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

Year 2024, Volume: 21 Issue: 3, 634 - 647, 27.05.2024
https://doi.org/10.33462/jotaf.1319077

Abstract

This 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.

References

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  • Ağır, B. H., Poyraz, N., Yılmaz, H. İ. and Boz, İ. (2014). Organic product perception of consumers: Sample of Kayseri Province. XI. Congress of Agricultural Economics, 3-5 September, Samsun, Türkiye.
  • Aprile, M. C., Caputo, V. and Nayga, R. M. (2016). Consumers’ Preferences and Attitudes Toward Local Food products. Journal of Food Products Marketing, 22(1): 19–42. https://doi.org/10.1080/10454446.2014.949990
  • Argemí-Armengol, I., Villalba, D., Ripoll, G., Teixeira, A. and Álvarez-Rodríguez, J. (2019). Credence cues of pork are more important than consumers’ culinary skills to boost their purchasing intention. Meat Science, 154: 11–21. https://doi.org/10.1016/j.meatsci.2019.04.001
  • Bahsi, N. and Akça, A. (2019). A research on the determination of consumers’ perspectives on organic agricultural products: Case study in Osmaniye and Şanlıurfa Provinces. Journal of Agriculture and Nature, 22(1): 26–34. https://doi.org/10.18016/ksutarimdoga.vi.443228
  • Banytė, J., Brazionienė, L. and Gadeikienė, A. (2010). Ivestigation of green consumer profile: A case of Lithuanian market of eco-friendly food products. Economics and Management, 15: 374–383.
  • Bilgen, İ. (2017). Perceived quality in organic agriculture products: A research on the consumers in Istanbul. The Turkish Online Journal of Design, Art and Communication, 7(4): 678–685. https://doi.org/10.7456/10704100/013
  • Bougherara, D. and Combris, P. (2009). Eco-labelled food products: What are consumers paying for? European Review of Agricultural Economics, 36(3): 321–341. https://doi.org/10.1093/erae/jbp023
  • Bryła, P. (2016). Organic food consumption in Poland: Motives and barriers. Appetite, 105: 737–746. https://doi.org/10.1016/J.APPET.2016.07.012
  • Çakmakçı, Y. and Hurma, H. (2021). The relationship between socio-economic characteristics and environmental awareness levels of consumers and the factors effective in purchasing environmentally friendly food products. Turkish Journal of Agriculture - Food Science and Technology, 9(7): 1297–1303. https://doi.org/10.24925/turjaf.v9i7.1297-1303.4439
  • Curl, C. L., Beresford, S. A. A., Hajat, A., Kaufman, J. D., Moore, K., Nettleton, J. A. and Diez-Roux, A. v. (2013). Associations of organic produce consumption with socioeconomic status and the local food environment: Multi-Ethnic study of atherosclerosis (MESA). PLoS ONE, 8(7):1-8. https://doi.org/10.1371/journal.pone.0069778
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  • De-Groote, H. and Kimenju, S. C. (2008). Comparing consumer preferences for color and nutritional quality in maize: Application of a semi-double-bound logistic model on urban consumers in Kenya. Food Policy, 33(4): 362–370. https://doi.org/10.1016/j.foodpol.2008.02.005
  • Di Franco, G. (2016). Multiple correspondence analysis: one only or several techniques? Quality and Quantity, 50(3): 1299–1315. https://doi.org/10.1007/s11135-015-0206-0
  • Díaz-Pérez, M., Carreño-Ortega, Á., Salinas-Andújar, J. A. and Callejón-Ferre, Á. J. (2019). Application of logistic regression models for the marketability of cucumber cultivars. Agronomy, 9(1): 17. https://doi.org/10.3390/agronomy9010017
  • Doğan, G. H., and Gürel, E. (2016). The determination of attitudes and behaviors in organic product consumption of consumers living of central district of Kırşehir Province. Journal of Agricultural Faculty of Gaziosmanpasa University, 33(2016–2): 147–156. https://doi.org/10.13002/jafag1033
  • Echeverría, R., Montenegro, A. B., Albarrán, E. S. and Charry, L. (2022). Consumer willingness to pay for cheese with a social sustainability attribute. Ciência Rural, 52(5):1-8. https://doi.org/10.1590/0103-8478cr20210281
  • Eldesouky, A., Mesias, F. J. and Escribano, M. (2020). Perception of Spanish consumers towards environmentally friendly labelling in food. International Journal of Consumer Studies, 44(1): 64–76. https://doi.org/10.1111/ijcs.12546
  • Fahmida, A., Palash, M. S., Alam Monirul, G. M. and Amin, M. R. Md. (2020). Young consumers’ eco-friendly food purchasing consciousness-behaviorgap in Bangladesh. The Bangladesh Journal of Agricultural Economics, 41(2): 29–44. https://www.researchgate.net/publication/ 348658012
