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Lee, B. Agri-Food Traceability System User Intention. Encyclopedia. Available online: https://encyclopedia.pub/entry/20599 (accessed on 23 July 2024).
Lee B. Agri-Food Traceability System User Intention. Encyclopedia. Available at: https://encyclopedia.pub/entry/20599. Accessed July 23, 2024.
Lee, Beyfen. "Agri-Food Traceability System User Intention" Encyclopedia, https://encyclopedia.pub/entry/20599 (accessed July 23, 2024).
Lee, B. (2022, March 15). Agri-Food Traceability System User Intention. In Encyclopedia. https://encyclopedia.pub/entry/20599
Lee, Beyfen. "Agri-Food Traceability System User Intention." Encyclopedia. Web. 15 March, 2022.
Agri-Food Traceability System User Intention
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This entry is adapted from the peer-reviewed paper 10.3390/ijerph19031371

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.

continuance intention traceability system technology acceptance model information systems success model

1. Introduction

Food plays an important role in human life, so food safety is one of the major concerns worldwide [1]. Food safety incidents, such as mad cow disease, alert consumers to be more concerned about the food they eat. Recently, due to the possibility that the COVID-19 virus is transmitted from wildlife to humans [2], consumers’ worries about food safety are enormously high. Therefore, people are willing to spend more money on safe food, i.e., traceable food with its provenance certified.
The main concerns when customers purchase food relate to the source and the hygiene of the food itself. To better understand the food quality, customers request clarity from food suppliers. Businesses should be capable to verify the content and source of their commodities to safeguard the customers in opposition to deception. In this situation, traceability and verification are essential instruments for assuring customers in conditions of food disclosure and protection, and in permitting manufacturers to obtain knowledge of their goods. Traceability allows the tracing of the supply of food at every point in the manufacturing chain, allowing the value-management methods, and reducing the manufacture of hazardous foods [3]. Food verification is the procedure through which food is analyzed to confirm if it conforms with the explanation included in its description [4]. Traceability and verification are essential elements of food protection, and correspond to basic parts of the food supply chain. A consistent validation and traceability structure can represent an important method for the safety of customers, decreasing the risk of individuals utilizing impure or tainted foods, and improving supplier management and procedure protection. Customers demonstrated inadequate understanding regarding the significance of validation and traceability of food [5][6], making the distribution of the ability and dependability of tracking techniques to improve individuals’ understanding of the position of food supervision in health security and the honesty of traceability knowledge important [7]. Food supply chains are vital to well-being. During the pandemic, countries need to maintain their daily operations. Investigators have examined the pandemic’s influence on the food business in remarkable detail [8][9][10][11]. The COVID-19 pandemic has interrupted food supply chains globally [12]. Various regulations and guidelines have been established and published in government and private segments to strengthen the food business, and avoid contamination from propagating. The Chinese State Council additionally issued a bill to avoid the pandemic contamination threats in food chains [13].
In many countries, the food and agriculture sectors are considered to be a major production and supply chain in a nation. Complex food supply chains that lack transparency and traceability make it very difficult for the government to find the outbreak origins when food safety issues occur. Thus, relevant directives, laws, as well as standards and regulations were established to ensure the traceability of food products. Consequently, the development of traceability systems throughout the food supply chains is considered to be a solution that could resolve the impact of food crises fast. Food traceability systems have progressively become the consumers’ concern, the manufacturers’ marketing strategies, and governments’ policy initiatives for the following three reasons: firstly, consumers believe food with traceability is safer than those that cannot be traced. Secondly, a food manufacturer can price traceable food higher than those without traceability. Thirdly, the government believes it helps to trace back the food origin, and speeds up the processing time during food crisis outbursts. However, even with the support of the government, not all food traceability system deployment is successful [14]; the users’ continuance intention needs to be further explored, especially during the COVID-19 pandemic.
The technology acceptance model (TAM), information systems success model (IS success model), and expectation confirmation model (ECM) have been widely used in information systems studies to understand consumers’ behavior intention. Traceability systems involve information and logistic systems, so these models are applicable to the traceability systems.

