A Competitive Intelligence Model Where Strategic Planning is Not Usual: Surety Sector in Mexico
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.
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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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 it