How Do Manufacturing Enterprises Construct E-Commerce Platforms for Sustainable Development? A Case Study of Resource Orchestration
Bibliographic record
Abstract
The existing literatures mainly focus on the pricing, strategic significance and sustainable development characteristics of the e-commerce platform, and lack deep research on mechanisms in the process of construction like main structure of recourses and driving force. This paper takes Haier as a Chinese example and explores how manufacturing enterprises create and develop the sustainable e-commerce platform. The research findings show that: (1) An e-commerce platform respectively carries the functions of sales channels, service differences and innovation incubation in different stages of the manufacturing enterprises’ sustainable development; (2) For managing e-commerce platform of manufacturing enterprises’ sustainable development, resource orchestration can effectively realize the integration of value creation and resource; (3) Finally, it further reveals that the driving power which resource orchestration continuously promotes for the sustainable e-commerce platforms to construct is from the co-creation value of manufacturers and users. This paper discusses the structure of e-commerce platforms based on the main characteristics of each resource, and systematically explores the mechanism and evolutionary driving force of resource orchestration to promote the construction of e-commerce platforms for the sustainable development. It complements and enriches the innovation ecosystem and resource orchestration theory, providing significant practical guidance to the sustainable development of manufacturing enterprises.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| 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".