Ratio Between Outsourcing Management Efficiency and Financial Results: Case Study in Brazilian Companies
Bibliographic record
Abstract
The objective of this article was to investigate the impact of the efficiency level in the management of outsourcing on corporate financial results. For this, two companies in the industrial sector that adopted the outsourcing method in their operations were selected, considering the period from 2015 to 2019. The research is a descriptive exploratory case study with a quali-quantitative method. For the data collection, an instrument was used consisting of 8 (eight) management process efficiency indicators (iTEPG), 4 (four) analysis criteria and 5 (five) evaluation standards, intended for the formation of the Efficiency Rate. To define the Efficiency Rate, a mathematical model built from the literature studied was used. In order to reach the research objective, analyses were carried out in the financial and management reports, based on the Business Process Outsourcing (BPO) method. The results showed that the negative impacts on these company's financial results are directly related to the low level of efficiency in the BPO management process, and it can be said with reasonable certainty that poor outsourcing management contributes decisively to negatively impact in the financial results of organizations.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".