Introduction
Bibliographic record
Abstract
Abstract International business strategy is a field where theory continuously seeks to meet business practice. Increasingly, scholars of international business strategy are concerned with the uncertainties and complexities of international operations, especially when firms commit significant resources to foreign markets. Over time, multinational enterprises have evolved in order to manage the challenges in their environments. The contributions in this volume address key remaining challenges and opportunities for the modern multinational enterprise. These contributions include refinements of traditional ideas about the role of firm-specific and country-specific advantages as well as new knowledge around how the heterogeneity observed in international business strategic behavior stems from the size, origin, governance and other characteristics of the firm. Further, we invite the reader to explore new dimensions of international business strategy, in order to understand the strategic implications of digitalization or the increased social pressure placed on MNEs to “do the right thing” and manage international operations responsibly, in ever changing social, economic and institutional environments. Each chapter provides insightful future research directions and implications for management and policy. This collection is a complete Handbook of International Business Strategy that should serve as a knowledge repository for scholars and managers alike.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.562 | 0.408 |
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".