Outsourcing contracts as instruments of risk management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it