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Record W4244109226 · doi:10.1504/ijbir.2020.111769

Innovation orientation and performance in the not-for-profit sector

2020· article· en· W4244109226 on OpenAlexaffabout
Mark Klassen, C. Brooke Dobni, Veronica Neufeldt

Bibliographic record

VenueInternational Journal of Business Innovation and Research · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsBusinessNot for profitBeneficiaryOptimismCompetitive advantageMarketingReputationIndustrial organizationMarket orientationKnowledge managementPsychologyComputer science

Abstract

fetched live from OpenAlex

Innovation is increasingly gaining attention by not-for-profits (NPOs) as their environment is becoming more competitive, complex and stakeholder driven. This objective of this research is to develop a better understanding of the association between innovation orientation and performance. This is accomplished through a survey of registered and active Canadian NPOs. The innovation orientation approach in this study has advantages given the holistic nature and inclusion of multiple innovation determinants to measure the state of innovation in NPOs. The results suggest that NPOs with high innovative orientations have a positive relationship with the performance metrics of beneficiary satisfaction, resource attraction, peer reputation, effectiveness, and optimism in meeting future objectives. In contrast, NPOs with low innovation orientations did not present any meaningful associations with the performance constructs suggesting that innovation may not have a robust impact on performance amongst low orientation NPOs.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.411
Threshold uncertainty score0.188

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.186
GPT teacher head0.440
Teacher spread0.254 · 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 teacher head, 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".

Quick stats

Citations9
Published2020
Admission routes2
Has abstractyes

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