Nationalism and Organizational Strategy
Bibliographic record
Abstract
While scholars who study strategy, organizations, and international business have long noted nationalism as an important force in organizations’ institutional environment, there has been little research on (1) what impact that nationalism has on organizational performance and strategy, (2) how organizations strategically deploy nationalism to manage their internal and external stakeholders, and (3) what kind of organizations are more likely to demonstrate a high level of nationalism. To address these questions, we propose this symposium composed of four paper presentations and a featured discussion on the topic of nationalism and organizational strategy. Theoretically, these papers argue that nationalism can create both uncertainties for organizations and opportunities for managers to bolster the meaning of their products and the purpose of their organizations. Empirically, these papers adopt a variety of methodology, including the difference-in-difference analysis, the in-depth case analysis, and the machine-learning based text analysis. Finally, these papers are also featured by the diversity of national contexts and study how nationalism affects organizations in the world’s major economic powerhouses, including US, China, Japan, and Europe. We expect this symposium to significantly advance the scholarly research on nationalism in organizational strategy. Nationalism, Firm Place Identity, and Firm Value: Evidence from the Catalan Secession Crisis Author: Ariel A. Casarin; U. Adolfo Ibañez Author: Sinziana Dorobantu; NYU Stern School of Business Author: Angel Saz-Carranza; ESADE Business School Unpacking Techno-Nationalism: Evidence from Made In China 2025 and The U.S. Policy Response Author: Jing Li; Simon Fraser U. Author: Daniel Shapiro; Simon Fraser U. Author: Peng Zhang; Beedie School of Business Simon Fraser U. Author: Anastasia Ufimtseva; Asia Pacific Foundation of Canada Firms’ Rhetorical Nationalism: Theory, Measurement, and Evidence from a Computational Analysis Author: Lori Qingyuan Yue; Columbia Business School Author: Jiexin Zheng; HKUST Business School Author: Kaixian Mao; Renmin U. of China Nationalism and Corporate Strategy: From Yamaha Pianos to Motorcycles Author: Yusaku Takeda; U. of Illinois at Urbana-Champaign Symposium Discussant Author: Jordan Siegel; U. of Michigan, Ross School of Business
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".