Factors influencing the formation of a user's perceptions and use of a DSS software innovation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Understanding how users form perceptions of a software innovation would help software designers, implementers and users in their evaluation, selection, implementation and on-going use of software. However, with the exception of some recent work, there is little research examining how a user forms his or her perceptions of an innovation over time. To address this research need, we report on the experiences of a health planner using a DSS software tool for health planning over a 12-month period. Using diffusion theory as outlined by Rogers, we interpret the user's perceptions of the software following Rogers' perceived characteristics of the innovation. Furthermore, we show how our user justifies her attitudes toward the technology using 5 important factors during 3-, 6- and 12-month interviews: stage of adoption, implementation processes, organizational factors, subjective norms, and user competence. Results are compared with key IS research in these areas, and the implications of these findings on the diffusion of decision support systems are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.014 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it