In Large-Scale Group Decision-Making (LSGDM), effectively implementing consensus models is pivotal for managing decision complexity. While trust-based LSGDM has garnered attention, there remains a need for deeper insights into the dynamics of interexpert trust and the impact of authority effects on the decision-making process. This study introduces a sophisticated model for large-scale group decision-making, incorporating considerations of expert “trustworthiness-authority.” Initially, the study assesses the trustworthiness of experts based on social network relationships and opinion similarity while using background information and consensus levels to establish their authority. Subsequently, experts are categorized into four distinct regions based on their trustworthiness and authority assessments. Furthermore, tailored consensus adjustment methods are proposed for each region based on social contagion theory to facilitate consensus achievement. Additionally, a case study is conducted to demonstrate the rationality and effectiveness of the proposed LSGDM model, considering expert “trustworthiness-authority.” Finally, the necessity and superiority of the proposed model are further verified through comparison analysis and sensitivity analysis.

Ecological knowledge and destination image are important drivers of ecotourism. Although previous studies have explored the antecedents of ecotourism intentions, our understanding of the factors driving demand for ecotourism is still limited. This study explored factors that affect ecotourism intention. Ecotourism knowledge and destination image were evaluated from the perspective of the theory of planned behavior. In total, 441 valid questionnaires (in paper and electronic format) were obtained and analyzed using a structural equation model. We developed an extended theoretical model of planned behavior. The results showed that ecological knowledge, attitude, and perceived behavioral control had a significant impact on ecotourism intention. Destination image directly affected attitudes toward ecotourism. Ecotourism attitude and subjective norms mediated the relationship between ecotourism knowledge and ecotourism intention. This study extended the theory of planned behavior to analyze ecotourism intention according to ecotourism knowledge and destination image. The findings of this study enrich the literature on tourism and consumer behavior, and also have practical implications for ecotourism operators aiming to design experiences for tourists, as well as for government and non-profit organizations attempting to promote ecotourism.

Supply chain finance has shown remarkable results in alleviating the financial constraints of small and medium-sized enterprises. However, most studies have concentrated on the two-tier supply chain while ignoring the financing issues of the deep-tier supply chain. This study introduced the blockchain-enabled financing mode now utilized in the manufacturing industry and constructed a three-tier agricultural supply chain considering the farmer’s financial constraints. The main contributions of this article are (1) presenting the blockchain-enabled financing mode in the three-tier agricultural supply chain and (2) investigating supply chain decision-making in the three-tier supply chain when the tier-2 supplier is financially constrained, comparing advance payment and the blockchain-enabled financing mode. Relevant parameter estimations were realized using the data of the last ten years, and numerical analyses were conducted. The results show that when the farmer’s bank’s financing capability exceeds the acquirer’s, the farmer is motivated to select the blockchain-enabled financing mode. However, a win-win situation among the three-tier supply chain is only achievable when the transmission-fee rate falls within a particular range. In addition, if government agencies wish to promote blockchain technology by subsidizing blockchain-enabled financing, they might support the farmer in obtaining a bank loan at a cheaper interest rate. All members of the supply chain would benefit from this.

Purpose: This study aims to explore conditions for the application of blockchain-enabled financing versus traditional prepayment. It seeks to understand how a short food supply chain can choose more efficient financing pattern under loan risk compensation policy, crucial for designing an optimal short food supply chain.Design/methodology/approach: Employing Stackelberg game, decisions of the short food supply chain regarding the adoption of different financing patterns are modeled. The theoretical model’s outcomes are validated using data from the Ministry of Agriculture and Rural Affairs of China, including publicly available market prices and costs of soybeans and wheat over the past decade. Adjusted market prices are derived using consumer price index for residential prices to eliminate the impact of inflation.Findings: The loan risk compensation rate, prepayment discount rate, blockchain-enabled financing interest rate and distribution of crop output create three distinct scenarios, prompting members of the short food supply chain to form various financing pattern preferences. Simultaneously, loan risk compensation effectively improves supply chain performance. Moreover, the impact of risk compensation is more pronounced at lower output rates.Originality/value: This research emphasizes that the blockchain-enabled financing pattern is not always dominant. Government departments can shape the financing pattern preferences of supply chain members by adjusting risk compensation rate, thereby promoting the application of the blockchain-enabled financing pattern. This paper contributes to the literature on the adoption of blockchain technology for financing short food supply chain by delving into the issue of financing pattern selection.

At this stage, countries around the world have their own operating management model for the procurement system of emergency equipment. This article analyzes the influencing factors affecting the operation of the emergency procurement system through a convolutional neural network analysis method, and the contract management of the emergency procurement system is realized. Management and monitoring and balance of interests on supply and demand also meet the requirements of the construction and improvement of emergency procurement systems at this stage. During the construction and improvement of the emergency procurement system, through the monitoring and management of the procurement system, standardize the management of emergency procurement contracts, and implement the management of the memorandum of emergency procurement contracts to maximize the benefits of supply and demand of emergency equipment, and meet the requirements of different emergency levels in the future equipment procurement requirements.

Rapid dissemination of information on social media challenges effective destination image management. Through text mining, this study explores how public emotions triggered by the tourism crisis event on social media affect users’ engagement behavior. It is found that specific discrete emotions—particularly anger and love—significantly enhance user engagement, identifying them as the most influential negative and positive drivers, respectively. Unlike previous studies that merely consider the presence or absence of emojis, this research shows that a higher proportion of emoji use weakens the impact of these emotional drivers. The study further uncovers the internal structure and the dynamic evolution of high-impact emotions using structural topic modeling. These findings provide actionable insights for tourism stakeholders, enabling them to refine public opinion strategies and respond swiftly to online emotion shifts.