Connecting Group Dynamics, Governance, and Performance: Evidence From Charter School Boards

2020 ◽  
Vol 49 (5) ◽  
pp. 1035-1057 ◽  
Author(s):  
Michael R. Ford ◽  
Douglas M. Ihrke

In this article, we build on the existing literatures on small group dynamics and public and nonprofit governance by exploring the link between small group dynamics, governance, and nonprofit performance. The results provide evidence that nonprofit governing boards can improve organizational performance by improving their governance behaviors. Specifically, we link survey data from Minnesota nonprofit charter school board members to hard measures of organizational performance in a path analysis predicting school-level math and reading proficiency levels. We find that boards exhibiting better group dynamics are more active in key governance areas, and that active governance is linked to increased organizational outcomes. Our findings advance scholarly understanding of nonprofit governance by identifying a pathway between nonprofit board governing dynamics and sustainable organizational performance gains. We conclude with practical advice on how nonprofit boards can increase their organizational performance through improved small group dynamics.

2009 ◽  
Author(s):  
Scott Atran ◽  
Marc Sageman ◽  
Jeremy Ginges ◽  
Justin Magouirk ◽  
Dominick Wright

2017 ◽  
pp. 1761-1776
Author(s):  
Sema A. Kalaian ◽  
Rafa M. Kasim ◽  
Nabeel R. Kasim

Regression analysis and modeling are powerful predictive analytical tools for knowledge discovery through examining and capturing the complex hidden relationships and patterns among the quantitative variables. Regression analysis is widely used to: (a) collect massive amounts of organizational performance data such as Web server logs and sales transactions. Such data is referred to as “Big Data”; and (b) improve transformation of massive data into intelligent information (knowledge) by discovering trends and patterns in unknown hidden relationships. The intelligent information can then be used to make informed data-based predictions of future organizational outcomes such as organizational productivity and performance using predictive analytics such as regression analysis methods. The main purpose of this chapter is to present a conceptual and practical overview of simple- and multiple- linear regression analyses.


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