Quantitative Verification of Cost-effectiveness Advantages: Research on China’s Smartphone Export Based on Demand Elasticity Model (2023–2024)
Bibliographic record
Abstract
In recent years, Chinese smartphone brands have achieved remarkable and far-reaching success in the global market, capturing significant market share and reshaping industry dynamics. This research aims to quantitatively verify that “cost-effectiveness” is its core competitive advantage from the perspective of economics, employing rigorous data analysis and theoretical frameworks to demonstrate how Chinese manufacturers deliver superior value propositions compared to international competitors. By collecting market data from IDC, Canalys and other institutions from the first quarter of 2023 to the second quarter of 2024, this article first describes the trend of China’s mobile phone exports, and then constructs a demand price elasticity model for empirical analysis. The calculation results show that the demand elasticity coefficient of Chinese smartphones is about -1. 8, indicating that the demand is elastic, and the price reduction strategy can effectively stimulate sales growth. The case study further confirms the success of Xiaomi with this model. Finally, this article discusses the challenges faced by this model and puts forward future prospects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".