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Record W4411656746 · doi:10.51847/qm37pavbr5

10.51847/qM37PAvBR5

2000· article· en· W4411656746 on OpenAlexvenueno aff

Bibliographic record

VenueTime to knit · 2000
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicDigital Platforms and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsE-commerceComputer scienceBusinessWorld Wide Web

Abstract

fetched live from OpenAlex

Aims: This paper aims at highlighting the importance of e-commerce adoption in Iran and refers to its technical, social-cultural and managerial implementation challenges, and by providing a ranking for each dimension of these challenges, wants to know solving or at least minimizing the adverse effects of which one has the most influenceon implementation of e-commerce in Iran.Study design:Quantitative research design.Place and Duration of Study: Iran, in 2013.Methodology: In this research system dynamics approach was used and the necessary data collected from previous researches, then analyzed by VENSIM software.Since the most of projects in Iran are short-term projects, a 5 year interval used for data analysis with the change rate of 20%.Because 20% is the lowest rate that best represents the effects of applied changes.Results: Data analysis showed that reducing every dimension of each challenge by 20% will have a great effect on the implementation of e-commerce in Iran.Conclusion:The research findings revealed that the shortage of internet service providers (as a technical challenge), Officials and decision makers' lack of familiarity with the structure and function of e-commerce (as a social-cultural challenge) and lack of strategic management (as a managerial challenge) are the most important implementation challenges of e-commerce in Iran

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.938
Threshold uncertainty score0.551

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 communication0.0010.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.9860.996

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 head0.007
GPT teacher head0.149
Teacher spread0.141 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2000
Admission routes1
Has abstractyes

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