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Record W3208531747 · doi:10.3390/jrfm14110515

The Impact of Corporate Social Responsibility as a Marketing Investment on Firms’ Performance: A Risk-Oriented Approach

2021· article· en· W3208531747 on OpenAlexvenueno aff
Mohamed Ibrahim, Mohamed M. El Frargy, Khaled Hussainey

Bibliographic record

VenueJournal of risk and financial management · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate social responsibilityBusinessProfit (economics)FrontierMarketingCompetitive advantageInvestment (military)Industrial organizationAccountingEconomicsPublic relationsMicroeconomics

Abstract

fetched live from OpenAlex

In light of the growing interest in corporate social responsibility (CSR), there is still controversy regarding its impact on firms’ performance. In this paper, we examine the impact of CSR initiatives, as a marketing investment, on firms’ performance. We treat CSR initiatives as investment and, consequently, the returns appear over the long term. We use the stochastic frontier analysis (SFA) approach which is a forward-looking financial market-based metric that captures the firm’s long-term performance. We focus on the banking industry as it confronts a variety compound of risk. We find that CSR implementation is positively reflected in profit efficiency, regardless of the strategic commitment to implementing CSR and bank size, as these variables do not influence the CSR–performance relationship. However, we find that bank age and competitive positioning have a significant impact on the CSR–performance relationship. Our study provides valuable insights to CSR practitioners and researchers, especially in the banking sector. We provide empirical evidence on the importance of CSR and its positive impact on bank performance in Egypt as one of the emerging markets.

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)0.008
metaresearch head score (Gemma)0.005
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 score0.465
Threshold uncertainty score0.718

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.019
GPT teacher head0.250
Teacher spread0.232 · 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

Citations7
Published2021
Admission routes1
Has abstractyes

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