Risk Analysis (Part 2 of 3): Application to Food Fraud

  • John W. Spink
Part of the Food Microbiology and Food Safety book series (FMFS)


This chapter presents the risk analysis application to food fraud prevention. The risk analysis concepts and theories are well known and widely researched but not often adapted to the unique fraud opportunity and resource-allocation decision-making needs for food fraud prevention.


  1. Andrew, P. S., Young, J. A., & Gibson, J. (1999). How now, mad-cow? Consumer confidence and source credibility during the 1996 BSE scare. European Journal of Marketing, 33(11/12), 1107.CrossRefGoogle Scholar
  2. Backer-Grøndahl, A., Fyhri, A., Ulleberg, P., & Amundsen, A. (2009). Accidents and unpleasant incidents: Worry in transport and prediction of travel behavior. Risk Analysis, 29(9), 1217.PubMedCrossRefGoogle Scholar
  3. Berlo, D. K., Lemert, J. B., & Mertz, R. J. (1969). Dimensions for evaluating the acceptability of message sources. Public Opinion Quarterly, 33(4), 563–576.CrossRefGoogle Scholar
  4. Broder, J. F. (2000). Risk analysis and the security survey. Boston: Butterworth-Heineman.Google Scholar
  5. Buchanan, R. (2007). Tools for prioritizing food safety concerns: An FDA perspective. Tools for prioritizing food safety concerns workshop. Greenbelt: CFSAN/FDA, JIFSAN.Google Scholar
  6. Burgoon, J. K., Birk, T., & Pfau, M. (1990). Nonverbal behaviors, persuasion, and credibility. Human Communication Research, 17(1), 140.CrossRefGoogle Scholar
  7. Capra, S., & Canale, R. (1998). Numerical methods for engineers. Boston: McGraw-Hill.Google Scholar
  8. Chaiken, S., & Maheswaran, D. (1994). Heuristic processing can bias systematic processing: Effects of source credibility, argument ambiguity, and task importance on attitude judgment. Journal of Personality and Social Psychology, 66, 460–473.PubMedCrossRefGoogle Scholar
  9. Chen, M. (2008). Consumer trust in food safety–A Multidisciplinary Approach and Empirical evidence from Taiwan. Risk Analysis, 28(6), 1553.PubMedCrossRefGoogle Scholar
  10. Cho, H., & Witte, K. (2005). Managing fear in public health campaigns: A theory-based formative evaluation process. Health Promotion Practice, 6(4), 482.PubMedCrossRefGoogle Scholar
  11. Claycamp, H. G. (2006). Rapid benefit-risk assessments: No escape from expert judgments in risk management. Risk Analysis, 26(1), 147–156.PubMedCrossRefGoogle Scholar
  12. Claycamp, H. G., & Hooberman, B. H. (2004). Antimicrobial resistance risk assessment in food safety. Journal of Food Protection, 67, 2063–2071.PubMedCrossRefGoogle Scholar
  13. CODEX, Codex Alimentarius. (2014). Procedural manual, twenty-second edition. Geneva/Rome: World Health Organization/Food and Agriculture Organization of the United Nations.Google Scholar
  14. Cox, A. L. (2009). Some limitations of frequency as a component of risk: An expository note. Risk Analysis, 29(2), 171.PubMedCrossRefGoogle Scholar
  15. Cvetkovich, G., Siegrist, M., Murray, R., & Tragesser, S. (2002). New information and social trust: Asymmetry and perseverance of attributions about hazard managers. Risk Analysis, 22(2), 359–367.PubMedCrossRefGoogle Scholar
  16. Earle, T. (2009). Trust, confidence, and the 2008 global financial crisis. Risk Analysis, 29(6), 785.PubMedCrossRefGoogle Scholar
  17. Eitzinger, C., & Wiedemann, P. (2008). Trust in the safety of tourist destinations: Hard to gain, easy to lose? New insights on the asymmetry principle. Risk Analysis, 28(4), 843.PubMedGoogle Scholar
  18. Etherton, J., Main, B., Cloutier, D., & Christensen, W. (2008). Reducing risk on machinery: A field evaluation pilot study of risk assessment. Risk Analysis, 28(3), 711–721.PubMedCrossRefGoogle Scholar
  19. Furukawa, K., Cologne, J., Shimizu, Y., & Ross, N. (2009). Predicting future excess events in risk assessment. Risk Analysis, 29(6), 885.PubMedCrossRefGoogle Scholar
  20. Gorn, G. J. (1982). The effects of music in advertising on choice behavior: A classical conditioning approach. Journal of Marketing, 46(1), 94.CrossRefGoogle Scholar
  21. Gotlieb, J. B., & Sarel, D. (1991). Comparative advertising effectiveness: The role of involvement and source credibility. Journal of Advertising, 20(1), 38.CrossRefGoogle Scholar
  22. Gotlieb, J. B., Schlacter, J. L., & St Louis, R. D. (1992). Consumer decision making: A model of the effects of involvement, source credibility, and location on the size of the price difference required to induce consumers to change suppliers. Psychol Mark (1986–1998), 9(3), 191.CrossRefGoogle Scholar
