Information technology outsourcing and architecture dynamic capabilities as enablers of organizational agility
Bibliographic record
Abstract
Grounded in the dynamic capabilities perspective, our study addresses the question of how information technology outsourcing capabilities can interact with other IT strategic capabilities to enable organizational agility through the ongoing reconfiguration of IT solutions. To answer our question, we built on the notion of microfoundations that undergird the high-level dynamic capabilities of sensing, seizing, and reconfiguring. Adopting a theory elaboration approach, we studied the case of a firm evolving in a turbulent environment, which had outsourced the quasi-totality of its IT services and had a mature IT architecture. From the case data, we specify two types of microfoundations: repeatability-related microfoundations (i.e. processes) and ability-related microfoundations (i.e. IT department structure, skills, simple rules, and communications) that undergird either information technology outsourcing dynamic capabilities or IT architecture dynamic capabilities. We propose a model that outlines how the interaction between repeatability-related microfoundations, supported by ability-related microfoundations, enables the reconfiguration of IT solutions. Our study also elucidates how a firm can follow a logic of opportunity enabled by their IT outsourcing and IT architecture dynamic capabilities.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".