Impact of Industry 4.0 drivers on the performance of the service sector: comparative study of cargo logistic firms in developed and developing regions
Bibliographic record
Abstract
This study investigates the impact of Industry 4.0 on the performance of the cargo logistic business (service sector) in Bangladesh and Canada. Our drivers of Industry 4.0 include big data, smart factory, cyber physical systems (CPS) and the Internet of things (IoT). However, there is dearth of research showcasing the effects of these drivers on the service sector in various countries. For this reason, we consider the Technology-Organisation-Environment (TOE) framework, as shaped by the institutional theory, within the context of this research. This research adopts a cross-sectional quantitative approach to identify the variation in sub-groups that refer to the samples in Bangladesh and Canada. Through purposive sampling, networking, and connections, a total of 210 (105 each) survey questionnaires, as completed by employees working in logistics companies, were gathered. Smart partial least square-structural equation modelling (PLS-SEM) was used to analyse the collected data, which revealed that Industry 4.0 has a significant role in promoting and improving the performance of the services industry of both economies. However, the impact of all drivers is more highly statistically significant for Canada than for Bangladesh. Thus, this research demonstrates the role of Industry 4.0 in terms of improving the performance of the logistics industries in contrasting economies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".