N. Safaie - Faculty of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran
H. Hamidi - Faculty of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran
M. Vali - Faculty of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran

This study developed unified theory of acceptance and use technology (UTAUT) to examine the predictive factors of mobile marketing adoption. Variables such as personal innovativeness, hedonic motivations, performance expectancy, mobility, and social influence were studied for mobile marketing acceptance. The predicted artificial neural networks (ANN) approach was applied to evaluate the data, and the results of the data were used for comparison with path analysis. The ANN model was derailed by the linear statistical model and was able to show the importance of all predictors that could not be identified by the path analysis model. The results show that personal innovativeness is the most effective factor in mobile marketing acceptance. Subsequently, the hedonic motivations, performance expectancy, mobility, social influence, trust, and facilitating conditions play a vital role. Furthermore, the results illustrate that price value, perceived risk, and effort expectancy were not effective.

Mobile marketing, technology acceptance, Partial Least Squares, Artificial Neural Networks, Unified Theory of Acceptance, Use Technology