Equity investment decisions for technology based ventures
Bibliographic record
Abstract
One major challenge facing early stage technology based companies is obtaining timely capital to aid their growth. This study sought to advance understanding of the decision making criteria currently used by Canadian equity investors to evaluate technology based companies which are seeking early stages of financing (seed, start–up or first stages). Sixty individuals belonging to one of three equity investor types participated: business angels (BAS, n=20), private venture capitalists (PVCs, n=20) and public venture capital funds (PVCFs, n=20). Data were collected using questionnaires administered through on–site personal interviews. Analyses reported here focus on group differences among decision making criteria as investors evaluated the business worthiness of one of their recent specific technology based business ventures. A total of 95 criteria were derived from previous investment literature and these subsequently received a priori assignment to one of these five categories: (1) characteristics of the entrepreneur(s), (2) characteristics of the market targeted by the venture, (3) characteristics of the venture offering, (4) investor(s) requirements and (5) characteristics of the investment proposal from the venture to the investor(s). Stability of ranked importance ordering for these five categories was tested using Friedman two–way ANOVA by ranks. Results revealed significant stability among the five categories within groups of each investor type: for BAS, the order of importance was (1,3,2,4,5); for PVCs, it was (1,2,3,4,5) and for PVCFs, it was (1,2,3,5,4). Next, specific key criteria as applied by these types of investors were located. For example, key criteria for BAS included: the entrepreneurs' familiarity with product and market; their familiarity with customer requirements; their ability to anticipate need for change; and evidence of truthfulness in their proposal. For PVCs, key criteria included: the entrepreneurs' ability to bring about change; attractive growth potential of market; cash out potential expected by the investors; and expected rate of return on investment to the investors. For PVCFs, key criteria included: evidence of truthfulness in the proposal; the entrepreneurs' familiarity with customer requirements; and their management commitment to success.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".