Banking Industry In Mauritius

Published Date: 02 Nov 2017

1.0 Introduction

Customers are more and more mindful of the service quality being provided by an organisation and they expect that their wishes are fulfilled. In an increasingly competitive environment, service quality as a critical measure of organisational performance attracts banking institutions and remains at the forefront of services marketing literature and practice (Lasser et al., 2000; Yavas and Yasin, 2001; Dhandabani, 2010). This study assesses what customers are expecting in terms of service quality from the commercial banks operating in Mauritius and what they are in fact receiving.

This chapter consists of the idea behind the study, problem statement, aim and objectives of the research, research questions and the research hypothesis. Banking industry is also scrutinized. The chapter finally outlines the structure of the dissertation.

Motivation

Mauritius which is among the leading economies in Africa, aims to rank among the top ten most investment- and business-friendly locations in the world (Michigan State University). To be able to fulfill this aim, the banking sector plays a major role and the service quality being offered by the banking sector is of utmost importance to attract private individuals, businesses, governments.

Huseyin et al., (2005) argued that good knowledge of the characteristics and advantages of service quality by banks do contribute for their success and their persistence in the international banking competitive environment (Ragavan and Mageh, 2013). Saravanan and Rao (2007) pointed out that service quality remains critical in the service industries, as businesses strive to uphold a competitive advantage in the market place and achieving customer satisfaction. Bheenick (2012), Governor of Central Bank of Mauritius (BOM), drew the attention that there is still scope for more competition and there is room for the banking sector to grow in size and sophistication to diversify consumer choice (Appendix 1). Banks compete with generally undifferentiated services and products in the market place, thereby service quality becomes a key competitive weapon (Stafford, 1996). This should alarm the existing commercial banks and make them realised how service quality is vital to compete for market shares and to avoid the tendency of existing customers switching to another bank. Customers are increasingly aware of the options on offer regarding the rising standards of service (Krishnaveni et al., 2004; Mishra et al., 2010). Customers therefore compare services being provided by different banks and then select the one offering superior quality.

The Mauritian banking sector contributes a great deal to the economy and the financial intermediation sector which is driven mainly by the banks posted a higher growth of 5.5 per cent. At 30 September 2011, the banks realised profit after tax of Rs15.9 billion while the GDP in 2011 was at 3.9 per cent.

Hence, the above information shows the importance of the banking sector for the Mauritian economy and how service quality may be a vital tool to gain competitive edge.

1.2 Banking industry in Mauritius

Today, the Mauritian banking sector has 21 commercial banks with 216 branches, 8 non-bank deposit taking institutions, 10 money–changers and 6 foreign exchange dealers. For the purpose of the study, service quality only in the banking industry more specifically that of commercial banks dealing mostly with retailed banking will be scrutinised. The service quality by offshore and Islamic banking will not be assessed.

The Table 1.1 illustrates the 216 branches that the 21 commercial banks operate; the number of ATMs which were around 436 in September 2012 and the approximate number of employees employed by each bank. Compared to the other commercial banks operating in Mauritius, the Century Banking Corporation Ltd is an Islamic bank and its operations depend on the Shari’a. Banyan Tree Bank Limited has been licensed in September 2012 and its operations are not yet fully established. Investec Bank (Mauritius) Limited, P.T Bank Internasional Indonesia, Standard Chartered Bank (Mauritius) Limited, Deutsche Bank (Mauritius) Limited are more offshore oriented.

Table 1.1: Commercial banks in Mauritius

Banks

No. of branches

No. of ATMs

No. of employees

ABC Banking Corporation Ltd

1

1

90

AfrAsia Bank Limited

1

0

102

Bank of Baroda

6

6

77

Bank One Limited

14

14

275

Banque des Mascareignes Ltée

12

11

158

BanyanTree Bank Limited

1

-

-

Barclays Bank PLC

24

24

1497

Bramer Banking Corporation Ltd

19

22

357

Century Banking Corporation Ltd

1

0

25

Deutsche Bank (Mauritius) Limited

1

0

250

Habib Bank Ltd

3

3

56

HSBC Bank (Mauritius) Limited

11

16

409

Investec Bank (Mauritius) Limited

1

0

80

Mauritius Post and Cooperative Bank Ltd

15

19

328

P.T Bank Internasional Indonesia

1

0

15

SBI (Mauritius) Ltd

13

14

226

Standard Bank (Mauritius) Limited

1

0

100

Standard Chartered Bank (Mauritius) Limited

1

0

51

State Bank of Mauritius Ltd (SBM)

39

104

1200

The Hongkong and Shanghai Banking Corporation Limited

11

15

409

The Mauritius Commercial Bank Ltd (MCB)

40

160

2700

Source: fieldwork

The banks in Mauritius are providing different banking and financial services to private, corporate and institutional clients. They offer personal banking, business and specialised services. Added to the existing products and services, the banks also put highly qualified and experienced professionals to the customer’s disposition to give them advice and to guide them.

