Elliott, M.; Elliott, L.; Sluis, E.V. A Predictive Analytics Understanding of Cooperative Membership Heterogeneity and Sustainability. Sustainability2018, 10, 2048.
Elliott, M.; Elliott, L.; Sluis, E.V. A Predictive Analytics Understanding of Cooperative Membership Heterogeneity and Sustainability. Sustainability 2018, 10, 2048.
Elliott, M.; Elliott, L.; Sluis, E.V. A Predictive Analytics Understanding of Cooperative Membership Heterogeneity and Sustainability. Sustainability2018, 10, 2048.
Elliott, M.; Elliott, L.; Sluis, E.V. A Predictive Analytics Understanding of Cooperative Membership Heterogeneity and Sustainability. Sustainability 2018, 10, 2048.
Abstract
The effects of heterogeneity of cooperative membership on cooperative and collective action sustainability has been previously discussed. However, despite the importance of membership heterogeneity in recent theoretical frameworks, empirical examinations have been limited. We determine the effect of changes to cooperative member heterogeneity on cooperative sustainability and discuss changes to heterogeneity overtime that can advance our understanding to cooperative sustainability long-term. This study uses USDA Agricultural Management Resource Survey data, coupled with USDA-Rural Development cooperative financial data at the state level, to quantify effects of cooperative member heterogeneity to sustainability of U.S. farmer cooperatives. We use random forest regression to interpret the significance of heterogeneity with cooperative sustainability at an aggregate level. The findings of this empirical study narrowly reconciles the theoretical understanding of the emergence of intra-cooperative issues while providing consistent empirical evidence and expectations for the sustainability of cooperatives in the near term.
Keywords
cooperatives; membership heterogeneity; random forest; collective action
Subject
Business, Economics and Management, Economics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
