MétaCan
Menu
Back to cohort
Record W4226342486 · doi:10.1080/09537287.2022.2058412

Improving operational efficiency through blockchain: evidence from a field experiment in cross-border trade

2022· article· en· W4226342486 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProduction Planning & Control · 2022
Typearticle
Languageen
FieldComputer Science
TopicBlockchain Technology Applications and Security
Canadian institutionsWilfrid Laurier University
FundersFundamental Research Funds for the Central UniversitiesNational Natural Science Foundation of China
KeywordsBlockchainTraceabilityAgency (philosophy)BusinessTrade financePaymentOperational efficiencyInterface (matter)Industrial organizationSupply chainField (mathematics)CommerceComputer scienceFinanceComputer securityEconomicsMarketing

Abstract

fetched live from OpenAlex

Based on the practical needs of cross-border trade between Yunnan Province, China, and Southeast Asian countries, this paper studies the impact of blockchain technology usage on operational performance. Owing to immutability and traceability, the implementation of a blockchain-based platform solves several existing problems of cross-border trade. We conducted a field experiment to verify the effect of blockchain technology on the operational efficiency of cross-border trade companies. The results show that operational, agency, and time costs significantly decrease. Blockchain usage reduces the intermediary agencies of cross-border trade and settlement payment for traders, also improves the timeliness of business processing and the efficiency of cross-departmental business collaboration. This study provides new insights on the interface of blockchain technology and supply-chain operation management.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.433
Threshold uncertainty score0.622

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.323
Teacher spread0.305 · 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