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Author
Pełka Marcin (Wroclaw University of Economics and Business, Poland), Irpino Antonio (Second University of Naples, Italy)
Title
The Application of Multidimensional Scaling of Data to Determining Changes in Retailer Customers' Preferences
Zastosowanie wielowymiarowego skalowania danych do określenia zmian preferencji klientów sklepów
Source
Wiadomości Statystyczne, 2024, nr 4, s. 41-58, wykr., tab., bibliogr. s. 57-58
Keyword
Skalowanie wielowymiarowe, Preferencje konsumenta, Pandemia, COVID-19, Rynek kosmetyków
Multidimensional scaling, Consumer preferences, Pandemic, COVID-19, Cosmetics market
Note
JEL Classification: C87, C30, L19, N84
streszcz., summ.
Abstract
Pandemia COVID-19 miała istotny wpływ na wiele obszarów ludzkiej aktywności, w tym na funkcjonowanie różnych rynków, dlatego ważne jest badanie jej oddziaływania. Celem badania omawianego w artykule jest wskazanie, które podejście w zakresie metody skalowania wielowymiarowego: danych klasycznych, danych symbolicznych interwałowych czy danych symbolicznych histogramowych jest najodpowiedniejsze do rozpoznania zmian preferencji klientów sklepów, które nastąpiły w okresie pandemii. Badanie dotyczyło rynku kosmetyków. Wykorzystano dane o zamówieniach na produkty do makijażu jednej z polskich firm o globalnym zasięgu złożonych przez 18 niewielkich, głównie rodzinnych, drogerii z woj. dolnośląskiego (zastosowano technikę doboru według wygody). Tego typu sklepy nie należą do sieci wielkich drogerii, dlatego są znacznie bardziej narażone na wszelkie wahania i zmiany na rynku. Uzyskane rezultaty wskazują, że w latach 2020 i 2021 na dolnośląskim rynku kosmetyków nastąpiły istotne zmiany w porównaniu z 2019 r. polegające na tym, że zmniejszyła się popularność kosmetyków do upiększania ust i policzków, a zwiększyła popularność kosmetyków do makijażu oczu i brwi. Najefektywniejszą metodą wychwycenia i analizy zmian zachodzących w czasie na rynku (w sensie miary dopasowania i współczynnika korelacji Pearsona) okazało się skalowanie wielowymiarowe danych symbolicznych histogramowych. (abstrakt oryginalny)

The COVID-19 pandemic has significantly affected several aspects of human activity, including the functioning of different markes, therefore it is important to research its impact. The aim of the study discussed in this paper is to determine which of the three approaches within the method of multidimensional scaling (i.e. multidimensional scaling of classical, symbolic interval-valued or symbolic histogram data) is most adequate for capturing the shifts in retailer customers' preferences that took place during the pandemic. The research concerned the health and beauty market. It was based on the data on orders for beauty products from a Polish producer of cosmetics of a global reach placed by 18 small, mainly family-managed, health and beauty retailers from Lower Silesia. The shops were selected through convenient sampling. Such shops are not a part of large health and beauty retailer chains, therefore they are more vulnerable to all the fluctuations and shifts on the market. The results of this study indicate that in 2020 and 2021, important changes took place on the Lower Silesian health and beauty market as compared to 2019. These changes involved cosmetics for eyes and eybrows gaining popularity at the expense of cosmetics for lips and cheeks. Multidimensional scaling of symbolic histogram data turned out to be the most effective method (in the sense of the measure of fit and the Pearson correlation coefficient) of capturing and analysing changes happening on a market over a period of time. (original abstract)
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Bibliography
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  1. Billard, L., & Diday, E. (2006). Symbolic Data Analysis. Conceptual Statistics and Data Mining. John Wiley & Sons. https://doi.org/10.1002/9780470090183.
