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      다국어 초록 (Multilingual Abstract) kakao i 다국어 번역

      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 studys 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
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      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 w...

      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 studys 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

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      참고문헌 (Reference)

      1 임좌상, "한국어 트위터의 감정 분류를 위한 기계학습의 실증적 비교" 한국멀티미디어학회 17 (17): 232-239, 2014

      2 진설아, "트위터 데이터를 이용한 네트워크 기반 토픽 변화 추적 연구" 한국정보관리학회 30 (30): 285-302, 2013

      3 배정환, "텍스트 마이닝을 이용한2012년 한국대선 관련 트위터 분석" 한국지능정보시스템학회 19 (19): 141-156, 2013

      4 박종도, "커뮤니티 기반 Q&A서비스에서의 질의 할당을 위한 이용자의 관심 토픽 분석에 관한 연구" 한국정보관리학회 32 (32): 397-412, 2015

      5 김진화, "온라인 리뷰를 활용한 사용자 이해 및 서비스 가치 증대" 한국정보시스템학회 20 (20): 21-36, 2011

      6 정원진, "스마트폰을 통한 모바일 쇼핑 시 모바일 쇼핑몰의 이용성이 구매의도에 미치는 영향" 대한경영학회 25 (25): 1769-1791, 2012

      7 이재영, "스마트 모바일 환경에서 모바일 쇼핑에 대한 소비자의 인식" 한국디지털디자인학회 11 (11): 399-410, 2011

      8 구철모, "모바일 커머스 사용 만족의 선행요인과 결과에 대한 연구" 한국경영정보학회 8 (8): 105-123, 2006

      9 이영철, "모바일 커머스 (M-Commerce) 대학생 이용자에 관한 탐색적 연구" 한국지역언론학회 12 (12): 382-418, 2012

      10 최상희, "동시출현단어분석을 이용한 연관영화정보 분석 연구" 한국정보관리학회 31 (31): 161-178, 2014

      1 임좌상, "한국어 트위터의 감정 분류를 위한 기계학습의 실증적 비교" 한국멀티미디어학회 17 (17): 232-239, 2014

      2 진설아, "트위터 데이터를 이용한 네트워크 기반 토픽 변화 추적 연구" 한국정보관리학회 30 (30): 285-302, 2013

      3 배정환, "텍스트 마이닝을 이용한2012년 한국대선 관련 트위터 분석" 한국지능정보시스템학회 19 (19): 141-156, 2013

      4 박종도, "커뮤니티 기반 Q&A서비스에서의 질의 할당을 위한 이용자의 관심 토픽 분석에 관한 연구" 한국정보관리학회 32 (32): 397-412, 2015

      5 김진화, "온라인 리뷰를 활용한 사용자 이해 및 서비스 가치 증대" 한국정보시스템학회 20 (20): 21-36, 2011

      6 정원진, "스마트폰을 통한 모바일 쇼핑 시 모바일 쇼핑몰의 이용성이 구매의도에 미치는 영향" 대한경영학회 25 (25): 1769-1791, 2012

      7 이재영, "스마트 모바일 환경에서 모바일 쇼핑에 대한 소비자의 인식" 한국디지털디자인학회 11 (11): 399-410, 2011

      8 구철모, "모바일 커머스 사용 만족의 선행요인과 결과에 대한 연구" 한국경영정보학회 8 (8): 105-123, 2006

      9 이영철, "모바일 커머스 (M-Commerce) 대학생 이용자에 관한 탐색적 연구" 한국지역언론학회 12 (12): 382-418, 2012

      10 최상희, "동시출현단어분석을 이용한 연관영화정보 분석 연구" 한국정보관리학회 31 (31): 161-178, 2014

      11 서선경, "동시출현단어 분석 기반 오픈 액세스 분야 지적구조에 관한 연구" 한국비블리아학회 24 (24): 207-228, 2013

      12 Mudambi, S. M, "What Makes a Helpful Review? A Study of Customer Reviews on Amazon. Com" 34 (34): 185-200, 2010

      13 Wu, J.-H., "What Drives Mobile Commerce?: An Empirical Evaluation of the Revised Technology Acceptance Model" 42 (42): 719-729, 2005

      14 Sandström, S, "Value in Use through Service Experience" 18 (18): 112-126, 2008

      15 Kouloumpis, E., "Twitter Sentiment Analysis: The Good the Bad and the Omg!" 11 : 538-541, 2011

      16 Chevalier, J. A., "The Effect of Word of Mouth on Sales: Online Book Reviews" 43 (43): 345-354, 2006

      17 Nord, C, "Text Analysis in Translation: Theory, Methodology, and Didactic Application of a Model for Translation-Oriented Text Analysis" Rodopi 2005

      18 Coughlan, M., "Survey Research: Process and Limitations" 16 (16): 9-15, 2009

      19 Somasundaran, S., "Supervised and Unsupervised Methods in Employing Discourse Relations for Improving Opinion Polarity Classification" 1 : 170-179, 2009

