en esta disciplina, útil para futuras investigaciones académicas. Dichos temas, junto con los aprendizajes de incidentes pasados, pueden ser usados por las Organizaciones de Marketing de Destinos (DMO, en sus siglas en inglés) para incorporarlos en su comunicación de crisis destinada a la recuperación de la imagen de los destinos turísticos en el actual escenario post pandemia o en futuras crisis o desastres.

In this study, we aim to investigate the connectedness spillovers among major cryptocurrency markets. Moreover, we also explore to identify factors driving this connectedness, particularly focusing on the sentimentality of total, short-term, and long-term return connectedness spillovers among cryptocurrencies under Twitter-based economic uncertainties and US economic policy uncertainty. Finally, we investigate the extent to which cryptocurrency markets serve as a safe haven, hedge, and diversifier from news-based uncertainties.

This study employs the connectedness approach following the combination of Ando et al. (2022) QVAR and Baruník and Krehlík's (2018) frequency connectedness methodologies into the framework proposed by Diebold and Yilmaz (2012, 2014). The data covered from November 10, 2017, to April 21, 2023, and the factors driving cryptocurrency connectedness spillovers are identified and examined. The sentimentality of total, short-term, and long-term return connectedness spillovers among cryptocurrencies, concerning Twitter-based economic uncertainties and US economic policy uncertainty, are analyzed. We apply the Wavelet quantile correlation (WQC) method developed by Kumar and Padakandla (2022) to explore the effects of Twitter-based economic uncertainties and US economic policy uncertainty on Cryptocurrency market connectedness risk spillovers. Besides, we check and present the robustness of WQC findings with the multivariate stochastic volatility method.

Our findings indicate that Ethereum and Bitcoin are net shock transmitters at the center of the connectedness return network. Ethereum and Bitcoin hold the highest market capitalization and value in the cryptocurrency market, respectively. This suggests that return shocks originating from these two cryptocurrencies have the most significant impact on other cryptocurrencies. Tether and Monero are the net receivers of return shocks, while Cardano and XRP exhibit weak shock-transmitting characteristics through returns. In terms of return spillovers, Ethereum is the most effective, followed by Bitcoin and Stellar. Further analysis reveals that Twitter economic policy uncertainty and US economic policy uncertainty are effective drivers of short-term and total directional spillovers. These uncertainty indices exhibit positive coefficient signs in short-term and total directional spillovers, which turn predominantly negative in different magnitudes and frequency ranges in the long term. In addition, we also document that as the Total Connectedness Index (TCI) value increases, market risk also rises. Also, our empirical findings provide significant evidence of Twitter-based economic uncertainties and US economic policy uncertainty that affect short-term market risks. Hence, we state that risk-connectedness spillovers in cryptocurrency markets enclose permanent or temporary shock variations. Besides, findings of the low value of long-term spillovers suggest that risk shocks in cryptocurrency markets are not permanent, indicating long-term changes require careful monitoring and control over market dynamics.

In this study, we find evidence that Twitter's news-based uncertainty and US economic policy uncertainty have a significant effect on short-term market risk spillovers. Furthermore, we observe that high cryptocurrency market risk spillovers coincide with periods of events such as the US-China trade tensions in January 2018, the Brexit process in February 2019, and the COVID-19 outbreak in November 2019. Next, we observe a decline in cryptocurrency market risk spillovers after March 2020. The reason for this mitigation of market risk spillover may be that the Fed's quantitative easing signals have initiated a relaxation process in the markets. Because the Fed's signal to fight inflation in March 2022 also coincides with the period when risk spillover increased in crypto markets. Based on this, we present evidence that the FED's communication mechanism with the markets can potentially affect both short- and long-term expectations. In this context, we can say that our hypothesis that uncertainty about the news causes short-term risks to increase has been confirmed. Our findings may have investment policy implications for portfolio managers and investors generally in terms of reducing financial risks.

Our paper contributes to the literature by examining the interconnectedness among major cryptocurrencies and the drivers behind them, particularly focusing on the role of news-based economic uncertainties. More broadly, we calculate the utilization of advanced methodologies and the incorporation of real-time economic uncertainty data to enhance the originality and value of the research, which provides insights into the dynamics of cryptocurrency markets.

Aiming at the problem of insufficient adaptability of robot motion planners under the diversity of end-effector constraints, this paper proposes Transformation Cross-sampling Framework (TC-Framework) that enables the planner to adapt to different end-effector constraints.

This work presents a standard constraint methodology for representing end-effector constraints as a collection of constraint primitives. The constraint primitives are merged sequentially into the planner, and a unified constraint input interface and constraint module are added to the standard sampling-based planner framework. This approach enables the realization of a generic planner framework that avoids the need to build separate planners for different end-effector constraints.

