Fostering, Promoting, and Encouraging Philanthropy: Mechanisms to Attract Younger Generations of Donors and Volunteers
Bibliographic record
Abstract
ABSTRACT For fundraising campaigns to attract support from all generations, it is important to understand how younger generations prefer to give and which strategies speak to them. This editorial discusses insights from nine papers dedicated to a special issue on understanding and attracting younger generations to increase the pool of donors and volunteers. Our discussion focuses on their giving preferences, influencing factors, engagement strategies, and expectations. This focus provides valuable insights for organizations aiming to engage these generations in philanthropy more effectively. We conclude with five propositions, three pillars, and five suggestions for future research. Our propositions highlight the differences and similarities among generations, the need to continue modernizing fundraising approaches, the role of engagement, and the expectations of younger generations. We summarise the strategies and motivational factors via three pillars: (1) foster a sense of belonging, (2) promote personal growth, and (3) encourage active participation in philanthropic activities. These pillars highlight the significance of traditional values such as altruism and recognition while emphasizing the growing importance of personal development and fostering a fun and social environment. Our suggestions for future research include a call for (1) longitudinal studies, (2) comparative analyses, (3) increased attention to Generation Alpha, (4) exploration of the role of digital technologies, and (5) assessing the current implementation of the suggestions by non‐profit organizations.
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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 | 0.024 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".