Sensitivity Analysis in Causal Mediation Effects for TAM Model

One of the goals of scientific research is to identify cause-effect relationships, which in many cases are made in non-experimental research design, based on correlation measures or using regression methods. A special case is a structural equation model (SEM) that is often and incorrectly labeled “causal” models. The aim of the paper is to identify causal relationships in relation to technology acceptance models (TAM) (Davis in MIS Q 13:319–340, 1989; Davis et al. in Manage Sci 35:982–1003, 1989) using the analysis of mediation effects and causal dependencies that stem from Markov’s causal rule. Identification of causal relationships is made using d-separation (Pearl in Stat Surv 3:96–146, 2009) and sensitivity analysis (Imai et al. in Stat Sci 1:51–71, 2010; Tingley et al. in J Stat Soft 59:1–38, 2013). The aim of this article is to assess the impact of unknown disturbing variables (confounders) affecting both the mediation and focal-dependent variables. The analysis allowed for simulations of correlated disturbances effect of dependent variables in the TAM model on the degree of average causal mediation effect bias. The TAM model was built on the basis of research conducted on a quota sample of 150 students of the Cracow University of Economics.