How the Readiness to Change and Intention to Remain in Employees? Evidence on the Sustainability of Hospitality Employees in Indonesia
Bibliographic record
Abstract
The presence of the COVID-19 pandemic has brought about an unusual global change.The impact of COVID-19 is not only limited to the health and social sectors, but also disrupts the global economy, including the hotel industry.This study aims to determine the effect of readiness to change, technology adoption and organizational culture that affect the intention to stay on hotel employees in Indonesia.This study uses a quantitative explanatory approach, namely research based on a theory or hypothesis that will be used to test a phenomenon that occurs.The population in this study were employees working in the hotel sector in Indonesia.A total of 207 employees were used as respondents in this study based on predetermined criteria.Data collection was carried out by online survey with 5 Likert scales.The results of the study indicate that readiness to change and organizational culture have a positive effect on the intention to stay, but the application of technology has a positive but not significant effect.While organizational culture mediates the effect of readiness to change into the intention to stay, the use of technology cannot mediate the relationship both.This means that the presence or absence of technology implementation does not affect an employee to stay in the company.These findings provide practical implications for hotel managers in making managerial decisions.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".