Modern Marketing Strategies for Development of the Company's Green Competitiveness
Bibliographic record
Abstract
The globalization of economic development, the expansion of digitalization processes, the transformation of marketing channels for the promotion of goods and services, as well as the peculiarities of enterprises is gaining new relevance. From this perspective, digital omnichannel strategies can potentially drive the growth of traditional economic performance and ensure the competitiveness of businesses. The article considers the theoretical basis for the implementation of omnichannel strategies for the development of the green competitiveness of enterprises. The authors summarized the existing scientific and applied experience in the implementation of omnichannel strategies. With the help of the Google Trends toolkit, a trend analysis was conducted, which outlined the scientific interest and interest of the business community. The results of trend analysis showed a gradual increase in interest in the search for and implementation of optimal ways of communication in marketing for the development of green competitiveness. In terms of interest, the leading countries are Germany, France, Canada and the United States. In addition to the existing ones, the following criteria are proposed, which reflect the degree of unification of marketing communication channels for the formation of green competitiveness of enterprises: differentiation of communication channels; absence of stakeholders losses when changing the communication channel; taking into account the experience of stakeholders in each iteration of communication with him; convergence of traditional and digital channels of development of green competitiveness of enterprises; use of brand identity in communication with stakeholders; personalization of stakeholders. The obtained results of the analysis and their graphical interpretation are relevant and form the basis for a better understanding of the problems of the omnichannel approach and search for promising ways to implement it in the formation of green competitiveness of enterprises.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".