A study of service quality of TPBank livebank in Ho Chi Minh

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<b>THE UNIVERSITY OF BOLTON DISSERTATION </b>

Assignment title: A Study of Service Quality of TPBank Livebank in HCM

Course/ Programme: BCs (Hons) Business Management

Student’s name: Tran Nguyen Thanh Mai

Assignment length: 10.000 – 12.000 words

Submission deadline: 1^st May 2023

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<b>DECLARATION </b>

I guarantee that I researched independently on the “A Study of Service Quality of TPBank Livebank in HCM” research project. By signing this document, I verify that all knowledge, information, and data included in the article were obtained from the company’s web-based, internet, and some other academic resources.

<b>Thank you! Ho Chi Minh, 1^st May 2023 </b>

<b>Tran Nguyen Thanh Mai </b>

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Finally, I would also like to thank my parents for their encouragement and support throughout the research process.

Due to the limited completion time and professional experiences, the study may have many errors. Therefore, I look forward to receiving comments and suggestions from teachers and everyone to improve my future career.

<b>Ho Chi Minh, 1^st May 2023 </b>

<b>Tran Nguyen Thanh Mai </b>

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<b>ABSTRACT </b>

<b>Purpose: The customers' expectations and perceptions are pivotal in determining </b>

the service quality of any business, and the digital banking sector is no exception. The purpose of the study is of utmost importance as it aims to delve deeper into the customers' expectations and perceptions regarding the Service Quality of TPBank LiveBank (LiveBank), a digital bank. The research conducted through five SERVQUAL dimensions will provide a comprehensive understanding of the factors that drive customers' satisfaction and dissatisfaction with the services offered by LiveBank. This analysis will enable the researchers to provide valuable recommendations and insights for enhancing service quality and improving customer experience.

<b>Methodology: The SERVQUAL scale, designed by Parasuraman et al. (1988), is a </b>

comprehensive questionnaire that is instrumental in determining the quality/satisfaction causal order. The questionnaire consists of 22 multiple-choice questions divided into five parts, namely Reliability, Assurance, Responsiveness, Empathy, and Tangibles. To maintain concision, the answers are duplicated for both expectation and perception, and the responses are recorded on a 7-point Likert scale that ranges from 1 “totally disagree” to 7 “totally agree”. The secondary data for the analysis of primary data are collected from the internet and TPBank's web-based resources. As a professional, it is imperative to use such a well-designed tool to accurately measure and analyze customer satisfaction.

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CHAPTER 2: LITERATURE REVIEW ... 13

2.1. Digital Banking concepts ... 13

2.2. Service quality concept ... 14

3.3. Onion layer 2: Research approach ... 30

3.2.4. Inductive: Building theory ... 30

3.2.5. Deductive: Testing theory ... 31

3.4. Onion layer 3 and 4: Research strategies and Research Choice... 31

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3.4.1. Research choice: choosing a quantitative, qualitative or multiple methods

Section 2: SERVQUAL questionnaire ... 73

Appendix 2: Histogram of Responsive dimension ... 76

Appendix 3: Research ethics ... 77

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<b>LIST OF FIGURES </b>

Figure 2.1: Unique characteristics of service ... 15

Figure 2.2: SERVQUAL measurement ... 20

Figure 3.1: Research Onion ... 27

Figure 4.1: Financial Statement of TPBank 2022 ... 40

<b>LIST OF TABLES </b>

Table 4.1: Mean score of customers' expectation and perception, Gap score ... 47

Table 4.2: Descriptive analysis ... 51

Table 4.3: Frequency and Percentage of customers' perception ... 53

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<b>CHAPTER 1: INTRODUCTION </b>

<b>1.1. </b>

<b>Research background </b>

Nowadays, the banking industry is critical to the operation of modern society. The impact of the “Fourth Industrial Revolution, Artificial Intelligence, and Digital Tranransformat” on consumer behaviour and the functioning of the economy is of great interest to both economists and policymakers (Tan et al., 2022). Then, the digitalization of service channels has led to significant changes in consumers’ impressions of the service experience (Son et al., 2020). For instance, before the introduction of Digital banking, clients tend to conduct transactions with one bank. However, nowadays, more information is now available, and search and transaction costs have been reduced, which has encouraged customers to use various banks at once.

That becomes even more crucial as the Covid-19 pandemic alters how people do banking activities. The banking industry observed an increase in the using online banking service and digital payments significantly, (Seetharaman, 2020; Naem and Ozuem, 2021). For example, in May 2021, an online survey of 600 US consumers was done through a press statement by Provident Bank, a reputable financial institution. The majority of respondents (91%) indicated they conduct their banking using digital methods. More than half of those clients (53%) said they had changed to digital banking because of the Covid-19 pandemic. Furthermore, 87% of customers are very satisfied or satisfied with the digital/mobile banking options available, and 63% use mobile banking apps frequently (Provident Bank, 2021)

In the current context, the quick change to process digitalization is both exciting and extremely sensitive. As banks are still reluctant to move to the online process, (Seetharaman, 2020). In contrast, banking companies are gradually eliminating or cutting back on their physical location and relying more on digital services (Wang et al., 2017). Digital banking can be considered as a process innovation that replaces

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physical branches to provide banking services without a physical store because it has substantially lower unit costs than traditional channels for various types of transactions, (DeYoung et al., 2007). As a result, consumers who conduct transactions with digital banks will typically pay lower service fees than those who do so with traditional banks.

Another crucial thing is that the expectation of consumers will significantly increase as the bank gains a certain level of customer service quality. In today's intensely competitive market, the client is the key performance indicator for all marketing, and ensuring their happiness is the first priority when creating any good or service. High-quality customer service is critical for the success and survival of any industry because it meets consumer needs and, as a result, will enhance brand loyalty, market share, and profitability (Kumari & Rani, 2011 cited in Kaur et al., 2012, p.399).

