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Research Trend Analysis of the Retail Industry: Focusing on the Department Store

유통업태 연구동향 분석: 백화점을 중심으로

  • Hoe-Chang YANG (Department of Distribution Management, Jangan University)
  • Received : 2023.09.22
  • Accepted : 2023.09.30
  • Published : 2023.10.30

Abstract

Purpose: As one of the continuous studies on the offline distribution industry, the purpose of this study is to find ways for offline stores to respond to the growth of online shopping by identifying research trends on department stores. Research design, data and methodology: To this end, this study conducted word frequency analysis, word co-occurrence frequency analysis, BERTopic, LDA, and dynamic topic modeling using Python 3.7 on a total of 551 English abstracts searched with the keyword 'department store' in scienceON as of October 10, 2022. Results: The results of word frequency analysis and co-occurrence frequency analysis revealed that research related to department stores frequently focuses on factors such as customers, consumers, products, satisfaction, services, and quality. BERTopic and LDA analyses identified five topics, including 'store image,' with 'shopping information' showing relatively high interest, while 'sales systems' were observed to have relatively lower interest. Conclusions: Based on the results of this study, it was concluded that research related to department stores has so far been conducted in a limited scope, and it is insufficient to provide clues for department stores to secure competitiveness against online platforms. Therefore, it is suggested that additional research be conducted on topics such as the true role of department stores in the retail industry, consumer reinterpretation, customer value and lifetime value, department stores as future retail spaces, ethical management, and transparent ESG management.

Keywords

Acknowledgement

본 연구는 IFDC 2023 에서 요약본으로 발표한 것으로, 이 논문은 장안대학교 2023 년도 연구비 지원에 의하여 연구되었음.

References

  1. Bianchi, F., Terragni, S., Hovy, D., Nozza, D., & Fersini, E. (2020). Cross-lingual contextualized topic models with zero-shot learning. arXiv preprint arXiv:2004.07737.
  2. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of machine Learning research, 3(Jan), 993-1022.
  3. Bodrunova, S. S., Orekhov, A. V., Blekanov, I. S., Lyudkevich, N. S., & Tarasov, N. A. (2020). Topic detection based on sentence embeddings and agglomerative clustering with markov moment. Future Internet, 12(9), 144. https://doi.org/10.3390/fi12090144
  4. Dennis, C., Murphy, J., Marsland, D., Cockett, T., & Patel, T. (2002). Measuring image: shopping centre case studies. The International Review of Retail, Distribution and Consumer Research, 12(4), 355-373. https://doi.org/10.1080/09593960210151153
  5. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
  6. Fevotte, C., & Idier, J. (2011). Algorithms for nonnegative matrix factorization with the β-divergence. Neural computation, 23(9), 2421-2456. https://doi.org/10.1162/NECO_a_00168
  7. Gao, T. L., & Ko, H. J. (2017). A study on the last mile delivery for B2C fulfillment of fresh food e-commerce in China. E-Trade Review, 15(2), 51-71. https://doi.org/10.17255/ETR.15.2.201705.51
  8. Griffis, S. E., Rao, S., Goldsby, T. J., & Niranjan, T. T. (2012). The customer consequences of returns in online retailing: An empirical analysis. Journal of Operations Management, 30(4), 282-294. https://doi.org/10.1016/j.jom.2012.02.002
  9. Griffiths, T, L., & Steyvers, M. (2004). Finding scientific topics, in Proceedings of the National Academy of Sciences, 101 (Supplement 1), 5228-5235. https://doi.org/10.1073/pnas.0307752101.
  10. Grootendorst, M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv preprint arXiv:2203.05794.
  11. Gross, M. (2015). Exploring the acceptance of technology for mobile shopping: An empirical investigation among Smartphone users. The International Review of Retail, Distribution and Consumer Research, 25(3), 215-235. https://doi.org/10.1080/09593969.2014.988280
  12. Holmes, A., Byrne, A., & Rowley, J. (2014). Mobile shopping behaviour: Insights into attitudes, shopping process involvement and location. International Journal of Retail & Distribution Management, 42(1), 25-39. https://doi.org/10.1108/IJRDM-10-2012-0096
  13. Jelodar, H., Wang, Y., Yuan, C., Feng, X., Jiang, X., Li, Y., & Zhao, L. (2019). Latent dirichlet allocation(LDA) and topic modeling: Models, applications, a survey. Multimedia Tools and Applications, 78(11), 15169-15211. https://doi.org/10.1007/s11042-018-6894-4
  14. Jiang, Y., Lai, P., Chang, C. H., Yuen, K. F., Li, S., & Wang, X. (2021). Sustainable management for fresh food e-commerce logistics services. Sustainability, 13(6). 3456; https://doi.org/10.3390/su13063456
  15. Kim, S. W., & Yang, K. D. (2022). Topic model augmentation and extension method using LDA and BERTopic. Korean Society for Information Management, 39(3), 99-132.
  16. Ko, Y. S., Lee, S. B., Cha, M. J., Kim, S. D., Lee, J. H., Ham, J. Y., & Song, M. (2022). Topic modeling insomnia social media corpus using BERTopic and building automatic deep learning classification model. Korean Society for Information Management, 39(2), 111-129.
  17. Kollmann, T., Kuckertz, A., & Kayser, I. (2012). Cannibalization or synergy? Consumers' channel selection in online-offline multichannel systems. Journal of Retailing and Consumer Services, 19(2), 186-194. https://doi.org/10.1016/j.jretconser.2011.11.008
  18. Korcham (2021). Distribution logistics statistics for 2021. Seoul: Korea Chamber of Commerce and Industry Distribution and Logistics Agency.
  19. Li, H., & Kannan, P. K. (2014). Attributing conversions in a multichannel online marketing environment: An empirical model and a field experiment. Journal of Marketing Research, 51(1), 40-56. https://doi.org/10.1509/jmr.13.0050
  20. MOTIE(2022). Ministry of Trade, Industry and Energy. Annual '21, December '21 sales trends of major distribution companies. Press release dated January 27, 2022.
  21. Nguyen, D. H., de Leeuw, S., Dullaert, W., & Foubert, B. P. (2019). What is the right delivery option for you? Consumer preferences for delivery attributes in online retailing. Journal of Business Logistics, 40(4), 299-321. https://doi.org/10.1111/jbl.12210
  22. Park, C. W. (2015). Practical distribution theory. Seoul: Cheongnam Book Publishing House.
  23. Shang, G., Pekgun, P., Ferguson, M., & Galbreth, M. (2017). How much do online consumers really value free product returns? Evidence from eBay. Journal of Operations Management, 53, 45-62. https://doi.org/10.1016/j.jom.2017.07.001
  24. Tokar, T., Williams, B. D., & Fugate, B. S. (2020). I heart logistics-Just don't ask me to pay for it: Online shopper behavior in response to a delivery carrier upgrade and subsequent shipping charge increase. Journal of Business Logistics, 41(3), 182-205. https://doi.org/10.1111/jbl.12239
  25. Yang, H. C. (2022). Analysis of distribution industry research trends using BERTopic and LDA. Journal of Creativity and Innovation (JCI), 15(4), 71-103.
  26. Yang, W. R., & Yang, H. C. (2022). Topic modeling analysis of social media marketing using BERTopic and LDA. Journal of Industrial Disribution & Business, 13(9), 39-52.
  27. Youn, M. K., & Kim, Y. O. (2017). Distribution theory. Seoul: Dunam Publishing House.