Framework for agricultural e-trading platform adoption using neural networks

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The research has been conducted in two phases—identification of predictors based on a scholarly articles’ review and generating the predictive function between the identified predictors and adoption using the multilayer perceptron neural networks method. A case study of the electronic national agriculture market platform is undertaken to understand the predictors in the context of a real project. The study has revealed significant predictors of priority (high to low) as fast transaction cycles, higher prices, easier to use, infrastructure availability, trust, social influence, low transaction costs and customer care. Post-adoption, the user expects to get benefits like transparency, quick trade settlement, reduced transaction cost, expanding the market reach, and increase in the product price realized by the farmer. The improvement in the critical predictors of the electronic trading platform may help the de-facto national initiative to succeed. In new markets, the prediction framework using the neural network may be used to identify users with the propensity to adopt e-commerce or digital initiatives in the agricultural sector and proactively approach them to get a high number of transactions—an essential ingredient of success.

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