I'm investigating the influence of top management team nationality diversity (TMTdiv, continuous between 0-1) and board nationality diversity (Boarddiv, continuous between 0-1) on firm internationalization (count of new foreign market entries).
I'm using a negative binomial regression due to overdispersion and I would like to know how to interpret the main and interaction effects of these two variables. All of my variables are standardized. If I understand correctly, the negative main effect of TMTdiv (-0.27052) indicates that for a one unit increase (i.e., one standard deviation increase) in TMTdiv while Boarddiv is zero (i.e., at average since data are standardized), the expected count of new foreign market entries decreases by a factor of exp(-0.27052)=0.76, or 0.24%. Similarly, for a one unit increase in Boarddiv while TMTdiv is zero, the expected outcome increases by exp(0.26567)=1.30, or 30%. As for the interaction, the effect of TMTdiv on new market entries increases by a factor exp(0.17965) ≈ 1.19, or 19% for a one unit increase in Boarddiv. Likewise, the effect of Boarddiv increases 19% for a one unit increase in TMTdiv.
My ex-ante hypothesis regarding TMTdiv stated a positive relationship between this variable and interationalization. My question is how to interpret a counterintuitive negative main effect of TMTdiv in light of a positive Boarddiv main effect and a positive interaction with Boarddiv as well? Is there perhaps a way to calculate a net effect? At which value of BoardDiv would the negative influence of TMTdiv on internationalization be minimized?
Call:
glm.nb(formula = Entry ~ Control1 + Control2
+ Control3 + Control4 + Control5 +
Control6 + Control7 + Control8 +
Control9 + factor(SIC1) + factor(Year) +
Control10 + TMTdiversity *
BoardDiversity, data = data,
init.theta = 1.090577033, link = log)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.1654 -1.1399 -0.4089 0.2878 2.4788
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.09543 0.31723 0.301 0.76354
Control1 0.26129 0.16089 1.624 0.10436
Control2 -0.10568 0.09433 -1.120 0.26254
Control3 0.01053 0.08214 0.128 0.89803
Control4 -0.07133 0.09095 -0.784 0.43285
Control5 0.07227 0.12183 0.593 0.55304
Control6 0.14621 0.10905 1.341 0.17999
Control7 0.07330 0.12111 0.605 0.54504
Control8 0.09151 0.09010 1.016 0.30979
Control9 0.08741 0.09077 0.963 0.33555
factor(SIC1)2 0.53152 0.36787 1.445 0.14849
factor(SIC1)3 1.06711 0.37244 2.865 0.00417 **
factor(SIC1)4 0.34078 0.37400 0.911 0.36220
factor(SIC1)5 0.85303 0.35066 2.433 0.01499 *
factor(SIC1)6 0.49310 0.34369 1.435 0.15137
factor(SIC1)7 1.06508 0.44184 2.411 0.01593 *
factor(SIC1)8 2.07867 0.45733 4.545 0.00000549 ***
factor(Year)2012 -0.81119 0.18053 -4.493 0.00000701 ***
factor(Year)2013 -0.25712 0.17094 -1.504 0.13254
Control10 -0.05225 0.08735 -0.598 0.54970
TMTdiv -0.27052 0.14007 -1.931 0.05345 .
Boarddiv 0.26567 0.13965 1.902 0.05711 .
TMTdiv:BoardDiv 0.17965 0.10642 1.688 0.09139 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for Negative Binomial(1.0906) family taken to be 1)
Null deviance: 402.74 on 296 degrees of freedom
Residual deviance: 307.19 on 274 degrees of freedom
AIC: 1130.7
Number of Fisher Scoring iterations: 1
Theta: 1.091
Std. Err.: 0.159
2 x log-likelihood: -1082.666