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The purpose of this paper is to present an optimisation of four-point star-shaped structures produced through additive manufacturing (AM) polylactic acid (PLA). The study also aims to investigate the compression failure mechanism of the structure.

A Taguchi L₉ orthogonal array design of the experiment is adopted in which the input parameters are resolution (0.06, 0.15 and 0.30 mm), print speed (60, 70 and 80 mm/s) and bed temperature (55°C, 60°C, 65°C). The response parameters considered were printing time, material usage, compression yield strength, compression modulus and dimensional stability. Empirical observations during compression tests were used to evaluate the load–response mechanism of the structures.

The printing resolution is the most significant input parameter. Material length is not influenced by the printing speed and bed temperature. The compression stress–strain curve exhibits elastic, plateau and densification regions. All the samples exhibit negative Poisson’s ratio values within the elastic and plateau regions. At the beginning of densification, the Poisson’s ratios change to positive values. The metamaterial printed at a resolution of 0.3 mm, 80 mm/s and 60°C exhibits the best mechanical properties (yield strength and modulus of 2.02 and 58.87 MPa, respectively). The failure of the structure occurs through bending and torsion of the unit cells.

The optimisation study is significant for decision-making during the 3D printing and the empirical failure model shall complement the existing techniques for the mechanical analysis of the metamaterials.

To the best of the authors’ knowledge, for the first time, a new empirical model, based on the uniaxial load response and “static truss concept”, for failure mechanisms of the unit cell is presented.

Drawing on social network and information diffusion theories, the authors study the impact of the structural characteristics of a seller’s local social network on her promotion effectiveness in social commerce.

The authors define a local social network as one formed by a focal seller, her directly connected users and all links among these users. Using data from a large social commerce website in China, the authors build econometric models to investigate how the density, grouping and centralization of local social networks affect the number of likes received by products posted by sellers.

Local social networks with low density, grouping and centralization are associated with more likes on sellers’ posted products. The negative effects of grouping and centralization are reduced when density is high.

The paper deepens the understanding of the determinants of social commerce success from a network structure perspective. In particular, it draws attention to the role of sellers’ local social networks, forming a foundation for future research on social commerce.

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 study aims to analyse the systemic risk emitted by all publicly listed commercial banks in a key emerging economy, India.

The study makes use of the Tobias and Brunnermeier (2016) estimator to quantify the systemic risk (ΔCoVaR) that banks contribute to the system. The methodology addresses a classification problem based on the probability that a particular bank will emit high systemic risk or moderate systemic risk. The study applies machine learning models such as logistic regression, random forest (RF), neural networks and gradient boosting machine (GBM) and addresses the issue of imbalanced data sets to investigate bank’s balance sheet features and bank’s stock features which may potentially determine the factors of systemic risk emission.

The study reports that across various performance matrices, the authors find that two specifications are preferred: RF and GBM. The study identifies lag of the estimator of systemic risk, stock beta, stock volatility and return on equity as important features to explain emission of systemic risk.

The findings will help banks and regulators with the key features that can be used to formulate the policy decisions.

This study contributes to the existing literature by suggesting classification algorithms that can be used to model the probability of systemic risk emission in a classification problem setting. Further, the study identifies the features responsible for the likelihood of systemic risk.

This paper seeks to revisit the interrelationship among tourists’ perceived value of the destination, tourist satisfaction and destination loyalty in the heritage tourism site of Hoi An. In addition, the moderating role of tourists’ perceived crowding, which has become remarkably common at the site, in such a triangle relationship will also be explored. In other words, this study aims to validate an extended model of perceived value – tourist satisfaction – destination loyalty – perceived crowding.

The study collects data from 403 tourists who visited Hoi An during peak season through an online questionnaire. The data were later analysed using AMOS and Warp partial least squares.

The results validate the significant and positive correlation among perceived value, customer satisfaction and destination loyalty. Also, perceived crowding was confirmed to affect the relationship among these three variables negatively. In terms of academic contributions, this paper empirically proved that low levels of tourist satisfaction and destination loyalty among tourists who highly value their visiting experience at World Heritage Sites (WHS) can be caused by perceived crowding.

So far, current literature has investigated the direct (either positive or negative) relationship between perceived crowding and post-visit behaviours of tourists (Nie et al., 2022; Papadopoulou, Ribeiro, & Prayag, 2023; Stemmer, Gjerald, & Øgaard, 2022). Broadening this area of research, the authors of this paper used the social interference theory and the stimulus-overload theory to explain the low level of tourist satisfaction and destination loyalty among tourists who highly value their visiting experience at WHS.

The purpose of this study is to solve the problem of healthcare waste management in developing countries. The buildup of medical waste has attracted the attention of all spheres of society due to the expanding population and developing economy. Timely collection and processing of medical waste are extremely important due to its potential hazards. Although the problem of planning medical waste management has been addressed in developed countries, it persists in several developing countries. This research is motivated by an example of a city in India characterized by a dense population, abundant health-care facilities and a lack of planning for managing large medical waste generated daily.

The authors address the problem of designing the network of collection and processing facilities for medical waste and optimizing the vehicle route that collects and transfers the waste between facilities. Due to distinct topographic restrictions in the considered city, the collection and transfer process needs to be conducted in two echelons – from hospitals to collection centers using smaller vehicles and then to the processing facilities using trucks. This work addresses these two problems as a two-echelon location-routing problem.

