This paper aims to predict a college football team’s competitiveness using physical resources, human resources and organizational resources.,Guided 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.,The 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.,The research focused exclusively on physical, organizational and human resources as sources of competitive advantage and not physiological and/or psychological variables.,College 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.,The 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.

Resource-based theory of college football team competitiveness

This study aims to predict the determinants of net income of 101 US university football programs.,Guided by stakeholder theory, financial capacity model and resource dependency theory, the dependent variable was net income (indicated as profit or loss) and independent variables were measured as the number of women and men’s team sports, average home attendances, win–loss records, conference ranking, endowment funds and age of football programs. Statistical analysis was performed using Kendell tau and binary logistic regression (BLR).,Net income was positively and statistically associated with home attendance, win–loss record, conference rankings and endowment funds, but not number of women’s sports, age of football program and number of men’s sports teams. The BLR indicated that home attendance was the best predictor of net income.,The research was delimited to 101 Football Bowl Subdivision football programs from public universities.,The findings indicate that home attendance and conference rankings had the highest association with net income, but the former was the best predictor of net income and not football tradition nor number of sports teams.,The study was pioneering in the predictive evaluation of the possible determinants of loss or profitability in college football programs.

Profit or loss? On the determinants of net income of United States college football programs

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The Age Discrimination in Employment Act, Title VII, and the Civil Rights Act of 1991: Three Acts and a Dog That Didn’t Bark

This paper poses the following question in the context of civil rights in education — is proportionality synonymous with parity? Since 1974 the Office of Civil Rights (OCR) has determined that in order for an educational institution to be in compliance with Title IX of the Educational Amendments of 1972, educational opportunities must be proportional to male/female demographics. In this paper, my empirical research shows that in fact interests are not proportional among men and women in many areas of education. In some areas, male interests are stronger than female interests, and in other areas the reverse is true. In support of my argument, I present a collection of studies that measure male and female demand in areas of sports and STEM, as well as demand for protection against sexual harassment. These studies cast light on the reality behind civil rights for both men and women in education, a reality that, I argue, civil rights theoreticians and advocates don’t want to see or cannot see. In September of 2015 President Obama issued an Executive Order to encourage the use of behavior science in lawmaking. The question remains as to why OCR continues to operate under the assumption that the interests of males and females are equal in all areas of education.

Daniel J. Emam

Papers

Manufacturing Equality: Title IX, Proportionality, and Natural Demand