MétaCan
Menu
Back to cohort
Record W2153470068 · doi:10.5267/j.msl.2014.9.009

Identifying barriers for ICT development in companies' registration office

2014· article· en· W2153470068 on OpenAlexvenueno aff
Rasool Mehdian, Gholamreza Hashemzadeh

Bibliographic record

VenueManagement Science Letters · 2014
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessInformation and Communications TechnologyProcess managementComputer scienceKnowledge managementOperations managementMarketingWorld Wide WebEconomics

Abstract

fetched live from OpenAlex

One of the necessary issues in business development is to register it with government, which helps an organization gain an official identification.However, registering a firm with government often involves with many challenges in different countries.The recent advances in information technology have created tremendous opportunities to expedite the process of business development.This paper presents an empirical investigation to prioritize various factors influencing on information and communication technology development in noncommercial business registration office in Iran.The proposed study designs a questionnaire with eight categories and distributes it among some experts.The results of our survey indicate that Creating a sense of urgency is number one important factor followed by Appropriate planning, Form a coalition FAQ, Fixed improvements, Empowering, Creating a good perspective, New approaches to institution-building and Explanation of Vision.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0050.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.092
GPT teacher head0.362
Teacher spread0.270 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueManagement Science LettersSame topicTechnology Adoption and User BehaviourFrench-language works237,207