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Despite the important role that suppliers have in enhancing the environmental performance of a buyer firm, previous research has not investigated the individual-level motivations of supplier employees (representatives) in supplier-to-supplier environmental knowledge sharing. Thus, we use insights from the coopetition literature to examine how buyer firms can encourage supplier-to-supplier environmental knowledge sharing with the aim of improving the buyer’s environmental performance.

We empirically test our model using an online vignette-based experiment administered to supply chain managers. We contextualized our results using insights from interviews with senior managers representing firms operating in a broad array of industries.

We find that a supplier representative’s personal environmental values influence their commitment to an environmental consortium with a rival firm, and they are subsequently willing to share proprietary environmental knowledge. In turn, these relationships are moderated by situational factors including competitive intensity and buyer power.

The study of coopetition is an emerging stream of research in operations management. Our findings improve the understanding on how a focal actor within a buyer–supplier coopetitive network can promote environmental knowledge sharing behavior.

The purpose of this study is to examine the extent to which a firm is able to leverage its information system (IS) innovativeness to improve supply chain resilience through developing and employing its analytics capability. It further considers how this mediating effect of analytics capability can be enhanced by internal and external integration.

Building on the logic of organizational information processing theory, a mediated moderation model is developed and tested using structural equation modeling and partial least squares regression based on survey responses from 247 working professionals.

The results indicate that IS innovativeness improves a firm’s supply chain resilience through enhanced analytics capability, with higher levels of internal and external integration further strengthening the effects of this mediating relationship.

This study is among the first to empirically test the effects of IS innovativeness and analytics capability on supply chain resilience and to examine the impacts of internal and external integration as key factors affecting the strength of these relationships. The findings complement existing literature through providing new insights into the linkage between IS strategy and supply chain resilience and highlighting the importance of relationships throughout the supply chain to enhance the efficacy of a firm’s analytics capability within this domain.

Software defect prediction (SDP) is a critical aspect of software quality assurance, aiming to identify and manage potential defects in software systems. In this paper, we have proposed a novel hybrid approach that combines Grey Wolf Optimization with Feature Selection (GWOFS) and multilayer perceptron (MLP) for SDP. The GWOFS-MLP hybrid model is designed to optimize feature selection, ultimately enhancing the accuracy and efficiency of SDP. Grey Wolf Optimization, inspired by the social hierarchy and hunting behavior of grey wolves, is employed to select a subset of relevant features from an extensive pool of potential predictors. This study investigates the key challenges that traditional SDP approaches encounter and proposes promising solutions to overcome time complexity and the curse of the dimensionality reduction problem.

The integration of GWOFS and MLP results in a robust hybrid model that can adapt to diverse software datasets. This feature selection process harnesses the cooperative hunting behavior of wolves, allowing for the exploration of critical feature combinations. The selected features are then fed into an MLP, a powerful artificial neural network (ANN) known for its capability to learn intricate patterns within software metrics. MLP serves as the predictive engine, utilizing the curated feature set to model and classify software defects accurately.

The performance evaluation of the GWOFS-MLP hybrid model on a real-world software defect dataset demonstrates its effectiveness. The model achieves a remarkable training accuracy of 97.69% and a testing accuracy of 97.99%. Additionally, the receiver operating characteristic area under the curve (ROC-AUC) score of 0.89 highlights the model’s ability to discriminate between defective and defect-free software components.

Experimental implementations using machine learning-based techniques with feature reduction are conducted to validate the proposed solutions. The goal is to enhance SDP’s accuracy, relevance and efficiency, ultimately improving software quality assurance processes. The confusion matrix further illustrates the model’s performance, with only a small number of false positives and false negatives.