Examining the Moderating Role of Network Density on the Relationship Between Social Norms and Behavioral Intention in the Context of Unfriending

Humans are social beings. People who live in a society consistently develop relationships with others and follow certain societal or group norms that help them maintain their relationships. Notably, what makes this possible is communication: Communication plays a pivotal role in the formation and evolution of social norms and social networks (Geber et al., 2019; Monge et al., 2008), and social norms and networks often guide the way people communicate with others (Hogg & Reid, 2006; Rice & Love, 1987). Therefore, investigating how social norms and social networks function in relation to communication helps us better understand human interaction as social beings.

Studies of social norms and social networks exist in two largely separate spheres, driven in part by the use of different methodologies. Yet, there is great potential for communication scholars to blend these two areas of research (e.g., Lapinski et al., 2019). This study bridges these two literatures by testing the moderating role of network density on the norm-behavior relationship. In the social norms literature, evidence shows that social norms influence behavior, and this relationship is often strengthened or weakened by various moderators (e.g., Rimal & Yilma, 2021). In the social network literature, it has been theorized that the influence of norms is stronger in a network in which people are more densely interconnected rather than sparsely interconnected (Coleman, 1988, 1990; Valente, 2010). Nonetheless, the evidence for the interplay between norms and network density has rarely been reported. Thus, this study is designed to tackle this issue by empirically investigating the interaction between social norms and network density, thereby contributing to a more comprehensive understanding of how these two spheres intersect and influence human behavior.

This study delves into the dynamics of unfriending behavior on social media platforms. Unfriending is the intentional removal of someone from one’s social media contacts (Sibona, 2014), thus ceasing all potential online interactions with the removed individual. Social media users typically resort to this behavior as a strategic form of avoidance when they encounter content they find inappropriate, offensive, or polarizing (Kim et al., 2022; Sibona, 2014; Skoric et al., 2018; Verswijvel et al., 2018). While unfriending can effectively prevent one from being exposed to potentially harmful content, research shows that it can also have negative consequences not only for those who unfriend (e.g., the formation of isolated and ideologically divided groups; Sasahara et al., 2021) but also those who are unfriended (e.g., psychological distress such as depression and frustration; Bevan et al., 2012).

This study particularly focuses on unfriending as its testing ground for several compelling reasons. Foremost among these is the role of social norms, which essentially entrench expectations of normalcy and appropriateness in social interactions (Lapinski & Rimal, 2005). Moreover, the very act of unfriending takes place in the digitally networked environment of social media, making it crucial to understand how an individual's network structure dovetails with social norms to shape such decisions. Notably, existing research indicates a significant positive correlation between perceived social pressures from one's close connections and the intention to unfriend (Verswijvel et al., 2019). Furthermore, certain aspects of network structures such as the size of one's network have been linked to unfriending actions (Barnidge et al., 2022), possibly because larger networks increase the chances of encountering disagreeable content. Despite these insights, there remains a paucity of research on how social norms and the structure of online social networks collectively drive unfriending behaviors. This study aims to bridge this gap in the literature.

In the subsequent sections, this paper provides an overview of the revised framework of normative influences (A. Chung & Rimal, 2016) and theories of social networks, serving as a basis for understanding how social norms and network density may interact with each other. Next, the paper moves on to the study hypotheses, followed by the method for testing the predictions, the results, and the discussion of the findings.

METHOD

Participants

In total, 220 college students signed up for the study from two different research participant pools at Michigan State University (MSU), and 200 of them participated in the study. Study participants received either $2 Amazon Gift Cards or class credits as compensation depending on the pools they used to sign up for the study. After removing 17 unusable cases (i.e., participants under the age of 18 and instances of duplicate participation), the data collected from 183 participants were used in the subsequent analyses. The sample was predominantly White (76%) and female (81%), with ages ranging from 18 to 29 (M = 20.42, SD = 1.69). The demographic characteristics of the study sample are presented in Table 1. There was no statistically significant difference in the dependent variable between the participants from the two pools or across the demographics.

Table 1.

Demographic Information of the Study Sample Including Number (N) and Percentage (%) of Participants Representing Each Category

Measurement

Most of the items used in this study were drawn from prior research, with revisions made to the wording to fit the current study context. These items were selected for their demonstrated reliability and validity in previous studies, thus ensuring the robustness of the measures employed. The study variables, unless otherwise specified, were measured on a 5-point Likert-type scale (1 = Strongly disagree, 5 = Strongly agree) in which higher scores indicated greater agreement with the statement or higher levels of the variable.

