Interpersonal and Computer-Mediated Competence for Prejudice Reduction: Learning to Interact Digitally and Physically During the Pandemic

Abstract

As racial and ethnic diversity have increased in America, prejudice too has expanded. Citizens are more wary of immigrants, with attitudes toward Asian immigrants in particular worsening during COVID-19. Yet less is known about the prejudice directed at other immigrant groups during this period, with research suggesting that feeling capable of interacting with new people could reduce misgivings about diversity. A web survey was conducted in April of 2020 to test the potential for digital and physical social competence to improve attitudes toward Mexican immigrants, as the largest immigrant group in the United States (N = 665). Interpersonal competence was inversely associated with prejudice toward Mexican immigrants, with interpersonal skills such as attentiveness, expressiveness, and mindfulness being especially valuable for prejudice reduction. Computer-mediated communication competence was indirectly associated with feeling less prejudiced, through interpersonal competence, and social presence also moderated the conversion of CMC competence into interpersonal competence, diminishing prejudice even further. Digital social capabilities encourage admiration and sympathy for immigrants by making users feel more capable of interacting with them locally. Networked settings now have the potential to train dissimilar users to interact together in person, as a way of reducing prejudice.

Keywords

subtle prejudice blatant prejudice immigration social presence social competence

The U.S. population had been distancing itself socially long before COVID-19 (Dinesen et al., 2020; Putnam, 2000, 2007). As neighborhoods diversified racially and ethnically, a perceived competition for resources ensued (Laurence et al., 2019; Major et al., 2016). Citizens today have smaller personal networks than in generations past and are less likely to talk with their neighbors (Kovacs et al., 2021; Parker et al., 2018). Trust and tolerance have diminished (Dinesen & Sønderskov, 2018; Rettie & Daniels, 2021), with COVID-19 exacerbating prejudice toward Asian immigrants (Croucher et al., 2020; Passini & Speltini, 2022). Hate crimes against this group were spiking (Department of Justice, 2022; Gover et al., 2020), while immigrants in general were also perceived as more threatening, as evidence that “blatant” prejudice was increasing (Esses & Hamilton, 2021; Laurence et al., 2019).

Yet it is less clear how feelings about immigrants could be improved at times of crisis, particularly in the absence of local contact. Communication competence is suggested to have some potential for this (Bouchillon, 2020, 2022; Schunk & DiBenedetto, 2020), defined as the motivation, knowledge, skill, and effectiveness a person uses to interact (Spitzberg, 2006; Spitzberg & Cupach, 1984). Interpersonal competence directs conversations that occur face to face (Spitzberg, 2006; Spitzberg & Cupach, 1984), while computer-mediated communication competence guides interactions using mobile phones and the Internet. In each of these settings, competent individuals exist in a “state of readiness” (Turner & Cameron, 2016, p. 218). They feel capable of interacting with new people and can generate a range of opportunities by doing so (Burt, 1992; Granovetter, 1973). This has ramifications for prejudice as well.

Interpersonal competence for example has been inversely related to the likelihood of experiencing prejudice (Świtaj et al., 2021). But it was difficult to source diverse interactions locally even before any pandemic, which complicates the process of becoming competent. So computer-based platforms are tested as a stopgap solution, given that aspects of CMC competence can also be felt to apply in person (Bouchillon, 2022). Skills like attentiveness, expressiveness, and mindfulness appear to cross over, while social presence allows this to happen more quickly (Bouchillon, 2022). Experiencing computer-based interactions as realistic and intimate enables networked social competence to be utilized offline (Bouchillon, 2020). It helps that people were using social technology more heavily during this period as well, and becoming more competent interacting through computers (Bouchillon, 2022; McClain et al., 2021). Assuming this translates to greater interpersonal competence, and it should, prejudice could be reduced.

The present study tests the potential for CMC competence to reduce prejudice directed the largest immigrant group in the United States—those from Mexico (Paxton & Ressler, 2018; Rosenbloom & Batalova, 2022). A web survey was conducted in April of 2020 to pursue this (N = 665), with three types of prejudice being considered. Subtle prejudice is defined as an absence of positive emotions toward the outgroup—specifically a lack of admiration or sympathy—while blatant prejudice is considered in terms of feeling threatened by them (Arancibia-Martini et al., 2016; Ungaretti et al., 2020). These measures are also combined into a “global” index of prejudice, comprising subtle and blatant components (Meertens & Pettigrew, 1997; Ungaretti et al., 2020), with results used to demonstrate the potential for networked social competence to reduce misgivings about diversity, even in a social vacuum.

Prejudice and Competence

Social learning theory posits that social contact with new people can be used to develop interpersonal tendencies, including a greater willingness to interact (Bandura, 1969, 1978; Spitzberg & Cupach, 1984). Interacting with diverse others seems to reduce prejudice as well, as part of the intergroup contact hypothesis (Allport, 1954; Turner & Cameron, 2016), with prejudice defined as “an aversive or hostile attitude toward a person who belongs to a group” (Allport, 1954, p. 7; Pettigrew & Tropp, 2008). It stems from stereotypical beliefs about the traits possessed by members of said group (Quillian, 1995), and these misgivings tend to increase with outgroup size and proximity (Kotzur & Wagner, 2021; Eric Oliver & Wong, 2003), as well as at high levels of community segregation (Laurence et al., 2019).

