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This research analyzes the impact of banks' peer effect in lending on the bank concentration‐crisis nexus for the period 2015–2021 in the United States. The analysis finds that a high level of bank concentration increases a crisis via the channel of banks' peer effect in lending, to which the imitative behavior of big banks is deemed to be the cause of deteriorating the damage of bank concentration on the stability of the banking sector. The results propose policy implications and what banks should do when treating financial market sustainability as their duty on the environmental, social, and governance (ESG) issues. [ABSTRACT FROM AUTHOR]

Blockchain technology holds considerable amount of potential for all types of industries by executing transactions in a verifiable, efficient, and permanent channel. It has been widely viewed as a standard requirement for making industry ready for the future, but when it comes to practical applications, it still arouses numerous risks/challenges that need to be addressed. Therefore, it is essential to address this gap and establish a comprehensive and effective practical framework to align the information technology revolution with sustainable value creation. The purpose of this research is to realize to what extent an enterprise legacy system's transformation benefits a blockchain-based system and to minimize its specific risk through a hybrid fuzzy MRDM (multiple rule-based decision making) model that integrates data envelopment analysis with rough set theory (DEA-RST) and the fuzzy DEMATEL approach grounded on a questionnaire derived from domain experts. We aim to point out the inherent risks of blockchain-based technology adoption and to assist senior engineers in designing or adopting a suitable architecture for practical operation and planning of any future integration and development. The potential risk evaluation of business blockchain adoption reveals that the priority improvement sequence based on dimensions is smart contract risk, value transfer risk, and standard risk. Furthermore, law and regulation are the most critical criteria. [ABSTRACT FROM AUTHOR]

The aim of this paper is to develop a mathematical model for a green product mix decision that incorporates capacity expansion features by using a mathematical programming approach. In order to satisfy customer orders when market demands exceed the company's production capacity, as well as maximise total profits, companies must study the feasibility of expanding capacity with regard to the production of parts. To place efficient controls on green product costs, we present the green product mix decision model for evaluating the benefits of expanding various types of capacity. By applying this model, companies that produce green products can make optimal decisions about further processing and capacity expansion. [ABSTRACT FROM AUTHOR]

Classification in an imbalanced dataset is a current challenge in machine learning communities, as the class-imbalanced problem deteriorates the performance of numerous classifiers. This study introduces a two-stage intelligent data preprocessing approach to tackle the class-imbalanced problem. By modifying the penalty parameter of the support vector machine (SVM), the discriminating boundary will move toward the majority class and in turn misclassify the majority class examples as minority class examples. That is, more misclassifications for the majority class examples are equivalent to a greater number of minority class examples. Executing the SVM as a preprocessor can be used to overcome the class imbalanced problem. Sequentially, the modified dataset undergoes the random forest to defy the curse of dimensionality. Finally, the preprocessed data are fed into a rule-based classifier to generate comprehensive decision rules. According to the empirical results, the presented architecture is a promising alternative for the class-imbalanced problem. [ABSTRACT FROM AUTHOR]