Shedding light on the reverse logistics’ decision-making: a social-media analytics study of the electronics industry in developing vs developed countries

ABSTRACT

Growing population leads to generating more waste and depletion of natural resources. Moreover, the cost of supplying some resources has increased substantially. Hence, the manufacturer is trying to focus on planning to get back old or partially/wholly unusable products and make the best disposition decisions on them. This research aims to build a multi-industry applied model using the deep learning method in social media analysis to make the best decision for returning products in reverse logistics, along with the sustainability and circular economy concerns. Furthermore, we outline the usage of social network analytics in aligning consumers’ expectations with supply chain policies, strategies, and decisions. An industry benchmark concerning circular economy concepts can be attained by applying the proposed model to different industries. We have proposed a generalisable model using social media analytics, consumer sentiment analysis, reverse logistics, and circular economy theory to attain a circular supply chain regarding sustainability concerns. Applying the proposed model to the electronics industry as a case study, the model was further validated with Twitter data analysis of developing versus developed countries for laptop devices. We collected over 70-million tweets using the Twitter Application Programming Interface (API) over fifteen months. The results approved the proposed model by leveraging the Twitter geolocation attribute to extract Twitter data from developing and developed countries. Moreover, the model is general enough to be used on various industries’ supply chains and provides managers and policymakers with deep insight into reverse logistics’ decision-making. It would be interesting to use real-time analytics and improve accuracy in future works. We made original contributions to reverse logistics decision-making in the circular economy context. Previous research, which has focused on supply chain decision-making, has been extended by providing theoretical and practical implications for social media analytics and the circular economy ecosystem. Thus, by scrutinising the consumers’ needs and expectations, we suggested the best decision on returned products to close an open-ended supply chain and achieve a circular economy. Furthermore, we derived industry benchmarks for both developing and developed countries separately. The results showed that the best decision on returning products in developing countries is different from developed countries. We advise top managers and policymakers to improve supply chain sustainability using social media analytics in developing and developed countries to substantially optimise waste and companies’ profits.

KEYWORDS:

• Circular economy
• reverse logistics
• deep learning
• social media analysis
• circular supply chain
• industry benchmark

HIGHLIGHTS

• The proposed model connects the consumers to the policymakers’ decision-making on the returning products align with the zero-waste strategies.

• A case study in comparison of the developing and developed countries validates the persistency of the framework

• Proposing an industry benchmark based on the social media analytics for circular supply chain

• The results proved focusing on consumers’ needs will convert the regular SC to the CSC regarding sustainability and CE concerns.

• Examining consumers’ sentiments, managers and policymakers are getting deep insight into the returned and new products.

Disclosure statement

No potential conflict of interest was reported by the author(s).