Simulation tests have demonstrated that the planner based on TC-framework can adapt to various end-effector constraints. Physical experiments have also confirmed that the framework can be used in real robotic systems to perform autonomous operational tasks. The framework’s strong compatibility with constraints allows for generalization to other tasks without modifying the scheduler, significantly reducing the difficulty of robot deployment in task-diverse scenarios.

This paper proposes a unified constraint method based on constraint primitives to enhance the sampling-based planner. The planner can now adapt to different end effector constraints by opening up the input interface for constraints. A series of simulation tests were conducted to evaluate the TC-Framework-based planner, which demonstrated its ability to adapt to various end-effector constraints. Tests on a physical experimental system show that the framework allows the robot to perform various operational tasks without requiring modifications to the planner. This enhances the value of robots for applications in fields with diverse tasks.

This study proposes a machine learning framework to predict customer complaints from production line tests in an automotive company's lot-release process, enhancing Quality 4.0. It aims to design and implement the framework, compare different machine learning (ML) models and evaluate a non-sampling threshold-moving approach for adjusting prediction capabilities based on product requirements.

This study applies the Cross-Industry Standard Process for Data Mining (CRISP-DM) and four ML models to predict customer complaints from automotive production tests. It employs cost-sensitive and threshold-moving techniques to address data imbalance, with the F1-Score and Matthews correlation coefficient assessing model performance.

The framework effectively predicts customer complaint-related tests. XGBoost outperformed the other models with an F1-Score of 72.4% and a Matthews correlation coefficient of 75%. It improves the lot-release process and cost efficiency over heuristic methods.

The framework has been tested on real-world data and shows promising results in improving lot-release decisions and reducing complaints and costs. It enables companies to adjust predictive models by changing only the threshold, eliminating the need for retraining.

To the best of our knowledge, there is limited literature on using ML to predict customer complaints for the lot-release process in an automotive company. Our proposed framework integrates ML with a non-sampling approach, demonstrating its effectiveness in predicting complaints and reducing costs, fostering Quality 4.0.

This paper explores how blockchain technology can enhance information quality within project management information systems (PMIS), thereby positively affecting knowledge management, learning capabilities and project portfolio success.

We employ a literature review and a theory-based approach to develop a conceptual framework and set of propositions that integrate key principles from blockchain technology, project management and dynamic capabilities theory. Subsequently, a focus group is conducted to refine our propositions, providing insights and examples demonstrating the potential value of blockchain in project management.

The findings suggest that blockchain significantly impacts the information quality within PMIS. This improvement in information quality enhances traceability, reliability and security of project data, facilitating better decision-making and governance. The focus group revealed blockchain’s benefits in managing confidential data and streamlining knowledge sharing processes, ultimately contributing to project portfolio success.

This research offers a novel conceptual framework and original insights into the application of blockchain in project management, particularly within the context of Industry 4.0, paving the way for future research on digital transformation in project management.

The purpose of this study was to examine how well a strength-based program grounded in positive psychology principles can advance the practical critical thinking skills of those pursuing the teacher training course.

This study used a single-group pre-test post-test design with 35 teacher-trainees from the Bachelor of Education course. The two-and-a-half-week strength-based program used the values in action survey to identify strengths. Pre- and post-test scores, measured with the Cornell Critical Thinking Test – Level Z, underwent Statistical Package for Social Sciences analysis including paired samples t-test for subcomponent and overall composite analysis.

Analysis of the pre- and post-test scores demonstrated a statistical significance in the critical thinking scores obtained by the teacher-trainees. Post-test scores were consistently significant. Out of the elements of critical thinking, induction, meaning, observation and credibility were more prominent. Deduction and assumption identification were also having a significant effect.

Most critical thinking programs focus on evaluating specific teaching methods for improving critical thinking skills. In education, positive psychology studies often center on students’ well-being, attention spans and academic success, aligning with wellness programs. Despite the importance of strengths in positive psychology, there is a lack of research on using a strength-based approach to boost critical thinking skills. This study aims to enhance teacher-trainees’ critical thinking by leveraging their individual strengths, moving away from traditional instructional strategies.

Employee participation in budgeting enhances motivation and commitment, leading to better performance by enhancing ownership. Within Iraqi higher education institutions (HEIs), the current investigation delves into the influence of participative budgeting on managerial performance. Drawing upon goal-setting theory, this study discerns budget goal commitment as a mediating variable while also examining directive leadership as a moderator in the dynamics of this association.

The current study used partial least squares structural equation modelling (PLS-SEM) using SmartPLS 4.0. 373 questionnaires were collected from managers in Iraqi public universities, with 58.37% responding.

The findings show that budget goal commitment partially mediates the connection between budget participation and managerial performance. Furthermore, the results showed that directive leadership increases the positive associations between budget goal commitment and managerial performance.

This study expands the existing literature by shedding light on the mediating role of budget goal commitment and the moderating influence of directive leadership. It also enriches the discourse in management accounting by providing a more elucidation of how employee involvement in the budgeting process enhances managerial performance.

