Exploring the relationships between Industry 4.0 implementation factors through systems thinking and network analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Industry 4.0 provides companies with the technological and theoretical means to enhance data‐driven decision‐making procedures. To facilitate the transformation process, several studies have identified factors that need to be considered when implementing Industry 4.0 on a broader level. However, the dynamic relationship between these factors has yet to be understood to provide companies with the in‐depth knowledge needed to effectively manage the transition. The principal aim of our research is therefore to map out the complex relationships between the identified factors, by adapting a novel approach that combines network analysis and causal loop diagrams. Results show that the roles of implementation factors are not static, and what role they play depends on their position in the network, complementing the findings of previous investigations about the drivers of change. Furthermore, our findings indicate that multiple intervention points exist, shedding more light on how to develop effective implementation strategies.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it