[b]Technology Acceptance Model (TAM) As a Predictor Model for Explaining Agricultural Experts Behavior in Acceptance of ICT [/b]
Journal Title: International Journal of Agricultural Management and Development - Year 2016, Vol 6, Issue 2
Abstract
This study aimed to develop Technology Acceptance Model (TAM) model to explain adoption of information technologies process. a Descriptive – correlation study was conducted and data were collected through a survey. Statistical population was West Azerbaijan Agricultural Extension agents who 120 of them were selected randomly using the Krejcie and Morgan table. A questionnaire was employed to measure the variables in the model. Its validity was confirmed by a panel of experts. The Cronbach's alpha coefficient ranged between from 0.704 to 0.816 show satisfied reliability. For data processing, partial leastsquares (PLS) method as a new approach to structural equation modeling was used. The results showed that among three variables for development oftechnology acceptance model including Job relevance, experience and organization willingness to invest, the first and second show significant effects.Thus,Job relevance and experience as an external variable was added to the basic TAM. Other relations between variablesin basic technology acceptance model in current study were also seen significant. Our developed TAM can explain 64% of the actual behavior of employee in information technology utilization. TAM is one of the most influential extensions of Ajzen and Fishbein’s theory of reasoned action (TRA) in the literature. The theories behind it assume that when a person forms an intention to act, that s/he will be free to act without limitation. While In the real world there will be many constraints, such as limited freedom to act. For example, people in organized working environments are forced to use most of the relevant applications irrespective of their opinion or attitude. In this research mentioned model was used as a strong model to predict actual use behavior that affected by three variables namely Job relevance, experience and organization willingness to invest.
Authors and Affiliations
Amir Alambaigi, Ismael Ahangari
Keywords
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