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This study aims to examine the association between globally responsible leadership (GRL) and pro-environmental behavior (PEB), specifically probing the mediating role of green management initiatives (GMI) in this relationship.

This study used a quantitative research design, using survey data from 390 participants working in manufacturing sector organizations in one of the emerging economies in the Asian region, namely, Pakistan. AMOS was used to test the hypothesized relationships.

The results reveal that GRL has a significant positive link with GMI and PEB. In addition, this study found that GMI mediates the association between GRL and PEB, suggesting that GRL indirectly promotes PEB through the implementation of GMI.

This study has several limitations, including its reliance on self-reported data, its cross-sectional design and its focus on participants from only one nation. Future research may benefit from using mixed-study designs and diverse samples from multiple industries and nations.

The results suggest that businesses can promote PEB among their staff by adopting GRL and implementing GMI. In doing so, businesses can demonstrate their commitment to sustainability, enhancing their credibility and competitive advantage.

This research contributes several new insights to the existing literature on sustainable leadership. First, it provides empirical evidence to support the hypothesis that GRL, GMI and PEB are interrelated. Second, it highlights the mediating role of GMI in this relationship.

This paper aims to realize an in-situ quality inspection system rapidly for new injection molding (IM) tasks via transfer learning (TL) approach and automation technology.

The proposed in-situ quality inspection system consists of an injection machine, USB camera, programmable logic controller and personal computer, interconnected via OPC or USB communication interfaces. This configuration enables seamless automation of the IM process, real-time quality inspection and automated decision-making. In addition, a MobileNet-based deep learning (DL) model is proposed for quality inspection of injection parts, fine-tuned using the TL approach.

Using the TL approach, the MobileNet-based DL model demonstrates exceptional performance, achieving validation accuracy of 99.1% with the utilization of merely 50 images per category. Its detection speed and accuracy surpass those of DenseNet121-based, VGG16-based, ResNet50-based and Xception-based convolutional neural networks. Further evaluation using a random data set of 120 images, as assessed through the confusion matrix, attests to an accuracy rate of 96.67%.

The proposed MobileNet-based DL model achieves higher accuracy with less resource consumption using the TL approach. It is integrated with automation technologies to build the in-situ quality inspection system of injection parts, which improves the cost-efficiency by facilitating the acquisition and labeling of task-specific images, enabling automatic defect detection and decision-making online, thus holding profound significance for the IM industry and its pursuit of enhanced quality inspection measures.