Browsing by Author "Yang, Ya-Lan"

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4G Mobile Phone Consumer Preference Predictions by Using the Rough Set Theory and Flow Graphs
(2010)
Huang, Chi-Yo
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Yang, Ya-Lan
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Tzeng, Gwo-Hshiung
;
Cheng, Shih-Tsung
;
Lee, Hong-Yuh
At the moment, when mobile phone users are demanding more handset features as well as broader bandwidth, the fourth generation (4G) wireless telecommunication standard is emerging. However, how to define appropriate handset features toward various market segmentations to fulfill customers\' needs and minimize the manufacturing cost has become one of the most important issues for the 4G handset manufacturers. Thus, a rule based consumer behavior forecast mechanism will be very helpful for marketers and designers of the handset manufacturers to understand and realize. 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 next generation handsets. Therefore, this research intends to define a Rough Set Theory (RST) based forecast mechanism for the 4G handset feature definitions. Possible handset features will first be summarized by literature reviews. After that, rules of consumers\' preferences toward the 4G handsets will be summarized by the RST. To analyze the data and uncover the knowledge inside the rules further, the flow graph will further be introduced for analyzing the information flow distribution. An empirical study on Taiwanese mobile phone users will be leveraged for verifying the feasibility and demonstrate the usability of the proposed forecast mechanism. Meanwhile, the proposed consumer behavior forecast mechanism can be leveraged on defining features of other high technology products/services.
Derivations of Factors Influencing Segmental Consumer Behaviors Using the RST Combined with Flow Graph and FCA
(2010)
Huang, Chi-Yo
;
Yang, Ya-Lan
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Tzeng, Gwo-Hshiung
;
Yu, Hsiao-Cheng
;
Lee, Hong-Yuh
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.