Innovation and Politics
Bibliographic record
Abstract
Scholars of innovation, organization, and strategy recognize the critical role of the political environment in shaping innovation processes and outcomes. While much of the existing research emphasizes how public policies influence innovation, other significant aspects of the interplay between politics and innovation remain underexplored. This symposium shifts attention to the active role of firms in shaping public policies, the incremental nature of policy change and its effects on firms’ adoption of new technologies, and the within-firm political dynamics that influence innovation. The symposium features four studies that provide novel insights into these pivotal issues. The first study investigates how firms lobby the U.S. Supreme Court to influence patent policy, introducing a unique dataset that underscores lobbying as a core component of firms’ innovation strategies. The second study employs advanced textual analysis techniques to capture incremental regulatory changes, highlighting their critical importance for innovation outcomes. The third study examines how chemical disclosure regulations drive divergent technology adoption strategies among public and private firms, revealing the combined effects of public policy and shareholder pressures. The fourth study explores how inventors’ political affiliations shape team dynamics, technological focus, and performance, uncovering the broader implications of political partisanship for innovation. By addressing corporate lobbying, incremental policymaking, and political values within organizations, this symposium offers a more nuanced understanding of how political contexts and dynamics shape innovation strategies and outcomes. It provides theoretical and empirical contributions that will interest scholars in technology and innovation management, as well as those examining the broader intersections of politics, institutions, and non-market strategy. LOBBYING THE COURT FOR PATENT POLICY: AN INTRODUCTION AND NEW DATASET Author: Elie J. Sung; HEC Paris Author: Yasir Dewan; HEC Paris COMPUTATIONAL QUANTIFICATION OF GREEN REGULATION Author: Michael Park; INSEAD Author: Shuping Wu; INSEAD Author: Zhen Ge; INSEAD Author: Patrick McLaughlin; Stanford University DIRECTED TECHNICAL CHANGE THROUGH DISCLOSURE: EVIDENCE FROM THE U.S. FRACKING INDUSTRY Author: Xiaoli Tang; Bocconi University HOW DO INVENTORS’ POLITICAL PREFERENCES AFFECT INNOVATION? Author: John M. De Figueiredo; Duke University Author: Trijeet Sethi; Author: Brian Silverman; University of Toronto
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".