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رویکرد داده کاوی برای انتخاب بازار خرده‌فروشی مصرف‌کنندگان: مطالعه موردی بازارهای خرده‌فروشی شهری در ایران

نوع مقاله : مقالات پژوهشی به زبان انگلیسی

نویسندگان

گروه اقتصاد کشاورزی، دانشکده کشاورزی، دانشگاه فردوسی مشهد، مشهد، ایران

چکیده

بازارهای خرده‌فروشی شهری، بازارهای خرده‌فروشی دولتی هستند که اخیراً در ایران با هدف افزایش رفاه مصرف‌کنندگان و تولیدکنندگان تأسیس شده‌اند. برای دستیابی به این هدف و گسترش حضور در بخش خرده‌فروشی شهری، درک جامعی از رفتار مصرف‌کننده در این بازارها ضروری است. این مطالعه با استفاده از الگوریتم C4.5 به بررسی عوامل مختلف اجتماعی-اقتصادی مؤثر بر تصمیم‌گیری مصرف‌کنندگان در بازارهای خرده‌فروشی پرداخته است. داده‌ها از طریق نظرسنجی از 189 مصرف‌کننده با استفاده از روش نمونه‌گیری تصادفی در شهر مشهد در سال‌های 1398 و 1399 جمع‌آوری شد. نتایج نشان داد که آگاهی از تخفیف‌های موجود به‌طور قابل توجهی انتخاب مصرف‌کننده را در بازارهای خرده‌فروشی شهری تحت تأثیر قرار می‌دهد. با این حال، علی‌رغم تخفیف‌های موجود، آگاهی در میان مصرف‌کنندگان پایین است که نیاز به بازنگری در استراتژی‌های تبلیغاتی می‌باشد. همچنین نتایج نشان داد تجربه خرید از بازارهای شهری، درآمد خانوار و تحصیلات از جمله عوامل تأثیرگذار بر انتخاب مصرف‌کننده می‌باشند. یافته‌های این مطالعه می‌تواند بینش‌های ارزشمندی برای سیاست‌گذاران و سهامداران فراهم آورد که در پی افزایش اثربخشی بازارهای خرده‌فروشی محلی در ایران هستند. علاوه بر این، با بهره‌گیری از این بینش‌ها در زمینه رفتار مصرف‌کننده و پویایی بازار، این بازارها می‌توانند رونق گرفته و نقش بسزایی در بهبود بخش خرده‌فروشی و اقتصاد ایران ایفا کنند. در این راستا، توصیه‌هایی مانند کمپین‌های تبلیغاتی، استراتژی‌های آموزش‌محور، همکاری با تولیدکنندگان محلی و سیاست‌های بازاریابی فراگیر با هدف بهبود دسترسی همه مصرف‌کنندگان به بازارهای خرده‌فروشی شهری ارائه گردید.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

A Data Mining Approach to Consumers’ Choice of Retail Market: The Case of Urban Retail Markets in Iran

نویسندگان [English]

  • H. Hashemzadeh
  • N. Yousefian
  • S. Esfandiari Bahraseman
  • A. Karbasi
  • A. Firoozzare

Department of Agricultural Economics, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran

چکیده [English]

Urban retail markets are state-owned retail markets that were recently established in Iran to increase the welfare of consumers and producers. To achieve this goal and expand its presence in the Iranian retail sector, it is essential to gain a comprehensive understanding of consumer behavior within these markets. This study examines the various socio-economic factors influencing consumers' decisions in the retail market by using the C4.5 algorithm. The data were collected using a random sampling method through a survey of 189 consumers, focusing on the population of Mashhad, Iran, during 2019-2020. Results revealed that awareness of available discounts significantly drives consumer choices in urban retail markets. Despite existing discounts, awareness among consumers remains low, suggesting a need to review promotional strategies within the marketing mix. The study also identifies previous purchases from urban markets, household income, and education as influential factors. Findings offer valuable insights for policymakers, market strategists, and stakeholders seeking to enhance the effectiveness of local retail markets in Iran. By leveraging insights into consumer behavior and market dynamics, these markets can thrive, benefiting Iran's retail sector and overall economy. Following the study, recommendations such as enhanced promotional campaigns, education-oriented strategies, loyalty programs, collaborations with local producers, and inclusive marketing policies was made aim to improve access for all consumers to urban retail markets.

