Credibility in crisis: Determining the availability and credibility of online food supply chain resources during the COVID-19 pandemic

Anissa M. Zagonel; Lauri Baker; Joelle Covarrubias; Angela Lindsey

doi:10.37433/aad.v2i3.145

Authors

Anissa M. Zagonel University of Florida, USA https://orcid.org/0000-0001-6359-5820
Lauri M. Baker University of Florida, USA https://orcid.org/0000-0002-4241-6077
Joelle Covarrubias University of Florida, USA https://orcid.org/0000-0003-4131-0112
Angela Lindsey University of Florida, USA https://orcid.org/0000-0002-9859-7962

DOI:

https://doi.org/10.37433/aad.v2i3.145

Keywords:

source credibility, quantitative content analysis, textual analysis, online resources

Abstract

Disruptions from COVID-19 forced agricultural business owners to navigate the uncertainty of market disruptions with limited information. As an effect, the quality of information available for agricultural businesses to adapt to changes was a concern. The purpose of this study was to determine the availability and credibility of resources for agricultural businesses to make informed decisions about food markets during COVID-19. Source credibility was the guiding framework to achieve the research objectives of 1) Describe resources available related to impacts of COVID-19 on the food supply chain, 2) Determine the credibility of available resources. A quantitative content and textual analyses were employed. Results revealed 401 terms used to describe resources (n = 779). Eleven of the top 36 terms were used over 100 times. These were: farmer, resources, farm, market, business, local, health, safe, supply, agriculture, and chain. The majority of resources (66%, f = 514) were mid-level credible sources (industry/business organization, online/print news source, nonprofit), and 32.2% (f = 251) were of the highest credibility (university scientists, USDA scientist, Extension). Implications of this work show an opportunity for university and Extension systems to publish resources and serve as credible sources related to this particular crisis.

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References

Anthony, L. (2021). AntWordProfiler (Version 1.5.1) [Computer Software]. Waseda University. https://www.laurenceanthony.net/software

Baker, L., Kandzer, M., Rampold, S., Chiarelli, C., Peterson, H., & McLeod-Morin, A. (2020). Agriculture and natural resources business owners economic and communication concerns early in the COVID-19 pandemic. Advancements in Agricultural Development, 1(3), 95–110. https://doi.org/10.37433/aad.v1i3.83

Baker, L., McLeod-Morin, A., Kent, K., & Lindsey, A. (2020). No online information outbreak: A quantitative content analysis of the CDC and USDA websites for available information on zoonotic disease. Advancements in Agricultural Development, 1(1), 25–37. https://doi.org/10.37433/aad.v1i1.19

Berelson, B. (1952). Content analysis in communication research. The ANNALS of the American Academy of Political and Social Science, 283(1), 197-198. https://doi.org/10.1177/000271625228300135

Burbules, N. C. (2001). Paradoxes of the web: The ethical dimensions of credibility. Library Trends, 49(3), 441–453. https://www.ideals.illinois.edu/bitstream/handle/2142/8352/librarytrendsv49i3f_opt.pdf

Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37–46. https://doi.org/10.1177/001316446002000104

Duvall, Z. (2020). Lessons from COVID-19. American Farm Bureau Federation. https://www.fb.org/viewpoints/lessons-from-covid-19

Hawkins, D. T. (1999). What is credible information? Online, 23(5), 86–89.

Hovland, C., Janis, I., & Kelley, H. (1953). Communication and persuasion. Yale University Press.

Krippendorf, K. (2013). Content analysis: An introduction to its methodology. SAGE.

Kumkale, G. T., Albarracín, D., & Seignourel, P. J. (2010). The effects of source credibility in the presence or absence of prior attitudes: Implications for the design of persuasive communication campaigns. Journal of Applied Social Psychology, 40(6), 1325–1356. https://doi.org/10.1111/j.1559-1816.2010.00620.x

Lacy, S., Watson, B. R., Riffe, D., & Lovejoy, J. (2015). Issues and best practices in content analysis. Journalism & Mass Communication Quarterly, 92(4) 791–811. https://doi.org/10.1177/1077699015607338

Lamm, A. J., Owens, C. T., Telg, R., Lamm, K. W. (2016). Influence of source credibility on agricultural water use communication. Journal of Applied Communications, 100(3), 121–132. https://doi.org/10.4148/1051-0834.1235

Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159–174. http://dx.doi.org/10.2307/2529310

Law, T. J. (2019, November 22). Google SERPs: Features and how to improve your ranking in 2019. Oberlo. https://www.oberlo.com/blog/serp-google-search-engine-results

Lioutas, E. D., & Charatsari, C. (2021). Enhancing the ability of agriculture to cope with major crises or disasters: What the experience of COVID-19 teaches us. Agricultural Systems, 187, Article 103023. https://doi.org/10.1016/j.agsy.2020.103023

Liu, Z. (2004). Perceptions of credibility of scholarly information on the web. Information Processing & Management, 40(6), 1027–1038. https://doi.org/10.1016/S0306-4573(03)00064-5

Nakat, Z., & Bou-Mitri, C. (2021). COVID-19 and the food industry: Readiness assessment. Food Control, 121, Article 107661. https://doi.org/10.1016/j.foodcont.2020.107661

Narine, L., & Meier, C. (2020). Responding in a time of crisis: Assessing extension efforts during COVID-19. Advancements in Agricultural Development, 1(2), 12–23. https://doi.org/10.37433/aad.v1i2.35

Nelson, M. (2020). Keeping rural communities connected while socially distanced. American Farm Bureau Federation. https://www.fb.org/market-intel/keeping-rural-communities-connected-while-socially-distanced

Pauchant, T. C., & Mitroff, I. I. (1990). Crisis management: Managing paradox in a chaotic world. Technological Forecasting and Social Change, 38(2), 117–134. https://doi.org/10.1016/0040-1625(90)90034-S

Pornpitakpan, C. (2004). The persuasiveness of source credibility: A critical review of five decades’ evidence. Journal of Applied Social Psychology, 34(2), 243-281. https://doi.org/10.1111/j.1559-1816.2004.tb02547.x

Rieh, S. Y. (2001). Judgment of information quality and cognitive authority in the Web. Journal of the American Society for Information Science and Technology, 53(2), 145–161. https://doi.org/10.1002/asi.10017

Sahin, O., Salim, H., Suprun, E., Richards, R., MacAskill, S., Heilgeist, S., Rutherford, S., Stewart, R. A., & Beal, C. D. (2020). Developing a preliminary causal loop diagram for understanding the wicked complexity of the COVID-19 pandemic. Systems, 8(2), 20. https://doi.org/10.3390/systems8020020

Stemler, S. (2000). An overview of content analysis. Practical Assessment, Research, and Evaluation, 7(17), 1–6. https://doi.org/10.7275/z6fm-2e34

Sternthal, B., Dholakia, R., & Leavitt, C. (1978). The persuasive effect of source credibility: Tests of cognitive response. Journal of Consumer Research, 4(4), 252–260. https://doi.org/10.1086/208704

Telg, R., Irani, T., Monaghan, P., Chiarelli, C., Scicchitano, M., & Johns, T. (2012). Preferred information channels and source trustworthiness: Assessing communication methods used in Florida’s battle against citrus greening. Journal of Applied Communications, 96(1), 1–12. https://doi.org/10.4148/1051-0834.1147

Thilmany, D., Jablonski, B., Angelo, B., Low, S., & Tropp, D. (2020). Mitigating immediate harmful impacts of COVID-19 on Colorado farms and ranches selling through local and regional food markets. Regional Economic Development Institute https://mountainscholar.org/handle/10217/217134