{"id":6970,"date":"2020-04-17T06:30:00","date_gmt":"2020-04-17T11:30:00","guid":{"rendered":"http:\/\/thesportjournal.org\/?p=6970"},"modified":"2020-06-02T13:41:08","modified_gmt":"2020-06-02T18:41:08","slug":"an-empirical-investigation-of-the-variables-influencing-contributions-in-ncaa-division-i-athletics-a-quantitative-analysis","status":"publish","type":"post","link":"https:\/\/thesportjournal.org\/article\/an-empirical-investigation-of-the-variables-influencing-contributions-in-ncaa-division-i-athletics-a-quantitative-analysis\/","title":{"rendered":"An Empirical Investigation of the Variables Influencing Contributions in NCAA Division I Athletics: A Quantitative Analysis"},"content":{"rendered":"\n

Authors:<\/strong> Kyle J. Brannigan, University of Northern Colorado & Dr. Alan L. Morse, University of Northern Colorado<\/p>\n\n\n\n

Corresponding Author:<\/strong>
Kyle J. Brannigan
4750 W29th Street APT 1210
Greeley CO, 80634
Kbrannigan429@gmail.com
845-216-0965<\/p>\n\n\n\n

Second Author:<\/strong>
Dr. Alan L. Morse
Butler-Hancock 261A University of Northern Colorado Sports & Exercise Science
Campus Box 118
Greeley, CO 80639
Alan.Morse@unco.edu<\/a><\/p>\n\n\n\n

An\nEmpirical Investigation of the Variables Influencing Contributions in NCAA\nDivision I Athletics: A Quantitative Analysis<\/strong><\/h3>\n\n\n\n

ABSTRACT<\/strong><\/p>\n\n\n\n

The\npurpose of this study was to identify variables that influence contributions to\nhelp athletic departments become more efficient with their fundraising efforts.\nIn addition, this study was expected to provide a better understanding of the\neffect each explanatory variable has on contributions. The researchers conducted\na multiple linear regression, using the data, which spanned over three years\n(2015, 2016, and 2017), to investigate what factors influence contributions to\nDivision I, public schools, in the Power Five conferences. A regression was\nconducted to clarify further the studies significance. The researchers tested\nfor assumptions, collinearity, correlations, normality, and variance. The\nsignificant variables in the study were 1) Average announced attendance for\nfootball 2) Enrollment, 3) Football winning percentage 4) Population of Metropolitan\nStatistical Area or MSA, 5) Fundraising years of experience. In addition, every\nconference was significant with the Southeastern Conference having the largest\npart correlation, which demonstrated influence for each variable. Other interesting\nfindings in this study were overall ticket sales were almost significant and Texas\nA&M is an influential observation because its contributions are much higher\nthan other institutions. The results of this study may aid athletic departments\nin determining focus to maximize donations. As enrollment was a significant\nfactor, the results further strengthen the case that athletic departments\nshould be using their alumni bases even more to solicit donations. Another\nimplication is that getting into a Power 5 conference can help your\ncontribution levels. In addition, it is crucial for athletic departments to focus\non hiring experienced directors of fundraising to guide the staff in maximizing\ndonations. Lastly, athletic departments may want to continue using ticket sales\nto solicit donations. If athletic departments take into consideration variables\nthat affect donations the most and focus on these variables, they may be able\nto increase overall athletic donations. <\/p>\n\n\n\n\n\n\n\n

Keywords: <\/strong>Contributions, donations, tickets, revenue, NCAA<\/p>\n\n\n\n