  2. Bock, H.-H., & Diday, E. (Eds.). (2000). Analysis of Symbolic Data. Explanatory Methods for Extracting Statistical Information from Complex Data. Springer Verlag. https://doi.org/10.1007/978-3-642-57155-8.
  3. Borg, I., & Groenen, P. J. F. (2005). Modern Multidimensional Scaling. Theory and Applications (2nd edition). Springer Science+Business Media.
  4. Braithwaite, J., Tran, Y., Ellis, L. A., & Westbrook, J. (2021). The 40 health systems, COVID-19 (40HS, C-19) study. International Journal for Quality in Health Care, 33(1), 1-7. https://doi.org/10.1093/intqhc/mzaa113.
  5. Brito, P. (2014). Symbolic data analysis: another look at the interaction of data mining and statistics. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 4(4), 281-295. https://doi.org/10.1002/widm.1133.
  6. Brito, P., & Dias, S. (Eds.). (2022). Analysis of distributional data. CRC Press. https://doi.org/10.1201/9781315370545.
  7. Coco, M., Guerrera, C. S., Santisi, G., Riggio, F., Grasso, R., Di Corrado, D., Di Nuovo, S., & Ramaci, T. (2021). Psychosocial Impact and Role of Resilience on Healthcare Workers during COVID-19 Pandemic. Sustainability, 13(13), 1-9. https://doi.org/10.3390/su13137096.
  8. Chanda, R., & Kaul, G. (2022). COVID-19: Effect on Indian Beauty Industry. In A. Chandani, R. Divekar, J. K. Nayak, & K. Chopra (Eds.), Pandemic, New Normal and Implications on Business (pp. 111-127). Springer. https://doi.org/10.1007/978-981-19-4892-3_8.
  9. De Leeuw, J. (1977). Applications of Convex Analysis to Multidimensional Scaling. In J. R. Barra, F. Brodeau, G. Romier, & B. van Cutsem (Eds.), Recent Developments in Statistics (pp. 133- 145). North Holland Publishing Company.
  10. De Leeuw, J., & Heiser, W. J. (1977). Convergence of Correction Matrix Algorithms for Multidimensional Scaling. In J. Lingoes (Ed.), Geometric Representations of Relational Data (pp. 735-752). Mathesis Press.
  11. De Leeuw, J., & Heiser, W. J. (1980). Multidimensional Scaling with Restrictions on the Configuration. In P. R. Krishnaiah (Ed.), Multivariate Analysis (pp. 501-522). North Holland Publishing Company.
  12. Diday, E., & Noirhomme-Fraiture, M. (Eds.). (2008). Symbolic Data Analysis and the SODAS Software. John Wiley & Sons. https://doi.org/10.1002/9780470723562.
  13. Gardner, K. (2021). Beauty During a Pandemic: The Impact of COVID-19 on the Cosmetic Industry (Doctoral dissertation, Honors College Middle Tennessee State University). https://jewlscholar.mtsu.edu/server/api/core/bitstreams/0e2d09ce-5a9d-4e84-b1da-29c9223d6d66/content.
  14. Gerstell, E., Marchessou, S., Schmidt, J., & Spagnuolo, E. (2020). How COVID-19 is changing the world of beauty. McKinsey & Company. https://www.mckinsey.com/~/media/McKinsey/Industries/Consumer%20Packaged%20Goods/Our%20Insights/How%20COVID%2019%20is%20changing%20the%20world%20of%20beauty/How-Covid-19-is-changing-the-world-of-beauty-vF.pdf.
  15. Groenen, P. J. F., Winsberg, S., Rodríguez, O., & Diday, E. (2006). I-Scal: Multidimensional Scaling of Interval Dissimilarities. Computational Statistics & Data Analysis, 51(1), 360-378. https://doi.org/10.1016/j.csda.2006.04.003.
  16. Güre, Ö. B., Kayri, M., & Şevgin, H. (2021). Investigation of Coronavirus Pandemic Indicators of the Countries with Hierarchical Clustering and Multidimensional Scaling. Eastern Journal of Medicine, 26(2), 308-315. https://doi.org/10.5505/ejm.2021.72681.