Confirmatory factor analyses (CFA) using the lessR package in R (Gerbing, 2014) were performed for the scales that contained at least four items. The internal consistency and parallelism theorems were employed to generate predicted correlations between all items of the same latent variable (Hunter & Gerbing, 1982). These predicted correlations were then subtracted from their respective obtained correlations to produce the errors/residuals (Boster, 2023). The adequacy of the measurement models was assessed by the following two criteria: ample factor loadings and small residuals. In line with previous literature, factor loadings greater than .51 were considered large, and RMSE (i.e., root mean squared error) less than .05 were considered small (Boster et al., 2011; Gerbing, 2014; Grayson-Sneed et al., 2016). When the data did not meet the criteria, items that had the weakest factor loadings or produced the largest errors of fit were removed from each measure until the data were consistent with the measurement model. When the data were consistent with the unidimensional measurement model, the items were summed and averaged to form indices. The full correlation matrix of the study variables is reported in Table 2, and the retained items and their respective factor loadings are included in Appendix C.

Table 2.

Correlation Matrix of the Main Variables

Key Variables

Intention to unfriend (Cronbach’s α = .92, M = 3.31, SD = 0.57) was measured by four items (Park & Smith, 2007). For example, “I intend to unfriend Sarah” and “I will keep my online friendship with Sarah (reverse coded).”

Perceived descriptive norms (Cronbach’s α = .94, M = 3.52, SD = 1.20) were measured by four items (Lapinski et al., 2014; Park & Smith, 2007). Sample items include, “I think most of the mutual friends I originally had with Sarah on social media have now unfriended Sarah,” and “I think the majority of the mutual friends I originally had with Sarah on social media have now unfriended Sarah.”

Perceived network density (Cronbach’s α = .87, M = 3.63, SD = 0.88) was measured by five items specifically designed for the current research, based on the conceptual definition of network density (Coleman, 1988, 1990). For instance, “I think the mutual friends I originally had with Sarah online are highly interconnected with each other,” and “A majority of the mutual friends I originally had with Sarah online are connected to each other.”

Covariate

Attitude toward the behavior of the target person online (Cronbach’s α = .91, M = 1.45, SD = 0.58) was measured by four bi-polar items adapted from the source likability scale (Roskos-Ewoldsen et al., 2002), such as “Negative – Positive,” and “Unfavorable – Favorable.” Each item was measured on a 5-point scale, in which higher scores indicate more positive attitude.

RESULTS

Hypotheses Testing

To test the study hypotheses, a two-way analysis of covariance (ANCOVA) was performed with the intention to unfriend as the dependent variable. Descriptive norms conditions and network density conditions (and their interaction term) were the independent variables. The attitude toward the target person’s online behavior, participants’ age, and gender were included in the analysis as covariates. The ANCOVA results are summarized in Table 3.

Table 3.

Two-Way Analysis of Covariance Results Using Intention to Unfriend as the Dependent Variable: The Independent Variables are Descriptive Norms Conditions (High vs. Low Descriptive Norms) and Density Conditions (High vs. Low Density)

Hypothesis 1 predicted that perceived descriptive norms would positively influence unfriending intention. The data showed a significant main effect for descriptive norms conditions, F(1, 176) = 7.26, p = .008, partial η² = .04. Participants who were exposed to high descriptive norms messages reported significantly greater intention to unfriend the target victim (adjusted M = 3.41, SE = 0.05) than did those who were exposed to low descriptive norms messages (Adjusted M = 3.19, SE = 0.06). Thus, the data were considered consistent with H1.

Hypotheses 2 predicted that the effect of perceived descriptive norms on the intention to unfriend would be moderated by network density, such that the normative influence would be stronger in a dense than sparse network. The data yielded a significant interaction effect between descriptive norms conditions and network density conditions, F(1, 176) = 4.96, p = .027, partial η² = .03. Simple effects analysis investigating the interaction pattern showed that the normative influence on behavior was stronger in a sparse than a dense network. In the sparse network condition, participants who were exposed to high descriptive norms messages reported significantly greater intent to unfriend (M = 3.47, SD = 0.52, adjusted M = 3.44, SE = 0.08) compared to those who were exposed to low descriptive norms messages (M = 3.07, SD = 0.63, adjusted M = 3.06, SE = 0.08), F(1, 176) = 12.40, p < .001, partial η² = .07. However, in the dense network condition, there was no significant effect of descriptive norms on behavior, although those who were exposed to high descriptive norms messages reported greater behavioral intention (M = 3.37, SD = 0.49, adjusted M = 3.38, SE = 0.08) than did those who were exposed to low descriptive norms messages (M = 3.29, SD = 0.56, adjusted M = 3.34, SE = 0.08), F(1, 176) = 0.11, p = .743. Despite the significant interaction effect, the data were considered inconsistent with H2 because of the unexpected interaction pattern (see Figure 1 for interaction plot).

Figure 1.

Adjusted Group Means by Experimental Conditions.Note. DN = Descriptive Norms. Error bars indicate the 95% confidence interval of each mean.

