Destination competitiveness in Russia: tourism professionals’ skills and competences
Bibliographic record
Abstract
Abstract Purpose The purpose of this study is to address one of the main issues in Russia's efforts to enhance tourism competitiveness: to educate a qualified workforce at the university level. Design/methodology/approach A survey of tourism professionals was conducted to assess importance and performance toward a set of hospitality- and tourism management-related skills and competences. An importance-performance analysis was performed to identify relative strengths and weaknesses. Findings Russian professionals need improved competences with respect to sustainable management, marketing and research skills. Research limitations/implications The study is limited to surveying professionals in the western part of Russia (St Petersburg, Moscow, Krasnodar and Sochi). Nevertheless, its implications for curriculum reform and development should be considered in the whole country. Practical implications The study identifies specific areas for Russian universities to address and focus on in their curriculum reform and development efforts. Social implications Better education at universities enhances students' employability at the time that supports tourism firms to perform better. Both together help to boost tourism destination competitiveness and sustainability, favoring progress and socio-economic development. Originality/value Few studies have addressed human resource development in Russia. This study investigates the need for developing skills and competences in hospitality and tourism in Russia. This country has a significant potential for tourism development. Other countries with a developing tourism sector should benefit from the results of this study.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".