The influence of hybrid working through readiness for change on sustainable performance management in the service sector in ASEAN
Bibliographic record
Abstract
The COVID-19 pandemic has accelerated the shift to hybrid working, especially in ASEAN's service sector. This transition requires high readiness for change to maintain performance. Hybrid working offers flexibility and improved work-life balance but also presents challenges like miscommunication and inequality. Successful implementation depends on organizational adaptability, employee readiness, and technological support. This study aims to analyze the influence of hybrid working through readiness for change on sustainable performance management in the service sector in ASEAN. This study uses a quantitative path analysis approach A sample of 385 respondents was analyzed via SmartPLS, with a focus on understanding these dynamics in the ASEAN service sector. Hybrid working models significantly impact sustainable performance management in ASEAN's service sector by enhancing flexibility, operational resilience, and employee satisfaction. These models allow organizations to adapt to changing conditions and improve job satisfaction, which fosters a proactive approach to change. This, in turn, supports sustainable performance by creating adaptable and resilient structures. Empirical evidence shows that hybrid working leads to higher engagement and better performance metrics, underscoring its effectiveness in managing performance sustainably in a dynamic business environment. Hybrid working models enhance readiness for change by boosting organizational agility, employee engagement, psychological safety, and continuous learning. These factors collectively prepare employees and organizations to adapt effectively to new strategies and technologies. In the ASEAN service sector, hybrid working is linked to higher readiness for change, supporting sustainable performance management and long-term resilience. Readiness for change is crucial for sustainable performance management in ASEAN’s service sector. It drives continuous improvement, fosters a proactive culture, and enhances adaptability to external pressures. High readiness for change correlates with better performance, as organizations effectively manage and leverage change for long-term success.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".