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Innovation Pathways for Nonprofits in Rural Ontario, Canada

2025· article· en· W4416004864 on OpenAlexaffabout
Kunle Akingbola, Yuanyuan Wu

Bibliographic record

VenueAcademy of Management Proceedings · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsLakehead University
Fundersnot available
KeywordsGovernment (linguistics)ProcurementSocial entrepreneurshipCompetition (biology)Rural areaPublic policySocial innovation

Abstract

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This community-engaged research examines the ability of nonprofit organizations in rural and small urban areas to develop specific initiatives, such as services, partnerships, and fundraising, and to achieve targeted social outcomes as growth potential proxies associated with social innovation (SI). Drawing on a sample of nonprofit organizations in Central Ontario, the preliminary findings suggest that there are diverse pathways to innovation in the organizations. Specifically, the rural nonprofit organizations, which are generally very small, deploys innovative practices that are consistent with their unique context. Nonprofit organizations advance a social, cultural, or environmental mission, and are in the forefront of providing social goods, helping vulnerable clients and communities to cope with emergent needs, economic and social problems, and changes in government policies. However, limited growth is a pressing challenge of many nonprofits in Canada, although organization growth is an imperative to survival and achieving scaling of social impacts. Growth is desired upon the perceived opportunities such as organizational and financial performance, and external threats caused by increased competition and public procurement policies (Tykkyläinen, 2019; Davies et al., 2019). Among various barriers to nonprofit growth, a resource-based view that emphasizes the nonprofit knowledge and capabilities is widely adopted (Davies et al., 2019; Shepherd & Patzelt, 2020). Innovation capabilities are an important drive for entrepreneurial growth and economic development, and skills and capabilities facilitating various innovations in nonprofits are raised as a key to address the growth issue (Weeawardena et al., 2021; Best, et al., 2021). Although nonprofits are known to have developed various adaptive strategies including the use of business management techniques, stepping up boundary-spanning activities and embracing social enterprise, many of the initiatives are short-term efforts (Alexander, 2000; Dart, 2004; Tucker et al., 2005). The threats to their survival and the challenges that these community organizations must navigate to be effective means that nonprofits must embrace innovation to deliver the outcomes expected by their stakeholders. Social Innovation is regarded as key to Canada’s strategy advancing its UN Sustainable Developmental Goals and enabling Canadian nonprofits to grow. The preliminary findings indicate that innovation is essential for these community organizations which are constrained by their limited resources. Rather than developing initiatives and specific innovation strategy based on generic template, the nonprofit organizations exemplify innovativeness that are related to their culture and opportunities that are available in the environment. While none of the organizations had a plan or strategy, innovation was driven by mandates, people, and required by constrained resources. Innovation is evidenced in many facets including new programs, fundraising approaches, collaboration engagement, and governance mechanisms. Innovation was encumbered by significant challenges, which contradictorily give rise to innovative solutions. From funding to informal approaches and scale of the innovation, the nonprofits managed the challenges to deploy innovation in different facets of their organization. The impact of the innovation that they deployed is often not the direct growth but increased local influence in securing resources and partners. The research identifies the relationship between the innovativeness and innovation pathway of nonprofit organizations, the unique features of the innovation pathways, and their outcomes for the clients, community, and the organization. The findings will help guide nonprofits’ innovation practices.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.129
Threshold uncertainty score0.937

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0120.002
Scholarly communication0.0050.001
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.026
GPT teacher head0.292
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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