To stay ahead of its competitors, the bank must adopt a policy to improve service constantly, (Avkiran, 1999). The trend demonstrates the strategic concept of developing a positive relationship with the customer, and finally, an “emotional connection” with them in an effort of ensuring loyalty and lifetime benefits, (Levy, 2022), is becoming depend more on the digital platform and customer interactions with the service’s online content. Banks need to continually examine their service quality to guarantee that consumers are always satisfied. There are a large number of research have committed that service quality is a critical factor in determining customer experience in the banking sector, then promoting customer loyalty (Avkiran, 1994). Maintaining high-quality customer service is an important component of an overall strategy for sustaining successful relationships. As a result, service quality has been considered as a crucial contributing factor able to retain customers for the firm (Ennew and Binks, 1996 cited in Avkiran, 1999, p.61). The SERVQUAL scale of Parasuraman et al. (1985, 1988) is an excellent tool in the terms of close examination of the relationship

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between factors to measure service quality. Therefore, accessing service quality is a hot topic for researchers and one of great importance to service providers and many regulatory agencies, which influence the competitiveness of an organization (Edvardsson, 1998), providing good services means matching the consumer’s expectations (Lewis and Mitchell, 1990).

Academic researchers have established that measuring customer satisfaction and service quality are two distinct dimensions that should be evaluated separately to gain a comprehensive understanding of the quality/satisfaction causal order. As Parasuraman et al. (1985) suggest, customer expectations of service providers' performance and evaluation of the actual service received are two measures of service quality that should be examined individually. Furthermore, Lewis and Booms (1983) posit that the deviation between customer perceptions and expectations of vital components of service quality represents the degree and direction of service quality (Lewis and Booms 1983 cited in Lewis, 1993, p.4). In summary, researchers emphasize the importance of understanding both customer expectations and their perceptions of actual service quality to gauge the overall satisfaction with the service provider.

According to Nguyen et al. (2020), Digital banking in Vietnam is developing rapidly, many banks are gradually launching digital technology transaction spaces such as Vietcombank (Digital Lab), OCB (Omi channel), and many other commercial banks (MPBank, Techcombank, etc.). Particularly, Tien Phong Commercial Join Stock Bank, abbreviated as TPBank. This is one of the leading joint stock commercial banks in Vietnam and was established on 5 May 2008, (TPBank, 2023). Standing an excellent strategy with a strong foundation, and the brand slogan “A Deeper Understanding”, TPBank has developed into a prominent digital bank in Vietnam with a variety of digital financial products. Particularly, since 2013, the bank has launched its first smart technology products in Vietnam, including the eCounter, eGold Technology Solution,

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and Multi-Functions Card. After that, in 2014, the bank made its mark by becoming the first to publish an eBank version built on the HTML5 platform, merging both mobile and internet banking into a single version. There are five different main digital banking products, including TPBank Mobile Application, Savy – Supper App for Savings, TPBank SoftPOS, TPBank QuickPay and LiveBank 24/7, (TPBank, 2023).

Over the last 10 years, TPBank has demonstrated a long-term vision and sustainable development roadmap by gradually establishing its national branch-office network and steadily growing its level of cooperation with variable partners. By the end of 2021, TPBank had over 500 transaction points, including LiveBank automated, and individual customers totaled 5 million.

The study has two purposes. First, it uses the SERVQUAL scale based on Parasuraman et al. (1985, 1988) gap theory and Likert scale (7 points) which can reveal the gap about the service quality (customer exepectations and perceptions) that they received during the transactions. The second purpose is to conduct an examination of the relationship through five SERVQUAL dimensions to describe and assess the Service Quality of TPBank LiveBank (LiveBank), a digital bank. In order to research more information and recommendations for the service quality of the firm. The chosen topic is the Service Quality of TPBank Livebank in HCM.

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<b>1.2. Research objective </b>

The principal objective of this paper are:

 To conduct a critical literature review of Service Quality  To investigate how TPBank Livebank’s Service Quality.

 To propose recommendations to improve the Service Quality of TPBank LiveBank

<b>1.3. Research question </b>

<b>The study aims to answer the following research questions: </b>

 Question 1: Is TPBank LiveBank's service quality good?

 Question 2: Does TPBank LiveBank HCM's service quality meet the needs of customers?

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<b>CHAPTER 2: LITERATURE REVIEW 2.1. Digital Banking concepts </b>

Traditional banking services have been controlled to produce "digital banking," which allows users to access banking products and services through online or electronic platforms (Haralayya, 2021). Due to the implementation of major digital platforms such as “ATMs, Internet banking, Mobile banking, cash deposit machines, and UPI transactions” (Venna and Janarthananpillai, 2022; Kaur et al., 2021).

Indriasari et al. (2022) showed that Digital banking has been expanded as a result of the development of the internet, cell phones, and new technologies like artificial intelligence (AI), blockchain, cloud computing, and open API. In other research, Alalwan et al. (2016) said that Digital Banking is important to the bank's marketing efforts, it allows banks to offer omnichannel services across digital banking channels (such as telephone, internet, and mobile banking), and these channels are able to bring banking services closer to the customer and create a strong relationship between clients and the banks (Ozili, 2018).

However, along with the continuous development of the application of digital banking in the banking system, Online Economic Transactions are under pressure for the banking industry, especially traditional banks, and digital banks. “With FinTech attracting millions of new customers, incumbents face a need for bold action that is becoming more urgent by the day”, (Bhapkar et al., 2021 cited in Nugroho and Hamsal, 2021, p.61). Belonging to the increase in new technology, populations have to begin adapting to the use of digitalized banking for their daily transactions, especially during the COVID-19 pandemic. Changes create risks to maintaining customer satisfaction.

The banking industry is high competitiveness, which can see that banks depend widely on their capacity to attract and retain clients, (Teeroovengadum, 2022). After that, the long-term financial performance of businesses is heavily influenced by

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customer loyalty, (Jones and Sasser, 1995 cited in McDougall and Levesque, 2000, p. 392). Therefore, many banks have concentrated on enlarging customer satisfaction strategies, which has been proven that is highly affect customer loyalty.

