Testing and Enhancing a Pivotal Organizational Structure Decision-Making Model
Meredith E. David,
Forest R. David and
Fred R. David
Additional contact information
Meredith E. David: Baylor University, USA
Forest R. David: University of Debrecen, Hungary
Fred R. David: Francis Marion University, USA
International Journal of Strategic Decision Sciences (IJSDS), 2021, vol. 12, issue 2, 1-19
Abstract:
This paper presents and empirically tests a new point-system-based mathematical decision-making model for determining the most effective organizational structure for any firm type. The model proposes that companies can determine their most effective structure by assessing 11 literature-based characteristics that best describe the firm. Through a survey of 143 executive MBA students, this paper provides results, conclusions, and implications of the first empirical test of a math-based organizational structure decision-making model. The research presented suggests that 11 key variables, or organizational characteristics, should be included in any predictive structure model. Corporate executives need and seek theoretical and practical guidance regarding how to best organize, structure, or re-structure their firm to gain and sustain competitive advantage. The model tested herein provides real-world guidance to managers regarding how to decide which organizational structure is most effective for any given firm.
Date: 2021
References: Add references at CitEc
Citations:
Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJSDS.294006 (application/pdf)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:igg:jsds00:v:12:y:2021:i:2:p:1-19
Access Statistics for this article
International Journal of Strategic Decision Sciences (IJSDS) is currently edited by Saeed Tabar
More articles in International Journal of Strategic Decision Sciences (IJSDS) from IGI Global
Bibliographic data for series maintained by Journal Editor ().