  23. Graver, P. A. (2001). Process hazard analysis – Failure Mode Effects Analysis (FMEA). FMEA Information Center.
  24. Green, E. C., & Witte, K. (2006). Can fear arousal in public health campaigns contribute to the decline of HIV prevalence? Journal of Health Communication, 11(3), 245–259.PubMedCrossRefGoogle Scholar
  25. Green, P. E., Wind, Y., & Jain, A. K. (1972). Benefit bundle analysis. Journal of Advertising Research, 12(2), 31.Google Scholar
  26. Green, P. E., Wind, Y., & Jain, A. K. (2000). Benefit bundle analysis. Journal of Advertising Research, 40(6), 32–37.CrossRefGoogle Scholar
  27. Haan, J., Rodammer, F., & Speier-Pero, C. (2015). The integration of business analytics into a business college undergraduate curriculum.Google Scholar
  28. Haimes, Y. (2009). On the definition of resilience in systems. Risk Analysis, 29(4), 498.PubMedCrossRefGoogle Scholar
  29. Hale, J. L., & Lemieux, R. (1995). Cognitive processing of fear-arousing message content. Communication Research, 22(4), 459.CrossRefGoogle Scholar
  30. Haley, R. I. (1995). Benefit segmentation: A decision-oriented research tool. Marketing Management, 4(1), 59–62.Google Scholar
  31. Hassenzahl, D. M. (2006). Implications of excessive precision for risk comparisons: Lessons from the past four decades. Risk Analysis, 26(1), 265–276.PubMedCrossRefGoogle Scholar
  32. Harris Interactive. (2008). Confidence in FDA hits new low, according to interactive study (Harris Poll): U.S. adults concerned about safety of prescription drugs. Rochester: Harris Interactive.Google Scholar
  33. Jablonowski, M. (1994). Words or numbers? Risk Management, 41(12), 47.Google Scholar
  34. Jablonowski, M. (2005). Do catastrophe models mislead? Risk Management, 52(7), 32.Google Scholar
  35. Kara-Zaitri, C., Keller, A. Z., Barody, I., & Fleming, P. V. (1991). An improved FMEA methodology. Reliability and Maintainability Symposium. Proceedings Annual 1991, Orlando, FL, 248–252.Google Scholar
  36. Kearny, A. T. (2009). Presentation, quoting pharmaceutical security institute (PSI). Washington, DC: GMA Economically Motivated Aduleration Working Group.Google Scholar
  37. Keller, C., Siegrist, M., & Visschers, V. (2009). Effect of risk ladder format on risk perception in high- and low-numerate individuals. Risk Analysis, 29(9), 1255.PubMedCrossRefGoogle Scholar
  38. Kmenta, S., & Ishii, K. (2000). Scenario-based FMEA: A life cycle cost perspective 2000. In ASME Design Engineering Technical Conferences. Baltimore, Maryland, ASME.Google Scholar
  39. Kopalle, P. K., & Assuncao, J. L. (2000). When (not) to indulge in “puffery”: The role of consumer expectations and brand goodwill in determining advertised and actual product quality. Managerial and Decision Economics, 21(6), 223.CrossRefGoogle Scholar
  40. Kramer, R. M. (1999). Trust and distrust in organizations: Emerging perspectives, enduring questions. Annual Review of Psychology, 50(1), 569–598.PubMedCrossRefGoogle Scholar
  41. Lafferty, B. A., Goldsmith, R. E., & Newell, S. J. (2002). The dual credibility model: The influence of corporate and endorser credibility on attitudes and purchase intentions. Journal of Marketing Theory and Practice, 10(3), 1.CrossRefGoogle Scholar
