| Title |
The Optimal Product-Line Design And Incentive Mechanism In A Supply Chain With Customer Environmental Awareness |
| ID_Doc |
18963 |
| Authors |
Li, ZT; Zhang, CH; Kong, W; Lyu, RX |
| Title |
The Optimal Product-Line Design And Incentive Mechanism In A Supply Chain With Customer Environmental Awareness |
| Year |
2023 |
| Published |
Journal Of Industrial And Management Optimization, 19.0, 1 |
| DOI |
10.3934/jimo.2021204 |
| Abstract |
Due to the increasing awareness of sustainable development, the manufacturer's product-line design gets wide attention. Nowadays, the traditional manufacturer that produces non-green products is considering whether to introduce upgraded green products. This paper studies the manufacturer's optimal product-line design considering the quality difference between non green and green products. Besides, our model also investigates the difference in unit production cost, green research and development (R&D) investment, and market segmentation. The results show that, from the manufacturer's perspective, producing green products is a better choice when non-green products are of low quality. In addition, the retailer is always inclined to sell green products. Further, the consumers' preference for non-green and green products is divided. And the consumer surplus under different product-line designs is analysed. Finally, two contracts are proposed and compared to encourage the manufacturer to produce green products. |
| Author Keywords |
Product-line design; Green product; Quality difference; Market seg-mentation; Consumer environmental awareness; Coordination |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000722198700001 |
| WoS Category |
Engineering, Multidisciplinary; Operations Research & Management Science; Mathematics, Interdisciplinary Applications |
| Research Area |
Engineering; Operations Research & Management Science; Mathematics |
| PDF |
https://www.aimsciences.org/data/article/export-pdf?id=729192d8-7aef-4e9f-a5dc-f7f3249a6003
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