DOI QR코드

DOI QR Code

빅데이터를 이용한 "배달음식" 관련 소비자인식 변화 연구: 코로나19 발생 전·후 차이비교

Consumer Perceptions Related to "Delivery food" Using Big Data: Comparison before and after the outbreak of COVID-19

  • Choon Mi Han (Department of Foodservice Management and Nutrition, The Graduate School of Sangmyung University) ;
  • Jin Kyoung Paik (Department of Foodservice Management and Nutrition, The Graduate School of Sangmyung University) ;
  • Gye Yeoun Jeoung (Department of Foodservice Management and Nutrition, The Graduate School of Sangmyung University) ;
  • Wan Soo Hong (Department of Foodservice Management and Nutrition, The Graduate School of Sangmyung University)
  • 투고 : 2023.03.24
  • 심사 : 2023.04.24
  • 발행 : 2023.04.30

초록

Since delivery food has become a new dietary culture, this study examines consumer awareness through big data analysis. We present the direction of delivery food for healthy eating culture and identify the current state of consumer awareness. Resources for big data analysis were mainly articles written by consumers on various websites; the collection period was divided into before and after COVID-19. Results of the big data analysis revealed that before COVID-19, delivery food was recognized as a limited product as a meal concept, but after COVID-19, it was recognized as a new shopping list and a new product for home parties. This study concludes by suggesting a new direction for healthy eating culture.

키워드

과제정보

본 연구는 2022년도 식품의약품안전처의 연구개발비(22192식품위008-2)로 수행되었으며 이에 감사드립니다.

참고문헌

  1. Albalaw iA, Hambly C, Speakman JR. 2022. Frequency of restaurant, delivery and takeaway usage is not related to BMI among adults in Scotland. Nutr., 12(9):1-12
  2. Amit S, Charles J, Preeti Nayalc, Haroon Iqbal Maseehb, Aman Kumard, Achchuthan Sivapalan. 2022. Online food delivery: A systematic synthesis of literature and a framework development. Int. J. Hosp. Manag,. 104(0):103240
  3. Drieger P. 2013. Semantic network analysis as a method for visual text analytics. Procedia Soc. Behav. Sci., 79(6):4-17 https://doi.org/10.1016/j.sbspro.2013.05.053
  4. Han CM. 2022. Consumer Perception of Subscription Economy by Big Data Analysis -Analysis for Food and Beverage Product Composition for Small Business Owner-. J. Foodserv. Manag., 25(1):169-186
  5. Han CM, Choi SG, Hong WS. 2022. A study on consumer perception on "RMR (Restaurant Meal Replacement)" products using big data. J. Foodserv. Manag., 25(6):123-142
  6. Heo SJ, Bae HJ. 2020. Analysis of the consumption pattern of delivery food according to food-related lifestyle. J. Consum. Mark., 53(5):547-561
  7. Hofacker CF, Malthouse EC, Sultan F. 2016. Big data and consumer behavior: Imminent opportunities. J. Consum. Mark., 33(2):89-97 https://doi.org/10.1108/JCM-04-2015-1399
  8. Kang DM, Park JH, Byun, SG, Yoon SH, Jo IC, Cha MJ, Kang TW. 2020. Value Judgement of Delivery Food using Sentiment. Analysis. Proceedings of KIIT Conference, Seoul, Korea, pp 446-448
  9. Kim HS, Leem HS, Yong HS. 2002. Design and development of the clustering algorithm considering weight in spatial data mining. J. Intell. Inf. Syst., 8(2):177-187
  10. Korea Rural Economic Institute. 2020. "Purchasing food online is increasing due to Corona 19, and price is emphasized when purchasing" KREI holds '2020 Food Consumption Behavior Survey Online Result Presentation Contest' online. pp 1140
  11. Kwak YK, Jeon DH. 2021. A Study on the effect of Environmental Consciousness and Eco-guilt on Proenvironmental Behavior of Food Delivery Comsumer -A moderating role of Eco-guilt-. J. Foodserv. Manag., 24(6):133-157
  12. Lee YG. 2020. Impact and Prospects of COVID-19 on the Japanese Food Industry. Korea Rural Economic Institute. Types World Agric. Food Ind., 1-21
  13. Lerman J. 2013. Big data and its exclusions. Rev. Online. 55(0):2013-2014
  14. Li F. 2010. Textual analysis of corporate disclosures: a survey of theliterature. J. Account. Lit., 29(0):143-165
  15. Lohr, S. 2012. The age of big data. New York Times, 11(0):2012
  16. Matz SC, Netzer O. 2017. Using Big Data as a window into consumers' psychology. Curr. Opin. Behav. Sci., 18(10):7-12 https://doi.org/10.1016/j.cobeha.2017.05.009
  17. Poelman MP, Thornton L, Zenk SN. 2020. A cross-sectional comparison of meal delivery options in three international cities. Eur. J. Clin. Nutr., 74:1465-1473 https://doi.org/10.1038/s41430-020-0630-7
  18. Qaiser S., Ali R. 2018. Text Mining: Use of TF-IDF to Examine the Relevance of Words to Documents. Int. J. Comput. Appl. 181(1):25-29
  19. Richardson, L. 2020. Platforms, markets, and contingent calculation: The flexible arrangement of the delivered meal. Antipode, 52(3):619-636 https://doi.org/10.1111/anti.12546
  20. Song HG. 2019. A Study on Air Quality Weather Factors and Eating Out Consumption : Focused on the Big Data of Family Restaurants and Food Delivery Restaurants in Seoul. J. Foodserv. Manag. 22(4):147-169
  21. Song HG. 2021. A study on food tourism perception using big data: comparison before and after the outbreak of COVID-19. J. Foodserv. Manag., 24(5):177-200 https://doi.org/10.47584/jfm.2021.24.5.177
  22. Talwar S, Kaur P, Ahmed U, Bilgihan A, Dhir A. 2023. The dark side of convenience: How to reduce food waste induced by food delivery apps. Br. Food J., 125(1):205-225 https://doi.org/10.1108/BFJ-02-2021-0204
  23. Tan, AH. 1999. Text mining: The state of the art and the challenges. In Proceedings of the pakdd 1999 workshop on knowledge discovery from advanced databases. 8(0):65-70
  24. Yallop A, Seraphin H. 2020. Big data and analytics in tourism and hospitality: opportunities and risks. J. Tour. Futures., 6(3):257-262 https://doi.org/10.1108/JTF-10-2019-0108
  25. Yu, DG. 2021. The Perceived Service Quality of Delivery Food Application affecting Consumers' Consumption Value and Loyalty. J. Foodserv. Manag., 24(6):83-106
  26. Yun SMi, Park DH. 2022. Astudy on the behavior of singleperson households choosing delivery food menus before and after COVID-19: by applying social network analysis. J. Hosp. Tour. Stud., 24(5):37-52
  27. Bae MW. The era of 10,000 won for lunch... Delivery and dining out decreased, and the number of home-cooked meals increased 2023. Available from: https://url.kr/wl2efp, [accessed 2023.02.09.]
  28. Lee JH. Family -to -home market share expansion competition in the second leg 2021. Available from: https://url.kr/k24toz. [accessed 2021.06.03.]
  29. Song HL. Family crisis diagnosis, in -depth lighting of singleperson households, solutions? 2019. Available from: https://https://url.kr/6lpdys, [accessed 2019.01.22]