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 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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".