Information Technology Maturity Evaluation in a Large Brazilian Cosmetics Industry
Bibliographic record
Abstract
Maturity evaluation has been one of the most important research topics for many of knowledge management areas. Thus, the purpose of this paper is to present a maturity framework for Information Technology- IT area showing the dimensions to be managed in terms of stages of development for technology management. The literature review was based on the analysis of the technological transformations within the organizations, assessing the importance of the technology and the way in which it is transforming companies, as well as, the maturity for IT determining the potential framework that will serve as object of the research deployment. Following a qualitative and exploratory approach, the research method considered was a case study carried out in a large Brazilian cosmetics industry. A semi-structured questionnaire was applied in samples of professionals from the IT area. The data worked were primary, with a temporal cross-section and the data were evaluated by way of content analysis. The result of the research provided the basis to evaluate the maturity of the IT of this cosmetics industry, with the following objectives: analyze the company’s current level of development from the dimensions of people, processes, technology and management. This study brings out important contribution once few articles considering the thematic of IT maturity were found in the literature. The main theoretical contribution is based on the opportunity of deploying a methodology as a reference whereas the practical contribution is linked to the framework presented which can be a reference for practitioners on IT maturity evaluation in other 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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".