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Record W1995230133 · doi:10.1080/10495141003601229

Corporate Support for Employee Volunteerism Within Canada: A Cross-Cultural Perspective

2010· article· en· W1995230133 on OpenAlexaffabout
Mary Runté, Debra Z. Basil, Robert Runté

Bibliographic record

VenueJournal of Nonprofit & Public Sector Marketing · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsPerspective (graphical)Divergence (linguistics)Corporate social responsibilityPerceptionBusinessConvergence (economics)Public relationsOrganizational cultureMarketingPolitical scienceEconomic growthPsychologyEconomics

Abstract

fetched live from OpenAlex

Company support for employee volunteerism (CSEV) is one mechanism whereby businesses meet the escalating expectation for corporate social responsibility (CSR). Institutional theory is applied to examine patterns of convergence and divergence in CSR programs cross culturally, with a particular focus on intra-country cultural differences. Using a national (Canada) survey of businesses, we examine cross-cultural differences regarding CSEV in two regions of Canada—French Canada (Quebec) and English Canada. Our results suggest that cultural differences, rooted in historical conditions, may shape CSEV program implementation in Canada. Quebec companies are less likely to engage in CSEV. If they do encourage employee volunteerism, they may exclude certain cause types from support and appear to focus more on the external benefits of CSEV, such as community perception, than do firms in English Canada. Recognizing that no nation is culturally homogenous, our study illustrates that CSR and CSEV may vary significantly whenever or wherever cultural differences occur. Businesses and nonprofit organizations need to consider culture as an important variable when implementing corporate volunteer programs.

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)0.009
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.444
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.001
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 head0.047
GPT teacher head0.331
Teacher spread0.284 · 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

Citations9
Published2010
Admission routes2
Has abstractyes

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