Surviving a rough patch through agility and technology innovation: Navigating young technopreneurial competitiveness with success in Industrial Revolution 4.0
Bibliographic record
Abstract
Creativity and innovation are now being encouraged in businesses because of technopreneurship, especially as the Fourth Industrial Revolution (IR 4.0) picks up. In addition, the evolution brought by technology has influenced our everyday lives, jobs and communication, making it easier for enterprises to deal with changes. The rapid changes brought about by these inventions have driven young technology entrepreneurs to make quick changes to their business models. This study analyzes how various elements help determine the agility and competitiveness of our young entrepreneurs starting businesses in Malaysia during Industry 4.0. However, these organizational enablers belong to 03 (three) main groups: individual traits (innovativeness, initiative and risk-taking), organizational tools (e.g., innovation, technologies and human resources) and institutional assistance (such as finances and support services). Initially, 18 (eighteen) technopreneurs were invited for semi-structured interviews to provide their experiences and detailed ideas. This research team then administered a survey to 204 (two hundred andfour) technopreneurs and they analyzed the data using the SmartPLS technique. Evidence from the interviews shows that having these enablers enables technopreneurs to remain nimble and compete well, a fact demonstrated by the significant connections found between all the enablers and also agility which is closely linked to competitiveness. All in all, this research provides important info and solid proof that being agile is key for young business owners to succeed under tough conditions.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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 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".