In its subtler form, prejudice entails a defense of ingroup values, an exaggeration of cultural differences, and difficulty expressing positive emotions toward those who are different (Pettigrew & Meertens, 1995). Blatant prejudice refers to feeling threatened by them (Lissitsa & Kushnirovich, 2019; Pettigrew & Meertens, 2001), and past studies have also relied on a fear of the outgroup or an opposition to intimate contact to measure blatant prejudice (Pettigrew & Meertens, 1995). Subtle and blatant prejudice items can also be combined, to create a “global” index of prejudice (Cárdenas Castro, 2010, p. 118), while knowledge about an outgroup, direct contact with them, and empathy have each shown the ability to reduce prejudice in past research (Allport, 1954; Pettigrew & Tropp, 2008).

Training individuals to interact with new people succesfully could be used to address prejudice as well (Pettigrew & Tropp, 2006, 2008; Świtaj et al., 2021; Zhou et al., 2019), with interpersonal competence measuring this preparedness in a localized sense. It is conceptualized as a set of face-to-face social capabilities that can be used in combination or separately to direct the process of interacting (Spitzberg & Cupach, 1984). These competencies include motivation, knowledge, skill, and effectiveness (Bochner & Kelly, 1974), with motivation as “the energizing component” (Spitzberg, 2006, p. 637). It allows one to initiate conversations with new people, while knowledge about how to interact with them accumulates through sustained contact. Skills then are “tactics and routines that people employ in the service of their motivation and knowledge,” with attentiveness, expressiveness, and mindfulness identified as three particularly salient skills (Spitzberg, 2006, p. 638; Wang et al., 2018). Effectiveness is another valuable skill, such that it eventually became a standalone competency. It speaks to an ability to reach desired outcomes interpersonally (Bouchillon, 2022; Spitzberg, 2006).

A similar process unfolds online, with Spitzberg (2006) defining computer-mediated communication as any human interaction occurring through “digitally based technologies” (p. 630). This includes “email, mobile phone calls, videoconferencing, and virtual reality, along with technologies that have yet to be developed” (Bouchillon, 2022, p. 24). CMC competence refers to the motivation, knowledge, skill, and effectiveness a person uses to interact through technology (Bouchillon, 2022; Spitzberg, 2006), with the CMC competence battery gauging aspects of technical proficiency (e.g., “I am knowledgeable about how to interact through computers”), as well as prosocial tendencies (e.g., “I show concern for and interest in the people I converse with using computers”; see Spitzberg, 2006).

But whether digital competencies contribute to prejudice reduction is less clear (Turner & Cameron, 2016, p. 236), with interpersonal competence thought to be more valuable for this (Imperato et al., 2021). Demographic influences complicate the process as well (Bouchillon, 2020, 2022), where feelings about immigrants are often politically dependent (Davis & Perry, 2021; Wright & Esses, 2019). Age can also relate to fearing differences (Eric Oliver & Wong, 2003; St John & Heald-Moore, 1996), while income and educational attainment seem to dispel prejudice (Benegal & Holman, 2021; Carvacho et al., 2013), as can diverse social contact (Côté & Erickson, 2009), having larger social networks (van Zalk & Kerr, 2014), and trusting the average person (van der Linden et al., 2017).

The present study controls for these influences to isolate associations between competence and prejudice during COVID-19. Interpersonal competence is suggested to reduce global prejudice by fostering the belief that one is capable of interacting with new people locally (Bochner & Kelly, 1974; Pettigrew & Tropp, 2006, 2008). Feelings about Mexican immigrants were measured during the pandemic to test this, with subtle prejudice operationalized as a lack of admiration or sympathy, while blatant prejudice gauges how threatening one perceives them to be (Arancibia-Martini et al., 2016; Cárdenas Castro, 2010; Pettigrew & Meertens, 1995). A research question asks which components of interpersonal competence—motivation, knowledge, skill, or effectiveness—are the most valuable for prejudice reduction, while interpersonal competence in general is hypothesized to be inversely associated with global prejudice (Sussman, 1997; Świtaj et al., 2021), as well as subtle prejudice, which is the less entrenched form (Lissitsa & Kushnirovich, 2019; Ungaretti et al., 2020). Blatant prejudice is often more difficult to address.

RQ1

Which if any interpersonal competencies are directly associated with prejudice in its global form, subtle form, or blatant form?

Interpersonal competence will be inversely associated with global prejudice.

Interpersonal competence will be inversely associated with subtle prejudice.

Social Compensation for Prejudice Reduction

Interpersonal and computer-mediated forms of competence also tend to increase together, with networked communication suggested to aid in the development of face-to-face social tendencies, even over time (Bouchillon, 2022; Hwang, 2011). Some of the capabilities one acquires interacting through computers also apply in person (Bouchillon, 2019, 2020), which is important, because citizens now have fewer structured ways of working together locally (Jennings & Stoker, 2004; Paxton & Ressler, 2018). This was especially true during the pandemic, when personal networks shrank by 16% on average (Kovacs et al., 2021), and the populace was relying on digital modes of communication more heavily instead (De’ et al., 2020).

