با همکاری انجمن اقتصاد کشاورزی ایران

نوع مقاله : مقالات پژوهشی

نویسندگان

دانشگاه فردوسی مشهد

چکیده

کشاورزی ارگانیک از یک سو نقش بسیار مهمی در بهبود کیفیت محیط زیست و منابع طبیعی ایفا می­نماید و از سوی دیگر، اثر مثبتی بر عرضه مواد غذایی با کیفیت مطلوب و ارتقاء سلامت جامعه دارد. ترجیحات مصرف­کنندگان برای خرید محصولات ارگانیک به عوامل متعددی بستگی دارد و درجه اهمیت هر یک از این عوامل میان مصرف­کنندگان مختلف متفاوت است. لذا، رتبه­بندی و بررسی عوامل مؤثر بر ترجیحات مصرف­کنندگان برای خرید محصولات ارگانیک (میوه­جات، سبزی­جات و صیفی­جات) هدف اصلی این مطالعه می­باشد. این پژوهش به صورت پیمایشی و اطلاعات آن در سال 1395 از 175 خانوار در شهر مشهد جمع­آوری گردیده است. با استفاده از نرم‌افزار MATLAB، استفاده از مدل شبکه عصبی مصنوعی با در نظر گرفتن همزمان سه متغیر وابسته و رهیافت تحلیل حساسیت از وجوه متمایز این مطالعه نسبت به مطالعات پیشین است. در این مطالعه از متغیرهای سن، جنسیت، تحصیلات، تعداد افراد خانوار، تعداد افراد زیر 10 سال و بالای 65 سال، شناخت از محصولات ارگانیک، داشتن اطلاع از عرضه محصولات ارگانیک، ظاهر محصول، ارزش غذایی، سهولت دسترسی، قیمت، عرضه در طول ایام سال و داشتن برچسب اطلاعاتی به عنوان متغیرهای ورودی شبکه عصبی مصنوعی استفاده گردید. نتایج مطالعه نشان داد که متغیر قیمت در میان تمامی عوامل اثرگذار بیشترین تأثیر را بر تمایل به مصرف محصولات ارگانیک دارد. اثر قیمت بر تمایل به مصرف محصولات ارگانیک میان مصرف­کنندگان فردی متفاوت و مستقل از محصول مورد بررسی است. لذا، برای توسعه تولید محصولات ارگانیک و رشد بازار آن پیشنهاد می­گردد که استراتژی­های بازاریابی مبتنی بر آمیخته قیمت طراحی و به کار گرفته شوند.

کلیدواژه‌ها

عنوان مقاله [English]

Rating and Investigating Factors Affecting Consumer Preferences for Organic Products in Mashhad City

نویسندگان [English]

  • M. Ghorbani
  • A.H. Tohidi
  • P. Alizadeh

Ferdowsi University of Mashhad

چکیده [English]

