2.1 Service Quality:
Academic research has become increasingly focused on service quality over the last few decades. Gummesson  was among the first to propose the concept of service quality was strongly associated with trust and perception. Gronroos  then gave the notion of “total service quality” and this concept considers the difference between expected service quality and perceived service quality for measuring total service quality.
The concept of service quality has been defined by many researchers in the past. According to Parasuraman et al., service quality refers to “the difference between customer expectations of what a firm should provide (i.e. expectations) and the perceived service performance” [35, 36].
Asubonteng et al.  defined service quality as “the difference between customers’ expectations for service performed before the service encounter and their perception of the service received”. Gefen  defined service quality as “the subjective comparison that customers make between the quality of service that they wish to receive and what they get”. As per Parasuraman et al. , service quality is an abstract of three features that are very unique to services: the inseparability of production and consumption, heterogeneity, and intangibility.
Parasuraman et al.  proposed a very popular definition of service quality and according to them, service quality means “conformance to customer specifications” – that is, Customers' perception of service quality is very important, not management's. Service quality is recognized as a driver of corporate marketing and financial performance . Service quality is capable of delivering strategic benefits, such as operational efficiency, improved customer loyalty, improved customer retention rates, etc. .
Service quality is observed as a key determinant of financial performance and corporate marketing . The financial performance of banks is impacted by the Service quality of mobile banking applications.
2.2 Online Service Quality:
In online service, the customer interacts with the service provider via technology. In the last decades, many research scholars studied online service quality and define it in different ways. Zeithaml et al.  defined online services as “web services delivered through the internet”. Ghosh et al.  conceptualized an online service as an interactive information service. Rowley (2006) defined online service as “deeds, efforts, or performances whose delivery is mediated by information technology” .
Delivering services at a higher level of service quality create a positive influence on customer satisfaction [36, 15]. The quality of service is a very important factor affecting the success or failure of e-commerce . The quality of service in e-commerce can also enhance customer retention and loyalty [35, 55].
2.3 Mobile Banking Service Quality:
The term mobile banking typically refers mostly to the use of mobile devices, such as a mobile phone, tablet, etc., to manage financial accounts . M-banking is an essential tool for developing relationships between financial institutions and their customers [38, 44]. Service Quality of mobile banking applications represents the overall evaluation made by users of mobile banking applications regarding the service quality attribute of the mobile banking application . There is no research conducted in the area of mobile banking application service quality in India. So literature review is conducted in the area of mobile banking/finance service quality.
Earlier researchers have used different approaches to determine important dimensions of service quality for mobile banking. Prior research has explored the factors that determine the quality of mobile services. Using text mining techniques, Leem and Eum  used sentiment analysis to measure the service quality of mobile banking apps in Korea, concluding that mobile bank managers should pay more attention to the service quality dimensions of enjoyment and practicality. Zhou et al.  researched to study various factors affecting the service quality of mobile banking and concluded that financial institutions and decision-makers with concern to mobile banking should consider the importance of security, assurance, system quality, and interface design. Mostafa  conducted an extensive literature review and identified four important mobile banking service quality dimensions: enjoyment, ease of use, security/privacy, and usefulness. Shankar et al.  explored important dimensions for mobile banking service quality using qualitative research methods (netnography, focusgroup, in-depth interviews, and critical incident techniques) and the results of the study demonstrated that content, efficiency, interactivity, customer support, and security are key dimensions for measuring service quality of mobile banking. Trialih et al.  examined the relationship between mobile banking service quality and customer satisfaction in Indonesia and concluded that easiness to operate, efficiency, convenience, assurance and security, and reliability and responsiveness are important service quality dimensions for measuring mobile banking service quality. Research conducted by Shareef et al.  on the adoption of mobile banking services found that perceived security, trust, perceived ease of use, and perceived functional benefits are important factors in the adoption of mobile banking services. Kumar  identified six dimensions for measuring online banking service quality and these dimensions were: assurance, mobile application design, customization, functionality, service recovery, and fulfillment. Jun and Palacious  applied the critical incident technique and identified 17 service quality dimensions for mobile banking: m-banking application quality (convenience, ease of use, content, speed, accuracy, security, diverse mobile application service features, and aesthetics), and m-banking customer service quality (continuous improvement, competence, reliability, courtesy, responsiveness, credibility, understanding the customer, access and communication). In addition, the research revealed that five dimensions, such as ease of use, diverse mobile application service features, accuracy, and continuous improvement are measured as the most important sources of customer satisfaction/dissatisfaction for mobile banking users. Bhatiasevi  researched mobile banking adoption in Thailand and applied the unified theory of acceptance and usage of technology and this study concluded that performance expectancy, effort expectancy, and perceived convenience impact people's intentions to use mobile banking. Arvidsson  studied consumer attitudes toward mobile payment services and found that the important factors for consumers who use a mobile payment service are ease of use. In addition, Arvidsson  concluded that low perceived security risks and high trust have a strong positive relationship with the adoption of mobile payment services. Chemingui and Lallouna  identified four important factors affecting the adoption of mobile financial services, such as system quality, compatibility, trialability, and perceived enjoyment. Amiri Aghdaie and Faghani  used the SERVQUAL model to measure the relationship between mobile banking user satisfaction and mobile banking services; and concluded that responsiveness, empathy, reliability, and tangibility have a significant correlation with the satisfaction of mobile banking users. Lu et al.  proposed three mobile service quality dimensions: outcome quality, environmental quality, and interaction quality. Chen  conducted research work on measuring the effects of information quality, system quality, and information presentation quality on customer satisfaction for mobile banking, and concluded that information quality and system quality create a significant impact on mobile banking customer satisfaction. Tan and Chou  proposed seven determinants of mobile service quality: personalization, perceived ease of use, variety, content, perceived usefulness, feedback, experimentation, and personalization. Lim  identified five determinants of mobile service quality: network quality, customer services, billing systems, pricing plans, mobile data services, and customer services.
