Ownership Types and Strategic Groups in an Emerging Economy*
Bibliographic record
Abstract
ABSTRACT Existing strategic group studies have rarely examined ownership type as a variable to classify firms in an industry. Using Chinese firms of different ownership types, we suggest that ownership type can be a parsimonious and important variable that managers use to cognitively classify firms into different strategic groups. While ownership itself is an objective variable, we contend that different ownership types lead to different managerial outlook and mentality due to a number of macro and micro foundations giving rise to various managerial cognitions. Employing the Miles and Snow typology, we find that state‐owned enterprises (SOEs) and privately‐owned enterprises (POEs) tend to adopt defender and prospector strategies, respectively, while collectively‐owned enterprises (COEs) and foreign‐invested enterprises (FIEs) exhibit an analyser orientation that falls between defenders and prospectors on the strategy continuum. Three statistical tests suggest that ownership types can be used to successfully predict strategic group memberships in China's emerging economy.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".