A theoretical framework to improve the quality of manually acquired data

© 2018 Elsevier B.V. We present a framework for organisations to prevent errors in data entry. It states that data entry errors can be prevented by a strong intention of data producers to enter data correctly and by a high task-technology fit. Two empirical studies support the framework and demonstrate that a high task-technology fit is relatively more important than the data producers’ intention. The framework refines the theory of planned behaviour, and extends the explanatory domain of the task-technology fit construct. The empirical evidence underlines the importance of the task-technology fit construct, an often-neglected construct in information systems research.

Publication year:2019

BOF-keylabel:yes

IOF-keylabel:yes

BOF-publication weight:10

CSS-citation score:1

Authors from:Higher Education

Accessibility:Open