Investigate Competitive Intelligence Process: An Exploratory Study in Tunisian Companies
Bibliographic record
Abstract
The purpose of this study is to explore the aspects of management of competitive intelligence process in Tunisian companies. This exploratory study was conducted using semi-in-depth interviews with six executives in six companies in Tunisia. The results revealed by this study that the competitive intelligence as a competitive tool is unknown to the vast majority of Tunisian companies. It is not still a formalized stage, but it is in an embryonic way. Second, two most important of information are collected: profiles of potentials customers, and opportunities in new market. Thirdly, for analysing and synthesising information the results indicate that managers knows and uses the majority of methods to analyze competitive information gathered especially in making decisions that fall the marketing department. Fourthly, all companies spend most of their time in collecting information. Although planning what and how to collect information and analysis they receive relatively less attention. This research suggests that overriding influence on successful competitive intelligence process is the existence of a management support, culture and structure, which encourage and develop competitive intelligence activities in companies.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".