Is there any utility in using SHAP values for feature attribution in cases of Linear models and GLMs?

So I have been reading up on SHAP values. I get that it works on the principle of game theory . Basically, just like we would want to allocate a payoff among the participants fairly, the same could be done to a statistical model.

For e.g. if we have a Linear regression model and we have ice cream sales as dependent variable. The independent variables are weather, location of the ice cream store, cost of the ice creams, some marketing …

attribution cases datascience feature game game theory linear reading shap theory utility values