Toward a Shared Agenda for Necessity Entrepreneurship Research: Definitions, Theories, and Perspectives
Bibliographic record
Abstract
Abstract Salient future research opportunities emerge when we rethink how to define and theorize necessity entrepreneurship (NE). In terms of defining NE, we outline its complex and contextualized nature, emphasizing the need for more nuanced conceptualizations that go beyond the conventional binary distinction of necessity versus opportunity entrepreneurship. Specifically, we use Wittgenstein’s family resemblance approach to identify the key elements that characterize necessity entrepreneurs, such as operating with limited resources, engaging in everyday activities, being socially embedded, and showing involvement in informal economies. This poses a first critical question to NE scholars: should they continue to define NE narrowly, using a single indicator, or broadly, using a multifaceted approach? We then reflect on the current state of theorizing on NE. We review the diverse set of theories that are currently employed by NE scholars, drawing from frameworks like existence-relatedness-growth (ERG) theory, social network theory, and effectuation. Building on that review, we pose a second critical question: should NE researchers continue to opt for theoretical eclecticism or aim for integrative theories that simultaneously capture the behavior of entrepreneurs, their ventures, and the contexts in which they operate? In addition to addressing these definitional and theoretical questions, we also identify key areas for future research, including the study of NE as a state of mind, a communal experience, a structural condition, and a moral dilemma. We call for deeper inquiry into the social, economic, and environmental impacts of NE, encouraging scholars to explore the overlooked complexities of necessity-driven entrepreneurial activities.
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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 | 0.045 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.005 | 0.057 |
| Scholarly communication | 0.023 | 0.049 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.009 | 0.019 |
| Insufficient payload (model declined to judge) | 0.005 | 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".