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Record W2047652610 · doi:10.1177/1548051811429572

Antecedents, Work-Related Consequences, and Buffers of Job Burnout Among Indian Software Developers

2011· article· en· W2047652610 on OpenAlexaff
Pankaj Singh, Damodar Suar, Michael P. Leiter

Bibliographic record

VenueJournal of Leadership & Organizational Studies · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsAcadia University
Fundersnot available
KeywordsBurnoutPsychologyJob attitudeSocial psychologyJob performanceEmotional exhaustionContext (archaeology)Job designJob satisfactionApplied psychologyInterpersonal communicationClinical psychology

Abstract

fetched live from OpenAlex

This study examines the antecedents, consequences, and buffers of job burnout among software developers using job demands resources theory. Data were collected from 372 software developers in India using questionnaires. Results reveal that software developers experiencing more role ambiguity, role conflict, schedule pressure, irregular shifts, group noncooperation, psychological contract violation, and work–family conflict are at a greater risk of job burnout. The most important antecedent of job burnout was found to be work–family conflict. Job burnout increased job performance but decreased organizational commitment and interpersonal relationships. Subjective well-being and practicing yoga and meditation were inversely related to burnout-linked job performance. Subjective well-being, social support, and practicing yoga and meditation were also found to decrease the adverse association of job burnout with organizational commitment and interpersonal relationships. In the context of work-related consequences, job burnout had the biggest adverse association with organizational commitment, and practicing yoga and meditation was found to be the most influential buffer to counter the adverse consequences of job burnout.

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)0.000
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 score0.013
Threshold uncertainty score0.650

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.073
GPT teacher head0.248
Teacher spread0.175 · 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

Citations118
Published2011
Admission routes1
Has abstractyes

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