Social compensation theory asserts that by learning how to interact with new people through computers, users develop social tendencies that compensate for interpersonal shortcomings (Amichai-Hamburger et al., 2015; Steinfeld et al., 2008). This explains why those who were distancing heavily during the pandemic saw increases in CMC competence, and added to interpersonal competence because of it (Bouchillon, 2022). Using computers to socialize left them feeling similarly capable in person, despite being absent from local life. The present study works to extend this influence to prejudice reduction. Interpersonal competence is hypothesized to lessen the tendency to stigmatize outgroups such as Mexican immigrants (see H1; Bouchillon, 2022; Świtaj et al., 2021), which should permit CMC competence to address global prejudice indirectly, through interpersonal competence, by increasing the sense that one is capable of interacting with new people locally (Sussman, 1997; Świtaj et al., 2021). Positive emotions toward immigrants are expected to result (Pettigrew & Meertens, 1995), with interpersonal competence hypothesized to mediate associations between CMC competence and global/subtle prejudice.

CMC competence will be indirectly associated with lower levels of (a) global prejudice and (b) subtle prejudice, through interpersonal competence.

Social Presence and Moderated Mediation

Social competence can develop rapidly in networked settings (Bouchillon, 2020, 2022), and citizens were motivated to replicate interpersonal life using computers during the pandemic (De’ et al., 2020). Besides becoming more competent, this led networked social interactions to feel more realistic and intimate to them, as fundamental aspects of social presence (Bouchillon, 2022; Oh et al., 2018). Presence is defined as a sense of “being there,” of feeling like computer-based settings are representative of real life, as well as feeling close to other users (Biocca et al., 2003, p. 456). Presence has also been shown to increase the size of the association between CMC competence and interpersonal competence (Bouchillon, 2020, 2022), where networked social capabilities apply in person more fully as the settings in which they are developed are experienced as realistic and intimate (Bouchillon, 2022).

Supplementing interpersonal capabilities through computers should lessen the tendency to stigmatize outgroups such as Mexican immigrants, and presence is suggested to encourage this type of social compensation (Sussman, 1997; Świtaj et al., 2021). It aids in the development of interpersonal competence, which is thought to allow CMC competence to reduce prejudice indirectly (see H3; Bouchillon, 2022). Moderated mediation is hypothesized, with CMC competence expected to contribute to interpersonal competence more fully at higher levels of social presence, spilling over to greater reductions in global and subtle prejudice (Lissitsa & Kushnirovich, 2019; Ungaretti et al., 2020). Blatant prejudice is, again, more difficult to address, but maybe not impossible.

Social presence will moderate the association between CMC competence and interpersonal competence, relating to greater reductions in (a) global prejudice and (b) subtle prejudice, both indirectly.

Blatant Prejudice and Serial Mediation

Given that subtle and blatant aspects of prejudice also tend to move together (Cárdenas Castro, 2010; Meertens & Pettigrew, 1997), this provides an opportunity. Positive emotions toward immigrants offer a means of reducing perceived threat (Esses & Dovidio, 2002; Zhou et al., 2019), with interpersonal competence hypothesized to be inversely associated with subtle prejudice, by increasing admiration and sympathy for Mexican immigrants (see H2; Pettigrew & Meertens, 2001). CMC competence is thought to lessen subtle prejudice more indirectly, by adding to interpersonal competence during a period of social distancing (see H3b). Reductions in subtle prejudice should relate to feeling less threatened by Mexican immigrants as well (Bouchillon, 2020, 2022; Meertens & Pettigrew, 1997), with serial mediation hypothesized to conclude. CMC competence is suggested to aid in the development of interpersonal competence (Bouchillon, 2022), as a means of reducing subtle prejudice, and possibly blatant prejudice by way of it (Bouchillon, 2022; Lissitsa & Kushnirovich, 2019; Ungaretti et al., 2020).

CMC competence will be inversely associated with blatant prejudice through increasing interpersonal competence and reducing subtle prejudice.

Method

A convenience sample of U.S. residents was drawn from the Dynata web panel in April of 2020, just after the COVID-19 pandemic had begun (N = 665). The survey was exempted from review by the Internal Review Board. Dynata recruits panelists from multiple places, including social media, other survey panels, and various websites (ESOMAR, 2018). They receive credit for taking surveys, which can be redeemed for things like Amazon gift cards, and an additional incentive of $.50 was offered for participating. The questionnaire was distributed through Qualtrics. The sample-to-variable ratio was 35 for each independent variable in regressions, surpassing suggestions from past research to use at least 15 to 20 observations per independent variable (Hair et al., 2018; Memon et al., 2020). Subtle and blatant aspects of prejudice toward Mexican immigrants in particular were measured, along with demographics, communication competence, and social distancing.

Focal Variables

Computer-Mediated Communication Competence

Sixteen items from Spitzberg (2006) were used to assess CMC competence (see Appendix), measured in terms of motivation (items a, b, c, and d), knowledge (items e, f, g, and h), skill (items i, j, k, and l), and effectiveness (items m, n, o, and p). Responses fell on a 5-point Likert scale, with the 16 items being combined to create the index of CMC competence (Cronbach’s α = .944, M = 3.56, SD = .67).

Interpersonal Competence

Sixteen items from Spitzberg and Cupach (1984) were used to measure interpersonal competence (see Appendix), also in terms of motivation (items a, b, c, and d), knowledge (items e, f, g, and h), skill (items i, j, k, and l), and effectiveness (items m, n, o, and p). The 16 items were combined to create the index of interpersonal competence (Cronbach’s α = .923, M = 3.9, SD = .49).

