Are people better employees than machines? Dehumanizing language and employee performance appraisals

Abstract

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).

Of mice and machines: The asymmetric dehumanization of immigrants as a function of perceived economic contribution

“…However, thinking about immigrants in terms of economic contribution may not always lead to more positive attitudes (Palma & Esses, 2023), and may even lead to different forms of dehumanization. For example, outgroups that are seen as hardworking, economically competitive, and high status, tend to also be viewed less warmly (Fiske, 2018;Hong et al, 2023) which in turn is associated with ascribing less emotion towards these groups and viewing them as more mechanistic (Bai & Zhao, in press;Fowler & Utych, 2021;Lee & Fiske, 2006;Savaş et al, 2021). While research on the dehumanization of immigrants has historically focused on their animalistic dehumanization (Esses et al, 2021), perceptions of immigrant skill and economic ability may vary as a function of admission category, with some immigrants being perceived as more competent (e.g., economic migrants) compared to others.…”

“…Blatant dehumanization occurs when people or groups are, at face value, explicitly likened to animals or machines. For example, likening immigrants to vermin or workers to "cogs in a machine" are forms of blatant dehumanization (e.g., Fowler & Utych, 2021;Marshall & Shapiro, 2018). In contrast, subtle dehumanization occurs when people are denied traits or characteristics associated with humanity.…”

“…Thus, while groups stereotyped to be low on both warmth and competence tend to be the traditional targets of dehumanization (Harris & Fiske, 2006), differences in perceived competence may influence whether disliked outgroups are seen mechanistically or not. For example, research on worker objectification has found that likening workers to machines increases perceived competence (Fowler & Utych, 2021). Similarly, research on the racial asymmetry of dehumanization has found that Black individuals, who are typically ascribed low warmth and low competence stereotypes tend to be more animalistically dehumanized.…”

“…For example, while temporary foreign workers in Canada and migrant workers in the United States may come to these countries to work, they often occupy low wage positions (Lu & Hou, 2023) and often do not receive the same rights, protections, and access to government and social services as immigrants with permanent residency (Nakache & Kinoshita, 2010;Salami et al, 2015). Despite this lack of prestige, temporary foreign workers / migrant workers may still experience greater mechanistic dehumanization, similar to how workers are objectified in society (Baldissarri et al, 2022;Fowler & Utych, 2021). Similarly, international students are often seen as potential 'skilled' workers for the receiving country's economy (Coustere et al, 2023), they can also be seen as potential competition for educational opportunities, future employment, and housing (Esses et al, 2001;Nickel, 2024).…”

“…There is a growing interest in understanding how different groups are dehumanized across society. For example, research on worker objectification has found that workers tend to experience a mixture of both animalistic and mechanistic dehumanization (Baldissarri et al, 2022), and that likening workers to machines tend to increase how competent they are perceived by others (Fowler & Utych, 2021). In the context of race and ethnicity, mechanistic dehumanization is typically applied to outgroups that are stereotyped as high competence and low warmth, while those perceived to be low on both dimensions experience animalistic dehumanization (Bai & Zhao, in press;Martínez et al, 2012), and that different forms of dehumanization affect how these groups are evaluated in the workplace (Bai & Zhao, in press).…”