Training transfer in school: integrating the theory of organizational support into the theory of planned behavior

There has been growing trend all around the world that more and more organizations are investing a remarkable amount of resources in organizational training, making it prudent to focus on training transfer. training transfer is of great significant for education system since teaching as a profession confronted with uncertainty demands that teachers apply their newly acquired knowledge and skills to classroom. As demonstrated by studies, however, employees have trouble transferring their knowledge into the workplace. Therefore, calls have been made by scholars to dissect transfer of training on the job.
The present paper is a response to this call. As the underlying theoretical model, the theory of planned behavior (TPB) is used to explain and predict teachers’ intention to use educational technologies in teaching. The TPB posits that intention is the primary antecedent of actual behavior which is determined by three major determinants, that is, attitude, subjective norm and perceived behavioral control. As empirical studies, however, demonstrate the relationship between the three determinants and intention isn’t perfect, leaving a proportion of variance in intention unexplained. One possible explanation for this imperfect relationship is that other variable may be involved. Hence, we incorporated supervisory support, as a possible organization-level factor, into the TPB to improve the variance of intention. Supervisory support has been found to account for other behaviors in organization such as OCB and it can be assumed to impact intention to use educational technology in teaching. Based on the above argument, the research hypotheses are as follow:
Teachers’ Attitude predicts teachers’ intention to use educational technology in teaching,
Teachers’ subjective norm predicts teachers’ intention to use educational technology in teaching,
Teachers’ perceived behavioral control predicts teachers’ intention to use educational technology in teaching,
Teachers’ perceived behavioral control predicts teachers’ actual behavior of educational technology use in teaching,
Teachers’ intention to use educational technology in teaching predicts their actual behavior of educational technology use in teaching,
Supervisory support predicts teachers’ intention to use educational technology in teaching.
to examine the hypotheses, correlational-predictive research design was employed. The target population consisted of all Kermanshah junior high school teachers, of whom a random sample of 183 was selected. To gather data, two researcher made surveys were administered:
TPB survey: pre-validated items were adopted and customized to develop this questionnaire. Eventually, a 20 item survey was designed.
Supervisory survey: pre-validated questionnaires were used and customized to develop this scale. Eventually, a 4 item survey was designed.
To rate that responses, 5 point Likert type scale was used. Before being administered, the surveys was presented to a panel of experts to comment on the items relevance, readability and comprehensibility. Some adjustments were made to the surveys based on the feedbacks and comments. The criterion validity of the scales was also confirmed using confirmatory factor analysis. Afterwards, the surveys were administered to a sample of 35 teachers in order to determine their reliability. By means of Chronbach’s alpha, the reliability of the scales was established. By means of Smart PLS2, structural equation modelling was used to test the proposed model as well as the proposed hypotheses.
SEM is a two stem process. In the first step, the measurement model is validated. In so doing, three criteria are used: 1) Cronbach’s reliability and composite reliability to assess the internal consistency, which their values should be greater than 0.6, 2) loading factors and AVE are used to assess the convergent validity, which their value should be greater than 0.7 and 0.5 respectively, and 3) Fornell-Larcker metric is used to assess the divergent validity. According to this criterion, the correlation of a variable should be greater than its correlation with other variables in the model.
After the measurement model is validated, the researcher is allowed to move on to the second stem, which is the assessment of the structural model. In so doing, three measures are used: 1) R2 to determine the amount of variance of the criterion variable estimated by the predicted, 2) path coefficient to establish the significance of the relationship, and 3) Q2 to determine the predictive relevance of the model.
According to the outputs of the software application, the measurement models were all proven to be valid and reliable. The results also indicated that the TPB alone explained 65% of intention variance. Adding supervisory support into the model improved R2 value of intention by 8%.
The present paper has theoretical as well as practical implications. From a theoretical standpoint, the present study is the first study of its own to integrate the theory of organizational support into the TPB. The findings revealed that among the determinants of intention to use educational technology in teaching, attitude is the strongest predicted followed by supervisory support and perceived behavioral control. The findings revealed that subjective norm isn’t able to predict intention to use educational technology in teaching, perceived behavioral control and intention were proven to jointly explain and predict teachers’ actual behavior of educational technology use in classroom. Since attitude turned out to be the strongest predictor of intention to use educational technology in teaching, we suggest principles shift their focus onto teachers’ attitude. As Lowe, Eves & Carrol (2002) argued, if you wish to impact individuals’ behavior and performance, you should work on their positive and negative attitudes and perceptions. Along this endeavor, principles may set goals and talk teachers through the advantages of educational technology. Principles can also be supportive by providing teachers with educational technology means like computer and projectors. In addition, school principles may design and execute training courses to upgrade teachers’ ability to use educational technology in teaching.
Like other scientific studies, the present study is confronted with limitations. Self-report survey was the means of choice to collect data which might have biased the results. Another limitation is concerned with the research design. The cross-sectional design was employed for investigation which is unable to track changes across time. Finally, the population was restricted to Kermanshah junior high school teacher, making it hard to generalize the results to other groups. Therefore, it is recommended that future studies employ longitudinal or qualitative research deign to explore training transfer among teacher at different level of education system.