The strategized business model for successful technopreneurs in Malaysian Small-Medium Enter-prise (SME) using Business Intelligence (BI) as a moderator , Pages:649-660
Bibliographic record
Abstract
The rise of digital marketing channels and e-commerce has compelled businesses to shift to a digital economy, where the use and use of technology in business operations necessitates the development of technopreneurs rather than entrepreneurs. Moreover, technopreneurs can transform a technical idea into and market-ready product that consists of intellectual wealth as the gateway to financial wealth. This study aims to inspect the relationship between strategizing business models for transformation and development as well as the moderating role of Business Intelligence (BI) towards becoming a successful technopreneur. The study obtained 313 respondents among entrepreneurs from Small-Medium Enterprise (SME) in Malaysia using a simple random sampling method by utilizing an online survey (Google Forms) to participate in the main survey. They are recognized as highly knowledgeable respondents who use digital technology to improve their businesses; as a result, they possess the knowledge necessary to give a trustworthy response based on their actions and experiences to succeed as technopreneurs. The collected dataset was analyzed using SmartPLS software to test the hypotheses, and a structural model was utilized in the study to assess direct relationships. Based on p-values and t-statistics, the bootstrapping technique was used to increase the significance of both direct and indirect impacts (path coefficient). The findings of this study highlight that strategized business models and successful technopreneurs may become an innovative high technology-intensive context with the moderating role of BI technology prowess and entrepreneurial talent and skills that develop technopreneurs in the new age of entrepreneurs who make use of the digital 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 | 0.002 | 0.003 |
| 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 | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".