Knowledge exchanges, trust, and secretive geographies in merger and acquisition processes
Bibliographic record
Abstract
Our current understanding of knowledge generation over geographical distance relies heavily on studies that focus on producer–user or headquarter–subsidiary settings. Less attention has been paid to the geographical particularities of knowledge exchanges in mergers and acquisitions, which involve high costs and an extraordinary degree of risk and uncertainty with potentially significant (positive or negative) consequences for the respective firms and regions alike. To keep the risks associated with such complex long-distance transactions at bay, buying firms strongly depend on robust knowledge about the structure and value of the target units while the sellers require reliable knowledge about the goals of the acquisition and the price the buyer is willing to pay. This paper aims to investigate the spatiality of related knowledge exchanges during merger and acquisition procedures by analyzing the role of face-to-face contacts and investigating the mechanisms to establish trust in undertaking such risky endeavors. Our empirical analysis focuses on national and international corporate acquisitions and takeovers involving firms located in Germany. It is based on semi-structured in-depth interviews with actors involved in mergers and acquisitions, conducted since 2012. We distinguish between the two extremes of relational and auction-based merger and acquisition procedures and systematically analyze in a process perspective (a) the conditions under which knowledge is exchanged over distance, (b) the importance of temporary proximity and how secretive geographies of meetings evolve, and (c) the ways in which trust is created and uncertainties are reduced.
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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 | 0.000 |
| 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".