Determination of asymmetric information in multistage agricultural Supply Chains: a sequence of research activity

Abstract

Due to the increasing globalization and the resulting emergence of new market participants, the issue of information asymmetries is of immense importance. Based on globalization, increasingly complex network supply chains are emerging in which information transparency between the actors is insufficiently pronounced. The occurrence of information asymmetry (IA) is particularly pronounced in agricultural supply chains, where different types and sizes of actors, such as small farmers, large producers, or small retailers etc., are involved in the supply chain. The insufficient information transparency in the food value chain, is the result of IA, which leads to mistrust between the actors involved and ultimately to opportunistic behavior among the actors. In this regard, it was of utmost necessity to identify, define and reduce the IA in food supply chains between the actors. The aim of the research project will be to extend the current state of knowledge regarding the topic of information asymmetries by defining variables influencing asymmetric information and calculating information asymmetries. To be able to achieve the goal of the research project, quantitative (e.g., surveys) and qualitative (e.g., expert interviews) empirical methods were used. Prior to this, an extensive literature review was undertaken to identify possible variables influencing asymmetric information, which also served as a basis for the quantitative and qualitative analysis in which the entire supply chain, starting from the seed producer to the end customer, was considered. In the course of this, "neutral actors" such as consulting companies, agricultural cooperatives and food networks were also consulted. They were able to provide extensive insight into possible variables influencing information asymmetry. Through the presentation of the research gap as well as the definition of these variables a comprehensive influencing variables model could finally be created. Furthermore, essential future research actions such as the explorative investigation of variables influencing information asymmetry and a possible IA calculation method, which could be useful, were roughly outlined. In conclusion the topic of information asymmetries in food value chains is highly complex, but this research project allows us to structure IA and make it more informative (or: accessible) for science.