Ethics, Adverse Selection, Target Method of Sale Strategies, and Akerlof’s “Lemons” Problem
Bibliographic record
Abstract
This study examines the acquisition dynamics associated with the target management’s choice to initiate the sale of the firm using the auction method. Specifically, we examine opportunistic merger and acquisition (M&A) dynamics related to the target-initiated method-of-sale decision (auctions vs. one-on-one negotiations), as a noteworthy example of Akerlof’s (1970) theory of the market for lemons. While we find a strong positive relationship between proxies of adverse selection risk and the likelihood of target initiation, robustness tests suggest target initiation itself is a unique indicator of information asymmetry in an acquisition environment. We also find that most target-initiated transactions follow an auction as the method of sale, which increases target information asymmetry advantages. While wealth accrued to both bidders and targets increases in non-target-initiated auctions, this benefit disappears when the target initiates the acquisition, causing both bidders and targets to suffer wealth losses. According to Akerlof’s theory, these wealth losses represent the cost of perceived dishonesty due to enhanced adverse section risk, which provides noteworthy implications for both business and society.
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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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".