Social innovations by nonprofits: Inter- and intra-organizational factors
Bibliographic record
Abstract
Socially innovative programs and initiatives are those that organizations undertake to create social change for service users and within communities. The government sector having less involvement in addressing emergent challenges and social issues that contribute to inequality, social exclusion, and meaningful social outcomes for social service users, is an emergent trend in the contemporary welfare state. As a result, direct service nonprofits have become increasingly important actors in this regard. However, minimal research investigating the characteristics of the inter- and intra-organizational context of direct service nonprofits, that are supportive in fulfilling this emerging role, exists. As a corrective, this study, utilizing a mixed-methods design, included both survey (n=241) and interview (n=31) data collected from a random sample of executive directors of direct service nonprofits in Alberta, Canada. Survey data were collected on several key variables related to the inter-organizational context. Qualitative data were collected through in-depth one to one interviews to identify aspects of the intra-organizational environment that are supportive in developing a social innovation orientated organizational culture. Three analytical techniques were utilized: Exploratory principal factor analysis; Structural equation modelling; and analytic induction. The results are presented following a three paper/essay dissertation format. The first shows the results of the exploratory factor analysis and highlight a three factor model of social change efforts (operationalized as social innovation) undertaken by direct service nonprofits. These include socially transformative, product, and process based social innovations. The second presents the multivariate analysis of the inter-organizational factors that explain the extent organizations undertake these three types of social innovation. Aspects of collaboration (including extent, quality and interconnectivity) and degree of marketization are found to be statistically significant. The third, utilizing the qualitative interviews, highlight aspects of staff engagement and development, along with executive leadership roles, in facilitating the development of a social innovation orientated organizational culture. In conclusion, the findings provide a model of a social welfare environment that aims to enhance the capacity of nonprofits in creating social change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".