There is a dearth of research in the field of social marketing that attempts to understand why consumers prefer to use plastic packages when using online food delivery services. In addressing this issue, this study aims to investigate the role of moral disengagement, myopia and environmental apathy in the young generations' intentions to use plastic bags while ordering food online. It also examines the mediating role of moral disengagement and the moderating role of guilt in the context of the online food delivery service industry in China.

An online survey was designed to collect data, which yielded 256 usable responses. The partial least squares structural equation modelling (PLS-SEM) technique (SmartPLS 4.0) was used to test the study hypotheses.

The results indicate that environmental apathy, myopia and moral disengagement exert significant negative effects on consumer intention to use plastic. In addition, moral disengagement was able to mediate the links between “environmental apathy”, “myopia” and “plastic usage intention”. Lastly, consumers’ guilt was found to be a significant moderator in the link between moral disengagement and plastic usage intention.

This research holds significant importance for social marketers in the online food delivery service industry. Particularly, by understanding consumers' negative behavioural aspects, social marketers can implement marketing strategies that emphasise green practices for environmental well-being.

This is a pioneer study that focuses on the negative aspects of consumer behaviour, such as myopia, environmental apathy and moral disengagement, to understand what drives young consumers to use plastic. Additionally, this study investigates several new relationships in the social marketing field, such as the mediating effect of moral disengagement between myopia, environmental apathy and plastic usage intention. It also tests the moderating effect of guilt on the link between moral disengagement and use intention.

This study aims to establish a multi-physics-coupled model for an electrostatic particulate matter (PM) sensor. The focus lies on investigating the deposition patterns of particles within the sensor and the variation in the regeneration temperature field.

Computational simulations were initially conducted to analyse the distribution of particles under different temperature and airflow conditions. The study investigates how particles deposit within the sensor and explores methods to expedite the combustion of deposited particles for subsequent measurements.

The results indicate that a significant portion of the particles, approximately 61.8% of the total deposited particles, accumulates on the inside of the protective cover. To facilitate rapid combustion of these deposited particles, a ceramic heater was embedded within the metal shielding layer and tightly integrated with the high-voltage electrode. Silicon nitride ceramic, selected for its high strength, elevated temperature stability and excellent thermal conductivity, enables a relatively fast heating rate, ensuring a uniform temperature field distribution. Applying 27 W power to the silicon nitride heater rapidly raises the gas flow region's temperature within the sensor head to achieve a high-temperature regeneration state. Computational results demonstrate that within 200 s of heater operation, the sensor's internal temperature can exceed 600 °C, effectively ensuring thorough combustion of the deposited particles.

This study presents a novel approach to address the challenges associated with particle deposition in electrostatic PM sensors. By integrating a ceramic heater with specific material properties, the study proposes an effective method to expedite particle combustion for enhanced sensor performance.

This study aims to investigate the performance analysis, science mapping and future direction of artificial intelligence (AI) technology, applications, tools and software used to preserve, curate and predict the historical value of cultural heritage.

This study uses the bibliometric research method and utilizes the Scopus database to gather data. The keywords used are “artificial intelligence” and “cultural heritage,” resulting in 718 data sets spanning from 2001 to 2023. The data is restricted to the years 2001−2023, is in English language and encompasses all types of documents, including conference papers, articles, book chapters, lecture notes, reviews and editorials.

The performance analysis of research on the use of AI to aid in the preservation of cultural heritage has been ongoing since 2001, and research in this area continues to grow. The countries contributing to this research include Italy, China, Greece, Spain and the UK, with Italy being the most prolific in terms of authored works. The research primarily falls under the disciplines of computer science, mathematics, engineering, social sciences and arts and humanities, respectively. Document types mainly consist of articles and proceedings. In the science mapping process, five clusters have been identified. These clusters are labeled according to the contributions of AI tools, software, apps and technology to cultural heritage preservation. The clusters include “conservation assessment,” “exhibition and visualization,” “software solutions,” “virtual exhibition” and “metadata and database.” The future direction of research lies in extended reality, which integrates virtual reality (VR), augmented reality (AR) and mixed reality (MR); virtual restoration and preservation; 3D printing; as well as the utilization of robotics, drones and the Internet of Things (IoT) for mapping, conserving and monitoring historical sites and cultural heritage sites.

The cultural heritage institution can use this result as a source to develop AI-based strategic planning for curating, preservation, preventing and presenting cultural heritages. Researchers and academicians will get insight and deeper understanding on the research trend and use the interdisciplinary of AI and cultural heritage for expanding collaboration.

This study will help to reveal the trend and evolution of AI and cultural heritage. The finding also will fill the knowledge gap on the research on AI and cultural heritage.

Some similar bibliometric studies have been conducted; however, there are still limited studies on contribution of AI to preserve cultural heritage in wider view. The value of this study is the cluster in which AI is used to preserve, curate, present and assess cultural heritages.