Delays that Cost: Usability Gaps and Consumer Behavior
Bibliographic record
Abstract
The increased adoption of mobile banking has enabled greater financial access and autonomy. However, this progress masks subtle usability issues that may undermine user outcomes. One such issue is system-induced delay, that is, the lag between user action (e.g., a transaction) and the system’s reflection of updated financial information (e.g., available balance). Although seemingly minor, these delays may disrupt consumers’ cognitive processes and financial planning, particularly when the system’s interface becomes a behavioral guide. Stylized patterns observed in Canada where the share of borrowers carrying credit card balances showed an increasing trend, despite economic growth and increased banking convenience via mobile banking post-2016. This highlights a compelling tension: real-time systems are not always behaviorally “real-time.” This study examines whether and how these usability gaps shape repayment behavior. Anchored in Information Systems and behavioral research, the study investigates whether delays in credit card updates within mobile apps influence users’ repayment decisions. Bridging the human-computer interaction and behavioral IS domains, the research draws on cognitive load theory and IS trust literature to theorize that such delays may impair users’ ability to track balances accurately, erode trust in system reliability, and trigger behavioral shifts such as postponing repayment or misjudging credit availability. The study employs a sequential, multi-method design. Phase one involves an online survey to capture user perceptions of delay and app reliability across banking institutions. Phase two exploits a natural experiment using institution-level variation in system responsiveness, comparing repayment behaviors across two banks with differing update protocols. Phase three features a randomized lab experiment simulating delay exposure, measuring trust, cognitive load (NASA-TLX and HRV), and repayment choices under controlled conditions. Semi-structured interviews complement these phases by offering interpretive depth into user reasoning and routines. This integrated design aligns with established IS methodological pluralism. Beyond the Canadian context, the research can speak to global digital banking infrastructures where update lags persist, highlighting the need for intelligent responsiveness in financial technologies. Theoretically, the study extends understanding of how usability features can exert causal effects on economic behavior. Practically, it offers insight for both policymakers and practitioners. For policymakers, this could strengthen the case for including information timeliness in consumer protection standards, in the banking sector. It could also provide insights for financial institutions aiming to promote responsible user outcomes in intelligent financial systems.
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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 | 0.007 | 0.046 |
| 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 | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".