The Blackwell Handbook of Cross‐Cultural Management
Bibliographic record
Abstract
Preface. Editorsa Introduction. Part I: Frameworks For Cross--Cultural Management:. 1. National Culture and Economic Growth: Richard H. Franke (Loyola College), Geert Hofstede (Tilburg University), and Michael H. Bond (Chinese University of Hong Kong). 2. Generic Individualism and Collectivism: Harry C. Triandis (University of Illinois). Part II: Strategy, Structure, and Inter--organizational Relationships:. 3. Cultures, Institutions, and Strategic Choices: Towards an Institutional Perspective on Business Strategy: Mike W. Peng (The Ohio State University). 4. Knowledge Acquisition through Alliances: Opportunities and Challenges: Paul Almeida (Georgetown University), Robert Grant (Georgetown University) and Anupama Phene (University of Utah). 5. Cooperative Strategies Between Firms: International Joint Ventures: Louis Hebert (The University of Western Ontario) and Paul W. Beamish (The University of Western Ontario). 6. The Importance of the Strategy--Structure Relationship in MNCs: William Egelhoff (Fordham University). Part III: Managing Human Resources Across Cultures:. 7. Human Resource Practices in Multinational Companies: Chris Brewster (Cranfield School of Management). 8. Goal Setting, Performance Appraisal, and Feedback Across Cultures: Pino G. Audia (London Business School) and Svenja Tams(London Business School). 9. Employee Development and Expatriate Assignments: Mark Mendenhall (University of Tennessee), Torsten M. Kuehlmann (University of Bayreuth), Guenter K. Stahl, and Joyce S. Oslund. Part IV: Motivation, Rewards, and Leadership Behavior:. 10. Culture, Motivation, and Work Behavior: Richard M. Steers (University of Orgeon) and Carlos J. Sanchez--Runde (IESE University of Navarre). 11. Cross--Cultural Leadership: Peter B. Smith (University of Sussex) and Mark F. Peterson (Florida Atlantic University). 12. Women Leaders in the Global Economy: Nancy J. Adler (McGill University). Part V: Interpersonal Processes:. 13. Structural Identity Theory and the Dynamics of Cross--Cultural Work Groups: P. Christopher Earley (Kelley School of Business) and Marty Laubach. 14. Cross--Cultural Communication: Richard Mead (University of London) and Colin J. Jones (University of Hull Business School). 15. Cross--Cultural Negotiation and Conflict Management: Michele J. Gelfand (University of Maryland) and Christopher McCusker (Yale School of Management). Part VI: Corporate Culture and Values:. 16. Justice, Culture, and Corporate Image: The Swoosh, the Sweatshops, and the Sway of Pulbic Opinion: Robert J. Bies (Georgetown University) and Jerald Greenberg (Ohio State University). 17. Trust in Cross--Cultural Relationships: Jean L. Johnson (Washington State University) and John B. Cullen (Washington State University). 18. Business Ethics Across Cultures: Diana C. Robertson (Emory University). Index.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.096 | 0.046 |
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".