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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".