THE STRATEGIC ROLE OF INFORMATION TECHNOLOGY SOURCING: A DYNAMIC CAPABILITIES PERSPECTIVE
Bibliographic record
Abstract
Grounded in the theory of dynamic capabilities, our study offers a conceptualization of IS strategy that comprises two sets of dynamic capabilities: enterprise IT architecture dynamic capability and IT sourcing dynamic capability. We borrow from extant IS literature and define enterprise IT architecture dynamic capability as the capacity of an organization to purposefully extend, create or modify its IT competencies for tight alignment with the firm’s business strategy; and we offer the concept of IT sourcing dynamic capability that we define as the capacity of an organization to purposefully extend, create or modify its IT resource base to support the creation or modification of IT competencies for tight alignment with the firm’s business strategy. We theorize on how these two sets of capabilities combine to form the firm IS strategy, which either helps a firm respond to rapid changes in the environment or bring about changes in the business strategy, which may in turn provoke changes in the environment and thus provide a competitive advantage. Our theorizing will be informed by a case study of two business units facing rapid environmental change.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| 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".