Factors influencing the formation of a user's perceptions and use of a DSS software innovation
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".