소비자의 인터넷 활동과 계란 구매 간의 연관성 분석 - 계란 살충제 성분 검출 사건 사례를 중심으로 -

• 영문명: Analysis of the Relationship between Internet Activity and Egg Purchases : A Case of the Egg Pesticide Detection Incident in 2017
• 발행기관: 한국농식품정책학회
• 저자명: 노호영(Rho, Ho Young) 김태영(Kim, Taeyoung) 김성용(Kim, Sung-Yong)
• 간행물 정보: 『농업경영.정책연구』농업경영·정책연구 46권 3호, 358~386쪽, 전체 29쪽
• 주제분류: 농수해양 > 식품과학
• 파일형태: PDF
• 발행일자: 2019.09.30

영문 초록

We analyze the relationship between Internet activity and egg purchases following the incident of egg pesticide detection in 2017. For the purpose, we collected the internet search index provided by NAVER Data Lab and consumers’ sentiment indices (negative, positive, and neutral) generated from hundred thousands of texts extracted from cafés and blogs of NAVER and Daum, and consumers daily egg purchases which are obtained from the Agri-food Consumers Panel Survey conducted by the Korea Rural Development Administration. The main results are as follows. First, the Internet search index and negative sentiment index of 1 days ago had significant effects on the purchasing amount of eggs, while their purchase amounts did not affect Internet activities. Second, the 1% increase in the internet search index and the relative negative sentiment index decreases the egg purchase by 0.053% and 0.17%, respectively, while the relative positive sentiment index does not affect the egg purchase. These findings show that social media usage data such as Internet search volume, sentiment index can be more efficient and quicker measures (or tools) than existing alternatives to identify food consumption issues or to predict short-time trends of food products. Therefore, all data related to internet and social media usage could serve as a tool to provide useful information in terms of making marketing strategy of food producers and food processing companies, and establishing the crisis management policy of agricultural policy authorities.

키워드

Egg Pesticide Detection Incident Internet Search Index Internet Sentiment Index Egg purchase VAR (Vector AutoRegressive) Model Impulse Response Function Multilevel Mixed-Effect Tobit Model