Timing and pricing of micro-acquisitions: A Perspective from effort justification theory
Bibliographic record
Abstract
Micro-acquisition marketplaces are a recent phenomenon in the world of entrepreneurship that facilitate the matching of buyers and sellers of relatively small-scale startups or pre-startup projects. Unlike traditional acquisitions, in micro-acquisition markets, sellers typically decide on the timing and offset an initial asking price for the deal. However, cognitive biases are likely to interfere with these decisions and lead to suboptimal decisions that prevent efficient matching. We argue that the age of a project at the time of listing can act as a proxy for the time and effort that has been spent by the entrepreneur to develop the project. Building on effort justification and cognitive dissonance theories , we argue that there is a curvilinear relationship between project age and price; and that this relationship is moderated by the level of revenue achieved by the project at the time of listing. Using data from Acquire.com , we find empirical support for these patterns indicating that entrepreneurs may justify a higher price because of higher effort up to a certain threshold, and especially for higher revenue projects.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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".