The venture capital investment process in emerging markets
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Bibliographic record
Abstract
Purpose The purpose of this paper is to focus on the investigation of the venture capital investment process in the emerging markets of Central and Eastern Europe (CEE), including Hungary, Poland, the Czech Republic, Slovakia, Romania, and Russia. The study aims to describe the mechanics by which venture capital firms operating in the CEE region process deals. Design/methodology/approach The paper is based on a two‐phase interview interaction process with venture capitalists operating in the CEE region. In the first semi‐structured (exploratory) phase of the study, 14 venture capitalists agreed to participate in one‐hour interview and aimed at discussing their venture capital process. In the second phase of the study (confirmatory), 24 venture capital firms commented on the actual fit of the proposed nine‐stage model into their past investments. Findings The study has two conclusions. Firstly, the study confirms the existence of a nine‐stage venture capital investment model, comprised of deal origination, initial screening, feedback from the investment committee and due diligence Phase I, feedback from the investment committee (due diligence Phase I), pre‐approval completions, formal approvals and due diligence Phase II, deal completion, monitoring, and exit. Secondly, the proposed model defines the venture capital process in terms of three channels of activity: document channel, information channel, and decision channel. Originality/value The study is important for at least four reasons. Firstly, the study focuses on the investigation of the entire venture capital process. Previous research in the area focuses on some specific facets of the venture capital process. Secondly, the paper investigates the connection between decision‐making, information gathering and written communication within a venture capital fund. Thirdly, the study focuses on the most recent period of development of the CEE industry. Many venture capital firms only recently crystallized their venture capital process. Lastly, the study proposes areas of further research for academics and makes suggestions for practitioners.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it