کلیدواژه‌ها [English]

  • Consumer behavior
  • Data mining
  • Decision tree
  • Machine learning

©2024 The author(s). This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0)

مراجع
  1. Alsini, N., Kutbi, H.A., Hakim, N., Mosli, R., Eid, N., & Mulla, Z. (2023). Factors influencing grocery shopping choices and the prevalence of food label use among Saudi mothers: a cross-sectional pilot study. Nutrition & Food Science, 53(2), 432-444. https://doi.org/10.1108/NFS-11-2021-0345
  2. Applebaum, W. (1951). Studying customer behavior in retail stores. Journal Market, 172–178. https://doi.org/10.1177/002224295101600204
  3. Barh, D. (2020). Artificial Intelligence in Precision Health: From Concept to Applications. Academic Press. https://doi.org/10.1016/B978-0-12-817133-2.09988-2
  4. Berry, M.A., & Linoff, G.S. (2000). Mastering data mining: The art and science of customer relationship management. Industrial Management & Data Systems. https://doi.org/10.1108/imds. 2000.100.5.245.2
  5. Bhatti, K.L., Latif, S., & Latif, R. (2015). Factors affecting consumer's store choice behavior. International Journal of Application or Innovation in Engineering & Management (IJAIEM), 4(9), 71-77.
  6. Büyükdağ, N., Soysal, A.N., & Ki̇tapci, O. (2020). The effect of specific discount pattern in terms of price promotions on perceived price attractiveness and purchase intention: An experimental research. Journal of Retailing and Consumer Services, 55, 102112. https://doi.org/10.1016/j.jretconser.2020.102112
  7. Chandrakala, V.G., Sowmya, C.U., & Nagesha, H.G. (2023). A study on factors influencing the consumer buying behavior with reference to organized apparel retail outlets. Journal of Innovations in Business and Industry, 1(2), 85-92. https://doi.org/10.61552/JIBI.2023.02.005
  8. Chen, T., Samaranayake, P., Cen, X., Qi, M., & Lan, Y. (2022). The impact of online reviews on consumers’ purchasing decisions: Evidence from an Eye-Tracking study. Frontiers in Psychology, 13, 865702. https://doi.org/10.3389/fpsyg.2022.865702
  9. Cherfi, A., Nouira, K., & Ferchichi, A. (2018). Very fast C4.5 decision tree algorithm. Applied Artificial Intelligence, 32(2), 119-137. https://doi.org/10.1080/08839514.2018.1447479
  10. Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37-46. https://doi.org/10.1177/001316446002000104
  11. Daoudi, H., & İldem Develi, E. (2023). Impact of gender and monthly income on consumer buying behavior. Journal of International Trade, Logistics and Law, 9(1), 241-250.
  12. Dev, V.A., & Eden, M.R. (2019). Gradient boosted decision trees for lithology classification. In Computer Aided Chemical Engineering, 47, 113-118. https://doi.org/10.1016/B978-0-12-818597-1.50019-9
  13. Dominici, A., Boncinelli, F., Gerini, F., & Marone, E. (2021). Determinants of online food purchasing: The impact of socio-demographic and situational factors. Journal of Retailing and Consumer Services, 60. https://doi.org/10.1016/j.jretconser.2021.102473
  14. Donoghue, S., Wilken-Jonker, I., Steffens, F.E., & Kirsten, J.F. (2021). South African consumers' willingness to pay a premium for Karoo Lamb: The influence of subjective and objective knowledge, label information and demographics. Journal of Retailing and Consumer Services, 63. https://doi.org/10.1016/j.jretconser.2021.102664
  15. Gauri, D.K., Jindal, R.P., Ratchford, B., Fox, E., Bhatnagar, A., Pandey, A., & Howerton, E. (2021). Evolution of retail formats: Past, present, and future. Journal of Retailing, 97(1), 42-61. ISSN 0022-4359. https://doi.org/10.1016/j.jretai.2020.11.002