  • Fox, J. and Monette, G. (1992). Generalized collinearity diagnostics. Journal of the American Statistical Association, 87(417):178–183.
  • Fox, J. and Weisberg, S. (2019). An R Companionto Applied Regression Third Edition. SAGE Publications Inc. https://toc.library.ethz.ch/objects/pdf03/z01_978-1-5443-3647-3_01.pdf.
  • Greenacre, M. (2015). Correspondence Analysis. In J. D. Wright (Ed.), International encyclopedia of the social & behavioral sciences (2nd ed., pp. 1-5). Elsevier. https://doi.org/10.1016/B978-0-08-097086-8.42005-2
  • Husson, F., Le, S. and Pagès, J. (2017). Exploratory multivariate analysis by example using R. CRC Press.
  • IBM (2022). Machine Learning. https://www.ibm.com/cloud/learn/machine-learning
  • İnan, R., Bekar, A. and Urlu, H. (2021). An assessment of consumers of organic food purchase behavior and attitudes. Journal of Tourism and Gastronomy Studies, 9(1): 220–235. https://doi.org/10.21325/jotags.2021.786
  • Ingwersen, E. W., Stam, W. T., Meijs, B. J. V., Roor, J., Besselink, M. G., Groot Koerkamp, B., de Hingh, I. H. J. T., van Santvoort, H. C., Stommel, M. W. J. and Daams, F. (2023). Machine learning versus logistic regression for the prediction of complications after pancreatoduodenectomy. Surgery, 174(3): 435-440. https://doi.org/10.1016/J.SURG.2023.03.012
  • Jollife, I. T. and Cadima, J. (2016). Principal component analysis: A review and recent developments. In Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences (Vol. 374, Issue 2065). Royal Society of London. https://doi.org/10.1098/rsta.2015.0202
  • Kashif, U., Hong, C., Naseem, S., Khan, W. A. and Akram, M. W. (2020). Consumer preferences toward organic food and the moderating role of knowledge: A case of Pakistan and Malaysia. Ciencia Rural, 50(5): 1–13. https://doi.org/10.1590/0103-8478cr20190842
  • Kassambara, A. and Mundt, F. (2020). Factoextra: Extract and Visualize the Results of Multivariate Data Analyses. R package version 1.0.7. https://CRAN.R-project.org/package=factoextra
  • Klopcic, M., Kuipers, A. and Hocquette, J. F. (2012). Consumer attitudes to food quality products: Emphasis on Southern Europe (EAAP publication no. 133). Wageningen Academic Publishers. https://edepot.wur.nl/242193
  • Nam, K., Lim, H. and Ahn, B. il. (2020). Analysis of consumer preference for milk produced through sustainable farming: The case of mountainous dairy farming. Sustainability, 12(7): 1-15. https://doi.org/10.3390/su12073039
  • Nascimento, A. G. M., Toledo, B. S., Guimarães, J. T., Ramos, G. L. P. A., da Cunha, D. T., Pimentel, T. C., Cruz, A. G., Freitas, M. Q., Esmerino, E. A. and Mársico, E. T. (2022). The impact of packaging design on the perceived quality of honey by Brazilian consumers. Food Research International, 151: 110887. https://doi.org/10.1016/J.FOODRES.2021.110887
  • Newbold, P. (1995). Statistics for Business and Economics. Prentice-Hall International. Onianwa, O., Wheelock, G. and Mojica, M. (2005). An analysis of the determinants of farmer-to-consumer direct-market shoppers. Journal of Food Distribution Research, 36(1): 1-5.
  • Oraman, Y., Unakıtan, G., Yılmaz, E. and Başaran., B. (2011). Analysis of the factors affecting consumer’s some traditional food products preferences by multidimensional scaling method. Journal of Tekirdag Agricultural Faculty, 8 (1): 33-40. https:// dergipark.org.tr/tr/pub/jotaf/issue/19043/201415
  • Petrescu, D. C., Vermeir, I. and Petrescu-Mag, R. M. (2020). Consumer understanding of food quality, healthiness, and environmental impact: A cross-national perspective. International Journal of Environmental Research and Public Health, 17(1): 1-20. https://doi.org/10.3390/ijerph17010169
  • Polimeni, J. M., Iorgulescu, R. I. and Mihnea, A. (2018). Understanding consumer motivations for buying sustainable agricultural products at Romanian farmers markets. Journal of Cleaner Production, 184: 586–597. https://doi.org/10.1016/J.JCLEPRO.2018.02.241
  • R Core Team. (2020). A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Vienna, Austria. https://www.r-project.org/
  • Ramya, N. and Ali, S. M. (2016). Factors affecting consumer buying behavior. International Journal of Applied Research, 2(10): 76–80. www.allresearchjournal.com
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Çevre Dostu Gıda Ürünlerine Yönelik Tüketici Algısının Denetimsiz Makine Öğrenmesi Kullanılarak Belirlenmesi

