Embedded big data in packaged foods: examining consumer sentiment during the purchasing process post-COVID-19

Abstract

PurposeThis study aims to examine consumers' purchasing and consumption behavior regarding big data embedded in packaged food post-Covid-19. The second purpose is to determine whether consumer purchasing behavior varies depending on the variety and volume of big data on food packages.Design/methodology/approachSemi-structured interviews were conducted to investigate consumer sentiment regarding big embedded data in packaged foods during purchasing. Based on samples from packaged foods sold in international chain stores, interview data collected from 24 participants were subjected to systematic analytical procedures.FindingsThe results revealed that before Covid-19, consumers had positive thoughts about the expiration date, brand, and product contents but did not care much about other data. At the same time, post-Covid-19, there were changes in their attitudes and behaviors on this issue. Post-Covid-19, it has been observed that consumers have positive attitudes and behaviors toward human health and food safety issues regarding unprocessed big data in packaged foods.Originality/valueThis study provides a different perspective on consumer purchasing behavior through big data on packaged foods post-Covid-19. Embedded information in packaged foods provides important data regarding consumer purchasing behavior. As a powerful source of consumer sentiment, this data also provides a reference for consumer purchasing decisions.