Bibliographic record
Abstract
Management challenge No one said being a manager is easy, and this opening chapter illustrates why. With increasing globalization comes increased pressure for both change and competitiveness. Understanding this changing environment is our first challenge. The second is building mutually beneficial interpersonal and multicultural relationships with people in different parts of the globe in order to overcome these challenges and take advantage of the opportunities presented by the turbulent global environment. We argue here that an important key to succeeding in the global business environment is developing sufficient multicultural competence to work and manage successfully across cultures. Chapter outline ▪ Globalization, change, and competitiveness page 13 ▪ The emerging global landscape 19 ▪ Management and multicultural competence 25 ▪ Summary points 28 Applications 1.1 Canada Post 15 1.2 Hamburgers and A380s 18 1.3 Apple iPhone 19 1.4 Ethanol and the price of tortillas in Mexico 23 1.5 Launching a new venture in India 25 A competitive world offers two possibilities. You can lose. Or, if you want to win, you can change. Lester Thurow Sloan School of Management, Massachusetts Institute of Technology, United States In the future, the ability to learn faster than your competitors may be the only sustainable competitive advantage. Arie de Geus Corporate planning director, Royal Dutch Shell, the Netherlands We live in a turbulent and contradictory world, in which there are few certainties and change is constant. Over time, we increasingly come to realize that much of what we think we see around us can, in reality, be something entirely different. We require greater perceptual accuracy just as the horizons become more and more cloudy. Business cycles are becoming more dynamic and unpredictable, and companies, institutions, and employees come and go with increasing regularity. Much of this uncertainty is the result of economic forces that are beyond the control of individuals and major corporations. Much results from recent waves of technological change that resist pressures for stability or predictability. Much also results from the failures of individuals and corporates to understand the realities on the ground when they pit themselves against local institutions, competitors, and cultures. Knowledge is definitely power when it comes to global business, and, as our knowledge base becomes more uncertain, companies and their managers seek help wherever they can find it.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".