Year 2024, Volume: 21 Issue: 3, 634 - 647, 27.05.2024
https://doi.org/10.33462/jotaf.1319077

Abstract

Bu çalışma, çevre dostu konseptler (organik gıda, iyi tarım ve doğal üretim) altında pazarlanan gıda ürünleri ile doğrudan çiftçiler tarafından satılan gıdalar, konvansiyonel gıdalar ve çiftçi kooperatifi markalı gıdalara yönelik tüketici algısını karşılaştırmalı olarak belirlemeyi amaçlamaktadır. Bu amaçla belirlenen 171 tüketici ile yüz yüze anket yapılmıştır. Tüm analizleri gerçekleştirmek için R programı kullanılmıştır. Elde edilen verilerden tüketici algısının belirlenmesi için Lojistik Regresyon (LR), Yazışma Analizi (CA) ve Temel Bileşenler Analizi (PCA) gibi makine öğrenmesi yöntemleri kullanılmıştır. Tanımlayıcı istatistik sonuçları, ankete katılanların yüzde 51,5'inin erkek ve yüzde 48,5'inin kadın olduğunu gösterdi. Araştırmaya katılan tüketicilerin yaş ortalamasının 36,4 olduğu tespit edildi. LR bulgularına göre, tüketici sosyoekonomik özelliklerinin çeşitli gıdaların (organik etiketli gıdalar, iyi tarım uygulamaları gıdaları, üretici kooperatifi markalı gıdalar vb.) satın alınmasında önemli bir etkisi vardır. PCA sonucunda insanların organik markalı gıdaları ve iyi tarım uygulamaları gıdalarını diğer gıdalardan daha sağlıklı, daha lezzetli ve daha güvenilir olarak algıladıkları saptanmıştır. Ancak bu tür gıdaların maliyetinin pahalı ve elde edilmesinin zor olduğuna inandıkları tespit edilmiştir. Öte yandan, çiftçi kooperatifi markalı gıdaların ve doğrudan çiftçi tarafından satılan gıdaların fiyatını daha düşük olarak algılamaktadırlar. Ayrıca CA bulgularında çeşitli gıda grupları ile satın alma yerleri arasında bağlantı olduğu belirlenmiştir. Çiftçiler tarafından doğrudan satılan ürünler daha çok halk pazarlarından satın alınır iken, iyi tarım uygulamalı gıdalar ve doğal etiketli (marketlerden)gıdalar satın alınırken, daha çok bakkal ve süpermarketleri tercih etmektedirler. Bu açıdan bakıldığında, çevre dostu gıda ve diğer gıda ürünlerinin, ürün özelliklerine göre müşteriler tarafından farklı değerlendirildiği sonucuna varmak mümkündür. Satın alınabilirlik ve erişilebilirlik açısından olumlu bir bakış açısı oluşturan pazarlama tekniklerinin kullanılması ve tüketicilerin bu ürünlere yönelik mevcut algılarını yükselten politikalar ve üretim tekniklerinin geliştirilmesinin bu ürünlerin tüketimini artıracağı düşünülmektedir.

