Mediation of social capital in the effect of collaborative leadership on the performance of tourism companies
Bibliographic record
Abstract
The COVID-19 phenomenon led to an increase in the digitalization of the tourism sector, reducing the demand for services and affecting business performance. There is no doubt that leadership plays a fundamental role in the management of organizations. Therefore, it is necessary to delve deeper into the study of the collaborative style to build social capital and measure the impact it can generate on the performance of tourism companies. The influence of collaborative leadership, in its dimensions of resources and work environment, as well as the mobilization of interest groups, on financial and non-financial performance was analyzed, in addition to the mediation of social capital in this relationship. It was carried out under a quantitative approach, not experimenting in the design, taking the data only once, the sample was made up of 782 representatives of Peruvian tourism companies, using self-administered questionnaires and SEM for the analysis. The results indicate that resources and the work environment positively impact financial performance, but not social capital. Furthermore, stakeholder management influences both non-financial performance and social capital. Likewise, it is confirmed that social capital positively affects both dimensions of organizational performance. A partial mediation of social capital was found, as stakeholder management was associated with non-financial performance, with no mediation in the relationship between resources and work environment on financial performance. These findings highlight the need to strengthen collaborative leadership to improve the performance levels of companies that provide tourism services.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".