Impact of Marketing Strategy on the Competitiveness of Tourism and Hotel Businesses
Bibliographic record
Abstract
The relevance of the study is conditioned by the need to analyse modern marketing theory and its provisions and systematise current methods of marketing promotion of travel companies.In this regard, the purpose of the study is to develop an algorithm for creating a marketing strategy for a tourist and hotel business enterprise, describe the process of identifying competitors, disclose parameters for assessing the competitiveness of tourist and hotel business enterprises, systematise strategies for increasing demand during periods of low demand for tourist services, list elements of a modern marketing strategy for the development of tourist and hotel business enterprises, present the main characteristics of brand positioning in the market.The main approach in the study is the method of system analysis, which was used to consider a complex system of relationships between marketing strategy and the effectiveness of the tourism business.In addition, the following methods of scientific knowledge were used: synthesis, deduction, classification, data grouping, comparison, generalisation, and analysis of information sources.The study presents the results of the analysis, developed an algorithm for creating a marketing strategy for a tourist and hotel business enterprise, described the process of identifying competitors, identified the parameters for assessing the competitiveness of tourist and hotel business enterprises, systematised strategies for increasing demand during periods of low demand for tourist services, listed elements of a modern marketing strategy for the development of tourist and hotel business enterprises, presents the main characteristics of brand positioning in the market.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".