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A Comparative Analysis of Social Commerce and Open Market Using User Reviews in Korean Mobile Commerce

사용자 리뷰를 통한 소셜커머스와 오픈마켓의 이용경험 비교분석

  • 채승훈 (아주대학교 e-비즈니스학과) ;
  • 임재익 (아주대학교 e-비즈니스학과) ;
  • 강주영 (아주대학교 e-비즈니스학과)
  • Received : 2015.11.22
  • Accepted : 2015.12.12
  • Published : 2015.12.30

Abstract

Mobile commerce provides a convenient shopping experience in which users can buy products without the constraints of time and space. Mobile commerce has already set off a mega trend in Korea. The market size is estimated at approximately 15 trillion won (KRW) for 2015, thus far. In the Korean market, social commerce and open market are key components. Social commerce has an overwhelming open market in terms of the number of users in the Korean mobile commerce market. From the point of view of the industry, quick market entry, and content curation are considered to be the major success factors, reflecting the rapid growth of social commerce in the market. However, academics' empirical research and analysis to prove the success rate of social commerce is still insufficient. Henceforward, it is to be expected that social commerce and the open market in the Korean mobile commerce will compete intensively. So it is important to conduct an empirical analysis to prove the differences in user experience between social commerce and open market. This paper is an exploratory study that shows a comparative analysis of social commerce and the open market regarding user experience, which is based on the mobile users' reviews. Firstly, this study includes a collection of approximately 10,000 user reviews of social commerce and open market listed Google play. A collection of mobile user reviews were classified into topics, such as perceived usefulness and perceived ease of use through LDA topic modeling. Then, a sentimental analysis and co-occurrence analysis on the topics of perceived usefulness and perceived ease of use was conducted. The study's results demonstrated that social commerce users have a more positive experience in terms of service usefulness and convenience versus open market in the mobile commerce market. Social commerce has provided positive user experiences to mobile users in terms of service areas, like 'delivery,' 'coupon,' and 'discount,' while open market has been faced with user complaints in terms of technical problems and inconveniences like 'login error,' 'view details,' and 'stoppage.' This result has shown that social commerce has a good performance in terms of user service experience, since the aggressive marketing campaign conducted and there have been investments in building logistics infrastructure. However, the open market still has mobile optimization problems, since the open market in mobile commerce still has not resolved user complaints and inconveniences from technical problems. This study presents an exploratory research method used to analyze user experience by utilizing an empirical approach to user reviews. In contrast to previous studies, which conducted surveys to analyze user experience, this study was conducted by using empirical analysis that incorporates user reviews for reflecting users' vivid and actual experiences. Specifically, by using an LDA topic model and TAM this study presents its methodology, which shows an analysis of user reviews that are effective due to the method of dividing user reviews into service areas and technical areas from a new perspective. The methodology of this study has not only proven the differences in user experience between social commerce and open market, but also has provided a deep understanding of user experience in Korean mobile commerce. In addition, the results of this study have important implications on social commerce and open market by proving that user insights can be utilized in establishing competitive and groundbreaking strategies in the market. The limitations and research direction for follow-up studies are as follows. In a follow-up study, it will be required to design a more elaborate technique of the text analysis. This study could not clearly refine the user reviews, even though the ones online have inherent typos and mistakes. This study has proven that the user reviews are an invaluable source to analyze user experience. The methodology of this study can be expected to further expand comparative research of services using user reviews. Even at this moment, users around the world are posting their reviews about service experiences after using the mobile game, commerce, and messenger applications.