Prejudice

Five items were used to represent prejudice (Pettigrew & Meertens, 1995; Ungaretti et al., 2020), with the first two items gauging the subtle form (e.g., the absence of positive emotions toward Mexican immigrants), while the final three items measured blatant prejudice, or feeling threatened by them (see Appendix). Responses fell on a 5-point Likert scale from strongly disagree to strongly agree (Arancibia-Martini et al., 2016; Pettigrew & Meertens, 1995). The two subtle prejudice items were reverse coded and shown to be correlated (r(664) = .713, p < .001). They held together reliably using the Spearman–Brown coefficient (r_kk = .833, M = 2.61, SD = .94). Responses to the blatant prejudice items held together reliably as well (Cronbach’s α = .725, M = 2.6, SD = .92), with subtle and blatant aspects of prejudice ultimately being combined, to test prejudice in its global form (Cronbach’s α = .806, M = 2.6, SD = .82). A principal component analysis with a promax rotation was also used to confirm the existence of distinctions between subtle and blatant prejudice. Both subscales are part of an older battery of prejudice items (Pettigrew & Meertens, 1995), and when subtle and blatant questions were included together in the PCA, a two-component solution was suggested. The majority of variance (57%) was explained by blatant prejudice (eigenvalue = 2.87), with 16% explained by subtle prejudice (eigenvalue = .82). Table 1 presents the pattern matrix for the principal component analysis.

Table 1.

Pattern Matrix for Subtle/Blatant Prejudice.

	Factors
	1	2
Do not admire Mexican immigrants	−.018	.934
Do not feel sympathy for Mexican immigrants	.003	.921
Mexicans take jobs from Americans	.755	.165
Can never really be comfortable together	.845	−.067
Politicians care too much about immigrants	.790	−.040
Percentage of variance	57.35%	16.38%
Eigenvalue	2.87	.82
Cronbach’s alpha	.725	.830
Subscale M	2.6	2.61
Subscale SD	.92	.94

Bold denotes the components that load together.

Social Presence

Eleven items were used to measure social presence, asking respondents to evaluate how realistic and intimate computer-based interactions are experienced as being (Goffman, 1963; Gunawardena, 1995). Bouchillon (2022) detailed the factor structure of the social presence scale. Five immediacy items were drawn from Goffman (1963), and six intimacy items came from Short and colleagues (1976). Responses to the immediacy items fell on a 5-point Likert scale from strongly disagree to strongly agree, while intimacy items asked respondents to rate computer-based communication on an anchored scale from 1 to 5 (see Appendix). The 11 items were combined to create the index of social presence (Cronbach’s α = .937, M = 3.15, SD = .82).

Control Variables

Demographics

Demographic control variables included sex (57.4% female; 42.4% male), race (.9% American Indian; 6.3% Asian; 2.4% Biracial; 6.5% Black; 83.9% White), ethnicity (9.8% Hispanic; 90.2% non-Hispanic), age (Mdn = 57, M = 55.1, SD = 15.39), and region of residence (20% Northeastern; 20.2% Midwestern; 34% Southern; 25.7% Western). Sex, race, ethnicity, and region were represented using dummy codes in regressions.

Annual Household Income

One item from the General Social Survey was used to control for household income (Smith et al., 2008), with responses falling on a 7-point scale from “less than $25,000” to “$150,000 or more” (Mdn = $87,499.50, M = 3.73, SD = 1.84).

Educational Attainment

A single item was drawn from the Citizenship, Involvement, and Democracy Survey to measure the highest level of education an individual had completed (Howard et al., 2006). Responses fell on an 8-point scale from “high school (incomplete)” to “doctorate or professional degree (completed)” (Mdn = “bachelor’s degree,” M = 4.44, SD = 1.48).

Political Party

Party affiliation was also controlled for (Weisberg, 1999), with the sample comprising 190 Republicans (28.6%), 203 Independents (30.5%), 234 Democrats (35.2%), and 35 reporting “Other” as their party of choice (5.3%). Dummy codes were used to represent Democrats and Republicans in regressions, with Independents and Others being combined as the baseline.

Neighborhood Diversity

A single item asked, “Of the people in your neighborhood or apartment building, what percentage would you say are from a racial or ethnic minority group?” (Howard et al., 2006). Responses used a percentage slider ranging from 0 to 100 in increments of 1 (Mdn = 29%, M = 34%, SD = 26.85).

Network Size

An index of 10 occupations from the General Social Survey (see Appendix) was used to control for the size of a personal network (Bouchillon, 2019; Lin & Dumin, 1986; Smith & Son, 2014; van der Gaag et al., 2008). Responses were summed to represent cumulative social connections, and these held together reliably using the Kuder–Richardson formula (KR-20 = .778, M = 2.19, SD = 2.3).

Generalized Trust

Four items measured trust in the average person (Bouchillon, 2021; Rosenberg, 1956; Valenzuela et al., 2009), with responses falling on a 5-point scale (see Appendix). The items were combined to create the index of generalized trust (Cronbach’s α = .906, M = 3.47, SD = .73).

Internet Use

Respondents were asked, “In the past week, on average, about how much time per day did you spend using the Internet through your computer or phone?” Responses fell on a 7-point scale from “No time” to “5 hours or more” (Mdn = “3–4 hours,” M = 5.22, SD = 1.66).

Social Distancing

One item was used to control for social distancing, asking, “How much would you say you’ve isolated yourself at this time?” Responses ranged from “not really at all,” “a little,” “a moderate amount,” “a great deal,” to “completely” (Mdn = “a great deal,” M = 3.87, SD = .87).

