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What Role is "Business Intelligence" Playing in Developing Countries? A Picture of Brazilian Companies

2009· book-chapter· en· W309491588 on OpenAlexaff
Maira Petrini, Marlei Pozzebon

Bibliographic record

VenueIGI Global eBooks · 2009
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicBig Data and Business Intelligence
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsBusinessDeveloping countryBusiness intelligenceValue (mathematics)Point (geometry)Knowledge managementInformation technologyInvestment (military)Industrial organizationMarketingProcess managementEconomicsComputer scienceEconomic growthPolitical science

Abstract

fetched live from OpenAlex

Constant technological innovation and increasing competitiveness make the management of information a considerable challenge, requiring decision-making processes built on reliable and timely information from internal and external sources. Although available information increases, this does not mean that people automatically derive value from it. After years of significant investment to establish a technological platform that supports all business processes and strengthens the operational structure’s efficiency, most organizations are supposed to have reached a point where the implementation of information technology (IT) solutions for strategic purposes becomes possible and necessary. This explains the emergence of “business intelligence” (BI); a response to information needs for decision-making through intensive IT use. This chapter looks at BI projects in developing countries – specifically, in Brazil. If the management of IT is a challenge for companies in developed countries, what can be said about organizations struggling in unstable contexts such as those often prevailing in developing countries?

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.942
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.039
GPT teacher head0.274
Teacher spread0.235 · 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.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations26
Published2009
Admission routes1
Has abstractyes

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