1.3 Problem Statement

Reputation of an organisation is very fragile and any service quality problems are not favorable for its name. Ha and Jang (2009) argues that service failure occurs when customer perceptions do not meet customer expectations. The problem with service failure is that it may lead to a destroyed relationship between the customer and the organisation.

Commercial banks operating in Mauritius are being awarded and are gaining international recognition. This can be epitomised by the MCB which has been rewarded "Best SEM-7 Company, Online Reporting and Corporate Governance Disclosures Awards" by PricewaterhouseCoopers for 2012, the "Best Local Bank in Mauritius" at EMEA Finance Award for 2012, " Best Emerging Market Bank 2012" in the Global Finance Magazine (MCB Annual Report 2012). Similarly, AfrAsia Bank has been selected as the "Best Local Private Bank in Mauritius" in 2011 and 2012 and as the "Best Private Bank for the Super Affluent in Mauritius" by Euromoney Private Banking Survey in 2012 (Afrasia Bank Annual Report 2012).

Many customers depend on the commercial banks to deliver high quality services daily. The question does arise how banks that though awarded, have shortcomings in their service quality. At the MCB which ranked 18th in Africa and 662nd among the top 1000 banks and the SBM clients have been victims of phishing (L’express, 2nd March 2012) (Appendix 2). Following these cases, the Mauritius Bankers Association (MBA), on behalf of its members, in a press release, alerted bank customers to various fraudulent e-mails (Appendix 3).

Moreover, according to Bheenick (2012), fee income is now a non-negligible part of banks’ total income and there have been complaints from various quarters that bank charges are on the high side. A Working Group on fees, charges and commissions has been set up to study the reasonableness of bank charges corresponding to the services offered. But then, till now no action has been undertaken to rectify the problem.

The problems mentioned above call for the assessment of service quality.

1.4 Aim

The aim of the study is to assess customers’ service quality expectations and perceptions of commercial banks.

1.5 Objectives

The main goal of the study is to identify the customers’ expectations and perceptions on service quality factors in banks as per Servqual model using a modified version of Servqual questionnaire. An insight on the different aspects of service quality by the banks is provided. The order of importance of the items under each dimension of Servqual model quality will be ranked. The Servqual approach and the paired sample tests will be used to assess the difference between customer’s expectations and perceptions. Similarly, the goal is to reduce the number of variables into a few meaningful objectives, to determine the new dimensions of service quality and to identify the most important factors using Factor Analysis (FA).

Finally, recommendations following the findings of the study will be made based on statistical techniques used.

1.6 Research Questions

The study seeks to answer the following questions:

Are the services being offered by the commercial banks in Mauritius meeting customers’ expectations?

What are the perceptions of the customers about the services that they are receiving?

Is there a gap between the expectations and the perceptions of the customers?

1.7 Hypothesis

The following hypothesis may be formulated for this study:

H1. There is no difference between customers’ expectations and perceptions.

1.8 Format of the study

The following is an outline of the dissertation:

Chapter 1: Introduction

In this chapter a general outline of the study is given. It introduces the motivation behind the study and the problem statement. Aims, hypothesis, research question and objectives have been formulated and the banking industry is evaluated.

Chapter 2: Literature Review

In chapter two, the theoretical framework will be presented. Different concepts such as service quality, customer expectations and perceptions, and perceived service quality are described. Finally, the SERVQUAL model is outlined and empirical studies are reviewed.

Chapter 3: Methodology

This chapter will describe the methods that will be used for making the survey and for analysis. The questionnaire structure as well as the method for choosing the sample of population is also described. Lastly, the data analysis techniques are studied.

Chapter 4: Analysis of Findings

In chapter four, the data, the findings and the analysis of the result will be dealt with. Gaps between expectations and perceptions are analysed using Servqual approach and paired sample t-tests. Factor Analysis is used to reduce the number variables of the SERVQUAL items. Data collected from the respondents will be analysed using Statistical Package for Social Scientists (SPSS) software and Excel 2007.

