Journal of Industrial Convergence (산업융합연구)

DAEHAN Society of Industrial Management (대한산업경영학회)
권호 표지
DOI QR코드

DOI QR Code

Research Trends in e-commerce Using Topic Modeling: Focusing on SCOPUS Database

토픽 모델링을 활용한 e-commerce 연구 동향: SCOPUS DB 데이터를 중심으로

  • Tae-Gu Kang (Dept.of Digital Marketing, Konyang Cyber University)
  • 강태구 (건양사이버대학교 디지털마케팅학과)
  • Received : 2024.09.11
  • Accepted : 2024.10.20
  • Published : 2024.10.28
Download PDF
⟨ Previous Next ⟩

Abstract

E-commerce has emerged as a key economic driver in the digital age, and the importance of the e-commerce market has been highlighted, leading to rapid expansion in related research areas. This paper analyzes the research trends on e-commerce from 1996, when e-commerce emerged and research began, to the present day. To this end, we used R and LDA topic modeling techniques and conducted a validity test on the number of topics and an analysis of the predictive value of the topic model centered on the core keyword "e-commerce" using the SCOPUS, a foreign academic database. The analysis of topics showed that ecommerce, model, study, data, and online were among the important topics. Logistics was also found to be important. In the rapidly changing and complex e-commerce market environment, it is important to respond to the diversification of business models and the establishment of a stable revenue structure to survive. As the continuous growth of the e-commerce market is predicted, the results of this study can be used as basic data for entering the e-commerce market and expanding business through countermeasures and strategies.

e-commerce는 디지털 시대의 경제 핵심 요소로서 e-commerce 시장의 중요성 부각과 함께 관련 연구 영역 또한 빠르게 확장되고 있다. 본 논문은 e-commerce가 본격적으로 등장하여 연구가 시작된 1996년부터 최근까지의 e-commerce 에 대한 연구 동향을 분석하였다. 이를 위해 R과 LDA 토픽 모델링 기법으로 해외 학술 데이터베이스인 SCOPUS를 활용하여 핵심키워드인 e-commerce를 중심으로 토픽수에 대한 타당성과 토픽모델의 예측치분석을 실행과 함께 토픽에 대한 분석을 진행하였다. 토픽에 대한 분석 결과 ecommerce, model, study, data, online 등이 토픽 중 중요한 것으로 나타났다. 또한 logistics도 중요한 것으로 나타났다. 빠르고 복잡하게 변화하는 e-commerce 시장환경에서 생존을 위해서는 비즈니스 모델의 다각화와 안정적인 수익 구조 기반 마련을 위한 대응 방안 및 전략 수립의 중요성을 알 수 있다. e-commerce 시장의 지속적인 성장이 예측되는 만큼 본 연구결과를 통해 e-commerce 시장 진입 및 사업 확장을 위한 방안 및 전략 수립을 위한 기초 자료로 활용될 수 있을 것으로 보인다.

