Interpretation of MGA and MICOM results

[kini]

Dear All,
I have observed heterogeneity in my data. So I separated my data into two data groups based on the categorical moderator. I ran MICOM and I have achieved configural and compositional invariance. However, I do not have equality of composite means and variances. Thus my measurement model shows partial invariance and hence I can proceed to MGA. After this, I checked for MGA and none of the paths show any significant difference across the two groups, establishing structural invariance of my model.
I am confused how to interpret this case. What does it mean to have partial invariance in the measurement model but complete invariance in the structural model? How to proceed further? Any guidance would be much appreciated. Thanks.

Reference:
Henseler, Jörg, Christian M. Ringle, and Marko Sarstedt. "Testing measurement invariance of composites using partial least squares." International marketing review 33.3 (2016): 405-431.

[ClovisWyman]

Dear All,
I have observed heterogeneity in my data. So I separated my data into two data groups based on the categorical moderator. I ran MICOM and I have achieved configural and compositional invariance. However, I do not have equality of composite means and variances. Thus my measurement model shows partial invariance and hence I can proceed to MGA.getting over it After this, I checked for MGA and none of the paths show any significant difference across the two groups, establishing structural invariance of my model.
I am confused how to interpret this case. What does it mean to have partial invariance in the measurement model but complete invariance in the structural model? How to proceed further? Any guidance would be much appreciated. Thanks.

Reference:
Henseler, Jörg, Christian M. Ringle, and Marko Sarstedt. "Testing measurement invariance of composites using partial least squares." International marketing review 33.3 (2016): 405-431.

Try to report and discuss your results in detail, such as the fit indices, the parameter estimates, the chi-square difference tests, and the effect sizes. You may also want to explore the reasons for the partial invariance in the measurement model, such as identifying the non-invariant indicators and testing for possible sources of bias. You may also want to check the validity and reliability of your measures, such as the convergent and discriminant validity, the composite reliability, and the average variance extracted