DIGITAL LIBRARY ARCHIVE
HOME > DIGITAL LIBRARY ARCHIVE
< Previous   List   Next >  
Smart Store in Smart City: The Development of Smart Trade Area Analysis System Based on Consumer Sentiments
Full-text Download
In-Jin Yoo (Graduate School of Business IT, Kookmin University)
Bong-Goon Seo (Graduate School of Business IT, Kookmin University)
Do-Hyung Park (College of Business Administration / Graduate School of Business IT, Kookmin University)
Vol. 24, No. 1, Page: 25 ~ 52
10.13088/jiis.2018.24.1.025
Keywords
Sentiment analysis, Consumer emotions, Social network indicators, Big-data analysis, Social network analysis, Trading area analysis, Smart city
Abstract
This study performs social network analysis based on consumer sentiment related to a location in Seoul using data reflecting consumers’ web search activities and emotional evaluations associated with commerce. The study focuses on large commercial districts in Seoul. In addition, to consider their various aspects, social network indexes were combined with the trading area’s public data to verify factors affecting the area’s sales. According to R square’s change, We can see that the model has a little high R square value even though it includes only the district’s public data represented by static data. However, the present study confirmed that the R square of the model combined with the network index derived from the social network analysis was even improved much more. A regression analysis of the trading area’s public data showed that the five factors of ‘number of market district,’ ‘residential area per person,’ ‘satisfaction of residential environment,’ ‘rate of change of trade,’ and ‘survival rate over 3 years’ among twenty two variables. The study confirmed a significant influence on the sales of the trading area. According to the results, ‘residential area per person’ has the highest standardized beta value. Therefore, ‘residential area per person’ has the strongest influence on commercial sales. In addition, ‘residential area per person,’ ‘number of market district,’ and ‘survival rate over 3 years’ were found to have positive effects on the sales of all trading area. Thus, as the number of market districts in the trading area increases, residential area per person increases, and as the survival rate over 3 years of each store in the trading area increases, sales increase. On the other hand, ‘satisfaction of residential environment’ and ‘rate of change of trade’ were found to have a negative effect on sales. In the case of ‘satisfaction of residential environment,’ sales increase when the satisfaction level is low. Therefore, as consumer dissatisfaction with the residential environment increases, sales increase. The ‘rate of change of trade’ shows that sales increase with the decreasing acceleration of transaction frequency. According to the social network analysis, of the 25 regional trading areas in Seoul, Yangcheon-gu has the highest degree of connection. In other words, it has common sentiments with many other trading areas. On the other hand, Nowon-gu and Jungrang-gu have the lowest degree of connection. In other words, they have relatively distinct sentiments from other trading areas. The social network indexes used in the combination model are ‘density of ego network,’ ‘degree centrality,’ ‘closeness centrality,’ ‘betweenness centrality,’ and ‘eigenvector centrality.’ The combined model analysis confirmed that the degree centrality and eigenvector centrality of the social network index have a significant influence on sales and the highest influence in the model. ‘Degree centrality’ has a negative effect on the sales of the districts. This implies that sales decrease when holding various sentiments of other trading area, which conflicts with general social myths. However, this result can be interpreted to mean that if a trading area has low ‘degree centrality,’ it delivers unique and special sentiments to consumers. The findings of this study can also be interpreted to mean that sales can be increased if the trading area increases consumer recognition by forming a unique sentiment and city atmosphere that distinguish it from other trading areas. On the other hand, ‘eigenvector centrality’ has the greatest effect on sales in the combined model. In addition, the results confirmed a positive effect on sales. This finding shows that sales increase when a trading area is connected to others with stronger centrality than when it has common sentiments with others. This study can be used as an empirical basis for establishing and implementing a city and trading area strategy plan considering consumers’ desired sentiments. In addition, we expect to provide entrepreneurs and potential entrepreneurs entering the trading area with sentiments possessed by those in the trading area and directions into the trading area considering the district-sentiment structure
Show/Hide Detailed Information in Korean
Smart Store in Smart City: 소비자 감성기반 상권분석 시스템 개발
유인진 (국민대학교 비즈니스 IT전문대학원)
서봉군 (국민대학교 비즈니스 IT전문대학원)
박도형 (국민대학교 경영대학/비즈니스 IT전문대학원)
Keywords
감성 분석, 소비자 감성, 관계지표, 빅데이터 분석, 소셜 네트워크 분석, 상권 분석, 스마트 도시
Abstract
본 연구는 소비자들이 상권에 대하여 수행하는 웹 탐색 활동과 감성평가를 반영하는 데이터인 지역구 연관감성어휘를 기반으로 서울시 내 대형 상업 공간으로 정의할 수 있는 각 지역구 간의 연관 감성 네트워크에 대하여 소셜 네트워크 분석을 수행하였다. 나아가 도출한 소셜 네트워크 지표를 지역구 공공 데이터와 결합하여보다 다각적 측면을 고려한 지역구 상권의 매출액에 영향을 미치는 요인들을 검증하였고 그 영향력의 변화 또한 확인해 보았다. 정적 데이터로 표현되는 공공 데이터만을 통해 구성된 모형으로도 높은 설명력을 가지는 것을 확인할 수 있었으나, 소셜 네트워크 분석 결과로 도출된 네트워크 지표와 결합된 모형에서는 그 설명력이더욱 향상된 것이 확인되었다. 공공 데이터에 대한 회귀 분석 결과, 투입된 22개의 요인들 중 ‘골목 상권 수,’ ‘1인당 거주면적,’ ‘주거환경만족도,’ ‘거래증감률,’ ‘3년 이상 생존율’의 5개의 요인이 지역구 상권 매출액에 유의한 영향을 미치는 것이 확인되었다. 이후 공공 데이터와 네트워크 지표 결합 모형에서 투입된 지표들은 ‘에고네트워크의 밀도,’ ‘연결 중심성,’ ‘근접 중심성,’ ‘매개 중심성,’ ‘아이겐벡터 중심성’이며, 이 중 ‘연결 중심성’과‘아이겐벡터 중심성’이 매출액에 유의한 영향을 미치며 모형 내에서 가장 높은 영향력을 보유한 것이 확인되었다. 본 연구는 각 상권이 소비자가 원하는 감성을 고려한 도시 전략 계획 수립과 이행의 실증적 근거로 활용될수 있을 것이며, 상권에 진입하거나 재창업하는 자영업자나 잠재 창업자를 바탕으로 지역구 상권이 보유한 감성과 그 관계 구조를 고려한 상권 진입 방향성을 제공할 수 있을 것이다.
Cite this article
JIIS Style
Yoo, I.-J., B.-G. Seo, and D.-H. Park, "Smart Store in Smart City: The Development of Smart Trade Area Analysis System Based on Consumer Sentiments", Journal of Intelligence and Information Systems, Vol. 24, No. 1 (2018), 25~52.

