Legitimacy and distinctiveness of Nigerian-Canadian transnational firms
Bibliographic record
Abstract
Building legitimacy is a major challenge facing transnational entrepreneurs. Yet, overcoming the liability of foreignness and becoming legitimate are crucial for survival and growth.Hence, new transnationals must use convincing claims to gain stakeholder support. However, our knowledge is sparse on how entrepreneurs use claims to build legitimate distinctiveness. We examine the websites of 20 Nigerian-Canadian transnationals using two sets of data: 1) claims collected from Nigerian-Canadian transnationals’ websites and classified by argument types, and 2) survey of a 20-member review panel that assessed the selected websites. We used a quantitative approach consisting of ranking, clustering and regression analysis. We found a positive correlation between legitimacy and distinctiveness, indicative of the importance of both dimensions for transnationals. We contribute to the legitimate distinctiveness and transnational literatures, and provide managerial recommendations for transnational entrepreneurs, not only from Nigeria but more broadly, to guide the construction of legitimate and distinct claims.
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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) | 0.002 | 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".