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Record W2065971986 · doi:10.1177/0170840613515564

The Extensiveness of Corporate Social and Environmental Commitment across Firms over Time

2014· article· en· W2065971986 on OpenAlexaff
Pratima Bansal, Jijun Gao, Israr Qureshi

Bibliographic record

VenueOrganization Studies · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsUniversity of ManitobaWestern University
FundersHong Kong Polytechnic University
KeywordsRubricBusinessCorporate social responsibilityScale (ratio)MarketingPublic relationsAccountingSociologyPolitical science

Abstract

fetched live from OpenAlex

Corporate social commitment (CSC) and corporate environmental commitment (CEC) are often combined under the general rubric of corporate social responsibility. Although the two sets of activities are similar, they are also very different. Both CSC and CEC respond to issues raised by stakeholders, but CEC tends to be more “technical”. This characteristic demands that CEC fit with the organization, which exposes greater economic opportunities than CSC. As a result, we argue that the extent to which these practices are implemented differs across firms over time. We analyze the extensiveness of implementation of CSC and CEC across 266 firms from 1991 to 2003, using latent growth curve modeling and one-way ANOVA. We find that firms moved towards at least a moderate level of CSC over time, but tended to bifurcate in the extent to which they implemented CEC practices, towards either the high or low end of the scale, over time. In this paper, we contribute to the institutional analysis of practice diffusion by examining how the characteristics of different kinds of practices shape the extensiveness of firm adoption patterns. As well, this research also speaks to corporate social responsibility researchers, pointing to the need to sometimes discriminate between social and environmental practices.

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)0.000
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 score0.008
Threshold uncertainty score0.570

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
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 head0.017
GPT teacher head0.234
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.

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

Citations121
Published2014
Admission routes1
Has abstractyes

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