IEEE Style
In-Jin Yoo, Bong-Goon Seo, and Do-Hyung Park, "Smart Store in Smart City: The Development of Smart Trade Area Analysis System Based on Consumer Sentiments", Journal of Intelligence and Information Systems, vol. 24, no. 1, pp. 25~52, 2018.

ACM Style
Yoo, I.-J., Seo, B.-G., and Park, D.-H., 2018. Smart Store in Smart City: The Development of Smart Trade Area Analysis System Based on Consumer Sentiments. Journal of Intelligence and Information Systems. 24, 1, 25--52.
Export Formats : BiBTeX, EndNote

Warning: include(/home/hosting_users/ev_jiisonline/www/admin/archive/advancedSearch.php) [function.include]: failed to open stream: No such file or directory in /home/hosting_users/ev_jiisonline/www/archive/detail.php on line 429

Warning: include() [function.include]: Failed opening '/home/hosting_users/ev_jiisonline/www/admin/archive/advancedSearch.php' for inclusion (include_path='.:/usr/local/php/lib/php') in /home/hosting_users/ev_jiisonline/www/archive/detail.php on line 429
@article{Yoo:JIIS:2018:712,
author = {Yoo, In-Jin and Seo, Bong-Goon and Park, Do-Hyung},
title = {Smart Store in Smart City: The Development of Smart Trade Area Analysis System Based on Consumer Sentiments},
journal = {Journal of Intelligence and Information Systems},
issue_date = {March 2018},
volume = {24},
number = {1},
month = Mar,
year = {2018},
issn = {2288-4866},
pages = {25--52},
url = {http://dx.doi.org/10.13088/jiis.2018.24.1.025 },
doi = {10.13088/jiis.2018.24.1.025},
publisher = {Korea Intelligent Information System Society},
address = {Seoul, Republic of Korea},
keywords = { Sentiment analysis, Consumer emotions, Social network indicators, Big-data analysis, Social network analysis, Trading area analysis and Smart city },
}
%0 Journal Article
%1 712
%A In-Jin Yoo
%A Bong-Goon Seo
%A Do-Hyung Park
%T Smart Store in Smart City: The Development of Smart Trade Area Analysis System Based on Consumer Sentiments
%J Journal of Intelligence and Information Systems
%@ 2288-4866
%V 24
%N 1
%P 25-52
%D 2018
%R 10.13088/jiis.2018.24.1.025
%I Korea Intelligent Information System Society