The Impact of the Digital Capability of College Students’ New Enterprises on Business Model Innovation Driven by the Digital Economy: The Mediating Effect of Digital Opportunity Discovery
Bibliographic record
Abstract
Based on the theoretical frameworks on dynamic capabilities and business model innovation, we conducted a comprehensive survey and analysis involving 451 Chinese university student enterprises. The primary objective was to investigate the synergistic mechanism between these two factors, assessing their impact on business model innovation and tracing the evolutionary path. The study revealed the following key findings: (1) positive correlations exist between digital capabilities and business model innovation; (2) entrepreneurial passion serves as a mediator in the positive relationship between digital capabilities and the discovery of digital opportunities; (3) digital opportunity discovery acts as a mediator in the relationship between digital capabilities and business model innovation; (4) under the mediation of dynamic capabilities, digital opportunity discovery significantly promotes business model innovation. Our research contributes to the empirical exploration of digitization in enterprises, shedding light on the collaborative influence of digital capabilities and digital opportunity discovery on business model innovation. Importantly, it elucidates the contextual boundaries that influence business model innovation through diverse pathways, enhancing our comprehensive understanding of the dynamic landscape in the evolution of digital business transformations.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".