Incentives for the Adoption of Blockchains in Ontario's Agri-Food Sector: The Role of Risk and Information

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Westover, Jamieson
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University of Guelph

There is a growing interest in better understanding the opportunities, benefits and applications of blockchains - i.e., distributed ledger technologies - in the Canadian agri-food sector to enhance the transparency and traceability of agricultural supply chains. This thesis uses a Best-Worst Scaling approach to elicit producers’ relative raking of incentives that may promote the adoption of blockchains. A latent class cluster analysis is used to identify groups of respondents that share similar preferences for adoption incentives and to examine the influence of risk and information on producers’ relative ranking of adoption incentives. The results show that producers are heterogeneous in their preferences for adoption incentives. Producers that prefer adoption incentives external to the farm (e.g., government subsidies) are more risk-averse than producers that prefer internal adoption incentives (e.g., farm differentiation). The thesis concludes by discussing the implications of these findings for efforts to better understand blockchain adoption incentives more generally.

blockchains, technology, technology adoption, agricultural economics, digital agriculture, food traceability, agriculture supply chain, best-worst scaling, latent class cluster analysis, agricultural technology, digital farming