Work Practices Mediated by Motivation Enhancing Productivity and Performance of Airports Post-Privatization – An Empirical Evidence
Bibliographic record
Abstract
Purpose: Airport privatisation is rapidly gaining ground, leading to a significant increase in research interest. Amid rapid airport privatisation, Indian airports offer a unique lens to study the impact of work practices on productivity and performance mediated by motivation. Theoretical Framework: The study draws upon relevant theories including high-performance work systems (HPWS) and motivation theories impacting productivity and performance. Method: This study investigates the detailed thematic analysis and self-administered surveys (Likert scale) collected from 50 professionals in 9 major Public-Private Partnership (PPP or 3Ps) airports in India on various aspects of work practices which includes work design, digitisation, and flexibility, with motivation mediating productivity and performance including effectiveness, efficiency, and quality outcomes. Their reliability and validity were analysed using Cronbach's alpha, Pearson correlation, and Mediating analysis using Process 4.2. Purposive sampling is employed in this study. Result: The study finds a positive impact of work practices on employee productivity and performance through motivation. Importantly, it reveals motivation as a key mediator, offering valuable insights for aviation professionals. The analysis confirms model accuracy by representing strong prediction and regression value alignment.
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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 | 0.007 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".