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Record W2337904916

Nigeria SMEs Participation in Electronic Economy: Problems and the Way Forward

2008· article· en· W2337904916 on OpenAlexvenueno aff
Adekunle, Paul Adesola, Tella, Adeyinka

Bibliographic record

VenueThe Journal of Internet Banking and Commerce · 2008
Typearticle
Languageen
FieldEngineering
TopicICT Impact and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsIndustrialisationThe InternetBusinessSmall and medium-sized enterprisesDigital economyScale (ratio)Developing countryEconomyEconomic systemIndustrial organizationEconomicsEconomic growthMarket economyComputer scienceFinance
DOInot available

Abstract

fetched live from OpenAlex

For both developing and developed countries, small and medium scale firms play important roles in the process of industrialization and economic growth. Apart from increasing per capita income and output, Small and Medium Scale Enterprises (SMEs) create employment opportunities, enhance regional economic balance through industrial dispersal and generally promote effective resource utilization considered critical to engineering economic development and growth. The development of SMEs is seen as accelerating the achievement of wider economic and socio-economic objectives, including poverty alleviation. Unleashing its influence on the way traditional business is conducted hitherto is the phenomenon of electronic commerce (e-commerce). The Internet through its reduction in distance-related costs is seen by many as a potential source of economic revitalization. This Internet economy has continued to grow at an unprecedented rate. Thus, this paper focused on the extent of Nigerian’s SMEs participation in this Internet economy. This will be achieved by considering the conceptual framework of Internet/electronic economy, some applications of Internet/electronic economy, Nigeria SME participation in electronic economy, the benefits and limitations of electronic economy in general, the problems hindering Nigeria SMEs’ participation in electronic economy and finally pointing out the way forward.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.770
Threshold uncertainty score0.149

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 communication0.0000.000
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 head0.014
GPT teacher head0.234
Teacher spread0.220 · 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 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

Citations21
Published2008
Admission routes1
Has abstractyes

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