Factors Hindering the Adoption of the Customs Electronic Licensing System (CELS) by Clearing and Forwarding Agents in Zambia
Bibliographic record
Abstract
While the Zambia Revenue Authority (ZRA) aims at improving and making it much easier, more efficient and faster to process clearing agents’ licenses added to mass sensitization and training on the Customs Electronic Licensing System (CELS), the adoption and utilization of the system among the clearing and forwarding agents in all the Zambian border posts is very low. This study aimed at determining the factors that impede the adoption and implementation of the CELS by the clearing agents and recommending the best ways in which ZRA can enhance the utilization of the electronic platform and boost compliance. The study was conducted within the population of the Zambia Revenue Authority and duly licensed Clearing Agents domiciled at Kenneth Kaunda International Airport (KKIA) as well as those working at Nakonde, Chirundu, Mwami, Katima Mulilo, Kasumbalesa and Kazungula border posts. We employed the mixed method approach, the quantitative and qualitative methods with a study sample of 263 licensed clearing and forwarding agents. However, analysis was based on 178 agents (the response rate was 68%). Binary Logistic was fitted on the date to determine the bottlenecks to the adoption of CELS. Factors which determined as bottlenecks to the adoption of the CELS are “Not Having ICT Skills”, “Not Having an electronic Device PC/Smartphone”, “No Access to Internet”, “Difficulty of Use of the CELS” and “Not Having Knowledge on CELS”. The challenges faced by clearing and forwarding agents include; not having electronic devices personal computers (PCs) or Smartphones, not having the required information technology skills, not having access to internet, and not having adequate knowledge about the system. To promote compliance among the agents, ZRA needs to do the following; make the possession of a computer or smartphone to be a mandatory requirement for an individual to be given the clearing and forwarding license; provide the required information technology skills training specifically on CELS; provide free internet only accessible to the registered agents to allow them to fully access the CELS, as well sensitization programs to ensure that all agents have knowledge on CELS.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".