Drivers of Governance Modes and Reconfiguration
Bibliographic record
Abstract
This symposium seeks to shed light on what drives firms to engage in different governance modes. Four papers comprise this symposium, and a discussant will build bridges among the different pieces and raise the conversation to a higher level of discussion of governance modes and resource reconfiguration. Two of the papers focus on the antecedents driving firms to engage in a particular governance mode (i.e., alliances, exit), whereas the other two seek to explore dynamic components in the sequential use of different modes (i.e., alliances vs. independent operations; acquisitions and divestitures). These four papers expand among different technology governance modes, theoretical lenses, and single- vs. multi-mode of governance. CEO Ideology and Investor Reactions to Alliances Presenter: Srikanth Paruchuri; Pennsylvania State U. Presenter: Razvan Lungeanu; Northeastern U. Alliance Performance and Subsequent Make-or-Ally Choices. Evidence from the Aircraft Manufacturing Presenter: Charlotte Ren; Fox School of Business, Temple U. Presenter: Louis Mulotte; Tilburg U. Presenter: Pierre Dussauge; HEC Paris Presenter: Jaideep Anand; Ohio State U. The Influence of Organizational Investors on Unrelated Businesses’ Exits Presenter: Xavier Castaner; U. of Lausanne Presenter: Nikolaos Kavadis; U. Carlos III de Madrid Exploring the Inter-Related Use of Acquisitions & Divestitures in Reconfiguration Strategy Presenter: Elena Vidal; City U. of New York, Baruch College Presenter: William G. Mitchell; U. of Toronto
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".