Modelling ragpickers' productivity at the bottom of the pyramid: the use of artificial neural networks (ANNs). Issue 4 (8th March 2022) - Johnson, Neil; Prasad, Sameer; Vahedian, Amin; Altay, Nezih; Jain, Ashish
Abstract : Purpose: In this research, the authors apply artificial neural networks (ANNs) to uncover non-linear relationships among factors that influence the productivity of ragpickers in the Indian context. Design/methodology/approach: A broad long-term action research program provides a means to shape the research question and posit relevant factors, whereas ANNs capture the true underlying non-linear relationships. ANN models the relationships between four independent variables and three forms of waste value chains without assuming any distributional forms. The authors apply bootstrapping in conjunction with ANNs. Findings: The authors identify four elements that influence ragpickers' productivity: receptiveness to non-governmental organizations, literacy, the deployment of proper equipment/technology and group size. Research limitations/implications: This study provides a unique way to analyze bottom of the pyramid (BoP) operations via ANNs. Social implications: This study provides a road map to help ragpickers in India raise incomes while simultaneously improving recycling rates. Originality/value: This research is grounded in the stakeholder resource-based view and the network–individual–resource model. It generalizes these theories to the informal waste value chain at BoP communities.