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Record W4312754398 · doi:10.52567/pjsr.v4i03.751

IMPACT OF CORPORATE SOCIAL RESPONSIBILITY ON FINANCIAL PERFORMANCE: MEDIATING ROLE OF QUALITY OF WORK LIFE

2022· article· en· W4312754398 on OpenAlexaff
Muhammad Ali, Rabia Ishfaq, Wasif Ali

Bibliographic record

VenuePakistan Journal of Social Research · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicBusiness and Economic Development
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsNexus (standard)Corporate social responsibilityStructural equation modelingBusinessAgency (philosophy)Work (physics)Quality (philosophy)AccountingPrivate sectorFinancial sectorPublic relationsFinancePolitical scienceEconomicsSociologyEconomic growth

Abstract

fetched live from OpenAlex

This study examines the influence of perceived corporate social responsibility (CSR) on corporate financial performance (CFP). Based on win-win paradigm, this study uncovers the mediating role of Quality of Work Life (QWL) on CSR-CFP nexus. A questionnaire based cross-sectional survey was conducted to accumulate data from 355 employees working in public and private sector banks of Pakistan. Structural Equation Modeling (SEM) was used to examine the hypotheses. The results depict that CSR is positively related with CFP, and employee QWL partially mediates this relationship. According to our noesis, the review of previous literature regarding the association of CSR and CFP reveals that QWL has not been empirically tested as a mediator between these two variables. This study findings proposes that banks engaged in CSR activities are actually establishing a healthy work environment for their employees which ultimately helps the banks to improve their financial performance. This study highlights the importance of CSR activities for enhancing the CFP of the banking sector in the developing countries. Keywords: Corporate Social Responsibility, Quality of Work Life, Agency Theory, Financial Performance.

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)0.012
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.110
GPT teacher head0.399
Teacher spread0.289 · 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.

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

Citations0
Published2022
Admission routes1
Has abstractyes

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