Authors: Kyle J. Brannigan, University of Northern Colorado & Dr. Alan L. Morse, University of Northern Colorado

Corresponding Author:
Kyle J. Brannigan
4750 W29th Street APT 1210
Greeley CO, 80634
Kbrannigan429@gmail.com
845-216-0965

Second Author:
Dr. Alan L. Morse
Butler-Hancock 261A University of Northern Colorado Sports & Exercise Science
Campus Box 118
Greeley, CO 80639
Alan.Morse@unco.edu

An Empirical Investigation of the Variables Influencing Contributions in NCAA Division I Athletics: A Quantitative Analysis

ABSTRACT

The purpose of this study was to identify variables that influence contributions to help athletic departments become more efficient with their fundraising efforts. In addition, this study was expected to provide a better understanding of the effect each explanatory variable has on contributions. The researchers conducted a multiple linear regression, using the data, which spanned over three years (2015, 2016, and 2017), to investigate what factors influence contributions to Division I, public schools, in the Power Five conferences. A regression was conducted to clarify further the studies significance. The researchers tested for assumptions, collinearity, correlations, normality, and variance. The significant variables in the study were 1) Average announced attendance for football 2) Enrollment, 3) Football winning percentage 4) Population of Metropolitan Statistical Area or MSA, 5) Fundraising years of experience. In addition, every conference was significant with the Southeastern Conference having the largest part correlation, which demonstrated influence for each variable. Other interesting findings in this study were overall ticket sales were almost significant and Texas A&M is an influential observation because its contributions are much higher than other institutions. The results of this study may aid athletic departments in determining focus to maximize donations. As enrollment was a significant factor, the results further strengthen the case that athletic departments should be using their alumni bases even more to solicit donations. Another implication is that getting into a Power 5 conference can help your contribution levels. In addition, it is crucial for athletic departments to focus on hiring experienced directors of fundraising to guide the staff in maximizing donations. Lastly, athletic departments may want to continue using ticket sales to solicit donations. If athletic departments take into consideration variables that affect donations the most and focus on these variables, they may be able to increase overall athletic donations.

Keywords: Contributions, donations, tickets, revenue, NCAA

INTRODUCTION

Donor contributions are the largest revenue generator in the National Collegiate Athletic Association (NCAA) for division one athletics (Fulks, 2017). Previous research has been conducted on factors that influence donor contributions (Coughlin & Erekson, 1984; Daughtrey & Stotlar, 2000; Wells et al. 2005). The importance of funding through contributions is most visible through the construction of enhanced facilities; however, the increase in funds also helps to draw more national attention, increase application rates, and increase academic donations (Sigelman & Bookheimer, 1983). From 2008, fundraising surpassed ticket sales as the single largest source of revenue for Division I athletic departments (Fulks, 2008). With contributions being a vital part of revenue generation today, it is becoming more important to examine further the factors that influence contributions. Findings from previous studies indicated donor motivation to be associated with perks, such as priority seating, parking, and power in decision making, social and philanthropic purposes, and to contribute to the academic success of student-athletes (Gladden, 2005; Mahoney, 2003; Shapiro et al. 2010). Other scholars have found winning percentages in football and basketball, national championships, ticket prices, conference affiliation, number of living alumni, size of fundraising staff, and county income to be reasons for donor contributions (Daughtrey & Stotlar, 2000; Stinson & Howard, 2007; Wells et al. 2005). One inconsistent finding across studies is that winning has a positive impact on donor contributions. This specific variable is both significant (Anderson, 2012; Reynolds et al., 2017; Stinson & Howard 2008), and insignificant (Cohen et al.; 2010; Turner et al. 2001; Wells et al. 2005), depending on the study. The purpose of the current study is to identify variables that influence contributions to help athletic departments be more efficient in fundraising efforts and to provide a better understanding of the effect each explanatory variable has on contributions.

