American Journal of Applied Sciences 9 (4): 496-504, 2012 ISSN 1546-9239 © 2012 Science Publications Correspondent Author: Margaretha Ari Aggorowati, Department of Statistics, Institute Technology Sepuluh Nopember, Indonesia 496 Restructuring and Expanding Technology Acceptance Model Structural Equation Model and Bayesian Approach 1 Margaretha Ari Aggorowati, 1 Nur Iriawan, 1 Suhartono and 2 Hasyim Gautama 1 Department of Statistics, Institute Technology Sepuluh Nopember, Indonesia 2 Department of Statistics, Institute of Statistics, Indonesia Abstract: Problem statement: Technology Acceptance Model (TAM) is one of models that analyze user behavior to accept and use a new technology. SEM is the most statistical method which use in TAM analysis that provides the estimation strength of all hypothesized relationship between variables in a theoretical model. Consider to employing the standard SEM in TAM analysis which expected large data, the sample size become a crucial problem. Population census data processing is Indonesian government statistical program that needs supporting a computer technology in order to obtain accurate data and less time processing. It is needed to understand the user acceptance in mandatory environment with limited users. Approach: Estimation SEM with Bayesian method is an alternative to solve the sample size problem. This study the developing TAM in the implementation of census data processing system with limitation of sample size and extension of statistical methods of TAM’s analysis with Structural Equation Model (SEM) Bayesian approach. The TAM theory of this study implemented the constructs of TAM3: subjective norm, output quality, result demonstrability, perception of external control, compatibility and experience, perceived ease of use, perceived of usefulness. The others constructs are organizational interventions: management support, design characteristic, training, organizational support. Results: The result have shown that from the model there are significant relations between first: management support to subjective norm, second: subjective norm to perceived of usefulness, third: training, perception of external control to perceived ease of use. Residual analysis show that residuals are close to zero. Conclusion: Estimation of TAM using SEM and Bayesian methods with MCMC and Gibbs Sampler algorithm could handle the small sample size problem. Key words: Information Technology (IT), Technology Acceptance Model (TAM), Structural Equation Model (SEM), Bayesian INTRODUCTION Information Technology (IT) is technology artifact and it has not been coming in vacuum area. The implementation of information technology could be different in every field. How the IT reach the optimum performance will depend on the user’s acceptance of the technology. Since 1980 more researchers have been focusing on the user’s intention to use a new technology (Zhang et al., 2008). Technology Acceptance Model (TAM) is one of models that analyze user behavior to accept and use a new technology. TAM has been implemented in many field studies. TAM became popular, because it is simple and easy to understand (King and He, 2006). As a theory, like an organic being, TAM has ceaselessly evolved (Lee et al., 2003). Some researchers have expanded to find the progress of TAM (Samah et al., 2011; Mohd et al., 2011). The studies have developed in a specific field or in a comprehensive study with meta analysis. In conjunction with the progress of diffusion innovation technology, TAM’s analysis has been employed in many areas of researches. It could be focus on theoretical perspectives or practical views. The goal of TAM studies is having explanation of user acceptance in a new technology and the restriction that induce the user acceptance. It performed an analysis of the implementation a new technology which fit with user requirement in different circumstance. The literature study from 108 leading journals, show the most common problems which became limitation in TAM researches can be grouped in some