| Literature DB >> 29370291 |
Diogo Veríssimo1, Hamish A Campbell2, Simon Tollington1,3, Douglas C MacMillan1, Robert J Smith1.
Abstract
Non-governmental organisations (NGOs) play a key role in biodiversity conservation. The majority of these organisations rely on public donations to fund their activities, and therefore fundraising success is a determinant of conservation outcomes. In spite of this integral relationship, the key principals for fundraising success in conservation are still guided by expert opinion and anecdotal evidence, with very few quantitative studies in the literature. Here we assessed the behaviour of monetary donors across twenty-five different species-focused conservation campaigns organised by an NGO conservation and environmental society. The Australian Geographic Society (AGS) carried out fundraising campaigns over a five and half year period using an identical methodology in thirty-four of its country-wide network of outlet shops. AGS owns and operates these shops that sell toys and games related to science and nature. We tested how the following factors influenced monetary donations from members of the public:1) campaign duration, 2) appeal and familiarity of species, 3) species geographic distribution relative to the fundraising location, 4) level of income and education of potential donors, 5) age and gender profile of potential donors. Contrary to past research, we found most of these factors did not significantly influence the amount of donations made to each campaign by members of the public. Larger animals did elicit a significantly higher amount donated per transaction than smaller animals, as did shops located in poorer neighbourhoods. Our study findings contrast with past research that has focused largely on hypothetical donations data collected via surveys, and demonstrates the complexity and case-specific nature of relationships between donor characteristics and spending patterns. The study highlights the value of assessing real-world fundraising campaigns, and illustrates how collaboration between academia and NGOs could be used to better tailor fundraising campaigns to maximise donations from individual citizens.Entities:
Mesh:
Year: 2018 PMID: 29370291 PMCID: PMC5785011 DOI: 10.1371/journal.pone.0191888
Source DB: PubMed Journal: PLoS One ISSN: 1932-6203 Impact factor: 3.240
Fig 1Location of the 34 shops of the Australian Geographic Society where flagship campaigns were run between January 2008 and June 2013.
Larger circles indicate higher number of shops. Generated using QGIS 2.14.3 and open access data from http://biogeo.ucdavis.edu.
Variables used in analysis to understand drivers of donations to fundraising campaigns run by the Australian Geographic Society.
| Name | Definition |
|---|---|
| Revenue | Total revenue of a campaign, corrected for inflation using the inflation calculator of the Australian Bureau of Statistics. Continuous variable |
| Customers | Total number of transactions in a shop during a fundraising campaign. Continuous variable |
| Shop | Unique identifier for each shop. Categorical variable |
| Campaign | Unique identifier for each conservation campaign. Categorical variable |
| Time | Order in which the fundraising campaigns took place. Continuous variable |
| Duration | Number of months a fundraising campaign lasted. Continuous variable |
| Income | Index of Economic Resources score of residents in the area where a shop was located (Statistical Area 2 level of the Australian Statistical Geography Standard). This is a measure of access to economic resources. Continuous variable |
| Education | Index of Education and Occupation score of residents in the area where a shop was located (Statistical Area 2 level of the Australian Statistical Geography Standard). This is a measure of the education and occupation status of the residents. Continuous variable |
| Age | Median age of residents living in the area where the shop was located (at the Statistical Area 2 level of the Australian Statistical Geography Standard). Continuous variable |
| Gender | Proportion of female residents in the area where the shop was located (at the Statistical Area 2 level of the Australian Statistical Geography Standard). Continuous variable |
| Flagship type | Topic of the fundraising campaign, divided into the following categories: mammal, bird, amphibian, reptile, fish, biological group, ecosystem or a project flagship (e.g. seed bank). Categorical variable |
| Body mass | Average individual mass of the species targeted by fundraising campaigns [ |
| Familiar | If a given fundraising campaigns targeted a familiar species (as defined by more than 50% of respondents recognizing the species). Binary variable |
| Appeal | If a campaign focused on an appealing species (as defined as an average ranking of 5 or less, out of 10). Binary variable as not all campaigns targeted species |
| Location | If the flagship type of a fundraising campaign existed in the Australian state where a shop was located. Binary variable |
Fig 2Mean number of customers (± SE) per shop by month.
Fig 3Mean revenue (± SE) raised per customer for the different conservation fundraising campaigns.
Model-averaged estimates for generalised linear mixed models coefficients (β), standard errors (SE) and confidence intervals for conservation flagship related drivers of donations to the AGS fundraising campaigns.
Only candidate models at ΔAIC < 4 candidate set were considered. Model averaged coefficients are ranked for relative importance using weighted absolute t statistics.
| Variable | β | SE | Lower 95% CI | Upper 95% CI | Absolute t-values |
|---|---|---|---|---|---|
| Intercept | 0.29 | 0.02 | 0.26 | 0.32 | |
| Time | 0.11 | 0.02 | 0.06 | 0.16 | 4.45 |
| Body mass | 0.05 | 0.02 | 0.01 | 0.10 | 0.57 |
Model-averaged estimates for generalised linear mixed models coefficients (β), standard errors (SE) and confidence intervals for socio-economic and demographic drivers of donations to the AGS fundraising campaigns.
Only candidate models in the ΔAIC < 4 candidate set were considered. Model averaged coefficients are ranked for relative importance using weighted absolute t statistics.
| Variable | β | SE | Lower 95% CI | Upper 95% CI | Absolute t-values |
|---|---|---|---|---|---|
| Intercept | 0.29 | 0.02 | 0.26 | 0.32 | |
| Time | 0.11 | 0.02 | 0.06 | 0.16 | 4.34 |
| Income | -0.05 | 0.02 | -0.09 | -0.02 | 1.59 |