A mixed-integer programming model is developed to minimize the cost of opening the facilities and transporting medical waste. Several managerial insights are drawn up to assist planners and decision-makers.

This study follows a case study approach to provide a descriptive and prescriptive approach to hospital waste management in the ancient city of Varanasi. The city has witnessed unplanned growth over the years and is densely populated. The health-care facilities in the city have a large catchment area and attract patients from neighboring districts. The situation analysis based on secondary data and unstructured interviews of the stakeholders suggests that the ad hoc approach prevails in present hospital waste management in the city.

In view of the increasing trend in food policies targeting the promotion of consumer interest in locally produced foods and growing developments in willingness-to-pay (WTP) methodologies, the authors investigate consumer preference for packaged traditional drink asaana.

The study used a simple random sample of 336 consumers to draw on perception index and contingent valuation methods to evaluate consumers' perceptions of the attributes of packaged asaana – a traditional maize-based beverage produced in Ghana (also known as Ghana Coca-Cola). A tobit regression model was employed to analyze consumers’ WTP for the product.

Analyzing the factors that influence consumers' WTP for packaged asaana using the tobit regression model, the study established the existence of positive health and nutrition, economic benefits and purchasing decision-making perceptions for asaana. While the results further showed that consumers are willing to pay a premium for well-packaged asaana, demographics such as age, income level, labeling, price of the product and savings were found to exert significant influence on consumers’ WTP for packaged asaana. Salient recommendations for food processors and relevant government agencies and food policy implications are identified.

Comprehending WTP provides valuable understanding regarding consumer qualms, actions and WTP for more secure traditional drinks and an examination of how the different factors that influence WTP for local beverages help boost local beverage production and guarantee employment.

Analyzing WTP data for traditional drinks reveals important implications for production, marketing and public health policies. Certification systems for traditional beverages may be beneficial, and the findings can be used to create public awareness campaigns about the safety of local drinks.

Assessing the WTP among Ghanaian consumers for traditional drinks, specifically asaana, is a ground-breaking study. The contingent evaluation (CE) and tobit regression approaches utilized in this research are strong, and the results obtained can guide decisions related to traditional drink production, marketing and the development of public health policies.

Based on the trait activation theory, the current study systematically integrates how autonomy interacts with proactivity to influence the relationship between ethical leadership style and employee creativity.

Using simple random sampling and questionnaires, a sample of 475 engineering employees of 3 leading telecommunication companies in Ghana were obtained. The analysis was done using structured equation modeling (SEM), using SmartPLS.

The results showed that ethical leadership style provides employees with job autonomy which facilitates individual creativity. Employee proactivity also moderates a positive relationship between autonomy and creativity such that high-proactive employees are well placed to produce more creative outcomes when given autonomy. At the individual level, personal characteristics determine the degree of creativity.

The current study implies that telecommunication companies should put in more efforts to train and encourage leaders to be ethical in leaders' dealings with employees and employees must be rewarded for taking initiative.

With a focus on the integrative approach from a developing economy, this work is novel in exploring how contextual and personal features impact creativity.

The shortage of organ donors is an under-researched global issue that demands immediate attention. This attention should begin at the government level and related organizations. In Malaysia, the shortage of organ donations has been a pressing issue faced by the Ministry of Health Malaysia (MOH) for a considerable length of time. In reaction to this issue, the MOH deployed the Organ Donation Awareness Strategic Campaign Plan by using the platform of social media to disseminate information regarding organ donation to the public. However, the number of registrations is still low among Malaysians. Moreover, the observation from the literature shows that there are limited studies which have been initiated to focus on social media in the context of organ donation campaigns.

The quantitative research design has been used to understand the issue. Three hundred and eighty-four completed questionnaires were collected from the target sample, which comprised university students in Malaysia. For this study, partial least squares structural equation modelling was used for data analysis.

The result shows that information usefulness is vital because it will lead individuals to adopt organ donation information on social media. More specifically, predictors that positively influence youth or university students to accept information as useful are visual information, information sharing, accessibility of information, needs of information and attitude towards information. Subsequently, information usefulness positively influences information adoption. In the meantime, information quality and credibility do not significantly affect information usefulness.

The findings of this study may assist MOH or interested parties in designing a sound marketing strategy in the context of organ donation promotion by providing empirical evidence.

The study provides empirical evidence about information characteristics in the context of organ donation promotion.

The goal of this narrative review was to look at the link between the Mediterranean diet (MD) and the telomere length. Furthermore, this study aims to understand the impact of the MD on obesity-related telomere length.

Relevant literature was reviewed to explore the potential influence of the MD on telomere length and its association with obesity.

The MD is one of the healthiest diets of all known dietary patterns, and it is also linked to the telomere length. Except for fruits and vegetables, the main findings for other MD components are inconsistent. In terms of antioxidant and antiinflammatory properties, using the MD as a weight loss approach is a good method. For predicting changes in obesity characteristics, the initial telomere length is critical. However, there are not many studies in the field that have looked at the MD as a weight loss approach and its link to the telomere length. As a result, more research is needed to understand these connections in various groups.

This study is unique since it examines the MD, telomere length and obesity-related consequences. This study examines the MD, telomere length and obesity to determine if the MD can help lose weight while maintaining telomere length. As there are few studies on MD weight loss and telomere length, the work emphasizes the need for greater research in this area. This study fills a research gap and improves the understanding of nutrition, telomere biology and obesity-related outcomes.