摘要：Negative effects of heavy meat consumption have been critically discussed in politics, the public and science for a long time. As there is heterogeneity in consumer behaviour, targeted measures regarding behaviour management can hardly be implemented on the basis of an average consumption levels but should take into account different consumer segments. Therefore, this study performs a segmentation and characterisation of fresh-meat-shoppers based on household panel data provided by the GfK. A cluster analysis was performed based on the average per capita monthly purchasing shares of five different meat types. A multinomial logistic regression was used to characterize the different segments regarding sociodemographic aspects, people’s attitudes towards food and meat shopping, total purchasing intensity and different shopping locations. The authors found a four-cluster solution, identifying a segment of poultry lovers (24 %), a segment of beef, lamb & speciality purchasers (17 %), a segment of mixed product eaters (15 %) and a segment of pork buyers (45 %). Households assigned to the largest cluster of “pork buyers” have an above-average monthly meat purchase while being price-sensitive. Future policy instruments such as meat tax could address this buyer segment in particular, and probably decrease overall meat purchases. However, it should not be neglected that “beef, lamb & speciality purchasers” also have an above-average monthly meat purchase, causing particularly negative environmental effects. As this buyer segment has a rather high income and reports to be less price sensitive, it might be a challenge to influence their purchasing behaviour by taxing meat products.
其他摘要：Negative effects of heavy meat consumption have been critically discussed in politics, the public and science for a long time. As there is heterogeneity in consumer behaviour, targeted measures regarding behaviour management can hardly be implemented on the basis of an average consumption levels but should take into account different consumer segments. Therefore, this study performs a segmentation and characterisation of fresh-meat-shoppers based on household panel data provided by the GfK. A cluster analysis was performed based on the average per capita monthly purchasing shares of five different meat types. A multinomial logistic regression was used to characterize the different segments regarding sociodemographic aspects, people’s attitudes towards food and meat shopping, total purchasing intensity and different shopping locations. The authors found a four-cluster solution, identifying a segment of poultry lovers (24 %), a segment of beef, lamb & speciality purchasers (17 %), a segment of mixed product eaters (15 %) and a segment of pork buyers (45 %). Households assigned to the largest cluster of “pork buyers” have an above-average monthly meat purchase while being price-sensitive. Future policy instruments such as meat tax could address this buyer segment in particular, and probably decrease overall meat purchases. However, it should not be neglected that “beef, lamb & speciality purchasers” also have an above-average monthly meat purchase, causing particularly negative environmental effects. As this buyer segment has a rather high income and reports to be less price sensitive, it might be a challenge to influence their purchasing behaviour by taxing meat products.
