Consumer reactions to food safety scandals: A research model and moderating effects
Bibliographic record
Abstract
Introduction Food safety scandals are defined as well-known eventsrelated to food safety issues or harms associatedwith some food brands or products (Röhr et al,2005). Perrier's chemical benzene-contaminatedmineral water (James, 1990), Coca-Cola'sfungicide-contaminated soft-drinks (BBC News, 1999),the E. coli outbreakin Taco Bell's lettuce (CNN, 2006), Sanlu'smelamine-contaminated baby formula (BBC News, 2008),the European horsemeat adulteration (BBC News,2013), KFC and Pizza Hut's sale of expired meat(Bora, 2014), Caraga candy poisonings (ABS-CBN,2015), the listeriosis outbreak involvingcontaminated beef in Ontario (Weatherill, 2009), andMars product recall after plastics were found inSnickers chocolate bars (Quinn et al, 2016)represent a few examples of food safetyincidents. While minor food product imperfections may mildlyinconvenience consumers, food safety issues canconversely result in serious injuries for consumers’health. For instance, due to Sanlu'smelamine-contaminated baby formula, four infantsdied from kidney damage and 54,000 babies werehospitalised (NBC News, 2008). Furthermore, foodsafety scandals may represent striking threats tofood companies. For example, food safety scandalscan generate negative consumer responses toward afood company deemed accountable of commercialisingharmful products (Verbeke, 2001), affect competingfood brands’ sales, even if the latter were notinvolved in the scandal (Bakhtavoryan et al, 2014),and even damage consumer confidence in the safetyand quality of the whole food industry (Berg, 2004).Food safety issues therefore have severeimplications for individual wellbeing, publicwelfare and environmental health (see Chapter 1,this volume). Due to the concern that food safety issues generate, itis quite easy to justify concern for food and foodsystems, and it is of the utmost importance toinvestigate how consumers respond to food safetyscandals and the companies deemed accountable forthese incidents. To this end, scholars and marketersare now calling for more research into thepsychological mechanisms through which consumersform attributions of responsibility toward foodcompanies involved in food safety scandals, thepsychological processes through which attributionsof blame drive negative consumer responses towardthe food brand at fault, and the variables that mayinfluence judgements of blame and subsequentresponses in the context of a food safety scandal(Regan et al, 2015).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".