Yes, we are familiar with that article and two of us here (Keith Chrzan and myself) sometimes use the Swait-Louviere test when comparing the results from two aggregate logit models, for two different groups of respondents.
While it would be possible to do this with ACBC data, it would be very tricky to do, since you would have to deal with the complex organization of the ACBC choice data files, which involve different layouts for the BYO, Screener Section, and Choice tournaments. Because you would need to be multiplying the design matrix of one group of respondents to try to match the scale factor to be able to compare to a different group of respondents, you'd need to then effects-code or dummy-code the raw choice data files for ACBC. This would be a super laborious process.
In my opinion, the two approaches (one Bayesian and one Frequentist) I have been encouraging you to use for multiple posts now would be easier to conduct and probably even better: 1) use of covariates in HB estimation, then examining the differences between groups of respondents on the part-worth parameters via the alpha file of population mean estimates, 2) use of the normalized part-worth utilities (the point estimates) per individual, via F-test or T-test on the part-worth parameters or on the attribute importance scores, comparing one group to another.
If you are using a continuous variable (such as your individualism scores), you can run a regression analysis with the individualism as independent variable and the normalized part-worth utilities (or the importance score to summarize the weight of attributes) from HB as dependent variables. The t-value for the beta weight would give you your statistical test.
If you are just going to use the individualism score to divide the respondents into two groups, then you could compare respondents using F-Test or T-Test using standard cross-tabulation software statistical testing.