A Study on High-tech Startup Failure : Antecedents, Outcome and Context
Bibliographic record
Abstract
A significant number of startups fail during their first years of operations, and most of \nthem crash within five years. A wide range of reasons for startup failures has been \nidentified in the literature. However, most of the reasons for startup failures are too \ngeneral in that they focus on startups in general. In this regard, not every factor may be \nresponsible for the failures of some startups. \n \nAlthough there are adequate investigations that have provided substantial evidence about \ndifferent reasons that cause startups failure, this study aimed to review these reasons \ncollectively to determine how they relate to high-tech startup failure. \n \nThis study used a qualitative research method to collect and analyze data from 15 \nfounders of high-tech startups in the United States (US), Finland, and Canada. The \nresearcher conducted interviews through Skype and analyzed data using thematic analysis \nto derive relevant themes to the study. Likewise, the researcher conducted a profound, \nsystematic review to identify themes relating closely to startup failures. \n \nThe results showed that high-tech startups failures relate closely to product and market \nchallenges (product timing difficulties, product design problems, improper or absence of \nselling strategy/ distribution channels, and small market size), financial problems (initial \nundercapitalization and debt burden), and management issues ( lack of competent teams \nand human errors). \n \nThe study showed that a wide range of factors leads to the failure of high-tech startups. \nTherefore, founders and personnel working in these high-tech startups should pay \nattention to the identified areas to minimize the chances of failure.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".