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Work flow policy and within-worker and between-workers variability in performance.

Kenneth Howard Doerr1, Tali Freed, Terence R Mitchell

  • 1Naval Postgraduate School, Graduate School of Business and Public Policy, Monterey, CA 93943, USA. khdoerr@nps.navy.mi

The Journal of Applied Psychology
|October 28, 2004
PubMed
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Workflow policies significantly alter worker performance variability. Work sharing increases heterogeneity and variability, while fixed assignments decrease them, impacting operational efficiency and cost estimations.

Area of Science:

  • Operations Management
  • Human Factors Engineering
  • Industrial Psychology

Background:

  • Understanding the impact of workflow policies on worker performance variability is crucial for optimizing operational efficiency.
  • Previous research has not fully elucidated how different workflow structures influence both between-worker (heterogeneity) and within-worker performance variability.

Purpose of the Study:

  • To investigate the effects of distinct workflow policies on worker heterogeneity and within-worker variability in performance times.
  • To analyze how work sharing and fixed assignment policies influence performance metrics compared to individual performance.

Main Methods:

  • A laboratory experiment was conducted to measure worker heterogeneity and within-worker variability.
  • Three workflow conditions were implemented: individual performance, work sharing, and fixed assignment.

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Main Results:

  • The work sharing policy was found to increase both worker heterogeneity and within-worker variability.
  • Conversely, the fixed assignment policy led to a decrease in worker heterogeneity and within-worker variability.
  • The study also examined the main effects of workflow policies on mean performance times and overall variability.

Conclusions:

  • Workflow policies have a demonstrable impact on performance variability, influencing both individual consistency and differences between workers.
  • The choice of workflow policy (e.g., work sharing vs. fixed assignment) can significantly alter performance dynamics.
  • Ignoring these effects may result in inaccurate performance predictions and suboptimal operational decisions.