This research investigated how data privacy practices may impact reverse logistics in the automotive engineering sector, particularly by examining whether environmental efficiency plays a mediating role. This research uses the Resource-Based View (RBV) and Dynamic Capabilities Theory (DCT) to understand how data privacy-driven processes support environmental practices and reverse logistics optimization. Primary research is done through structured questionnaires of automotive engineering professionals. The partial least squares structural equation modeling (PLS-SEM) approach tested the relationships amongst data privacy, environmental efficiency, and reverse logistics. However, the results further clarify how key intermediate outcomes, after all, improved environmental efficiency, affected by robust data privacy practices, may enhance reverse logistics processes. The nexus of data privacy and environmental efficiency highlights the critical need to embed respect for private sector information into logistics strategies that achieve superior business performance and also protect corporate sustainability. The findings suggested that environmental consequences must be considered in the flexibility of data-privacy measures with important strategic implications for firms operating in a complex and more environmentally conscious market. This study makes a novel contribution to the extant literature by empirically detecting how environmental efficiency mediates data privacy practices and reverse logistics. These findings will be useful for industry practitioners to use data privacy to enable sustainable logistics management of business operations within automotive engineering.

The objective of this study was to examine the role of Industry 4.0 in the sustainable supply chain (SSC). The relationship between Industry 4.0, data storage, data assessment, performance evaluation, data inquiry and SSC were examined. To achieve the study objective, the mediation effect of data storage, data assessment, performance evaluation and data inquiry between Industry 4.0 and SSC was examined. Employees of textile companies were selected for data collection in Indonesia. Results of the study highlighted that Industry 4.0 had a significant positive effect on SSC. Better implementation of Industry 4.0 showed a positive effect on SSC. In addition to this, Industry 4.0 had a positive effect on data storage, data assessment, performance evaluation and data inquiry. Finally, data storage, data assessment, performance evaluation and data inquiry had a positive effect on SSC.

The objective of the current study is to examine the role of Industry 4.0 in supply chain sustainability (SCS). To examine the effect of Industry 4.0 on SCS, the big data technology is considered. As the supply chain process requires a significant data handling system among the companies, however, companies are lacking in this area. Therefore, the relationship between data storage, data transformation, data utilization, order management and SCS were examined. Data was collected from the rubber industry. For the purpose of data collection, questionnaires were used, and data were collected from the rubber companies of Indonesia. Results of this study shows that Industry 4.0 has a vital role in SCS. Implementation of Industry 4.0 among the rubber companies shows a positive effect on SCS. Particularly, the applications of big data technology have a vital role in order management and SCS. Big data technology has a significant positive effect on SCS. Big data technology has a positive role to promote order management which further influences positively on SCS.