Recently, there has been a lot of buzz on social media, particularly in the form of vlogs, about newly launched semi-high speed trains in India popularly known as Vande Bharat Express. However, no information is available about the extent to which people trust the vlogs promoting the trains and the trains themselves. Therefore, this research aims to investigate the impact of watching vlogs about semi-high speed trains on the trust and attitude towards them, and how they perceive the risks associated. This study is guided by the trust transfer theory to investigate how trust transference can lead to a traveler’s intent to use semi-high speed trains. This study involved 338 participants. The relationship between variables was examined using SmartPLS 4 software. The findings indicate that trust in semi-high speed trains can be established through vlogs leading to intention to use. On the theoretical side, it provides insight into how trust, attitude, and perceived risk can affect the adoption of new technology, while on the practical side, it helps to understand how vlog coverage can be used as a tool to increase trust and ultimately drive adoption. Vlog coverage, trust in vlog content, trust in semi-high speed trains and behavioural intention altogether are not well understood in current literature despite the important implication for managers, academicians and consumers alike. This study contributes to the field of transportation and railways, social media and communication, and hospitality and tourism research. The study helps policy makers to understand users’ characteristics regarding the latest social media tools and adopt them accordingly to provide a better governance policy.

Interdependence between the United States (U.S.), European Union (EU) and Asia in the semiconductor industry, driven by specialization, can serve as a preventive measure against disruptions in the global semiconductor supply chain. Moreover, with rising geopolitical tensions, the cost-intensive nature of the semiconductor industry and a slowdown in demand, interdependence and partnership provide countries with opportunities and benefits. Specifically, by analyzing global trade patterns, developing the Interdependence Index within the semiconductor market, and applying the Grubel-Lloyd Index to the U.S., the EU, and Asian countries from 2011 to 2022, our findings reveal that interdependence enhances regional semiconductor supply chains, such as the establishment of semiconductor foundries in the U.S., Japan, and the EU; reduces dependence on a single supplier, such as the U.S. distancing from China; and increases market share in different semiconductor segments, as demonstrated by Taiwan in automobile chips. The evidence indicates that China heavily depends on foreign sources to meet its semiconductor demand, while Taiwan and South Korea specialize as foundry service providers with lower Interdependence Index values. The U.S., with a robust presence in semiconductor manufacturing and design, has a moderate dependence on semiconductor imports, whereas the EU demonstrates a higher level of interdependence because it lacks semiconductor foundries. The stage-specific analyses indicate that the U.S. and the EU rely on Asia for semiconductor devices, while China and Taiwan have a higher dependence on American intermediate inputs and European lithography machines.

A logistics service company in Batam faces challenges related to warehouse load fulfillment and sorting inaccuracies. This study aims to identify proposed efficiency improvements to the goods distribution system using the cross-docking method. The research method chosen is cross-docking, a technique that eliminates the storage process in the warehouse, thus saving time and cost. The research findings show significant benefits, especially in achieving zero inventory efficiency. Data processing and discussion revealed that efficiencies were apparent by increasing the sorting tables from 1 to 6, with an output of 90,000 kg during aircraft loading and unloading (compared to approximately 77,000 kilograms). This efficiency arises from the larger output of the sorting tables compared to the input, eliminating the need for warehousing and adding ten trucks. As a result, the shipment can be completed in one trip, with no goods stored in the warehouse. The analysis shows that implementing cross-docking in the company increases efficiency in distributing goods to forwarding partners.

Knowledge transfer, assimilation, transformation and exploitation significantly impact performing business activities, developing innovations and moving forward to new business models such as transferring to a circular economy. However, organizations’ decisions or willingness to transition to a circular economy are very often also influenced by the external environment. The study aims to determine the influence of the external environment on the transfer from a linear to a circular economy while mediating knowledge assimilation. The quantitative research involved 159 Nordic capital companies operating in Estonia and Lithuania. The survey has been performed by means of the CATI method. The analysis has been done also by applying structural equation modelling (SEM). In order to perform mediation analysis, IBM SPSS and a special PROCESS macro have been used. The study showed that knowledge assimilation partially mediates the relationship between the external environment and the transfer to the circular economy. Hence, the external environment’s direct effect is much more significant than the indirect. The added value of the study also consists in extending the concept of circular economy by including some aspects of absorptive capacity and the external environment.

This study applies machine learning methods such as Decision Tree (CART) and Random Forest to classify drought intensity based on meteorological data. The goal of the study was to evaluate the effectiveness of these methods for drought classification and their use in water resource management and agriculture. The methodology involved using two machine learning models that analyzed temperature and humidity indicators, as well as wind speed indicators. The models were trained and tested on real meteorological data to assess their accuracy and identify key factors affecting predictions. Results showed that the Random Forest model achieved the highest accuracy of 94.4% when analyzing temperature and humidity indicators, while the Decision Tree (CART) achieved an accuracy of 93.2%. When analyzing wind speed indicators, the models’ accuracies were 91.3% and 93.0%, respectively. Feature importance revealed that atmospheric pressure, temperature at 2 m, and wind speed are key factors influencing drought intensity. One of the study’s limitations was the insufficient amount of data for high drought levels (classes 4 and 5), indicating the need for further data collection. The innovation of this study lies in the integration of various meteorological parameters to build drought classification models, achieving high prediction accuracy. Unlike previous studies, our approach demonstrates that using a wide range of meteorological data can significantly improve drought classification accuracy. Significant findings include the necessity to expand the dataset and integrate additional climatic parameters to improve models and enhance their reliability.