INTRODUCTION<\/strong><\/p>\n\n\n\n

Donor\ncontributions are the largest revenue generator in the National Collegiate Athletic\nAssociation (NCAA) for division one athletics (Fulks, 2017). Previous research\nhas been conducted on factors that influence donor contributions (Coughlin\n& Erekson, 1984; Daughtrey & Stotlar, 2000; Wells et al. 2005). The\nimportance of funding through contributions is most visible through the\nconstruction of enhanced facilities; however, the increase in funds also helps\nto draw more national attention, increase application rates, and increase\nacademic donations (Sigelman & Bookheimer, 1983). From 2008, fundraising\nsurpassed ticket sales as the single largest source of revenue for Division I\nathletic departments (Fulks, 2008). With contributions being a vital part of\nrevenue generation today, it is becoming more important to examine further the\nfactors that influence contributions. Findings from previous studies indicated\ndonor motivation to be associated with perks, such as priority seating,\nparking, and power in decision making, social and philanthropic purposes, and\nto contribute to the academic success of student-athletes (Gladden, 2005; Mahoney,\n2003; Shapiro et al. 2010). Other scholars have found winning percentages in\nfootball and basketball, national championships, ticket prices, conference\naffiliation, number of living alumni, size of fundraising staff, and county\nincome to be reasons for donor contributions (Daughtrey & Stotlar, 2000;\nStinson & Howard, 2007; Wells et al. 2005). One inconsistent finding across\nstudies is that winning has a positive impact on donor contributions. This\nspecific variable is both significant (Anderson, 2012; Reynolds et al., 2017;\nStinson & Howard 2008), and insignificant (Cohen et al.; 2010; Turner et\nal. 2001; Wells et al. 2005), depending on the study. The purpose of the\ncurrent study is to identify variables that influence contributions to help\nathletic departments be more efficient in fundraising efforts and to provide a\nbetter understanding of the effect each explanatory variable has on\ncontributions. <\/p>\n\n\n\n

LITERATURE\nREVIEW<\/strong><\/p>\n\n\n\n

Multiple\nstudies have investigated factors that affect contributions (Coughlin &\nErekson, 1984; Reynolds et al., 2017; Shapiro et al., 2010; Stinson &\nHoward, 2007; Wells et al., 2005). Coughlin and Erekson (1984) used a\nregression model with eight independent variables to estimate the influences on\nathletic contributions. Independent variables used in the study were athletic\nsuccess, conference affiliation, and population variables. Coughlin and Erekson\n(1984) found basketball winning percentages, conference affiliation, football\nattendance, state population, and ticket sales as significant determinates of\ndonor contributions. More recent studies confirm these findings (Baade &\nSunberg, 1996; Cohen et al., 2010; Wells et al. 2005). However, Coughlin and\nErekson (1984) overlooked other variables that scholars showed to influence\ncontributions. They omitted variables involving enrollment, alumni size, along\nwith fundraising staff size and experience (Reynolds et al., 2017; Wells et\nal., 2005). Significant variables not overlooked were, bowl appearances,\nbasketball success, conference affiliation, and attendance (Cohen et al., 2010;\nCoughlin and Erikson, 1984; Martinez et al., 2010; Wells et al., 2005). One\nconstant that exists is the contradiction between basketball and football\nwinning percentages and the influence of the success of the sports has on donor\ncontributions (Brooker & Klastorin, 1981; Ko et al., 2013; Reynolds et al.,\n2017; Sigelman & Bookheimer, 1983; Wells et al., 2005). In addition, other\ncontradictions exist in significant variables, which include state income and\nthe number of living alumni (Stinson & Howard, 2010; Wells et al., 2005).\nFindings have differed as to whether these variables have a positive impact on\ndonor contributions. No study has expanded on these contradictions nor compared\nall variables to see what influences contributions the most.<\/p>\n\n\n\n

