A Competitive Intelligence Model Where Strategic Planning is Not Usual: Surety Sector in Mexico
Bibliographic record
Abstract
Nowadays, the importance of the strategy for an enterprise becomes evident by verifying the changes that characterize its environment. Changes in legislation and regulation models and a greater market fragmentation are clear examples of the threats that lead the change. At the same time, the opportunities that the environment offers through the reduction of entrance barriers and a strong possibility of investment extension have increased. In order to be able to survive in an increasingly competitive environment, organizations must adapt their products to the market. For this to happen, it is necessary that the organization develops a retrieval, analysis and information interpretation process with strategic value about the industry and the competitors in it, which is transmitted to those in charge of the organization at the right time. The objective of this study was to develop a competitive intelligence model in an environment where strategic planning is not common and structural conditions are adverse. The research took place in the surety bond industry in Mexico, and the model obtained allows the surety companies with little strategic planning to know and identify their specific information requirements in order to lead competitiveness in a better way and the quality of their products and services at the same time. The outcome of this study demonstrates that competitive intelligence must suit the enterprise’s activity thus overcoming the barriers offered to this practice by the environment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".