Chapter 5: Recommendations and Conclusion

In chapter five, the recommendations within the context of the study and conclusions of the dissertation will be highlighted. Moreover, proposal for further research will be made.

1.9 Conclusion

Chapter one gives a brief and general description of the research and presents the statement of the problem, objectives of the study, research questions, motivation of the study, an overview of banking industry and finally the structure of the research. Chapter two will present service quality concept adopted in this study and reviews the literature and explain in details the constructs of the study.

CHAPTER 2: LITERATURE REVIEW

2.0 Introduction

This chapter focuses on the relevant literature on the concept of service quality, customers’ expectations and perceptions on service quality. Moreover, the importance of service quality in banking industry will be examined. The Servqual model which will be used to measure service quality will also be reviewed and related articles are scrutinized.

2.1 Service Quality/Concept of Quality

Quality is the keyword for survival of organisations in the global economy (Rahaman et al., 2011). Crosby (1979) and Parasuraman et al., (1985) defined quality as "conformance to requirements" and according to Parasuraman et al., 1985, even though the substance and determinants of quality may be undefined, its importance to firms and consumers is unequivocal (Parasuraman et al., 1985). Delivering excellent service is a winning strategy. Quality service sustains customers' confidence and is essential for a competitive advantage ( Berry et al., 1994). Quality in most services, occurs during service delivery, usually in an interaction between the customer and contact personnel of the service firm (Zeithaml et al., 1988).

Many researchers (Bateson 1977, Berry 1980, Lovelock 1981, Shostak 1977, Parasuraman et al., 1985) asserted that most services are intangibles and due to this, a firm may find it challenging to understand how consumers perceive their services and assess service quality (Parasuraman et al., 1985). Zeithlaml (1981) and Parasuraman et al., (1985) argued that most services cannot be counted, measured, inventoried, tested, and verified in advance of sale to assure quality. Parasuraman et al., (1985) suggest that service quality is ‘performance based’ rather than objects, therefore "precise manufacturing specifications concerning uniform quality can rarely be set". Service quality is intangible, heterogenic, and inseparable, which makes service quality difficult to measure and understand (Parasuraman et al., 1985). Services have unique characteristics physical products do not have, such as intangibility, inseparability, variability, and perishability (Kotler, 1994; Rust et al., 1996; Lee et al., 2000). As an illustration, because services are usually produced and consumed simultaneously, customers often interact directly with the firm's personnel (Zeithaml and Bitner, 1996; Lee et al., 2000). Service is therefore a key component of value that drives any company's success. Quality service helps a company maximize benefits and minimize non-price burdens for its customers (Berry et al., 1994).

Service quality which has been conceptualized as an overall assessment of service by the customers, is a key decision criterion in service evaluation by the customers (Lewis and Booms, 1983; Ganguli and Kumar Roy, 2010). Duffy and Ketchand (1998) defined service quality as customers' appraisals of the service core, the provider, or the entire service organization. Most definitions also focus on the customer, and on the fact that services are provided as solutions to customer problems (Grönroos, 2001). We may conclude that service quality is linked to activities, interactions and solutions to customer problems (Edvardsson, 2005). The service quality in a dyadic service encounter is recognised as dependent upon the interactive process between the service provider and the service receiver (Svensson, 2001). Service quality is a measure of how well the service level delivered meets customer expectations, thereby delivering quality service means conforming to customer expectations on a consistent basis (Lewis and Booms 1983, Parasuraman et al., 1985).

The success behind superior service is to understand and respond to customer expectations. This is because customers compare perceptions to expectations when judging the quality of a firm’s service offering (Parasuraman et al., 1988). Parasuraman et al., (1985) stated in their study for banking sector that "customers’ perceptions of quality are influenced by various gaps which lead to service quality shortfalls and, in particular, that the quality perceived in a service is a function of the gap between customers’ desires/expectations and their perceptions of the service that is actually received". The service quality is identified as the determinants of customers’ satisfaction in banking (Naceur et al., 2002; Ghost and Gnanadhas, 2011). In addition, Parasuraman et al., (1985) described service quality as a form of attitude that results from the comparison of consumer expectations with service performance delivered. For Parasuraman et al., (1985) customer expectation and perception are the two key ingredients in service quality.

