The complete model

Task Performance

Every factor researchers have brought together to explain task performance, combined from 19 study models into one ranked driver set — the model the priority engine reasons over.

39 drivers · 19 contributing models · Handbook entry →

§1The drivers, by consensus

Ranked by how many independent models include each driver (consensus), then pooled effect. “In N of 19” is a robustness signal — a driver in many models is one many researchers agreed matters; a driver in one is a single lead worth following.

DriverConsensusPooled rGradeHeterogeneity
Unstructured Employment Interviews5 / 19+0.25AI²≈0.96
Structured Employment Interviews4 / 19+0.41AI²≈0.99
Autonomy3 / 19+0.27AI²≈0.90
Job Knowledge2 / 19+0.40A
Competence2 / 19+0.38A
Employment Interview2 / 19+0.34AI²≈0.98
General Mental Ability GMA2 / 19+0.33AI²≈1.00
Organizational Justice2 / 19+0.29AI²≈0.99
Procedural Justice2 / 19+0.29AI²≈0.99
Relatedness2 / 19+0.25A
Organizational Citizenship Behavior OCB1 / 19+0.51A
Situational Interviews1 / 19+0.49A
Structured Interviews1 / 19+0.42A
Helping Behavior1 / 19+0.40AI²≈0.99
Integrity1 / 19+0.36A
Assessment Centers1 / 19+0.33A
Work Sample1 / 19+0.30A
Intrinsic Motivation1 / 19+0.26A
Motivation1 / 19+0.26A
Ethical Leadership1 / 19+0.25B
Servant Leadership1 / 19+0.25A
Role Ambiguity1 / 19−0.21A
Positive Affect1 / 19+0.20A
Emotional Exhaustion1 / 19−0.19A
Person Group Fit1 / 19+0.19A
Conscientiousness1 / 19+0.18A
Person Job Fit1 / 19+0.17A
Burnout1 / 19−0.16B
Person Organization Fit1 / 19+0.15A
Negative Affect1 / 19−0.15A
Goal Orientation1 / 19+0.13A
Interactional Justice1 / 19+0.13B
Learning Orientation1 / 19+0.13A
Authentic Leadership1 / 19+0.12B
Power Distance1 / 19−0.10B
Role Conflict1 / 19−0.07B
Collectivism1 / 19−0.02B
Job Knowledge Test1 / 19Astudy-reported
Performance Feedback1 / 19Astudy-reported

“Pooled r” is the meta-analytic prior where one exists; drivers marked study-reported have a model but not yet a pooled prior — a concrete acquisition target. This is the value-of-information view in miniature: the priority engine can rank which driver to measure first by consensus, strength, and how much is still unknown.

§2The models it’s built from

Each contributing study model — the researcher’s theory of this outcome — with its size and grade, so the meta-model is auditable back to its sources.

Source studyPredictorsGrade
doi:10.1037/apl00009949A
doi:10.1037/0033-2909.124.2.2625A
doi:10.1037/0021-9010.79.4.5994A
doi:10.1111/j.1744-6570.2005.00672.x3A
doi:10.1007/s11031-016-9578-23A
doi:10.1037/0021-9010.86.3.4253A
doi:10.1177/01492063166320583A
doi:10.1177/01492063166654613B
doi:10.1037/0021-9010.92.5.13322A
doi:10.1037/a00189382B
doi:10.1037/a00131152A
doi:10.1007/s10869-010-9201-62A
doi:10.1111/joms.124022A
doi:10.1037/a00130792A
doi:10.1037/0021-9010.79.2.1842A
doi:10.1037/a00356612A
doi:10.1111/j.2044-8325.1988.tb00467.x2B
doi:10.1080/1359432x.2023.22093202A
doi:10.1177/0149206300026001042A
Composed live from the registry. The complete model is the union of all study-level models of this outcome; study-level models are derived from the effect corpus and re-derived every loop cycle.