A mathematical framework for assessing food fraud intentions

Authors

  • Shadreck Muyambo
    osField Research & Development Co., 25A Scott Road Hatfield, Harare, Zimbabwe
  • Jack A Urombo
    Department of Mathematical Sciences, Harare Institute of Technology, P. O. Box BE277 Belvedere, Harare, Zimbabwe
    Quanta Research, Scientific and Technical (QRST), Harare, Zimbabwe
  • Garry K. Masoha
    osField Research & Development Co., 25A Scott Road Hatfield, Harare, Zimbabwe
  • Livingstone Jenya
    osField Research & Development Co., 25A Scott Road Hatfield, Harare, Zimbabwe

Keywords:

Food fraud, Ethical fading, Unethicality, Bounded ethicality, Malicious intent, Similarity distance

Abstract

This work presents a framework for assessing subjects’ intentions to engage in food fraud. The model demonstrated that the decision to commit fraud arises from psychological mechanisms of malicious intent (Π) and ethical fading-bounded ethicality (Ω). Based on these psychological processes, the framework utilises a survey tool to generate finite sets of binary responses, fT (theorised response set derived from governing moral notions) and fA (actual response set given by subjects). The similarity function, fT ϕ fA, maps a dichotomous variable, with arguments (p, q), to a continuous variable, f [(p, q) 7→ f ]. The model metrics have Jaccard index characteristics with a range of 0.0-1.0, where fraud intention is low when f → 0.0 and high when f → 1.0. The number of questions used in a survey indicates special characteristics of the model, for instance, its rate of change (Rf ) and the number of possible response patterns (Γ). Survey data (N=54) was used to validate the model. The results show that the f values for respondents range from 0.21-0.69 with a mean of 0.44. Indicating that the respondents have moderate to neutral inclinations towards fraud. The statistical difference between f -values obtained from Π and Ω data indicates that they have the same effect on food fraud intention (p > 0.05). The model is essential when assessing the fraud intention of an individual or population by examining and understanding factors contributing to fraud and their numerical impacts. This is a significant step towards developing a fraud prevention framework.

Dimensions

[1] J. Spink & D. C. Moyer, “Defining the public health threat of food fraud”, Journal of food science 76 (2011) R157. https://doi.org/10.1111/j.1750-3841.2011.02417.x.

[2] L. Manning, R. Smith & J. M. Soon, “Developing an organizational typology of criminals in the meat supply chain”, Food Policy 59 (2016) 44. https://doi.org/10.1016/j.foodpol.2015.12.003.

[3] S. M. van Ruth, W. Huisman & P. A. Luning, “Food fraud vulnerability and its key factors”, Trends in Food Science & Technology 67 (2017) 70. https://doi.org/10.1016/j.tifs.2017.06.017.

[4] Y. Bouzembrak, B. Steen, R. Neslo, J. Linge, V. Mojtahed & H. Marvin, “Development of food fraud media monitoring system based on text mining”, Food Control 93 (2018) 283. https://doi.org/10.1016/j.foodcont.2018.06.003.

[5] K. Giannakas & A. Yiannaka, “Food fraud: Causes, consequences, and deterrence strategies”, Annual Review of Resource Economics 15 (2023) 85. https://doi.org/10.1146/annurev-resource-101422-013027.

[6] M. H. Bazerman & A. E. Tenbrunsel, “Blind spots: why we fail to do what’s right and what to do about it”, Princeton University Press, 2011. http://dx.doi.org/10.1515/9781400837991.

[7] D. Chugh & M. C. Kern, “A dynamic and cyclical model of bounded ethicality”, Research in Organizational Behavior 36 (2016) 85. https://pages.stern.nyu.edu/kscarbro/dolly/2016ROB.pdf.

[8] T. W. Kim, R. Monge & A. Strudler, “Bounded ethicality and the principle that ‘ought’ implies ‘can’ ”, Business Ethics Quarterly 25 (2015) 341. https://doi.org/10.1017/beq.2015.25.

[9] A. E. Tenbrunsel & D. M. Messick, “Ethical fading: the role of self-deception in unethical behavior”, Social justice research 17 (2004) 223. http://dx.doi.org/10.1023/B:SORE.0000027411.35832.53.

[10] I. Ajzen, “From intentions to actions: A theory of planned behavior”, in Action control: From cognition to behavior, J. Kuhl and J. Beckmann (Ed.), Springer-Verlag, New York, 1985, pp. 11–39. https://www.researchgate.net/profile/Icek-Ajzen/publication/238719086ActionControl/links/0046353a6edba381e0000000/Action-Control.pdf.

[11] I. Ajzen, Understanding attitudes and predictiing social behavior, Englewood cliffs, 1980. https://www.scienceopen.com/book?vid=c20c4174-d8dc-428d-b352-280b05eacdf7.

[12] M. Fishbein & I. Ajzen, Belief, attitude, intention, and behavior: an introduction to theory and research, Readind, MA, Addison-Wesley, 1975. https://people.umass.edu/aizen/f&a1975.html.

