Receiving Social Support Motivates Long-Term Prosocial Behavior
Bibliographic record
Abstract
Abstract Prosocial behavior—actions aimed to benefit other individuals, groups, or communities—are important for promoting and maintaining a healthy society. Extant research on the factors driving prosocial behavior has mainly addressed short-term effects, overlooking the factors that motivate long-term prosocial behavior. Building on attachment theory, we theorize that an interpersonal factor, receiving social support, can foster prosocial behavior in the long-term, both in the environment where the support was received and beyond it. We argue that receiving social support positively predicts felt security—a sense of being safe, cared for, and loved—which in turn associates with higher motivation to engage in behaviors that benefit others. We test our hypotheses with cross-sectional, longitudinal, retrospective, and experimental data. In Study 1, data from a sample of international business school alumni validate past research and show a significant positive relationship between receiving social support and engaging in prosocial behavior both within and beyond the environment in which support was received. Study 2 leverages data of US adults in a multi-wave study to show that receiving social support predicts prosocial activities several years later. Study 3 uses a retrospective survey to show that receiving social support relates positively to long-term prosocial behavior through higher felt security. Study 4 experimentally manipulates social support and further demonstrates that receiving social support fosters prosocial behavior through boosting felt security. Overall, our findings show that receiving social support motivates long-term prosociality through its positive association with felt security.
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".