Knowledge management is no more a gray area in the tourism industry in India – a study on transformation leadership and executive communication
Bibliographic record
Abstract
Purpose Considering the important role of knowledge management in the global growth of world economies, the importance of knowledge management in the tourism industry can hardly be overestimated. Knowledge management, empowering leadership and innovation can open new research prospects for online travel and tourism, thereby increasing organizational capacity. In this context, this study aims to investigate the relationship between knowledge management practices and transformational leadership, along with the mediating role in this relationship of leadership communication. Knowledge management practices are discussed through the lens of managers’/organizational perspectives working in the online travel and tourism industry. Design/methodology/approach Using a descriptive research method, the data were collected from a total of 174 managers/senior executives working in the online travel and tourism sectors. The proposed conceptual framework was evaluated using structural equation modeling through AMOS 21 and Process Macro (SPSS-Plugin). Findings The results revealed a significant increase in the adoption of knowledge management practices in the online travel and tourism sector under transformative leadership. The results also highlighted that effective communication is a vital contributor and significant complementary mediator in the relationship between transformative leadership and knowledge management practices. Practical implications The results of this study suggest that transformational leaders, who are equipped with the “high touch” nature of leadership and the “high tech” aspect of the contemporary workplace, are the best fit to manage online travel and tourism organizations. To have an adequate knowledge management system, knowledge managers should adopt a transformative leadership style and receive comprehensive training in developing leadership abilities such as effective communication competencies. Originality/value To the best of the authors’ knowledge, this study is the first to demonstrate that, in emerging online travel and tourism sectors, organizational knowledge can be managed through effective leadership communication.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".