Bibliographic record
Abstract
Purpose This study aims to investigate the question involving what factors affect angel investors’ decision-making in funding new start-ups with specific reference to their evolving business models. Without funding and access to networks and experience, certain entrepreneurs will not get their business model through the start-up phase. Design/methodology/approach Data arise from 20 semi-structured interviews with angel investors in New Zealand plus supplementary interviews with business incubator managers and textual data. Findings The findings suggest a degree of causation-based decision-making, in that certain linear thinking was evident. The implication is that, without the ability of the entrepreneurs to convince the investors about key criteria in their decision-making, investment is unlikely. Nevertheless, a degree of effectuation-based decision-making was also evident, the implication being investors having to balance risk/reward decisions in the context of their own perceptions of affordable losses against an evolving business model. However, angel investors may take on co-investment, including from overseas, that takes decision-making away from management teams. Originality/value The study draws attention to the need to consider entrepreneurial ecosystems in angel investor’s decision-making and especially those with a small domestic market that may require management teams to look for scalability internationally. Furthermore, an effectuation lens contributes to knowledge in respect of predictive and control criteria, in particular, assessing risks and rewards against affordable losses involving an evolving business model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 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".