References

  • Abdi, H. and Williams, L. J. (2010). Principal Component Analysis (Issue 2). www.utdallas.edu/.
  • Ağır, B. H., Poyraz, N., Yılmaz, H. İ. and Boz, İ. (2014). Organic product perception of consumers: Sample of Kayseri Province. XI. Congress of Agricultural Economics, 3-5 September, Samsun, Türkiye.
  • Aprile, M. C., Caputo, V. and Nayga, R. M. (2016). Consumers’ Preferences and Attitudes Toward Local Food products. Journal of Food Products Marketing, 22(1): 19–42. https://doi.org/10.1080/10454446.2014.949990
  • Argemí-Armengol, I., Villalba, D., Ripoll, G., Teixeira, A. and Álvarez-Rodríguez, J. (2019). Credence cues of pork are more important than consumers’ culinary skills to boost their purchasing intention. Meat Science, 154: 11–21. https://doi.org/10.1016/j.meatsci.2019.04.001
  • Bahsi, N. and Akça, A. (2019). A research on the determination of consumers’ perspectives on organic agricultural products: Case study in Osmaniye and Şanlıurfa Provinces. Journal of Agriculture and Nature, 22(1): 26–34. https://doi.org/10.18016/ksutarimdoga.vi.443228
  • Banytė, J., Brazionienė, L. and Gadeikienė, A. (2010). Ivestigation of green consumer profile: A case of Lithuanian market of eco-friendly food products. Economics and Management, 15: 374–383.
  • Bilgen, İ. (2017). Perceived quality in organic agriculture products: A research on the consumers in Istanbul. The Turkish Online Journal of Design, Art and Communication, 7(4): 678–685. https://doi.org/10.7456/10704100/013
  • Bougherara, D. and Combris, P. (2009). Eco-labelled food products: What are consumers paying for? European Review of Agricultural Economics, 36(3): 321–341. https://doi.org/10.1093/erae/jbp023
  • Bryła, P. (2016). Organic food consumption in Poland: Motives and barriers. Appetite, 105: 737–746. https://doi.org/10.1016/J.APPET.2016.07.012
  • Çakmakçı, Y. and Hurma, H. (2021). The relationship between socio-economic characteristics and environmental awareness levels of consumers and the factors effective in purchasing environmentally friendly food products. Turkish Journal of Agriculture - Food Science and Technology, 9(7): 1297–1303. https://doi.org/10.24925/turjaf.v9i7.1297-1303.4439
  • Curl, C. L., Beresford, S. A. A., Hajat, A., Kaufman, J. D., Moore, K., Nettleton, J. A. and Diez-Roux, A. v. (2013). Associations of organic produce consumption with socioeconomic status and the local food environment: Multi-Ethnic study of atherosclerosis (MESA). PLoS ONE, 8(7):1-8. https://doi.org/10.1371/journal.pone.0069778
  • Dardak, R. A., Abidin, A. Z. Z. and Kasim Ali, A. (2009). Consumers’ perceptions, consumption and preference on organic product: Malaysian perspective (Persepsi, penggunaan dan kecenderungan pengguna terhadap produk organik: Perspektif rakyat Malaysia). Economic and Technology Management Review, 4:95-107.
  • De-Groote, H. and Kimenju, S. C. (2008). Comparing consumer preferences for color and nutritional quality in maize: Application of a semi-double-bound logistic model on urban consumers in Kenya. Food Policy, 33(4): 362–370. https://doi.org/10.1016/j.foodpol.2008.02.005
  • Di Franco, G. (2016). Multiple correspondence analysis: one only or several techniques? Quality and Quantity, 50(3): 1299–1315. https://doi.org/10.1007/s11135-015-0206-0
  • Díaz-Pérez, M., Carreño-Ortega, Á., Salinas-Andújar, J. A. and Callejón-Ferre, Á. J. (2019). Application of logistic regression models for the marketability of cucumber cultivars. Agronomy, 9(1): 17. https://doi.org/10.3390/agronomy9010017
  • Doğan, G. H., and Gürel, E. (2016). The determination of attitudes and behaviors in organic product consumption of consumers living of central district of Kırşehir Province. Journal of Agricultural Faculty of Gaziosmanpasa University, 33(2016–2): 147–156. https://doi.org/10.13002/jafag1033
  • Echeverría, R., Montenegro, A. B., Albarrán, E. S. and Charry, L. (2022). Consumer willingness to pay for cheese with a social sustainability attribute. Ciência Rural, 52(5):1-8. https://doi.org/10.1590/0103-8478cr20210281
  • Eldesouky, A., Mesias, F. J. and Escribano, M. (2020). Perception of Spanish consumers towards environmentally friendly labelling in food. International Journal of Consumer Studies, 44(1): 64–76. https://doi.org/10.1111/ijcs.12546
