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

Der-Chiang Li, Yao San Lin, Yu Cheng Huang

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)2525-2533
Number of pages9
JournalExpert Systems With Applications
Volume36
Issue number2 PART 1
DOIs
Publication statusPublished - 2009 Mar 1

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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