Allocation of Blame After A Safety Incident
Authors: Kalsher Michael J. , Obenauer William G. 
Published: September 2018

Background A significant body of evidence has accrued concerning how people form perceptions and allocate responsibility (e.g., Karlovac & Darley, 1988; Pennington & Hastie, 1986). Human factors researchers have extended this line of research by examining how people allocate blame in product liability cases involving injury or death. Collec-tively, these studies have investigated how perceptions of blame are assigned in a broad range of contexts and to an array of potentially blameworthy entities, including manu-facturers, retailers, consumers, employers and employees, to name but a few. The present study builds on previous research in this area by systematically varying information pertaining to a realistic, but fictitious, incident in which a worker (depicted as experienced vs. inexperienced) is killed when his hand comes into contact with an energized bus bar while feeding electrical wiring into a ground-based electrical pedestal. We systematically manipulated quality of the warning (an actual warning on a commercial product vs. a re-de-signed alternative) and the fatally injured worker’s on-the-job experience (less than one month vs. 4 years). Previous research has shown that quality of warning interacts with efforts on the part of various potentially blameworthy enti-ties (e.g., manufacturers, distributors, employers)to do their part in disseminating warning materials down the chain to end users (Kalsher, Viale & Williams, 2003). We manipu-lated the worker’s level of experience to determine its im-pact on the relative ratio of blame attributed to the manu-facturer and the fatally injured worker. We predicted the “experienced” worker would receive greater blame than the “inexperienced” worker, independent of warning quality. Results and Discussion Overall, participants apportioned most of the blame to the electrician ( M=58.60, SD = 26.07), who was depicted as having failed in his duty to secure protective covers over the energized bus bars. The manufacturer received the next highest percentage of blame ( M=19.87, SD=24.33), fol-lowed by the decedent ( M=14.60, SD=20.02) and job fore-man ( M=14.00, SD=16.70). Manufacturer blame was entered as the dependent vari-able in a 2 (warning: original, re-designed) by 2 (worker experience: under one month, four years) ANOVA. The model was significant, F(3,182)=4.73, p<0.01, eta-squared = 0.07. The effect of warning quality on blame allocated to the manufacturer was significant, F(1,182)=13.35, p<0.001, eta-squared = 0.07, indicating that manufacturers received significantly less blame ( M=13.86, SD=20.47) for the accident given the (better) re-designed warning than they did in the original (poor) warning condition ( M=26.71, SD=26.58). Neither the effect of worker experience nor the interaction of experience and warning condition were sig-nificant ( ps>0.10). The ANOVA described above was replicated using blame apportioned to the decedent as the dependent variable. The model was not significant ( p>0.10), indicating that neither warning quality nor level of experience influenced the amount of blame apportioned to the fatally injured worker. These findings offer the following insights. Contrary to our predictions, the worker’s level of experience did not influence allocations of blame, including to the manufac-turer. Warning quality, however, did influence how blame was apportioned, in particular to the manufacturer. Conse-quently, these findings add to the growing body of evidence suggesting that safety sells. Specifically, evaluators con-sistently assign less blame to manufacturers when they are perceived as adequately warning about their products’ re-sidual hazards.