Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.im.2018.05.014 | Information & Management |
Keywords | Field | DocType |
Data quality,Data entry,Manually acquired data,Information quality | Information systems research,Empirical evidence,Knowledge management,Data entry,Theory of planned behavior,Engineering,Empirical research | Journal |
Volume | Issue | ISSN |
56 | 1 | 0378-7206 |
Citations | PageRank | References |
0 | 0.34 | 16 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tom Haegemans | 1 | 7 | 1.45 |
| Monique Snoeck | 2 | 440 | 66.62 |
| Wilfried Lemahieu | 3 | 171 | 23.09 |