| Title |
Promoting consumer's attitude toward refurbished mobile phones: A social media analytics approach |
| ID_Doc |
69223 |
| Authors |
Sharifi, Z; Shokouhyar, S |
| Title |
Promoting consumer's attitude toward refurbished mobile phones: A social media analytics approach |
| Year |
2021 |
| Published |
|
| DOI |
10.1016/j.resconrec.2021.105398 |
| Abstract |
Resource depletion and environmental issues persuade manufacturers to reuse and recycle electronic products. One green strategy in mobile device manufacturing is to refurbish and reuse these products. Consumer feedback in the secondary market impress decision-makers and manufacturer strategies. This research provides a framework that focuses on data mining techniques to investigate consumer attitudes toward refurbished phones by one of the reverse supply chain practices: refurbishment. Furthermore, the consumer's opinions on refurbished mobile phones found on Twitter have been analyzed, and the most appropriate selling strategy has been proposed. Accordingly, the customer's strong motivating factors were identified by assessing approximately 25,000 tweets. The obtained data are analyzed according to three categories: environmental versus financial motivators, refurbished mobile phone features, and main components of refurbished mobile phones. The results indicate that the environmental factor is slightly higher motivating when compared to the financial factor. Other important features were prioritized from the consumer's point of view (highest to lowest): price, product warranty, quality, and seller's reputation. In a separate analysis, camera, display, battery, performance, innovative technologies, and internal memory were found to be the top components of a mobile device that customers pay attention to the most. Interrelationships between identified features were established using interpretive structural modeling (ISM) to accomplish a comprehensive framework. Classification analysis was performed by the matrix of cross-impact multiplications applied to classifications (MICMAC) technique to identify these features based on their driving and dependence power. Camera, screen, battery, internal storage, and performance were significant features that affected customer's attitudes. |
| Author Keywords |
Opinion mining; Refurbishing; Closed-loop supply chain; Twitter analysis; Customer's behavior; Sentiment analysis |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) |
| EID |
WOS:000652020200048 |
| WoS Category |
Engineering, Environmental; Environmental Sciences |
| Research Area |
Engineering; Environmental Sciences & Ecology |
| PDF |
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