Use of Social Media in the Promotion of University-based Entrepreneurship Centres
Bibliographic record
Abstract
The overall problem this study addressed is how university-based entrepreneurship centres (EC) are using social media to promote themselves and whether this varies based on the regional location of the EC. This study examined how ECs are connecting with internal and external stakeholders to highlight their congruence with the social and scholarly values of the university and the larger ecosystem and helps in understanding how ECs are promoting the support they provide to their key stakeholders to drive innovation and economic development. The study used institutional theory and symbolic management to analyze the social media disseminated by ECs to engage stakeholders. The study included interviews of 12 directors and/or leads of communications at ECs across Canada, followed by a website and Twitter review. The findings of this study revealed that ECs are using social media as an integral means to drive stakeholder engagement and establish themselves as legitimate players in their relevant ecosystems. Symbolic imagery was prevalent in the social media distributed by the ECs with an emphasis on successful programs and events. Targeted channels allowed the EC to build their legitimacy with appropriate stakeholders in the university and the larger entrepreneurial ecosystem. Regional location of the EC does seem to affect the stakeholders valued by an EC. All shared that their primary stakeholders were; students, the university, alumni, mentors and funders. However, ECs in smaller cities appeared to have a more local focus, where those in large cities were looking to engage on a national and international level. ECs shared that their communication tools need to reflect the audience they are looking to engage, and that this needs to align with their university to establish their perceived legitimacy in the ecosystem.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".