Navigating grand challenges: How environmental dynamism shapes robust action and business model innovation
Bibliographic record
Abstract
Abstract Research Summary This paper develops a theoretical framework that links environmental dynamism, robust action, and business model innovation (BMI) in response to grand challenges. It argues that as environments shift from relative stability to rapid change and disruption, incumbent firms are likely to deepen their engagement in robust action—through participatory architecture, multivocal inscription, and distributed experimentation. These strategies, in turn, lead firms toward BMIs of increasing scope and degree of novelty—from evolutionary (modular, new‐to‐firm) to adaptive (architectural, new‐to‐firm) and focused (modular, new‐to‐industry) BMIs. However, regardless of the level of environmental dynamism, incumbent firms remain generally reluctant to pursue complex BMIs—those both architectural and new‐to‐industry—due to the heightened challenges of managing inter‐organizational partnerships, aligning divergent stakeholder interests, and reconciling external demands with entrenched internal routines. Managerial Summary In today's volatile and fast‐evolving environment, business leaders are under increasing pressure to adopt more substantive approaches to addressing grand challenges. Robust action strategies, which emphasize collaboration, openness to diverse perspectives, and experimentation, offer a potentially effective means for promoting large‐scale transformations needed to address grand challenges. This study suggests that depending on environmental turbulence, companies may pursue robust action in distinct ways. This, in turn, requires changes of varying scope and novelty in their business models, from incremental adjustments to a radical redesign of their core business model elements. However, companies often resist complex business model changes due to organizational inertia, the need to coordinate complex partner networks, and conflicting stakeholder interests.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".