Previous\nresearchers acknowledge that contributions are the most valuable source of\nrevenue for college athletic departments (Coughlin & Erikson, 1984; Stinson\n& Howard, 2010). Moreover, 18% of NCAA division I-A schools\u2019 revenue\nstemmed from contributions (Stinson & Howard, 2010). Researchers continue\nto find that institutions are doing a better job at soliciting and creating\nlifelong connections with donors thus creating donor retention (Coughlin &\nErikson, 1984; Sargeant & Woodliffe, 2007; Stinson & Howard, 2010).\nThis builds on Wells, et al, (2005) showing the size and experience of your\nfundraising staff plays a significant factor in the success of generating donor\ncontributions. Past researchers have been able to show that contributions are a\nmain revenue source for Division 1 institutions as well as demonstrating the\nsignificance of having a good fundraising staff. With contributions being more\nrelevant now than ever, this may create opportunities for researchers to figure\nout what factors are most important to donor contributions. In order to add to\nthe existing literature and aid athletic departments in gaining the most\nrevenue out of donor contributions, the current study uses a quantitative\nanalysis to investigate the variables that most influence donor contributions.<\/p>\n\n\n\n

This\nstudy will add to the existing literature and provide clarification regarding\ndonor contributions. Past researchers have not examined all these factors using\na quantitative approach. Researchers have found a variety of factors proven to\ninfluence donations. However, these studies limited themselves to certain\nvariable groups. The current study aimed to find the most influential variables\nin relation to donor contributions. Based on previous literature, researchers\nhave found conference affiliation, and winning variables had the most\nsignificant impact on donor contributions (Anderson, 2012; Daughtrey &\nStotlar, 2000; Sigelman & Bookheimer, 1983; Stinson & Howard, 2008;\nWells et al., 2005). The researchers hypothesized that enrollment, conference\naffiliation, overall ticket sales, and rights and licensing, will be\nsignificant. Enrollment speaks for the size of the school as well as its\npossible alumni base, with larger enrollment numbers schools may have more\npeople to solicit for donations. Overall ticket sales typically include the\nopportunity to donate and join the organization’s database, which make\npurchasers available for donor solicitations. Both conference affiliation and\nrights, and licensing variables allow for exposure, which could influence more\ndonations. <\/p>\n\n\n\n

METHODS<\/strong><\/p>\n\n\n\n

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

The\nresearchers conducted a multiple linear regression, using the data, which\nspanned three years (2015, 2016, and 2017), to investigate what factors influence\ncontributions to Division I public schools in the Power 5 conferences. A\nregression was conducted to clarify further the studies significance.\nResearchers tested for assumptions, collinearity, correlations, normality, and\nvariance. <\/p>\n\n\n\n

Variables<\/em><\/p>\n\n\n\n

The\npurpose of this study was to investigate what factors most influence donor\ncontributions in Division 1 athletics. A multiple regression analyses model was\ncreated to explain more clearly the relationship between contributions and\ndivision one athletic programs. In addition, the study controlled the potential\nastounding variables to best isolate the relationship. The other variables were\nchosen based on previous literature.  <\/p>\n\n\n\n

Dependent Variable<\/em><\/p>\n\n\n\n

Contributions\nis the dependent variable for the study. The contributions variable is defined\nas the amount of money donated to an athletic institution. This variable was\nanalyzed in each institution included in the sample. <\/p>\n\n\n\n

Explanatory Variables<\/em><\/p>\n\n\n\n

The\nexplanatory variables studied were rights\/licensing, student fees, school\nfunds, average announced attendance for football, average announced attendance\nfor men’s basketball (MBB), average announced attendance for women’s basketball\n(WBB), overall ticket sales, football home winning percentage, MBB winning\npercentage, WBB winning percentage, national championships, conference\nchampionships, march madness appearances, bowl appearance, enrollment,\npopulation of metro statistical area (MSA), median household income, fundraising\nstaff experience conference affiliation. Fundraising experience was based on\nthe years of experience for the director of fundraising in the athletic\ndepartment. For conference affiliation, a dummy variable was used to better\ndistinguish each conference. These variables were chosen based on past research,\nwhich found them to be significant in influencing donor contributions, as well\nas factors the researchers feel most influence contributions. <\/p>\n\n\n\n

