Determination of Consumer Perceptions of Eco-Friendly Food Products Using Unsupervised Machine Learning

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dc.authoridCAKMAKCI, CIHAN/0000-0001-6512-9268
dc.authoridHURMA, HARUN/0000-0003-1845-3940
dc.authorid, Yusuf Cakmakci/0000-0002-5136-9102
dc.contributor.authorCakmakci, Yusuf
dc.contributor.authorHurma, Harun
dc.contributor.authorCakmakci, Cihan
dc.date.accessioned2024-10-29T17:59:14Z
dc.date.available2024-10-29T17:59:14Z
dc.date.issued2024
dc.departmentTekirdağ Namık Kemal Üniversitesi
dc.description.abstractThis study aims to comparatively determine the consumer perception of food products marketed under ecologically friendly concepts (organic food, good agriculture, and natural production) and food sold directly by farmers, conventional food, and farmer cooperative branded food. For this purpose, a face-to-face survey was conducted with 171 identified consumers. R program was used to perform all of the analyses. Machine learning methods such as Logistic Regression (LR), Correspondence Analysis (CA), and Principal Component Analysis (PCA) are used for determining consumer perception from obtained data. Descriptive statistics results showed that 51.5 percent of those polled were male and 48.5 percent were female. It found that the mean age of the consumers was joined to the survey was 36.4. According to the LR findings, consumer socioeconomic characteristics have a considerable impact on the purchase of various foods (such as organic labeled foods, good agricultural practices foods, producer cooperative branded foods, etc.). It has been discovered as the result of the PCA, people perceived organic branded food and good agricultural practices foods, which are healthier, more flavorful, and more trustworthy than other food. however, it has been discovered that they believe the costs of these types of food are expensive and that they are difficult to obtain. On the other hand, they perceive the pricing of farmer cooperative branded foods and food sold directly by the farmer to be lower. Furthermore, it was observed in CA findings that there was a correlation between these various food groups and purchase locations. While products sold directly by farmers are mostly purchased from public markets, they prefer grocery stores and supermarkets when purchasing foods with good agricultural practices and natural labeled (from the markets). When seen from this perspective, it is possible to conclude that ecologically friendly food and other food products are regarded differently by customers based on product characteristics. The use of marketing techniques that create a positive perspective in terms of affordability and accessibility and the development of policies and production techniques that boost consumers' current perceptions of these items are considered will promote the consumption of these products.
dc.identifier.doi10.33462/jotaf.1319077
dc.identifier.endpage647
dc.identifier.issn1302-7050
dc.identifier.issn2146-5894
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85195695125
dc.identifier.scopusqualityQ3
dc.identifier.startpage634
dc.identifier.trdizinid#BAŞV!
dc.identifier.urihttps://doi.org/10.33462/jotaf.1319077
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1238085
dc.identifier.urihttps://hdl.handle.net/20.500.11776/14672
dc.identifier.volume21
dc.identifier.wosWOS:001243820700006
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.publisherUniv Namik Kemal
dc.relation.ispartofJournal of Tekirdag Agriculture Faculty-Tekirdag Ziraat Fakultesi Dergisi
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectEco-friendly foods
dc.subjectConsumer perceptions
dc.subjectMachine learning
dc.subjectProduct appreciation
dc.subjectPurchasing decision
dc.titleDetermination of Consumer Perceptions of Eco-Friendly Food Products Using Unsupervised Machine Learning
dc.title.alternativeDetermination of Consumer Perceptions of Eco-Friendly Food Products Using Unsupervised Machine Learning
dc.typeArticle

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