Resource-based theory of college football team competitiveness

Resource-based theory of college football team competitiveness PurposeThis paper aims to predict a college football team’s competitiveness using physical resources, human resources and organizational resources.Design/methodology/approachGuided by the resource-based theory, the study used archival data of 101 college football teams. The dependent variable was competitiveness (indicated by win-loss records), the independent variables were physical resources (operationalized as home attendance and total revenues), human resources (measured as coaches’ salary and coaches’ experience) and organizational resources (specified as conference rankings and the number of sports). Kendall Tau correlation and binary logistic regression were used to examine the associative and predictive competitive advantages.FindingsThe binary logistic regression model showed an overall percentage predictive correctness of 71.3%, with a Negelkerke R2 of 41.1% of the variance of all predictors – with coaches’ experience, total revenues and home attendance being the best predictors of generating competitive advantages that produced superior win-loss records.Research limitations/implicationsThe research focused exclusively on physical, organizational and human resources as sources of competitive advantage and not physiological and/or psychological variables.Practical implicationsCollege football teams aspiring to be competitive may benefit from this study by applying a three-fold strategy of hiring well-paid high performing and experienced coaches who can increase attendance and revenues.Originality/valueThe study was unique in two ways – one, it made clear the positive significance of coaches’ experience as a source of competitive advantage, and second, it highlighted the catalytic effects of revenues and attendance in fueling competitiveness. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Organizational Analysis Emerald Publishing

Resource-based theory of college football team competitiveness

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Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
1934-8835
DOI
10.1108/IJOA-04-2018-1403
Publisher site
See Article on Publisher Site

Abstract

PurposeThis paper aims to predict a college football team’s competitiveness using physical resources, human resources and organizational resources.Design/methodology/approachGuided by the resource-based theory, the study used archival data of 101 college football teams. The dependent variable was competitiveness (indicated by win-loss records), the independent variables were physical resources (operationalized as home attendance and total revenues), human resources (measured as coaches’ salary and coaches’ experience) and organizational resources (specified as conference rankings and the number of sports). Kendall Tau correlation and binary logistic regression were used to examine the associative and predictive competitive advantages.FindingsThe binary logistic regression model showed an overall percentage predictive correctness of 71.3%, with a Negelkerke R2 of 41.1% of the variance of all predictors – with coaches’ experience, total revenues and home attendance being the best predictors of generating competitive advantages that produced superior win-loss records.Research limitations/implicationsThe research focused exclusively on physical, organizational and human resources as sources of competitive advantage and not physiological and/or psychological variables.Practical implicationsCollege football teams aspiring to be competitive may benefit from this study by applying a three-fold strategy of hiring well-paid high performing and experienced coaches who can increase attendance and revenues.Originality/valueThe study was unique in two ways – one, it made clear the positive significance of coaches’ experience as a source of competitive advantage, and second, it highlighted the catalytic effects of revenues and attendance in fueling competitiveness.

Journal

International Journal of Organizational AnalysisEmerald Publishing

Published: Sep 2, 2019

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