Using Multiple Correspondence Analysis to Evaluate Selected Aspects of Behaviour of Consumers Purchasing Local Food Products
The paper compares selected aspects of behaviour of consumers when purchasing local food products in the Czech Republic or food products labelled as local or regional using correspondence analysis vis‑ŕ‑vis customer gender. Its purpose is to identify aspects which each group of the consumers (that i...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Mendel University Press
2017-01-01
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| Series: | Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis |
| Subjects: | |
| Online Access: | https://acta.mendelu.cz/65/6/2083/ |
| Summary: | The paper compares selected aspects of behaviour of consumers when purchasing local food products in the Czech Republic or food products labelled as local or regional using correspondence analysis vis‑ŕ‑vis customer gender. Its purpose is to identify aspects which each group of the consumers (that is men vs. women) consider important when purchasing local food products. Information about the preferences of customers purchasing local food is important for marketing activities of retailers and producers. Data from 269 respondents have been processed in total according to gender and purchasing frequency. Correspondence analysis was used in order to evaluate consumer preferences and to identify their behaviour. MCA is an exploratory multivariate statistical analysis that allows investigation of several qualitative parameters. Appearance and information on the packaging, qualitative parameters (visually declared), anticipated health benefits and declared nutritional values were the focus of the research. This paper is significant in terms of using sign schemes, correlation and correspondence analysis, therefore the analysis demonstrates differences in these aspects between men’s and women’s behaviour when purchasing local food products inter alia, according to their purchasing frequency, which previous papers have not considered while using multivariate statistical methods. |
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| ISSN: | 1211-8516 2464-8310 |