Five Configurations of Opportunism in International Market Entry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract We investigate the conditions under which opportunism occurs in international market entry. Examining 133 entries into new markets by 38 Chinese exporters, we uncover instances of opportunistic behaviour on the part of importers. We study five variables affecting such behaviour: managerial experience, market entry share; market distance, young age, and network size . While we find no single variable on its own associated with opportunism, we do find that in concert they form five configurations of opportunism. In one configuration, even older firms with experienced managers and a large network are subject to partners behaving opportunistically when they are entering a distant market. We conclude that simplistic predictions based on the presence of a single antecedent should make way for a configurational approach whereby a set of conditions must be in place for opportunism to materialize.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it