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누가, 언제 알고리즘 평가 제도를 공정하다고 인식할까? 알고리즘 기반 평가 제도에 대한 직원의 공정성 인식 연구

Who and when will algorithm-based performance evaluation be perceived as fair? The relationship between algorithm-based performance evaluation and employee perception of fairness

김미정|이중학|홍운기
한국기업경영학회 (Korean Corporation Management Association)|2024.10|기업경영연구 (Korean Corporation Management Review)|제31권 제5호|pp. 51-75|DOI http://dx.doi.org/10.21052/KCMR.2024.31.5.51

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키워드

자기효능감self-efficacygoal interdependence공정성 인식fairness perception알고리즘 평가 제도목표의존성Algorithm-based performance evaluation
주제분류경영학

초록

기업 내 과업 활동이 디지털화됨에 따라 조직에서는 알고리즘을 활용하여 직원의 태도와 성과를 측정하고 평 가하려는 시도가 관찰되고 있다. 알고리즘 평가 제도에 관한 기존 연구는 알고리즘 평가 제도 도입이 직원과 조 직의 성과에 미치는 영향에 초점을 두었지만, 알고리즘 평가 제도의 대상이 되는 직원들의 수용성 및 공정성에 관한 이해는 부족하였다. 누가, 언제 알고리즘 평가 제도를 공정하다고 인식할까? 이 질문에 답하기 위해 본 연구 는 공정성 이론에 기반하여 알고리즘 평가 제도에 대한 직원의 공정성 인식을 예측하는 이론적 모델을 제시하였 다. 연구 모델을 검증하기 위해 먼저 직장인들의 알고리즘 평가 제도에 대한 장단점 인식 조사를 파일럿 연구를 통해 수행하였고, 이를 바탕으로 알고리즘 평가 제도에 대한 실험 시나리오를 만들어 국내 정규직 직원 300명을 대상으로 설문을 진행하였다. 분석 결과 개인의 업무 목표가 조직 내 다른 구성원들과 상호 의존적인 경우 알고 리즘 HRM의 결정에 상사가 적절하게 개입해야 더욱 공정하다고 인식하였다. 또한, 자기 효능감이 높아 스스로 업무를 충분히 완수하여 성과를 달성할 수 있다고 믿는 직원들은 상사가 개입할 경우 알고리즘 평가 제도에 대한 공정성 인식이 저해될 수 있음을 보였다. 이러한 결과를 바탕으로 이론적 기여와 실무적 시사점을 논의하였다.

Abstract

As employee work activities become digitized, organizations are trying to utilize data and algorithms to measure and evaluate employees' attitudes and performance. Previous studies have predominantly focused on leveraging algorithm-based performance evaluation to accurately predict employee behaviors and enhance performance. However, there has been a lack of understanding from the employee perspective regarding the acceptability and fairness of algorithm-based performance evaluation. Who perceives algorithm-based performance evaluation as fair, and under what circumstances? To address this question, we draw from organizational justice theory to develop a theoretical model predicting employee perception of fairness for algorithm-based performance evaluation. We argue that goal interdependence is positively related to perceived fairness in algorithm-based performance evaluation when supervisors intervene in decisions made by the algorithm. We also argue that self-efficacy is positively related to perceived fairness in algorithm-based performance evaluation when supervisors are unable to intervene. To test our theoretical model, we conducted a pilot study using topic modeling to identify perceived strengths and weaknesses of algorithm-based performance evaluation. Building on the insights from the pilot study, we tested our theoretical model with a sample of 300 full-time employees. Lastly, we discussed the theoretical contributions and practical implications of our findings.

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