<b>2.2. Service quality concept </b>

The previous few decades have seen an examination of the banking industry's service quality. Since it was defined by different individuals in various ways, there is no universal definition of service. However, service quality can be demonstrated as an overall evaluation of a particular service by a customer and determined the customer's expectations are met as well as their subsequent level of satisfaction (AI-jazzazi and Sultan, 2017).

For instance, according to Parasuraman et al. (1985), “Service quality as perceived by the customer is the degree and director of discrepancy between customer service perceptions and expectations”. It is also a foundation for creating the SERVQUAL instrument and its remake. Nevertheless, Bolton and Drew disagreed with this definition, concluding that the many explanatory factors for customers' attitudes at a particular time could not be the same as the variables that caused customers' attitudes to change over time, these claims do not examine the impact of changing service offers on perceived service quality. Hence, they used the common premise that service quality is similar to attitude as the foundation to propose that satisfaction is an antecedent of service quality and that service quality is viewed as a function of the consumer's residual perception of the service (Bolton and Drew, 1991).

According to a study by Zeithaml et al. (1985), there are four distinct qualities of service that must be understood in order to fully comprehend service quality. They are intangibility, heterogeneity, inseparability, and perishability (such as Regan (1963); Sasser (1976); Uni and Upah (1980). Others said that it must be addressed that some of its key characteristics, including Intangibility, Heterogeneity (or variability), and

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Inseparability (Figure 2.1). This paper will focus on three different characteristics: Intangibility, Heterogeneity, and Inseparability:

<i><b>Figure 2.1: Unique characteristics of service </b></i>

<i><b>Source: Zeithaml et al. 1985 </b></i>

<b>2.2.1. Intangibility </b>

The majority of today's services are intangible, which is commonly cited as the distinction between services and products (Muide and Pirrie, 2006; Parasuraman et al., 1985; Haywood-Farmer, 1988). ). According to them, it is unusual to be able to develop

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precise manufacturing standards for uniform quality because they are performances rather than things. A large number of services cannot be counted, measured, inventoried, tested, or confirmed prior to sale (McDougall and Snetsinger, 1990), in contrast, physical goods (tangible) are searched highly and assessed easily (Darby and Karni, 1973).

Intangibility has also drawn criticism because a service performance typically involves a lot of tangible goods (Shostack, 1977 cited in Moeller, 2010). For many customers of auto repair, for instance, the service is completely intangible; they frequently cannot see what is being done and many are unable to assess what has been done. It is more challenging to demonstrate what they (the customers) receive for their payment (Muide and Pirrie, 2006). For intangibility, the company may find it difficult to understand how customers perceive its offerings and evaluate service quality. It is essential to get a suitable image for service products than for physical goods, and for lower purchasing risks for consumers, they suggest using brand names (Onkvisit and Shaw, 1989). This is because measuring services has become more complex as a result (Turley and Moore, 1995 cited in Brady et al., 2005, p.402).

According to de Chernatony and Segal-Horn (2001), the relative of intangibility in the service industry is not the ratio of intangible to tangible assets necessary to offer services. Instead, it concerns how expectations of the brand promise are delivered and how excellent customer service is gained. This is occurring for a service organization through the inseparability of production and consumption, another crucial aspect of services.

<b>2.2.2. Inseparability </b>

The inseparability is introduced by Say (1836), who declared that a service is produced simultaneously with its consumption, making them inseparable (Say, 1836

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cited in Moeller, 2010, p. 363-364), and “there is a marked distinction between physical goods and services in terms of the sequence of production and consumption”

<b>Physical goods: Production----Storage----Sold----Consumed. Service: Sold---Produces and consumed at the same time. </b>

(Siddiqi, 2011; Muide and Pirrie, 2006)

Since the service is first sold, then produced, and consumed simultaneously, it is obvious that the quality of the service is sold and provided to the consumer intact without the need for factory design (Edgett and Parkinson, 1993). Berry (1984) highlighted the essential relevance of the service production process as an integral element of it by defining service as "a deed, a performance, an effort" (Berry, 1984 cited in Haywood-Farmer, 1988, p.20). Moreover, inseparability "forces the buyer into intimate touch with the production process”, as the customer must be present in the majority of the production processes of many services (such as haircuts, medical examinations, home banking, etc) (Nicoulaud, 1989), because services differ from goods in that their production and consumption processes frequently occur simultaneously (Sierra and McQuitty, 2005). As a result, direct distribution, marketing, and production operations become extremely interactive because the producer and the seller are the same (Upah, 1980; Gronroos, 1987 cited in Zeithaml et al., 1985, p.34). Hence, client input is crucial to the performance quality of the service (Parasuraman et al., 1985).

<b>2.2.3. Heterogeneity </b>

The term “Heterogeneity” is used in the service context meaning the service difficult to standardize. A service’s nature and quality can differ depending on “producer to producer, from customer to customer, and from day to day” (Edgett and Parkinson, 1993).

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Services often follow a set formula in order to meet the needs of each customer; however, the absence of uniformity in the services produces considerable challenges throughout the product development process (including design, manufacture, and delivery) (Nicoulaud, 1989). It is more challenging to regulate the output of service firms than that provided tangible products (Zeithaml et al., 1985). According to Lovelock (1993), there is a significant likelihood of variation in service delivery due to employee judgment or customer personalization throughout the creation of services. These various tasks require both necessities of technical and communication skills.

<b>2.3. Theoretical framework (Service quality measurement) </b>

Nowadays, most people believe and accept that customers’ expectations of quality are rising, in addition, they are becoming increasingly critical of the service they received (Lewis, 1991). Customers frequently choose a variety of criteria that are essential to them when assessing the service quality that they received from banking, these criteria differ in level of importance. From the perspective of the consumer, these key characteristics will be used to determine service quality (Loudon and Della Bitta, 1988 cited in Joseph et al., 1999, p. 183). However, in the past few decades, many methods have been created tend to concentrate on the expectation and reduce important issues (Joseph et al., 1999), and many authors have conducted extensive research on service quality (Brochado, 2009). The various models for measurement service quality namely the SERVQUAL model (Parasuraman et al., 1988), the SERPERF model (Cronin and Taylor, 1992); the BANKSERV model (Avkiran, 1994), the BSQ model (Bahia and Nantel, 2000) and so on.

