Business School, Beijing Information Science and Technology University, Beijing, China.
Zhongguancun Smart City Co., Ltd, Beijing, China.
BMC Med Ethics. 2024 Oct 23;25(1):118. doi: 10.1186/s12910-024-01121-0.
Institutions are increasingly employing algorithms to provide performance feedback to individuals by tracking productivity, conducting performance appraisals, and developing improvement plans, compared to traditional human managers. However, this shift has provoked considerable debate over the effectiveness and fairness of algorithmic feedback. This study investigates the effects of negative performance feedback (NPF) on the attitudes, cognition and behavior of medical researchers, comparing NPF from algorithms versus humans. Two scenario-based experimental studies were conducted with a total sample of 660 medical researchers (algorithm group: N1 = 411; human group: N2 = 249). Study 1 analyzes the differences in scientific misconduct, moral disengagement, and algorithmic attitudes between the two sources of NPF. The findings reveal that NPF from algorithms shows higher levels of moral disengagement, scientific misconduct, and negative attitudes towards algorithms compared to NPF from humans. Study 2, grounded in trait activation theory, investigates how NPF from algorithms triggers individual's egoism and algorithm aversion, potentially leading to moral disengagement and scientific misconduct. Results indicate that algorithm aversion triggers individuals' egoism, and their interaction enhances moral disengagement, which in turn leads to increased scientific misconduct among researchers. This relationship is also moderated by algorithmic transparency. The study concludes that while algorithms can streamline performance evaluations, they pose significant risks to scientific misconduct of researchers if not properly designed. These findings extend our understanding of NPF by highlighting the emotional and cognitive challenges algorithms face in decision-making processes, while also underscoring the importance of balancing technological efficiency with moral considerations to promote a healthy research environment. Moreover, managerial implications include integrating human oversight in algorithmic NPF processes and enhancing transparency and fairness to mitigate negative impacts on medical researchers' attitudes and behaviors.
机构越来越多地使用算法来提供绩效反馈，通过跟踪生产力、进行绩效评估和制定改进计划，来代替传统的人工管理者。然而，这种转变引发了关于算法反馈的有效性和公平性的大量争论。本研究调查了消极绩效反馈（NPF）对医学研究人员的态度、认知和行为的影响，比较了算法和人工提供的 NPF。通过两项基于情景的实验研究，共对 660 名医学研究人员进行了研究（算法组：N1=411；人工组：N2=249）。研究 1 分析了两种来源的 NPF 在科学不端行为、道德脱离和算法态度方面的差异。研究结果表明，与人工提供的 NPF 相比，算法提供的 NPF 表现出更高水平的道德脱离、科学不端行为和对算法的消极态度。研究 2，基于特质激活理论，研究了算法提供的 NPF 如何引发个体的自我中心主义和算法厌恶，从而可能导致道德脱离和科学不端行为。结果表明，算法厌恶会引发个体的自我中心主义，它们的相互作用会增强道德脱离，从而导致研究人员的科学不端行为增加。这种关系也受到算法透明度的调节。研究得出结论，虽然算法可以简化绩效评估，但如果设计不当，它们会对研究人员的科学不端行为构成重大风险。这些发现通过强调算法在决策过程中面临的情感和认知挑战，扩展了我们对 NPF 的理解，同时也强调了在促进健康的研究环境中平衡技术效率和道德考虑的重要性。此外，管理启示包括在算法 NPF 过程中纳入人工监督，并提高透明度和公平性，以减轻对医学研究人员态度和行为的负面影响。
