Toward digital community economies: California’s alternative food networks navigating market digitalization
Sasha Pesci & Ryan E. Galt
Abstract
This paper explores how farmers in California who sell through direct market channels (e.g. farmers markets, community-supported agriculture) experience and respond to market digitalization trends. Consumers increasingly use the internet to find information about or buy products, a trend that accelerated with the COVID-19 pandemic. As a result, online information and communication technologies are becoming prerequisites for participating in many parts of market economies, including much of the agri-food sector, particularly in California. Direct market farmers increasingly use online sales and marketing tools, and the number of sales platforms for direct market farms is growing. This paper reports findings based on 28 semi-structured interviews related to farmers’ motivations to use online sales and marketing, the barriers they experience in accessing these tools, and their attitudes toward market digitalization. We draw from critical agrarian studies and community economies literature to analyze how market changes in capitalism impact and change agriculture, even in systems that seek to avoid or limit their relationship with the pressures of capitalism. Through a farmer-centered and community-engaged research approach, we examine the extent to which farmers’ social and environmental values can buffer capitalism’s pressures to adopt new technologies. We found that while some farmers perceive online tools as eroding social and environmental connections, others perceive them as supporting these connections. Findings from this study highlight the importance of critically examining the opportunities and tensions of market digitalization for Alternative Food Networks.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.