  • Fahmida, A., Palash, M. S., Alam Monirul, G. M. and Amin, M. R. Md. (2020). Young consumers’ eco-friendly food purchasing consciousness-behaviorgap in Bangladesh. The Bangladesh Journal of Agricultural Economics, 41(2): 29–44. https://www.researchgate.net/publication/ 348658012
  • Fox, J. and Monette, G. (1992). Generalized collinearity diagnostics. Journal of the American Statistical Association, 87(417):178–183.
  • Fox, J. and Weisberg, S. (2019). An R Companionto Applied Regression Third Edition. SAGE Publications Inc. https://toc.library.ethz.ch/objects/pdf03/z01_978-1-5443-3647-3_01.pdf.
  • Greenacre, M. (2015). Correspondence Analysis. In J. D. Wright (Ed.), International encyclopedia of the social & behavioral sciences (2nd ed., pp. 1-5). Elsevier. https://doi.org/10.1016/B978-0-08-097086-8.42005-2
  • Husson, F., Le, S. and Pagès, J. (2017). Exploratory multivariate analysis by example using R. CRC Press.
  • IBM (2022). Machine Learning. https://www.ibm.com/cloud/learn/machine-learning
  • İnan, R., Bekar, A. and Urlu, H. (2021). An assessment of consumers of organic food purchase behavior and attitudes. Journal of Tourism and Gastronomy Studies, 9(1): 220–235. https://doi.org/10.21325/jotags.2021.786
  • Ingwersen, E. W., Stam, W. T., Meijs, B. J. V., Roor, J., Besselink, M. G., Groot Koerkamp, B., de Hingh, I. H. J. T., van Santvoort, H. C., Stommel, M. W. J. and Daams, F. (2023). Machine learning versus logistic regression for the prediction of complications after pancreatoduodenectomy. Surgery, 174(3): 435-440. https://doi.org/10.1016/J.SURG.2023.03.012
  • Jollife, I. T. and Cadima, J. (2016). Principal component analysis: A review and recent developments. In Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences (Vol. 374, Issue 2065). Royal Society of London. https://doi.org/10.1098/rsta.2015.0202
  • Kashif, U., Hong, C., Naseem, S., Khan, W. A. and Akram, M. W. (2020). Consumer preferences toward organic food and the moderating role of knowledge: A case of Pakistan and Malaysia. Ciencia Rural, 50(5): 1–13. https://doi.org/10.1590/0103-8478cr20190842
  • Kassambara, A. and Mundt, F. (2020). Factoextra: Extract and Visualize the Results of Multivariate Data Analyses. R package version 1.0.7. https://CRAN.R-project.org/package=factoextra
  • Klopcic, M., Kuipers, A. and Hocquette, J. F. (2012). Consumer attitudes to food quality products: Emphasis on Southern Europe (EAAP publication no. 133). Wageningen Academic Publishers. https://edepot.wur.nl/242193
  • Nam, K., Lim, H. and Ahn, B. il. (2020). Analysis of consumer preference for milk produced through sustainable farming: The case of mountainous dairy farming. Sustainability, 12(7): 1-15. https://doi.org/10.3390/su12073039
  • Nascimento, A. G. M., Toledo, B. S., Guimarães, J. T., Ramos, G. L. P. A., da Cunha, D. T., Pimentel, T. C., Cruz, A. G., Freitas, M. Q., Esmerino, E. A. and Mársico, E. T. (2022). The impact of packaging design on the perceived quality of honey by Brazilian consumers. Food Research International, 151: 110887. https://doi.org/10.1016/J.FOODRES.2021.110887
  • Newbold, P. (1995). Statistics for Business and Economics. Prentice-Hall International. Onianwa, O., Wheelock, G. and Mojica, M. (2005). An analysis of the determinants of farmer-to-consumer direct-market shoppers. Journal of Food Distribution Research, 36(1): 1-5.
  • Oraman, Y., Unakıtan, G., Yılmaz, E. and Başaran., B. (2011). Analysis of the factors affecting consumer’s some traditional food products preferences by multidimensional scaling method. Journal of Tekirdag Agricultural Faculty, 8 (1): 33-40. https:// dergipark.org.tr/tr/pub/jotaf/issue/19043/201415
  • Petrescu, D. C., Vermeir, I. and Petrescu-Mag, R. M. (2020). Consumer understanding of food quality, healthiness, and environmental impact: A cross-national perspective. International Journal of Environmental Research and Public Health, 17(1): 1-20. https://doi.org/10.3390/ijerph17010169
  • Polimeni, J. M., Iorgulescu, R. I. and Mihnea, A. (2018). Understanding consumer motivations for buying sustainable agricultural products at Romanian farmers markets. Journal of Cleaner Production, 184: 586–597. https://doi.org/10.1016/J.JCLEPRO.2018.02.241
  • R Core Team. (2020). A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Vienna, Austria. https://www.r-project.org/
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There are 48 citations in total.