2. Traceability System

After the second mad cow disease crisis occurred in Europe in 1996, the European Union launched an agricultural traceability system to improve food safety. The prevalence of COVID-19 has made people more concerned about food safety because the origin of the disease might be from wildlife. Food traceability refers to the ability to track the flow of food or ingredients through specific stages of production, processing, and distribution [15]. When potential food safety or security issues are identified, traceability systems allow the implementation of corrective actions and pausing risks to public health. The use of food traceability systems could quickly isolate the polluted items from the supply chain, and prevent contaminated products from reaching consumers. When public food safety issues occur, government authorities interfere to avoid the potential number of illnesses or deaths of the public, and to diminish the damage on the markets. For tracing back to the food origin quickly, countries such as Australia, India, China, New Zealand, South Korea, Taiwan, Thailand, and the United States have developed agricultural traceability systems in past decades.
Besides the demand from the authorities, traceability systems could be motivated by economic incentives, in that manufacturers can differentiate their food products in the market based on their credibility [16]. Companies can attract consumer interest by showing the traceability of organic, non-GMO food, or point of origin, so the product price, customer satisfaction, and profit margin could be raised. To the consumers, food with traceability meets their expectations for food safety. Consumers are willing to pay a premium price for traceable food for the assurance of food safety [17][18][19].

3. Technology Acceptance Model (TAM)

The TAM has been widely adopted by scholars for its streamlined structure and strong explanatory power, and was first proposed by Davis [20] to explain the factors influencing the user acceptance of computer systems. The model contains only four constructs: user behavior is influenced by behavioral intention, which in turn is impacted by perceived ease of use and perceived usefulness. Davis [20] defined perceived usefulness as the subjective perception of users where they believe that using certain technologies can improve the performance of their work. Based on the “perceived value” defined by Zeithaml [21], it was defined perceived value as the overall assessment of the usefulness of the traceability system based on users’ perceptions of what they receive compared with what they give. According to the TAM, cognitive beliefs have effects on behavioral intention, so it was substituted the construct perceived usefulness with perceived value as Figure 1, and proposed the following hypotheses:
/media/item_content/202203/62313a8939610ijerph-19-01371-g001.png
Figure 1. Research Model I (TAM).
Hypothesis 1.1 (H1.1).
Perceived ease of use has a positive impact on perceived value.
Hypothesis 1.2 (H1.2).
Perceived ease of use has a positive impact on behavioral intention.
Hypothesis 1.3 (H1.3).
Perceived value has a positive impact on behavioral intention.

