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Cybervetting Best Practices: An Integrative Framework for Developing, Validating, and Implementing Social Media Assessment for Personnel Selection

Anna N. Hoover et al.

Employee Responsibilities and Rights Journal2025https://doi.org/10.1007/s10672-025-09538-4article
AJG 2ABDC C
Weight
0.44

What the paper says

Abstract The prevalence of organizations’ use of applicants’ social media profiles for selection (i.e., cybervetting) warrants further consideration of social media assessment best practices. Using a technology-as-designed framework (Landers & Marin, 2021), we present two features of social media platforms, namely a level of structure and degree of professional content focus, that guide whether a social media platform presents a viable option for use in a social media assessment. Consequently, we suggest LinkedIn as platform ripe for current use in social media assessment due to its high degree of structure and professional focus. Next, we review knowledge, skills, ability, and other characteristics (KSAOs) previously evaluated for use in social media assessment and propose KSAOs that future research should evaluate. Furthermore, we detail the steps organizations should take to develop, implement, and validate social media assessment to ensure the validity, legal defensibility, and fairness of the selection method. Finally, future research directions are offered.

3 citations

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https://doi.org/https://doi.org/10.1007/s10672-025-09538-4

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@article{anna2025,
  title        = {{Cybervetting Best Practices: An Integrative Framework for Developing, Validating, and Implementing Social Media Assessment for Personnel Selection}},
  author       = {Anna N. Hoover et al.},
  journal      = {Employee Responsibilities and Rights Journal},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1007/s10672-025-09538-4},
}

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Cybervetting Best Practices: An Integrative Framework for Developing, Validating, and Implementing Social Media Assessment for Personnel Selection

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Evidence weight

0.44

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.32 × 0.4 = 0.13
M · momentum0.57 × 0.15 = 0.09
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.