Abstract
Shapley Value Regression is a regression tool that provides reliable estimates for predictor shares in a model. The problem is vital in market research practice where the relative importance of variables considered is very often of primary interest, while collinearity among the predictors makes it impossible to be derived from the usual ordinary least square models. This paper compares the SVR performance with the results of OLS by means of simulation study. The bias and stability of beta estimates together with the prediction accuracy is assessed and the conditions for SVR superiority are determined. The applications in market research and decision making are also discussed.