Using text mining to measure mobile banking service quality

Byung–Hak Leem · Seong-Won Eum

76 citations0/0 files storedIndustrial Management & Data Systems

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Customer Service Quality and Loyalty Digital Marketing and Social Media Sentiment Analysis and Opinion Mining

Abstract

Purpose The purpose of this study is to propose a method of measuring service quality as well as suggesting to detect customer complaints through analysis of customer online reviews of mobile bank, which is unstructured data. Design/methodology/approach This study uses text mining approach for customer online reviews analysis. The research procedure includes: (1) extracting users' reviews for Kakao Mobile Bank, (2) pre-processing of the extracted review data, (3) analyzing the sentiment of each review, (4) measuring the service quality score of each dimension by analyzing keyword frequency and network for each polarity, (5) evaluating total score for mobile bank service quality, and (6) detecting customer complaints on online reviews. Findings There are some findings. First, from the customer's point of view, it was possible to see which factors are important among the various dimensions of service quality and which factors should be managed well in mobile banking setting. Second, by periodically finding customer complaints, service failures can be prevented early, and service quality and customer satisfaction can be improved. Practical implications From a practical point of view, mobile bank managers should pay more attention to the service quality dimensions of practicality and enjoyment. In addition, the results mean that the app design and aesthetics with the most negative reviews should be reviewed from the user's perspective rather than from the company's point of view. Second, it is possible for them to establish a systematic complaint management system that can prevent service failure in advance by detecting customer complaints early. Third, it is possible for them to make quick decisions regarding service quality with the help of real-time customer response through dashboard construction. Originality/value This paper is a pioneer study measuring service quality with sentiment analysis, one of the text mining applications, using customers' reviews of a mobile bank.

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References

1988SERVQUAL: A multiple-Item Scale for measuring consumer perceptions of service quality 2004E-S-QUAL 2000The Impact of Technology on the Quality-Value-Loyalty Chain: A Research Agenda 2019The influence of e-banking service quality on customer loyalty 2017Mobile banking service quality and customer relationships 2015M-S-QUAL: Mobile service quality measurement 2018Measuring service quality from unstructured data: A topic modeling application on airline passengers’ online reviews 2016Examining the key dimensions of mobile banking service quality: an exploratory study

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2024Unveiling the drivers of satisfaction in mobile trading: Contextual mining of retail investor experience through BERTopic and generative AI 2022Determining banking service attributes from online reviews: text mining and sentiment analysis 2021Does service quality influence operational and financial performance of third party logistics service providers? A mixed multi criteria decision making -text mining-based investigation 2022Mobile banking service quality and customer value co-creation intention: a moderated mediated model

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