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Analysing organic food buyers' perceptions with Bayesian networks: a case study in Turkey

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  • E. Cene
  • F. Karaman

Abstract

Bayesian network (BN) is an efficient graphical method that uses directed acyclic graphs (DAG) to provide information about a set of data. BNs consist of nodes and arcs (or edges) where nodes represent variables and arcs represent relations and influences between nodes. Interest in organic food has been increasing in the world during the last decade. The same trend is also valid in Turkey. Although there are numerous studies that deal with customer perception of organic food and customer characteristics, none of them used BNs. Thus, this study, which shows a new application area of BNs, aims to reveal the perception and characteristics of organic food buyers. In this work, a survey is designed and applied in seven different organic bazaars in Turkey. Afterwards, BNs are constructed with the data gathered from 611 organic food consumers. The findings match with the previous studies as factors such as health, environmental factors, food availability, product price, consumers' income and trust to organization are found to influence consumers effectively.

Suggested Citation

  • E. Cene & F. Karaman, 2015. "Analysing organic food buyers' perceptions with Bayesian networks: a case study in Turkey," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(7), pages 1572-1590, July.
    • Handle: RePEc:taf:japsta:v:42:y:2015:i:7:p:1572-1590
    DOI: 10.1080/02664763.2014.1001331
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    References listed on IDEAS

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    2. Rizwan Abbas & Gehad Abdullah Amran & Irshad Hussain & Shengjun Ma, 2022. "A Soft Computing View for the Scientific Categorization of Vegetable Supply Chain Issues," Logistics, MDPI, vol. 6(3), pages 1-17, June.

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