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Looking In, Looking Out: CSR Image Incongruence and Employees’ Conflicting Reactions

2025· article· en· W4416006583 on OpenAlexaff
Zishuo Ye, Ruodan Shao, Zhen Zhang, Shenjiang Mo

Bibliographic record

VenueAcademy of Management Proceedings · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsYork University
Fundersnot available
KeywordsCorporate social responsibilityAmbivalenceModerationPerceptionLoyaltyIdentity (music)Organizational behaviorSocial identity theory

Abstract

fetched live from OpenAlex

Prior research on corporate social responsibility (CSR) suggests that employees’ views of organizational CSR contribute to their favorable evaluations of their organizations, which in turn lead to their positive attitudes and behaviors at work. Although internal and external stakeholders often have differing expectations of an organization—leading to potentially divergent evaluations of the organization’s CSR—little research has examined how the incongruence between employees’ own CSR evaluations and their perceptions of outsiders’ CSR evaluations may influence employees’ reactions toward their organizations. By integrating the organizational image literature with intergroup emotion theory, we develop a model that explains when, why, and for whom CSR image incongruence may lead to positive and negative outcomes (i.e., employees’ emotional ambivalence about their organizations, and their subsequent reactions in terms of loyalty boosterism and distancing). Moreover, we identify employees’ moral identity internalization as a moderator that can mitigate the effects of CSR image incongruence on employee responses. Our model is supported by a scenario-based experiment and a time-lagged survey study using polynomial regression and response surface analyses. Theoretical and practical implications of our findings are discussed.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.232
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.001
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 head0.020
GPT teacher head0.300
Teacher spread0.280 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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