Entrepreneurial Ecosystems as Amplifiers of the Lean Startup Philosophy: Management Control Practices in Earliest‐Stage Startups*
Bibliographic record
Abstract
ABSTRACT Entrepreneurial ecosystems play a key role in the development of startups not only by providing support—such as flexible office space and access to skilled employees, mentors, and investors—but moreover by promoting concrete ideals about “good” entrepreneurship. However, we know less about the role that ecosystems play in managerial practices of startups. In our empirical analysis of management control systems (MCSs) in earliest‐stage startups, we witness a strong influence of entrepreneurial ideals—above all, the Lean Startup philosophy—on the MCSs analyzed. Building on cross‐sectional field study data resulting from a comprehensive field‐immersion strategy and 50 interviews with key actors in an entrepreneurial ecosystem as well as with founder‐managers of startups, we consider the entrepreneurial ecosystem as a collective meso‐level community that mediates between macro‐level institutional pressures and micro‐level practices of startups. We show how this community, through a variety of what we term amplifying mechanisms, actively deinstitutionalizes a legacy entrepreneurial philosophy epitomized by the business plan concept. At the same time, the community propagates the Lean Startup philosophy so that this alternative has become the dominant institutional philosophy in the studied ecosystem and its startups. Due to the amplifying mechanisms exerted by the meso level, startups use MCSs that play a crucial role in the rapid experimentation and learning process toward finding a scalable business model that is characteristic of the Lean Startup philosophy. We highlight that this philosophy of scientific experimentation has, to a significant degree, transformed intuitive entrepreneurial processes into a set of transactions that can be steered and accelerated by MCSs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".