View in EDS HTML Full Text PDF Full Text

Gender, Network Recall, and Structural Holes.

Saved in:
Bibliographic Details
Title: Gender, Network Recall, and Structural Holes.
Authors: Quintane, Eric1 (AUTHOR), Brashears, Matthew E.2 (AUTHOR) brasheam@mailbox.sc.edu, González‐Gómez, Helena V.3 (AUTHOR), Brands, Raina4 (AUTHOR)
Source: Personnel Psychology. Dec2025, Vol. 78 Issue 4, p641-657. 17p.
Subject Terms: *Gender, *Social networks, Social network theory, Recollection (Psychology)
Abstract: Accurate recall of social networks is critical to individuals' success in organizations, as it enables them to leverage networks more effectively. Understanding whether men or women exhibit greater recall accuracy is particularly important given persistent gender inequalities in workplaces and the role of networks in shaping access to social resources and opportunities. We propose that women and men exhibit different levels of recall accuracy, which depend on the structural characteristics of the network. Specifically, women's greater reliance on the triadic closure mental schema—assuming a relationship between two individuals who are both connected to the same third party—enhances their recall accuracy in more cohesive networks with many closed triads but diminishes it in networks with more structural holes. Across three studies, including a demographically diverse sample representative of the US population, we confirm that women exhibit superior network recall accuracy on average and show that this advantage is contingent on network structure. This research advances our understanding of gender differences in network cognition and offers a potential cognitive explanation for women's underrepresentation in brokerage positions, which require recognizing open triads. [ABSTRACT FROM AUTHOR]
Copyright of Personnel Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: ehh
DbLabel: Education Research Complete
An: 189263922
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Gender, Network Recall, and Structural Holes.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Quintane%2C+Eric%22">Quintane, Eric</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Brashears%2C+Matthew+E%2E%22">Brashears, Matthew E.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> brasheam@mailbox.sc.edu</i><br /><searchLink fieldCode="AR" term="%22González‐Gómez%2C+Helena+V%2E%22">González‐Gómez, Helena V.</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Brands%2C+Raina%22">Brands, Raina</searchLink><relatesTo>4</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Personnel+Psychology%22">Personnel Psychology</searchLink>. Dec2025, Vol. 78 Issue 4, p641-657. 17p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Gender%22">Gender</searchLink><br />*<searchLink fieldCode="DE" term="%22Social+networks%22">Social networks</searchLink><br /><searchLink fieldCode="DE" term="%22Social+network+theory%22">Social network theory</searchLink><br /><searchLink fieldCode="DE" term="%22Recollection+%28Psychology%29%22">Recollection (Psychology)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Accurate recall of social networks is critical to individuals' success in organizations, as it enables them to leverage networks more effectively. Understanding whether men or women exhibit greater recall accuracy is particularly important given persistent gender inequalities in workplaces and the role of networks in shaping access to social resources and opportunities. We propose that women and men exhibit different levels of recall accuracy, which depend on the structural characteristics of the network. Specifically, women's greater reliance on the triadic closure mental schema—assuming a relationship between two individuals who are both connected to the same third party—enhances their recall accuracy in more cohesive networks with many closed triads but diminishes it in networks with more structural holes. Across three studies, including a demographically diverse sample representative of the US population, we confirm that women exhibit superior network recall accuracy on average and show that this advantage is contingent on network structure. This research advances our understanding of gender differences in network cognition and offers a potential cognitive explanation for women's underrepresentation in brokerage positions, which require recognizing open triads. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Personnel Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=189263922
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1111/peps.12691
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 17
        StartPage: 641
    Subjects:
      – SubjectFull: Gender
        Type: general
      – SubjectFull: Social networks
        Type: general
      – SubjectFull: Social network theory
        Type: general
      – SubjectFull: Recollection (Psychology)
        Type: general
    Titles:
      – TitleFull: Gender, Network Recall, and Structural Holes.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Quintane, Eric
      – PersonEntity:
          Name:
            NameFull: Brashears, Matthew E.
      – PersonEntity:
          Name:
            NameFull: González‐Gómez, Helena V.
      – PersonEntity:
          Name:
            NameFull: Brands, Raina
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 12
              Text: Dec2025
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 00315826
          Numbering:
            – Type: volume
              Value: 78
            – Type: issue
              Value: 4
          Titles:
            – TitleFull: Personnel Psychology
              Type: main
ResultId 1