Browse > Article
http://dx.doi.org/10.13088/jiis.2022.28.4.119

Youtube Mukbang and Online Delivery Orders: Analysis of Impacts and Predictive Model  

Choi, Sarah (School of MIS, Hanyang University)
Lee, Sang-Yong Tom (School of MIS, Hanyang University)
Publication Information
Journal of Intelligence and Information Systems / v.28, no.4, 2022 , pp. 119-133 More about this Journal
Abstract
One of the most important current features of food related industry is the growth of food delivery service. Another notable food related culture is, with the advent of Youtube, the popularity of Mukbang, which refers to content that records eating. Based on these background, this study intended to focus on two things. First, we tried to see the impact of Youtube Mukbang and the sentiments of Mukbang comments on the number of related food deliveries. Next, we tried to set up the predictive modeling of chicken delivery order with machine learning method. We used Youtube Mukbang comments data as well as weather related data as main independent variables. The dependent variable used in this study is the number of delivery order of fried chicken. The period of data used in this study is from June 3, 2015 to September 30, 2019, and a total of 1,580 data were used. For the predictive modeling, we used machine learning methods such as linear regression, ridge, lasso, random forest, and gradient boost. We found that the sentiment of Youtube Mukbang and comments have impacts on the number of delivery orders. The prediction model with Mukban data we set up in this study had better performances than the existing models without Mukbang data. We also tried to suggest managerial implications to the food delivery service industry.
Keywords
Online Delivery; Youtube Mukbang; Sentiment Analysis; Machine learning; Predictive model;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 권태용, 곽재영, 신주일, 양다영, 여현구, 이영재, & 윤상후. (2018). 기상자료와 기계학습을 통한 배달주문 수요예측. 한국기상학회 학술대회 논문집, 540-540.
2 이맹탁, 이준영, & 심성욱. (2020). 유튜브 (YouTube) 뷰티 인플루언 서 속성이 콘텐츠 태도, 제품 태도, 구전의도, 구매의도에 미치는 영향 연구. 광고학연구, 31(5), 117-142.
3 조정태, & 최상현. (2015). 영화리뷰 감성 분석을 통한 평점 예측 연구. 경영과 정보연구, 34(3), 161-177.
4 홍석경, & 박소정. (2016). 미디어 문화 속 먹방과 헤게모니 과정. 언론과 사회, 24(1), 105-150.
5 Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.   DOI
6 Guelman, L. (2012). Gradient boosting trees for auto insurance loss cost modeling and prediction. Expert Systems with Applications, 39(3), 3659-3667.   DOI
7 Peng, W., & Park, D. H. (2011, July). Generate adjective sentiment dictionary for social media sentiment analysis using constrained nonnegative matrix factorization. In Fifth International AAAI Conference on Weblogs and Social Media.
8 Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267-288.   DOI
9 김보라. (2019.09.26).BBQ치킨, JTBC 드라마 '멜로가 체질' 등장에 매출 평균 138% 상승. 백세시대. http://www.100ssd.co.kr/news/articleView.html?idxno=64279
10 Chiu, C. (2002). A case-based customer classification approach for direct marketing. Expert systems with Applications, 22(2), 163-168.   DOI
11 김정엽. (2016.05.04). 배달음식1호, 1968년7월냉면. 서울엔. http://www.seouland.com/arti/culture/culture_general/354.html
12 송정은, & 장원호. (2013). 유투브(YouTube) 이용자들의 참여에 따른 한류의 확산: 홍콩의 10-20대 유투브(YouTube) 이용자조사를 중심으로. 한국콘텐츠학회논문지, 13(4), 155-169.   DOI
13 권재영, 김시내, 박은지, & 송종우. (2015). 국내 배달음식 이용 건수 분석 및 예측. 응용통계연구, 28(5), 977-990.   DOI
14 김경재, 한인구, & 안현철. (2005). Support Vector Machine 을 이용한 고객구매예측모형. 지능정보연구, 11(3), 69-81.
15 김경진. (2019.04.25). [ONE SHOT] 유튜브 분야별 최고 인기 채널 & 인기 유튜버 톱3. 중앙일보 https://dcnewsj.joins.com/article/2343250
16 김대룡, 김다영, & 변수지. (2016). 날씨에 따른 배달음식 주문건수 예측. 한국기상학회 학술대회 논문집, 480-481.
17 김영민, 정석재, & 이석준. (2014). 소셜 미디어 감성분석을 통한 주가 등락 예측에 관한 연구. Entrue Journal of Information Technology, 13(3), 59-70.
18 Akaike, H. (1974). A new look at the statistical model identification. IEEE transactions on automatic control, 19(6), 716-723.   DOI
19 Schwarz, G. (1978). Estimating the dimension of a model. The annals of statistics, 461-464.
20 Firat, D. (2019). YouTube advertising value and its effects on purchase intention. Journal of Global Business Insights, 4(2), 141-155.   DOI
21 김보라. (2019.09.27). '멜로가체질'뜨자BBQ매출 껑충_PPL전략'눈에띄네'. 시장경제. https://www.meconomynews.com/news/articleView.html?idxno=33472
22 전승엽, 박성은, 김유정. (2017.09.11). [디지털스토리] 한국인이 가장 많이 시키는 배달음식은. 연합뉴스. https://www.yna.co.kr/view/AKR20170905129800797
23 정지선, 김동성, & 김종우. (2015). 온라인 언급이 기업 성과에 미치는 영향 분석: 뉴스 감성분석을 통한 기업별 주가 예측. 지능정보연구, 21(4), 37-51.   DOI