Any mathematical model must be preceded by a systemic analysis for determining all involved variables, incluing the more spam task decisions variables linked to our task, the efficiency indicators that reflect the quality of any possible decision and the decisions variables of process in modeling stage. So, if you are working well, all the variables will be taken in account.

Thank you Dear José Arzola-Ruiz

Bests,

The classic Technology Acceptance Model already includes perceived usefulness and perceived ease of use as independent variables, and I presume that the literature suggests several other predictors.

if you are using regression as your method for predicting acceptance, then it may help to enter your independent variables as separate blocks of predictors in several steps. This is known as hierarchical regression (but be sure not to confusion this with hierarchical linear models).

Thank you dear David L Morgan

Bests,

Technology Acceptance Model is a making decision model, therefore, more variables independent (if the variables reflect the real process and the variables are really independents) result on better decision, but the problem when you have more than one decision variable is the compromise between them. The best option in my opinion is to investigate what happening when you add or remove some of the decision variables in the model, depends what you assess based on system global indicator (sum of all the contributions), you can use some weights in order to find the compromise between all the variables involved as a calibration model.