2.2 Customers’ Expectations of Service Quality

Customer expectations are beliefs about a service that serve as standards against which service performance is judged (Zeithaml et al., 1993); what customers think a service provider should offer rather than what might be on offer (Parasuraman et al., 1988). Edvardsson et al., (1994) added that expectations are formed from a variety of sources such as the customer’s personal needs and wishes while Dubey (2011) defined customers’ expectations as beliefs about service delivery that functions as standards or reference points against which performance is judged. The latter argued that expectations have a central role in influencing satisfaction with services and also pointed out that in the banking sector, expectations of the customers have undergone a great change with the availability of banking services everywhere through the help of technology.

2.3 Customers’ Perceptions of Service Quality

Services are produced, delivered and consumed during – in time and space –overlapping processes in which customers have a role as co-producers carrying out activities and deeds as well as being part of interactions for instance with front-line employees, other customers and technology which will influence or decide both process quality and outcome quality. Therefore, Edvardsson (2005) argued that service quality perceptions are formed during the production, delivery and consumption processes.

2.4 Importance of service quality in banking industry

Recently, there has been a keen interest, especially in banking, where banks are looking at the life time value of the customer base rather than focusing on the cost of transactions (Ambler 1995, Mishra et al., 2010). Therefore, the strategic focus of the banks changed from production-oriented to customer-oriented. The attempts of the banks to increase the number of services, innovate new products, invest in hi-tech infrastructure and develop customer’s relationship management systems to attract customers from rival banks with incentive schemes, package services, better service quality and competitive service standards (Dubey, 2011).

Dubey (2011) sustained that customers become the focal point for the banking business so that bankers have to involve themselves totally in anticipating, identifying, reciprocating and satisfying their customers in a mutually rewarding manner. Realizing this, there is a strong feeling in the modern banks that each individual employee working with them has to act as a marketing person who contributes to the total satisfaction of their customers.

A banking firm uses resources like establishment, technology, marketing effort and employee quality to deliver a high level of service for the customers (Mukherjeel et al., 2003). Customers expecting better services, have begun demanding different services and their choice for delivery channels are changing (Dubey, 2011). As such new techniques are being adopted by banks and therefore banks should find out whether the customers are really satisfied with their services or not (Dubey, 2011). Moreover, a better service delivery helps banks to differentiate their offerings, extract more business from existing customers and attract new customers and thus leads to improvement in financial performance of the banks (Mukherjeel et al., 2003). Anderson and Sullivan (1990) and Mishra et al., (2010) argued that it is expensive to capture customers from other companies and the solution is to provide a greater degree of service improvement to make a customer switch from a competitor.

Among others, provision of high quality services enhances customer retention rates, helps attract new customers through word of mouth advertising, increases productivity, leads to higher market shares, lowers staff turnover and operating costs, and improves employee morale, financial performance and profitability (Julian and Ramase-shan, 1994; Lewis, 1989; 1993; Yavas et al., 1997).

2.5 Factors affecting the banking industry

Changes in the financial services such as banks have arisen as a result of economic deregulation, government policies, globalisation and information communication technology and consequently resulted into intense competition in the financial service industry.In order to remain competitive, financial institutions are providing an increasingly assorted bouquet of financial services (Hinson et al., 2006).

In the banking industry, bank branches alone are no longer sufficient to provide banking services to cater the needs of today’s sophisticated and demanding customers. The provision of banking services through electronic channels (e-channels) namely ATMs, PC banking, phone banking and banking kiosks have provided an alternative means to acquire banking services more conveniently (Ong Hway-Boon and Cheng Ming Yu; 2003).

2.6 Measuring Service Quality using SERVQUAL Model

Defining service quality as a measure of excellence in terms of perceptions is not sufficient according to Langevin (1988) who maintains that "it is obvious that understanding customer expectations and meeting customer needs is the single most critical issue and determinant of service quality" (Duffy and Ketchand, 1998). Parasuraman et al., (1988) argue that the difference between perceptions of a service and expectations for that service which is more important and should be used as the measure of service quality.

Early conceptualizations (Grönroos 1982, 1984; Parasuraman, Zeithaml, and Berry 1985) are based on the disconfirmation paradigm employed in the physical goods literature which suggests that quality results from a comparison of perceived with expected performance, as is reflected in Grönroos's (1982, 1984) seminal conceptualization of service quality that "puts the perceived service against the expected service" (Brady and Cronin, 2001). Grönroos (1982) identifies two service quality dimensions: functional quality which represents how the service is delivered and technical quality reflects the outcome of the service act, or what the customer receives in the service encounter (Brady and Cronin, 2001).

