What are the obstacles hindering digital transformation for small and medium enterprise freight logistics service providers? An interpretive structural modeling approach
Bibliographic record
Abstract
Digital Transformation (DT) allows logistics service providers (LSPs) to gain competitive advantages by reducing costs, and creating customer experience, innovation and efficiency. This paper proposes a systematic framework to analyse the obstacle factors hindering DT of Thailand small and medium enterprises freight LSPs. First, thirteen obstacles are identified through the extensive literature and validated by a panel of experts. Second, a nine-level hierarchical structure is determined based on Interpretive Structural Modelling to demonstrate the complex interrelationships among obstacle factors. Finally, thirteen obstacles are categorized regarding the driving and dependence power by employing Matrix Impact of Cross Multiplication Applied to Classification approach. The results indicate a lack of digital culture being the most important obstacle hindering DT, followed by lack of support and commitment from top management and lack of risk taking initiative. This finding could help LSPs who aim for DT to take appropriate steps to alleviate obstacles.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| 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".