Introduction: Organic farming plays an important role in protecting the environment, maintaining non-renewable resources, improving the food quality, reducing the production of unnecessary products, and promoting market- oriented agricultural sector. In fact, organic farming make a significant contribution in improving the quality of the environment and natural resources, and also it has a positive effect on the quality of food supply and the promotion of public health. Given the many benefits of organic products, the market for these products has been increasingly considered by researchers, government officials and consumers. First step in developing the market for organic products is to meet the needs and demands of consumers. Recognizing consumer behavior and investigating the factors affecting it contributes significantly in success of any economic system. Besides, in advanced marketing studies, the process of identifying consumer choice is very crucial. Contrary to economists' views, consumers give little weight to benefits and costs in their decision making, and their choices are based on people's behavior, habits and other factors that may speed up the decision making. Consumer preferences for organic products depend on many factors and the importance of each of these factors varies among different consumers. Therefore, the main aim of this study is to rate and evaluate factors affecting the consumer preferences for organic products (fruitage, vegetables and cucurbits) in Mashhad city.
Materials and Methods: Many marketing researchers use regression models to evaluate consumer decisions. In these models, decision variables are definitive part of utility function which is used to calculate how to choose a product. Linearity of utility function is the vital hypothesis. To specify a non-linear model, it is necessary to use variables that can show non-linear effects (For example, including the quadratic term of variables). However, this requires the insertion of assumptions about the nature of the utility function which ultimately leads to specification bias, and subsequently misinterpretation and unreasonable applications in marketing studies. Modeling complex processes is one of the advantages of artificial neural networks, and in this approach, it is not necessary to specify a mathematical relationship between the variables. The nonlinear and complex interactions can be considered between system variables using artificial neural network model. In this study in order to rate and evaluate factors affecting consumers preferences for organic products (fruitage, vegetables and cucurbits) an artificial neural network has been used that is consist of three dependent or target variables. Also, in order to evaluate the importance of the explanatory variables of the artificial neural network, partial derivatives approach has been used. Therefore, the use of three output variables on artificial neural networks simultaneously and partial derivative approach was distinctive features of this study compared with previous ones. Data is collected through questionnaires from a total of 175 households living in Mashhad. Age, gender, education, household size, number of household members under 10 years, number of household members over 65 years, price, having information on organic products, product appearance, having information on the supply of organic products, nutritional values, ease of access, the supply of organic products during the year and having labels were the input variables of artificial neural network. Consumer preferences for the purchase of organic fruitage, vegetables and cucurbits were the target variables of the artificial neural network.
Results and Discussion: The results indicate that price has the greatest influence on willingness to consume organic products among all other factors. The price effect on willingness to consume organic products is different among individual consumers, and it's independent of the product. This finding suggested that the price of organic products had a significant impact on consumer purchasing decisions in comparison with other marketing mix elements.
Conclusion: The adoption and implementation of marketing strategies based on price play a very important role in the growth of organic products markets. The results of the study indicate that, for each consumer and each product, the price had almost the similar effects on willingness to choose. Hence, it is recommended that the similar pricing strategies be used for these three organic products.

کلیدواژه‌ها [English]