SERVQUAL – one of the most popular methods, defined service quality in terms of the difference between customer expectations and performance perception, these include five gaps (Parasuraman et al., 1985):

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1. Gap 1: The difference between customer expectation and management perception gap

2. Gap 2: The difference between management perception and service quality specification gap

3. Gap 3: The difference between service quality specification and service delivery gap

4. Gap 4: The difference between service delivery and external communications gap

5. Gap 5: The difference between expected service and perceived service gap After exploring the SERVQUAL instrument for measuring customers’ perception of service quality. The main idea is based on 22 items and 5 dimensions (RATER) (Parasuraman et al., 1988):

1. Tangible: physical facilities, the appearance of personnel, tools or equipment 2. Reliability: the ability to perform the promise right the first time

3. Responsiveness: timeline of service; the willingness or readiness of employees to provide service

4. Assurance: employee expertise, courtesies, and capacity to foster confidence 5. Empathy: the attention of firm provides their customers

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<i><b>Figure 2.2: SERVQUAL measurement </b></i>

<i><b><small>Source: Kumar et al. (2009) </small></b></i>

This scale has survived the tests of the time, and researchers encourage to use SERVQUAL to assess the service quality perception of various industries: enterprise, commerce, and non-profit. Publications have been related to the theory and application of SERVQUAL, for example, “hospitality (Johns, 1993); car servicing (Bouman and van der Wide, 1992); business schools (Rigotti and Pitt, 1992); higher education (Ford et al., 1993; McElwee and Redman, 1993)”, cited in (Buttle, 1996). Since its validity offers an excellent measurement of acceptance, SERVQUAL has been specially created to target the important characteristics of service delivery (Brysland and Cury, 2001), then, attracting lots of interest in service quality measurement. However, despite SERVQUAL being the popular method, especially in the banking industry, however, many authors also highlighted the questions about the necessity and the suitable capacities of accessing “expectations, the interpretation, and operationalization of expectation, the reliability and validity of SERVQUAL’s difference score formulation and SERVQUAL’s dimensionality”, (Cui et al., 2003), and this instrument has been reproduced or changed

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in the research of later authors (Kumar et al., 2010). The following criticisms were mentioned:

The SERVQUAL model is mostly criticized for having a high degree of correlation between the different dimensions. The majority of previous research demonstrated the model’s components as an independent factor with its importance meaning an absolute difference between expectations and perceptions. Nevertheless, recent studies by SERVQUAL developers have highlighted the different opinions and inconsistent results from the replication studies about SERVQUAL (Cui et al., 2003). Excluding tangible, there is a different level of overlap between the four aspects of reliability, assurance, empathy, and responsiveness. In other words, according to Parasuraman et al. (1988), this scale does not accurately represent the five components based on the 22 SERVQUAL items as rated by customers (Llosa et al., 1998). Moreover, Babakus and Boller (1992) previously doubted the validity and reliability of SERVQUAL's multiple scores. Babakus and Mangold (1989) also suggested that the SERVQUAL should get only one factor rather than five, however, others proposed more than five dimensions (Asubonteng et al., 1996).

The Likert scale also has some potential drawbacks. The mid-range number just only be vaguely involved with the different opinion levels of customers (Gilmore, 2003).

In addition, in Carman’s research, SERVQUAL should be changed by each service, despite the original design being for any service (Carman, 1990). According to some research, for instance, SERVQUAL scale is inappropriate in the environment of the retail store making them “that retailers and consumer researchers should not treat SERVQUAL as an ‘off the shelf’ measure of perceived service quality. Much refinement is needed for specific companies and industries” (Finn and Lamb, 1991 cited in Brown et al., 1993, p.128)

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Moreover, a study by Cronin and Taylor (1992) claimed that the current conceptualization and organization of service quality (SERVQUAL) is insufficient. The scale is based on a gap hypothesis developed by Parasuraman et al. (1985), which showed the gap between consumers' expectations of service providers' performance in general and their actual perceptions of a particular company. There is little theoretical or empirical evidence, however, to support this relation (Carman, 1990), and the marketing literature appears to provide significant support for the superiority of service quality measurement based on simple performance (Cronin and Taylor, 1992). Therefore, they investigated SERVPERF, a “performance-only” based service quality measure, across four industries (banking, pest control, dry cleaning and fast food).

The SERVPERF scale is a variation of the SERVQUAL scale that only has 22 components. They have been utilized as a potential substitute for SERVQUAL and in relationship analyses between service quality, customer satisfaction, and purchase intention by Cronin and Taylor (1992) due to the validity of 22 individual performance scale components being able to adequately identify the service quality area. Then, it was discovered that only perceptions of performance have a direct impact on service quality, cited by Cronin and Taylor (1991). Additionally, they argued that SERVQUAL and SERVPERF should be viewed as having a one-dimensional structure rather than the five-dimensional structure proposed by Parasuraman et al. (1994), written by Gilmore (2003). In fact, the SERVPERF method describes a significant improvement over the SERVQUAL scale (Taylor and Cronin, 1994; ), which not only performs better than SERVQUAL in terms of reducing the number of measurement items by 50% (44 items reduced to 22) but also able to give “a more convergent and discriminant valid explanation of service quality construct” overall through the use of an item scale (Jain and Gupta, 2004). SERVQUAL and SERVPERF have already been compared in previous years, the vague of these individual research makes many vaguenesses about the measurement. For example, according to Jain and Gupta (2004); and Kettinger and Lee

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(1997), SERVPERF was more strong bond to overall service quality than SERVQUAL whereas Quester and Romaniuk (1997) claimed in contrast, cited by Francois Carrillat et al. (2007).

