A Correlational Analysis to Assess Major Obstacles Associated with the Internationalization of Saudi Startup Enterprises
Bibliographic record
Abstract
Startups’ constant tendency to grow and scale up through internationalization is occasionally collided with a number of barriers in the domains of legalization and regulation, market and customer, environment and competitiveness, information and knowledge, resources and accessibility, and economy and culture. The sharpness of these barriers might intensify when it comes to startups from emerging markets. Consequently, a need for assessing the major obstacles associated with the internationalization of emerging markets’ startups is emphasized. In this regard, a correlational analysis has been used to identify and assess the role of these obstacles in restricting Saudi startup enterprises to operate internationally. A total of 103 participants were included in the data collection process of the study from Saudi startup enterprises. The findings have shown that liability of foreignness, managerial dispute and organizational distrust, and immaturity of home market were the most influential barrier towards internationalization process of SMEs. Coping implications were suggested to mitigate the impact of each barrier and possible avenues for future research in the area of startups’ internationalization were recommended.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".