Marketing Plans for Technological Innovation Centers: 05 Success Cases
Bibliographic record
Abstract
Marketing theory since developed by Prof. Jerome McCArthy (1960) fixing the 4Ps mode and later systemized by Prof. Philip Kotler (1962), has been studied and applied almost by default in private companies and particularly in mass consumer products. The functionality and application of marketing theory to technological innovation processes have several publications, articles and contributions based on specific experiences and field cases in different parts of the world, but we realize that we are still far from a structured body of knowledge and universally applicable methodologies.In Perú, the first Technological Innovation Centers (CITEs in Spanish) was created in 1988, focused in supporting leather and footwear sub-sector; now in 2025, there exists 45 CITEs, installed all over the country serving variety of production chains, including agroindustry, fishing and aquaculture, leather and footwear, forestry timber, camelid textiles, and others.Part of the strategy for creating CITEs is their articulation with specific production chains to improve its competitiviness, which is why most of them are CITEs that promote hard technologies, industrial processing or transformation technologies.However, the very nature of their condition and their actions means that these organisations have a strong bias towards the product rather than the market, towards supply rather than demand, which opens up an important space for marketing, with the necessary adaptations to reflect the complexity of technological innovation, to contribute.The biggest challenge of this proposal is the adaptation of the theoretical frameworks of marketing whose universal reference are brands such as Coca-Cola, McDonald's, Xerox, Burger King, Starbucks (B2C approaches) to the market of technological innovation processes (B2B).
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".