Can Consumers’ Altruistic Inferences Solve the CSR Initiative Puzzle? A Meta-analytic Investigation
Bibliographic record
Abstract
Abstract Research into consumer responses to corporate social responsibility (CSR) initiatives has expanded in the past four decades, yet the evidence thus far provided does not paint a cohesive picture. Results suggest both positive and negative consumer reactions to CSR, and unless such mixed findings can be reconciled, the outcome might be an amalgamation of disparate empirical results rather than a coherent body of knowledge. The current meta-analysis therefore tests whether the mixed findings might reflect consumers’ distinct, altruistic inferences across various contingency factors. On the basis of 337 effect sizes, involving 584,990 unique respondents, in 162 studies published between 1996 and 2021, this study reveals that altruistic inferences are central to the current CSR paradigm, such that they mediate the effects of CSR initiatives on consumer responses across multiple contingencies. The mediation by altruistic inferences is stronger (weaker) in conditions favorable to dispositional (situational) motive attributions. Furthermore, consumers respond more favorably to cause marketing or philanthropy rather than business-related CSR initiatives, when the initiative is environmental (vs. social), the firm’s offering is utilitarian (vs. hedonic), the CSR initiative takes place in self-expressive (vs. survival) cultures and in earlier (vs. later) periods. These findings offer several ethical implications, and they inform both practical recommendations and an agenda for further research directions.
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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 | 0.109 | 0.264 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.016 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".