In agreement with the propositions put forward by Grönroos (1982) and Smith and Houston (1982), Parasuraman et al., (1985, 1988) considered service quality as a difference between consumer expectations of ‘what they want’ and their perceptions of ‘what they get’ (Mishra et al., 2010). Parasuraman et al., (1985, 1988) defined perceived service quality as "a global judgment, or attitude, relating to the superiority of the service." The authors linked the concept of service quality to the concepts of perceptions and expectations in this way: "Perceived quality is viewed as the degree and direction of discrepancy between consumer's perceptions and expectations ".

Thus, the authors suggested that customers' assessment of overall service quality is based on the gap between their expectations and their perceptions of performance levels. Parasuraman et al., (1985) presented a model of five service quality gaps, and then developed a scale, "SERVQUAL", which was adopted by many researchers (Chen et al., 2005). The table 2.2 illustrates the conceptual model of service quality.

Table 2.1: The conceptual model of Service Quality

Source: Zeithaml et al., 1988

The first gap shows the difference between consumer expectations and management perceptions of consumer expectations while the second gap illustrates the difference between management perceptions of consumer expectations and service quality specifications. The third gap points out the difference between service quality specifications and the service actually delivered while the forth gap discloses the difference between service delivery and what is communicated about the service to consumers. Finally, the fifth gap asserts the difference between consumer expectations and perceptions which in turn depends on the size and direction of the four gaps associated with the delivery of service quality on the marketer's side (Zeithaml et al., 1988).

The construct of quality measured by SERVQUAL scale involves service quality (as opposed to object quality) (Mishra et al., 2010). SERVQUAL (Parasuraman et al., 1988) is perhaps the most widely-known and researched scale of service quality. It focuses on human interactions during the service encounter (Ganguli and Kumar Roy, 2010).

2.7 Service quality dimensions

Parasuraman et al, (1988) developed a 22-item instrument, called as SERVQUAL for assessing customer perceptions of service quality in service organizations. Initially, the researchers took ten dimensions of service quality- Tangibles, Reliability, Responsiveness, Competence, Courtesy, Credibility, Security, Access, Communication, and Understanding the customer- as the input to derive some items for the SERVQUAL scale. The new dimensions of service quality are five in number: tangibles, reliability, responsiveness, assurance and empathy. It has now a variety of applications in banking industry, especially in assessing customers’ expectations and perceptions of service quality delivered by different banks. It also helps in identifying the areas of managerial attention for future improvement (Mishra et al., 2010). While SERVQUAL can be used in its present form to access and compare quality across a wide variety of firms, appropriate adaptation of the instrument may be desirable when only a single service is investigated (Parasuraman et al., 1988).

It is argued that the key to ensuring good service quality perception is in meeting or exceeding what customers expect from the service.

In an equation form, the measurement of service quality can be expressed as follows:

SQi = Pi - Ei

Where:

SQi = perceived service quality of an attribute

Pi = Average perception of individual ‘i’ with respect to performance of a service attribute

Ei = Average expectation of individual ‘i’ with respect to performance of a service attribute

Parasuraman et al., (1988) held that when perceived or experienced service is less than expected service, it implies less than satisfactory service quality. But, when perceived service is less than expected service, the obvious inference is that service quality is more than satisfactory. Parasuraman et al., (1988) posited that while a negative discrepancy between perceptions and expectations — a ‘performance-gap’ as they call it causes dissatisfaction, a positive discrepancy leads to consumer delight (Mishra et al., 2010). Closing this gap might require toning down the expectations or heightening the perception of what has actually been received by the customers or a little of both.

2.8 SERVQUAL Dimensions

The SERVQUAL dimensions: tangibles, reliability, responsiveness, assurance and empathy are the basis for service quality measurement (Parasuraman et al., 1988).

The SERVQUAL model when assessed collectively, appears to be unambiguous from the others because it uses terms that describe one or more determinants of a quality service encounter (Brady and Cronin, 2001). Undeniably, the relative importance of the SERVQUAL factors may vary across each dimension depending on individual or situational differences. In turn, the SERVQUAL dimensions capture how consumers differentiate performance on these dimensions (Brady and Cronin, 2001).

The five dimensions identified from empirical studies are:

2.8.1 Tangibles

Tangibles include the physical evidence of the service- physical facilities, appearance of personnel, tools or equipment used to provide the service, physical representations of the service such as a plastic credit card or a bank statement and other customers in the service facility (Parasuraman et al., 1985). These are all factors that customers notice before or upon entering the bank. Such visual factors help consumers form their initial impressions and among others, the bank should enhance ambient conditions such as lighting, temperature and spatial layout of furnishings (Yavas et al., 1997).