4. TAM-IS Integrated Model

The information systems success model (IS success model), explaining the constitution of IS success, was developed by Delone and McLean [22], and improved a decade later. The updated IS success model [23][24][25] is considered to be one of the most influential theories in the field of information systems research because it explains the production, use, and net benefits of IS. Information, system, and service quality in the IS production phase will impact the consumers’ intention to use and their satisfaction, which will then affect individual or organizational productivity.
The TAM-IS integrated model (Figure 2) is the second proposed model. This model concerned the users’ continuance intention of an agri-food traceability system. There are six variables in this model, including system quality, information quality, and service quality, which were derived from the IS success model as the predecessors of perceived ease of use and perceived value, and had a direct impact on continuance intention.
/media/item_content/202203/62313a976a0e3ijerph-19-01371-g002.png
Figure 2. Research Model II (TAM-IS Success Integrated Model).
Mustapha and Obid [26] showed a direct positive relationship between online tax service quality and perceived ease of use. Bahari et al. [27] showed a similar result in their hotel website design studies. Li and Shang [28] suggested that e-government service quality affected perceived service value. Ali and Younes [29] asserted that information quality positively affected the perceived ease of use. Machdar [30] has a similar finding in her accounting class. Tsao et al. [31] indicated system quality and e-service quality both had a positive effect on the perceived value of consumers or sellers. In an e-commerce research, Putri and Pujani [32] proved the effects of system quality, information quality, and e-service quality on perceived value. Therefore, it is reasonable to infer similar concepts, and propose the following hypotheses:
Hypothesis 2.1 (H2.1).
System quality has a positive and significant effect on perceived ease of use.
Hypothesis 2.2 (H2.2).
Information quality has a positive and significant effect on perceived ease of use.
Hypothesis 2.3 (H2.3).
Service quality has a positive and significant effect on perceived ease of use.
Hypothesis 2.4 (H2.4).
System quality has a positive and significant effect on perceived value.
Hypothesis 2.5 (H2.5).
Information quality has a positive and significant effect on perceived value.
Hypothesis 2.6 (H2.6).
Service quality has a positive and significant effect on perceived value.
Sarmento and Mesquita [33] found that ease of use has a significant positive effect on perceived value. In the research of online exchanges [34], easy navigation of the C2C website was vital for consumers to perceive value from using the platform. Based on TAM, perceived ease of use had a direct impact on behavior intention. As to the relationship between perceived value and behavior intention, Jin and Lee [35] found that perceived value had a direct influence on behavioral intention in their water park research. Similar conclusions could be drawn in the studies of Meng et al. [36], Jen et al. [37], and Kamtarin [38]. Therefore, the following hypotheses are proposed:
Hypothesis 2.7 (H2.7).
Perceived ease of use has a positive and significant effect on perceived value.
Hypothesis 2.8 (H2.8).
Perceived ease of use has a positive and significant effect on continuance intention.
Hypothesis 2.9 (H2.9).
Perceived value has a positive and significant effect on continuance intention.
Hypothesis 2.10 (H2.10).
There are mediation effects in this model.

5. TAM-ECM Integrated Model

The expectation confirmation model (ECM), which evolved from the expectation confirmation theory [39], focuses on the comparison between consumers’ expectations before purchasing a product or service, and their performance in using the product or service to determine consumers’ satisfaction with the product. Because ECT showed good explanatory and predictive power in the traditional marketing field, scholars apply it to different types of information system products that established the ECM model [40]. The ECM model aims to understand the impact of confirmation and perceived value on continuance intention through satisfaction, and has been widely used in the various IS contexts, such as distance education [41][42][43], online services [44][45], and mobile apps [46][47].
The TAM-ECM integrated model is the third proposed model, as shown in Figure 3. This model introduced the constructs perceived ease of use and perceived value from the TAM, and confirmation and satisfaction from the ECM. The research of paid mobile apps showed that confirmation affected both perceived value and satisfaction [48].
/media/item_content/202203/62313aa87d387ijerph-19-01371-g003.png
Figure 3. Research Model III (TAM-ECM Integrated Model).
Hypothesis 3.1 (H3.1).
Confirmation has a positive and significant effect on perceived value.
Hypothesis 3.2 (H3.2).
Confirmation has a positive and significant effect on satisfaction.
Hypothesis 3.3 (H3.3).
Perceived ease of use has a positive and significant effect on perceived value.
Hypothesis 3.4 (H3.4).
Perceived ease of use has a positive and significant effect on continuance intention.
Hypothesis 3.5 (H3.5).
Perceived value has a positive and significant effect on continuance intention.
Hypothesis 3.6 (H3.6).
Satisfaction has a positive and significant effect on continuance intention.
Hypothesis 3.7 (H3.7).
There are mediation effects in this model.
Habits were defined as automatic behaviors without self-instruction [49]. Khalifa and Liu [50] asserted that habits might have effects on the determinants of continuance purchase intention. In the IS context, Limayem et al. [51] defined habit as the use of a particular IS that has become automatic in response to certain situations. Limayem and Cheung [52] believed habits are a moderator in IS continuance usage. Also, empirical studies have examined the moderating effect of habits on the relationship between perceived value and repeat purchase intention [53][54], and the relationship between satisfaction and repeat purchase intention [50][54][55]. Thus, habit is included in the TAM-ECM integrated model to test its moderating effects on the linkages between continuance intention purchase intention and its antecedents (i.e., perceived value, satisfaction). Thus, the following hypotheses were proposed:
Hypothesis 3.8 (H3.8).
User habits have a moderator effect on the relationship between perceived value and continuance intention.
Hypothesis 3.9 (H3.9).
User habits have a moderator effect on the relationship between satisfaction and continuance intention.

