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Afet Dönemlerinde E-Ticaret Sektöründe Uygulanan Fiyat Dalgalanmaları Analizi: İçecek Kategorisi için Türkiye Örneği

Year 2024, Volume: 7 Issue: 4, 1826 - 1850, 16.09.2024
https://doi.org/10.47495/okufbed.1466023

Abstract

Günümüzde gelir yönetimi anlayışı, müşterilerin internet ortamında birçok veriyi karşılaştırmalı olarak elde ederek rekabet durumunu daha akıllı ve kısa sürede analiz etmelerine olanak sağlamaktadır. Mağazaların fiziksel ortamlarından yapılan alışverişler, hem müşterilerin yaşadığı bedensel yorgunluklara hem de fiyat alternatiflerini daha uzun zaman dilimlerinde daha göreceli olarak değerlendirmelerine neden olmasından dolayı son yıllarda yerini e-ticaret siteleri üzerinden gerçekleştirilen online alışverişlere bırakmıştır. E-ticaret özellikle müşterilere zaman kazandırmasından dolayı son yıllarda daha çok tercih edilmeye başlanmışken, dünya genelinde yaşanan COVID-19 salgını nedeniyle yaşanan kapanmalarla da tercih eğilimini daha hızlı artırmıştır. Dinamik fiyatlandırma ise, online alışveriş siteleri için oldukça cazip görünen ve son yıllarda sıklıkla kullanılan bir strateji haline gelmiştir. Ele alınan bu çalışma kapsamında, Türkiye’de çok tercih edilen bir online alışveriş sitesinde içecek kategorisinde yer alan en çok satan ürünler, COVID-19 dönemi birinci, ikinci, üçüncü dalgalanma dönemleri ve 11 ili etkileyen deprem döneminde uygulanan online fiyatlandırma yaklaşımları çerçevesinde incelenmiştir. Gelir yönetimi yaklaşımlarının ele alınan afet dönemleri içerisinden en çok etkilediği dönemleri tespit etmek amacıyla, farklı kriterlere bağlı olarak çok kriterli karar verme yöntemlerinden AHP ve TOPSİS yöntemleri kullanılmış olup, COVID-19’un ilk periyodunun en yüksek etkiye sahip olduğu tespit edilmiştir.

Project Number

Adana Alparslan Türkeş Bilim ve Teknolojisi Üniversitesi Bilimsel Araştırma Projeleri - 22303014