Moreover, if emerging the consumer perceptions of the importance of different services during the analysis, both weighted service quality scales are able to manage exactly to inadequate areas. Nonetheless, SERVPERF suffers from weakness because it directly manages attention to areas where there is no shortage of customer perception. Conversely, SERVQUAL is assumed to have superior weight than SERVPERF, on its ability to more accurately analyze service quality deficiencies (Jain and Gupta, 2004), even in a developing economy (Wakefield and Blodgett, 1990 cited in Angur et al., 1999, p.121). This is because it can measure customer-defined service quality gaps and the weighting of importance that customers assign to different services.

Another method of service quality is BANKSERV by Avkiran (1994, 1999) which is created to capture the service quality from the consumer’s perception. According to that, customer service quality was defined as “Perceived service quality is a global judgment, or attitude, relating to superiority of the service, whereas satisfaction is related to a specific transaction”. Additionally, this scale is frequently utilized in commercial bank branches, especially concentrated retail banking.

The method includes 17 items with four dimensions after having been conceptualized, namely:

1. Staff conduct - Customers will see branch employees as professional if they are responsive, courteous, and well-dressed.

2. Access - The sufficiency of workers to serve consumers during off-peak times and during business hours.

3. Communication – Delivering timely notices and effectively providing financial advice to clients in order to keep the consumer demands

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4. Credibility – Keep consumers informed and maintain staff customer trust by fixing errors.

In order to overcome any potential psychometric problems associated with the SERVQUAL scale, BANKSERV (Avkiran, 1994) was developed as a special scale to measure service quality (Ehigie, 2006). Moreover, it also avoids the negative wording of question items found in the previous SERVQUAL tool.

Such as the SERVQUAL model and others, the BSQ model also being a population scale used to measure service quality in retail banks (Narteh, 2018). This scale was generated for alternating the SERVQUAL to measure the perceived service quality in retail banking, after many critics (Bahia and Nantel, 2000). BSQ (Bank Service Quality) model includes 31 items and devices into six dimensions on the 10 components of the SERVQUAL model by Parasuraman et al. (1985):

1. Effectiveness and assurance 2. Access

3. Price 4. Tangibles

5. Service portfolio 6. Reliability

Moreover, not only Bahia and Nantel (2000) combined some additional features that have been encouraged by Carman (1990) about the “courtesy and access” into the BSQ scale but also the marketing mix items from Boom and Bitner (1981) such as “7Ps” (product/ service, place, process, participants, physical surroundings, price, and promotion), written by Spathis et al. (2004).

According to numerous studies, the BSQ model is more accurate than SERVQUAL dimensions. For example, in a paper by Glavei et al. (2006), in order to assess the level

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of providing service quality in retail banks of five Balkan countries, they prefer to use BSQ. Furthermore, the integration between reliability, efficiency, and assurance items in the elements of factor analysis of banking service quality across countries. This was discovered in the Petridou et al. (2007) study, on the quality of banking services provided in Greece and Bulgaria. The findings demonstrate that BSQ may utilize multinationals in a flexible manner and emphasize various indications depending on the research environment (Nateh, 2018).

However, like other methods, it also has relevant criticals while using BSQ. First, as Bahia and Nantel (2000), the scale is alternatively based on expert opinion and published literature. Second, the service quality is based solely on customer perception and not on the difference or gap between expectations and perceptions as SERVQUAL (Glavei et al., 2006).

Despite the criticism of many authors, SERVQUAL still seems to be the most realistic model for measuring service quality, especially in the financial service field. In fact, SERVQUAL has always as a main drive for most businesses in their service quality improvement initiatives (Newman, 2001). The investigations of Parasuraman et al. (1985,1988,1991) have been modified throughout time to fit a variety of fields, and the original SERVQUAL has been improved to enable retesting of its validity and reliability. The findings indicate that the reliability value (perception minus expected gap) across the five SERVQUAL dimensions is consistently high in various circumstances, indicating that there is a high level of intrinsic property between the five dimensions (Kaur et al., 2012). For instance, Asubonteng et al. (1996) concurred that all 22 SERVQUAL items included in the studies seemed appropriate for evaluating service quality in diverse settings and that "The balance between the various parts of SERVQUAL may change industry by industry but their relevance should not." Furthermore, they claim that

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managers can adapt how they use SERVQUAL as a planning tool by accepting specific features of the model.

<b>2.4. Hypothesis </b>

The service quality model designed by Parasuraman et al. (1988) is quite similar to the disconfirmation theory. According to this model, service quality (or the service quality gap) is caused by customers comparing their expectations before receiving service to their impressions of the service experience itself. As a result, there are three conclusions to be drawn from this comparison:

1. H1: Confirmation that if consumers' perceptions and expectations correspond, they are satisfied with the service they received.

2. H2: “Positive disconfirmation” if the experience exceeds expectations, the service quality is seen to be excellent, and customers are satisfied.

3. H3: “Negative disconfirmation” If the experience does not fulfil expectations, the service quality is regarded to be bad, and customers are unsatisfied.

<b>CHAPTER 3: RESEARCH METHODOLOGY 3.1. Introduction </b>

The planning and designing stages play an essential role in the report. It should clearly show that what the research is concentrated through the collecting and processing of data and analysis procedure. Hence, the study will adopt the Research Onion created by Saunders et al. (2006; 2007) to present and select the research objectives. The method being deal with the data collection and analysis layer of the research process onion respectively as a figure bellows.

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<i><b>Figure 3.1: Research Onion </b></i>

<i><b>Source: Saunders et al. (2006; 2007) </b></i>

<b>3.2. Onion layer 1: Research philosophy </b>

Saunders et al. (2006; 2007) claimed that the research philosophy is an initial stage in the process of examining and developing knowledge in a particular filed. These assumptions will serve as the basis for the authors' research strategy, and the methodologies chosen will be a component of those assumptions. It also allows investigators in shaping their understanding of the research issue, methodologies used, and findings interpretation:

<b>3.2.1. Positivism </b>

Positivist research presented that only observable phenomena may produce practical data. Positivism recognized social science as an organized method for combining deductive logical thinking with precise empirical observation of individual behaviour to discover and examine broad trends of human beings (Saunders et al., 2015). In other words, it applies reliable knowledge by making measurements and

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observations in the real world from a scientific perspective. The resources being researched can only be done objectively and cannot involve social actors (individual thoughts or viewpoints). For example, it is claimed that "the researcher is independent of and neither affects nor is affected by the subject of the research" (Remeyi et al., 1998:33 cited in Saunders et al.,2006; 2007, p.103; Antwi and Hamza, 2015). In addition, Crossan (2003) believed that speculation and assumptions relating to knowledge of metaphysics are dismissed. And positivism does not investigate or analyze human behavior, such as emotions.

