An Empirical Evaluation of the Effects of Gender Differences and Self-efficacy in the Adoption of E-banking in Nigeria
Bibliographic record
Abstract
The issues of gender disparity in the usage of information technology (IT), as well as self-efficacy, have received considerable interest and attention among researchers in recent times. Prior research has identified that gender differences and self-efficiency affect the attitude towards adoption and use of technology. In general, females are believed to be disadvantaged compared to their male counterparts with respect to IT usage and acceptance. The reasoning is that males are mostly more exposed to technology and tend to have more proficiency with such tools. Very little information exists in the extant literature regarding perceptions in developing parts of the world, including Africa. In this chapter, an empirical evaluation of the issues in the context of e-banking will be made in Lagos (Nigeria) and its environs. An extended Technology Acceptance Model (TAM) will be used as a conceptual framework to guide the discourse. Data analysis was done on SPSS 15.0. The study’s results showed that gender differences moderated the acceptance of e-banking of users in the research context. Namely, computer self efficacy and perceived ease of use were of concerns to females, but less so for their male counterparts. Also, perceived usefulness of e-banking is discovered to be the most influencing factor for male users. The study’s implications for research and practice are discussed in the chapter.
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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 | 0.003 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".