JOURNAL BROWSE
Search
Advanced SearchSearch Tips
A Comparative Analysis of Social Commerce and Open Market Using User Reviews in Korean Mobile Commerce
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
A Comparative Analysis of Social Commerce and Open Market Using User Reviews in Korean Mobile Commerce
Chae, Seung Hoon; Lim, Jay Ick; Kang, Juyoung;
  PDF(new window)
 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.
 Keywords
User review;LDA topic modeling;sentimental analysis;Co-occurrence analysis;Social commerce;Open market;Mobile commerce;
 Language
Korean
 Cited by
1.
사용자 리뷰의 평가기준 별 이슈 식별 방법론: 호텔 리뷰 사이트를 중심으로,변성호;이동훈;김남규;

지능정보연구 , 2016. vol.22. 3, pp.23-43 crossref(new window)
2.
토픽모델링 기법을 활용한 산업별 직무만족요인 비교 조사 : 잡플래닛 리뷰를 중심으로,김동욱;강주영;임재익;

한국IT서비스학회지, 2016. vol.15. 3, pp.157-171 crossref(new window)
3.
Analysis of Success Factors for Mobile Commerce using Text Mining and PLS Regression,;;;;

한국컴퓨터정보학회논문지, 2016. vol.21. 11, pp.127-134 crossref(new window)
4.
Analysis of Factors Influencing Food Purchasing Behavior of Consumers In Mobile Shopping Malls : Focusing on the Comparison of Three Types of Mobile Shopping Malls,;;;

International Journal of Contents, 2016. vol.12. 4, pp.45-52 crossref(new window)
5.
온라인 쇼핑에서 웹루밍으로의 쇼핑전환 의도에 영향을 미치는 요인에 대한 연구,최현승;양성병;

지능정보연구 , 2016. vol.22. 1, pp.19-41 crossref(new window)
6.
온라인 리뷰의 감성과 독해 용이성이 리뷰 유용성에 미치는 영향: 가산형 리뷰 유용성 정보 활용,루스 안젤리 크루즈;이홍주;

지능정보연구 , 2016. vol.22. 1, pp.43-61 crossref(new window)
7.
한국과 미국 간 모바일 앱 리뷰의 감성과 토픽 차이에 관한 탐색적 비교 분석,조혁준;강주영;정대용;

한국IT서비스학회지, 2016. vol.15. 2, pp.169-184 crossref(new window)
 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. crossref(new window)

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.

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. crossref(new window)

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. crossref(new window)

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. crossref(new window)

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. crossref(new window)

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. crossref(new window)

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. crossref(new window)

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. crossref(new window)

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. crossref(new window)

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. crossref(new window)

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. crossref(new window)

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. crossref(new window)

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.

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. crossref(new window)

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. crossref(new window)

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. crossref(new window)

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.

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. crossref(new window)

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. crossref(new window)

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. crossref(new window)

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. crossref(new window)

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.

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. crossref(new window)

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. crossref(new window)

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. crossref(new window)

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. crossref(new window)

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.