A survey with 247 customers was carried out in four german horticultural shops to identify the relationship between plant signs and consumer preferences. Objective were poinsettia plants. Approximatly 50 to 60 percent of the variance could be explained by the plant signs included into regression analysis and 70% with the two main components of a principal component analysis. For each of the shops where the survey took place different plant signs were identified as relevant. The results show that it is not possible to explain customer decisions by using a simple additive approach where the final decision is seen as the result of the addition of partial decisions. But the combination of principal component analysis and regression analysis may help to show which plants signs are most important for growers to satisfy customers needs. Also it seems to be neccesary to record the customer rankings not only as scalar values. Confrontation of PCA-results with the results of multiple regression points out that using multiple regression has limited validity in this kind of experiments if there are variables with large ranges and intercorrelations.
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