Procedures<\/em><\/p>\n\n\n\n

The\ndata for annual contributions were collected from usatoday.com (2017). Student\nfees, school funds, and rights and licensing fees were also derived from the\nUSA today database. The researchers used the NCAA website, university websites,\nand phone calls to gather all sports attendance data, average ticketing\npricing, winning percentages, national championships, conference championships,\nMarch Madness appearances, conference affiliation, and bowl appearances. University\nwebsites and common data sets were used to gather university enrollment.\nLastly, the U.S. Census Bureau was used to collect income per capita for the\ncounty as well as the population of the Metropolitan Statistical Area (MSA). <\/p>\n\n\n\n

Statistical design<\/em><\/p>\n\n\n\n

Correlations\nwere also accounted for while the variance inflation factor was used to assess\ncorrelations. These tests allowed the researchers to discover different levels of\nsignificance in all factors. A multiple linear regression was used to test the\nsignificance of the variables.<\/p>\n\n\n\n

RESULTS<\/strong><\/p>\n\n\n\n

Table 1:<\/strong> Variable outcomes<\/p>\n\n\n\n

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

a. Dependent Variable: Contributions <\/p>\n\n\n\n

Table\n1 displays descriptive data for all 28 variables included in the study. The\nresults show that average announced attendance for football, football-winning percentage;\nfundraising staff years of experience, population of metropolitan statistical\narea, and enrollment for every Power 5 conference school were significant\nvariables. Overall tickets sales were very close to being significant at .062.\nThis may be important to note because in studies with this variable, typically\nticket sales is significant. The overall regression model showed statistical significance\nyielding a p-value less than .001. This implies at least one or more\nindependent variables influences contributions. The overall model showed an R-squared\nvalue of .652, more importantly the adjusted R-squared value is .578. Thus,\napproximately 58% of the variance in contributions are explained by the\nindependent variables.                         <\/p>\n\n\n\n

VIF\nis a measurement that determines whether two variables may be explaining the\nsame thing; therefore, VIF should show how much multi-collinearity is in the\nmodel (displayr.com, 2018). In Table 1, variance inflation factor (VIF) values\nappeared to be acceptable, despite some collinearity issues. Average football\nattendance and overall ticket sales appear to be similar and have some\ncollinearity issues; however, the VIF values are not alarmingly high. Going\nfurther, the partial correlation plots showed the linearity assumptions were\nmet. <\/p>\n\n\n\n

\"Figure<\/figure>\n\n\n\n
\"Figure<\/figure>\n\n\n\n

The\nstudy also tested for normality and variance. Normality does not appear to be\nviolated from the histogram (Figure 1) although the normal PP plot (Figure 2)\ndoes show that normality may be violated. This could mean that results may not\nbe trustworthy. However, according to the scatter plot (Figure 3) the constant\nvariance assumption does not appear to be violated. In addition, the overall\nregression model had high significance levels. Thus, proving the overall study\nis significant. <\/p>\n\n\n\n

\"Figure<\/figure>\n\n\n\n

Table 2: <\/strong>Regression table <\/p>\n\n\n\n

ANOVAa<\/sup><\/strong><\/p>\n\n\n\n\n\n \n \n \n \n
Model<\/td>\n Sum of Squares<\/td>\n df<\/td>\n Mean Square<\/td>\n F<\/td>\n Sig.<\/td>\n <\/tr>\n
Regression<\/td>\n 16248809260344990.000<\/td>\n 27<\/td>\n 601807750383148.000<\/td>\n 8.799<\/td>\n .000b<\/td>\n <\/tr>\n
Residual<\/td>\n 8685715310953492.000<\/td>\n 127<\/td>\n 68391459141366.086<\/td>\n  <\/td>\n  <\/td>\n <\/tr>\n
Total<\/td>\n 24934524571298488.000<\/td>\n 154<\/td>\n  <\/td>\n  <\/td>\n  <\/td>\n <\/tr>\n<\/tbody>\n<\/table>\n\n\n\n

