Markov Workforce Models

Markov Workforce Models is a stochastic modeling approach in workforce forecasting that uses transition probabilities between discrete organizational states to predict internal labor supply, distinguishing itself by assuming memoryless movement and stationary distributions over time.

21

Publications

359

Citations

27

Authors

22

Institutions

Publications per year

2017–2026

1

Authors

27

  1. JE
    James E. Ciecka

    DePaul University, United States

    4 publications · 56 citations

    4

    h-index

  2. GR
    Gary R. Skoog

    DePaul University, United States

    4 publications · 56 citations

    4

    h-index

  3. TD
    Tim De Feyter

    Vrije Universiteit Brussel, Belgium

    3 publications · 56 citations

    3

    h-index

  4. MG
    Marie‐Anne Guerry

    Vrije Universiteit Brussel, Belgium

    3 publications · 56 citations

    3

    h-index

  5. KV
    Kurt V. Krueger

    Centers for Disease Control and Prevention, United States

    2 publications · 30 citations

    2

    h-index

1–5 of 27

Venues

  1. Journal of Forensic Economics

    JFE · 3 publications · 41 citations

    3

    h-index

  2. Journal of the Operational Research Society

    OR · 2 publications · 34 citations

    2

    h-index

  3. Linear Algebra and its Applications

    1 publications · 21 citations

    1

    h-index

  4. Stochastic Analysis and Applications

    1 publications · 22 citations

    1

    h-index

  5. Academy of Management Review

    AMR · 1 publications · 15 citations

    1

    h-index

1–5