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

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

Raji Srinivasan and Gulen Sarial-Abi

JM Insights in the Classroom

Teaching Insights

Insight: Consumer are more forgiving of brand failures due to a marketing algorithm error than human error because they are considered to have less mind and are therefore held responsible for the error and the brand failure. 

When algorithms fail, consumers are less forgiving of these brand failures when algorithms are anthropomorphized, use machine learning, or are used for subjective or interactive tasks where they are more humanized.

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​​​​Classroom Code(s): Brand Management; Consumer Behavior; Digital Marketing; Marketing Strategy; Principles of Marketing, Core Marketing, Intro to Marketing Management; Social Media Marketing; Technology Marketing

Full Citation: ​
Complete citation: Srinivasan, Raji, Gulen Sarial-Abi (2021), “When Algorithms Fail: Consumers’ Responses to Brand Harm Crises Caused by Algorithm Errors,” Journal of Marketing.

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Article Abstract
Abstract: 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.

Special thanks to Demi Oba and Holly Howe, Ph.D. candidates at Duke University, for their support in working with authors on submissions to this program.

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Raji Srinivasan is Sam Barshop Professor of Marketing Administration, University of Texas at Austin, USA.

Gülen Sarial-Abi is Associate Professor of Marketing, Copenhagen Business School, Denmark.