Multi-level Approach to the “Social Marketing” Context for Innovation: Impact on Organizational Relationships
Bibliographic record
Abstract
The aim of the article is to explore the social aspects of innovation at three levels of research: individual, group, and organization. A multi-level approach enhances the understanding of how organizational context shapes and is shaped by the actions and perceptions of individuals. It may provide more precise research findings and more rigorous theory testing by clarifying the level of analysis. A resource-based approach and adaptation theories are used in relation to the organizational level, while at the individual level, psychological theories are applied. We propose a theoretical approach which could link creativity and competencies at the individual level, managerial / leader action and organizational culture with innovation to marketing innovation as a process and outcome of organizational level. The earlier studies used the approach that focused attention on the innovation process and innovation outcomes rather than on developing the ability to take specific innovative action and focused research on the selected level of innovation process management. It is therefore necessary to take into account the complexity of the research subject and include the actual problems resulting from the needs of multi-level innovation management and respect for the diversity of its conditions in the research.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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.
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".