Returned Entrepreneurs and Ability to Generate Innovations: Case of Kosovo
Bibliographic record
Abstract
Numerous scientific studies underscore the importance of returned entrepreneurs for a country's economy. Returned entrepreneurs are individuals who have worked or studied abroad and subsequently returned to their homeland to initiate entrepreneurial ventures, bringing with them experience and knowledge from abroad. The motivations of these returned entrepreneurs vary significantly. This study aims to investigate the motives, knowledge, and experiences of returned entrepreneurs, specifically in the context of Kosovo, and how they contribute to creating innovations. A qualitative data approach is often considered the most appropriate methodology for such investigations, and this study employs it accordingly. To this end, 12 structured interviews were conducted with returned entrepreneurs, which helped us answer the study's objectives. Notably, the motivations of returned entrepreneurs differ across developing countries. The results of this study provide scientific evidence of these differences, thereby contributing to the entrepreneurship literature. The findings highlight that one of the main motivations of entrepreneurs in the Kosovar context relates to knowledge/experiences and connections formed abroad, a finding consistent with human capital theory and Internationalisation Theory. Another significant discovery of this study is that returned entrepreneurs in Kosovo have generated innovations related to new products/services, work methods, production methods, sales methods, and the application of new technologies. The generation of such innovations is regarded as a primary source of a country's economic growth. Therefore, this study is expected to have theoretical implications, as well as political implications beneficial to the local economy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".