The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence categorization, a person will feel a distinct way about a group, which influences their behavior toward those individuals.
Discrimination and the Stereotype Content Model
According to the SCM, group stereotypes can be broken down into four categories: (1) low for both warmth and competence; (2) high for both warmth and competence; (3) low in warmth, high in competence; and finally (4) high in warmth, low in competence. Respectively, these classifications provoke feelings of contempt, admiration, envy and pity—emotions that may result in certain groups being discriminated against. In the United States, two examples of this are women and the homeless.
While looking at gender in the workplace, research has demonstrated that employed mothers are viewed as warm yet incapable (Cuddy & Fiske, 2004). On one hand, the pity that this categorization evokes may make colleagues more likely to provide help—for example, by offering to stay a little later to finish up a project, knowing that the mother has to pick up her children from school. However, it can also result in a type of “passive harm.” Here, managers may not actively try to sabotage or fire their maternal colleagues, but they may fail to either promote them or provide job-relevant training. Paradoxically, employed fathers are still seen as capable and don’t experience the same degree of neglect in the office.
In contrast, the homeless are classically stereotyped as “low” in both warmth and competence, and seen as a societal burden. This stereotype breeds contempt, and as a result, this group is actively discriminated against, being the recipient of both “passive” and “active“ harm—for example, being ignored or aggressively harassed by passersby on the street, respectively (Cuddy, Fiske & Glick, 2007; Fiske, 2012). Such behavior may have a biological basis, as studies have shown that the homeless (and other members of the low warmth, low competence classification of the SCM, like drug addicts) fail to robustly activate a neural region typically involved in social function. As this area is normally stimulated when members of any of the other three SCM categories are encountered, this suggests that the homeless are dehumanized in the brain (Harris & Fiske, 2007; Fiske, 2012). Fortunately, this can be circumvented by encouraging people to put themselves in the shoes of the disadvantaged, for example by humanizing them and imagining what types of food they like to eat. Thus, through application of the SCM, people can better understand why they react the way they do towards certain groups, and can modify their behavior to prevent discrimination.
Extending the Stereotype Content Model to Inanimate Objects
While the SCM has classically been applied to interactions between social groups, some researchers are evaluating whether this model can predict feelings towards inanimate objects. For example, investigators have demonstrated that specific product brands can be categorized using a warmth (intention) and competence (ability) scale (Kervyn, Fiske & Malone, 2012). Popular brands, like those manufacturing a favorite candy bar or soda, are typically viewed as friendly, capable and well-intentioned toward consumers—a perception that fosters admiration and loyalty, translating into more purchases. In contrast, brands that have received negative press—like an oil company involved in a spill—are viewed as ill-intentioned and incompetent, which evokes contempt and dissuades potential buyers. Interestingly, ambivalent product categories also exist, and include envied luxury items (classified as ill-intentioned but capable), and pitied government-subsidized brands, like the post office (seen as potentially incapable but trustworthy).
However, applications of the SCM are not limited to consumerism. Other researchers are evaluating whether warmth-competence stereotypes influence peoples’ perceptions of machines (Oliveira, R., Arriaga, P., Correia, F. & Paiva, A. 2018 and 2019). Interestingly, such studies have demonstrated that people most admire and prefer robots that appear warm—for example, those programmed to offer words of encouragement during a simulated card game—even if such machines are calibrated to appear incompetent and make mistakes during a specific task. This may be due to the perception that fallible robots appear more human. Thus, through application of the SCM, engineers can improve android design, creating machines that people want to interact with in the future.
