Reconciling Diverse Approaches to Opportunity Research Using the Structuration Theory
Bibliographic record
Abstract
Diverse approaches have been used to investigate entrepreneurial opportunity, but have commonly produced an opportunity formation-recognition dichotomy. The structuration theory of Anthony Giddens is used to dissolve this dichotomy. Six approaches are discussed: (1) neoclassical equilibrium theory, (2) coevolutionary lock-in, (3) triggers for structural change, (4) effectuation/embeddedness/relationality, (5) path creation, and (6) prior knowledge and feedback. These theories often yield contradictory explanations and predictions of how entrepreneurs find or create opportunities. Structuration theory suggests that human action is guided by “scripts” that are formed in social and business structures. A script is accepted and used if it works, and rejected if it does not. An action that is legitimate, competent, and powerful will be repeated; when repeated, group and individual actions become scripts; they appear to be structural because they are observed in many places. Application of structuration theory to opportunity research suggests entrepreneurship is both a recognition and formation of scripts: a selection of common scripts and production (by modification) of uncommon scripts. An entrepreneur must understand and react to information (signals) about the effectiveness of scripts in recognizing and forming opportunities. Entrepreneurs learn and adopt common scripts; using uncommon ones has risks. The entrepreneur's difficult task is to choose scripts that are legitimate with business and social structures, competent enough to compete, and powerful enough to make a difference. Entrepreneurial activity is thus both enabled and constrained by business and social structure.(TNM)
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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 | 0.026 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.018 | 0.012 |
| Science and technology studies | 0.005 | 0.044 |
| Scholarly communication | 0.014 | 0.032 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".