The role of digital B2B platforms with industry 4.0 technological ecosystems(integration of cloud computing, artificial intelligence and internet of things) as a growth lever
Bibliographic record
Abstract
This study investigates how digital business-to-business (B2B) platforms transform manufacturing industries through Industry 4.0 ecosystems and emerging digital technologies such as AI, Cloud Computing, and IoT. Employing methodological triangulation, the research provides comprehensive insights into platform-enabled digital transformation pathways through systematic literature review, case study analysis, and secondary data examination. The research applies the Technology-Organisation-Environment (TOE) framework to develop the Growth Lever Framework for Digital B2B Platforms, using Alibaba.com as a comprehensive case study. By analysing digital transformation mechanisms, the study reveals critical insights into the orchestration of global digital platforms as technological ecosystems. Key findings highlight the complex dynamics of digital platform evolution; Technological integration success is contingent upon platform governance structures, with users prioritising efficient supplier communication and comparison systems. Resource optimisation emerges as a critical mechanism, enabling manufacturers to strategically allocate digital transformation investments while maintaining operational capabilities. Significant regional variations emerge between emerging markets (Pakistan, Brazil, India) and developed markets (UK, Canada), revealing the profound impact of regulatory environments on platform success. The Growth Lever Framework and Digital Platform Ecosystem Technological Readiness Model (DPETRM) created within this study contribute theoretical perspectives by conceptualising how B2B platforms drive manufacturing transformation. By examining the interplay of technological capabilities, organisational structures, and environmental factors, the research offers strategic guidance for stakeholders navigating increasingly complex and emerging digital technological ecosystems.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".