دانلود مقاله ISI انگلیسی شماره 62021
ترجمه فارسی عنوان مقاله

بررسی طولی مسیرهای تغییر خودکارآمدی کامپیوتر در طول تمرین

عنوان انگلیسی
A longitudinal examination of computer self-efficacy change trajectories during training
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
62021 2013 9 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Computers in Human Behavior, Volume 29, Issue 4, July 2013, Pages 1816–1824

ترجمه کلمات کلیدی
خودکارآمدی عمومی کامپیوتر، خودکارآمدی کامپیوتری ویژه نرم افزار، اضطراب، روشهای طولی، مدل سازی دست نخورده
کلمات کلیدی انگلیسی
General computer self-efficacy; Software specific computer self-efficacy; Anxiety; Longitudinal methods; Latent growth modeling

چکیده انگلیسی

Computer self-efficacy (CSE) is known to enhance individual competence and performance in learning and using technology as well as improve technology attitudes and beliefs. Using longitudinal studies, CSE increases over time during organizational technology training. While these studies have been instrumental in our understanding of how self-efficacy operates in organizations, some critical questions remain unanswered. In particular these studies cannot answer how long it takes for one’s CSE to increase during training, nor can it describe the shape of the change trajectory (linear? some other shape?). The answers to these questions will provide organizations a much clearer perspective on training expectations, understanding when benefits from training through enhanced CSE might be expected, and when to start and ramp up/down training efforts. This study examines these issues by collecting repeated waves of data from 230 respondents in a technology lab training setting and using a relatively new structural equation modeling technique, latent growth modeling. Findings suggest that it takes about 2 months of training for individuals to display significant increases in CSE, and that the growth trajectory for CSE in non-linear. In the proposed model, anxiety is a significant predictor of CSE change, while CSE change significantly predicted software-specific self-efficacies.