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<\/p>\n\n\n\n

Table 3:<\/strong> R-Squared table<\/p>\n\n\n\n

Model Summaryb<\/sup><\/strong><\/p>\n\n\n\n\n\n \n \n \n
Model<\/td>\n R<\/td>\n R Square<\/td>\n Adjusted R Square<\/td>\n Std. Error of the Estimate<\/td>\n Change Statistics<\/td>\n Durbin-Watson<\/td>\n <\/tr>\n
R Square Change<\/td>\n F Change<\/td>\n df1<\/td>\n df2<\/td>\n Sig. F Change<\/td>\n <\/tr>\n
1<\/td>\n .807a<\/td>\n .652<\/td>\n .578<\/td>\n 8269912.886<\/td>\n .652<\/td>\n 8.799<\/td>\n 27<\/td>\n 127<\/td>\n .000<\/td>\n 2.157<\/td>\n <\/tr>\n<\/tbody>\n<\/table>\n\n\n\n

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
\nb. Dependent Variable: Contributions<\/p>\n\n\n\n

The\nregression model in Table 2 allows us to see the overall regression model\nshowed significance at 8.799 yielding a p-value less than .001. Again, this indicates\nthat at least one or more of independent variables does have an influence on\ncontributions. The overall model (Table 3) showed an adjusted R-squared value\nof .578; this explains that approximately 58% of the variance in contributions\nare explained by the independent variables. The significant variables are 1) Average\nannounced attendance for football, 2) Enrollment, 3) Football winning\npercentage, 4) Population of MSA, and 5) Fundraising years of experience. In addition,\nevery conference was significant with the SEC having the largest part\ncorrelation and thus has the most influence on donor contributions of all the Power\n5 conferences. <\/p>\n\n\n\n

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

The\nfindings of this study supported the initial hypothesis. However, some\nvariables found to be significant were not hypothesized in this study. It was hypothesized\nthat enrollment, conference affiliation, ticket sales, winning variables and\nrights and licensing fees would have the most impact on donor contributions. However,\nrights and licensing fees were not deemed significant. In addition, it was not hypothesized\nthat fundraising staff experience would have the impact it did. However, the researchers\ndid correctly hypothesize that conference affiliation; enrollment and winning\nwould be significant. It should also be noted that overall ticket sales were\nvery close to being significant, and basketball winning was close as well. <\/p>\n\n\n\n

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

CONCLUSIONS<\/strong><\/p>\n\n\n\n

The\npurpose of this study was to identify variables that influence contributions to\nhelp athletic departments be more efficient with their fundraising efforts and\nto provide a better understanding of the effect each explanatory variable has\non contributions. Previous literature indicated factors that are significant,\nbut there seemed to be uncertainty in the understanding of significant\nexplanatory variables. A multiple linear regression was used in this study to\nidentify these significant variables and provide a better understanding of the\nexplanatory variables. This study supports that average announced attendance\nfor football, enrollment, football winning percentage, population of MSA; fundraising\nstaff years of experience and conference affiliation have a positive\nrelationship with donor contributions. Some reasons for this may be that\nconference affiliation allows for greater exposure. All these conferences have\nlarge amounts of games on national television and often get national exposure\non sports shows, which may explain why being in a Power 5 conference can affect\ndonations. <\/p>\n\n\n\n

Overall\ntickets sales were close to being significant and has been significant in past\nstudies. It is common when purchasing tickets that a donation is required,\nwhich may contribute to this measure (Wells et al., 2005). Thus, ticket sales\nhave a direct impact on increasing donations. In addition, ticket sale\ndatabases create more donation revenue opportunities.  <\/p>\n\n\n\n

Winning\nin the past has been found significant as well. Winning may lead to more\nexposure, especially since these teams are often on national television. Winning\nbig games as well as bowl games allow for a great amount of exposure and allow\nticket departments more power when trying to solicit donations. Of course,\nathletic departments cannot control winning or losing but they may be able use\nwinning to help increase attendance and solicit more donations. Larger\npopulations to solicit in ticket sales yield more donations, which athletic\ndepartments cannot control but can use to its advantage. Having a fundraising\nstaff that knows how to relate to both the people in your area and your\nexisting database may be a positive way for athletic departments to garnish\nmore donations. <\/p>\n\n\n\n

