In this paper we show that Shapley-Value may even have benefits in conditions of mild collinearity.
This problem is well-known under conditions of severe multi-collinearity, and alternatives like the Shapley-Value approach have been proposed to mitigate this issue. A common problem is the independent variables are correlated and this makes it difficult to get a good estimate of the importance of the “drivers”. Driver modeling is and can also be used to determine the drivers of value, likelihood to recommend, etc. The goal of such research is quite often to determine the relative importance of various sub-components of the product or service in terms of predicting and explaining overall satisfaction.
Linear (and other types of) regression are often used in what is referred to as “driver modeling” in customer satisfaction studies.
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