Factors Impacting Senior Citizens’ Adoption of E-Banking Post COVID-19 Pandemic: An Empirical Study from India
Bibliographic record
Abstract
The global economy and the financial sector have suffered due to the COVID-19 epidemic. The banking industry has seen an increase in digital channels and payments, consumer behavior changes, regulatory and supervisory relief, and new operational resilience challenges due to the COVID-19 pandemic. Therefore, seniors have had to adopt new channels and technologies instead of traditional cash and traditional channels. However, older people in India are not tech-savvy and avoid e-banking. Thus, stakeholders (e.g., bank authorities, governments) must focus on variables affecting the older population’s use of e-banking to reduce financial isolation. Thus, this study uses an extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework to examine senior citizens’ intentions to use e-banking. Data from “456” senior citizens from central India were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) techniques. The study identified different significant predictors (e.g., performance expectancy, effort expectancy, perceived risk, self-efficacy, perceived trust, and anxiety) of older users’ intention to use e-banking post-COVID-19. This is the first study from central India to determine elderly people’s intention to use online banking during and after the COVID-19 pandemic. The findings will help bank authorities and other stakeholders increase senior citizens’ financial inclusion in India.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".