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.
ASJC Scopus subject areas
- 決策科學 (全部)