Scalability in Platforms for Local Groceries: An Examination of Indirect Network Economies

• 成果类型: Article
• 署名作者: Wang, Lina; Rabinovich, Elliot; Richards, Timothy J.
• 署名单位: Arizona State University; Arizona State University-Tempe; Arizona State University
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• DOI: 10.1111/poms.13536
• 发表日期: 2022
• 页码: 318-340
• 关键词: platform design and scalability; structural estimation; grocery food; network externalities

摘要

Despite a significant rise in consumer interest in local foods, supply constraints limit access to these products in many markets. Online platforms for local foods may help solve these constraints. However, to our knowledge, there is no empirical research on the economic viability of these platforms. We study this problem by analyzing a two-sided platform subject to indirect network effects. If present, these effects will create a virtuous cycle where consumers' demand for products sold through the platform rises in the number of vendors and suppliers' demand for product distribution through the platform increases in consumer demand. In the case of our study's platform, analyses reveal the existence of indirect network effects, as consumers prefer a variety of local vendors and vendors derive greater surplus from greater consumer demand. Therefore, platforms like the one we analyze may serve as viable alternatives for the commercialization of local foods, and could provide greater access to these products. Importantly, however, our analysis also reveals the existence of not only nonlinearities in the strength of indirect network effects, but also non-monotonic effects. Non-monotonicity derives from consumers' attraction to the platform marginally decreasing in the number of local vendors and from the existence of marginally increasing costs as more of these vendors join in. As a result, indirect network economies are subject to a cap imposed by the number of vendors participating in these platforms. Through counterfactual simulations, we evaluate the magnitude of this constraint and offer recommendations on how to minimize its impact.