Furthermore, science aims to create objective techniques which can produce data that most closely resembles reality. Hence, positivist researchers frequently use quantitative explanations (Saunders et al., 2015). However, it is gradually being applied qualitative data collection methods in later studies.

<b>3.2.2. Realism </b>

Similar to positivism, realism is a scientific strategy for increasing the value of knowledge through empirical investigation using both qualitative and quantitative approaches for data collection. According to Saunders et al. (2019), there are two types of realism: direct realism and critical realism. When these philosophies are compared, the meaning of the information gathered and its comprehension is more clarified. The first is direct realism (naive realism), it is argued that "what you see is what you get," meaning that people's experiences correctly represent the outside world. However, real structures exist independently and frequently diverge from our perceptions of reality (Dobson, 2001).

The second is critical realism. In contrast to early realism, critical realists contend that people only perceive indirect experiences such as feelings or representations of real-world observations. In other words, this technique demonstrates how frequently attendants’ senses mislead us about what is actually happening (Saunders et al., 2019).

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As critical realists stated that reality can never be a social product since existing in the change of social analysis. Individuals’ perceptions about reality things may change regularly, however, the construct and process of it seem to be unchanged. According to Baskar’s research the lack of definitional distinction: real objects are the subject of valuable observation (reality and valid observation of reality) is an essential weakness. These are adopted in two different ways: one is "intransitive" and "relatively enduring," and the other is “transitive” and “changing” Bhaskar, 1989 cited in Dobson, 2001). In the critical realism approach, it is impossible to make a "clear prediction" when applying theory and testing it to predict social situations because of its ability to test the wider tendency. Therefore, it is impossible to falsify the social observation base.

In fact, the main objective of realism research is to improve the knowledge of these persistent structures and systems. As a result, a comparison of these two concepts is always useful for selecting methodological approaches and ensuring methodological coherence (Dobson, 2001).

<b>3.2.3. Interpretivism </b>

The interpretive theory is formed from individuals’ perception of reality and truth, as well as seeing them as social agents. Similar to realism, its development is dependent on critical realism, however from a subjective standpoint (Ryan, 2018). Accordingly, interpreters represent that reality can only be approached through a variety of techniques, including “hermeneutics, phenomenology and symbolic interactionism” (language, consciousness, common sense, and methods) (Crotty, 1998 cited in Saunders et al., 2006, 2007, p. 149). Some researchers argue that the discursive perspective is appropriate for business and management studies that capture the rich complexity of social circumstances. Hence, the biggest challenge for researchers is to accept the position of the research subjects and understand their vision and their point of view.Because different individuals have different cultural backgrounds, and different

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circumstances, then create experiences of different experiential realities. A suitable and commonly used analytical method for data collection in interpretivism is qualitative analysis (Saunders et al., 2019).

In conclusion, research philosophy serves as the initial statement for any research project. All of its methods play an important role in conducting research, especially for articles on business and management. Depending on the research goal, choosing the right research method can help them achieve the depth of the main purpose of the study accurately. In the dissertation, the researcher will concentrate on realism, particularly critical realism. It is possible to clarify the participant’ thought in two stages. First, get customers' expectations and perceptions when participating in the experience. Second, process the data and recognize the difference between the perception and actual experience of each participant.

<b>3.3. Onion layer 2: Research approach </b>

There are two common approaches namely inductive and deductive research approaches (Figure 2). The inductive research approach (building theory) is appropriate for the researcher using significant results from the investigation, then creating a new observation theory about the phenomenon. In contrast, the deductive research approach (testing theory) is a process test by applying an established hypothesis or generalization to conduct data collection (Spens and Kovács, 2006).

<b>3.2.4. Inductive: Building theory </b>

According to Azungah (2018), inductive analysis refers to a method that mainly uses a detailed reading of data to find out related concepts and topics. It requires the researcher to pay attention to all the details (data) for paragraphs of the text in order to create concepts that are relevant to the research question. This is an important process that involves arranging and capturing meaning between data and document analysis to make sense of new concepts. In inductive analysis, the findings and data are derived

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directly from the analysis of the raw data, not from “a priori expectations and models”. In other words, the process of inductive research does not depend on common frames of knowledge, their generalization through logical arguments. The deductive method is appropriate to the trend of small sample research, as well as the collection of qualitative data and the use of various methods for data collection (Azungah, 2018; Saunders et al., 2006; 2007).

<b>3.2.5. Deductive: Testing theory </b>

The deductive analysis is based on established theories. This approach has an important role in explaining the causal relationship between variables and enables for hypothesis testing through surveys to confirm the theory or improve it as needed. Studies using deductive analysis will use a highly structured approach to the benefit of replicating existing theory; and are performed in a quantitative data set, however, this does not mean that qualitative data cannot be used (Sauders et al., 2006; 2007).

In the thesis, the deductive research approach will be employed: “where the focus on using data to test theory”, cited by Sauders et al. (2012). The student uses concepts and questionnaires that have already been created and tested to collect the relevant data (using the SERVQUAL scale in this paper). In addition, a large figure of data is found to be more suitable for deductive than inductive methods.

<b>3.4. Onion layer 3 and 4: Research strategies and Research Choice </b>

<b>3.4.1. Research choice: choosing a quantitative, qualitative or multiple methods research design </b>

Before deciding on a research approach, the student should identify the objective, the type of research, and the information required to make an informed decision. Two common methods (mono method) are qualitative data collection through interviews and quantitative data collection through questionnaires. The distinction between both

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research is non-numeric data (eg. Text, video, or audio) and numeric data (numbers), Saunders et al. (2012).

