Bibliographic record
Abstract
Psychological attachment to an entrepreneurial opportunity may motivate the entrepreneur to persevere but can also bias decisions made in the entrepreneurial process, especially on market entry. This thesis investigates how psychological attachment to an entrepreneur’s idea influences decision making at the commercialization stage with special emphasis on control tendencies. Data collected from 106 fourth-year students from the Engineering Design Program at a top engineering-focused Canadian university revealed some interesting results. In the model estimated, the higher the subject’s psychological attachment to the opportunity, the more control oriented the subject was. Interestingly, psychological attachment is a strong predictor of control tendency even when subjects’ perceptions of projected returns (value) are statistically controlled in the analysis. Furthermore, psychological attachment correlates with proxy measures of the level of cognitive evaluation: the indication, affective constructs like psychological attachment elicit affect-laden evaluation of outcomes in a way that is divergent from the cognitive evaluation of commercialization situations. \n \nWithin a framework of financial decision making, even as subjects generally acknowledged outside investor expertise in a potential commercialization partnership, the main finding was that high levels of attachment are more likely to lead to control-oriented funding preferences over optimal financing preferences. Further, alternative research explanations for control tendency failed to hold, as individual personality-type factors were not significant in explaining the variability in control tendency. Therefore, control tendency may be dependent on attachment to the creative process as opposed to an individual’s personality construct. The results provide insight into the role that affective constructs like psychological attachment and control tendency may play in important decision making in the entrepreneurship process.
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 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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".