Effects of innovative climate, knowledge sharing, and communication on sustainability of digital start-ups: Does social media matter?
Bibliographic record
Abstract
Start-ups are built by the charismatic leaders with innovative ideas, creativity, and distinguished expertise. However, they face huge challenges for growth and survival in an uncertain and competitive business eco-system. The aim of the present study is to examine the effect of entrepreneurial orientations with social media mediation on digital start-up sustainability. To conduct this research, active start-ups at the science and technology park of Tehran, Shiraz and Yazd universities have been identified in 2021. A 25-item questionnaire was used to collect data from 195 digital start-up managers. Data analysis has been done using SmartPLS3 software. The findings indicate that an innovative atmosphere, knowledge sharing and efficient communications have positive effects on the sustainability of digital startups, and the social media plays role as mediator. The results showed that social media contributes to increasing participation of employees in decision making process. Social media helps employees to share their knowledge, and establishes better interactions with the customers and stakeholders. As a result of better interaction, the company can respond to business challenges faster and operate in a more stable environment. Therefore, in today's turbulent market, attention to sustainability to achieve economic growth and development has been the focus of knowledge-based companies.
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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 | 0.003 | 0.013 |
| 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 | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".