A Study on the Influence of Entrepreneurial Competence Characteristics on the Sustainability of Entrepreneurs -Focused on the Mediating Effects of Entrepreneurial Mentoring
Bibliographic record
Abstract
Background/Objectives: Many studies have shown that the ability of a startup to have a significant impact on the sustainability of the startup, but no studies have been conducted on whether the ability of the startup to influence startup sustainability using startup mentoring. Therefore, this study investigated whether the founder's competency characteristics influence sustainability through the medium of start-up mentoring.Methods/Statistical analysis: The study subjects were early founders, and the survey was conducted as a survey method. The survey items consisted of 62 questions including 12 demographics. The Likert 5-point scale was used for the measurement. For the empirical analysis, frequency analysis, descriptive statistical analysis, exploratory factor analysis, reliability analysis, correlation analysis, regression analysis, and mediation effect analysis were performed using SPSS Ver. 22 statistical package.Findings: The results of the study confirm that entrepreneurial competence characteristics are partially mediated by the characteristics of the technical capability and the strategic thinking capability on the impact of sustainability, and through the research, the organizational capability of entrepreneurial competence characteristics are completely mediated on the impact on the sustainability.Improvements/Applications: In order to secure the sustainability of start-ups, mentors should conduct mentoring by understanding the entrepreneurial competence characteristics. Mentoring that does not fit the entrepreneurial competence characteristics only forces the founder to regenerate time and effort. Mentors should participate in entrepreneurial mentoring with a sense of mission for the national economy and job creation, and government support policies should be tailored to the characteristics of entrepreneurs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".