Can corporate social responsibility deter consumer dysfunctional behavior?
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine how and when a reputation for corporate social responsibility (CSR) can deter dysfunctional consumer behaviors (DCBs) such as shoplifting or negative word-of-mouth (WOM) in response to firm failures. The authors predict that congruency of the CSR activities and the basis for the firm failure (e.g. environmental protection, environmental harm) provides protection for firms while incongruency (e.g. environmental protection, social harm) does not. The authors base this prediction on the process of retroactive attribution and sense-making. Design/methodology/approach Across two studies the research finds support that a reputation for CSR can deter consumer dysfunctional behavior. Study 1 uses an experimental design with a Mturk sample, and a behavioral outcome using an overpayment situation, to examine when consumers will act honestly and recognize overpayment. Study 2 uses secondary data, across three novel data sources (Google trends data, an existing data set of consumer perceptions of CSR and Factiva to uncover press coverage of negative firm events). Study 2 examines how CSR reputation impacts consumers’ participation in negative WOM in response to firm failures. Findings Study 1 finds support for CSR congruency as a protection mechanism against dysfunctional behavior in response to negative events. The authors find that dysfunctional behaviors in conditions of congruency, while incongruent and a control condition do not provide such protections. Study 2 supports these findings using Google trends data in the form of online negative WOM. The authors find that when firms are known for their social performance, negative events in the social domain result in significantly lower levels of negative WOM. Originality/value The current paper makes the novel prediction that consumers will use a current negative event (corporate social irresponsibility) to re-evaluate previous CSR. Thus, in contrast with prior research, the authors argue that a negative event is not affected by previous CSR but that previous CSR is affected by a negative event. Furthermore, the authors posit that the congruency between the transgression and previous CSR moderates consumer perceptions, such that incongruent CSR and transgression contexts lead to increased DCBs through consumers’ retroactive sense-making process.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".