Enhancing Entrepreneurial Outcomes: Inclusion Through IT-Enabled Innovation
Bibliographic record
Abstract
Entrepreneurial ecosystems play a pivotal role in shaping innovation and inclusion outcomes. Equity-seeking entrepreneurs—such as women, immigrants, and racialized individuals—can be systematically marginalized when accessing resources, funding, and social networks. While technological resources are often celebrated as enablers of entrepreneurial opportunity, access to digital tools, and thus their impact, is uneven and contingent on broader ecosystem dynamics (Nambisan, 2017; Srinivasan & Venkatraman, 2018). In this study, we ask the research question: How do the dynamics of entrepreneurial ecosystems influence the pathways of equity-seeking entrepreneurs and their use of IT-enabled innovations? Drawing on Social Capital Theory (Adler & Kwon, 2002) and Social Identity Theory (Ashforth & Mael, 1989), our comparative case study analyzes two successful mid-sized Canadian ecosystems with contrasting investment cultures, support structures, and inclusivity mandates. Our findings are based on interviews with entrepreneurs, ecosystem leaders, and policymakers, alongside observations and ecosystem documents. Results show that ecosystem dynamics strongly mediate the value and accessibility of digital tools. In ecosystems with inclusive leadership and intentional support for marginalized groups, IT-enabled innovations—such as WhatsApp for peer mentoring, generative AI for marketing, and cloud platforms for collaboration—help entrepreneurs overcome geographic, financial, and social capital barriers (Nootjarat et al., 2012). In contrast, in ecosystems shaped by conservative values or wealth migration, digital tools tend to benefit mostly well-connected entrepreneurs—unless IT tools are intentionally promoted through inclusive programs or trusted intermediaries. The study advances 10 propositions that explain how lightweight and context-appropriate IT tools can be used by entrepreneurs to enhance bonding and bridging social capital, support iterative learning, and foster inclusive leadership pipelines, even where formal institutional support is weak. This work contributes to the IS field by theorizing the mechanisms through which digital tools act not only as functional enablers but as social and institutional equalizers. Our research offers practical insights for policymakers, incubators, and ecosystem architects to design equitable innovation environments.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".