Algorithms in personnel selection, applicants' attributions about organizations' intents and organizational attractiveness: An experimental study
通过两项实验发现，求职者认为组织使用算法选拔人员更多是为了降低成本或剥削求职者，而人类专家决策则被视为提升质量或关心求职者福祉，这种归因导致算法使用降低了组织吸引力。
Abstract Machine‐learning algorithms used in personnel selection are a promising avenue for several reasons. We shift the focus to applicants' attributions about the reasons why an organization uses algorithms. Combining the human resources attributions model, signaling theory, and existing literature on the perceptions of algorithmic decision‐makers, we theorize that using algorithms affects internal attributions of intent and, in turn, organizational attractiveness. In two experiments ( N = 259 and N = 342), including a concurrent double randomization design for causal mediation inferences, we test our hypotheses in the applicant screening stage. The results of our studies indicate that control‐focused attributions about personnel selection (cost reduction and applicant exploitation) are much stronger when algorithms are used, whereas commitment‐focused attributions (quality enhancement and applicant well‐being) are much stronger when human experts make selection decisions. We further find that algorithms have a large negative effect on organizational attractiveness that can be partly explained by these attributions. Implications for practitioners and academics are discussed.