Testing Assumptions

Exploratory regressions were used to predict global, subtle, and blatant forms of prejudice from the full range of focal and control variables. Multicollinearity was not an issue in regressions. The highest VIF value was 2.53, and the lowest tolerance value was .395, both of which fall within acceptable ranges (Craney & Surles, 2007). Q-Q plots indicate that linear relationships existed between the set of regressors and predicted outcomes, with scatterplots of standardized residuals and standardized predicted values appearing to be homoscedastic as well. Little’s tests using estimated means indicate that data were missing completely at random from the regression predicting global prejudice from interpersonal competence, χ2 (43, N = 665) = 38.02, p = .687, and from the regression predicting global prejudice from interpersonal competence subscales, χ2 (55, N = 665) = 44.37, p = .847. Data were missing completely at random from regressions of subtle and blatant prejudice on interpersonal competence as well, χ2 (47, N = 665) = 40.01, p = .755, and from regressions of subtle and blatant prejudice that used interpersonal competence subscales, χ2 (59, N = 665) = 46.54, p = .880). Robust standard errors are utilized in PROCESS to account for any heteroskedasticity (Hayes, 2017).

Results

Research Question 1 asked which interpersonal competencies were related to prejudice. A principal component analysis was used to divide interpersonal competence into motivation, knowledge, skill, and effectiveness subscales (see Table 2). Item loadings needed to be greater than .5 to be used, and to load at least .15 more strongly than on the next strongest component (Bouchillon, 2020; Reise et al., 2000). After interpersonal competencies were distinguished and placed into the full regression, interpersonal skill was inversely associated with prejudice (β = −.123, B = −.168, p = .013; R² = .281, F(20, 632) = 15.31, p < .001). As traits like attentiveness, expressiveness, and mindfulness develop, negative perceptions of the largest immigrant group in the country are reduced (see Table 3). That said, no interpersonal competencies were inversely associated with subtle prejudice, but effectiveness came close (β = −.102, B = −.148, p = .059; R² = .445, F(21, 631) = 26.09, p < .001). Effectiveness was also close to predicting blatant prejudice, albeit positively (β = .099, B = .14, p = .063; R² = .383, F(21, 631) = 20.7, p < .001), with interpersonal skills identified as the most important aspect of competence for addressing global prejudice.

Table 2.

Pattern Matrix for Interpersonal Competence.

	Factors
	1	2	3	4
Want to go smoothly	.925	.039	.017	−.178
Want to understand	.514	−.030	−.160	.534
Want to be involved	.727	.154	−.179	.228
Want to be appropriate	.871	−.059	.186	−.153
Know rules	.091	−.101	.854	−.069
Know how to change topics	−.068	.054	.743	.068
Know how to adapt	.114	.081	.702	.031
Know what empathy means	.438	−.071	.346	.086
Show involvement	.243	.092	.230	.341
Range of behaviors	.040	−.053	.161	.609
Understanding	−.205	.046	−.069	.996
Mindful	.131	−.103	.101	.705
Successful	−.142	.569	.272	.162
Effective	−.092	.525	.413	.023
Attain social goals	.032	.926	−.071	−.095
Social contact works out	.129	.909	−.129	−.027
Percentage variance	46.8%	10.6%	4.9%	4.5%
Eigenvalue	7.49	1.69	.78	.73
Cronbach’s alpha	.819	.791	.767	.731
Subscale M	4.07	3.65	3.95	3.81
Subscale SD	.57	.65	.6	.6

Table 3.

Regression Predicting Global Prejudice From Interpersonal Competence Subscales.

Model		Unstandardized Coefficients		Standardized Coefficients	Sig.
Model		B	Std. Error	β	Sig.
	(Constant)	4.38	.33		.000
	Female	.01	.06	.012	.868
	White	.098	.08	.119	.209
	Hispanic	−.115	.11	−.14	.291
	Age	−.001	.002	−.013	.744
	Education	−.096	.02	−.173	.000
	Income	.003	.02	.007	.853
	South	.03	.06	.037	.624
	Republican	.45	.08	.544	.000
	Democrat	−.373	.07	−.452	.000
	Neighborhood diversity	.001	.001	.024	.567
	Internet use	−.0002	.02	−.0004	.992
	Personal network size	−.036	.01	−.102	.003
	Generalized trust	−.081	.04	−.072	.073
	Social distancing	−.022	.04	−.023	.549
	CMC competence	.028	.08	.023	.711
	Social presence	−.004	.05	−.004	.935
	Interpersonal motivation	−.094	.08	−.065	.234
	Interpersonal knowledge	−.019	.08	−.013	.816
	Interpersonal skill	−.168	.07	−.123	.013
	Interpersonal effectiveness	.01	.06	.008	.876

R² = .281, F(20, 632) = 15.31, p < .001.

Hypothesis 1 predicted that interpersonal competence would be inversely associated with global prejudice. This was tested using PROCESS, with the full range of control variables being included (Hayes, 2017). Results indicate that interpersonal competence was inversely associated with global prejudice (β = −.185, B = −.309, p < .001; see Table 4). As one's interpersonal capability increases, bias against Mexican immigrants is diminished, with education (β = −.17, B = −.095, p < .001), Democratic party membership (β = −.465, B = −.384, p < .001), and personal network size (β = −.096, B = −.034, p = .005) also being inversely associated with global prejudice. Republican party membership appeared to contribute to prejudice (β = .548, B = .453, p < .001), with the model accounting for 28% of the variance in global prejudice overall. H1 finds support.

Table 4.

Regression Predicting Global Prejudice.

