On the determinants, gains and challenges of electronic banking adoption in Nigeria
Bibliographic record
Abstract
Purpose The purpose of this study is to examine the gains, challenges and determinants of electronic banking adoption in Nigeria. Design/methodology/approach This paper applied the generalized structural equation modelling (GSEM) to a large sample of respondents surveyed from five of the six geopolitical zones of Nigeria to model the determinants of electronic banking. In addition to many other advantages, GSEM can be used as a likelihood function. As a result, this paper proposes GSEM as the most appropriate tool for modelling the socioeconomic determinant of electronic banking adoption. Findings About three-quarter of respondents adopted at least a form of electronic banking. However, only a tenth of users used e-banking for purchase of goods or services, implying low electronic payment adoption. The low adoption of electronic payment was due to poor digital security infrastructure which made users vulnerable to widespread electronic frauds. The findings also show that the adoption of e-banking platforms or services was characterized by users' socioeconomic status. For example, the odds of adopting internet/mobile banking decreases with older users but increase with higher educational attainment and income, whereas the odds of adopting e-banking platforms such as short message service (SMS) and point of sale (POS) banking increases with older users and informally employed users respectively. Practical implications For a sustainable cashless economy and financial inclusion in Nigeria, policy consolidation that provides safe e-banking services is necessary. Also, e-banking service providers should deliver specific contents and services that match the physical and economic characteristics of users. Originality/value Generalized structural equation modelling (GSEM) is a robust likelihood function method that combines the power of structural equation modelling with the generalized linear model. The application of GSEM to predict the likelihood of adopting a banking technology or Service has not been explored in electronic banking literature. Also, as a fast-growing economy with a heterogeneous population, Nigeria presents an interesting context to study the determinants of electronic banking.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".