Table 2.
Respondent’s demographic profile.
| Category | Frequency (n) | Percent (n = 361) |
|---|---|---|
| Gender | Male | 42 |
| Female | 58 | |
| Age | 18–25 | 20 |
| 26–35 | 49 | |
| 36–45 | 19 | |
| 46 and above | 12 | |
| Educational Qualification | Undergraduate | 61 |
| Postgraduate | 36 | |
| Doctorate (Ph. D.) | 2 | |
| Annual Income | Below ₹3 Lakhs | 20 |
| ₹3–6 Lakhs | 48 | |
| ₹6–10 Lakhs | 19 | |
| Above ₹10 Lakhs | 14 | |
| Years of Banking Experience | 1–3 years | 20 |
| 4–7 years | 48 | |
| 8–10 years | 19 | |
| Above 10 years | 14 | |
| Awareness of AI-enabled banking services | Yes | 78 |
| No | 22 | |
| Usage of AI-based banking services | Yes | 77 |
| No | 23 | |
| Frequency of interaction | Never | 5 |
| Rarely | 13 | |
| Sometimes | 38 | |
| Often | 30 | |
| Very often | 14 | |
| Perception of data security | Yes | 71 |
| No | 29 | |
| Trust factors | Data security | 27 |
| Transparency | 24 | |
| Ethical practices | 20 | |
| Regulatory compliance | 12 | |
| Human support | 18 | |
| Continued usage intention | Yes | 80 |
| No | 20 | |
| Ethical AI influence on trust | Not at all | 4 |
| Slightly | 11 | |
| Moderately | 24 | |
| Significantly | 35 | |
| Very significantly | 25 | |
| Confidence-building factors | Clear explanations | 22 |
| Secure transactions | 28 | |
| Human oversight | 20 | |
| Regulatory compliance | 12 | |
| Fair treatment | 18 |