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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".