Causal hypotheses:from qualitative to quantitative (SEM style)

The statistical testing of hypotheses about causal sequencing of variables seems to more clearly show nowadays the 2-step process directly stated in Ch. 10 in Conrady & Jouffe, (chapter written with Felix Elwert), process mentioned in my Semnet posting:
1. Causal Identification, followed by
2. Computing the Effect Size.

I found a good example of this approach, within the SEM (Structural Equation Modeling) approach, in a a recent article in Computers in Human Behavior, by Yen & Wu, the sequence is simply: 1. A 'qualitative' (i.e. no numbers!) part: how variables/concepts are expected to 'string themselves' out; and 2. A quantitative part, the post-SEM estimation phase, with numbers attached this time.
1.What one expects:

 2. What one finds (numerically):

References:

Conrady, S. and L. Jouffe, Bayesian Networks and BayesiaLab: 

A Practical Introduction for Researchers. 2015: Bayesia USA: 

free online: http://www.bayesia.com/book

Yen, Y.-S., & Wu, F.-S. (2016). Predicting the adoption of mobile financial services: 

The impacts of perceived mobility and personal habit. Computers in Human Behavior, 

65, 31-42. doi: http://dx.doi.org/10.1016/j.chb.2016.08.017