Outsourcing contracts as instruments of risk management
Bibliographic record
Abstract
Purpose This paper aims to examine contracts in public jurisdictions to compare academic theories related to outsourcing risks and risk management strategies to current practice in order to extend and refine theory concerning what risk management strategies can, or should, be included in outsourcing contracts. Design/methodology/approach An automated content analysis tool is used to rigorously compare contract documents in two public jurisdictions to a comprehensive outsourcing risk framework from previous research. Findings The findings indicate that although IS outsourcing risk factors are widely acknowledged in the literature, they are not fully specified in the outsourcing contracts that are implemented in some public organizations. This research surfaces some of the differences in the techniques implemented through actual contracts to manage the risks inherent in IS outsourcing, including some strategies not previously identified in the literature. Also, not all risks need to be addressed in the contract to have a successful outsourcing engagement. Practical implications The improved framework for thinking about risk management strategies in the contracting process shown within the paper can provide important ideas and insights for managers contemplating or renewing outsourcing engagements. Originality/value This paper uses content analysis to rigorously compare academic theory to actual practice to extend theory. Specifically, it discovers several risk management strategies that have not been presented in previous research.
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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 | 0.024 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".