  16. Golriz Ziaie, Z., Moghaddasi, R., & Yazdani, S. (2015). Estimation of customer satisfaction index of food markets, Case study: Mashhad Urbanity’s Hypermarkets. Journal of Agricultural Economics and Development, 29(2), 181-191. ISSN 2008-4722. https://doi.org/10.22067/JEAD2.V0I0.40763
  17. Gomes, A. (2018). Influencing factors of consumer behavior in retail shops available at SSRN: https://ssrn.com/abstract=3151879or http://dx.doi.org/10.2139/ssrn.3151879
  18. Greenacre, L., & Akbar, S. (2019). The impact of payment method on shopping behavior among low income consumers. Journal of Retailing and Consumer Services, 47, 87-93. https://doi.org/10.1016/j.jretconser.2018.11.004
  19. Han, J., Kamber, M., & Pei, J. (2022). Data mining: concepts and techniques, Morgan Kaufmann. https://doi.org/10.1016/B978-0-12-381479-1.00020-4
  20. Hanaysha, J. (2018). An examination of the factors affecting consumer’s purchase decision in the Malaysian retail market. PSU Research Review, 2(1), 7-23. https://doi.org/10.1108/PRR-08-2017-0034
  21. Hecht, A.A., Perez, C.L., Polascek, M., Thorndike, A.N., Franckle, R.L., & Moran, A.J. (2020). Influence of food and beverage companies on retailer marketing strategies and consumer behavior. International Journal of Environmental Research and Public Health, 17(20), 7381. https://doi.org/10.3390/ijerph17207381
  22. Hingley, M., Lindgreen, A., & Chen, L. (2009). Development of the grocery retail market in China: A qualitative study of how foreign and domestic retailers seek to increase market share. British Food Journal, 111(1), 44-55. https://doi.org/10.1108/00070700910924227
  23. Jofreh, M. (2013). An investigation of business activities in Iran retailing industry. Research Journal of Applied Sciences, Engineering and Technology 6(5), 858-861.
  24. Kol, O., & Levy, S. (2023). Men on a mission, women on a journey - Gender differences in consumer information search behavior via SNS: The perceived value perspective. Journal of Retailing and Consumer Services, 75, https://doi.org/10.1016/j.jretconser.2023.103476
  25. Kotsiantis, S.B. (2013). Decision trees: a recent overview. Artificial Intelligence Review, 39(4), 261-283. https://doi.org/10.1007/s10462-011-9272-4
  26. Kotu, V., & Deshpande, B. (2018), Chapter 4 – Classification, Data science: concepts and practice (Second Edition), Morgan Kaufmann, 65-163.
  27. Kumar, D. (2018). Factors influencing consumers' choice of retail store format in Assam, India. Journal of Marketing Management, 1(17), 22-37.
  28. Kumar, M.G., Hemanth, K., Gangadhar, N., Kumar, H., & Krishna, P. (2014). Fault diagnosis of welded joints through vibration signals using Naïve Bayes algorithm. Procedia Materials Science, 5, 1922-1928. https://doi.org/10.1016/j.mspro.2014.07.514
  29. Laine, K. (2014). The factors influencing the choice of grocery store among finnish consumers. Business. ttps://urn.fi/URN:NBN:fi:amk-201405096933
  30. Lintang, M., Pandiangan, N., & Hyronimus, D. (2022). Use of the C4.5 Algorithm to Analyze Student Interest in Continuing to College. SHS Web of Conferences.
  31. Liyanage, L., PLGSD, P., & Rathnayake, T. (2020). Determinants of consumers' selection of supermarkets for grocery shopping; Empirical Evidence from Western Province, Sri Lanka. In International Conference on Marketing Management.
  32. Makgosa, R., & Sangodoyin, O. (2017). Retail market segmentation: the use of consumer decision-making styles, overall satisfaction and demographics. The International Review of Retail, Distribution and Consumer Research, 28(1), 64–91. https://doi.org/10.1080/09593969. 2017.1334690
