Comparing regression coefficient with same model but two distinct samples I am currently completing my dissertation. My study is cross-cultural and looks at predictors and inhibitors to adoption of technology in two countries (Thailand and Australia). I have a hypothesised model with IV's (Ease of Use, Usefulness, Need for Interaction, Risk, and Social Influence) directly linked to a single DV (intention to use). Both models are the exact same, so are the IVs and DVs (and related items), and sample sizes are similiar. 
I have run regression analysis on both the Thai and Australian sample individually. I have the regression coefficient outputs with signifiance etc. What I am trying to find out now is how to best test the following question (or something similiar): "Social influence (IV) will have a stronger relationship in Thailand with intention to use (DV) m-banking than in Australia". 
Is this the best way to test whether individual constructs fit better in one country then another? I want to test each individual construct to find out which has a more significant relationship between that IV and DV. 
I apologise if this question has been answered already somewhere on the site or sounds very simplistic. I am using SPSS v19.0 btw. Thanks in advance!
 A: The best way to test this is to combine the two samples, then add a variable for country and then test the interaction between the other IVs and country. This gives you everything you would get for an ordinary regression - effect sizes, standard errors, p values etc. for the interaction you want to test. 
A: @whuber makes an important point about it being subtle that I commented on but wish to put in as an answer. 
It’s understandable that one would think of two studies as being like two strata within one study.  But as David Cox once pointed out, there are much greater concerns about homogeneity or what can be taken as common.  For instance, the within group variance is often taken to be the same in different factors but this is seldom true between two different studies.  
It is good that you have the same IVs in both, but were there measured with the same accuracy over the same range? This type of extra concern about heterogeneity error/lack of fit may justify thinking of it as a meta-analysis. 
Your situation is briefly addressed in Greenland S, O' Rourke K: Meta-Analysis. Page 652 in Modern Epidemiology, 3rd ed. Edited by Rothman KJ, Greenland S, Lash T. Lippincott Williams and Wilkins; 2008
