Handbook of Organization Measurement

Emotional Exhaustion

construct.emotional_exhaustion

The complete model → · Measurement plan →

§3The math on what we know

Synthesized priors from the live registry: pooled r, studies (k), total N, top study grade, and heterogeneity. Rows flagged for heterogeneity or single-study evidence are directional, not settled — read them as a central tendency.

What drives emotional exhaustion

PredictorrkNGradeConfidence
Negative Affect+0.54150Asingle study
Organizational Politics+0.42150Asingle study
Work Life Conflict Wlc+0.42150Asingle study
Work Interference With Family Wif+0.3819,177Asingle study
Abusive Supervision+0.3614,343Asingle study
Psychological Detachment From Work−0.3617,007Asingle study
Optimism−0.33150Asingle study
Autonomy−0.30114,825Asingle study
Job Insecurity+0.2713,350Asingle study
Resilience−0.2716,966Asingle study

What emotional exhaustion predicts

OutcomerkNGradeConfidence
Job Satisfaction−0.47150Asingle study
Turnover Intention+0.382100Acoherent
In Role Performance−0.22150Bsingle study
Voluntary Turnover+0.21118,740Asingle study
Task Performance−0.192100Acoherent
Organizational Citizenship Behavior OCB−0.19150Bsingle study

§4As a model node

Where emotional exhaustion sits when you drop it into a model — what feeds it, what it moves. Read left to right as a small, actionable causal claim.

Inputs (drivers)

  • Negative Affect+0.54
  • Organizational Politics+0.42
  • Work Life Conflict Wlc+0.42
  • Work Interference With Family Wif+0.38
Emotional Exhaustion

Outputs (outcomes)

  • Job Satisfaction−0.47
  • Turnover Intention+0.38
  • In Role Performance−0.22
  • Voluntary Turnover+0.21
Composed live from the Principia registry. Effect sizes are synthesized priors (random-effects meta-analysis); grades are the top study-quality grade in each pool; I² is pooled heterogeneity. Source-instrument item wording is withheld pending copyright clearance (PRN-207); the Principia rendering is owned and shown in full.