This study enhances blockchain security by developing a comprehensive machine learning framework that automates the detection and classification of smart contract vulnerabilities. Our main innovation is the systematic transformation of the unlabeled BCCC-VulSCs-2023 dataset into a multi-label classification resource. We achieve this transformation through automated vulnerability labeling using the Oyente tool, which enables the simultaneous detection of seven different types of vulnerabilities. We employ a robust methodology that utilizes traditional machine learning algorithms, including Random Forest, Gradient Boosting, Decision Tree, Logistic Regression, and Gaussian Naive Bayes. This approach is further enhanced by advanced feature selection techniques, such as Recursive Feature Elimination (RFE) and Principal Component Analysis (PCA), along with thorough hyperparameter optimization. Our method demonstrates significant improvements over existing approaches. In binary classification, the optimized Random Forest classifier achieves an accuracy of 92.8% and an F1-score of 92.9% when combined with RFE. Furthermore, it exhibits satisfactory multi-label performance, with a precision of 72.6% across various vulnerability categories. These results highlight the potential of interpretable machine learning models for effective smart contract security auditing. Such models could reduce financial risks and promote trust in the blockchain ecosystem through automated vulnerability assessments.

This study investigates how retailer overconfidence shapes pricing and inventory decisions in a supply chain with strategic consumers. Forward-looking consumers may defer purchases in anticipation of markdowns, making inventory both an operational choice and a commitment device that influences expected availability and regular-season pricing. We develop a two-period game-theoretic model with one manufacturer, one retailer, and rational consumers, while allowing the retailer to hold overconfident demand beliefs in the form of overestimation or overprecision. The analysis shows that overestimation generally leads to higher inventory commitments, which depress regular-season prices and improve product availability, while retailer profit may decline because of margin compression and excess-inventory risk. Meanwhile, overprecision pulls inventory decisions toward the perceived mean demand, reducing inventory when the benchmark level is high and increasing it when the benchmark level is low. Its effects on price and inventory are therefore more conditional, especially on consumer patience. These findings show that retailer overconfidence affects not only forecasting accuracy but also equilibrium pricing and the distribution of gains across the supply chain. The study contributes to the literature by integrating overconfidence and strategic consumer behavior within a unified framework and offers implications for pricing, inventory management, and supply chain management.

E-commerce is a rapidly growing industry that stands to gain the most from emerging technologies. Online shopping and trading have become the preferred activities for customers and other stakeholders in the e-commerce value chain. The primary issues stakeholders face include trust, secure payments, transaction costs, and transparency in supply chains and financial transactions. Blockchain technology has the potential to solve all of these issues, providing a robust framework for a more secure and efficient e-commerce ecosystem. This research delves into the potential applications of blockchain in e-commerce and presents a new model of an e-commerce ecosystem based on blockchain. In addition, a survey was conducted to gauge customers’ readiness to adopt blockchain-based e-commerce services. The acceptance study used the UTAUT2 model, and data analysis was performed with SmartPLS. The study results suggest that blockchain significantly enhances consumer trust and satisfaction. Furthermore, the analysis revealed that the statistically significant variables affecting the adoption of blockchain in e-commerce are Effort expectancy, Perceived efficiency, Price value, and Trust. It is crucial to educate individuals about the effort and trust required when implementing new technologies. The research provides a theoretical contribution by refining the UTAUT2 model and proposing a blockchain-based e-commerce ecosystem model. Its practical implications offer businesses and policymakers strategies to enhance trust, improve usability, and establish regulatory clarity for real-world blockchain adoption.

Generally, it is difficult for consumers to accurately authenticate the product information disclosed by online e-commerce platforms in traditional e-commerce supply chain. Therefore, due to the information immutability and traceability of blockchain technology, blockchain has become an important tool for consumers to judge product information. To analyze the application strategies of blockchain technology, we construct Stackelberg game model in an e-commerce supply chain, consisting of one supplier and two retailers, and explore blockchain adoption strategies from the perspectives of supply chain competition. First, we indicate the equilibrium results of product price, the supplier and retailers profits and consumer surplus, and show the accuracy of consumers quality judgement on the products produced by the supplier always has a positive impact. Furthermore, we discuss the optimal strategies with blockchain technology effect and find retailers competition and consumers quality judgement on the products would impact blockchain adoption decisions and the profits of the whole supply chain. It indicates that retailers different blockchain technology implement decisions are beneficial to the social welfare of the whole supply chain. Finally, we analyze the impact of blockchain technology on optimal decision-making and consumers judgment of product information. And we find that when the proportion of expert consumers in the supply chain is high, the supplier prefers to improve product quality management capability and retailers would apply blockchain technology. The main contribution of this study is to combine the analysis of blockchain implement with different consumers judgement in competitive e-commerce supply chain, which reveals the value of blockchain technology service, transaction modes and retailers competitive relationship in supply chain.

The quasi-experimental evidence suggests that Non-Fungible Token (NFT) Spin-Off NFTs strategies significantly dampen raw asset valuations. Utilizing the launch of Mutant Ape Yacht Club (MAYC) by Bored Ape Yacht Club (BAYC) as an exogenous event and CryptoPunks as a control group, this study identifies the causal effects based on the trading data of 1,583 matched samples from MayDecember 2021, using the Difference-in-Differences (DID) method. The findings indicate that the issuance of MAYC leads to a significant decrease in the BAYC floor price by 39.461 ETH. Mechanism analysis reveals two transmission channels: 1) loyalty erosion, evidenced by a decrease of 324.625 normalized units in highly loyal BAYC holders post-launch compared to the control; and 2) substitution effect, where a 1-unit increase in the overlap between BAYC holders minting MAYC and BAYC sellers correlates with an average BAYC price decrease of 20.6 units. This study provides the first quantitative demonstration of the negative valuation effect of Spin-Off NFTs strategies on the original pool and identifies the dual mechanisms of loyalty loss and substitution, that offers important strategic warnings for digital asset portfolio management.