DISCUSSION

Social norms are posited to exert a more substantial influence on a group’s behavior when the individuals within are densely interconnected rather than sparsely connected. Although this interplay between social norms and network density is a recurrent theme in social network literature, direct empirical evidence to support it is scarce. This study addressed this gap by empirically examining the interaction effect in the context of unfriending behaviors. The findings revealed that the perceived prevalence of unfriending within a person’s social network positively influenced their intention to unfriend. Additionally, a significant interaction between social norms and network density was observed, with normative influences on the intention to unfriend being stronger in sparse networks than in dense ones. These findings illuminate the critical role that an individual’s perception of normative behaviors and the structure of their online network play in shaping their decision to unfriend, as well as the importance of empirical testing of theory. In subsequent sections, the results and their implications for both social norms and network literature will be discussed.

The perceived prevalence of unfriending a particular individual among one’s social network members has been found to significantly shape one’s own intention to unfriend the person. This supports theories asserting the direct impact of social norms on both behaviors and behavioral intentions (e.g., Rimal & Yilma, 2021) and aligns with findings from Verswijvel et al. (2019), which

The influence of prevalence perceptions on behavioral intention was also contextual (Chung & Rimal, 2016). Specifically, we found that the impact of perceived descriptive norms on unfriending intention varied depending on the interconnectivity among a person's group members. Social network literature has long argued that such normative influence is likely to be stronger in a network where people are densely connected rather than sparsely connected with each other (e.g., Coleman, 1988). However, we discovered that the effect of norms on behavioral intention was more potent in a sparse network rather than a dense one. This inconsistent interaction pattern with our prediction is both intriguing and served as the driving force behind this study. Frequently, studies rely on the empirical validity of theories without direct evidence or without identifying the boundary conditions that may apply to a given theory (Kraimer et al., 2023; Walther, 2009). While further evidence is required to draw a proper conclusion regarding the interaction between social norms and network density, the unexpected pattern can be attributed to at least the following three reasons.

First, even low-prevalent behavior in a dense network can strongly drive normative conformity if people within the network expect the behavior to become highly prevalent in the future. Recent literature on dynamic norms (e.g., M. Chung & Lapinski, 2023) elucidates this point: Behavior that is currently gaining traction but remains unpopular leads people to expect that it will become popular in the future, motivating them to engage in the behavior now. Although trending information was not presented in the current study, we cannot exclude the possibility that participants expected most of their highly interconnected mutual friends would soon unfriend the target. This expectation becomes particularly plausible when considering that a high-density network offers ample opportunities for communication, which can, in turn, foster behavioral contagion among network members (e.g., through social learning; Monge & Contractor, 2003). The intricate relationship between network density, normative perceptions, and behavioral trends requires further examination and could redefine our understanding of how social norms operate within highly interconnected networks.

Second, the density argument might apply more to injunctive norms than to descriptive norms. One of the key theoretical mechanisms of the density effect is that high-density networks enable members to monitor each other's behavior and collectively sanction non-normative, deviant actions (e.g., Coleman, 1988, 1990). The motivation to avoid social penalties is considered a driver for injunctive norms (Cialdini et al., 1990) rather than for descriptive norms. Although social network literature has not made clear distinctions between different types of social norms, it is reasonable to expect that the collective monitoring and sanctioning mechanism of the density effect would be more pertinent to injunctive norms. Given that our insights emphasize the need for a nuanced understanding of the relationship between norms and network density, we suggest that future research exploring this interaction should consider focusing on injunctive norms.

Third, normative influences on behavior may be more pronounced in sparsely connected networks than in densely connected ones, particularly when individuals perceive those in their sparse networks as distinct from themselves. Arguably, sparse networks are likely to be more heterogeneous than dense networks, driven by infrequent interactions and notable disconnections among network members (Valente, 2010). Observing a diverse array of people engaging in a specific behavior within such networks can provide compelling social proof, affirming the behavior’s effectiveness in a given situation and its perceived universal benefits. This concept may be reflected in the findings of Rimal et al.'s (2005) study, where college students reported a greater belief in their ability to consistently practice yoga upon learning that the most common yoga practitioners were pregnant women, rather than their fellow college students. This argument may seem at odds with social norm theories, which posit that perceived similarity with a reference group enhances normative conformity (e.g., Rimal & Real, 2005). Nonetheless, the literature on the impact of perceived similarity with a reference group on normative influence is in fact inconsistent (Carcioppolo & Jensen, 2012; Carcioppolo et al., 2017; Rimal, 2008). This underscores the need for further research to delve into these complexities and to refine our understanding of the interplay between social norms and network density.

Conclusion

The social norms literature suggests that perceived norms lead to normative conformity, and the relationship between norms and behavior can often be contextual. In social network literature, it has been theorized that normative influence is stronger in networks where people are more densely interconnected than in those that are sparsely connected. This experimental study explored this relationship in the context of unfriending intention. The findings revealed that perceived descriptive norms did influence behavioral intention, and intriguingly, the normative influence was stronger in a sparse network rather than a dense one. These results underscore the influence of social norms on the decision to unfriend and highlight the necessity of empirical testing of theoretical propositions. They also emphasize the importance of further research to investigate the complex interplay between norms and network density in various contexts.

Disclosure Statement

This project received fellowship funding from the Charles J. Strosacker Foundation and was also supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2021S1A5C2A02088387).

The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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