Model		Unstandardized Coefficients		Standardized Coefficients	Sig.
Model		B	Std. Error	β	Sig.
	(Constant)	4.42	.32		.000
	Female	.016	.06	.019	.799
	White	.088	.08	.106	.259
	Hispanic	−.106	.11	−.128	.327
	Age	−.0005	.002	−.008	.829
	Education	−.095	.02	−.17	.000
	Income	.003	.02	.008	.841
	South	.035	.06	.042	.573
	Republican	.453	.07	.548	.000
	Democrat	−.384	.07	−.465	.000
	Neighborhood diversity	.001	.001	.021	.611
	Internet use	−.0001	.02	−.0001	.997
	Personal network size	−.034	.01	−.096	.005
	Generalized trust	−.074	.04	−.067	.094
	Social distancing	−.021	.04	−.022	.563
	CMC competence	.045	.08	.037	.554
	Social presence	−.006	.05	−.006	.906
	Interpersonal competence	−.309	.08	−.185	.000

R² = .281, F(17, 635) = 17.72, p < .001.

Hypothesis 2 expected interpersonal competence would be inversely associated with subtle prejudice as well. Using the PROCESS macro, interpersonal competence was inversely related to subtle prejudice (β = −.184, B = −.353, p < .001; see Table 5). As interpersonal capability increases, it becomes easier to espouse admiration and sympathy for Mexican immigrants, with the regression accounting for 44% of the variance in subtle prejudice. Interpersonal competence was unrelated to blatant prejudice in a separate regression (β = .001, B = .002, p = .987; see Table 6), but subtle prejudice contributed to blatant prejudice (β = .494, B = .483, p < .001), and blatant prejudice was related to subtle prejudice (β = .439, B = .449, p < .001). These measures appear to prop one another up. Demographically, Democratic party membership was negatively associated with subtle prejudice (β = −.26, B = −.246, p < .001), while Republican party membership related to subtle prejudice (β = .362, B = .342, p < .001). Democratic party membership (β = −.183, B = −.169, p = .023) was inversely associated with blatant prejudice as well, as were measures of education (β = −.087, B = −.054, p = .022) and personal network size (β = −.069, B = −.028, p = .047).

Table 5.

Regression Predicting Subtle Prejudice.

Model		Unstandardized Coefficients		Standardized Coefficients	Sig.
Model		B	Std. Error	β	Sig.
	(Constant)	3.17	.45		.000
	Female	.007	.06	.007	.913
	White	.005	.09	.005	.956
	Hispanic	−.323	.12	−.342	.007
	Age	.003	.002	.043	.2
	Education	−.046	.02	−.072	.054
	Income	.033	.02	.065	.06
	South	−.096	.06	−.102	.122
	Republican	.342	.08	.362	.000
	Democrat	−.246	.07	−.26	.000
	Neighborhood diversity	−.002	.001	−.054	.125
	Internet use	−.019	.02	−.034	.307
	Personal network size	−.008	.01	−.018	.56
	Generalized trust	−.05	.05	−.039	.341
	Social distancing	.005	.04	.004	.897
	CMC competence	.034	.08	.024	.682
	Social presence	−.052	.05	−.045	.324
	Interpersonal competence	−.353	.09	−.184	.000
	Blatant prejudice	.449	.04	.439	.000

R² = .446, F(18, 634) = 31.07, p < .001.

Table 6.

Regression Predicting Blatant Prejudice.

Model		Unstandardized Coefficients		Standardized Coefficients	Sig.
Model		B	Std. Error	β	Sig.
	(Constant)	1.63	.43		.000
	Female	.011	.06	.012	.868
	White	.085	.1	.092	.383
	Hispanic	.178	.14	.193	.193
	Age	−.003	.002	−.047	.193
	Education	−.054	.02	−.087	.022
	Income	−.026	.02	−.052	.154
	South	.12	.06	.129	.065
	Republican	.152	.08	.164	.054
	Democrat	−.169	.07	−.183	.023
	Neighborhood diversity	.002	.001	.068	.071
	Internet use	.017	.02	.03	.397
	Personal network size	−.028	.01	−.069	.047
	Generalized trust	−.031	.05	−.025	.534
	Social distancing	−.024	.04	−.023	.51
	CMC competence	.015	.1	.011	.886
	Social presence	.041	.06	.037	.528
	Interpersonal competence	.002	.1	.001	.987
	Subtle prejudice	.483	.04	.494	.000

R² = .378, F(18, 634) = 22.51, p < .001.

Hypothesis 3a predicted CMC competence would be indirectly associated with lower levels of global prejudice, through increasing interpersonal competence. Results of a mediation analysis using the PROCESS macro (Model 4) and the full set of control variables indicate that CMC competence did not have a significant direct effect on global prejudice (β = .037, B = .045, p = .554), or a significant total effect (β = −.06, B = −.073, p = .245). However, indirect effects can exist even in the absence of direct and total effects (Hayes, 2017), and bootstrapping the confidence interval indicates that CMC competence was indirectly related to lower levels of global prejudice, through interpersonal competence. That is, individuals who are socially proficient through computers also believe themselves to be competent in person (β = .525, B = .382, p < .001), which is related to feeling less prejudiced against Mexican immigrants (β = −.185, B = −.309, p < .001; unstandardized point estimate = −.118, 95% CI [−.194, −.059]).

