A matter of trust? Perceptions about the adoption of virtual banking services in Hong Kong
Bibliographic record
Abstract
Purpose The paper aims to corroborate the literature on the perceptions about the adoption of virtual banking services and identify factors driving potential users’ intention to subscribe to these services in Hong Kong. Design/methodology/approach This paper employs the partial least square-structural equation modeling technique to analyze a set of data collected from surveying a sample of virtual bank non-users in Hong Kong, aiming to identify the important factors affecting these users to try virtual banking services in an Asian financial centre. Findings The results indicate that trust in virtual banks is the most important factor driving respondents’ intention to try these banks’ services, followed by perceived compatibility, performance expectancy, and familiarity with internet banking. Trust, in turn, is positively affected by perceived compatibility and performance expectancy, and negatively influenced by perceived risk. The results also show that, in addition to their direct effects, perceived compatibility and performance expectancy have positive indirect effects on respondents’ usage intention of these services through their initial trust in these banks. With no significant direct effect, perceived risk has a negative indirect effect on respondents’ usage intention. Practical implications Our findings are of interest to virtual banks in an overbanked small city, helping them formulate effective marketing strategies to expand their customer base, which would improve the sustainability of these banks in Hong Kong. These findings are also of interest to the city's policymakers, who can use them to revise their policies on better developing the virtual bank market in the future. Our findings will offer valuable lessons for virtual banks and their policymakers in other markets as well. Originality/value The findings presented in this paper are relevant and original, given that Hong Kong is a tiny city ranked third in the Global Financial Centre Index and has just introduced virtual banks to its overbanked market. Our study is the first to investigate the factors determining consumers’ usage intention of virtual banking services and their initial trust in virtual banks in Hong Kong. Our study is also the first to investigate the indirect effects of the factors on consumers’ usage intention of virtual banking services through their initial trust. These features make our study an interesting case study with much insight into consumers’ expectations of virtual banks.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".