产品(数学)
投资(军事)
产品策略
业务
投资策略
直觉
产业组织
产品设计
经济
环境经济学
新产品开发
产品管理
营销
微观经济学
利润(经济学)
哲学
几何学
数学
认识论
政治
政治学
法学
作者
Yang Xia,Yang Hui,Hongfu Huang,Siyuan Zhu
标识
DOI:10.1016/j.jclepro.2023.139227
摘要
As environmental consciousness among consumers grows, firms are recognizing the importance of adopting carbon abatement technology as a crucial strategy to meet the rising demand for low-carbon products. Our study specifically examines the influence of a low-carbon investment strategy on a firm’s product line design, aiming to better align with consumer needs. Considering the impact of low-carbon investments and consumer environmental concern on the product line design has not been adequately examined in previous research. To fill this gap in research, our study proposes a model of a firm that designs the product line while determining the price and quality of each product. Our findings indicate that the single-product strategy is strictly inferior to the product line strategy when considering the low-carbon investment. This implies that the conventional wisdom that either the single-product strategy or the product line strategy could be an equilibrium strategy does not hold. Intuitively, the firm’s low-carbon investment in a specific product segment solely impacts the corresponding segment’s product quality and pricing decisions. While this intuition holds for the low-carbon investment strategy solely targeting high-end products, it does not apply when the low-carbon investment strategy applies solely to low-end products. Furthermore, our results show that the firm adopts the low-carbon investment strategy and traditional (without low-carbon investment) strategy when consumer environmental concern is relatively high and low, respectively. Finally, our results show that any of the four strategies (the traditional strategy and the low-carbon investment strategies for only low-end products, only high-end products, and both segments) could be optimal, depending on certain conditions, in the product line scenario.
科研通智能强力驱动
Strongly Powered by AbleSci AI