Similarly, H3b predicted CMC competence would relate to decreasing subtle prejudice through interpersonal competence. Results of a second mediation analysis using the PROCESS macro (Model 4) indicate that CMC competence was indirectly related to lower levels of subtle prejudice, by increasing interpersonal competence. Networked social capabilities are felt to apply locally (β = .522, B = .38, p < .001), and competence in this localized form seems to promote positive emotions like admiration and sympathy for immigrants (β = −.184, B = −.353, p < .001; unstandardized point estimate = −.134, 95% CI [−.203, −.067]). Hypothesis 3 finds support.

Hypothesis 4a predicted that social presence would moderate the indirect effect of CMC competence on global prejudice. First-stage conditional moderated mediation was tested using PROCESS (Model 7), with all focal and control variables being included in the analysis. Results indicate there was a significant interaction between CMC competence and presence when predicting interpersonal competence (β = .133, B = .116, p = .014). Presence appears to strengthen the conversion of digital competence into its physical counterpart (see Figure 1), which was inversely associated with prejudice (β = −.184, B = −.308, p < .001). In terms of simple slopes, the direct effect of CMC competence on interpersonal competence increased between low (conditional effect = .302, p = .000), medium (conditional effect = .399, p = .000), and high (conditional effect = .496, p = .000) levels of presence. High and low levels of presence were tested as being ±1 SD from the mean. The size of the indirect effect of CMC competence on global prejudice through interpersonal competence increased with social presence as well (unstandardized index of moderated mediation: −.036, 95% CI [−.078, −.01]), specifically between low (conditional indirect effect = −.093; 95% CI [−.153, −.045]), medium (conditional indirect effect = −.123, 95% CI [−.204, −.062]), and high levels of presence (conditional indirect effect = −.153, 95% CI [−.26, −.074]). Sensing realness and closeness in combination can permit computer-based social lessons to apply in person, as a path to reducing global prejudice indirectly. Figure 2 illustrates the moderated mediation.

Figure 1.

Moderation.

Figure 2.

Moderated mediation.

The fact that presence aids in the conversion of CMC competence into interpersonal competence was expected to help with reducing subtle prejudice as well (see H4b), with PROCESS (Model 7) being used once again. There was a significant interaction between CMC competence and presence in predicting interpersonal competence (β = .126, B = .111, p = .019), and interpersonal competence was inversely related to subtle prejudice in turn (β = −179, B = −.342, p < .001). The indirect effect of CMC competence on subtle prejudice through interpersonal competence grew at higher levels of social presence as well (unstandardized index of moderated mediation: −.038, 95% CI [−.067, −.012]), increasing between low (conditional indirect effect = −.104; 95% CI [−.175, −.046]), medium (conditional indirect effect = −.136, 95% CI [−.208, −.068]), and high levels of presence (conditional indirect effect = −.167, 95% CI [−.250, −.087]). Experiencing networked settings as realistic and intimate allows social competencies to cross over, and positive feelings about immigrants begin to emerge.

Hypothesis 5 predicted serial mediation, with CMC competence expected to diminish blatant prejudice indirectly, by adding to interpersonal competence, and reducing subtle prejudice. This was tested using Model 6 in PROCESS, still controlling for the full range of variables. Results indicate that CMC competence did not have significant direct (β = .011, B = .015, p = .886) or total effects (β = −.032, B = −.043, p = .606) on blatant prejudice. But CMC competence related to lower levels of blatant prejudice indirectly, through interpersonal competence (β = .525, B = .382, p < .001) and subtle prejudice (β = −.235, B = −.449, p < .001). Reductions in subtle prejudice seem to lessen blatant prejudice in turn (β = .494, B = .483, p < .001; unstandardized point estimate = −.083, 95% CI [−.124, −.047]), with computer-based social learning now spanning the digital/physical divide. Competence crossing over is associated with positive emotions toward immigrants, and feeling less threatened by them. Figure 3 depicts serial mediation. H5 is supported.

Figure 3.

Serial mediation.

Discussion

Competent individuals are said to communicate “in a manner that enhances the self and supports the normative standards for appropriate behavior established by the larger group” (Sussman, 1997, p. 9). Yet it becomes difficult to promote competence and acceptance of diversity when interpersonal contact has diminished, or ceased entirely (Allport, 1954). A workaround was sought during COVID-19, with the pandemic appearing to exacerbate feelings of prejudice in America (Croucher et al., 2020). But it did not initiate them (Shin & Dovidio, 2018), as the populace had been distancing itself for some time already (Laurence et al., 2019; Putnam, 2000, 2007). So the present study sought to determine whether social competence developed through computers would relate to reductions in prejudice, as proof that digital settings could be used to address our tendency to withhold positive emotions from immigrants, and to feel threatened by them.

To begin, interpersonal competence was shown to relate to lower levels of global prejudice, with skills like attentiveness, expressiveness, and mindfulness being especially valuable for prejudice reduction. They prepare users to interact with a wide range of others more adeptly, and when global prejudice was divided into subtle and blatant components, interpersonal competence was inversely related to subtle prejudice but not blatant prejudice. It can establish admiration and sympathy for Mexican immigrants, if not reduce the sense of threat outright. However, finding ways of addressing subtle prejudice should diminish blatant prejudice as well, given that aspects of prejudice tend to move together, and subtle prejudice is less deeply ingrained (Lissitsa & Kushnirovich, 2019; Ungaretti et al., 2020). Blatant prejudice also explained the majority of variance in global prejudice, which interpersonal competence was still shown to reduce.

