Development and validation of a model to investigate the impact of individual factors on instructors’ intention to use e-learning systems

E-learning is becoming an increasingly important part of higher education institutions. However, instructors’ use of e-learning systems in community colleges in the United States is relatively sparse. Thus, the purpose of this study was to investigate some individual factors that may affect instructors’ intention to use e-learning systems in community colleges. In this study, we proposed a theoretical model predicting instructors’ intention to use e-learning systems in community colleges based on their resistance to change, perceived value of e-learning systems, computer selfefficacy, and attitude toward e-learning systems. The sample for this study included 119 (over 41% response rate) full-time, part-time, and adjunct instructors in different academic departments at a community college. Our findings indicate that the theoretical model developed was able to predict instructors’ intention to use e-learning systems. All four predictive variables have significant effects on intention to use e-learning systems. Two statistical methods were used to formulate and test predictive models: Multiple Linear Regression (MLR) and Ordinal Logistic Regression (OLR). Results of both models were consistent on resistance to change as having the greatest weight on predicting instructors’ intention to use e-learning systems, while computer self-efficacy in both analyses was found to have the least weight. We conclude the paper with a discussion, which includes a summary of the results, limitations of this research study, as well as implications for practice and future research

A study of factors that affect instructors' intention to use e-learning systems in two-year colleges

Instructors’ use of e-learning systems in higher education institutions is a central concern of researchers, academicians, and practitioners. Higher education institutions are investing substantial resources to incorporate and maintain the infrastructure of e-learning systems; however, instructors’ use of e-learning systems in two-year colleges is relatively limited. In this context, the goal of this study was to investigate the factors that may affect instructors’ intention to use e-learning systems in two-year colleges. Based on literature review on technology acceptance, this study proposed a theoretical model predicting instructors’ intention to use e-learning systems in two-year colleges based on their resistance to change, perceived value of e-learning systems, computer self-efficacy (CSE), and attitude toward e-learning systems. Consequently, this study investigated the effect of four independent variables on the dependent variable, intention to use e-learning systems. A Web-based survey was designed to empirically assess the effect of aforementioned constructs on instructors’ intention to use e-learning systems in two-year colleges. The Web-based survey was developed as a multi-item measure using Likert-type scales. Existing validated scales were used to develop the Web-based survey. The target population of this study was instructors of public and private two-year colleges. The sample for this study was 119 (over 41% response rate) full-time, part-time, and adjunct instructors in different academic departments at a two-year college. Two statistical methods were used to formulate and test predictive models: Multiple Linear Regression (MLR) and Ordinal Logistic Regression (OLR). Both MLR and OLR results showed that the theoretical model was able to predict instructors’ intention to use e-learning systems. All four independent variables have significant effects on the dependent variable. Results of both analyses were consistent on resistance to change as having the greatest weight on predicting instructors’ intention to use e-learning systems, while CSE in both analyses was found to have the least weight. This study contributes to the body of knowledge by providing empirical results for the key constructs that affect two-year college instructors’ intention to use e-learning systems. Results of this research may also help IT practitioners to concentrate their efforts on ways to address resistance to change as it was found to be the most significant factor affecting e-learning accepted by two-year college instructors

A Study of Factors that Affect Instructors\u27 Intention to Use E-Learning Systems in Two-Year College

Instructors\u27 use of e-learning systems in higher education institutions is a central concern of researchers, academicians, and practitioners. Higher education institutions are investing substantial resources to incorporate and maintain the infrastructure of e-learning systems; however, instructors\u27 use of e-learning systems in two-year colleges is relatively limited. In this context, the goal of this study was to investigate the factors that may affect instructors\u27 intention to use e-learning systems in two-year colleges. Based on literature review on technology acceptance, this study proposed a theoretical model predicting instructors\u27 intention to use e-learning systems in two-year colleges based on their resistance to change, perceived value of e-learning systems, computer self-efficacy (CSE), and attitude toward e-learning systems. Consequently, this study investigated the effect of four independent variables on the dependent variable, intention to use e-learning systems. A Web-based survey was designed to empirically assess the effect of aforementioned constructs on instructors\u27 intention to use e-learning systems in two-year colleges. The Web-based survey was developed as a multi-item measure using Likert-type scales. Existing validated scales were used to develop the Web-based survey. The target population of this study was instructors of public and private two-year colleges. The sample for this study was 119 (over 41% response rate) full-time, part-time, and adjunct instructors in different academic departments at a two-year college. Two statistical methods were used to formulate and test predictive models: Multiple Linear Regression (MLR) and Ordinal Logistic Regression (OLR). Both MLR and OLR results showed that the theoretical model was able to predict instructors\u27 intention to use e-learning systems. All four independent variables have significant effects on the dependent variable. Results of both analyses were consistent on resistance to change as having the greatest weight on predicting instructors\u27 intention to use e-learning systems, while CSE in both analyses was found to have the least weight. This study contributes to the body of knowledge by providing empirical results for the key constructs that affect two-year college instructors\u27 intention to use e-learning systems. Results of this research may also help IT practitioners to concentrate their efforts on ways to address resistance to change as it was found to be the most significant factor affecting e-learning accepted by two-year college instructors