Consumers with Weaker Applications Are Less Receptive to Algorithmic Evaluation

Many organizations are adopting algorithms for evaluating various consumer applications (e.g., loans, insurance). This research explores how consumers react to this practice, with the goal of understanding what leads some consumers to react more positively or negatively to being evaluated by an algorithm than others. In the context of consumers applying for access to valued services, opportunities, or benefits, applicant strength (i.e., how strong an applicant believes their case is based on the information they have about their standing) influences their reactions toward algorithmic versus human evaluation. Algorithmic evaluation will have a greater deterrent effect on weaker than on stronger applicants. This asymmetry is explained by weaker applicants’ stronger preference for characteristics of human evaluators, such as flexibility and leniency, that they believe may improve their chances of receiving a favorable outcome. Consumers’ preferences ultimately impact willingness to apply, such that using algorithmic evaluations disproportionally discourages weaker applicants from applying. This research contributes to the literature on consumer responses to algorithms by identifying applicant strength as a novel determinant and by extending the focus from algorithms as advisors to algorithms as evaluators of consumers.