Understanding the Role of Quality and Quantity of Digital Technologies for Equity Fundraising
Bibliographic record
Abstract
This study examines the impact of the quantity (number of digital tools) and quality (sophistication of technologies) of digital technologies on the extent of equity fundraising in firms, emphasizing the role of organizational learning and market knowledge in this relationship. Drawing on the resource-based view and dynamic capabilities frameworks, we explore how adoption of a higher number of digital technologies and of the highest quality shapes firms' resource mobilization strategies. While younger firms are known as more agile and innovative, mature firms benefit to a greater extent from the adoption of quantity and quality of digital technologies for fundraising, raising questions regarding the role of accumulated market experience and organizational learning for resource mobilization. Using micro-level data from Pitchbook Canada comprised of 5,607 Canadian companies during 2010-2023 we demonstrate that more mature firms, in particular 11 years since the establishment and older, are more able to increase equity fundraising, given their focus on an increased number of digital tools adoption of highly advanced technologies. The study contributes to entrepreneurship finance and digital transformation literature by differentiating between technology adoption strategies, the role of firm maturity for fundraising and the ability of firms to raise external equity financing with technologies.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".