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Enabling socially responsible operations: A decision-making model for a firm contracting with decision-biased smallholders
Dutta N.,
Published in Springer
Volume: 320
Issue: 1
Pages: 509 - 533
Small farmers in emerging economies face numerous constraints, including financial distress and limited access to profitable markets. Thus, procurement contracts having provisions for resource inputs are essential in linking them to the mainstream agri-supply chains. Consequently, we frame a decision-making model for a risk-neutral firm offering advance payment contracts to such credit-constrained smallholders. The firm allows them to commit their supply quantities and pays a fraction of the per-unit guaranteed price for the crop as advance. Behaviorally, the decision-biased farmers are loss-averse and hyperbolic discounters. They have a subjective perception of crop yield based on the (timing of) advance. Our model establishes a criterion for the firm to shortlist the farmers for contracting. Subsequently, it predicts their commitment quantities under limited information on their behavioral parameters using the Prospect Theory framework. Under the model assumptions, we calculate the upper and lower bounds on their commitment quantities, thereby establishing the limits on the firm’s production quantity and profitability. Lastly, our model determines the optimal timing for paying the advances to the eligible farmers using two different strategies. The first favors a firm’s profit maximization objective, while the second maximizes its marginal cost savings from contracting over spot buying. Through analytical and numerical calculations, we establish that the latter approach significantly raises the farmers’ utilities without severely affecting the firm’s profit. Thus, making it fit for a social entrepreneur on a mission to create a socially responsible and economically viable procurement strategy. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
About the journal
JournalAnnals of Operations Research
Open AccessNo