Looking In, Looking Out: CSR Image Incongruence and Employees’ Conflicting Reactions
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".