Navigating culture mixing: a framework for global brand legitimacy in an era of deglobalization
Bibliographic record
Abstract
Purpose This conceptual paper explores how global brands navigate culture mixing to build and sustain legitimacy in response to the market shift from globalization to deglobalization. Design/methodology/approach This research adopts a conceptual approach, integrating the legitimation process perspective and the polyculturalist paradigm to develop a comprehensive framework for understanding global brand legitimacy through culture mixing. The framework begins by contextualizing the transition from globalization to deglobalization, highlighting the shifting market logic and its implications for legitimacy priorities. It then focuses on the active role of global brands in navigating legitimacy challenges through cultural environment considerations and culture-mixing strategies. Insights are derived from existing literature and case-based observations of global brand practices. Findings This paper introduces a dual-lens framework integrating host-country institutional sensitivity and brand cultural symbolism. Four adaptive culture-mixing strategies (i.e. material blending, heritage fusion, assimilation fusion and value blending) are identified and aligned with different stages of the legitimation process. Originality/value This paper advances the legitimation process perspective by contextualizing it within a deglobalized market logic and reframing culture mixing as a long-term legitimacy-building strategy. It offers actionable insights for global brands to balance global-local tensions and achieve sustained legitimacy in volatile environments.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.007 | 0.068 |
| Scholarly communication | 0.019 | 0.025 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".