From Placebo to Panacea: Studying the Diffusion of IT Management Techniques with Ambiguous Efficiencies: The Case of Capability Maturity Model
Bibliographic record
Abstract
In light of the inherent shortcomings of single-perspective approaches in IT diffusion research, in this paper, we develop a multi-perspective framework for studying the diffusion of IT management techniques. The framework is then applied to explain the diffusion of capability maturity model (CMM). This research contributes to information systems theory by (a) illustrating how several different theoretical perspectives (i.e., forced-selection, efficient choice, fashion, and fad) can be used to explain an IT management innovation diffusion; (b) identifying the specific limitations of each perspective; and (c) demonstrating how these perspectives can be reconciled and yield a holistic understanding of the diffusion trajectory. Building on 20+ years of CMM research, the propositions of this paper shed more light on the underlying dynamics driving the adoption decision among software vendors, and will inform IS scholars and practitioners about the types of actions that can foster the dissemination of emerging IT management techniques.
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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 | 0.018 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.008 | 0.023 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".