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Emergent Dynamics of Workforce Program Reductions: A Hybrid Multi-Level Analysis

Emergent Dynamics of Workforce Program Reductions: A Hybrid Multi-Level Analysis

Steven Cavaleri, Chester S. Labedz, George H. Stalker
Copyright: © 2012 |Pages: 71
ISBN13: 9781466616011|ISBN10: 1466616016|EISBN13: 9781466616028
DOI: 10.4018/978-1-4666-1601-1.ch083
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MLA

Cavaleri, Steven, et al. "Emergent Dynamics of Workforce Program Reductions: A Hybrid Multi-Level Analysis." Human Resources Management: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2012, pp. 1408-1478. https://doi.org/10.4018/978-1-4666-1601-1.ch083

APA

Cavaleri, S., Labedz, C. S., & Stalker, G. H. (2012). Emergent Dynamics of Workforce Program Reductions: A Hybrid Multi-Level Analysis. In I. Management Association (Ed.), Human Resources Management: Concepts, Methodologies, Tools, and Applications (pp. 1408-1478). IGI Global. https://doi.org/10.4018/978-1-4666-1601-1.ch083

Chicago

Cavaleri, Steven, Chester S. Labedz, and George H. Stalker. "Emergent Dynamics of Workforce Program Reductions: A Hybrid Multi-Level Analysis." In Human Resources Management: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 1408-1478. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-4666-1601-1.ch083

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Abstract

This paper reports on research that explores designing a hybrid system dynamics/agent modeling (HSDAM) simulation methodology to evaluate potential effects of a new human resources policy in a company. The study measures the effect of changes in the company’s pension policies on individual employee retention, promotion and employment longevity. The Delphi method for elicitation of expert views was used, as four expert panels composed of human resource specialists and general managers participated in model design and predicted employee behavior. The model integrates multi-level organizational data inputs from macro-level business data to granular individual-level employee information. Each simulation run used four years of workforce longitudinal data at the start. Initially, the expert panel predictions did not validate simulation results. However, once alteration of a key model parameter recalibrated individual employees as more economically rational, later runs provided strong support for the model and modeling approach. The simulation results confirmed, among several expert panel predictions, that setting a policy that decreased the likelihood of employee willingness to retire due to replacement income concerns could lead to other consequences with potentially adverse strategic implications for the firm.

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