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Multi-trait, multi-method talent assessment through a digital platform: Relationship with job performance
Ist Teil von
Journal of psychology in Africa, 2022-07, Vol.32 (4), p.370-378
Ort / Verlag
Philadelphia: Routledge
Erscheinungsjahr
2022
Link zum Volltext
Quelle
Taylor & Francis Online
Beschreibungen/Notizen
This study investigated whether a multi-method assessment approach increases variance for predicting future job performance compared to a single-method approach measuring similar constructs. Participants were managers (n = 539, female = 29%, average age 38 years, SD = 7 years) across a variety of organisations, including the utilities (n = 145), mining (n= 83), telecommunications (n = 98), Information Technology and professional service sectors (n = 213), drawn from samples across Namibia (n = 54), Nigeria (n = 53), Saudi Arabia (n = 105), South Africa (n = 177), the United Arab Emirates (n = 88), and the United Kingdom (n = 62). The managers used a digital platform to complete multi-trait multi-method (MTMM) measures which included competency-based video interviewing, work-related self-report measures, work simulations and aptitude assessments, each measuring work-related competencies. Structural equation modelling was used to determine paths between measures and work performance. Acceptable model fit for the measurement model was established through confirmatory factor analysis. A constrained and baseline model was used to investigate predictive paths to job performance. The baseline model proved to explain greater variance between measures and job performance than the constrained model. This provides support for deploying a multi-method assessment approach, including both self-rated and expert rated content, in a modern organisational context; and using an integrated digital platform.