IoT in Courier Services: Impact on Customer Satisfaction and Supply Chain Sustainability
Bibliographic record
Abstract
The increasing importance of integrating the Internet of Things (IoT) within the courier service sector in today's digital landscape highlights the necessity for an in-depth examination of how IoT intricately affects customer satisfaction and the sustainability of supply chains in this industry. The objective of this study is to assess the impact of IoT on both customer service and the sustainability of supply chains, with a specific focus on electronic document management, route optimization, and real-time information. A random sampling survey technique was employed to collect data from 310 participants in the Malaysian courier service company in the year 2022. The data analysis primarily relied on quantitative methods, with the utilization of multiple regression analysis as a key technique for evaluating the effects. The outcomes of this study provide validation for our hypotheses concerning the positive influence of electronic document and route optimization, while also emphasizing the critical aspect of managing real-time information. These findings contribute invaluable insights for courier service companies striving to harness the capabilities of IoT to enhance service quality and promote sustainable operations. Future research directions may delve deeper into the intricate dynamics of IoT's role to explore its scalability and cost-effectiveness within courier service companies
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".