Contextualising hotel career behaviour: testing a sociocultural and organizational career model
Bibliographic record
Abstract
This study applies Social Cognitive Career Theory (SCCT) to develop and empirically test a contextual model of hotel career outcomes in Ghana, focusing on how social and organizational factors shape employees' career attitudes and intentions. Drawing on survey data from 769 frontline hotel employees, Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed to assess both the measurement and structural models. The results reveal that subjective norms significantly and positively influence career attitudes. Transformational leadership not only strengthens employees' career attitudes and intentions but also moderates the relationship between subjective norms and career attitudes, amplifying their positive effect. Additionally, favourable career attitudes strongly predict employees' career intentions, underscoring the importance of cultivating positive perceptions to enhance employee commitment. The findings offer practical implications for the hospitality and tourism (H&T) industry, particularly across collectivist (e.g., African and Asian countries) and individualist (e.g., U.S., Australia, Canada, and Europe) contexts. Theoretically, the study extends SCCT by integrating contextual influences within the hotel industry and highlighting transformational leadership's moderating role. It also provides a foundation for future research to explore other contextual factors such as career development programs, organizational commitment, and psychological contracts in shaping career outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".