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Record W2770422057 · doi:10.1002/bse.2023

Institutional Antecedents of the Corporate Social Responsibility Narrative in the Developing World Context: Implications for Sustainable Development

2017· article· en· W2770422057 on OpenAlexaff
Frans Melissen, Andrew Ngawenja Mzembe, Uwafiokun Idemudia, Yvonne Novakovic

Bibliographic record

VenueBusiness Strategy and the Environment · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsYork University
Fundersnot available
KeywordsCorporate social responsibilityCorporate governanceTourismSustainable developmentContext (archaeology)Developing countryPolitical scienceNarrativeSociologyColonialismGlobal governancePublic relationsEnvironmental ethicsEconomic growthEconomicsManagement

Abstract

fetched live from OpenAlex

Abstract Efforts to understand the background to perceptions and manifestation of corporate social responsibility (CSR) in the developing world need to focus on establishing their link with the challenges of socio‐economic governance and societal expectations and cultural traditions. This signifies a departure from a western centric understanding of CSR but also an over‐focus on CSR as philanthropy. This study considers the Malawian tourism industry and finds that its colonial legacy, post‐colonialism development thinking and the national education system explain the prevalence of a ‘CSR as philanthropy’ agenda. When these factors interact with challenges of socio‐economic governance and societal expectations, however, the universality thesis that has often been associated with the theory and implementation CSR can be challenged. These findings therefore suggest a shift from the western centric CSR thinking to a CSR perspective that is strongly grounded in local values and norms and which meets the expectations of the global society. This indicates a way forward if CSR is to be adequately institutionalized in the developing world. Copyright © 2017 John Wiley & Sons, Ltd and ERP Environment

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.787
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.063
GPT teacher head0.280
Teacher spread0.217 · 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

Citations51
Published2017
Admission routes1
Has abstractyes

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