The Contingency Approach of Digitalization and Entrepreneurial Orientation on Smes Performance in Metal and Machinery Industry
Bibliographic record
Abstract
The ability to enter the global market has become a competitive necessity for many firms and one important for survival and growth in the era of globalization. At the same time, digitalization is transforming the locus of entrepreneurial opportunities and entrepreneurial practices, thus offering new perspectives on internationalization. When entering the global market, SMEs will require innovativeness capability, proactiveness, and risk-taking. However, there is a gap in the literature exploring the interplay of digitalization and entrepreneurial orientation in the internationalization process. The objective of the present study aims at developing insights that explain how SMEs in Slawi district in the metal and machinery industry can use the tactics and strategies associated with EO to achieve superior performance in the digitalization age. Results from a survey in 63 SMEs show that: 1) SMEs that display high levels of EO report a higher level of performance, 2) SMEs that display high levels of digitalization report a higher level of EO, 3) the relationship between EO and performance is moderated by digitalization and 4) the relationship between digitalization and performance is moderated by EO. These results indicate that for those firms, innovativeness capability, risk-taking, and proactiveness are crucial to their success in foreign markets. Instead, SMEs should develop a clear vision of digitalization that is characterized by innovation, being ahead of the competition, and a willingness to take risks.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".