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Record W2798020240 · doi:10.1111/emre.12180

Does charitable giving substitute or complement firm differentiation strategy? Evidence from Chinese private SMEs

2018· article· en· W2798020240 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEuropean Management Review · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsUniversité de MontréalHEC Montréal
FundersEconomic Research InstituteNational Natural Science Foundation of ChinaNational Science Foundation
KeywordsProduct differentiationComplement (music)ChinaBusinessService (business)MarketingEconomicsMicroeconomics

Abstract

fetched live from OpenAlex

Using R&D and advertising intensity to measure a firm's differentiation strategy, this study examines whether charitable giving substitutes or complements such a differentiation strategy. Evidence from a nationwide survey of private small‐ and medium‐sized enterprises (SMEs) across China shows that corporate charitable giving generally complements rather than substitutes differentiation strategy. In particular, the combined spending in R&D and advertising increases corporate charitable giving. In addition, the positive relationship between differentiation strategy and charitable giving is more prominent for firms located in service sector and in less developed markets. This study contributes important insights to our understanding of the relationship between corporate charitable giving and differentiation strategy.

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.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.475
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.002

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 head0.065
GPT teacher head0.307
Teacher spread0.242 · 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