Slides from presentation at Sloan Sports Analytics Conference, May 3, 2017

10. Hierarchical regression modeling results
Predictor Variables Model 1 Model 2 Model 3 Model 4
MSE (FIFA vs. TOP) 1.45 (.57) 1.00 (.51) -0.60 (.39) 0.15 (.74)
Dyadic
Inflation 2.23* (.09) 3.07** (.12) 2.42* (.16)
Economic Growth 1.84 (.10) 1.64 (.11) 1.78 (.11)
Agency Conflicts 1.44 (.80) 1.21 (.82) 0.32 (.62)
Seller-Related
Population – Future Host -0.27 (.40) -0.25 (.45)
GDP – Future Host 0.91 (.55) 1.00 (.68)
Country Risk Index -.79 (.43) -0.82 (.35)
Sponsorship Clutter 2.97** (.21) 2.41* (.23)
Customer-Related
Congruence -2.43* (.15)
Stability of Leadership -0.14 (.41)
Brand Equity -2.39* (.15)
Public Corporation -0.66 (.40)
N. American Corporation -0.56 (.37)
B2B 1.18 (.80)
Sponsorship Level -1.59 (.23)
AIC 189.9 185.7 183.1 171.7
Log-likelihood -93.93 -88.86 -83.56 -70.86
Wald χ² 2.09 8.73* 9.51* 19.11**
Results from Cox model, with exact discrete approximation for handling ties.
Standardized coefficients are listed, with standard errors in parentheses.
* p < .05; ** p < .01

16. .1.2.3.4.5
2 3 4 5 6
Analysis Time
Congruent Incongruent
Cox Model: Congruence Differentials
.1.2.3.4.5
Smoothedhazardfunction
2 3 4 5 6
Analysis Time
High Brand Equity Low Brand Equity
Cox Model: Brand Equity Differentials