Understanding information systems success: a hybrid view
Bibliographic record
Abstract
The information systems (IS) success model, introduced in 1992, provided IS research with a comprehensive set of dependent variables for project success. While the model addresses both process and variance considerations, the latter has dominated the research. Concurrently, the benefits of hybrid theories have been discussed in the literature, and there have been calls for an integrated view of IS success that includes the process perspective. We build on this momentum by presenting a hybrid model based on a longitudinal case study of the development and implementation of a patient-flow decision-support system at a large not-for-profit hospital. Our model remains true to the DeLone and McLean framing but elaborates on the process elements. The hybrid model expands our ability to analyse multiple dimensions of IS success and integrates diverse research findings into the IS success model, providing a revised version for future research to extend.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.018 | 0.010 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.023 | 0.040 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".