Based on $X \sim N_d(θ, σ^2_X I_d)$, we study the efficiency of predictive densities under $α-$divergence loss $L_α$ for estimating the density of $Y \sim N_d(θ, σ^2_Y I_d)$. We identify a large number of cases where improvement on a plug-in density are obtainable by expanding the variance, thus extending earlier findings applicable to Kullback-Leibler loss. The results and proofs are unified with respect to the dimension $d$, the variances $σ^2_X$ and $σ^2_Y$, the choice of loss $L_α$; $α\in (-1,1)$. The findings also apply to a large number of plug-in densities, as well as for restricted parameter spaces with $θ\in Θ\subset \mathbb{R}^d$. The theoretical findings are accompanied by various observations, illustrations, and implications dealing for instance with robustness with respect to the model variances and simultaneous dominance with respect to the loss.