The Impact of Cross-cultural Human Resource Management on Brand Consistency of Multinational Enterprises and Optimization of Management Strategies
Bibliographic record
Abstract
This article aims to explore the impact of cross-cultural human resource management on brand consistency in multinational enterprises and the directions for optimization. The research methods adopted include literature analysis and case study. It describes the historical background of three development stages in this field, analyzes the current situation and influence related to the topic, and takes McDonald's as an example to propose the content and implementation process of optimization strategies. The research findings show that the current cross-cultural human resource management has both positive and negative impacts on brand consistency in multinational enterprises: efficient management can promote cultural integration, strengthen employees' brand consensus, reduce cognitive biases, and contribute to brand consistency; inefficient management, on the other hand, can lead to cultural conflicts, internal divisions, and damage brand consistency. Taking McDonald's as an example, it effectively maintains brand consistency through strategies such as establishing a standardized system, advocating a diverse and inclusive culture, and promoting local talent. The conclusion indicates that multinational enterprises need to attach importance to cross-cultural human resource management, adopt targeted strategies to address cultural differences, and maintain brand image consistency and enhance global influence.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".