  33. Manuere, F. (2023). Factors affecting customers’ choice of supermarkets for grocery shopping in Chinhoyi Town. International Journal of Academic Research in Public Policy and Governance, 9(1), 10–20
  34. McHugh, M.L. (2012), Interrater reliability: the kappa statistic. Biochemia Medica, 22(3), 276-282.
  35. Meng, X., Zhang, P., Xu, Y., & Xie, H. (2020). Construction of decision tree based on C4.5 algorithms for online voltage stability assessment. International Journal of Electrical Power & Energy Systems, 118, https://doi.org/10.1016/j.ijepes.2019.105793
  36. Moitra, A.K., Bhattacharya, J., Kayal, J.R., Mukerji, B., & Das, A.K. (2021). Innovative exploration methods for minerals, oil, gas, and groundwater for sustainable development. https://doi.org/10.1016/C2020-0-00590-6
  37. Nguyen, T.D.E. (2019). Factors affecting customer loyalty of different strategic groups in the Vietnamese supermarket sector (Doctoral dissertation, University of hull).
  38. Nisbet, R., Miner, G.D., & Yale, K. (2018). Chapter 9 – Classification, Nisbet, R., Miner, G. D., & Yale, K., Handbook of statistical analysis and data mining applications. 169-186 in Academic press.
  39. Noor, Z. (2020). The effect of price discount and in-store display on impulse buying. Journal Ilmu-ilmu Sosial dan Humaniora. 22(2), 133-139. https://doi.org/10.24198/sosiohumaniora.v22i2.26720
  40. Ooi, M.P., Sok, H.K., Kuang, Y.C., & Demidenko, S. (2017). Alternating decision trees. In P. Samui, S. S. Roy, & V. E. Balas (Eds.), Handbook of Neural Computation. PP 345-371 in Academic Press. 
  41. Pandey, A., & Kaur, D.A. (2018). A comprehensive study on evolution, present scenario and future prospects of retailing. International Journal of Current Research in Life Sciences, 7(2), 1158-1162.
  42. Quinlan, J.R. (1996). Improved use of continuous attributes in C4.5. Journal of Artificial Research, 4, 77–90. https://doi.org/10.48550/arXiv.cs/9603103
  43. Quinlan, J.R. (2014). C4.5: Programs for Machine Learning. 58-60. https://books.google.com/books/about/C4_5.html?id=b3ujBQAAQBAJ
  44. Rahim, M.A., Mushafiq, M., Khan, S., & Arain, Z.A. (2021). RFM-based repurchase behavior for customer classification and segmentation. Journal of Retailing and Consumer Services, 61. https://doi.org/10.1016/j.jretconser.2021.102566
  45. Ram, S. (2022). Data Mining. Computer Sciences. from Encyclopedia.com: https://www.encyclopedia.com/computing/news-wires-white-papers-and-books/data-mining
  46. Rasheed, A., Shahid Yaqub, R., & Baig, F. (2018). Factors affecting impulse buying behaviors in shopping malls: Evidence from Bahawalpur Region, Pakistan. Journal of Marketing and Consumer Research. 39.
  47. Razu, M.A., & Roy, D. (2019). Prominent factors influencing consumers’ choice of retail store. Journal of Business and Management, 21(6), 62-66. https://doi.org/10.9790/487X-2106026266
  48. Reddy, G.S., & Chittineni, S. (2021). Entropy based C4.5-SHO algorithm with information gain optimization in data mining. Peer Journal Computer Science, 7. https://doi.org/10.7717/peerj-cs.424
  49. Roy, G., Debnath, R., & Mitra, P.S. (2021). Analytical study of low-income consumers’ purchase behaviour for developing marketing strategy. International Journal Syst Assur Engineering Management, 12, 895–909. https://doi.org/10.1007/s13198-021-01143-6
  50. Savaşkan, A., & Çatı, K. (2021). Investigation of consumer behavior in market shopping in the gender context. Elektronik Sosyal Bilimler Dergisi, 20(77), 255-272. https://doi.org/10.17755/esosder.767017
