Universities as change agents for green entrepreneurship: role of support systems and self-efficacy in fostering sustainable ventures in Sri Lanka
Bibliographic record
Abstract
Purpose This study aims to examine the influence of support systems – social, country and educational – on entrepreneurial self-efficacy (ESE) and green entrepreneurial intentions (GEI) among university students in Sri Lanka. The research integrates the theory of planned behaviour (TPB), social cognitive theory (SCT) and entrepreneurial event theory (EET) to establish a comprehensive framework for understanding GEI development. Design/methodology/approach Data were collected through a self-administered questionnaire from 482 final-year business students at 3 leading Sri Lankan universities, selected based on their UI GreenMetric World University Rankings 2023 status. The data were analysed using partial least squares structural equation modelling (PLS-SEM). Findings Results reveal that social, country and educational support significantly influence students' ESE, which in turn drives GEI. Additionally, ESE mediates the relationship between support systems and GEI, highlighting its crucial role in translating support into entrepreneurial action. Practical implications The findings provide university management and educators with actionable insights for fostering green entrepreneurship through structured learning experiences, mentorship programmes and targeted support systems. Particular emphasis is placed on addressing gender-specific barriers and creating comprehensive entrepreneurial ecosystems within universities. Originality/value This study pioneers the investigation of GEI precursors within Sri Lanka’s university context, where research on green entrepreneurship remains limited. It offers valuable insights into how universities in emerging economies can effectively nurture sustainable entrepreneurship while addressing institutional constraints. The findings establish a foundation for future research on policy frameworks supporting university-based green entrepreneurship initiatives.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".