An\nunderstanding of these variables and their significance may be important for\nathletic departments in order to maximize donor contributions. A focus on\nticket sales and enrollment may aid organizations in creating more donation\nrevenue, which has been proven vital to the success of modern-day Division I\ncollege athletic programs (Fulks, 2017; Sigelman & Bookheimer, 1983).\nFurthermore, athletic departments should employ a director of fundraising with\nfundraising experience, as fundraising staff experience was found significant.\nA director who knows how to create relationships and motivate staff may allow\nathletic departments to be more successful when soliciting athletic\ncontributions.  <\/p>\n\n\n\n

Support for findings in literature <\/em><\/p>\n\n\n\n

The\nliterature is mixed on what factors affect donor contributions. Some find\nwinning percentages to be significant (Anderson, 2012; Reynolds et al., 2017;\nStinson & Howard 2008), others do not (Cohen et al.; 2010; Turner et al.;\n2001; Wells et al.; 2005). Similar to Reynolds et al. (2017), the current study\nfound football winning percentage to have an impact on donor contributions. This\nstudy was also supported by Daughtrey and Stotlar\u2019s (2000) findings that\nconference affiliation affects contributions as well. <\/p>\n\n\n\n

APPLICATIONS IN SPORT<\/strong><\/p>\n\n\n\n

The\nresults of this study may aid athletic departments to maximize donations. As\nenrollment is a significant factor, athletic departments should be using their\nalumni bases and student populations more to solicit donations. Athletic\ndepartments may want to consider continuing using ticket sales to solicit\ndonations. Tying in donations with tickets and parking may be a way to use\nticket sales to solicit larger amounts of donations and overall revenue. Conference\nmembership also had an effect on contribution levels. Finally, athletic\ndepartments should hire experienced directors of fundraising as directors of\nfundraising drive donor contributions. These considerations may increase\noverall athletic donations. <\/p>\n\n\n\n

REFERENCES<\/strong><\/strong><\/p>\n\n\n\n

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    <\/p>\n","protected":false},"excerpt":{"rendered":"