국내 모바일 커머스 시장은 현재 소셜커머스가 이용자 수 측면에서 오픈마켓을 압도하고 있는 상황이다. 산업계에서는 모바일 시장에서 소셜커머스의 성장에 대해 빠른 모바일 시장진입, 큐레이션 모델 등을 주요 성공요인으로 제시하고 있지만, 이에 대한 학계의 실증적인 연구 및 분석은 아직 미미한 상황이다. 본 연구에서는 사용자 리뷰를 바탕으로 모바일 소셜커머스와 오픈마켓의 사용자 이용경험을 비교 분석하는 탐험적인 연구를 수행하였다. 먼저 본 연구는 구글 플레이에 등록된 국내 소셜커머스 주요 3개 업체와 오픈마켓 주요 3개 업체의 모바일 앱 리뷰를 수집하였다. 본 연구는 LDA 토픽모델링을 통해 1만여건에 달하는 모바일 소셜커머스와 오픈마켓 사용자 리뷰를 지각된 유용성과 지각된 편리성 토픽으로 분류한 뒤 감정분석과 동시출현단어분석을 수행하였다. 이를 통해 본 연구는 국내 모바일 커머스 상에서 오픈마켓 이용자들에 비해 소셜커머스 이용자들이 서비스와 이용편리성 측면에서 더 긍정적인 경험을 하고 있음을 증명하였다. 소셜커머스는 '배송', '쿠폰', '할인'을 중심으로 서비스 측면에서 이용자들에게 긍정적인 이용경험을 이끌어내고 있는 반면, 오픈마켓의 경우 '로그인 안됨', '상세보기 불편', '멈춤'과 같은 기술적 문제 및 불편으로 인한 이용자 불만이 높았다. 이와 같이 본 연구는 사용자 리뷰를 통해 서비스 이용경험을 효과적으로 비교 분석할 수 있는 탐험적인 실증연구법을 제시하였다. 구체적으로 본 연구는 LDA 토픽모델링과 기술수용모형을 통해 사용자 리뷰를 서비스와 기술 토픽으로 분류하여 효과적으로 분석할 수 있는 새로운 방법을 제시하였다는 점에서 의의가 있다. 또한 본 연구의 결과는 향후 소셜커머스와 오픈마켓의 경쟁 및 벤치마킹 전략에 중요하게 활용될 수 있을 것으로 기대된다.