References

  1. Han, W.; Gu, Y.; Wang, W.; Zhang, Y.; Yin, Y.; Wang, J.; Zheng, L.R. The design of an electronic pedigree system for food safety. Inf. Syst. Front. 2015, 17, 275–287.
  2. Xiao, K.; Zhai, J.; Feng, Y.; Zhou, N.; Zhang, X.; Zou, J.J.; Shen, Y. Isolation of SARS-CoV-2-related coronavirus from Malayan pangolins. Nature 2020, 583, 286–289.
  3. Aung, M.M.; Chang, Y.S. Traceability in a food supply chain: Safety and quality perspectives. Food Control 2014, 39, 172–184.
  4. Ballin, N.Z.; Laursen, K.H. To target or not to target? Definitions and nomenclature for targeted versus non-targeted analytical food authentication. Trends Food Sci. Technol. 2019, 86, 537–543.
  5. Maitiniyazi, S.; Canavari, M. Exploring Chinese consumers’ attitudes toward traceable dairy products: A focus group study. J. Dairy Sci. 2020, 103, 11257–11267.
  6. Pelegrino, B.O.; Silva, R.; Guimarães, J.T.; Coutinho, N.F.; Pimentel, T.C.; Castro, B.G.; Freitas, M.Q.; Esmerino, E.A.; Sant’Ana, A.S.; Silva, M.C.; et al. Traceability: Perception and attitudes of artisanal cheese producers in Brazil. J. Dairy Sci. 2020, 103, 4874–4879.
  7. Fanelli, V.; Mascio, I.; Miazzi, M.M.; Savoia, M.A.; De Giovanni, C.; Montemurro, C. Molecular Approaches to Agri-Food Traceability and Authentication: An Updated Review. Foods 2021, 10, 1644.
  8. U.S. Food and Drug Administration. Food Safety and the Coronavirus Disease 2019 (COVID-19); U.S. Food and Drug Administration: Silver Spring, MD, USA, 2020.
  9. Hailu, G. Economic thoughts on COVID-19 for Canadian food processors. Can. J. Agric. Econ./Revue Can. d’Agroecon. 2020, 68, 163–169.
  10. McEwan, K.; Marchand, L.; Shang, M.; Bucknell, D. Potential implications of COVID-19 on the Canadian pork industry. Can. J. Agric. Econ./Revue Can. d’Agroecon. 2020, 68, 201–206.
  11. Nakat, Z.; Bou-Mitri, C. COVID-19 and the food industry: Readiness assessment. Food Control 2021, 121, 107661.
  12. Galanakis, C.M. The food systems in the era of the coronavirus (COVID-19) pandemic crisis. Foods 2020, 9, 523.
  13. Iftekhar, A.; Cui, X. Blockchain-Based Traceability System That Ensures Food Safety Measures to Protect Consumer Safety and COVID-19 Free Supply Chains. Foods 2021, 10, 1289.
  14. Liao, P.A.; Chang, H.H.; Chang, C.Y. Why is the food traceability system unsuccessful in Taiwan? Empirical evidence from a national survey of fruit and vegetable farmers. Food Policy 2011, 36, 686–693.
  15. FMRIC (Food Marketing Research and Information Center). Handbook for Introduction of Food Traceability System (Guidelines for Food Traceability). 2008. Available online: http://www.fmric.or.jp/trace/en/ (accessed on 5 September 2014).
  16. Golan, E.H.; Krissoff, B.; Kuchler, F.; Calvin, L.; Nelson, K.E.; Price, G.K. Traceability in the US Food Supply: Economic Theory and Industry Studies; No. 1473-2016-120760; United States Department of Agriculture (USDA): Washington, DC, USA, 2004.
  17. Chen, M.F.; Huang, C.H. The impacts of the food traceability system and consumer involvement on consumers’ purchase intentions toward fast foods. Food Control 2013, 33, 313–319.
  18. Buaprommee, N.; Polyorat, K. The antecedents of purchase intention of meat with traceability in Thai consumers. Asia Pac. Manag. Rev. 2016, 21, 161–169.
  19. Yuan, C.; Wang, S.; Yu, X. The impact of food traceability system on consumer perceived value and purchase intention in China. Ind. Manag. Data Syst. 2020, 120, 810–824.
