The role of experience on techno-entrepreneurs’ decision making biases
Bibliographic record
Abstract
Entrepreneurs are the driving force behind the prospect and growth of the societies.Sound and wise decisions pave the way for them to carry out these highly important functions.Entrepreneurs are to discover and exploit opportunities.Therefore, they must gather sufficient and pertinent information.Entrepreneurs, like most human beings face complex and ambiguous decision-making situations, not to mention their lack of time and source to gather and process the data.Under these circumstances, entrepreneurs are prone making biases decisions.There are many reasons identified for this entrepreneurial decision making biases, such as the high cost of rational decision making, limitations in information processing, differences in their styles and procedures, or information overload, environmental complexity, environmental uncertainty.These biases are neither totally harmful nor completely useful and have to be seen as natural human characteristics.What makes entrepreneurial decision-making biases important is their effects on the decisions and thus the outcome of the enterprises.Entrepreneurial decision-making biases, deliberate or unintentional can seal the fate of the enterprises, therefore studying them meticulously is crucial.Literature has shown that experience could be an effective factor in decision-making biases.In this paper, we try to find out the impact of experience in Iranian high tech entrepreneurs' major decision-making biases by a qualitative approach.Finally, it was concluded that experience is influential in shaping overconfidence bias.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".