Bibliographic record
Abstract
Purpose This paper aims to illustrate the value of the outsourcing life cycle, as described in several industry models, including ISO 37500. Design/methodology/approach The authors present a comparison of outsourcing life cycles to provide an overview of current practices in the global outsourcing industry. Findings Several outsourcing life cycles have been defined by industry associations such as the International Association of Outsourcing Professionals (IAOP) and the National Outsourcing Association (NOA). Academic research has created several outsourcing life cycles, notably the model from the London School of Economics (Cullen and Willcocks, 2005). Finally, commercial models have been defined, for example the Vendor and Sourcing Management model from IDC (2014). Research limitations/implications Researchers will find the overview of different life cycles useful in assessing maturity of outsourcing organizations. Practical implications Practitioners will find the detailed description of ISO 37500 and the comparative life cycles to be illustrative of different approaches to managing outsourcing transactions. Both buyers and providers will be able to compare their own life cycle to industry standards. Originality/value Little or no research has been conducted on how outsourcing life cycles contribute to effective outsourcing. This paper provides a foundation for such research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".