Purpose-Driven Strategic Renewal, Open Innovation & Generative Change: Models, Governance, Practice
Bibliographic record
Abstract
Unprecedented challenges of disruptive technologies, climate change and social crises, as well as stakeholder disorientation, call for a re-evaluation of organizational capabilities, growth priorities, and the way organizations change and innovate for the future. Established companies face a sharp increase of exploration and exploitation tensions and the need for renewal of business practice, models, and governance is existential. In these conditions, examples of high-growth companies place the corporate purpose central to their strategy and innovation, leveraging it to restructure playing fields and value propositions (Knowles & Hunsacker, 2022; Malnight et al., 2019), to catalyze systematic change (Henderson, 2021), to serve multiple stakeholders’ interests (Battilana et al., 2022), and to radically reinvent themselves (Binns, O’Reilly & Tushman, 2022). In continuation of the AOM2023 symposium “Purpose-driven Innovation and Transformation” and building on existing and emerging empirical research, the aim of this symposium is to deepen the discussion and explore the under-researched role of corporate purpose in the specific fields of strategic renewal, generative change as well as groundbreaking and collaborative innovation. For a insightful and lively debate on these topics, we invited leading experts in organizational transformation and evolution, strategic renewal, disruptive and open innovation, visionary leadership, and ambidexterity. The members of the panel have academic as well as practitioner backgrounds and experiences.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.062 |
| Scholarly communication | 0.029 | 0.020 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".