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Record W3215372195 · doi:10.33423/jop.v21i1.4029

Engaging Employees Through Corporate Social Responsibility Programs: Aligning Corporate Social Responsibility and Employee Engagement

2021· article· en· W3215372195 on OpenAlexaboutno aff

Bibliographic record

VenueJournal of Organizational Psychology · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate social responsibilityPublic relationsNoticeStewardship (theology)Work (physics)BusinessSocial responsibilityEmployee engagementPlan (archaeology)Environmental stewardshipPolitical scienceEngineeringPolitics

Abstract

fetched live from OpenAlex

The message is clear: people want to work for organizations where they feel they are engaged and learning. Recently, the organization Benevity in Canada stated that "Today’s employees are expecting a greater sense of purpose in the workplace. In fact, 83% of Millennials say they would be more loyal to their employer when they feel they can make a difference on social and environmental issues at work." Companies that engage in social and environmental stewardship also benefit from employees who are more aware and involved. Once the Corporate Social Responsibility (CSR) strategy and programs have been defined, how do you ensure your communications strategy, plan and actions are aligned and will help deliver the desired results to get employees interested and engaged? An online survey with 100 respondents in Canada demonstrated a clear shift from years ago where now current applicants research a company’s CSR information when applying to work there. And once working inside the organization, employees notice CSR initiatives and want to get involved.

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)0.007
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-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 score0.221
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
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 head0.117
GPT teacher head0.342
Teacher spread0.225 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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