IT outsourcing success: A dynamic capability-based model
Bibliographic record
Abstract
This study proposes and tests a model of information technology outsourcing (ITO) capabilities as antecedents of ITO success. Building on the dynamic capabilities perspective (DCP), the model posits that ITO sensing, ITO seizing, and ITO orchestrating capabilities will influence ITO success by way of both successful reconfiguration of IT solutions and successful delivery of IT services. Building on extant ITO research, the model also hypothesizes that contract management capabilities and relationship management capabilities will influence ITO success via the successful delivery of IT services. Data from a cross-sectional survey of 152 large U.S.-based organizations in various industries were analyzed with PLS. The results support the hypothesis that successful reconfiguration mediates the effect of dynamic capabilities on ITO success. They partially support the hypothesis of successful delivery as mediator of the effect of dynamic capabilities on ITO success. The hypothesis of successful delivery as a mediator of the effect of relationship management capabilities and contract management capabilities on ITO success is supported only for relationship management capabilities. The study offers a theoretical anchoring for the conceptualization of ITO capabilities, which complements the rich and context-specific case-based literature of ITO capabilities and extends current research by adding to existing explanations of how ITO success is achieved.
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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 | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".