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Record W4414400179 · doi:10.1016/j.jbusres.2025.115705

Build or buy corporate social responsibility? Socially responsible brand acquisitions and firm CSR perceptions

2025· article· en· W4414400179 on OpenAlexafffund
Argiro Kliamenakis, Bianca Grohmann, H. Onur Bodur

Bibliographic record

VenueJournal of Business Research · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsConcordia UniversityWilfrid Laurier UniversityUniversity of Ottawa
FundersConcordia University
KeywordsCorporate social responsibilityCredibilityValue (mathematics)Corporate brandingPerceptionSocial responsibilityBrand awarenessBrand management

Abstract

fetched live from OpenAlex

• Acquisition (versus development) of socially responsible brands impacts firm CSR perceptions. • Brand development (vs. acquisition) is more effective in increasing firm CSR perceptions. • When a socially responsible brands’ symbolic value is high, acquisition harms brand credibility. • Acquisitions of highly symbolic socially responsible brands thus decrease firm CSR perceptions. • Brand architecture and communication strategies can mitigate this negative effect. Because development of socially responsible brands is costly and risky, acquisition of socially responsible brands (SRB) has emerged as a frequently employed approach to enhancing firms’ CSR profile. This research explores the effectiveness of SRB acquisition (versus brand development) in increasing firm CSR perceptions and the moderating role of symbolic brand value in this relationship. This research demonstrates that SRB acquisition (vs. development) is generally less effective in signaling firm CSR and that the effect of SRB acquisitions on firm CSR perceptions depends on the acquired brand’s symbolic value. Seven experiments show that when symbolic brand value is high, acquisition (vs. development) of SRB harms the acquired brand’s credibility and subsequently reduces firm CSR perceptions. Two additional studies explore the role of brand architecture and communication strategies in mitigating the negative impact of highly symbolic, SRB acquisitions on consumers’ perceptions of firm CSR.

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)0.011
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.514
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.007
Science and technology studies0.0020.001
Scholarly communication0.0010.002
Open science0.0010.001
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 head0.113
GPT teacher head0.386
Teacher spread0.273 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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