[13] S. M. van Ruth & O. Nillesen, “Which company characteristics make a food business at risk for food fraud?”, Foods 10 (2021) 842. http://doi.org/10.3390/foods10040842.

[14] K. Gussow & A. Mari¨et, “The scope of food fraud revisited”, Crime, Law and Social Change 78 (2022) 621. https://doi.org/10.1007/s10611-022-10055-w.

[15] A. Vinothkanna, O. I. Dar, Z. Liu & A. Jia, “Advanced detection tools in food fraud: a systematic review for holistic and rational detection method based on research and patents”, Food Chemistry 446 (2024) 138893. https://doi.org/10.1016/j.foodchem.2024.138893.

[16] Y. Guntzburger, J. Theolier, V. Barrere, I. Peignier, S. Godefroy & N. de Marcellis-Warin, “Food industry perceptions and actions towards food fraud: insights from a pan-Canadian study”, Food Control 113 (2020) 107182. https://doi.org/10.1016/j.foodcont.2020.107182.

[17] H. Jafarkarimi, A. T. H. Sim, R. Saadatdoost & J. M. Hee, “Designing a scenario-based questionnaire to assess behavioral intention in social networking sites’ ethical dilemmas”, in Blurring the boundaries through digital innovation: Individual, organizational, and societal challenges, F. D’Ascenzo, M. Magni, A. Lazazzara, S. Za (Ed.), Springer, Cham, 2016, pp. 145–159. https://doi.org/10.1007/978-3-319-38974-5_12.

[18] J. Spink, B. Bedard, J. Keogh, D. C. Moyer, J. Scimeca & A. Vasan, “International survey of food fraud and related terminology: preliminary results and discussion”, Journal of food science 84 (2019) 2705. https://doi.org/10.1111/1750-3841.14705.

[19] M. R. Rees, A. E. Tenbrunsel & M. H. Bazerman, “Bounded ethicality and ethical fading in negotiations: Understanding unintended unethical behavior”, Academy of Management Perspectives 33 (2019) 26. https://doi.org/10.5465/amp.2017.0055.

[20] G. N. Nyaga, J. M. Whipple & D. F. Lynch, “Examining supply chain relationships: do buyer and supplier perspectives on collaborative relationships differ?”, Journal of Operations Management 28 (2010) 101. https://doi.org/10.1016/j.jom.2009.07.005.

[21] L. Manning, “Fraud in the food system: critiquing the ‘bad apple’perspective”, Trends in Food Science & Technology 149 (2024) 104563. https://doi.org/10.1016/j.tifs.2024.104563.

[22] S. M. van Ruth & L. de Pagter-de Witte, “Integrity of organic foods and their suppliers: Fraud vulnerability across chains”, Foods 9 (2020) 188. https://doi.org/10.3390/foods9020188.

[23] A. E. Tenbrunsel & K. Smith-Crowe, “13 ethical decision making: Where we’ve been and where we’re going”, Academy of management Annals 2 (2008) 545. http://dx.doi.org/10.1080/19416520802211677.

[24] D. M. Boyle, J. F. Boyle & D. P. Mahoney, “Behavioral assessment and modification in fraud mitigation efforts”, Management Accounting Quarterly 20 (2018) 1. https://www.imanet.org//media/IMA/Files/Home/Insights-and-Trends/MAQ/MAQ-2018/Fall-2018/MAQ fall 2018 boyle.pdf.

[25] S. Fletcher & M. Z. Islam, “Comparing sets of patterns with the Jaccard index”, Australasian Journal of Information Systems 22 (2018) 1. http://dx.doi.org/10.3127/ajis.v22i0.1538.

[26] P. Jaccard, “Comparative study of floral distribution in a portion of the Alps and the Jura”, Bull Soc Vaudoise Sci Nat. 37 (1901) 547. http://dx.doi.org/10.5169/seals-266450.

[27] J. Holmes, “The structure of the set of idempotents in a Banach algebra”, Illinois Journal of Mathematics 36 (1992) 102. https://doi.org/10.1215/ijm/1255987609.

[28] B. D. Basic, Distance Measures, Springer Berlin Heidelberg, Berlin, Heidelberg, 2011, pp. 397–398. https://doi.org/10.1007/978-3-642-04898-2626.

[29] J. Yu, J. Amores, N. Sebe & Q. Tian, A new study on distance metrics as similarity measurement, 2006 IEEE international conference on multimedia and expo, IEEE, 2006, pp. 533–536. https://doi.org/10.1109/ICME.2006.262443.

Published

2025-10-18

How to Cite

A mathematical framework for assessing food fraud intentions. (2025). African Scientific Reports, 4(3), 314. https://doi.org/10.46481/asr.2025.4.3.314

Issue

Volume 4, Issue 3, December 2025

Section

MATHEMATICAL SCIENCES SECTION
Copyright & Licensing

Copyright (c) 2025 Shadreck Muyambo, Jack A Urombo, Garry K. Masoha, Livingstone Jenya

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

A mathematical framework for assessing food fraud intentions. (2025). African Scientific Reports, 4(3), 314. https://doi.org/10.46481/asr.2025.4.3.314

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