Hello From the Other Side: External Stakeholder Paradoxes Matter for Organizational Ambidexterity
Bibliographic record
Abstract
Organizational ambidexterity (OA)—the simultaneous pursuit of explorative and exploitative innovations—engenders nested (paradoxes manifesting across organizational levels) and knotted paradoxes (intertwined co-occurring paradoxes at the same or across organizational levels). Both nested and knotted paradoxes are managed by internal stakeholders using approaches that are socially constructed by influences emanating from within and beyond an organization. External stakeholders also help shape organizational innovations but their influence on the management of paradoxes has largely been overlooked. In this study, we collected and analyzed data from internal ( n = 19) and external stakeholders ( n = 18) in organizations pursuing explorative and exploitative clean energy transitions at nine Canadian energy utilities. We find that the external stakeholders engage with knotted inclusivity–efficiency and existing–emerging needs paradoxes. Our findings add to OA and business and society literature by revealing how the approaches that internal and external stakeholders adopt for paradox management have implications for each other, and thus OA. We also provide practical insight into how external stakeholder paradoxes can be managed through stakeholder inclusivity and enhanced transparency of innovation plans, with positive implications for OA.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".