Comparison of logistic regression results for the Polish and Lithuanian samples
| Variable | OR (PL) | 95% CI (PL) | p (PL) | OR (LT) | 95% CI (LT) | p (LT) |
|---|---|---|---|---|---|---|
| Average assessment of barriers | 0.60 | 0.41–0.89 | < 0.01 | 0.576 | 0.0767–4.322 | 0.591 |
| Large enterprise status | 2.15 | 1.00–4.62 | < 0.05 | 1.904 | 0.4698–7.716 | 0.367 |
Comparison of technology adoption levels (LTAI) between SMEs and large enterprises
| Country | Category | LTAI mean | U | p value |
|---|---|---|---|---|
| Poland | Small and medium-sized | 0.271 | 306.0 | .007 |
| Large | 0.591 | |||
| Lithuania | Small and medium-sized | 0.041 | 259.5 | .118 |
| Large | 0.048 |
Results of χ2 significance tests for comparing the use of logistics technologies in Poland and Lithuania
| Technology | χ2 | p value |
|---|---|---|
| Smart cargo tracking | 10.50 | .0012 |
| Intelligent vehicle technology | 38.47 | < .0001 |
| Data analytics & predictive | 29.10 | < .0001 |
| IoT supply-chain tracking | 14.25 | .0002 |
| e-CMR | 5.93 | .015 |
Smart technologies use in food logistics
| Smart technology | Key applications in food logistics | Benefits |
|---|---|---|
| Blockchain |
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| Internet of Things (IoT) |
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| Big data analytics |
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| Cloud computing |
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| Smart contracts |
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| 5G connectivity |
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| Artificial intelligence (AI) |
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| Drones |
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| Digital platforms |
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| Supply chain digital integration |
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Descriptive statistics of the LTAI indicator for Poland and Lithuania
| Parameter | Poland | Lithuania |
|---|---|---|
| Average LTAI | .431 | .044 |
| Median | .500 | .000 |
| Standard deviation | .296 | .067 |