Blockchain technology has increasingly attracted the attention of the financial service sector, customers, and investors because of its distinctive characteristics, such as transparency, security, reliability, and traceability. The paper is based on a Systematic Literature Review (SLR). The study comprehended the literature and the theories. It deployed the technology-organization-environment (TOE) model to consider technological, organizational, and environmental factors as antecedents of blockchain adoption intention. The paper contributes to blockchain literature by providing new insights into the factors that affect the intention to adopt blockchain technology. A theoretical model incorporates antecedents of blockchain adoption intention to direct an agenda for further investigations. Researchers can use the model proposed in this study to test the antecedents of blockchain adoption intention empirically.

Hazards are the primary cause of occupational accidents, as well as occupational safety and health issues. Therefore, identifying potential hazards is critical to reducing the consequences of accidents. Risk assessment is a widely employed hazard analysis method that mitigates and monitors potential hazards in our everyday lives and occupational environments. Risk assessment and hazard analysis are observing, collecting data, and generating a written report. During this process, safety engineers manually and periodically control, identify, and assess potential hazards and risks. Utilizing a mobile application as a tool might significantly decrease the time and paperwork involved in this process. This paper explains the sequential processes involved in developing a mobile application designed for hazard analysis for safety engineers. This study comprehensively discusses creating and integrating mobile application features for hazard analysis, adhering to the Unified Modeling Language (UML) approach. The mobile application was developed by implementing a 10-step approach. Safety engineers from the region were interviewed to extract the knowledge and opinions of experts regarding the application’s effectiveness, requirements, and features. These interview results are used during the requirement gathering phase of the mobile application design and development. Data collection was facilitated by utilizing voice notes, photos, and videos, enabling users to engage in a more convenient alternative to manual note-taking with this mobile application. The mobile application will automatically generate a report once the safety engineer completes the risk assessment.

This study conducts a comparative analysis of various machine learning and deep learning models for predicting order quantities in supply chain tiers. The models employed include XGBoost, Random Forest, CNN-BiLSTM, Linear Regression, Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), Bidirectional LSTM (BiLSTM), Bidirectional GRU (BiGRU), Conv1D-BiLSTM, Attention-LSTM, Transformer, and LSTM-CNN hybrid models. Experimental results show that the XGBoost, Random Forest, CNN-BiLSTM, and MLP models exhibit superior predictive performance. In particular, the XGBoost model demonstrates the best results across all performance metrics, attributed to its effective learning of complex data patterns and variable interactions. Although the KNN model also shows perfect predictions with zero error values, this indicates a need for further review of data processing procedures or model validation methods. Conversely, the BiLSTM, BiGRU, and Transformer models exhibit relatively lower performance. Models with moderate performance include Linear Regression, RNN, Conv1D-BiLSTM, Attention-LSTM, and the LSTM-CNN hybrid model, all displaying relatively higher errors and lower coefficients of determination (R²). As a result, tree-based models (XGBoost, Random Forest) and certain deep learning models like CNN-BiLSTM are found to be effective for predicting order quantities in supply chain tiers. In contrast, RNN-based models (BiLSTM, BiGRU) and the Transformer show relatively lower predictive power. Based on these results, we suggest that tree-based models and CNN-based deep learning models should be prioritized when selecting predictive models in practical applications.

This study aims to examine how marketing mix and trust theories influence users’ intentions to adopt herbal platform services in Thailand and examine the impact of these intentions on actual service usage, placing a special focus on the integration of technologies in the context. The significant potential for growth in Thailand’s herbal business and the currently underutilized online platforms, it is crucial for stakeholders to understand the determinants of investment intentions. Merging marketing mix and trust theories, this research offers a comprehensive analysis of factors influencing the use of herbal platform, highlighting the relevance of herbal in enhancing service adoption. This study utilized a quantitative approach, gathering data through online surveys from 416 users of online herbal platforms in Thailand using SEM to examine the impact of gender on consumers’ decisions to use these platforms. This study provides insights into effective business strategies for herbal companies and contributes novel perspectives to the literature on herbal services. It specifically examines cognitive and emotional trust impacts and explores gender dynamics within the context of Health development. The study clarifies the roles of these factors and assesses the impact of gender on platform adoption, highlighting the importance of m-Health services in facilitating this process. Enhancing user engagement with herbal platform services requires prioritizing influential determinants, streamlining the investment experience, and underscoring the sector’s contribution to economic revitalization. Authorities should prioritize simplifying the investment landscape and initiating advocacy campaigns, while platform developers are advised to improve the user experience, bolster educational efforts, and heighten awareness of the investment advantages within the herbal industry. This research provides stakeholders with insights into the factors that enhance Thais’ engagement with herbal market platforms, especially via online channels. Identifying these key drivers is anticipated to boost participation in the herbal market, thereby contributing positively to Thailand’s economy.

This study aims to explore the mediating role of perceived organizational support(POS) in the relationship between university teachers' competence and job performance. Through a questionnaire survey of 968 undergraduate university teachers in China, 879 valid questionnaires were collected. The study employed quantitative methods, constructing a university teacher competence scale comprising foundational competence, teaching competence, research competence, and innovation competence, as well as a job performance scale encompassing task performance, relationship performance, and adaptive performance. Structural equation modeling and SOBEL tests were used for data analysis. The results showed that POS exhibited different mediating effect patterns between various competence dimensions and job performance dimensions: no significant mediating effect was found in task performance; partial mediating effects were observed in relational performance and adaptive performance; and a complete mediating effect was identified between foundational competence and adaptive performance. The study provides theoretical support and practical guidance for university teachers management, emphasizing the importance of establishing a competence-based human resources management system, strengthening teachers perceptions of organizational support, and establishing diverse evaluation standards. Future research could further explore the impact of different cultural backgrounds and organizational types on mediating effects.

A method for studying the resilience of energy and socio-ecological systems is considered; it integrates approaches developed at the International Institute of Applied Systems Analysis and the Melentyev Institute of Energy Systems (MESI) of the Siberian Branch of the Russian Academy of Sciences. The article discusses in detail the methods of using intelligent information technologies, in particular semantic technologies and knowledge engineering (cognitive probabilistic modeling), which the authors propose to use in assessing the risks of natural and man-made threats to the resilience of the energy sector and social and ecological systems. More attention is paid to the study and adaptation of the integral indicator of quality of life, which makes it possible to combine these interdisciplinary studies.