  42. Lapinski, Maria Knight and P. Nwulu (2008). Can a short film impact HIV-related risk and stigma perceptions? Results from an experiment in Abuja, Nigeria. Health Communication. 23(5): 403–412.PubMedCrossRefGoogle Scholar
  43. Levitt, S. D., & Dubner, S. J. (2005). Freakonomics. New York: HarperCollins.Google Scholar
  44. Lewis, R., & Tyshenko, M. (2009). The impact of social amplification and attenuation of risk and the public reaction to mad cow disease in Canada. Risk Analysis, 29(5), 714.PubMedCrossRefGoogle Scholar
  45. Lindell, M., Arlikatti, S., & Prater, C. (2009). Why people do what they do to protect against earthquake risk: Perceptions of hazard adjustment attributes. Risk Analysis, 29(8), 1072.PubMedCrossRefGoogle Scholar
  46. Magee, R., & Kalyanaraman, S. (2007). Antecedent variables in persuasion processes: The effect of worldview on the processing of persuasive messages. Conference Papers – International Communication Association, International Communication Association.Google Scholar
  47. Malka, A., Krosnick, J., & Langer, G. (2009). The association of knowledge with concern about global warming: Trusted information sources shape public thinking. Risk Analysis, 29(5), 633.PubMedCrossRefGoogle Scholar
  48. McAfee, A., & Brynjolfsson, E. (2012). Big data: The management revolution. (cover story). Harvard Business Review, 90(10), 60–68.PubMedGoogle Scholar
  49. Meehan, T. (2016). What is Big Data? Loss prevention. January–February: 1.Google Scholar
  50. Meijnders, A., Midden, C., Olofsson, A., Öhman, S., Matthes, J., Bondarenko, O., Gutteling, J., & Rusanen, M. (2009). The role of similarity cues in the development of trustin sources of information about GM food. Risk Analysis, 29(8), 1116.PubMedCrossRefGoogle Scholar
  51. MSU-FFI, Food Fraud Initiaive (2018). Blog series, Food Fraud Initiative, Michigan State University, developed and presented by John Spink, URL:
  52. Nielsen, J., & Shapiro, S. (2007). Side effects from fear: The automatic inhibition of threat-relevant brand advertising. Advances in Consumer Research, 34, 192.Google Scholar
  53. NIST, United States National Institute of Science and Technology (2015). NIST Big Data interoperability framework Big Data Public Working Group, Special publication, URL: Special Publication: 1500–1506.
  54. NRC, National Research Council. (1996). In P. C. Stern & H. V. Fineberg (Eds.), Understanding risk: Informing decisions in a democratic society, National Academy of Science NAS. Washington, DC: National Academies Press.Google Scholar
  55. Onodera, K. (1997). Effective techniques of FMEA at each life-cycle stage. Reliability and Maintainability Symposium. 1997 Proceedings Annual, pp. 50–56.Google Scholar
  56. Peter, J. P., & Olsen, J. C. (2005). Consumer behavior & marketing strategy. New York: McGraw-Hill Irwin.Google Scholar
  57. Pittinger, C. A., Brennan, T. H., Badger, D. A., Hakkinen, P. J., & Catherine Fehrenbacher, M. (2003). Aligning chemical assessment tools across the hazard-risk continuum. Risk Analysis, 23(3), 529–535.PubMedCrossRefGoogle Scholar
  58. Ray, M. L., & Wilkie, W. L. (1970). Fear: The potential of an appeal neglected by marketing. American Marketing Association., 34, 54–62.Google Scholar
  59. Sandman, P. M. (1988). Risk communication: Facing public outrage. Management Communication Quarterly, 2(2), 235.CrossRefGoogle Scholar
