| Literature DB >> 35321034 |
Benjamin S Freeling1, Matthew J Dry2, Sean D Connell1.
Abstract
Everyone has an opportunity to contribute to climate solutions. To help people engage with this opportunity, it is critical to understand how climate organizations and fundraisers can best communicate with people and win their financial support. In particular, fundraisers often rely on practical skills and anecdotal beliefs at the expense of scientific knowledge. Fundraisers could be motivated to achieve a substantial boost in funding for climate solutions, if there is evidence of the financial gains that science-based fundraising makes available. In this Perspective, we provide a preliminary foray into such evidence. We bring together findings from philanthropic research and climate psychology to identify what factors can help captivate donors. Then, through an experimental study of a charitable appeal for a climate charity, we show how putting these factors into practice may contribute toward an increase in donated money. This provides optimism that evidence-based fundraising can inspire donors to contribute much-needed resources toward climate solutions.Entities:
Keywords: communication; conservation; effective altruism; non-profit; philanthropy
Year: 2022 PMID: 35321034 PMCID: PMC8936950 DOI: 10.3389/fpsyg.2022.768823
Source DB: PubMed Journal: Front Psychol ISSN: 1664-1078
The eight components used to craft messages of different impact levels.
| Message characteristic | Meaning | Level in high-impact message | Level in med-impact message | Level in low-impact message |
| Impact | Does the message state the concrete impact of donating? | A concrete measure of the averted carbon dioxide emissions per dollar donated. | A statement that emissions are averted, but with no concrete measure. | A statement that emissions are averted, but with no concrete measure. |
| Motives | Does the message invoke altruistic or self-interested motives? | A statement that a donation will help preserve the environment. | A statement that a donation will help preserve the environment. | A statement that a donation will give a feeling of satisfaction. |
| Endorsement | Is the charity endorsed by an authority figure? | An endorsement by a policy researcher from a well-known university. | An endorsement by a policy researcher from a well-known university. | No endorsement by an authority figure. |
| Co-benefits | Does the message mention positive side effects of donating? | A statement that donations also increase employment in developing countries. | No mention of positive side effects of donating. | No mention of positive side effects of donating. |
| Frame | Is the message framed in terms of climate or a different issue? | Framed in terms of climate change. | Framed in terms of climate change. | Framed in terms of air pollution and its impact on human health. |
| Proximity | Does the message focus on consequences of the issue that are nearby in space and time? | Emphasis of consequences in the same country and year of the study. | Emphasis of consequences in a different continent and future century. | Emphasis of consequences in a different continent and future century. |
| Social norms | Does the message mention how the reader’s peers feel about the issue? | A statement that university students are concerned about the issue. | No mention of university students. | No mention of university students. |
| Growing risk | Does the message emphasize that the issue is increasing in severity? | A statement that the risk is growing more urgent each year. | No mention of the growing risk. | No mention of the growing risk. |
Linear regression model for the effects of message impact and personal characteristics on money donated.
| Donation | ||||
| Predictors | Estimates | std. Error |
|
|
| Intercept | 14.83 | 5.03 | 2.95 |
|
| Impact | −5.46 | 2.37 | −2.30 |
|
| PC1 | −3.67 | 4.42 | −0.83 | 0.409 |
| PC2 | −9.28 | 6.10 | −1.52 | 0.133 |
| PC3 | −8.68 | 6.14 | −1.41 | 0.162 |
| PC1*Message | 2.74 | 2.46 | 1.12 | 0.268 |
| PC2*Message | 6.75 | 3.14 | 2.15 |
|
| PC3*Message | 4.48 | 2.75 | 1.63 | 0.108 |
| Observations df | 70 62 | |||
| R2/R2 adjusted | 0.157/0.062 | |||
| AIC | 381.426 | |||
*p < 0.05.