  51. Shamsher, R. (2016). Store image and its impact on consumer behavior. Elk Asia Pacific Journal of Marketing and Retail Management, 7, (2), 1-27. ISSN 0976-7193. https://www.researchgate.net/project/STORE-IMAGE-AND-ITS-IMPACT-ON-CONSUMER-BEHAVIOR
  52. Šostar, M., & Ristanović, V. (2022). Assessment of influencing factors on consumer behavior using the AHP model. Sustainability, 15(13), 10341. https://doi.org/10.3390/su151310341
  53. Sugumaran, V., & Ramachandran, K.I. (2007). Automatic rule learning using decision tree for fuzzy classifier in fault diagnosis of roller bearing. Mechanical Systems and Signal Processing, 21, 2237–2247. https://doi.org/10.1016/j.ymssp.2006.09.007
  54. Sun, K., Likhate, S., Vittal, V., Kolluri, V.S., & Mandal, S. (2007). An online dynamic security assessment scheme using phasor measurements and decision trees. IEEE Transactions on Power Systems, 22(4), 1935-1943. https://doi.org/10.1109/TPWRS.2007.908476
  55. Taylor, M.J., Kwasnica, V., Reilly, D., & Ravindran, S. (2019). Game theory modelling of retail marketing discount strategies. Marketing Intelligence and Planning, 37(5), 555-566. ISSN 0263-4503.
  56. Terano, R., Yahya, R., Mohamed, Z., & Saimin S. (2015). Factor influencing consumer choice between modern and traditional retailers in Malaysia. International Journal of Social Science and Humanity, 5(6). https://doi.org/10.7763/IJSSH.2015.V5.509
  57. Thakur, P., Mehta, P., Devi, C., Sharma, P., Singh, K.K., Yadav, S., Lal, P., Raghav, Y.S., Kapoor, P., & Mishra, P. (2023). Marketing performance and factors influencing farmers choice for agricultural output marketing channels: The case of garden pea (Pisum sativum) in India. Frontiers in Sustainable Food Systems, 7, 1270121. https://doi.org/10.3389/fsufs.2023.1270121
  58. Tian, X., Cao, S., & Song, Y. (2021). The impact of weather on consumer behavior and retail performance: Evidence from a convenience store chain in China. Journal of Retailing and Consumer Services, 62. https://doi.org/10.1016/j.jretconser.2021.102583
  59. Veeck, A., & Veeck, G. (2000). Consumer segmentation and changing food purchase patterns in Nanjing”, PRC. World Development, 28(3), 457-471. https://doi.org/10.1016/S0305-750X(99)00135-7
  60. Vindigni, G., Peri, I., Consentino, F., Selvaggi, R., & Spina, D. (2022). Exploring consumers attitudes towards food products derived by new plant breeding techniques. Sustainability, 14(10). https://doi.org/10.3390/su14105995
  61. Walkinshaw, N. (2013). Reverse-engineering software behavior, Memon, A., Advances in Computers PP 1-58 in Elsevier.
  62. Yildirim, K., Cagatay, K., & Hidayetoğlu, M.L. (2015). The effect of age, gender and education level on customer evaluations of retail furniture store atmospheric attributes. International Journal of Retail & Distribution Management, 43(8), 712-726. https://doi.org/10.1108/IJRDM-01-2013-0034
  63. Zaki, M.J., & Meira, W. (2020). Data mining and machine learning: Fundamental concepts and algorithms. Cambridge University Press.
  64. Zulqarnain, H., Zafar, A.U., & Shahzad, M. (2015). Factors that affect the choice of consumers in selecting retail store, for grocery shopping. International Journal of Multidisciplinary and Current Research, 3(1), 1167-1172S. http://ijmcr.com
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اقتصاد و توسعه کشاورزی
دوره 38، شماره 4
دی 1403
صفحه 370-351
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  • تاریخ دریافت: 31 اردیبهشت 1403
  • تاریخ بازنگری: 16 تیر 1403
  • تاریخ پذیرش: 18 تیر 1403
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