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Record W1520496057 · doi:10.4102/sajbm.v33i3.703

Competitive intelligence practices: A South African study

2002· article· en· W1520496057 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

VenueSouth African Journal of Business Management · 2002
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCompetitive and Knowledge Intelligence
Canadian institutionsUniversity of Ottawa
FundersNational Research FoundationUniversity of Ottawa
KeywordsCompetitive intelligenceOrder (exchange)Software deploymentCompetitive advantageControl (management)BusinessMarketingEconomic growthEconomicsManagementFinanceEngineering

Abstract

fetched live from OpenAlex

Competitive Intelligence (CI) as a business discipline and as a business practice is still in its infancy in South Africa. Only a few higher education courses in CI exist in South Africa and only a few studies on CI practices in South African firms have been done. The question that arises is: What is the level of development and deployment of CI in South Africa? From this study it is clear that most of the responding firms believe that CI can be used to create a competitive advantage and that CI is a legitimate and necessary activity for increasing their firms’ intelligence. It is, however, also clear that South African firms are not well equipped yet to conduct good intelligence practices, especially in the areas of process and structure, analysis and awareness. Recommendations are made in order to increase the firms’ CI awareness in order to improve South African firms’ competitiveness.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.797
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.057
GPT teacher head0.260
Teacher spread0.203 · 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