      20 Broderick, A. J., "Service Quality in Internet Banking: The Importance of Customer Role" 20 (20): 327-335, 2002

      21 Agarwal, A., "Sentiment Analysis of Twitter Data" 30-38, 2011

      22 Melville, P., "Sentiment Analysis of Blogs by Combining Lexical Knowledge with Text Classification" 1275-1284, 2009

      23 Abbasi, A., "Sentiment Analysis in Multiple Languages: Feature Selection for Opinion Classification in Web Forums" 26 (26): 12-, 2008

      24 LOU, D.-c., "Semantic Polarity Analysis and Opinion Mining on Chinese Review Sentences [J]" 11 : 30-45, 2006

      25 Dragut, E. C, "Polarity Consistency Checking for Domain Independent Sentiment Dictionaries" 27 (27): 838-851, 2015

      26 Davis, F. D., "Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology" 13 (13): 319-340, 1989

      27 Pang, B., "Opinion Mining and Sentiment Analysis" 2 (2): 1-135, 2008

      28 Froehle, C. M., "New Measurement Scales for Evaluating Perceptions of the Technology-Mediated Customer Service Experience" 22 (22): 1-21, 2004

      29 Xianghua, F., "Multi-Aspect Sentiment Analysis for Chinese Online Social Reviews Based on Topic Modeling and Hownet Lexicon" 37 : 186-195, 2013

      30 Hu, M., "Mining and Summarizing Customer Reviews" 168-177, 2004

      31 Piao, S, "Mining Opinion Polarity Relations of Citations" 366-371, 2007

      32 Collier, J. E., "Measuring Service Quality in E-Retailing" 8 (8): 260-275, 2006

      33 Blei, D. M, "Latent Dirichlet Allocation" 3 : 993-1022, 2003

      34 Zhang, W., "Improve the Effectiveness of the Opinion Retrieval and Opinion Polarity Classification" 1415-1416, 2008

      35 Denecke, K, "Health Web Science" Springer 83-98, 2015

      36 Park, S., "Efficient Extraction of Domain Specific Sentiment Lexicon with Active Learning" 56 : 38-44, 2015

      37 Kostyra, D. S., "Decomposing the Effects of Online Customer Reviews on Brand, Price, and Product Attributes" 2015

      38 Ha, S., "Consumer E-Shopping Acceptance: Antecedents in a Technology Acceptance Model" 62 (62): 565-571, 2009

      39 Pikkarainen, T., "Consumer Acceptance of Online Banking: An Extension of the Technology Acceptance Model" 14 (14): 224-235, 2004

      40 Pavlou, P. A., "Consumer Acceptance of Electronic Commerce: Integrating Trust and Risk with the Technology Acceptance Model" 7 (7): 101-134, 2005

      41 Koufaris, M., "Applying the Technology Acceptance Model and Flow Theory to Online Consumer Behavior" 13 (13): 205-223, 2002

      42 Amoako-Gyampah, K, "An Extension of the Technology Acceptance Model in an Erp Implementation Environment" 41 (41): 731-745, 2004

      43 Venkatesh, V., "A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies" 46 (46): 186-204, 2000

      44 Jøsang, A., "A Survey of Trust and Reputation Systems for Online Service Provision" 43 (43): 618-644, 2007

      45 Pang, B., "A Sentimental Education:Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts" 2004

      46 Teh, Y. W., "A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation" 1353-1360, 2006

      47 Nilson Korea Click, "35th Survery Reports of Internet User" 2015

      48 Nilson Korea Click, "32th Survery Reports of Internet User" 2014

      49 Statistics Korea, "2015 1/4 Trend in Online Shopping"

      50 KISA, "2014 Mobile Internet Usage Statistics"

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      연월일 이력구분 이력상세 등재구분
      2027 평가 재인증평가 신청대상 (재인증)
      2021-01-01 등재 등재학술지 유지 (재인증) KCI등재
      2018-01-01 등재 등재학술지 유지 (등재유지) KCI등재
      2015-03-25 학회명변경 영문명 : 미등록 -> Korea Intelligent Information Systems Society KCI등재
      2015-03-17 학술지명변경 외국어명 : 미등록 -> Journal of Intelligence and Information Systems KCI등재
      2015-01-01 등재 등재학술지 유지 (등재유지) KCI등재
      2011-01-01 등재 등재학술지 유지 (등재유지) KCI등재
      2009-01-01 등재 등재학술지 유지 (등재유지) KCI등재
      2008-02-11 학술지명변경 한글명 : 한국지능정보시스템학회 논문지 -> 지능정보연구 KCI등재
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      2003-01-01 등재 등재후보 1차 PASS (등재후보1차) KCI등재후보
      2001-07-01 등재 등재후보학술지 선정 (신규평가) KCI등재후보
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      기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
      2016 1.51 1.51 1.99
      KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
      1.78 1.54 2.674 0.38
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