Keywords

References

  1. Abbasi, A., H. Chen, and A. Salem, "Sentiment Analysis in Multiple Languages: Feature Selection for Opinion Classification in Web Forums," ACM Transactions on Information Systems (TOIS), Vol.26, No.3(2008), 12.
  2. Agarwal, A., B. Xie, I. Vovsha, O. Rambow, and R. Passonneau, "Sentiment Analysis of Twitter Data," Proceedings of the Workshop on Languages in Social Media, (2011), 30-38.
  3. Amoako-Gyampah, K. and A. F. Salam, "An Extension of the Technology Acceptance Model in an Erp Implementation Environment," Information & Management, Vol.41, No.6(2004), 731-745. https://doi.org/10.1016/j.im.2003.08.010
  4. Bae, J.-h., J.-e. Son, and M. Song, "Analysis of Twitter for 2012 South Korea Presidential Election by Text Mining Techniques," Journal of Intelligence and Information Systems, Vol.19, No.3(2013), 141-156. https://doi.org/10.13088/jiis.2013.19.3.141
  5. Blei, D. M., A. Y. Ng, and M. I. Jordan, "Latent Dirichlet Allocation," the Journal of machine Learning research, Vol.3(2003), 993-1022.
  6. Broderick, A. J. and S. Vachirapornpuk, "Service Quality in Internet Banking: The Importance of Customer Role," Marketing Intelligence & Planning, Vol.20, No.6(2002), 327-335. https://doi.org/10.1108/02634500210445383
  7. Chevalier, J. A. and D. Mayzlin, "The Effect of Word of Mouth on Sales: Online Book Reviews," Journal of marketing research, Vol.43, No.3(2006), 345-354. https://doi.org/10.1509/jmkr.43.3.345
  8. Choi, S., "An Analysis of Related Movie Information Using the Co-Word Method," Journal of the Korean Society for Information Management, Vol.31, No.4(2014), 161-178. https://doi.org/10.3743/KOSIM.2014.31.4.161
  9. Collier, J. E. and C. C. Bienstock, "Measuring Service Quality in E-Retailing," Journal of service research, Vol.8, No.3(2006), 260-275. https://doi.org/10.1177/1094670505278867
  10. Coughlan, M., P. Cronin, and F. Ryan, "Survey Research: Process and Limitations," International Journal of Therapy and Rehabilitation, Vol.16, No.1(2009), 9-15. https://doi.org/10.12968/ijtr.2009.16.1.37935
  11. Davis, F. D., "Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology," MIS Quarterly, Vol.13 No.3(1989), 319-340. https://doi.org/10.2307/249008
  12. Denecke, K., "Sentiment Analysis from Medical Texts," Health Web Science, Springer, 2015, 83-98.
  13. Dragut, E. C., H. Wang, P. Sistla, C. Yu, and W. Meng, "Polarity Consistency Checking for Domain Independent Sentiment Dictionaries," IEEE Transactions on Knowledge and Data Engineering, Vol.27, No.3(2015), 838-851. https://doi.org/10.1109/TKDE.2014.2339855
  14. Froehle, C. M. and A. V. Roth, "New Measurement Scales for Evaluating Perceptions of the Technology-Mediated Customer Service Experience," Journal of Operations Management, Vol.22, No.1(2004), 1-21. https://doi.org/10.1016/j.jom.2003.12.004
  15. Ha, S. and L. Stoel, "Consumer E-Shopping Acceptance: Antecedents in a Technology Acceptance Model," Journal of Business Research, Vol.62, No.5(2009), 565-571. https://doi.org/10.1016/j.jbusres.2008.06.016
  16. Hu, M. and B. Liu, "Mining and Summarizing Customer Reviews," Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, (2004), 168-177.
  17. Josang, A., R. Ismail, and C. Boyd, "A Survey of Trust and Reputation Systems for Online Service Provision," Decision support systems, Vol.43, No.2(2007), 618-644. https://doi.org/10.1016/j.dss.2005.05.019
  18. Jin, S. A., C. E. Heo, Y. K. Jeong, and M. Song, "Topic-Network Based Topic Shift Detection on Twitter," Journal of the Korean Society for Information Management, Vol.30, No.1(2013), 285-302. https://doi.org/10.3743/KOSIM.2013.30.1.285
  19. Jung, W.-J., "The Effects of Usability of Mobile Shopping Malls on Customer's Intention to Buy," Korean Journal of Business Administration, Vol.25, No.3(2012), 1769-1791.
  20. Kim, J., H. Byeon, and S. H. Lee, "Enhancement of User Understanding and Service Value Using Online Reviews," The Journal of Information Systems, Vol.20, No. 2(2011), 21-36. https://doi.org/10.5859/KAIS.2011.20.2.21
  21. KISA, "2014 Mobile Internet Usage Statistics," Korea Internet & Security Agency, 2014.
  22. Koo, C., Y. J. Kim, and K. Nam, "Antecedents of Mobile Commerce Satisfaction and Outcomes: Empirical Test," Information Systems Review, Vol.8, No.3(2006), 105-123.
  23. Kostyra, D. S., J. Reiner, M. Natter, and D. Klapper, "Decomposing the Effects of Online Customer Reviews on Brand, Price, and Product Attributes," International Journal of Research in Marketing, (2015).
  24. Koufaris, M., "Applying the Technology Acceptance Model and Flow Theory to Online Consumer Behavior," Information systems research, Vol.13, No.2(2002), 205-223. https://doi.org/10.1287/isre.13.2.205.83
  25. Kouloumpis, E., T. Wilson, and J. Moore, "Twitter Sentiment Analysis: The Good the Bad and the Omg!," Icwsm, Vol. 11(2011), 538-541.