LITERATURE REVIEW

Multiple studies have investigated factors that affect contributions (Coughlin & Erekson, 1984; Reynolds et al., 2017; Shapiro et al., 2010; Stinson & Howard, 2007; Wells et al., 2005). Coughlin and Erekson (1984) used a regression model with eight independent variables to estimate the influences on athletic contributions. Independent variables used in the study were athletic success, conference affiliation, and population variables. Coughlin and Erekson (1984) found basketball winning percentages, conference affiliation, football attendance, state population, and ticket sales as significant determinates of donor contributions. More recent studies confirm these findings (Baade & Sunberg, 1996; Cohen et al., 2010; Wells et al. 2005). However, Coughlin and Erekson (1984) overlooked other variables that scholars showed to influence contributions. They omitted variables involving enrollment, alumni size, along with fundraising staff size and experience (Reynolds et al., 2017; Wells et al., 2005). Significant variables not overlooked were, bowl appearances, basketball success, conference affiliation, and attendance (Cohen et al., 2010; Coughlin and Erikson, 1984; Martinez et al., 2010; Wells et al., 2005). One constant that exists is the contradiction between basketball and football winning percentages and the influence of the success of the sports has on donor contributions (Brooker & Klastorin, 1981; Ko et al., 2013; Reynolds et al., 2017; Sigelman & Bookheimer, 1983; Wells et al., 2005). In addition, other contradictions exist in significant variables, which include state income and the number of living alumni (Stinson & Howard, 2010; Wells et al., 2005). Findings have differed as to whether these variables have a positive impact on donor contributions. No study has expanded on these contradictions nor compared all variables to see what influences contributions the most.

Previous researchers acknowledge that contributions are the most valuable source of revenue for college athletic departments (Coughlin & Erikson, 1984; Stinson & Howard, 2010). Moreover, 18% of NCAA division I-A schools’ revenue stemmed from contributions (Stinson & Howard, 2010). Researchers continue to find that institutions are doing a better job at soliciting and creating lifelong connections with donors thus creating donor retention (Coughlin & Erikson, 1984; Sargeant & Woodliffe, 2007; Stinson & Howard, 2010). This builds on Wells, et al, (2005) showing the size and experience of your fundraising staff plays a significant factor in the success of generating donor contributions. Past researchers have been able to show that contributions are a main revenue source for Division 1 institutions as well as demonstrating the significance of having a good fundraising staff. With contributions being more relevant now than ever, this may create opportunities for researchers to figure out what factors are most important to donor contributions. In order to add to the existing literature and aid athletic departments in gaining the most revenue out of donor contributions, the current study uses a quantitative analysis to investigate the variables that most influence donor contributions.

This study will add to the existing literature and provide clarification regarding donor contributions. Past researchers have not examined all these factors using a quantitative approach. Researchers have found a variety of factors proven to influence donations. However, these studies limited themselves to certain variable groups. The current study aimed to find the most influential variables in relation to donor contributions. Based on previous literature, researchers have found conference affiliation, and winning variables had the most significant impact on donor contributions (Anderson, 2012; Daughtrey & Stotlar, 2000; Sigelman & Bookheimer, 1983; Stinson & Howard, 2008; Wells et al., 2005). The researchers hypothesized that enrollment, conference affiliation, overall ticket sales, and rights and licensing, will be significant. Enrollment speaks for the size of the school as well as its possible alumni base, with larger enrollment numbers schools may have more people to solicit for donations. Overall ticket sales typically include the opportunity to donate and join the organization’s database, which make purchasers available for donor solicitations. Both conference affiliation and rights, and licensing variables allow for exposure, which could influence more donations.

METHODS

Similar to the method used by McEvoy and Morse (2007), and Coughlin and Erekson (1984), the researchers used a multiple linear regression analyses to discover what variables affected donor contributions to Division 1 public schools in Power 5 conferences, what of all influencing factors affect contributions, and the differences in affect each factor has. A three year span (2015-2017) of data was collected from these schools because they are the highest revenue and contribution gaining institutions in division one athletics (NCAA.com, 2018). Data was not available for private institutions, and thus they were excluded from the study. Attendance and winning variables were recorded for football as well as men’s and women’s basketball as these sports were the most attended, most broadcasted, and most revenue generating sports in college athletics (NCAA.com, 2018). Additionally, to the researcher’s knowledge, no study has combined these factors along with others to test which are most significant. Enrollment, student fees, and incoming funding variables were included based on previous literature. The average attendance for each team based on home attendance were used.

The researchers conducted a multiple linear regression, using the data, which spanned three years (2015, 2016, and 2017), to investigate what factors influence contributions to Division I public schools in the Power 5 conferences. A regression was conducted to clarify further the studies significance. Researchers tested for assumptions, collinearity, correlations, normality, and variance.

Variables

The purpose of this study was to investigate what factors most influence donor contributions in Division 1 athletics. A multiple regression analyses model was created to explain more clearly the relationship between contributions and division one athletic programs. In addition, the study controlled the potential astounding variables to best isolate the relationship. The other variables were chosen based on previous literature. 

Dependent Variable

Contributions is the dependent variable for the study. The contributions variable is defined as the amount of money donated to an athletic institution. This variable was analyzed in each institution included in the sample.

