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Record W4283075404 · doi:10.5539/ijef.v14n7p18

The Transition of E-Commerce Industry in Bangladesh: Added Concerns & Ways of Recovery

2022· article· en· W4283075404 on OpenAlexvenueno aff
Md. Shahnur Azad Chowdhury, Mohammad Arafat Uddin Bappi, Mohammad Nahid Imtiaz, Sayema Hoque, Serajul Islam, Md. Shariful Haque

Bibliographic record

VenueInternational Journal of Economics and Finance · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinTech, Crowdfunding, Digital Finance
Canadian institutionsnot available
Fundersnot available
KeywordsTransparency (behavior)BusinessMarketingCoronavirus disease 2019 (COVID-19)State (computer science)LawPolitical scienceComputer science

Abstract

fetched live from OpenAlex

The retail business is undergoing a huge upheaval around the world, and Bangladesh is following suit. In Bangladesh, ecommerce is still a new and developing business that is rapidly expanding. We have outlined the current fresh issues and areas for improvement in Bangladesh’s ecommerce sector in this research. It also tries to depict the overall recent controversial scenarios of ecommerce in Bangladesh. The paper is organized by anatomizing various secondary sources on ecommerce news and articles. Moreover, During the covid-19 situation this sector has glimpsed a remarkable upthrust as to people are barred from going outside to get their regular commodities. Following this uprising, even in this post pandemic condition, ecommerce business in Bangladesh has emerged tremendously. But some ecommerce ventures fabricated fraudulence and treachery with consumers which let this potential sector on the edge of destruction now. Necessary numerous steps must be needed to make available different facilities and establishing policies to rebuild trust in ecommerce as well as to ensure transparency. Thus, this article comprises suggestions and ways of recovery and for improvements in overall legal framework and operational activities to overcome current sensitive state of ecommerce in Bangladesh.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.810
Threshold uncertainty score0.269

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.036
GPT teacher head0.231
Teacher spread0.194 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations1
Published2022
Admission routes1
Has abstractyes

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