ResultsStructural equation modelling (SEM) is a statistical technique fortesting and estimating causal relations using a combination of sta-tistical data and qualitative causal assumptions. Careful researchersacknowledge the possibilities of distinguishing between measure-ment and structural models and explicitly take measurement errorinto account (Henseler, Ringleand, & Sinkovics, 2009). There aretwo families of SEM techniques: (i) covariance-based techniquesand (ii) variance-based techniques. Partial least squares (PLS) is avariance-based technique and is used in this investigation since:(i) not all items in our data are distributed normally (p < 0.01 basedon Kolmogorov–Smirnov’s test); (ii) the research model has notbeen tested in the literature; (iii) the research model is consid-ered as complex. Smart PLS 2.0 M3 (Ringle, Wende, & Will, 2005)was the software used to analyze the relationships defined by thetheoretical model.In the next two subsections we (first) examine the measurementmodel in order to assess internal consistency, indicator reliability,convergent validity, and discriminant validity, and (secondly) testthe structural model.