References

  • Ballestar MT., Pilar GC., Jorge S. Predicting customer quality in e-commerce social networks: a machine learning approach. Review of Managerial Science 2019; 13: 589-603.
  • Bandyopadhyay S., Thakur SS. Product prediction and recommendation in e-commerce using collaborative filtering and artificial neural networks: A hybrid approach. Intelligent Computing Paradigm: Recent Trends 2020; 59-67.
  • Chornous G., Yaroslava H. Modeling and forecasting dynamic factors of pricing in e-commerce. IT&I. 2020; 71-82.
  • Den B., Arnoud V. Dynamic pricing and learning: historical origins, current research, and new directions. Surveys in Operations Research and Management Science 2015; 20(1): 1-18.
  • Desticioğlu Taşdemir B., Kumcu S., Özyörük B. Comparison of E-Commerce Sites with Pythagorean Fuzzy AHP and TOPSIS Methods. Intelligent and Fuzzy Systems (INFUS) 2023. Lecture Notes in Networks and Systems.
  • Dung T., My HT., Mai NH., Linh C. Application of Fuzzy-AHP-Topsis in online shopping selection on B2C e-commerce websites. Valley International Journal Digital Library 2020; 1196-1206.
  • Fisher M., Santiago G., Jun L. Competition-based dynamic pricing in online retailing: A methodology validated with field experiments. Management Science 2018; 64(6): 2496-2514.
  • Gabor MR., Mihaela K., Flavia DO. Yield management - a sustainable tool for airline e-commerce: dynamic comparative analysis of e-ticket prices for romanian full-service airline vs. low-cost carriers. Sustainability 2022; 14: 15150.
  • Ijegwa AD. A bayesian based system for evaluating customer satisfaction in an online store. Intelligent Systems and Applications: Proceedings of the 2018 Intelligent Systems Conference (IntelliSys) Volume 2, Springer International Publishing, 2019.
  • Le MT. Sustainable evaluation of e-commerce companies in Vietnam: a multi-criteria decision-making framework based on MCDM. Mathematics 2024; 12(11): 1681.
  • Li R., Sun T. Assessing factors for designing a successful B2C E-Commerce website using fuzzy AHP and TOPSIS-Grey methodology. Symmetry 2020; 12(3): 363.
  • Liu D., Bocheng C. Dynamic pricing for e-tailers with two B2C platform online-stores. ICSSSM12, IEEE, 2012.
  • Loukili M., Fayçal M., Raouya EY. Implementation of machine learning algorithms for customer churn prediction. Journal of Information Systems and Telecommunication (JIST) 2023; 3: 196.
  • Mamakou, X.J., Roumeliotou, K.P. Evaluating the electronic service quality of E-shops using AHP-TOPSIS: the case of Greek coffee chains during the COVID-19 lockdown. Journal of Electronic Commerce in Organizations (JECO) 2022; 20(1): 1-17.
  • Poh L.Z., Connie T., Ong T.S., Goh M. Deep reinforcement learning-based dynamic pricing for parking solutions. Algorithms 2023; 16(1): 32.
  • Saaty T. The analytic hierarchy process. USA: Mcgraw-Hill International Book Company 1980.
  • Šaković J.J. The relationship between e-commerce and firm performance: the mediating role of internet sales channels. Sustainability 2020; 12(17): 6993.
  • Serth S. An interactive platform to simulate dynamic pricing competition on online marketplaces. IEEE 21st International Enterprise Distributed Object Computing Conference (EDOC). IEEE, 2017.
  • Ullah I., Adhikari D., Ali F., Ali A., Khan H., Sharafian A., Bai X. Revolutionizing e-commerce with consumer-driven energy-efficient WSNs: a multi-characteristics approach. IEEE Transactions on Consumer Electronics 2024.
  • Ulmer MW. Dynamic pricing and routing for same-day delivery. Transportation Science 2020; 54: 1016-1033.
  • Victor V. Factors influencing consumer behavior and prospective purchase decisions in a dynamic pricing environment—an exploratory factor analysis approach. Social Sciences 2018; 7(9): 153.
  • Victor V. Investigating the dynamic interlinkages between exchange rates and the NSE NIFTY index. Journal of Risk and Financial Management 2021; 14(1): 20.
  • Yapıcıoğlu AY. Glocalization of consumption culture through global brand advertisements. Global Media Journal: Turkish Edition 2019; 9(18).
  • Ye X. Information asymmetry evaluation in hotel e-commerce market: Dynamics and pricing strategy under pandemic. Information Processing & Management 2023; 60: 103117.

Price Fluctuation Analysis in E-Commerce Sector During Disaster Period: A Case Study for Beverage Category in Türkiye

Year 2024, Volume: 7 Issue: 4, 1826 - 1850, 16.09.2024
https://doi.org/10.47495/okufbed.1466023

Abstract

Today's revenue management approach allows customers to analyze the competitive situation more consciously and in a shorter time by obtaining many comparative data on the internet. Shopping in the physical stores has been replaced by online shopping through e-commerce sites in recent years, as it causes both the physical fatigue experienced by customers and the fact that they are able to evaluate price alternatives more relatively in longer periods of time. While e-commerce has become more preferred in recent years, especially because it saves time for customers, it has also increased its preference trend more rapidly with the shutdown experienced worldwide due to the COVID-19 epidemic. Dynamic pricing, on the other hand, has become a very tempting strategy for online shopping sites and has become frequently used in recent years. Within the scope of this study, the best-selling products in the beverage category on a most preferred online shopping site in Turkey were examined within the framework of the online pricing approaches applied during the first, second and third waves of the COVID-19 period and the earthquake period that affected 11 provinces. In order to determine the most affected periods by means of revenue management approaches among the disaster periods, AHP and TOPSIS, which are multi-criteria decision-making methods, were used depending on different criteria, and it was determined that the first period of COVID-19 had the highest impact.

