Platform Economy and Global Supply Chain Integration: A Technology-Driven Approach to Business Model Innovation
Bibliographic record
Abstract
There is no question that the platform economy has integrated deeply into global supply chains through advanced technologies like blockchain, cloud computing, AI, and IoT.This work investigates how digital platforms can improve the transparency, coordination and efficiency of supply chains by addressing known inefficiencies such as delays in data updates or general time based failures adopted for access.This research uses case study methodology focussing on multinational enterprises (MNEs) which involve platform based technologies in their supply chains.Data collection consisted of semi-structured interviews, operational data from the supply chain system and document analysis.This paper calculated the relative impact of platform technology adoption on supply chain efficiency by using Structural Equation Modeling (SEM) model.The results indicate a highly significant positive association (0.82) between digitalization and improvements in supply chain productivity In particular, blockchain provided 25% of the efficiency gains, cloud computing added another 30%, and AI & machine learning amounted to just under a quarter (24%), with IoT contributing two in ten.Ability to blend these technologies provide facility in supply chain coordination and flexibility, notably for industries focusing on data based decision making like real time tracking of products.The results of the research provide further confirmation that using platform technologies is an important factor in increasing levels of integration within a supply chain, as has been found elsewhere in the literature.These implications are key to supply chain strategy for global companies, as such technologies enable resilience and agility.However the study was limited by a small sample size, and an early stage in the adoption of platform technologies.Theoretically interesting and highly relevant to practice, future research could study different industries or regions which may vary in response to how disruptions occur so that patterns observed do not simply arise from specifics of a particular industry.Another stream for investigation is whether platform adoption brings long-term changes on overall supply chain performance or just improves resilience against previous types of disruption.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.009 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".