Explanatory Variables

The explanatory variables studied were rights/licensing, student fees, school funds, average announced attendance for football, average announced attendance for men’s basketball (MBB), average announced attendance for women’s basketball (WBB), overall ticket sales, football home winning percentage, MBB winning percentage, WBB winning percentage, national championships, conference championships, march madness appearances, bowl appearance, enrollment, population of metro statistical area (MSA), median household income, fundraising staff experience conference affiliation. Fundraising experience was based on the years of experience for the director of fundraising in the athletic department. For conference affiliation, a dummy variable was used to better distinguish each conference. These variables were chosen based on past research, which found them to be significant in influencing donor contributions, as well as factors the researchers feel most influence contributions.

Procedures

The data for annual contributions were collected from usatoday.com (2017). Student fees, school funds, and rights and licensing fees were also derived from the USA today database. The researchers used the NCAA website, university websites, and phone calls to gather all sports attendance data, average ticketing pricing, winning percentages, national championships, conference championships, March Madness appearances, conference affiliation, and bowl appearances. University websites and common data sets were used to gather university enrollment. Lastly, the U.S. Census Bureau was used to collect income per capita for the county as well as the population of the Metropolitan Statistical Area (MSA).

Statistical design

Correlations were also accounted for while the variance inflation factor was used to assess correlations. These tests allowed the researchers to discover different levels of significance in all factors. A multiple linear regression was used to test the significance of the variables.

RESULTS

Table 1: Variable outcomes

Coefficientsa

Model Unstandardized Coefficients Standardized Coefficients t Sig. Correlations Collinearity Statistics
B Std. Error Beta Zero-order Partial Part Tolerance VIF
(Constant) -14140629.089 7424368.684   -1.905 .059          
RightsLicensing .056 .076 .056 .741 .460 .444 .066 .039 .476 2.099
StudentFees -.140 .277 -.039 -.504 .615 -.273 -.045 -.026 .457 2.187
Overallticketsales .235 .125 .238 1.880 .062 .610 .165 .098 .171 5.836
SchoolFunds -.290 .257 -.084 -1.125 .263 -.336 -.099 -.059 .490 2.041
AVGAnnouncedAttendanceforFootball 206.221 80.332 .358 2.567 .011 .631 .222 .134 .141 7.095
AnnouncedAttendanceforMBB -65.280 287.499 -.022 -.227 .821 .010 -.020 -.012 .283 3.529
AnnouncedAttendanceforWBB -92.572 437.009 -.018 -.212 .833 .143 -.019 -.011 .381 2.623
FootballWinning 298135.548 130180.315 .134 2.290 .024 -.001 .199 .120 .802 1.247
MBBWinning 82491.593 48332.380 .106 1.707 .090 .138 .150 .089 .713 1.402
WBBWinning 13775.662 49285.282 .020 .280 .780 .175 .025 .015 .545 1.836
MBballNationalChampionships -7104230.124 6831739.735 -.063 -1.040 .300 .015 -.092 -.054 .742 1.347
WBballNationalChampionships 5456228.863 9372959.825 .034 .582 .562 .084 .052 .030 .784 1.276
FBNationalChamps 2030812.291 6152570.896 .022 .330 .742 .079 .029 .017 .614 1.628
FootballConferenceChampionships 1808687.184 2895728.266 .040 .625 .533 .199 .055 .033 .685 1.460
Mbballconfchamps 4904028.497 2728097.507 .121 1.798 .075 .078 .158 .094 .607 1.647
Wbballconfchamps 372790.987 3896994.119 .007 .096 .924 -.054 .008 .005 .594 1.685
MMarchMadnessAppearances 1377037.138 1813169.387 .054 .759 .449 .026 .067 .040 .547 1.829
WMarchMadnessAppearances 1735085.092 1823442.732 .068 .952 .343 .209 .084 .050 .531 1.882
BowlAppearances -1343333.491 1666249.255 -.055 -.806 .422 .213 -.071 -.042 .593 1.685
Enrollment 334.379 81.670 .297 4.094 .000 .200 .341 .214 .522 1.915
PopulationofMSA -3.827 1.649 -.144 -2.321 .022 -.011 -.202 -.122 .710 1.408
MedianHouseholdIncome -84.536 81.675 -.077 -1.035 .303 -.092 -.091 -.054 .494 2.026
FundraisingstaffexpYears -191323.360 88309.919 -.133 -2.166 .032 -.046 -.189 -.113 .725 1.379
ACC 13433902.713 3137919.977 .383 4.281 .000 -.065 .355 .224 .342 2.920
Big12 11393056.933 2690974.130 .325 4.234 .000 .078 .352 .222 .466 2.148
Pac12 7346618.798 2937432.195 .226 2.501 .014 -.290 .217 .131 .336 2.974
SEC 11696114.134 2420960.331 .400 4.831 .000 .419 .394 .253 .400 2.501
Big 10 -11393056.933 2690974.130 -.390 -4.234 .000 -.170 -.352 -.222 .324 3.090

