Given that normality of the response variable is not an underlying assumption of linear regression and that for linear regression the assumption is only of the normality of residuals, what is the motivation for applying Box-Cox - or indeed any transformations (e.g. log) to make the response variable 'more normal'?
By making the response variable 'more normal', often the residuals will be more normal - as seems to be the motivation here https://data.library.virginia.edu/interpreting-log-transformations-in-a-linear-model/ for the log transform
So that we can apply hypothesis testing / inferencing techniques that assume normality of the response variable, as this would seem to imply - https://ncss-wpengine.netdna-ssl.com/wp-content/themes/ncss/pdf/Procedures/NCSS/Box-Cox_Transformation_for_Simple_Linear_Regression.pdf
Some other factors?