Digitalization in public sector in emerging economies: The enablers and inhibitors influence electronic customs in Vietnam
Bibliographic record
Abstract
This paper investigates how customs officials perceive the implementation of e-customs will influence business performance in Vietnam, a developing country with a lower technological environment. A survey of customs officials was conducted, and data were analyzed by structural equation modelling. The outcomes discover two significant enablers related to relative advantages and the new exploring factor Culture while Finance & Human Resources and Legislation as the inhibitors. Additionally, the study also emphasized that e-customs implementation had a positive influence on firm performance in Vietnam. In addition, the study provides different viewpoints of cultural dimensions in case study of applying e-customs in Vietnam in comparison with previous studies. Culture with attributions related to uncertain acceptance and individualism encourage innovation in other literature reviews, however, the study indicates uncertainty avoidance and collectivism as Vietnam also promotes e-customs deployment. Vietnam with high power distance and short-term orientation became old themes. This emerging country switched to low distance and long-term orientation in terms of e-customs innovation. In contrast to previous studies related to constraints from technology in emerging economies, technological factors are not an obstacle for Vietnam. Furthermore, previous literature reviews inflected legislation and regulations of government as one of the limitations that should be examined in further and this research carried-out this investigation in one of emerging economies. The results of the paper support policy makers who can have essential solutions to enhance e-customs implementation as well as enterprises’ managers set-up strategy to adapt with the modernization environment.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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".