  17. Jolliffe, I. T. (2002). Principal Component Analysis (2nd edition). Springer-Verlag.
  18. Kutlar, A., Gülmez, A., & Öncel, A. (2021). The Analysis of the Effect of Covid 19 on Macroeconomic Indicators via MDS and Clustering Methods [Preprint]. https://doi.org/10.21203/rs.3.rs-194221/v1.
  19. Lee, J., & Kwon, K. H. (2022). Sustainable changes in beauty market trends focused on the perspective of safety in the post-coronavirus disease-19 period. Journal of Cosmetic Dermatology, 21(7), 2700-2707. https://doi.org/10.1111/jocd.14493.
  20. Mair, P., de Leeuw, J., & Groenen, P. J. F. (2015). More on Multidimensional Scaling and Unfolding in R: smacof Version 2. https://cran.r-project.org/web/packages/smacof/vignettes/smacof.pdf.
  21. Mościcka, P. (2023). Body care activities and its consequences related to COVID-19 pandemic. Journal of Cosmetic Dermatology, 22(1), 16-20. https://doi.org/10.1111/jocd.15314.
  22. Mościcka, P., Chróst, N., Terlikowski, R., Przylipiak, M., Wołosik, K., & Przylipiak, A. (2020). Hygienic and cosmetic care habits in Polish women during COVID-19 pandemic. Journal of Cosmetic Dermatology, 19(8), 1840-1845. https://doi.org/10.1111/jocd.13539.
  23. Pikoos, T. D., Buzwell, S., Sharp, G., & Rossell, S. L. (2020). The COVID-19 pandemic: Psychological and behavioral responses to the shutdown of the beauty industry. International Journal of Eating Disorders, 53(12), 1993-2002. https://doi.org/10.1002/eat.23385.
  24. Ratajczak, P., Landowska, W., Kopciuch, D., Paczkowska, A., Zaprutko, T., & Kus, K. (2023). The Growing Market for Natural Cosmetics in Poland: Consumer Preferences and Industry Trends. Clinical, Cosmetic and Investigational Dermatology, 16, 1877-1892. https://doi.org/10.2147/CCID.S411032.
  25. Swachta, M. (2022). Analiza unfolding jako narzędzie identyfikacji zmian na rynku kosmetycznym przed i w trakcie pandemii Covid-19 [Unpublished bachelor's thesis]. Wrocław University of Business and Economy.
  26. Ścieszko, E., Budny, E., Rotsztejn, H., & Erkiert-Polguj, A. (2021). How has the pandemic lockdown changed our daily facial skincare habits?. Journal of Cosmetic Dermatology, 20(12), 3722-3726. https://doi.org/10.1111/jocd.14059.
  27. Unger, J. K. (2022). The impact of Covid-19 on buying decision-making: changes in beauty consumer behavior during lockdown [Master's thesis, LUT University]. https://lutpub.lut.fi/handle/10024/164690.
  28. Walesiak, M., & Dudek, A. (2017). Selecting the Optimal Multidimensional Scaling Procedure for Metric Data with R Environment. Statistics in Transition new series, 18(3), 521-540. https://doi.org/10.59170/stattrans-2017-027.
  29. Walesiak, M., & Dudek, A. (2022). mdsOpt - Searching for Optimal MDS Procedure for Metric and Interval-Valued Data. https://cran.r-project.org/web/packages/mdsOpt/vignettes/mdsOpt.pdf.
  30. Werner-Lewandowska, K., Lubiński, P., & Słoniec, J. (2021). The Effect of Covid-19 on Consumer Behavior in Poland - Preliminary Research Results. European Research Studies Journal, 24(2), 405-416. https://doi.org/10.35808/ersj/2272.
Cited by
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ISSN
0043-518X
Language
eng
URI / DOI
http://dx.doi.org/10.59139/ws.2024.04.3
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