a. Dependent Variable: Contributions

Table 1 displays descriptive data for all 28 variables included in the study. The results show that average announced attendance for football, football-winning percentage; fundraising staff years of experience, population of metropolitan statistical area, and enrollment for every Power 5 conference school were significant variables. Overall tickets sales were very close to being significant at .062. This may be important to note because in studies with this variable, typically ticket sales is significant. The overall regression model showed statistical significance yielding a p-value less than .001. This implies at least one or more independent variables influences contributions. The overall model showed an R-squared value of .652, more importantly the adjusted R-squared value is .578. Thus, approximately 58% of the variance in contributions are explained by the independent variables.                        

VIF is a measurement that determines whether two variables may be explaining the same thing; therefore, VIF should show how much multi-collinearity is in the model (displayr.com, 2018). In Table 1, variance inflation factor (VIF) values appeared to be acceptable, despite some collinearity issues. Average football attendance and overall ticket sales appear to be similar and have some collinearity issues; however, the VIF values are not alarmingly high. Going further, the partial correlation plots showed the linearity assumptions were met.

Figure 1
Figure 2

The study also tested for normality and variance. Normality does not appear to be violated from the histogram (Figure 1) although the normal PP plot (Figure 2) does show that normality may be violated. This could mean that results may not be trustworthy. However, according to the scatter plot (Figure 3) the constant variance assumption does not appear to be violated. In addition, the overall regression model had high significance levels. Thus, proving the overall study is significant.

Figure 3

Table 2: Regression table

ANOVAa

Model Sum of Squares df Mean Square F Sig.
Regression 16248809260344990.000 27 601807750383148.000 8.799 .000b
Residual 8685715310953492.000 127 68391459141366.086    
Total 24934524571298488.000 154      

a. Dependent Variable: Contributions
b. Predictors: (Constant), SEC, FundraisingstaffexpYears, FootballConferenceChampionships, MBballNationalChampionships, FootballWinning, Wbballconfchamps, Enrollment, Mbballconfchamps, PopulationofMSA, Big12, WBballNationalChampionships, SchoolFunds, MBBWinning, BowlAppearances, WMarchMadnessAppearances, StudentFees, MedianHouseholdIncome, MMarchMadnessAppearances, FBNationalChamps, Pac12, WBBWinning, RightsLicensing, AnnouncedAttendanceforWBB, Overallticketsales, ACC, AnnouncedAttendanceforMBB, AVGAnnouncedAttendanceforFootball

Table 3: R-Squared table

Model Summaryb

Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics Durbin-Watson
R Square Change F Change df1 df2 Sig. F Change
1 .807a .652 .578 8269912.886 .652 8.799 27 127 .000 2.157

a. Predictors: (Constant), SEC, FundraisingstaffexpYears, FootballConferenceChampionships, MBballNationalChampionships, FootballWinning, Wbballconfchamps, Enrollment, Mbballconfchamps, PopulationofMSA, Big12, WBballNationalChampionships, SchoolFunds, MBBWinning, BowlAppearances, WMarchMadnessAppearances, StudentFees, MedianHouseholdIncome, MMarchMadnessAppearances, FBNationalChamps, Pac12, WBBWinning, RightsLicensing, AnnouncedAttendanceforWBB, Overallticketsales, ACC, AnnouncedAttendanceforMBB, AVGAnnouncedAttendanceforFootball
b. Dependent Variable: Contributions

The regression model in Table 2 allows us to see the overall regression model showed significance at 8.799 yielding a p-value less than .001. Again, this indicates that at least one or more of independent variables does have an influence on contributions. The overall model (Table 3) showed an adjusted R-squared value of .578; this explains that approximately 58% of the variance in contributions are explained by the independent variables. The significant variables are 1) Average announced attendance for football, 2) Enrollment, 3) Football winning percentage, 4) Population of MSA, and 5) Fundraising years of experience. In addition, every conference was significant with the SEC having the largest part correlation and thus has the most influence on donor contributions of all the Power 5 conferences.

