Technology Adoption of Medical Faculty in Teaching: Differentiating Factors in Adopter Categories
Department of Biostatistics & Medical Informatics, Akdeniz University, Medical School, 07059 Antalya, Turkey, Tel: +90 (242) 227 43 43, firstname.lastname@example.org
Department of Computer Education & Instructional Technology, Middle East Technical University Faculty of Education, 06531 Ankara, Turkey, Tel: +90 (312) 210 4057, Fax: +90 (312) 210 1112, email@example.com
Department of Biostatistics & Medical Informatics, Akdeniz University, Medical School, 07059 Antalya, Turkey, Tel: +90 (242) 227 43 43, firstname.lastname@example.org
ABSTRACT: Despite large investments by higher education institutions in technology for faculty and student use, instructional technology is not being integrated into instruction in higher education institutions including medical education institutions. While the diffusion of instructional technologies has reached a saturation point among early adopters of technology, it has remained limited among the mainstream faculty. This investigation explores instructional technology usage patterns and the characteristics of medical school faculty as well as contributing factors to IT adoption. The focus of the study was to explore the differences between faculty members who have adopted new technology and those reluctant or resistant to IT adoption, and to determine whether faculty characteristics contribute to the prediction of faculty adopter categories. Faculties from the disciplines of basic and clinical science at a state university Faculty of Medicine were surveyed to gather data concerning faculty characteristics, adoption patterns, perceptions of computer-use self efficacy, the value of IT, barriers and incentives to adoption and preferences related to help and support in technology adoption. The data analysis was based on Rogersí theories of diffusion and adopter categories. Significant differences were found between early adopters and the mainstream faculty in terms of individual characteristics, adoption patterns, perceptions of barriers and technology learning preferences The results indicated that computer use self efficacy and rank significantly contribute to the prediction of faculty adopter group.
Keywords: Technology adoption, Diffusion of innovation, Adopter categories, Medical faculty technology use