MétaCan
Menu
Back to cohort
Record W1545302324

Mobile Banking: Innovation for the Poor

2011· article· en· W1545302324 on OpenAlexfundno aff
Tashmia Ismail, Khumbula Masinge

Bibliographic record

VenueUpSpace Institutional Repository (University of Pretoria) · 2011
Typearticle
Languageen
FieldEngineering
TopicICT Impact and Policies
Canadian institutionsnot available
FundersMaastricht Economic and Social Research Institute on Innovation and Technology, United Nations UniversityInternational Development Research Centre
KeywordsWork (physics)BusinessPolitical scienceEconomic growthEngineeringEconomics
DOInot available

Abstract

fetched live from OpenAlex

Access to, and the cost of, mainstream financial services act as a barrier to financial inclusion for many in the developing world. The convergence of banking services with mobile technologies means however that users are able to conduct banking services at any place and at any time through mobile banking thus overcoming the challenges to the distribution and use of banking services. This research examines the factors influencing the adoption of mobile banking by people at the Base of the Pyramid (BOP) in South Africa, with a special focus on trust, cost and risk Data for this study was collected through paper questionnaires in townships around Gauteng. This research has found that customers in the BOP will consider adopting mobile banking as long as it is perceived to be useful and to be easy to use. But the most critical factor for the customer is cost; the service should be affordable. Furthermore, the mobile banking service providers, both the banks and mobile network providers, should be trusted. Trust was found to be significantly negatively correlated to perceived risk. Trust therefore plays a role in risk mitigation and in enhancing customer loyalty.

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)0.002
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.002
Scholarly communication0.0050.005
Open science0.0000.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0220.003

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 head0.023
GPT teacher head0.207
Teacher spread0.184 · 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 designObservational
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

Citations28
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueUpSpace Institutional Repository (University of Pretoria)Same topicICT Impact and PoliciesFrench-language works237,207