Navigating the sustainability landscape: How entrepreneurial intentions and competitive strategies drive success in the exhibition industry
Bibliographic record
Abstract
This study investigates the impact of entrepreneurial intentions and competitive factors on sustainable business performance in the exhibition industry. Analyzing recent literature, the research examines how strong entrepreneurial intentions drive the adoption of sustainable practices, innovation, and value creation, while considering the long-term effects of actions. The study also explores the influence of competitive factors, such as innovation capability, market orientation, networking ability, entrepreneurial orientation, and competitive strategies, on an entrepreneur's ability to achieve sustainable performance. The findings suggest that entrepreneurs committed to starting and growing their businesses are more likely to achieve superior financial and non-financial results while contributing to industry sustainability. The study emphasizes the importance of aligning strategies with sustainability goals, developing dynamic capabilities, utilizing competitive intelligence, and leveraging big data analytics to navigate challenges and create value. The research concludes that cultivating strong entrepreneurial intentions and effectively managing competitive factors are crucial for achieving long-term success and sustainable performance in the exhibition industry and beyond.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".