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Record W1488901915

Small and Medium Enterprises (SME) Adjustments to Information Technology (IT) in Trade Facilitation: The South Korean Experience

2009· preprint· en· W1488901915 on OpenAlexfundno aff
Junsok Yang

Bibliographic record

VenueEconstor (Econstor) · 2009
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsTrade facilitationFacilitationBusinessSmall and medium-sized enterprisesIndustrial organizationInformation technologyInternational tradeEconomicsTrade barrierManagementPolitical scienceFinance
DOInot available

Abstract

fetched live from OpenAlex

This report examines how IT was incorporated into cargo clearance procedures in Korea, and what its implications are for traders, SMEs in particular. After a short introduction in Section I, Section II examines the definition of SMEs in Korea, and SMEs' role in Korean trade. In Section III, we describe the history of the adoption of IT in Korean cargo clearance. The introduction of IT to cargo clearance procedures in Korea can be roughly divided into two stages. The first stage includes the implementation of: Preparation for Customs Clearance Automation (1980s-1992), EDI Customs Clearance Automation Six Year Plan (1992-1997), Establishment of Paperless Customs Clearance System (1997-2001); and the Plan for Establishment of Infrastructure for Information Technology and Knowledge Management (2001-2003). The main accomplishment of the first stage was a Value Added Network / Electronic Data Interchange (VAN/EDI) which linked KCS and traders in 1996. The system was subsequently expanded so that traders could access the system through the Internet. The second stage begun in 2003, and has nearly reached completion in 2008. The goal of the second stage is to build an e-trade system where IT is used at every stage of trade, encompassing not only government-business (traders) transactions such as cargo clearance, but all trade-related transactions including business-business transactions as well. This second stage involves the establishment of an e-trade network and uTradeHub,” which ties not only government with traders, but other trade-related organizations and private agencies such as shippers, insurers, banks and financial institutions. These projects were carried out with considerations for SMEs in mind. Section IV describes the results of the adoption of IT into cargo clearance. We find that IT has significantly lowered costs and sped up the cargo clearance process. Section V includes some case examples of individual firms which use the e-trade network for cargo clearance. Lastly, section VI tries to draw some lessons for other countries which seek to adopt IT into cargo clearance. These lessons include: 1) Adopting IT to cargo clearance must be a part of a comprehensive customs procedure reform. 2) Legal framework must accompany the adoption of IT and e-trade 3) Single network and single standard may be more useful than variety 4) Keep It Simple 5) The e-trade system and paperless trade system is meant to be used by the widest number of people. 6) Trust must be built between SMEs and government agencies. 7) Usefulness of e-trade will increase exponentially when more countries join.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.040
GPT teacher head0.224
Teacher spread0.184 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2009
Admission routes1
Has abstractyes

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