Assessing the Dynamics of Digital Entrepreneurial Intentions
Bibliographic record
Abstract
This study aims to examine the key drivers influencing the digital entrepreneurial intention (DEI) of business students regarded as potential entrepreneurs. Using the EEM theory, the practical implications of the study are based on data collected from 400 business students at ten private universities in Bangladesh. To analyze the proposed hypotheses, a Partial Least Squares Structural Equation Modeling (PLS-SEM) approach was used. The results show that digital entrepreneurial education (DEE) enhances digital competency (DC) and has a positive influence on digital entrepreneurial intention (DEI). In addition, DC has a positive effect on DEI and plays a mediating role in the relationship between DEE and DEI. Our results emphasize the importance of digital competency as a mediator between digital entrepreneurial education and intention. The study's cross-sectional design limits causal inferences. The focus on business students may also affect the generalizability of the findings. Future research could benefit from longitudinal studies and more diverse samples, including cross-country comparisons to validate the outcomes in different contexts.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".