In this way, “qualitative” is related to an interpretive philosophy because researchers must make sense of the subjective and socially created meaning expressed about the phenomenon being examined. This study uses a variety of data collection techniques and analytical procedures to develop a theoretical framework through the study of the participants' meanings and the relationships between them. Qualitative research, like quantitative research, can be applied to realism and pragmatic philosophies. Furthermore, for the research approach, qualitative research design will adopt an inductive, naturalistic, and emergent research design approach that is used to develop new perspectives compared to the previous literature. However, in recent years, qualitative research has begun to incorporate a deductive approach to test current theoretical views. Through these methods, the collected data will not be standardized, so the research process is both natural and highly interactive from the perception of the survey participants. Major research strategies used for qualitative research are action research, case study research, ethnography, grounded theory, and narrative research, Saunders et al. (2012).

In contrast, “quantitative” is associated with positivism. It can also be aligned with interpretive, realist, and pragmatic philosophies thanks to its ability to collect large-scale data (data about human, organizational, or opinion-based data, sometimes when called “qualitative”). As a result, quantitative research is frequently regarded as ideal for deductive approaches, which employ data to test the validity of theories. Furthermore, it can integrate an inductive approach to constructing theory through evidence. This study looks into the link between variables that are measured numerically and examined using a variety of statistical techniques. Furthermore, quantitative research is mostly related to experimental and survey research methodologies, Saunders et al.

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(2012). According to Bhandari (2020), quantitative data analysis is frequently used to standardize data collecting and generalize findings. It allows huge volumes of data from large samples to be handled and evaluated using consistent and reliable techniques. Furthermore, this study can be reproduced in a variety of cultural situations, times, and people groups, allowing researchers to effectively compare factual facts. Repeating these processes several times helps to standardize the data and clearly identify abstractions. However, the definitions would not be accurately provided for the study because the replies were only explained as numbers. Additionally, even with standardized procedures, distorted structures can still have an impact on quantitative research, such as missing data, erroneous measurements, or the use of the wrong sample techniques (Bhandari, 2020).

Another method that researchers can employ is "multiple methods research design," which is based on a discussion of two philosophical perspectives, realism and critical realism. Some academics contend that even if there is an objective reality, societal factors will have an impact on people's thoughts and understanding. In order to appropriate the philosophies (realism and critical realism), researchers often use deductive or inductive methods or both. Then, using quantitative analysis of officially published data to test theories, and qualitative research methods to explore perceptions, and develop a rich theoretical perspective (Tashakkori et al. Teddlie, 2010 cited in Saunders et al., 2012, p.164).

The research will focus on "quantitative" research to collect precise data on the expectations and perceptions of LiveBank clients. The advantages state that quantitative methods can assist researchers in clearly classifying the majority of "digital data" into groupings of customs, opinions, and behaviours. The quantitative approach collects data in an organized, and objective to generalize researcher findings. From there, provide obvious justifications and explanations for each component group.

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<b>3.4.2. Research strategy </b>

The research strategy provides the overall objective of the research as well as the methodology used. According to Saunders et al. (2006; 2007), it consists of seven different strategies, namely, experiment, survey, case study, action approach, grounded theory, ethnography, and archival research (Figure 2).

As mentioned before, the business research will focus on quantitative. Both survey and experiment research can be used as a formal method to examine the hypotheses, whether statistically or predictably. The findings can be extended to larger groups based on the chosen sample technique.

<i><b>Experiment </b></i>

Experiments are a form of research found in many natural sciences. It is an excellent technique for examining the connections or occurrences between persons or phenomena in a controlled and defined context. As a result, experiment tends to be used in explanatory and exploratory research to answer "how" and "why" questions (experimental research), and the observational patterns used in this method are usually small experimental samples (e.g. employees or students of a particular institution) (Sauders et al., 2006; 2007). The purpose of an experiment is to study the probability that a change in one independent variable causes a change in another dependent variable (small relation) (Hakim, 2000 cited in Saunders et al., 2012, p.174). The feasibility of using an experimental strategy will depend on the nature of the research question. Since the testing technique uses hypotheses rather than open-ended research questions, it is possible to transform questions into hypotheses that assist the researcher in testing expected relationships between variables. However, most business and management research questions will be designed to discover relations between variables rather than to test for predictable relationships. This is a significant distinction

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of this strategy. In particular, quantitative research designs will highlight the key differences between experimental and survey strategies (Sauders et al., 2006; 2007).

<i><b>Survey </b></i>

The survey strategy is a popular method in business and management research with the answers "what", "who", "where", "how much", and "how many", hence, it frequently appears in exploratory and descriptive research. The use of questionnaires in surveys is common because it allows for the collecting of standardized data from a sizable population (social and behavioral sciences) in a method that is both very economical and convenient for comparison (eg. large figures of quantitative data). Following that, it can provide potential explanations for specific interactions between variables and create models of these relationships. Adopting a survey technique gives researchers more control over the study process, and sampling allows the researcher to obtain "representative findings" at a lower cost than that gather data for the entire population. In order to obtain accurate data, it is critical for researchers to ensure a high response rate during data collection time. However, many researchers complain that their study time is a long time because of the dependent on survey participants’ information (Sauders et al., 2006; 2007; 2012).

Both the experiment and survey strategies have significant advantages and disadvantages. To evaluate the service quality of digital bank TPBank HCM via transaction customers, data collection must be linked to customer feedback on their perceptions and perceptions to find out different and relevant data. Quantification is the method that will be used as the technique of this study and goes along with the survey strategy. Because the ability to collect data on a large scale, through observations, interviews or questionnaires helps researchers save time and costs.

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<b>3.5. Onion layer 5: Time horizon </b>

Depending time horizon, there are two types of research: Longitudinal and sectional. In which, a researcher wants to study samples at a certain time is called cross-sectional research. By contrast, longitudinal research studies samples over a period of year.

