Application of the 4Es in Online Crowdfunding Platforms: A Comparative Perspective of Germany and China
Bibliographic record
Abstract
As a dynamic way to raise funds for professional and private projects in recent years, crowdfunding has made tremendous progress, especially through online platforms. However, research on this subject is still young, leaving room for different perspectives. We therefore approach the marketing mix adaptability of online crowdfunding platforms and its impact on campaign efficiency and company strategy in two major economies: Germany and China. With the help of case examples based on secondary data, we performed an in-depth analysis of the 4E marketing mix benefits on crowdfunding, highlighting best practice approaches. We critically discuss the 4Es marketing mix approach, focusing on experience, value exchange, and marketing scales, and clarify the compatibility between crowdfunding and 4Es to better understand how these theories are applied to crowdfunding activities. As a result, the suitability of the 4E marketing mix adapted to crowdfunding needs is shown. From a market-oriented perspective, managers of crowdfunding platforms, as well as project owners from Germany and China, will be better able to attract their target audience by applying the 4E adaptation provided.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".