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Record W4400439991 · doi:10.5465/amproc.2024.318bp

The Dual Labor Effects of Corporate Philanthropy

2024· article· en· W4400439991 on OpenAlexaff
Luis Ballesteros, Vontrese Deeds Pamphile

Bibliographic record

VenueAcademy of Management Proceedings · 2024
Typearticle
Languageen
FieldComputer Science
TopicEconomic Growth and Development
Canadian institutionsQuest University Canada
Fundersnot available
KeywordsDual (grammatical number)BusinessLabour economicsEconomicsPhilosophy

Abstract

fetched live from OpenAlex

This study explores the proposition that the specific social causes targeted by corporate philanthropy influence its effects on labor performance. We introduce a categorization based on social psychology, distinguishing between welfare-shocks philanthropy, aimed at providing welfare restitution for victims of disruptions, and chronic-conditions philanthropy, focused on welfare improvement for those facing longstanding problems. Our experimental evidence demonstrates that exposure to welfare-shocks philanthropy significantly enhances workers’ production, accuracy, and efficiency. In contrast, labor performance tends to decrease under chronic-conditions philanthropy and remains unchanged in the absence of philanthropy. These results broadly generalize in matched difference-in-difference estimates spanning 12 years of philanthropic activity by U.S. corporations. Our findings suggest that not all philanthropic efforts are equally motivating for employees and underscore the strategic importance of the philanthropic focus in influencing labor performance, both positively and negatively. Consequently, this study helps reconcile previous varying results regarding the strategic value of corporate philanthropy, offering a nuanced understanding of how this non-financial incentive can shape workforce dynamics and guide the design of more effective philanthropic strategies.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.670
Threshold uncertainty score0.323

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 studies0.0000.000
Scholarly communication0.0000.000
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 head0.013
GPT teacher head0.229
Teacher spread0.215 · 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 designTheoretical or conceptual
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

Citations0
Published2024
Admission routes1
Has abstractyes

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