Bibliographic record
Abstract
This chapter examines Levitt's idea that MNEs should not worry very much about customizing to cultural preferences. According to Levitt, technology has largely homogenized consumer preferences – most consumers simply want quality, reliability and low price. Therefore, MNEs should focus on offering such products and services. MNEs should standardize their products and services worldwide in order to achieve economies of scale, and should implement global strategies across all markets. These ideas will be examined and then criticized using the framework presented in Chapter 1. Significance ‘The world's needs and desires have been irrevocably homogenized. This makes the multinational corporation obsolete and the global corporation absolute’. This statement sums up Theodore Levitt's bold assertions in his wonderfully written, landmark HBR article, ‘The globalization of markets’. In terms of this book's framework, Levitt sees the multi-centred MNE being gradually replaced by centralized exporters and international projectors . He argues that advances in technology, communications and travel have revolutionized commerce and trade in all parts of the globe, basically conferring additional value to non-location-bound FSAs, and strengthening the MNE's ability to deploy and exploit such non-location-bound FSAs, irrespective of cultural, economic, institutional or spatial distance. Customers throughout the world are thirsty for new products that can now be made available universally. While MNEs have traditionally customized their products to cater to perceived cultural differences across countries and regions, these preferences are converging as technology brings the world closer together into one global market.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.462 | 0.215 |
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".