Harnessing Digital Technologies for Entrepreneurial Innovation
Bibliographic record
Abstract
This thesis, organized in a 3-paper format, explores the potential of digital technologies to support entrepreneurial innovation using a capability-based perspective. The fast emergence of powerful, readily accessible, and affordable new technologies has transformed the processes and outcomes of entrepreneurial innovation. Both startups and business incubators that support startups, as a result, require an up-to-date and nuanced understanding of how digital technologies can be leveraged to create business value in their respective contexts. In the first paper, a multilevel framework is inductively developed to explain how digitally-enabled influential factors at the ecosystem level shape innovation environments through business incubators; how startups carry out digitally-enabled entrepreneurial actions; and how business incubators and startups jointly form digitally-enabled intra-incubator action patterns to co-create innovation outcomes at the ecosystem level. Findings of this study confirm the saliency of digital enablement at all three analytical levels (i.e., entrepreneurial ecosystems, business incubators, and startups) and throughout the innovation processes. The second paper takes a mixed-method approach to explore the value of digital capabilities to startups. Using case data collected from 65 Canadian startups, this study inductively identifies four types of digital capabilities that are commonly developed and used by early-phase startups: digital platform capability, digital infrastructure capability, digital adaptation capability, and digital knowledge capability. Drawing on the insights derived, fuzzy-set qualitative comparative analysis was conducted to examine high-performing digital capability configurations for startups at the business validation and transition stages. The results show that the high-performing configurations differ from validation to transition, and that digital startups and their less-digital counterparts rely on different digital capability configurations to succeed. The third paper investigates further a key finding from the first paper – that is, today’s business incubators are being transformed from siloed support infrastructure to active resource orchestrators in entrepreneurial ecosystems. Specifically, this study examines the influence of dynamic digital capabilities on both the agility and performance of publicly-funded business incubators. The research framework is tested and supported using survey data. It is found that resource orchestration capabilities fully mediate the impact of dynamic digital capabilities on incubator performance, and partially mediate the latter’s impact on incubator agility.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".