
	Science International
	Year: 2014 \| Volume: 2 \| Issue: 3 \| Page No.: 64-71 DOI: 10.17311/sciintl.2014.64.71

Consumer Behavioural Buying Patterns on the Demand for Detergents Using Hierarchically Multiple Polynomial Regression Model

H.J. Zainodin, Noraini Abdullah and G. Khuneswari

Abstract: Background: Market studies on consumer preferences on product items had shown that consumer’s behavioural patterns and intentions are sources of business profit level. In the advent wave of global businesses, the behavioural buying patterns of consumers have to be studied and analysed. Hence, this research illustrated the procedures in getting the best polynomial regression model of the consumer buying patterns on the demand for detergent that had included interaction variables. Methods: The hierarchically multiple polynomial regression models involved were up to the third-order polynomial and all the possible models were also considered. The possible models were reduced to several selected models using progressive removal of multicollinearity variables and elimination of insignificant variables. To enhance the understanding of the whole concept in this study, multiple polynomial regressions with eight selection criteria (8SC) had been explored and presented in the process of getting the best model from a set of selected models. Results: A numerical illustration on the demand of detergent had been included to get a clear picture of the process in getting the best polynomial order model. There were two single independent variables: the "price difference” between the price offered by the enterprise and the average industry price of competitors’ similar detergents (in US$) and advertising expenditure (in US$). Conclusion: In conclusion, the best cubic model was obtained where the parameters involved in the model were estimated using ordinary least square method.

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How to cite this article:

H.J. Zainodin, Noraini Abdullah and G. Khuneswari, 2014. Consumer Behavioural Buying Patterns on the Demand for Detergents Using Hierarchically Multiple Polynomial Regression Model. Science International, 2: 64-71.

DOI: 10.17311/sciintl.2014.64.71

URL: https://scialert.net/abstract/?doi=sciintl.2014.64.71

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