Bibliographic record
Abstract
While researchers have used a variety of models to explain information system (IS) implementation outcomes, few have analyzed the same project or set of projects with different models looking for complementary explanations. Recognizing the multilevel nature of IS implementation, our study rises to this challenge by conducting an alternate template analysis of three cases of IS implementation in hospitals. First, we explain individual use, group resistance, and organizational adoption with models situated at the same level of analysis as each outcome. At the individual level, we use a model of cognitive absorption to explain individual system usage. At the group level, the political variant of interaction theory is used to explain group resistance to IS implementation. At the organizational level, we use organizational configurations to explain IS adoption in terms of emergence and routinization. We identify each model’s limits and prediction failures, and we show that using alternate models helps to remedy a model’s prediction failures and overcome its limits. Finally, we propose an alternate-template theory of IS implementation outcomes that takes into account all three levels of analysis, their respective outcomes, and the time dimension. This multilevel, longitudinal theory provides a better understanding of IS implementation and further elucidates what may initially have seemed to be contradictory results.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".