Predictors of accuracy in perceiving informal social networks

Bondonio, Daniele

doi:10.1016/s0378-8733(98)00007-0

Cited by 75 publications

(49 citation statements)

References 32 publications

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“…Considered in tandem, these results suggest that popular individuals demonstrate enhanced interpersonal sensitivity (i.e., attunement to group members' status differences), whereas unpopular individuals show more generalized interpersonal responsiveness (i.e., elevated valuation responses to all group members regardless of status). In support of the inference that popular perceivers have heightened interpersonal sensitivity (7,35,36), we also found that they were more accurate in predicting how each of the other group members perceived them across various personality attributes (SI Text and Fig. S3).…”

Section: And Person Judgment Tasks (See Ref 23 For Review)supporting

confidence: 74%

“…Another view consistent with our data is that popular individuals achieve their status because they are particularly skilled social perceivers. At the behavioral level, heightened interpersonal acuity has been linked to popularity in social networks of children (7) and adults (35,36), and we likewise found that popular individuals more accurately predicted how individual group members viewed them (Fig. S3).…”

Section: Discussionsupporting

confidence: 57%

“…In studies both of adults and children, popular individuals have more accurate perceptions of the affiliative social network structure that underlies differences in popularity (7,35,36). In addition, human and nonhuman primate experiments have shown that although low-status individuals pay attention to group members of any status, high-status group members attend selectively to one another (6,37).…”

Section: And Person Judgment Tasks (See Ref 23 For Review)mentioning

confidence: 99%

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Neural mechanisms tracking popularity in real-world social networks

Zerubavel

Bearman

Weber

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

102

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Differences in popularity are a key aspect of status in virtually all human groups and shape social interactions within them. Little is known, however, about how we track and neurally represent others' popularity. We addressed this question in two real-world social networks using sociometric methods to quantify popularity. Each group member (perceiver) viewed faces of every other group member (target) while whole-brain functional MRI data were collected. Independent functional localizer tasks were used to identify brain systems supporting affective valuation (ventromedial prefrontal cortex, ventral striatum, amygdala) and social cognition (dorsomedial prefrontal cortex, precuneus, temporoparietal junction), respectively. During the face-viewing task, activity in both types of neural systems tracked targets' sociometric popularity, even when controlling for potential confounds. The target popularity-social cognition system relationship was mediated by valuation system activity, suggesting that observing popular individuals elicits value signals that facilitate understanding their mental states. The target popularity-valuation system relationship was strongest for popular perceivers, suggesting enhanced sensitivity to differences among other group members' popularity. Popular group members also demonstrated greater interpersonal sensitivity by more accurately predicting how their own personalities were perceived by other individuals in the social network. These data offer insights into the mechanisms by which status guides social behavior.social status | fMRI | social network | popularity | social cognition

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Section: And Person Judgment Tasks (See Ref 23 For Review)supporting

confidence: 74%

Section: Discussionsupporting

confidence: 57%

Section: And Person Judgment Tasks (See Ref 23 For Review)mentioning

confidence: 99%

See 1 more Smart Citation

Neural mechanisms tracking popularity in real-world social networks

Zerubavel

Bearman

Weber

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

102

View full text Add to dashboard Cite

show abstract

“…This validation notwithstanding, egonetwork data remain an oft-used but suboptimal alternative to whole-network data. Although ego-network data correlate well with dyadic data based on information gathered from both members of each pair (Bondonio, 1998;McEvily, 1997) and measures from ego-network data correlate highly with measures from whole-network data (Everett & Borgatti, 2005), several studies have documented inaccuracies in how respondents perceive their social networks (for a review, see Bernard, Killworth, Kronenfeld, & Sailer, 1984). Future research can productively complement our analysis with fully validated ego-network data or whole-network data.…”

Section: Limitations and Future Research Directionsmentioning

confidence: 76%

Change Agents, Networks, and Institutions: A Contingency Theory of Organizational Change

Battilana

Casciaro

2012

AMJ

312

194

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Authors' names appear in alphabetical order. Acknowledgements:We would like to thank Wenpin Tsai and three anonymous reviewers for their valuable comments on earlier versions of this paper. We also wish to acknowledge the helpful comments we received from Michel Anteby, Joel Baum, Stefan Dimitriadis, Martin Gargiulo, Ranjay Gulati, Morten Hansen, Herminia Ibarra, Sarah Kaplan, Otto Koppius, Tal AbstractWe develop a contingency theory for how structural closure in a network, defined as the extent to which an actor's network contacts are connected to one another, affects the initiation and adoption of change in organizations. Using longitudinal survey data supplemented with eight indepth case studies, we analyze 68 organizational change initiatives undertaken in the United Kingdom's National Health Service. We show that low levels of structural closure (i.e., structural holes) in a change agent's network aid the initiation and adoption of changes that diverge from the institutional status quo, but hinder the adoption of less divergent changes.

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“…We used CSS to measure children's classroom peer social networks. Originally proposed by Krackhardt [1987], CSS is commonly used to examine adults' perceptions of their workplace networks [e.g., Bondonio, 1998;Casciaro, 1998;Krackhardt, 1990]. More recently, J.W.…”

Section: Methodsmentioning

confidence: 99%

An Examination of Network Position and Childhood Relational Aggression: Integrating Resource Control and Social Exchange Theories

Neal

Cappella

2012

Aggressive Behavior

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Applying resource control theory and social exchange theory, we examined the social network conditions under which elementary age children were likely to engage in relational aggression. Data on classroom peer networks and peer-nominated behaviors were collected on 671 second- through fourth-grade children in 34 urban, low-income classrooms. Nested regression models with robust cluster standard errors demonstrated that the association between children's number of relationships and their levels of relational aggression was moderated by the number of relationships that their affiliates had. Children with more peer relationships (i.e., higher network centrality) exhibited higher levels of relational aggression, but only when these relationships were with peers who had fewer connections themselves (i.e., poorly connected peers). This finding remained significant even when controlling for common predictors of relational aggression including gender, overt aggression, prosocial behavior, victimization, social preference, and perceived popularity. Results are discussed in terms of their implications for advancing the literature on childhood relational aggression and their practical applications for identifying children at risk for these behaviors.

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Predictors of accuracy in perceiving informal social networks

Cited by 75 publications

References 32 publications

Neural mechanisms tracking popularity in real-world social networks

Neural mechanisms tracking popularity in real-world social networks

Change Agents, Networks, and Institutions: A Contingency Theory of Organizational Change

An Examination of Network Position and Childhood Relational Aggression: Integrating Resource Control and Social Exchange Theories

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