Objective: Although performance appraisals are based on objective procedures, cognitive biases from appraisers may create avenues for errors in judgment of employee performance. Dehumanizing language, or metaphors that characterize humans in nonhuman terms (e.g., cogs in a machine), is one important way cognitive biases can occur Method: We conduct a survey experiment to determine if dehumanizing language affects perceptions of employee value or competency within the context of performance appraisals. Result: Findings show that when employees are referred to in mechanistic terms, respondents perceive that employee to be deserve hire compensation, and be more competent, as compared to referring to employees in human or animalistic terms.
Conclusion:Conclusions suggest dehumanizing language is an important type of cognitive bias that affects individuals in administrative environments, and the managerial and ethical implications of its use require further examination.Performance management has been a key component of administrative practice and theory since the days of scientific management, but more recently, the performance paradigm has placed it at the center of the human resource management (HRM) research agenda (Battaglio, 2015;Durant et al., 2006;Taylor, 1919). Central to this agenda are tools for performance appraisal, in which managers evaluate outputs at both individual and organizational-levels related to organizational missions, goals, and values. Performance appraisals have increased in sophistication over time, as well as our understanding of their design, implementation, and effects on both employees and organizations (Battaglio, 2015;Belle, Cantarelli, and Belardinelli, 2017). In order for performance appraisal to be effective, it must be built on objective processes that eliminate biases; otherwise, they may be counterproductive to organizational goals or employee motivation (Nalbanian, 1981;Wiemann, Meidert, and Weibel, 2019). Significant research focuses on reducing error by identifying and developing strategies to mitigate common types and sources of bias (Battaglio, 2015). However, scant attention has been paid to cognitive biases inherent in how humans interpret performance information during this process, which inserts subjectivity into a supposedly objective process (Belle, Cantarelli, and Belardinelli, 2017).