Personality traits, individual resilience, openness to experience and young digital entrepreneurship intention
Bibliographic record
Abstract
Digital entrepreneurship can be a potential alternative solution for addressing challenges faced by young people and future workers in Asia. Additional studies are required to enhance comprehension of digital entrepreneurship given the insufficiency of research conducted in this domain. This research seeks to uncover possible determinants that could impact the desire to engage in digital entrepreneurship, with a specific focus on personal traits, resilience, and the level of educational services. The participants in this study are university students as they represent the potential future workforce and potential digital entrepreneurs. A total of 517 sample data (212 Malaysian, 305 Indonesian) were collected through online surveys towards students in Malaysia and Indonesia. The study used a brief version of The Big Five Personality Traits, CD-RISC resilience scale, Liñán & Chen entrepreneurship intention scale, and Parasuraman, Zheitaml, Berry SERVQUAL to gather data. To analyze the data, the study employed structural equation modeling. The results suggest that the intention to pursue digital entrepreneurship is affected by both an individual's openness to experience and their resilience. Additionally, the study revealed that service quality is a factor that affects both digital entrepreneurship intention and resilience. This study provides new understanding of digital entrepreneurship intention antecedents and implies that improvement on education quality service can foster student’s intention to digital entrepreneurship and their resilience.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".