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Record W4395660224 · doi:10.24857/rgsa.v18n9-015

Work Practices Mediated By Motivation Enhancing Productivity and Performance of Airports Post-Privatization – An Empirical Evidence

2024· article· en· W4395660224 on OpenAlexaff
Sawmya Shanmuganathan, L.R.K. Krishnan

Bibliographic record

VenueRevista de Gestão Social e Ambiental · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicAviation Industry Analysis and Trends
Canadian institutionsAlberta Oil Sands Technology and Research Authority
Fundersnot available
KeywordsProductivityWork (physics)Empirical evidenceBusinessIndustrial organizationEmpirical researchEconomicsEconomic growthEngineering

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.494

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.066
GPT teacher head0.300
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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