Effects of the Decision-Making Process on the Competitive Advantage in Costs Obtained After Implementing Outsourcing
Bibliographic record
Abstract
This article aims to answer how the decision process model used to adjudicate on the implementation of outsourcing in the company affected the competitive advantage in costs obtained by the organization after the implementation of subcontracting. Therefore, the financial results obtained by the company in the period from 2015 to 2019 were analysed in order to know if there were competitive advantages in costs arising from the decision to implement outsourcing. Studies were also carried out on the structure of the decision-making process adopted by the entity to identify the level of efficiency of this model in the implementation of outsourcing, and finally, the impacts generated on these advantages due to the degree of efficiency of the tool application were verified. The research strategy was qualitative and quantitative, with an exploratory bias, bibliographic survey and structured interview. A mathematical model was used based on 14 (fourteen) decision-making efficiency indicators, 4 (four) efficiency level classification indicators and 5 (five) classification criteria for performance. The results showed that there is sufficient evidence to conclude that the low level of efficiency in the decision-making structure contributed to the high financial impact on Company Alpha and, consequently, to the negative impact on competitiveness acquired through the implementation of outsourcing in this entity, which caused financial disadvantages represented by losses in operating income and reduction in profitability.
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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 | 0.031 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".