<b>Cross-3.5.1. Longitudinal </b>

In longitudinal research, the researcher collects data from samples at different time intervals and is not limited to a certain point. Longitudinal capabilities enable the study of development and change and also provide researchers with a measure of control for some of the variables being studied.

<b>3.5.2. Cross-sectional </b>

Cross-sectional research involves the study of a specific event (or phenomena) at a certain period and has a time limit for each study. It tends to use a survey strategy in many theses. However, the researcher may also employ qualitative or mixed research methodologies. For example, many case studies are based on interviews. In this thesis will apply the cross-sectional research.

<b>3.6. Onion layer 6: Techniques and procedures </b>

<b>3.6.1. Research methodology </b>

According to Hox and Boeije (2005), data is a collection of qualitative or quantitative variables' values. Before researchers can present and analyze information, there must be a process of obtaining and summarizing data, which are facts or statistics from which inferences can be derived. It may be gathered from a primary source (first time collected by a researcher) or a secondary source (the data has already been gathered by other sources earlier). Both types of data collecting are essential in statistical analysis:

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The primary is real-time data with the goal of finding a solution to the “problem at hand”. Its sources include surveys, observations, experiments, questionnaires, personal interviews, case studies, and so on. Primary data collection is a complex process, which can be time-consuming in using a particular method, with limited resources and research scope. However, the advantage of primary data is that it is highly accurate and reliable since it is collected directly by the investigator Hox and Boeije (2005).

Secondary data, in contrast to primary data, is gathered by earlier authors for many objectives and at different times in the past. For example, nowadays, many new primary data are collected, these are added to the store of social knowledge, which is called "secondary data”. In other words, secondary data research analysis of documentary evidence. As a result, getting secondary data requires less time and money than gathering primary data. However, there are not appropriate for each problem being researched (cannot be understood clearly without enough context and knowledge), written by Hox and Boeije (2005).

<b>3.6.2. Data collection </b>

The thesis employs collecting quantitative data. The research strategies apply for this are experiment research and survey. However, just only the survey strategy is appropriate for the study’s objectives to collect data.

Herkenhoff and Fogli (2018) define a survey as a system for gathering data to characterize, compare, or otherwise explain the knowledge, attitudes, and/or behaviours of a certain population. Survey respondents are asked a set of questions in order to collect information. There are two main types of questions: questionnaires and interviews, and responses can be written, spoken, or electronic (such as electronically, by email, telephone, or in face-to-face). Additionally, the paper aims to investigate the “primary data” of service quality in the TPBank LiveBank by adopting the SERVQUAL and Likert scale. The current study is quantitative, using an “online” survey (the Google

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form-based survey) to measure the service quality of the bank. Therefore, the questionnaire will be applied in this study to help the researcher collect information about customers' personal, habits, perceptions and expectations.

<i><b>Sampling </b></i>

The sample frame for the paper is the LiveBank in HCM and includes customers who have transacted at these locations which started selection data in March 2023. Most survey data is posted through the social web, especially in the group of TPBank’s customers, which is the reliable information from their consumers.

To ensure that the questionnaire is appropriate for Vietnamese clients. It was introduced in English in the form of SERVQUAL and then translated into Vietnamese. The two superiors first reviewed the validity of the contents and wording before giving them to the public, in order to have the same meaning in both English and Vietnamese translation. Their suggestions were applied to reformat the questionnaire translation.

<i><b>Target population </b></i>

According to Henrique and Matos’s (2015) report, they examined how demographic values influence the banking sector during various loyalty periods. As a result, there are many different levels of loyal phases to the company, such as the demographic variables: gender, age, education and income. Hence, managers should adopt strategies of dividing the sample for these variables in increasing customer orientation and consumer segmentation. As a result, customers' personal information is divided by age group, income, employment and gender. The target population in the study is banking clients who have been using the LiveBank in HCM and above 18 to those under 55 years old.

The report's initial objective was to collect 200 survey sample responses. However, by choosing each qualified person from the specified database and ignoring inadequate

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samples, such as age mismatches or missing data. In addition, some feedbacks are also reduced because customers have never transacted at TPBank LiveBank. The real final results are 110 responses.

<b>3.6.3. Measurement instrument </b>

It was divided into three main sections to select the “primary data". Section one contains the individual information on respondents. Section two, the rest of the questionnaire is prepared for 22 multiple-choice of the SERVQUAL scale, designed by Parasuraman et al. (1988) which includes five parts namely: Reliability, Assurance, Responsiveness, Empathy, and Tangibles. Then duplicated the answers to expectation and perception in the same sentence to be more concise, and it focused on a 7-point Likert scale from 1 “totally disagree” to 7 “totally agree”. The secondary data are collected from internet, TPBank web-based in order to analize the primary data.

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<b>CHAPTER 4: FINDINGS AND DATA ANALYSIS 4.1. Introduction </b>

Digital banking and digital finance will be important in the world's upcoming industry revolution. In Vietnam, the competition for survival in the banking industry is more fierce than ever. Digital technology is expected to have a greater impact on the financial sector as modern distribution channels emerge. These channels include ATM/POS, home banking, and call centers (Nguyen and Dang, 2018). In particular, TPBank was the first financial institution in Vietnam to introduce the LiveBank VTM (Video Teller Machine) system in 2017. This system enables users to conduct deposit transactions, send money, deposit savings, open instant cards, and more without having to physically visit the counter. By the end of 2021, TPBank had 400 LiveBank positions with an average of 3,200 transactions per machine per month. LiveBank is also one of the five differentiators that set TPBank apart from other banks, which assists the bank in reducing the number of new branches and transaction offices that open each year, (TPBank, 2022).

The following is the financial statement for TPBank in 2022: TPBank's after-tax profit in 2022 is about VND 6,261 billion, up 30% from 2021.

Units: VND million, %

<i><b>Figure 4.1: Financial Statement of TPBank 2022 </b></i>

<i><b>Source: TPBank, 2023 </b></i>

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