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Purpose -The purpose of this paper is to investigate the effects of perceived product characteristics (i.e. involvement, product type, and switching cost) and consumer value consciousness on private brand purchase intent. Design/methodology/approach -A college student sample was surveyed to measure product characteristic perceptions for six product categories and to evaluate private brand purchase intent. Analysis of covariance was conducted for hypothesis testing. Findings -Support existed for the significant effects of all three product characteristics on the intent to purchase private brands. A moderating effect by value consciousness on the product type (search versus experience) emerged. Practical implications -It is critical that retailers identify appropriate product categories as they develop private brands. Private brand marketing strategies should be designed to reduce the level of product involvement and switching cost, and to increase consumer perception of search properties. Originality/value -The research is one of the few studies to test the effects of product characteristics extensively and to provide related marketing implications.

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A novel approach to predict surface roughness in machining operations using fuzzy set theory AbstractThe increase of consumer needs for quality metal cutting related products with more precise tolerances and better product surface roughness has driven the metal cutting industry to continuously improve quality control of metal cutting processes. In this paper, two different approaches are discussed. First, design of experiments (DOE) is used to determine the significant factors and then fuzzy logic approach is presented for the prediction of surface roughness. The data used for the training and checking the fuzzy logic performance is derived from the experiments conducted on a CNC milling machine. In order to obtain better surface roughness, the proper sets of cutting parameters are determined before the process takes place. The factors considered for DOE in the experiment were the depth of cut, feed rate per tooth, cutting speed, tool nose radius, the use of cutting fluid and the three components of the cutting force. Finally the significant factors were used as input factors for fuzzy logic mechanism and surface roughness is predicted with empirical formula developed. Test results show good agreement between the actual process output and the predicted surface roughness.