| Title |
Mastering supply chain's decision-making establishing SDG's goal: a social media analytics study of the electronic devices in developing and developed countries |
| ID_Doc |
23064 |
| Authors |
Shokouhyar, S; Shahidzadeh, MH |
| Title |
Mastering supply chain's decision-making establishing SDG's goal: a social media analytics study of the electronic devices in developing and developed countries |
| Year |
2024 |
| Published |
|
| DOI |
10.1007/s10479-024-06078-2 |
| Abstract |
This research proposed a multi-industry framework that aims to clarify reverse logistics decision-making through a social media analytics approach analyzing the electronics industry in developing compared to developed countries in separated case studies. By leveraging deep learning on social media data, this study intends to elucidate ideal product return policies and strategies aligned with sustainability, circular economic principles, and sustainable development goals. Furthermore, this work outlines the potential of social media analytics to align consumer expectations with supply chain decisions across geographic contexts and identify the missing potential stream of material in supply chains. Overall, this study utilizes social media data mining and sentiment analysis to shed light on optimal reverse logistics decision-making for electronics firms in diverse national settings pertinent to circular economy ideals. The juxtaposition of developing and developed nations provides an enhanced understanding of how optimal return policies may differ given geographic and economic contexts. In summary, this research elucidates data-driven reverse logistics insights via social media analytics to inform electronics sector circular economy strategies tailored to national development levels. |
| Author Keywords |
SDGs; Reverse logistics; Industry 5; Social media analytics; Deep learning |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:001282064200002 |
| WoS Category |
Operations Research & Management Science |
| Research Area |
Operations Research & Management Science |
| PDF |
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