Mobile Banking Service Quality Assessment
Summary
Mobile banking service quality assessment encompasses the systematic evaluation of digital financial services delivered via smartphone applications and online platforms. Central to this field are multidimensional frameworks that measure aspects such as reliability, responsiveness, assurance, empathy and tangibles. Traditional models adapted for electronic contexts—most notably SERVQUAL and its electronic counterpart, e-SERVQUAL—provide the conceptual foundation for comparing customer expectations with perceived performance. Assessment approaches range from structured questionnaires and psychometric scales to real-time analytics of user behaviour and feedback harvested from app stores or social media. Recent advances incorporate artificial intelligence and machine-learning algorithms to predict service performance and identify latent patterns in large datasets. The global significance of this research lies in its ability to inform banks’ strategic decisions, optimise user experience, enhance trust and foster financial inclusion by tailoring mobile interfaces to diverse populations and regulatory environments.
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Mobile Banking Service Quality Assessment publication trend
The graph below shows the total number of articles in mobile banking service quality assessment across all publications each year (not limited to Nature Index journals).
Technical terms
SERVQUAL: A five-dimension framework for assessing service quality based on gaps between customer expectations and perceptions across reliability, responsiveness, assurance, empathy and tangibles.
e-SERVQUAL: An adaptation of SERVQUAL tailored to electronic services, emphasising factors such as accessibility, ease of navigation and online security.
Sentiment analysis: Automated computational technique that categorises opinions expressed in text as positive, negative or neutral to gauge customer attitudes.
Text mining: The process of deriving high-quality information from text sources using natural language processing, statistical pattern learning and data visualisation.
References
- Applications of text mining in services management: A systematic literature review. International Journal of Information Management Data Insights (2021).
- An Artificial Intelligence System to Predict Quality of Service in Banking Organizations. Computational Intelligence and Neuroscience (2016).
- Influence of Service Quality and Trust in Customer Satisfaction of Mobile Banking Users. Journal of Economics Business and Government Challenges (2022).
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