Perennializing Information Technology Infrastructures: A Dynamic Capabilities Perspective
Bibliographic record
Abstract
In an era of heightened uncertainty and urgency, robust and flexible information technology infrastructures (ITI) – arrangements of shared IT services and technical components that power and support an organization’s strategy and processes – are vital to organizations. ITI play key strategic roles, are at the core of business operations and directly affect performance. However, managing the evolution and sustaining transformations of ITI can be very challenging. To cope with this sustainability challenge, organizations must develop specific dynamic capabilities to sustain ITI and their evolution under turbulent and changing business contexts. Still, the question for managers is: What actions should be deployed to sustain ITI and their transformations? Twenty key organizational actions that were identified by twenty-nine ITI experts, were grouped into three interrelated vectors: (1) Watching and developing knowledge and know-how to sustain ITI; (2) Visioning and governing ITI; (3) Standardizing and adopting a flexible approach to ITI.
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.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.003 | 0.023 |
| Scholarly communication | 0.018 | 0.029 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 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".