  • Marketing Strategy
  • Preferences
  • Organic products
1- Aschemann-Witzel J., and Zielke S. 2017. Can't buy me green? A review of consumer perceptions of and behavior toward the price of organic food. Journal of Consumer Affairs 51(1): 211-251.
2- Bentz Y., and Merunka D. 2000. Neural networks and the multinomial logit for brand choice modelling: a hybrid approach. Journal of Forecasting 19(3):177-200.
3- Bhatt M.A. 2012. Evaluation and associations: A neural-network model of advertising and consumer choice. Journal of Economic Behavior & Organization 82(1): 236-255.
4- Cantarella G.E., and de Luca S. 2005. Multilayer feedforward networks for transportation mode choice analysis: An analysis and a comparison with random utility models. Transportation Research Part C: Emerging Technologies 13(2): 121-155.
5- Chowdhury P., and Samuel M.S. 2014. Artificial neural networks: a tool for understanding green consumer behavior. Marketing Intelligence & Planning 32(5): 552-566.
6- Dimopoulos I., Chronopoulos J., Chronopoulou-Sereli A., and Lek S. 1999. Neural network models to study relationships between lead concentration in grasses and permanent urban descriptors in Athens city (Greece). Ecological Modelling 120(2): 157-165.
7- Dimopoulos Y., Bourret P., and Lek S. 1995. Use of some sensitivity criteria for choosing networks with good generalization ability. Neural Processing Letters 2(6): 1-4.
8- Dourandish A., Tohidi A., and Mousavi M. 2018. Factors affecting the price of saffron (using the hedonic pricing and artificial neural network model). Saffron agronomy and technology 6(1): 91-103. (In Persian with English abstract)
9- Fish K.E., Johnson J.D., Dorsey R.E., and Blodgett J.G. 2004. Using an artificial neural network trained with a genetic algorithm to model brand share. Journal of Business Research 57(1): 79-85.
10- Gevrey M., Dimopoulos I., and Lek S. 2003. Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecological Modelling 160(3): 249-264.
11- Gulseven O. 2018. Estimating factors for the demand of organic milk in Turkey. British Food Journal 120(9): 2005-2016.
12- Hayashi Y., Hsieh M.H., and Setiono R. 2009. Predicting consumer preference for fast-food franchises: A data mining approach. Journal of the Operational Research Society 60(9): 1221-1229.
13- Hu M.Y., Shanker M., and Hung M.S. 1999. Estimation of posterior probabilities of consumer situational choices with neural network classifiers. International Journal of Research in Marketing 16(4): 307-317.
14- Hu M.Y., and Tsoukalas C. 2003. Explaining consumer choice through neural networks: The stacked generalization approach. European Journal of Operational Research 146(3): 650-660.
15- Janssen M. 2018. Determinants of organic food purchases: Evidence from household panel data. Food Quality and Preference 68: 19-28.
16- Kim J., and Ahn H. 2009. A new perspective for neural networks: Application to a marketing management problem. Journal of Information Science and Engineering 25(5): 1605-1616.
17- Koocheki A., Mansori H., Ghorbani M., and Rajabzadeh M. 2013. Evaluation of factors affecting willingness to use of organic products in Mashhad County. Agricultural Economics and Development 27(3): 188-194. (In Persian with English abstract)
18- Koocheki A., Ghorbani M., Mansori H., and Rajabzadeh M. 2016. Components influencing on preferences of organic fruits and vegetables consumption in Mashhad. Agricultural Economics and Development 30(4): 323-330. (In Persian with English abstract)
19- Kumar A., Rao V.R., and Soni H. 1995. An empirical comparison of neural network and logistic regression models. Marketing Letters 6(4): 251-263.
20- Magnusson M.K., Arvola A., Koivisto Hursti U.K., Åberg L., and Sjöden P.O. 2001. Attitudes towards organic foods among Swedish consumers. British Food Journal 103(3): 209-227.
21- McNelis P.D. 2005. Neural networks in finance: gaining predictive edge in the market. Elsevier Academic Press, California.
22- Mohammadi H., Saghaian S.H., and Tohidi A. 2015. An empirical investigation of the exchange rate fluctuations and pass-through to Iran's pistachio export prices. Iranian Journal of Economic Research 20(65): 159-184. (In Persian with English abstract)
23- Mousavi S.N., Sajedi S.h., and Mozaffari Z. 2016. Valuation of organic dairy products, proteins and factors affecting willingness to pay: A case study of milk in the city of Shiraz. Journal of Applied Economics Studies in Iran 5(17): 273-300. (In Persian with English abstract)
24- Olden J.D., Joy M.K., and Death R.G. 2004. An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. Ecological Modelling 178(3): 389-397.
25- Padel S., and Foster C. 2005. Exploring the gap between attitudes and behaviour: Understanding why consumers buy or do not buy organic food. British Food Journal 107(8): 606-625.
26- Razeghi F., Haghi E., and Yunesian M. 2018. Data about knowledge and tendency towards organic foods use in Tehran. Data in Brief 16: 955-958.
27- Siegrist M., and Hartmann C. 2019. Impact of sustainability perception on consumption of organic meat and meat substitutes. Appetite 132: 196-202.
28- Singh A., and Verma P. 2017. Factors influencing Indian consumers' actual buying behaviour towards organic food products. Journal of Cleaner Production 167: 473-483.
29- Subba Rao P.V., Sikdar P.K., Krishna Rao K.V., and Dhingra S.L. 1998. Another insight into artificial neural networks through behavioural analysis of access mode choice. Computers, Environment and Urban Systems 22(5): 485-496.
30- Tohidi A., Zare Mehrjerdi M.R., Mehrabi Boshrabadi H., and Nezamabadi-pour H. 2015. Evaluation of Artificial Neural Network-Panel Data Hybrid Model in Predicting Iran’s Dried Fruits Export Prices. Scientific Journal Management System 12(3): 95-116. (In Persian with English abstract)
31- Vukasovič T. 2016. Consumers’ perceptions and behaviors regarding organic fruits and vegetables: Marketing trends for organic food in the twenty-first century. Journal of International Food & Agribusiness Marketing 28(1): 59-73.
32- Wang Q., and Sun J. 2003. Consumer preference and demand for organic food: Evidence from a Vermont survey. P. 1-12. In American agricultural economics association annual meeting. Montreal, Canada.
33- Yeh C.H., and Lin Y.C. 2006. Neural network models for transforming consumer perception into product form design. Lecture Notes in Computer Science 3973: 799-804.
34- Yiridoe E.K., Bonti-Ankomah S., and Martin R.C. 2005. Comparison of consumer perceptions and preference toward organic versus conventionally produced foods: a review and update of the literature. Renewable Agriculture and Food Systems 20(04): 193-205.
CAPTCHA Image