Details

Primary Language English
Subjects Marketing in Agricultural Management
Journal Section Articles
Authors

Yusuf Çakmakçı 0000-0002-5136-9102

Harun Hurma 0000-0003-1845-3940

Cihan Çakmakçı 0000-0001-6512-9268

Early Pub Date May 21, 2024
Publication Date May 27, 2024
Submission Date June 23, 2023
Acceptance Date September 13, 2023
Published in Issue Year 2024 Volume: 21 Issue: 3

Cite

APA Çakmakçı, Y., Hurma, H., & Çakmakçı, C. (2024). Determination of Consumer Perceptions of Eco-Friendly Food Products Using Unsupervised Machine Learning. Tekirdağ Ziraat Fakültesi Dergisi, 21(3), 634-647. https://doi.org/10.33462/jotaf.1319077
AMA Çakmakçı Y, Hurma H, Çakmakçı C. Determination of Consumer Perceptions of Eco-Friendly Food Products Using Unsupervised Machine Learning. JOTAF. May 2024;21(3):634-647. doi:10.33462/jotaf.1319077
Chicago Çakmakçı, Yusuf, Harun Hurma, and Cihan Çakmakçı. “Determination of Consumer Perceptions of Eco-Friendly Food Products Using Unsupervised Machine Learning”. Tekirdağ Ziraat Fakültesi Dergisi 21, no. 3 (May 2024): 634-47. https://doi.org/10.33462/jotaf.1319077.
EndNote Çakmakçı Y, Hurma H, Çakmakçı C (May 1, 2024) Determination of Consumer Perceptions of Eco-Friendly Food Products Using Unsupervised Machine Learning. Tekirdağ Ziraat Fakültesi Dergisi 21 3 634–647.
IEEE Y. Çakmakçı, H. Hurma, and C. Çakmakçı, “Determination of Consumer Perceptions of Eco-Friendly Food Products Using Unsupervised Machine Learning”, JOTAF, vol. 21, no. 3, pp. 634–647, 2024, doi: 10.33462/jotaf.1319077.
ISNAD Çakmakçı, Yusuf et al. “Determination of Consumer Perceptions of Eco-Friendly Food Products Using Unsupervised Machine Learning”. Tekirdağ Ziraat Fakültesi Dergisi 21/3 (May 2024), 634-647. https://doi.org/10.33462/jotaf.1319077.
JAMA Çakmakçı Y, Hurma H, Çakmakçı C. Determination of Consumer Perceptions of Eco-Friendly Food Products Using Unsupervised Machine Learning. JOTAF. 2024;21:634–647.
MLA Çakmakçı, Yusuf et al. “Determination of Consumer Perceptions of Eco-Friendly Food Products Using Unsupervised Machine Learning”. Tekirdağ Ziraat Fakültesi Dergisi, vol. 21, no. 3, 2024, pp. 634-47, doi:10.33462/jotaf.1319077.
Vancouver Çakmakçı Y, Hurma H, Çakmakçı C. Determination of Consumer Perceptions of Eco-Friendly Food Products Using Unsupervised Machine Learning. JOTAF. 2024;21(3):634-47.