Evaluating the total costs of purchasing via probabilistic and fuzzy reasoning

Transaction costs analysis is concerned with ways of aligning appropriate governance modes with the attributes of economic transactions. Nowadays transaction costs are universally accepted, despite the difficulty in measuring and quantifying them. Starting from the customary definition of transaction costs, this paper proposes a model for the buyer/seller relationship, focusing on the uncertainty characterizing the exchange and the connected costs. In particular, according to a well-known classification, the transaction costs connected to the purchasing phase are divided in ex ante (drafting and negotiating agreements) and ex post (monitoring and enforcing agreements) costs. More precisely, we propose to employ appropriate deterministic models for evaluating ex ante costs and suitable statistical distributions for ex post costs. Obviously, both such costs categories require the quantification of several parameters related to the buyer operating the transaction and to the uncertainty characterizing the buyer/seller relationship. Hence, in order to correctly evaluate the buyer behaviour, a fuzzy logic inference system is designed for synthesizing, starting from expert judgements, the required data to the transaction costs model. The reported simulation experiments show the effectiveness of the proposed model in estimating the transaction costs and total costs associated with a generic transaction.