Global Multisourcing Strategy: The Emergence of a Supplier Portfolio in Services Offshoring
Bibliographic record
Abstract
ABSTRACT In today's global services outsourcing arena, increasing numbers of companies adopt “multisourcing,” that is, they select and combine information technology (IT) and business services from multiple providers. The literature on IT outsourcing and supply chain management has identified critical tradeoffs involved in increasing the number of suppliers and has strongly recommended focusing on a handful of strategic partners to balance these tradeoffs. Committing to a few strategic partners, however, may prevent a firm from discovering new suppliers, or even supply regions. Such missed opportunities may be particularly limiting in the context of offshoring professional services, which has exhibited rapid changes in supplier markets in the last decade. Thus, firms may want to engage in a more intensive multisourcing in services. If they do so, their success will depend on a global sourcing process that effectively addresses the critical tradeoffs involved. To explore how a global sourcing process can support multisourcing, we conducted a qualitative longitudinal case study of a large financial services institution that developed a varied global supply base to obtain offshore professional services. Our analysis results in a theory that emphasizes (i) advantages of a multiple provider strategy in rapidly changing global supply markets; (ii) the critical role of middle managers in enabling continuous innovation in the supplier structure; and (iii) the importance of the global sourcing process combining top–down and bottom–up decision making in multisourcing.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".