Stakeholder Engagement in an Unconventional Form of Organizing: What Can We Learn from Hackathons?
Bibliographic record
Abstract
The concept of stakeholder engagement has developed around the premise that organizations that involve multiple stakeholders in organizational activities yield better outcomes than those that do not. Considering the importance of stakeholder engagement for addressing grand challenges in a sound and sustainable way, this paper explores how does stakeholder engagement unfold in unconventional form of organizing (UFO) dedicated to creating social value? For doing so, this article studies the stakeholder’s engagement mechanisms of AquaHacking; an initiative that has developed in the field of water conservation in Canada by adopting the organizing form of hackathons. Our analysis shows that in UFOs three mechanisms support the social value creation process: (1) harnessing fluidity, (2) maintaining attractiveness, and (3) ensuring commitment. Our findings also consider the challenges inherent to organizing in unconventional forms and the conditions for the viability of a UFO dedicated to social value creation.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".