Understanding Agri-Food Traceability System User Intention in Respond to COVID-19 Pandemic: The Comparisons of Three Models

Abstract

Scientists believed the outbreak of COVID-19 could be linked to the consumption of wild animals, so food safety and hygiene have become the top concerns of the public. An agri-food traceability system becomes very important in this context because it can help the government to trace back the entire production and delivery process in case of food safety concerns. The traceability system is a complicated digitalized system because it integrates information and logistics systems. Previous studies used the technology acceptance model (TAM), information systems (IS) success model, expectation confirmation model (ECM), or extended model to explain the continuance intention of traceability system users. Very little literature can be found integrating two different models to explain user intention, not to mention comparing three models in one research context. This study proposed the technology acceptance model (TAM), technology acceptance model-information systems (TAM-IS) success, and technology acceptance model-expectation confirmation model (TAM-ECM) integrated models to evaluate the most appropriate model to explain agri-food traceability system during the COVID-19 pandemic. A questionnaire was designed based on a literature review, and 197 agri-food traceability system users were sampled. The collected data were analyzed by partial least square (PLS) to understand the explanatory power and the differences between the three models. The results showed that: (1) the TAM model has a fair explanatory power of continuance intention (62.2%), but was recommended for its’ simplicity; (2) the TAM-IS success integrated model had the best predictive power of 78.3%; and (3) the system providers should raise users’ confirmation level, so their continuance intention could be reinforced through mediators, perceived value, and satisfaction. The above findings help to understand agri-food traceability system user intention, and provide theoretical and practical implications for system providers to refine their system design.

What does digital transformation do in agriculture? A systematic literature review of Agri-food sector's digitalisation

“…Most of the studies involved by this category deal with the relationship between the challenges of the pandemic and digitalisation, namely, what possibilities may be available in case of lockdowns or restrictions for farmers and further actors within the supply chain. In the conclusions, most of them highlighted that the appropriate information flow has an even more important role to play in the relationships of the actors within the supply chain which can be strengthened by digital development, upgrading, effective communication and trust between the actors, in order to continuous flow of products (Butu et al, 2020;Desa and Jia, 2020;Kumar, Padhee and Kumar, 2020;Bahn, Yehya and Zurayk, 2021;Rowan and Casey, 2021;Senesi, Palau and Neves, 2021;Zielińska-Chmielewska, Mruk-Tomczak and Wielicka-Regulska, 2021;Constantin, Pătărlăgeanu, et al, 2021;Oruma, Misra and Fernandez-Sanz, 2021;Osório et al, 2021;Priyadarshini and Abhilash, 2021;Rengarajan et al, 2022;Sridhar et al, 2022;Tseng et al, 2022;Guo et al, 2022;Popescu and Popescu, 2022). The next most popular research topic was the subcategory of "Integration with customers/suppliers, supply chain management".…”

Consumers’ purchase intentions towards traced foods: A comparative analysis between the United Kingdom and China

“…purchasing of non-traced food" or facilitate them adopting or "switching" to traced food (Nguyen et al, 2022). The TAM employs perceived usefulness and perceived ease of use to explain people's attitudes towards, and intention to use a new technology (Davis et al, 1989), including food traceability technology (Lin & Wu, 2021;Tseng et al, 2022).…”

“…These benefits can be associated to improved food safety and quality as well as lower environmental impacts (Dang & Tran, 2021). Another pull effect may relate to the greater ease of accessing and understanding the traced information for consumers (termed as "perceived ease of use" in the TAM), as extra cognitive effort is not needed to change choices (Kim & Woo, 2016;Lin & Kuo, 2020;Tseng et al, 2022), thereby increasing perceived benefits and acceptance of traced food (Kim & Woo, 2016). Thus, it is assumed: H2: Consumers' perceived benefits associated with tracing a food product positively affect their attitudes towards (a), and intention to buy, the traced product (b).…”

Effectiveness of Food Traceability System: Chinese Consumer Food Purchase Intention During the Pandemic

“…The consumer perceived value of a product reflects the overall performance (Yuan et al, 2020) and usefulness (Tseng et al, 2022) of FTS. As stated by Ingrassia et al (2017), some new details on food provided by FTS can be sufficient to increase consumers' perceived value of the product.…”