Global Multisourcing Strategy: Integrating Learning from Manufacturing into IT Service Outsourcing
Bibliographic record
Abstract
“Multisourcing” is emerging as an important strategy in today’s global information technology (IT) service outsourcing arena. The existing literature on IT services provides only limited understanding of this phenomenon. However, in the manufacturing domain, a similar concept, supply base management, has been extensively examined, especially in the operations management (OM) literature. In this study, we integrate related research in service and manufacturing to investigate how to successfully pursue IT service multisourcing. Specifically, we first conceptualize a firm’s multisourcing supply base along the dimensions of breadth and depth, and theorize about the impact of these two dimensions on outsourcing outcome. Second, based on this theoretical framework, we analyze four configurations of multisourcing supply base. Finally, we use case studies of two global financial services firms to illustrate the evolution of these configurations and identify patterns of multisourcing strategy in IT services.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".