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Record W2808456988 · doi:10.5430/ijba.v9n4p15

Information Technology Maturity Evaluation in a Large Brazilian Cosmetics Industry

2018· article· en· W2808456988 on OpenAlexvenueno aff
Cintia Nailor Pedrini, Guilherme F. Frederico

Bibliographic record

VenueInternational Journal of Business Administration · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBig Data and Business Intelligence
Canadian institutionsnot available
Fundersnot available
KeywordsMaturity (psychological)Exploratory researchCapability Maturity ModelCosmeticsKnowledge managementInformation technologyBusinessComputer scienceThematic analysisProcess managementMarketingQualitative researchSociology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.468
Threshold uncertainty score0.644

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.005
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.043
GPT teacher head0.338
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2018
Admission routes1
Has abstractyes

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