Explorative/Exploitative Business Model Change: The Antecedents of Responses to Ongoing Disruption
Bibliographic record
Abstract
Entrepreneurial disruptors entering established industries are the agents of Schumpeterian creative destruction. Although creative, they are nevertheless destructive for incumbents. Focused on business model disruptions, we develop a typology framework of heterogeneous incumbent adaptations to gaining momentum disruptive innovations, based on juxtaposing two strategic paths: i) the explorative adoption of disruptive business model, ii) the exploitative strengthening of an existing business model. Applying theories of managerial decision making, we derive and test hypotheses concerning situational and dispositional antecedents to managerial intentions to embrace each of the two adaptation strategies. Empirically, we study Canadian realtors at a time when a salient disruptive innovation was gaining momentum. Structural equation modeling results revealed that explorative business model change intentions were positively influenced by recognition of opportunity, perceived non-critical threat, and prior successful risk experience. On the other hand, exploitative intentions were negatively associated with perception of critical threat and tenure within the industry, and positively associated with prior successful risk experience. We contribute to the growing literature on business model disruptions by collecting prior research into one definable framework of incumbent responses, and by developing and testing replicable hypotheses of influences on incumbent strategic responses in a dynamic setting.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".