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Record W4392975176 · doi:10.1016/j.procs.2024.01.095

Implementation of a Business Intelligence System in the Brazilian Nuclear Industry: An Action Research

2024· article· en· W4392975176 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueProcedia Computer Science · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBig Data and Business Intelligence
Canadian institutionsÉcole de Technologie Supérieure
FundersMitacs
KeywordsComputer scienceAction (physics)Business intelligenceAction researchData scienceKnowledge managementEngineering managementProcess managementManagementBusiness

Abstract

fetched live from OpenAlex

The literature on information systems emphasizes the positive impact of information from business intelligence systems (BIS) on decision-making, especially in highly regulated environments. Assessing BIS effectiveness is vital to understanding its value and significance in improving operational performance and management. However, deploying BIS and understanding how BIS dimensions are interrelated and how they affect the decision-making process in organizations in the nuclear field still need to be explored. In order to address this research gap, this article investigates the process of implementing BIS in a Brazilian company from the nuclear industry using an action research methodology. Results suggest that the use of BIS in decision-making routines allowed company managers to expand their perception of previously neglected information, significantly helping in decision-making and prioritizing actions and/or solutions.

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.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.793
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.008
Science and technology studies0.0000.000
Scholarly communication0.0010.005
Open science0.0010.000
Research integrity0.0000.000
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 head0.181
GPT teacher head0.414
Teacher spread0.233 · 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