    Authors: Kyle J. Brannigan, University of Northern Colorado & Dr. […]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"jetpack_publicize_message":"","jetpack_is_tweetstorm":false,"jetpack_publicize_feature_enabled":true,"jetpack_social_options":[]},"categories":[994,291],"tags":[1581,1580,249,1579,1582],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/p4btio-1Oq","jetpack-related-posts":[{"id":188,"url":"https:\/\/thesportjournal.org\/article\/predicting-fund-raising-revenues-in-ncaa-division-i-a-intercollegiate-athletics\/","url_meta":{"origin":6970,"position":0},"title":"Predicting Fund Raising Revenues in NCAA Division I-A Intercollegiate Athletics","date":"January 1, 2005","format":false,"excerpt":"Submitted by: Chad McEvoy Abstract This study created a model to predict annual fund raising contributions to NCAA Division I-A athletic programs using 13 explanatory variables, re-examining an area of the literature unstudied in two decades. A statistically significant model was developed, explaining 60.7% of the variance in athletic contributions.\u2026","rel":"","context":"In "Contemporary Sports Issues"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":7914,"url":"https:\/\/thesportjournal.org\/article\/college-footballs-bottom-line-impact-exploring-the-relationship-of-football-performance-on-athletic-finances-for-division-i-institutions-today\/","url_meta":{"origin":6970,"position":1},"title":"College Football\u2019s Bottom-Line Impact: Exploring the Relationship of Football Performance on Athletic Finances for Division I Institutions Today","date":"July 23, 2021","format":false,"excerpt":"Authors: Spencer D. Wyld1 and David C. Wyld2 1 Walton College of Business, Department of Finance, University of Arkansas, Fayetteville, AR, USA2 Department of Management & Business Administration, Southeastern Louisiana University, Hammond, LA, USA Corresponding Author:David C. Wyld, DBA47042 Scott DriveHammond, LA 70401dwyld@selu.edu985-789-2127 Spencer D. Wyld, M.B.A., is a doctoral\u2026","rel":"","context":"In "Research"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/thesportjournal.org\/wp-content\/uploads\/2021\/07\/Figure1.jpg?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":6322,"url":"https:\/\/thesportjournal.org\/article\/student-athletes-vs-athlete-students-the-academic-success-campus-involvement-and-future-goals-of-division-i-student-athletes-who-were-university-bound-compared-to-those-who-would-not-have-attended\/","url_meta":{"origin":6970,"position":2},"title":"Student-Athletes vs. Athlete-Students: The academic success, campus involvement, and future goals of Division I student athletes who were university bound compared to those who would not have attended a university had they not been an athlete.","date":"February 21, 2019","format":false,"excerpt":"Authors: Brenda L. Vogel, Jeff Kress, and Daniel R. Jeske Corresponding Author:Jeff Kress, Ph.D. Department of Kinesiology1250 Bellflower Blvd. \u2013 MS 4901, HHS2-103Long Beach, CA 90840jeff.kress@csulb.edu949-375-3958 Brenda L.Vogel is a Professor of Criminology and Criminal Justice and the Director of the School of Criminology, Criminal Justice, and Emergency Management at\u2026","rel":"","context":"In "Sports Studies and Sports Psychology"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/thesportjournal.org\/wp-content\/uploads\/2019\/02\/Figure-5.png?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":6015,"url":"https:\/\/thesportjournal.org\/article\/sports-marketing-publicity-efforts-in-division-ii-intercollegiate-athletics\/","url_meta":{"origin":6970,"position":3},"title":"Sports Marketing & Publicity Efforts in Division II Intercollegiate Athletics","date":"September 13, 2018","format":false,"excerpt":"Authors: Robert Zullo Corresponding Author: Robert Zullo, PhD Westminster College 319 South Market Street New Wilmington, PA 16172 zullorh@westminster.edu 724-946-6835 Dr. Robert Zullo is an Associate Professor of Business and Sports Management at Westminster College in New Wilmington, Pennsylvania, located between Pittsburgh and Cleveland. He is also Program Coordinator for\u2026","rel":"","context":"In "Research"","img":{"alt_text":"Table 3","src":"https:\/\/i0.wp.com\/thesportjournal.org\/wp-content\/uploads\/2018\/09\/Table-3.png?resize=350%2C200","width":350,"height":200},"classes":[]},{"id":5391,"url":"https:\/\/thesportjournal.org\/article\/academic-fraud-in-revenue-and-nonrevenue-sports\/","url_meta":{"origin":6970,"position":4},"title":"Academic Fraud in Revenue and Nonrevenue Sports","date":"November 23, 2017","format":false,"excerpt":"Authors: John Adamek Corresponding Author: John Adamek, CSCS 4 Truman Place Moonachie NJ, 07074 Jfadamek21@gmail.com 201-543-9142 John Adamek is a strength and conditioning coach owner of Sports Science Integration. He is also a graduate student at the United States Sports Academy. Academic Fraud in Revenue and Nonrevenue Sports ABSTRACT The\u2026","rel":"","context":"In "Research"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":2408,"url":"https:\/\/thesportjournal.org\/article\/the-impact-of-elite-individual-athletic-performance-on-university-applicants-for-admission-in-ncaa-division-i-a-football-2\/","url_meta":{"origin":6970,"position":5},"title":"The Impact of Elite Individual Athletic Performance on University Applicants for Admission in NCAA Division I-A Football","date":"January 1, 2006","format":false,"excerpt":"Submitted by Chad McEvoy, Ed. D* 1* Illinois State University, Normal,IL 61761 Dr. Chad McEvoy is an assistant professor in the school of kinesiology and recreation at Illinois State University. He holds an Ed. 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