EXPRESS: When Algorithms Fail: Consumers’ Responses to Brand Harm Crises Caused by Algorithm Errors

Algorithms increasingly used by brands sometimes fail to perform as expected or even worse, cause harm, causing brand harm crises. Unfortunately, algorithm failures are increasing in frequency. Yet, we know little about consumers’ responses to brands following such brand harm crises. Extending developments in the theory of mind perception, we hypothesize that following a brand harm crisis caused by an algorithm error (vs. human error), consumers will respond less negatively to the brand. We further hypothesize that consumers’ lower mind perception of agency of the algorithm (vs. human) for the error that lowers their perceptions of the algorithm’s responsibility for the harm caused by the error will mediate this relationship. We also hypothesize four moderators of this relationship: two algorithm characteristics, anthropomorphized algorithm and machine learning algorithm and two task characteristics where the algorithm is deployed, subjective (vs. objective) task and interactive (vs. non-interactive) task. We find support for the hypotheses in eight experimental studies including two incentive-compatible studies. We examine the effects of two managerial interventions to manage the aftermath of brand harm crises caused by algorithm errors. The research’s findings advance the literature on brand harm crises, algorithm usage, and algorithmic marketing and generate managerial guidelines to address the aftermath of such brand harm crises.