  26. Lee, J. and S. Kim, "Customer's Cognitions on Mobile Shopping in Smart Mobile Environment," Journal of Digital Design, Vol.11, No.1(2011), 399-410. https://doi.org/10.17280/jdd.2011.11.1.038
  27. Lee, Y. C. and Y. J. Choi, "An Exploratory Research on College Students' Usages of Mobile Commerce," Journal of Communication Science, Vol.12, No.4(2012), 382-418.
  28. Lim, J.-S. and J.-M. Kim, "An Empirical Comparison of Machine Learning Models for Classifying Emotions in Korean Twitter," Journal of Korea Multimedia Society, Vol.17, No.2(2014), 232-239. https://doi.org/10.9717/kmms.2014.17.2.232
  29. LOU, D.-c. and T.-f. YAO, "Semantic Polarity Analysis and Opinion Mining on Chinese Review Sentences [J]," Journal of Computer Applications, Vol.11(2006), 30-45.
  30. Melville, P., W. Gryc, and R. D. Lawrence, "Sentiment Analysis of Blogs by Combining Lexical Knowledge with Text Classification," Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, (2009), 1275-1284.
  31. Mudambi, S. M. and D. Schuff, "What Makes a Helpful Review? A Study of Customer Reviews on Amazon. Com," MIS quarterly, Vol.34, No.1(2010), 185-200. https://doi.org/10.2307/20721420
  32. Nilson Korea Click, 32th Survery Reports of Internet User, Nilson Korea Market Report, 2014.
  33. Nilson Korea Click, 35th Survery Reports of Internet User, Nilson Korea Market Report, 2015.
  34. Nord, C., Text Analysis in Translation: Theory, Methodology, and Didactic Application of a Model for Translation-Oriented Text Analysis, Rodopi, 2005.
  35. Pang, B. and L. Lee, "Opinion Mining and Sentiment Analysis," Foundations and trends in information retrieval, Vol.2, No.1-2(2008), 1-135. https://doi.org/10.1561/1500000011
  36. Pang, B. and L. Lee, "A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts," Proceedings of the 42nd annual meeting on Association for Computational Linguistics, (2004).
  37. Park, J. D., "A Study on Mapping Users' Topic Interest for Question Routing for Communitybased Q&A Service," Journal of the Korean Society for Information Management, Vol.32, No.3(2015), 397-412. https://doi.org/10.3743/KOSIM.2015.32.3.397
  38. Park, S., W. Lee, and I.-C. Moon, "Efficient Extraction of Domain Specific Sentiment Lexicon with Active Learning," Pattern Recognition Letters, Vol.56(2015), 38-44. https://doi.org/10.1016/j.patrec.2015.01.004
  39. Pavlou, P. A., "Consumer Acceptance of Electronic Commerce: Integrating Trust and Risk with the Technology Acceptance Model," International journal of electronic commerce, Vol.7, No.3(2005), 101-134.
  40. Piao, S., S. Ananiadou, Y. Tsuruoka, Y. Sasaki, and J. McNaught, "Mining Opinion Polarity Relations of Citations," International Workshop on Computational Semantics (IWCS), (2007), 366-371.
  41. Pikkarainen, T., K. Pikkarainen, H. Karjaluoto, and S. Pahnila, "Consumer Acceptance of Online Banking: An Extension of the Technology Acceptance Model," Internet research, Vol. 14, No.3(2004), 224-235. https://doi.org/10.1108/10662240410542652
  42. Sandström, S., B. Edvardsson, P. Kristensson, and P. Magnusson, "Value in Use through Service Experience," Managing Service Quality: An International Journal, Vol.18, No.2(2008), 112-126. https://doi.org/10.1108/09604520810859184
  43. Seo, S. and E. Chung, "Domain Analysis on the Field of Open Access by Co-Word Analysis," Journal of the Korean Biblia Society For Library And Information Science, Vol.24, No.1(2013), 207-228. https://doi.org/10.14699/kbiblia.2013.24.1.207
  44. Somasundaran, S., G. Namata, J. Wiebe, and L. Getoor, "Supervised and Unsupervised Methods in Employing Discourse Relations for Improving Opinion Polarity Classification," Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, Vol.1(2009), 170-179.
  45. Statistics Korea, 2015 1/4 Trend in Online Shopping, 2015.
  46. Teh, Y. W., D. Newman, and M. Welling, "A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation," Advances in neural information processing systems, (2006), 1353-1360.
  47. Venkatesh, V. and F. D. Davis, "A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies," Management science, Vol.46, No.2(2000), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926
  48. Wu, J.-H. and S.-C. Wang, "What Drives Mobile Commerce?: An Empirical Evaluation of the Revised Technology Acceptance Model," Information & management, Vol.42, No.5(2005), 719-729. https://doi.org/10.1016/j.im.2004.07.001
  49. Xianghua, F., L. Guo, G. Yanyan, and W. Zhiqiang, "Multi-Aspect Sentiment Analysis for Chinese Online Social Reviews Based on Topic Modeling and Hownet Lexicon," Knowledge-Based Systems, Vol.37(2013), 186-195. https://doi.org/10.1016/j.knosys.2012.08.003
  50. Zhang, W., L. Jia, C. Yu, and W. Meng, "Improve the Effectiveness of the Opinion Retrieval and Opinion Polarity Classification," Proceedings of the 17th ACM conference on Information and knowledge management, (2008), 1415-1416.

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