Derivations of factors influencing segmental consumer behaviors using the RST combined with flow graph and FCA

Chi Yo Huang, Ya Lan Yang, Gwo Hshiung Tzeng, Hsiao Cheng Yu, Hong Yuh Lee, Shih Tsunsg Cheng, Sang Yeng Lo

Research output: Contribution to journalArticlepeer-review


Consumer behavior analysis and prediction are both important for marketers in general and high technology marketers in special. At the moment of fast evolutions of high technology products, precise predictions of consumer behaviors can serve as the foundation of product/specification definitions. Traditionally, qualitative approaches (e.g. brain storming) or multivariate statistical (e.g. principal component analysis, factor analysis, etc.) were applied widely on consumer behavior analysis. However, the qualitative methods can be objective while the statistical approaches could be hard to be manipulated. Thus, a rule-based prediction method can be very helpful for analyzing and predicting consumer behavior. Moreover, precise prediction rules for consumer behavior being derived by the forecast mechanism can be very useful for marketers and designers to define the features of the products. Therefore, this research intends to define a Cluster Analysis (CA), Rough Set Theory (RST), flow graph (FG) and formal concept analysis (FCA) based forecast mechanism for predicting segmental consumer behavior. An empirical study on 124 Taiwanese 4G handset users was leveraged for verifying the feasibility of the proposed forecast mechanism. The empirical study results demonstrate the feasibility of this proposed framework. Meanwhile, the proposed consumer behavior forecast mechanism can be leveraged on defining features of other high technology products/services.

Original languageEnglish
Pages (from-to)687-698
Number of pages12
JournalSmart Innovation, Systems and Technologies
Publication statusPublished - 2010


  • 4G
  • Consumer behavior
  • Flow graph (FG)
  • Formal concept analysis (FCA)
  • Mobile phone
  • Rough set theory (RST)

ASJC Scopus subject areas

  • General Decision Sciences
  • General Computer Science


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