The Conceptualization of Nonprofit Marketing Orientation: A Critical Reflection and Contributions Toward Closing the Practice–Theory Gap
Bibliographic record
Abstract
In this article three generations of research addressing the marketing construct in nonprofit organizations are critically analyzed: (a) market orientation in nonprofit organizations, (b) societal orientation, and (c) research contributions aiming to close the existing practice–theory gap on this topic. A qualitative study among 24 nonprofit marketers is conducted in Canada and Germany to develop a construct labeled nonprofit marketing orientation. It includes the dimensions of brand orientation, supporters’ orientation, commercial orientation, and service orientation. Furthermore, the authors describe the nomological net of nonprofit marketing orientation to present interconstruct relationships and to inform future research.
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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 | 0.071 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.008 | 0.091 |
| Scholarly communication | 0.012 | 0.021 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.016 |
| Insufficient payload (model declined to judge) | 0.002 | 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".