Moderating Role of Job Relevance in Information Technology Acceptance Model among Agricultural Extension Experts in West Azarbaijan Province

Subject Areas : Infomartion Technology

Amir Alambeigi ¹ , Ismaeil Ahangari ²

1 - Assistant Professor, Department of Agricultural Extension and Education, University of Tehran, Tehran, Iran
2 - M.S. Student, Department of Agricultural Extension and Education, University of Tehran, Tehran, Iran

Received: 2014-02-23 Accepted : 2015-03-07 Published : 2014-08-01

Keywords: structural equation modeling, technology acceptance model, job relevance, perceived usefulness, perceived ease of use,

Abstract :

considered one of the important issues in the field of organizational studies, among which Technology Acceptance Model of Davis provides a suitable framework to identify the most important affecting variables. Using this approach, this study was conducted with correlational method aiming to show the external variables affecting the dimensions of perceived usefulness and ease of use, as two dimensions of information acceptance model.  There is little research on this model in organizational context. Thus to develop the model, this study has focused on job relevance as a moderator between perceived ease influence on acceptance intentions and perceived usefulness. In addition, experience variable has been considered as an external variable. The statistical population was West Azerbaijan agricultural extension experts (N=180), among whom 120 people were selected randomly using the Krejcie and Morgan table. The required data were collected via questionnaire. The face validity of the questionnaire was confirmed by a panel of experts. The Chronbach's alpha coefficient was between 0.730 and 0.816 for different dimensions of questionnaire. The partial least squares method as a new approach to structural equation modeling was used for data analysis. The results showed that experience variable had significant effect on perceived usefulness and it was able to explain 77% variance of perceived usefulness. Furthermore, technology acceptance model in this study could predict 71% of the real use.