Keywords

References

  1. C. H. Park. (2020). Untact Economy and Changes in Business Eco-System, Implication for Inclusive Growth Policy - Impact of e-commerce on job and regional balance. The Journal of Internet Electronic Commerce Research, 20(5), 181-200.
  2. S. S. Kang. (2024). 'The trend is online'... World's 5th largest Korean e-commerce market. MAil Paemium. https://www.m-i.kr/news/articleView.html?idxno=1138585
  3. G. H. Lee & U. S. Kang. (2024). Competitive strategy in the domestic e-commerce market: Focusing on strengthening value chain-based resources and capabilities. Journal of Channel and Retailing, 25(1), 85-119. DOI: 10.17657/jcr.2024.1.31.5
  4. K. H. Lee & W. S. Kang. (2024). Competitive Strategy in Domestic Ecommerce Market: Focusing on Value Chain-based Resources and Capabilities. Journal of Channel and Retailing, 29(1), 85-119. DOI: 10.17657/jcr.2024.1.31.5
  5. J. K. Goo. (2021). E-commerce market preparing for the ICT Retail 4.0 era. Invest Korea. https://www.investkorea.org/ik-kr/bbs/i-112/detail.do?ntt_sn=491181
  6. S. K. Kim. (2023). E-commerce is facing a period of turbulence. What is the direction for companies to survive?. Seoul : KPMG Samjong.
  7. G. Day & R. Wensley (1988). Assessing advantage: A framework for diagnosing competitive superiority. Journal of Marketing, 52(2), 1-20.
  8. H. J. Jung. (2024). Considerations on the acceptance of digital technology in international trade. The International Commerce & Law Rwview, 101(2), 235-254.
  9. G. Zhu, M.Wang & C. Su. (2022). Prediction of consumer repurchase behavior based on LSTM neural network model. International Journal of System Assurance Engineering and Management, 13(3), 1042-1053.
  10. K. H. Kim. (2023). A Study on Customer Classification based on Customer Behavior in E-commerce Environment using Machine Learning. Doctoral dissertation. The Graduate School Chung-Ang University, Seoul.
  11. G. S. Dhanesh & G. Duthler. (2019). Relationship management through social media influencers: Effects of followers' awareness of paid dorsement. Public Relations Review, 45(3), 1017-1065. DOI: 10.1016/j.pubrev.2019.03.002
  12. l. Zou. (2024). A Study on the Impact of Logistics Service Quality, Relationship Quality and Customer Loyalty in B2C E-Commerce. Master dissertation. The Graduate School Hanyang University, Seoul.
  13. S. G. Kim., H. K. Cho & J. Y. Kang. (2016). (The) Status of Using Text Mining in Academic Research and Analysis Methods. Journal of information technology and architecture, 13(2), 317-329.
  14. J. H. Kim & S. S. Kim. (2021). A Study on the Analysis of Agricultural R&D Keywords Using Textmining Method. Journal of the Korea Academia-Industrial cooperation Society, 22(2), 721-732.
  15. D. H. Lee. (2024). A Social Network Analysis of 'The Journal of Internet Electronic Commerce Research' : from 2011 to 2020. The Journal of Internet Electronic Commerce Research, 24(3), 273-290. DOI: 10.37272/JIECR.2024.06.24.3.273
  16. Blei, D. M., A. Y. Ng. and M. I. Jordan, (2003). "Latent Dirichlet Allocation". Journal of Machine Learning Research, 3, 993-1022.
  17. E. M. Park & J. H. Seo. (2019). Effects of shared leadership, psychological empowerment and organizational justice on organizational commitment. Journal of Digital Convergence, 17(6), 177-184. DOI: 10.14400/JDC.2019.17.6.177
  18. S. T. Park & Y. K. Kim. (2019). A study on deriving an optimal route for foreign tourists through the analysis of big data. Journal of Convergence for Information Technology, 9(10), 56-63. DOI: 10.22156/CS4SMB.2019.9.10.056
  19. G. Li, J. S. Dai, E. M. Park & S. T. Park. (2017). A study on the service and trend of Fintech security based on text-mining: focused on the data of Korean online news. Journal of Computer Virology and Hacking Techniques, 13(4), 249-255. DOI: 10.1007/s11416-016-0288-9
  20. C. Sievert & K. Shirley. (2014). LDAvis: A method for visualizing and interpreting topics. In Proceedings of the workshop on interactive language learning, visualization, and interfaces. 63-70.
  21. E. H. Kim & Y. W. Suh. (2020). A Method of Calculating Topic Keywords for Topic Labeling. Korea Society of Digital Industry and Information Management, 16(3), 25-36. DOI: 10.17662/ksdim.2020.16.3.025
  22. J. B. Kim. (2024). Analyzing Trends in Organizational Effectiveness(Job Satisfaction, Organizational Commitment, Organizational Citizenship Behavior) Research: Focusing on SCOPUS DB. Journal of Industrial Convergence, 22(1), 65-73. DOI: 10.22678/JIC.2024.22.1.065
  23. J. B. Kim. (2024). Analyze Research Trends in Person-Organization Fit:Focusing on SCOPUS DB. Journal of Industrial Convergence, 22(7), 23-30. DOI: 10.22678/JIC.2024.22.7.023
  24. K. D. Suh., J. C. Choi, P. A. Choi & J. R. Jung. (2024). An Analysis of the Support Policy for Small Businesses in the Post-Covid-19 Era Using the LDA Topic Model. Journal of Industrial Convergence, 22(6), 51-59. DOI: 10.22678/JIC.2024.22.6.051
  25. K. G. Kim. (2022). Customer based Data Business Model Research in MyData Era. Master dissertation. The Graduate School Sogang University, Seoul.
  26. C. G. Park. (2017). A Study on Consumer Soju Purchase Decision Making and Brand Equity. Doctoral dissertation. The Graduate School Paichai University, Daejeong.