  20. Davis, F.D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989, 13, 319–340.
  21. Zeithaml, V.A. Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence. J. Mark. 1988, 52, 2–22.
  22. DeLone, W.; McLean, E. Information systems success: The quest for the dependent variable. Inf. Syst. Res. 1992, 3, 60–95.
  23. DeLone, W.H.; McLean, E.R. Information systems success revisited. In Proceedings of the 35th Annual Hawaii International Conference on System Sciences, Big Island, HI, USA, 10 January 2002; pp. 2966–2976.
  24. DeLone, W.; McLean, E. The DeLone and McLean model of information systems success: A ten-year update. J. Manag. Inf. Syst. 2003, 19, 9–30.
  25. DeLone, W.H.; McLean, E.R. Measuring e-commerce success: Applying the DeLone & McLean information systems success model. Int. J. Electron. Commer. 2004, 9, 31–47.
  26. Mustapha, B.; Obid, S.N.B.S. Tax service quality: The mediating effect of perceived ease of use of the online tax system. Procedia-Soc. Behav. Sci. 2015, 172, 2–9.
  27. Bahari, K.A.; Abdullah, D.; Kamal, M.S.B.; Johari, N.R.; Zulkafli, M.S. The influence of hotel website design quality, perceived ease of use and perceived usefulness on loyalty intention. Turk. Online J. Des. Art Commun. 2018, 701–710.
  28. Li, Y.; Shang, H. Service quality, perceived value, and citizens’ continuous-use intention regarding e-government: Empirical evidence from China. Inf. Manag. 2020, 57, 103197.
  29. Ali, B.M.; Younes, B. The impact of information systems on user performance: An exploratory study. J. Knowl. Manag. Econ. Inf. Technol. 2013, 3, 128–154.
  30. Machdar, N.M. The effect of information quality on perceived usefulness and perceived ease of use. Bus. Entrep. Rev. 2019, 15, 131–146.
  31. Tsao, W.C.; Hsieh, M.T.; Lin, T.M. Intensifying online loyalty! The power of website quality and the perceived value of consumer/seller relationship. Ind. Manag. Data Syst. 2016, 116, 1987–2010.
  32. Putri, W.K.; Pujani, V. The influence of system quality, information quality, e-service quality and perceived value on Shopee consumer loyalty in Padang City. Int. Technol. Manag. Rev. 2019, 8, 10–15.
  33. Sarmento, A.; Mesquita, A. (Eds.) User Perception and Influencing Factors of Technology in Everyday Life; Information Science Reference; American Psychological Association: Washington, DC, USA, 2013; pp. 303–309.
  34. Khasawneh, M.H.A.; Haddad, N. Analysis of the effects of ease of use, enjoyment, perceived risk on perceived value and subsequent satisfaction created in the context of C2C online exchanges. Int. J. Electron. Mark. Retail. 2020, 11, 217–238.
  35. Jin, N.; Lee, S.; Lee, H. The effect of experience quality on perceived value, satisfaction, image and behavioral intention of water park patrons: New versus repeat visitors. Int. J. Tour. Res. 2015, 17, 82–95.
  36. Meng, S.M.; Liang, G.S.; Yang, S.H. The relationships of cruise image, perceived value, satisfaction, and post-purchase behavioral intention on Taiwanese tourists. Afr. J. Bus. Manag. 2011, 5, 19–29.
  37. Jen, W.; Tu, R.; Lu, T. Managing passenger behavioral intention: An integrated framework for service quality, satisfaction, perceived value, and switching barriers. Transportation 2011, 38, 321–342.
  38. Kamtarin, M. The effect of electronic word of mouth, trust and perceived value on behavioral intention from the perspective of consumers. Int. J. Acad. Res. Econ. Manag. Sci. 2012, 1, 56.