Project Number

Adana Alparslan Türkeş Bilim ve Teknolojisi Üniversitesi Bilimsel Araştırma Projeleri - 22303014

References

  • Ballestar MT., Pilar GC., Jorge S. Predicting customer quality in e-commerce social networks: a machine learning approach. Review of Managerial Science 2019; 13: 589-603.
  • Bandyopadhyay S., Thakur SS. Product prediction and recommendation in e-commerce using collaborative filtering and artificial neural networks: A hybrid approach. Intelligent Computing Paradigm: Recent Trends 2020; 59-67.
  • Chornous G., Yaroslava H. Modeling and forecasting dynamic factors of pricing in e-commerce. IT&I. 2020; 71-82.
  • Den B., Arnoud V. Dynamic pricing and learning: historical origins, current research, and new directions. Surveys in Operations Research and Management Science 2015; 20(1): 1-18.
  • Desticioğlu Taşdemir B., Kumcu S., Özyörük B. Comparison of E-Commerce Sites with Pythagorean Fuzzy AHP and TOPSIS Methods. Intelligent and Fuzzy Systems (INFUS) 2023. Lecture Notes in Networks and Systems.
  • Dung T., My HT., Mai NH., Linh C. Application of Fuzzy-AHP-Topsis in online shopping selection on B2C e-commerce websites. Valley International Journal Digital Library 2020; 1196-1206.
  • Fisher M., Santiago G., Jun L. Competition-based dynamic pricing in online retailing: A methodology validated with field experiments. Management Science 2018; 64(6): 2496-2514.
  • Gabor MR., Mihaela K., Flavia DO. Yield management - a sustainable tool for airline e-commerce: dynamic comparative analysis of e-ticket prices for romanian full-service airline vs. low-cost carriers. Sustainability 2022; 14: 15150.
  • Ijegwa AD. A bayesian based system for evaluating customer satisfaction in an online store. Intelligent Systems and Applications: Proceedings of the 2018 Intelligent Systems Conference (IntelliSys) Volume 2, Springer International Publishing, 2019.
  • Le MT. Sustainable evaluation of e-commerce companies in Vietnam: a multi-criteria decision-making framework based on MCDM. Mathematics 2024; 12(11): 1681.
  • Li R., Sun T. Assessing factors for designing a successful B2C E-Commerce website using fuzzy AHP and TOPSIS-Grey methodology. Symmetry 2020; 12(3): 363.
  • Liu D., Bocheng C. Dynamic pricing for e-tailers with two B2C platform online-stores. ICSSSM12, IEEE, 2012.
  • Loukili M., Fayçal M., Raouya EY. Implementation of machine learning algorithms for customer churn prediction. Journal of Information Systems and Telecommunication (JIST) 2023; 3: 196.
  • Mamakou, X.J., Roumeliotou, K.P. Evaluating the electronic service quality of E-shops using AHP-TOPSIS: the case of Greek coffee chains during the COVID-19 lockdown. Journal of Electronic Commerce in Organizations (JECO) 2022; 20(1): 1-17.
  • Poh L.Z., Connie T., Ong T.S., Goh M. Deep reinforcement learning-based dynamic pricing for parking solutions. Algorithms 2023; 16(1): 32.
  • Saaty T. The analytic hierarchy process. USA: Mcgraw-Hill International Book Company 1980.
  • Šaković J.J. The relationship between e-commerce and firm performance: the mediating role of internet sales channels. Sustainability 2020; 12(17): 6993.
  • Serth S. An interactive platform to simulate dynamic pricing competition on online marketplaces. IEEE 21st International Enterprise Distributed Object Computing Conference (EDOC). IEEE, 2017.
  • Ullah I., Adhikari D., Ali F., Ali A., Khan H., Sharafian A., Bai X. Revolutionizing e-commerce with consumer-driven energy-efficient WSNs: a multi-characteristics approach. IEEE Transactions on Consumer Electronics 2024.
  • Ulmer MW. Dynamic pricing and routing for same-day delivery. Transportation Science 2020; 54: 1016-1033.
  • Victor V. Factors influencing consumer behavior and prospective purchase decisions in a dynamic pricing environment—an exploratory factor analysis approach. Social Sciences 2018; 7(9): 153.
  • Victor V. Investigating the dynamic interlinkages between exchange rates and the NSE NIFTY index. Journal of Risk and Financial Management 2021; 14(1): 20.
  • Yapıcıoğlu AY. Glocalization of consumption culture through global brand advertisements. Global Media Journal: Turkish Edition 2019; 9(18).
  • Ye X. Information asymmetry evaluation in hotel e-commerce market: Dynamics and pricing strategy under pandemic. Information Processing & Management 2023; 60: 103117.
There are 24 citations in total.

