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Purpose -This paper aims to investigate the satisfaction of dealers with their suppliers in the agricultural machinery sector. Design/methodology/approach -A dummy approach of the three-factor model is used to detect the dimensions that influence the overall satisfaction of agricultural machinery dealers. The model considers satisfiers, dissatisfiers and so-called performance factors that might lead to both satisfaction and dissatisfaction. Findings -Two dissatisfiers, after-sales and service methods and relationship with supplier, are detected. Furthermore, there is one satisfier, competitive outlook, and one performance factor, the product program.Research limitations/implications -The dummy approach detects the three factors implicitly. A Kano-questionnaire might be helpful to confirm the results. Practical implications -Producers should first fulfill the factors that have the highest negative impact: product program, followed by after-sales and service methods and relationship with supplier. After reaching a specific level within these factors, producers could seek to increase their dealers' satisfaction with the two factors, product program and competitive outlook. The product program thus represents the key factor for producers seeking to both decrease dissatisfaction and increase satisfaction. Originality/value -While different approaches of the three-factor model are used along with customer satisfaction, this paper is the first to detect different factors of dealer satisfaction in the agricultural machinery sector.

This article analyses dealer satisfaction data in the agricultural technology market in Germany. The dealers could rate their suppliers in the 'overall satisfaction' and in 38 questions which can be summarized in 8 dimensions. An ordinal regression model which is also known as the proportional odds model is used to analyse the ordinal scaled rating of the dealers. The ordinal regression model is a well examined method in econometric theory, but many authors prefer using a linear regression model due to better interpretation, even the assumptions of a linear regression do not fit the data. Since the estimated coefficients of an ordinal regression model can not be properly interpreted we show other methods for a better insight of the relationship of the dealer satisfaction and the influencing variables. These methods are easy to use and it is recommended to list some of them in empirical papers.

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