The Impact of Perceived Website Design on Customer Performance Expectations in Online Banking: A Communication-Centered Approach in the Era of AI and Digital Governance
Bibliographic record
Abstract
As digital platforms increasingly mediate interactions between organizations and the public, understanding how communication is conveyed through technological interfaces has become essential—especially in online banking, where trust, clarity, and user confidence are critical. This study examines the impact of perceived website design on customer performance expectations from a digital communication perspective, focusing on how visual cues, content organization, interface language, and usability features shape user perceptions and behaviors. In a landscape where banks are progressively adopting AI-driven interfaces and more centralized digital service structures, effective website communication remains foundational to user engagement. A structured questionnaire was completed by 390 online banking users. Face validity was confirmed by experts in communication, banking, and management, and reliability analysis produced a Cronbach’s alpha of 0.97. Data were analyzed using SPSS and Structural Equation Modeling (SEM). Findings show that performance expectation, website design quality, technical features, general and specialized content, social influence, and prior experience all have significant positive effects on users’ performance expectations and their adoption of online banking services. The results highlight the central role of effective digital communication—expressed through interface design, content clarity, navigation, and social cues—in shaping user trust, expectations, and engagement in technology-mediated financial environments. The study contributes to communication scholarship by demonstrating how website design operates as a communicative process influencing perception and decision-making in online contexts, while also pointing toward future opportunities to examine AI-mediated communication and system structure in digital banking. To the best of current academic knowledge, this study is among the first to conceptualize online banking website design as a communication-centered construct—treating interface features, content clarity, and usability as communicative signals that shape performance expectations—while acknowledging the growing influence of AI-driven systems and centralized versus decentralized digital service architectures on user interpretation and trust.
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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 | 0.005 | 0.031 |
| 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 | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".