Details

Primary Language Turkish
Subjects Multiple Criteria Decision Making, Industrial Engineering
Journal Section RESEARCH ARTICLES
Authors

Pırıl Tekin

Büşra Mat This is me

Project Number Adana Alparslan Türkeş Bilim ve Teknolojisi Üniversitesi Bilimsel Araştırma Projeleri - 22303014
Publication Date September 16, 2024
Submission Date April 6, 2024
Acceptance Date July 26, 2024
Published in Issue Year 2024 Volume: 7 Issue: 4

Cite

APA Tekin, P., & Mat, B. (2024). Afet Dönemlerinde E-Ticaret Sektöründe Uygulanan Fiyat Dalgalanmaları Analizi: İçecek Kategorisi için Türkiye Örneği. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 7(4), 1826-1850. https://doi.org/10.47495/okufbed.1466023
AMA Tekin P, Mat B. Afet Dönemlerinde E-Ticaret Sektöründe Uygulanan Fiyat Dalgalanmaları Analizi: İçecek Kategorisi için Türkiye Örneği. OKÜ Fen Bil. Ens. Dergisi ((OKU Journal of Nat. & App. Sci). September 2024;7(4):1826-1850. doi:10.47495/okufbed.1466023
Chicago Tekin, Pırıl, and Büşra Mat. “Afet Dönemlerinde E-Ticaret Sektöründe Uygulanan Fiyat Dalgalanmaları Analizi: İçecek Kategorisi için Türkiye Örneği”. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi 7, no. 4 (September 2024): 1826-50. https://doi.org/10.47495/okufbed.1466023.
EndNote Tekin P, Mat B (September 1, 2024) Afet Dönemlerinde E-Ticaret Sektöründe Uygulanan Fiyat Dalgalanmaları Analizi: İçecek Kategorisi için Türkiye Örneği. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi 7 4 1826–1850.
IEEE P. Tekin and B. Mat, “Afet Dönemlerinde E-Ticaret Sektöründe Uygulanan Fiyat Dalgalanmaları Analizi: İçecek Kategorisi için Türkiye Örneği”, OKÜ Fen Bil. Ens. Dergisi ((OKU Journal of Nat. & App. Sci), vol. 7, no. 4, pp. 1826–1850, 2024, doi: 10.47495/okufbed.1466023.
ISNAD Tekin, Pırıl - Mat, Büşra. “Afet Dönemlerinde E-Ticaret Sektöründe Uygulanan Fiyat Dalgalanmaları Analizi: İçecek Kategorisi için Türkiye Örneği”. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi 7/4 (September 2024), 1826-1850. https://doi.org/10.47495/okufbed.1466023.
JAMA Tekin P, Mat B. Afet Dönemlerinde E-Ticaret Sektöründe Uygulanan Fiyat Dalgalanmaları Analizi: İçecek Kategorisi için Türkiye Örneği. OKÜ Fen Bil. Ens. Dergisi ((OKU Journal of Nat. & App. Sci). 2024;7:1826–1850.
MLA Tekin, Pırıl and Büşra Mat. “Afet Dönemlerinde E-Ticaret Sektöründe Uygulanan Fiyat Dalgalanmaları Analizi: İçecek Kategorisi için Türkiye Örneği”. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, vol. 7, no. 4, 2024, pp. 1826-50, doi:10.47495/okufbed.1466023.
Vancouver Tekin P, Mat B. Afet Dönemlerinde E-Ticaret Sektöründe Uygulanan Fiyat Dalgalanmaları Analizi: İçecek Kategorisi için Türkiye Örneği. OKÜ Fen Bil. Ens. Dergisi ((OKU Journal of Nat. & App. Sci). 2024;7(4):1826-50.

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