How Board and CEO Characteristics Can Affect Italian and Canadian Nonprofit Financial Performance
Bibliographic record
Abstract
Abstract Purpose The literature considers three main models of nonprofit sector structure and development: liberal, welfare partnership, and social democratic. This study analyzes the cases of Italian and Canadian nonprofit organizations (NPOs) that operate in two third-sector contexts, widely known as “hybrids.” In particular, we aim to verify whether some features of governance, leadership, and volunteer participation have impacts on the financial performances of selected Italian and Canadian NPOs. Methodology/approach Differences between the two studied nonprofit contexts influenced the sampling, the data collection, and the methods of analysis. Data on Italian and Canadian NPOs are analyzed both together and separately, using multiple regression models. Revenues, fund-raising and other grants from the general public, and program expenses are used as measurements of financial performance. Findings Our analysis demonstrates that some board characteristics, as well as volunteer participation and representation on the board, have impacts on the nonprofit financial performance. The characteristics of the CEO studied in this work are not significantly associated with the level of financial performance. Research implications/limitations This study has several important implications for research on board characteristics, CEO characteristics and volunteer management and governance, as well as implications for practitioners. The limitations of this study are related mostly to the different methods used for sampling NPOs and collecting data in the two different country contexts due to the different level of availability of data. Originality/value The past literature has not adequately examined the relationships among the board and CEO characteristics, the role of volunteers in governance and financial performance.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".