Cargando…
Game-related assessments for personnel selection: A systematic review
Industrial development in recent decades has led to using information and communication technologies (ICT) to support personnel selection processes. One of the most notable examples is game-related assessments (GRA), supposedly as accurate as conventional tests but which generate better applicant re...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9554090/ https://www.ncbi.nlm.nih.gov/pubmed/36248590 http://dx.doi.org/10.3389/fpsyg.2022.952002 |
Sumario: | Industrial development in recent decades has led to using information and communication technologies (ICT) to support personnel selection processes. One of the most notable examples is game-related assessments (GRA), supposedly as accurate as conventional tests but which generate better applicant reactions and reduce the likelihood of adverse impact and faking. However, such claims still lack scientific support. Given practitioners’ increasing use of GRA, this article reviews the scientific literature on gamification applied to personnel selection to determine whether the current state of the art supports their use in professional practice and identify specific aspects on which future research should focus. Following the PRISMA model, a search was carried out in the Web of Science and Scopus databases, identifying 34 valid articles, of which 85.3% are empirical studies that analyze five areas: (1) validity; (2) applicant reactions; (3) design of GRA; (4) personal characteristics and GRA; and (5) adverse impact and faking. Together, these studies show that GRA can be used in personnel selection but that the supposed advantages of GRA over conventional tests are fewer than imagined. The results also suggest several aspects on which research should focus (e.g., construct validity, differences depending on the type of game, prediction of different job performance dimensions), which could help define the situations in which the use of GRA may be recommended. |
---|