Level of education and knowledge, foresight competency and international entrepreneurship
Bibliographic record
Abstract
Purpose Global economies are involved with enormous activities of internationalization that provide pure and untapped opportunities for entrepreneurs and businesses to place and promote their products. Design/methodology/approach The authors applied structural equation modeling (SEM) analysis with the partial least squares (PLS), conducting an empirical analysis of data from 28 European countries. Findings The results reveal that the higher level of education/knowledge in a country enhances the foresight competencies of entrepreneurs and that they both have a positive influence on the effective business creation. The findings of this paper also stress on the positive relationship between the effect of business creation and international intensity in economy level. Research limitations/implications The limitation of this study lies in the impossibility of obtaining a larger and more complete data. Consequently, this study uses national-level data from 28 European countries, which makes the sample too small. In addition, although innovation is one of the driving factors in both internationalization and entrepreneurship, because of the limitation, it has not been considered in this study. Practical implications The authors assert that countries, specifically European nations studied in this research, can improve their employment rate and value creation (through their products in international markets) by giving a special attention to the entrepreneurial-oriented human capitals. Social implications This research warns policymakers that they can have a serious contribution in promoting (international) entrepreneurship. They should draw a rigorous plan for formal and informal educational systems that effectively develops essential knowledge for launching new businesses and fosters the innovation and entrepreneurship. Originality/value This study set out to improve the understanding of the role of level of education/knowledge and foresight competencies, as the elements of human capitals, on international entrepreneurship.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".