Constructing marketing decision support systems using data diffusion technology: A case study of gas station diversification

Building a decision support system (DSS) using small data sets usually results in uncertain knowledge, likely leading to incorrect decisions and causing a large losses. However, gathering sufficient samples for building a DSS often has significant costs in many cases. To solve this problem, a case study of a particular business decision-making procedure in which only small data sets are available is discussed. The learning accuracy for the modeling phase in the DSS was improved using the mega-trend-diffusion technique, which includes two learning tools: Back-propagation network and Bayesian network. The case study, a business diversification decision for an oil company, shows that the proposed technique contributes to increasing the prediction precision using very limited experience.

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