  60. Sandman, P. M. (2004a). July 2004 issue of the synergist, pp. 24–25,
  61. Sandman, P. M. (2004b). The synergist, February 2004 issue of the synergist, pp. 22, 24.
  62. Sandman, P. M., Miller, P. M., Johnson, B. B., & Weinstein, N. D. (1993). Agency communication, community outrage, and perception of risk: Three simulation experiments. Risk Analysis, 13(6), 585–598.CrossRefGoogle Scholar
  63. Sanquist, T., Mahy, H., & Morris, F. (2008). An exploratory risk perception study of attitudes toward homeland security systems. Risk Analysis, 28(4), 1125.PubMedGoogle Scholar
  64. Schniederjans, M. J., Schniederjans, D. G., & Starkey, C. M. (2015). Business analytics principles, concepts, and applications: What, why, and how. Upper Saddle River: Pearson Education.Google Scholar
  65. Schoell, R., & Binder, C. (2009). System perspectives of experts and farmers regarding the role of livelihood assets in risk perception: Results from the structured mental model approach. Risk Analysis, 29(2), 205.PubMedCrossRefGoogle Scholar
  66. Shepherd, R., Barker, G., French, S., Hart, A., Maule, J., & Cassidy, A. (2006). Managing food chain risks: Integrating technical and stakeholder perspectives on uncertainty. Journal of Agricultural Economics, 57(2), 313–327. Scholar
  67. Siegrist, M., & Cvetkovich, G. (2001). Better negative than positive? Evidence of a bias for negative information about possible health dangers. Risk Analysis, 21(1), 199–206.PubMedCrossRefGoogle Scholar
  68. Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of big data challenges and analytical methods. Journal of Business Research, 70, 263–286.CrossRefGoogle Scholar
  69. Society of Automotive Engineers (SAE) (2002). Potential failure mode and effects analysis in design (Design FMEA) and potential failure mode and effects analysis in manufacturing and assembly processes (Process FMEA). Reference manual, Document number: SAE J 1739.Google Scholar
  70. Spink, J. (2009). Analysis of counterfeit risks and development of a counterfeit product risk model. PhD dissertation Ph.D., Michigan State University.Google Scholar
  71. Spink, J. (2014). Food Fraud prevention overview, introducing the Food Fraud Prevention Cycle (FFPC)/Food Fraud Prevention System, GFSI China focus day 2014, Beijing.Google Scholar
  72. Spink, J., & Levente Fejes, Z. (2012). A review of the economic impact of counterfeiting and piracy methodologies and assessment of currently utilized estimates. International Journal of Comparative and Applied Criminal Justice, 36(4), 249–271.CrossRefGoogle Scholar
  73. Spink, J., Elliott, C., Dean, M., Speier-Pero, C. (2019). Fraud Data Collection Needs Survey, NPJ Science of Food, 3(1), Pages 1–8 [Available on-line May 16, 2019].Google Scholar
  74. Spink, J., Zhang, G., Chen, W., & Speier-Pero, C. (2019). Introducing the food fraud prevention cycle (FFPC): A dynamic information management and strategic roadmap. Food Control, 105, 233–241.Google Scholar
  75. Stern, B. B. (1994). A revised communication model for advertising: Multiple dimensions of the source, the message, and the recipient. Journal of Advertising, 23(2), 5.CrossRefGoogle Scholar
  76. Taleb, N. N. (2007). The black swan: The impact of the highly improbable. New York: Random house.Google Scholar
  77. Tanner, J. F., Jr., Hunt, J. B., & Eppright, D. R. (1991). The protection motivation model: A normative model of fear appeals. Journal of Marketing, 55(3), 36.CrossRefGoogle Scholar
  78. Terpstra, T., Lindell, M., & Gutteling, J. (2009). Does communicating (flood) risk affect (flood) risk perceptions? Results of a quasi-experimental study. Risk Analysis, 29(8), 1141.PubMedCrossRefGoogle Scholar
  79. Tyler, T. R., & Degoey, P. (1996). Trust in organizational authorities. In Trust in organizations: Frontiers of theory and research (pp. 331–356). Thousand Oaks: Sage Publications.CrossRefGoogle Scholar
  80. Van Kleef, E., Houghton, J. R., Krystallis, A., Pfenning, U., Rowe, G., Van Dijk, H., Van der Lans, I. A., & Frewer, L. J. (2007). Consumer evaluations of food risk management quality in Europe. Risk Analysis, 27(6), 1565–1580.PubMedCrossRefGoogle Scholar
  81. Venables, D., Pidgeon, N., Simmons, P., Henwood, K., & Parkhill, K. (2009). Living with nuclear power: A Q-method study of local community perceptions. Risk Analysis, 29(8), 1089.PubMedCrossRefGoogle Scholar
  82. White, M. P., Pahl, S., Buehner, M., & Haye, A. (2003). Trust in risky messages: The role of prior attitudes. Risk Analysis, 23(4), 717–726.PubMedCrossRefGoogle Scholar
  83. WHO, World Health Organization (2007). General information on counterfeit medicines World Health Organization. 2007.Google Scholar
  84. Witte, K. (1992). Putting the fear back into fear appeals: The extended parallel process model. Communication Monographs, 59(4), 329.CrossRefGoogle Scholar
  85. Witte, K., & Morrison, K. (2000). Examining the influence of trait anxiety/repression-sensitization on individuals’ reactions to fear appeals. Western Journal of Communication, 64(1), 1.CrossRefGoogle Scholar
  86. Zambrano, L., Sublette, K., Duncan, K., & Thoma, G. (2007). Probabilistic reliability modeling for oil exploration & production (E&P) facilities in the tallgrass prairie preserve. Risk Analysis, 27, 1323–1333.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • John W. Spink
    • 1
  1. 1.Michigan State UniversityOkemosUSA

Personalised recommendations