Just a partial answer because I've never heard of this method. From what I read in the link you provided, it seems to be a single-step procedure (much like Bonferroni, except we rework the test statistics instead of the p-value) which is likely to be too conservative.
In R, there is a function
pairwise.prop.test() which allows any correction for multiple comparisons (single-step or step-down FWER methods or FDR-based), but it is quit what you already suggested (although Bonferroni is by far too conservative, but still very used in practice).
A resampling approach, using permutation, might be interesting too. The
coin R package provides a well-established testing framework in this respect, see §5 of Implementing a Class of Permutation Tests: The coin Package, but I never had to deal with permutation tests on categorical data in a post-hoc way.
About the analysis of subdivided contingency tables, I generally consider specific associations as a guide to develop additional hypotheses (as for any unplanned comparisons), but this is another question. I generally just use visualization tools, like mosaicplot from Michael Friendly, Pearson's residuals, and if I seek to explain specific patterns of association I use log-linear models.