CMC competence related to lower levels of global prejudice indirectly, through interpersonal competence, as evidence that social compensation was taking place during the pandemic (Amichai-Hamburger et al., 2015; Steinfeld et al., 2008). Becoming adept at using the Internet socially contributes to interpersonal proficiency (Nguyen et al., 2022), and social presence helped to convert CMC competence into interpersonal competence more fully. This reduced prejudice to an even greater degree, with perceptual realness and closeness permitting the lessons one learns through computers to be felt to apply in person. When competence translates, and it appears to, attitudes toward the largest immigrant group in the United States are improved, even in the absence of direct contact.

Results suggest that networked social capabilities carry, and this can be used to encourage positive emotions toward immigrants, potentially even reducing the sense of threat. In the longest indirect chain found here, CMC competence added to interpersonal competence, which was inversely associated with subtle prejudice, which related to lower levels of blatant prejudice. Simply believing one has the potential to interact with new people could be used to dispel prejudice, even in a social vacuum, and the process of becoming competent socially can now be initiated through computers. Some of what interactants learn crosses over, with interpersonal capability appearing to encourage positive emotions toward those who are different. Admiration and sympathy relate to lower levels of blatant prejudice in turn, as findings that contribute to a better understanding of the role of technology in intergroup contact theory (Allport, 1954; Imperato et al., 2021).

Remote settings experienced as realistic and intimate now have the potential to train citizens to interact with new and diverse others more willingly and capably. They can be used to generate social proficiencies that are flexible enough to be drawn upon in person, with presence increasing the size of the association between CMC competence and interpersonal competence. This contributes to more positive attitudes toward immigrants, as well as greater reductions in global prejudice. To encourage these outcomes, ways of developing CMC competence in digital settings should be sought in the future, beyond the general prognosis of using networked applications to interact with new people (see Bouchillon, 2019, 2022). Longitudinal surveys should be employed as well, to establish that competence is associated with lower levels of prejudice over time. Ultimately, an experiment is necssary to confirm that changes in competence are responsible for reductions in prejudice found here.

Because directionality cannot be determined in cross-sectional surveys such as this one, with previous findings being used to guide expectations about which forms of prejudice will be easier to reduce (Lissitsa & Kushnirovich, 2019; Ungaretti et al., 2020). The model also lacks control over personality factors that might influence sentiments about competence and prejudice (e.g., agreeableness), and it cannot rule out a reverse causal process—that prejudice will erode interpersonal competence over time. Bochner and Kelly (1974) argued that prejudiced individuals tend to avoid diverse social contact in general. Yet CMC competence could address prejudice even in the absence of direct contact, for allowing those who may struggle to interact in person to still learn how, and to feel less afraid of differences by doing so. Developing technologies that are conducive to presence would permit competence to migrate offline more quickly as well (Bouchillon, 2022).

Much debate has also surrounded measures of subtle and blatant prejudice, with research at times combining them into an index of global prejudice (Arancibia-Martini et al., 2016; Gattino et al., 2008). Other studies have found greater predictive value in using subtle and blatant aspects of prejudice as distinct explanatory variables (Álvarez-Castillo et al., 2018; Cárdenas Castro, 2010). The present research does both, with a principal component analysis suggesting that subtle and blatant prejudice are indeed distinct concepts, but they can also be combined into an overall index of prejudice (Cárdenas Castro, 2010). In addition, the direct association between interpersonal competence and subtle prejudice (but not blatant prejudice) is identified for maybe the first time. Feeling capable of interacting with new people locally encourages positive emotions toward Mexican immigrants, and improving attitudes toward one outgroup can bolster perceptions of other outgroups as well (D’urso et al., 2023; Fouka & Tabellini, 2021).

Mean values of subtle and blatant prejudice were also comparable in the present sample, which is interesting, given that past research suggests blatant prejudice should be lower, or at least, less reported. Citizens historically were somewhat reticent to admit they felt threatened by immigrants (Pettigrew & Meertens, 1995). But the absence of a meaningful difference is evidence that prejudiced individuals have become less concerned with how politically correct or not misgivings about racial and ethnic diversity are perceived as. This could relate to the use of an older, mostly White sample, and older individuals were also the most likely to socially distance during this period (Kim & Crimmins, 2020). Yet they too discovered an ability to convert computer-based social competence into interpersonal competence, with social presence expediting the process, and allowing prejudice to be reduced.

Ultimately, Kuo and Roysircar (2006) suggested that people traveling to North America would benefit from competence training, while the present research advances a complementary approach—that Americans could use the training—to reduce subtle and blatant feelings of prejudice that have slowly but surely become embedded in the culture (Laurence et al., 2019). Understanding how prejudice was manifest and could be mitigated during a global pandemic is a start, with social technology shown to have a real utility for teaching the populace how to navigate social divides. Experiencing networked communication as realistic and intimate expedites the conversion of digital competence into its physical counterpart, for increasing the size of the indirect association between CMC competence and prejudice reduction (Bouchillon, 2022). This means realistic, intimate technologies could pave the way for competence to develop remotely, for a populace that fails to interact in person to still address the sense of threat.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Data Availability Statement

This data is available from the author by request.

Appendix

Author Biography

Brandon C. Bouchillon is an associate professor of journalism whose work considers the influence of digital communication on traditional measures of social competence and social capital. It has appeared in places such as New Media & Society and Computers in Human Behavior. His first book, Competence, Presence, Trust, and Hyperpersonal-ness, was published with Lexington Books in 2022.

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