During the last two decades, service quality has emerged as the most explored area in the field of services marketing. Offering superior quality services is necessary for ensuring business success in today’s dynamic and competitive environment. There is no effort has been made to identify important service quality dimensions for a mobile banking app using text analytics in India. Table 1 shows the summary of important research work in the area of mobile banking/finance service quality.
Table 1: Summary of Important Research Work in the Area of Mobile Banking/Finance Service Quality
2.4 Machine Learning and online reviews:
Mobile applications are an important part of our lives today. Mobile applications are special-purpose software that is developed to perform precise tasks. Any mobile application has unique features to perform precise tasks. Mobile phones have Mobile OS; “Mobile OS” means mobile phone operating system. Mobile OS operates PDA, smartphones, and other mobile devices. Mobile phone uses various types of operating system like Android, Windows Phone OS, IOS, Blackberry OS; Firefox OS, etc…Any mobile applications can be downloaded from app stores like Google Play, Amazon App, Apple Store. All app stores offer the opportunity to users to make comments on the app and rate the app.
Users of mobile banking applications are producing huge amounts of text data in the form of reviews or comments on online platforms like Google Play Store, Appgrooves, etc. This data is very useful to study customer dynamics more rigorously. Online reviews allow organizations for performing consumer behavior analyses.
Customer online reviews are very helpful because it allows consumers to get information about products and services through other people’s opinions. Nowadays, positive reviews are very important, since the interactions and postings of online customers are viewed by many potential buyers of products and services every day. Online customer reviews are the second most reliable source for product or service information after the recommendation of friends and family. Analyzing online customer reviews is also critical for companies to gain knowledge about customers’ perceptions of their products and services. The outcome of customers review analysis is very useful to companies or organizations because it helps companies in improving the design of product/service, measuring product or service quality, framing positioning strategy, etc. . Optimistic reviews state customer satisfaction, while, negative reviews state customer dissatisfaction .
Analytics related to mobile apps is vital for understanding and analyzing the behavior of mobile app users. Today, most organizations use mobile apps for selling their products and services. Users can purchase products or services with a few swipes on mobile devices. Users’ experiences are also logged in the app. These experiences and opinions are very important for any organization for getting a competitive advantage.
ML (Machine Learning) is considered an area within AI (Artificial Intelligence). Machine learning includes various types of computerized methods of data mining usually in big and complex data to support classification, prediction, and decision-making procedures . Machine learning techniques are effectively used in determining customers’ satisfaction dimensions from the large quantity of text data. Machine learning techniques are that it identifies the preferences of the customers automatically from big datasets of online ratings and reviews of customers but it is not possible using a survey-based approach using statistical approaches  and for that machine learning is very useful in online reviews analysis.
2.5 Text Mining Approach:
Text is defined as “the unstructured data which consists of strings which are called words”. In the broad view, data mining is referred to the process of receiving hidden knowledge from any type of data. Text mining is a special type of data mining, which is useful to get implicit knowledge from text data. Text association, clustering, and classification are the tasks of text mining and they are very important in extracting implicit knowledge from the textual data. Text mining is very useful in extracting and analyzing text data for business insight and mostly it is used to understand the sentiments of users or customers .
Business insight can be extracted from textual elements, such as comments, reviews, tweets, and blog posts, using text mining. In most cases, it is used to identify emerging themes or topics or gauge user sentiments .