  39. Oliver, R.L. A cognitive model of the antecedents and consequences of satisfaction decisions. J. Mark. Res. 1980, 17, 460–469.
  40. Bhattacherjee, A. Understanding information systems continuance: An expectation-confirmation model. MIS Q. 2001, 25, 351–370.
  41. Cheng, Y.M. Investigating medical professionals’ continuance intention of the cloud-based e-learning system: An extension of expectation–confirmation model with flow theory. J. Enterp. Inf. Manag. 2020, 34, 1169–1202.
  42. Dai, H.M.; Teo, T.; Rappa, N.A.; Huang, F. Explaining Chinese university students’ continuance learning intention in the MOOC setting: A modified expectation confirmation model perspective. Comput. Educ. 2020, 150, 103850.
  43. Wang, T.; Lin, C.L.; Su, Y.S. Continuance Intention of University Students and Online Learning during the COVID-19 Pandemic: A Modified Expectation Confirmation Model Perspective. Sustainability 2021, 13, 4586.
  44. Jumaan, I.A.; Hashim, N.H.; Al-Ghazali, B.M. The role of cognitive absorption in predicting mobile internet users’ continuance intention: An extension of the expectation-confirmation model. Technol. Soc. 2020, 63, 101355.
  45. Wu, L.; Chiu, M.L.; Chen, K.W. Defining the determinants of online impulse buying through a shopping process of integrating perceived risk, expectation-confirmation model, and flow theory issues. Int. J. Inf. Manag. 2020, 52, 102099.
  46. Chiu, W.; Cho, H.; Chi, C.G. Consumers’ continuance intention to use fitness and health apps: An integration of the expectation–confirmation model and investment model. Inf. Technol. People 2020, 34, 978–998.
  47. Tam, C.; Santos, D.; Oliveira, T. Exploring the influential factors of continuance intention to use mobile Apps: Extending the expectation confirmation model. Inf. Syst. Front. 2020, 22, 243–257.
  48. Hsu, C.L.; Lin, J.C.C. What drives purchase intention for paid mobile apps?–An expectation confirmation model with perceived value. Electron. Commer. Res. App. 2015, 14, 46–57.
  49. Triandis, H.C. Dimensions of Cultural Variations as Parameters of Organizational Theories. Int. Stud. Manag. Organ. 1982, 12, 136–169.
  50. Khalifa, M.; Liu, V. Online consumer retention: Contingent effects of online shopping habit and online shopping experience. Eur. J. Inf. Syst. 2007, 16, 780–792.
  51. Limayem, M.; Cheung, C.M.K.; Chan, G. Explaining information systems adoption and post-adoption: Toward an integrative model. In Proceedings of the 24th International Conference on Information Systems, Seattle, WA, USA, 15–17 December 2003; pp. 720–731.
  52. Limayem, M.; Cheung, C.M. Understanding information systems continuance: The case of Internet-based learning technologies. Inf. Manag. 2008, 45, 227–232.
  53. Chiu, C.M.; Hsu, M.H.; Lai, H.; Chang, C.M. Re-examining the influence of trust on online repeat purchase intention: The moderating role of habit and its antecedents. Decis. Support Syst. 2012, 53, 835–845.
  54. Hsu, M.H.; Chang, C.M.; Chuang, L.W. Understanding the determinants of online repeat purchase intention and moderating role of habit: The case of online group-buying in Taiwan. Int. J. Inf. Manag. 2015, 35, 45–56.
  55. Anderson, R.E.; Srinivasan, S.S. E-satisfaction and e-loyalty: A contingency framework. Psychol. Mark. 2003, 20, 123–138.
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