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Record W3122707573

Revisiting the Effect of Family Involvement on Corporate Social Responsibility: A Behavioral Agency Perspective

2016· article· en· W3122707573 on OpenAlexaff
Victor Cui, Shujun Ding, Mingzhi Liu, Zhenyu Wu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFamily Business Performance and Succession
Canadian institutionsUniversity of OttawaUniversity of ManitobaUniversity of British Columbia
Fundersnot available
KeywordsIncentivePerspective (graphical)Corporate social responsibilityAgency (philosophy)BusinessPrincipal–agent problemPublic relationsCorporate governanceMicroeconomicsEconomicsFinancePolitical science
DOInot available

Abstract

fetched live from OpenAlex

This paper sheds light on the incongruent findings concerning the relationship between family involvement and firms’ corporate social responsibility (CSR). While prior studies have mainly taken the perspective of families’ socio-emotional wealth preservation, we approach this relationship from the perspective of behavioral agency theory, highlighting the important role played by CEOs’ family memberships. Specifically, we posit that family firms are more likely to invest in CSR when their CEOs are members of the controlling families. Furthermore, we examine how family firms can employ long-term incentives to encourage non-family CEOs to act in the interests of the controlling families to preserve SEW and thus enhancing family firms’ CSR performance. We tested our hypotheses using hand-collected data of family firms included in the S&P 500 index, in the period of 2003 to 2010. The empirical findings support our hypotheses that (a) family firms with family members as the CEOs have better CSR performance and (b) family firms tend to provide a high level of long-term incentives to non-family than family CEOs. In addition, long-term incentives strongly motivate CEOs to improve firms’ CSR performance, regardless of their family memberships.

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)0.001
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 score0.403
Threshold uncertainty score0.369

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.055
GPT teacher head0.294
Teacher spread0.240 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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