What does it mean if my adjusted r-squared decreases between my bivariate and multivariate regression models? is this a bad or good thing?

The $$R^2_{adj.}$$ is a version of the $$R^2$$ which is adjusted for multiple regressors. Whereas the $$R^2$$ rises the more regressors you add, even when there are irrelevant, the $$R^2_{adj.}$$ stays robust.
$$R^2=1-\frac{SSR}{SST};\quad R^2_{adj.}=1-\frac{SSR/(n-p-1)}{SST/(n-1)}$$
Hence, when you add more regressors to your bivariate model, and then your $$R^2_{adj.}$$ decreases, the model tends to deteriorate and you might call it a "bad thing."
• @LouisRich Yes, I would consider that model with the higher $R^2_{adj.}$ better, because it explains more variance in the dependent variable. Or put the other way round, the added regressors contribute nothing but "blur" to the model. Commented Dec 17, 2020 at 19:05
• @LouisRich I would'nt conclude that solely from the $R^2_{adj.}$. The effect of the $X$ variable is probably more important (does it change considerable? does it change sign? does it get insignificant?). Commented Dec 17, 2020 at 19:21