The enrollment variable had a part correlation of .341 and a p-value of less than .001. This variable had the highest part correlation of all variables in the study other than the SEC conference. With the exception of other conferences, enrollment was followed by average announced attendance for football, which had a part correlation of .222 followed by football winning percentage. The data demonstrated a gap between the two leading variables that affect contributions. Other variables that have been shown to affect donor contributions are fundraising staff experience with a part correlation of -.113 and population of MSA with a part correlation of -.122.

The findings of this study supported the initial hypothesis. However, some variables found to be significant were not hypothesized in this study. It was hypothesized that enrollment, conference affiliation, ticket sales, winning variables and rights and licensing fees would have the most impact on donor contributions. However, rights and licensing fees were not deemed significant. In addition, it was not hypothesized that fundraising staff experience would have the impact it did. However, the researchers did correctly hypothesize that conference affiliation; enrollment and winning would be significant. It should also be noted that overall ticket sales were very close to being significant, and basketball winning was close as well.

Some interesting findings in this study were overall ticket sales was almost significant. This variable had a part correlation of .98 and a p-value of .062. This is very close to the significance level and something that may be taken into consideration. Another interesting finding was that Texas A&M was a potential outlier. The reason Texas A&M is an influential observation is because its contributions are much higher than other institutions. This could also be the reason that the SEC conference has such an influence on donations as well. For example, Texas, another leading university, had an average contribution of $44,150,553 over the studies span compared to Texas A&M’s average contribution level of $71,871,773. Although the difference is vast, Texas A&M should not be omitted because the recorded values are legitimate. The results indicate that  it conference, enrollment, staff experience affect contributions.

CONCLUSIONS

The purpose of this study was to identify variables that influence contributions to help athletic departments be more efficient with their fundraising efforts and to provide a better understanding of the effect each explanatory variable has on contributions. Previous literature indicated factors that are significant, but there seemed to be uncertainty in the understanding of significant explanatory variables. A multiple linear regression was used in this study to identify these significant variables and provide a better understanding of the explanatory variables. This study supports that average announced attendance for football, enrollment, football winning percentage, population of MSA; fundraising staff years of experience and conference affiliation have a positive relationship with donor contributions. Some reasons for this may be that conference affiliation allows for greater exposure. All these conferences have large amounts of games on national television and often get national exposure on sports shows, which may explain why being in a Power 5 conference can affect donations.

Overall tickets sales were close to being significant and has been significant in past studies. It is common when purchasing tickets that a donation is required, which may contribute to this measure (Wells et al., 2005). Thus, ticket sales have a direct impact on increasing donations. In addition, ticket sale databases create more donation revenue opportunities.  

Winning in the past has been found significant as well. Winning may lead to more exposure, especially since these teams are often on national television. Winning big games as well as bowl games allow for a great amount of exposure and allow ticket departments more power when trying to solicit donations. Of course, athletic departments cannot control winning or losing but they may be able use winning to help increase attendance and solicit more donations. Larger populations to solicit in ticket sales yield more donations, which athletic departments cannot control but can use to its advantage. Having a fundraising staff that knows how to relate to both the people in your area and your existing database may be a positive way for athletic departments to garnish more donations.

An understanding of these variables and their significance may be important for athletic departments in order to maximize donor contributions. A focus on ticket sales and enrollment may aid organizations in creating more donation revenue, which has been proven vital to the success of modern-day Division I college athletic programs (Fulks, 2017; Sigelman & Bookheimer, 1983). Furthermore, athletic departments should employ a director of fundraising with fundraising experience, as fundraising staff experience was found significant. A director who knows how to create relationships and motivate staff may allow athletic departments to be more successful when soliciting athletic contributions. 

Support for findings in literature

The literature is mixed on what factors affect donor contributions. Some find winning percentages to be significant (Anderson, 2012; Reynolds et al., 2017; Stinson & Howard 2008), others do not (Cohen et al.; 2010; Turner et al.; 2001; Wells et al.; 2005). Similar to Reynolds et al. (2017), the current study found football winning percentage to have an impact on donor contributions. This study was also supported by Daughtrey and Stotlar’s (2000) findings that conference affiliation affects contributions as well.

APPLICATIONS IN SPORT

The results of this study may aid athletic departments to maximize donations. As enrollment is a significant factor, athletic departments should be using their alumni bases and student populations more to solicit donations. Athletic departments may want to consider continuing using ticket sales to solicit donations. Tying in donations with tickets and parking may be a way to use ticket sales to solicit larger amounts of donations and overall revenue. Conference membership also had an effect on contribution levels. Finally, athletic departments should hire experienced directors of fundraising as directors of fundraising drive donor contributions. These considerations may increase overall athletic donations.

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