First of all, especially considering that your model is not that simple, I suggest you to switch for this study from using term regression analysis to using term latent variable modeling (LVM) or, more commonly, structural equation modeling (SEM). The main reason is not the terminology, but emphasizing the fact that SEM encompasses a comprehensive analysis of both measurement model and structural model. In SEM terminology, to analyze a measurement model, you need to perform confirmatory factor analysis (CFA), after you've done EFA, while to analyze a structural model, you need to perform path analysis, also referred to as path modeling (PM) or simply SEM.
In terms of the SEM process, as I said earlier, it is quite a challenge to grasp all concepts and, especially, tie them all into one neat framework. So, I would suggest you to start with this excellent tutorial, after that - this paper (theoretical parts) to understand better SEM in general as well as two major approaches to SEM (CB-SEM and PLS-SEM) and then, perhaps, take a quick look at this paper to get a sense (don't try to understand everything right away) how the full SEM analysis (EFA $\rightarrow$ CFA $\rightarrow$ PM/SEM) should be performed and reported. Then you can return to this question to post small clarifying questions or post them as separate questions. Hope this helps.
Note. Two important aspects: 1) your full SEM model (both measurement and structural models) should be hypothesized by you, based on theory or, if theory doesn't exist for that knowledge domain, literature review as well as your assumptions and arguments; 2) the mapping between 26 items and 4 latent factors is exactly that hypothesized measurement model I was talking about.