2.8.2 Reliability

Reliability which involves consistency of performance and dependability, expect that the firm performs the service right the first time and honors its promises. Specifically, it involves accuracy in billing, keeping records correctly and performing the service at the designated time (Parasuraman et al., 1985).

2.8.3 Responsiveness

Responsiveness concerns the timeliness of service- giving prompt service, calling the customer back quickly- and the willingness or readiness of employees to provide service (Parasuraman et al., 1985). Yavas et al., (1997) asserted that responsiveness is similar to tangible elements and is closely linked to consumers’ satisfaction with a bank and their continued patronage decision such as commitment.

2.8.4 Assurance

Al-Hawary et al., (2011) adverted that the assurance dimension refers to the knowledge and courtesy of the company’s employees and their ability to inspire trust and confidence in the customer towards the service company.

2.8.5 Empathy

The empathy questions in the SERVQUAL battery deal with whether or not the bank gives individual attention to customers, has their best interest at heart, and understands the specific needs of customers. It is easy to see how banks with a seller’s market mentality would be weak in this area. This situation may be depressing at first glance, but in reality offers excellent prospects for those banks willing to change and adapt (Yavas et al., 1997). Zeithaml et al., (1990) asserted that empathy is about easy access, good communication and understanding the customer.

2.9 Criticisms and limitations of SERVQUAL

However, Cronin and Taylor (1992) criticized SERVQUAL and proposed an alternative scale called SERVPERF which have all the SERVQUAL scale dimensions but uses only perception as a measure of customer perceived service quality instead of the gap between expectation and perception as SERVQUAL approach (Ganguli and Kumar Roy, 2010). Further research works have been carried out with SERVQUAL, which either modified the dimensions or added new dimensions to the original five in order to accommodate for uniqueness of different types of service settings (Asubonteng et al., 1996; Babakus and Boller, 1992; Buttle, 1996; Carman, 1990; Lai et al., 2007)

Limitations with SERVQUAL are highlighted by the authors themselves (Parasuraman et al., 1991) and in other research studies (Babakhus and BoIler 1992; Carman 1990; Lewis 1993; Lewis and Mitchell 1990; and Smith 1992). They relate to respondent’s difficulties with negatively worded statements; using two lists of statements for the same items, the number of dimensions of service being assessed; ease of consumer assessment and timing of measurement—before, during or after a service encounter (Mishra et al., 2010).

Nevertheless, despite the criticism, SERVQUAL has been widely used in various contexts throughout other studies. The SERVQUAL instrument has been widely used because it "provides a basic skeleton... which can be adapted or supplemented to fit the characteristics or specific research needs of a particular organization..." ( Parasuraman et al., 1988).

2.10 Previous Research close to the study

Table 2.2:

Titles

Authors

Year

Organisation/University

Banking Service Quality

provided by commercial

banks and customer

satisfaction

Al-Hawary,

Alhamali,

Alghanim

2011

Al al-Bayt University, Jordan

King Saud University, Saudi Arabia

Applying Servqual model and factor analysis in assessing customer satisfaction with service quality: The case of mobile telecommunications in Macedonia

Zekiri

2011

South East European

University

An assessment on service quality in the Mauritian Banking Sector

Agathee

2010

UOM

Measuring perceived service

quality at UAE commercial

banks

Jabnoun

and Al

-Tamimi

2002

College of Business and

Management, University of Sharjah,

Sharjah, United Arab Emirates

Source: Fieldwork

2.11 Empirical Studies

The following are the main findings of the above mentioned studies:

Al-Hawary et al., (2011) examine the impact of constructs of service quality and customer satisfaction and the results of the study indicate that more emphasis should be placed on Assurance. The results also revealed that service quality can be used to predict customer satisfaction and that there is appositive relationship between service quality and customer satisfaction. FA most specifically Principal Component Analysis (PCA) was used to check the data reliability for the service quality.

In Zekiri’s study (2011), it was found that overall service quality perceived by the customers was not satisfactory, implying in other words that expectations were higher than perceptions. PCA was employed to find out the most important factor for customer’s expectations and perceptions with service quality dimensions. PCA revealed reliability as the most important factor for customer satisfaction and empathy as the most important for customers’ expectations.

Jabnoun and Al-Tamimi (2002) developed an instrument based on Servqual model to measure service quality in UAE banks and the instrument was tested for reliability and validity in their study. PCA resulted into three reliable dimensions of banking service and the difference in significance between the instrument’s dimensions obtained, was analysed and the human skill dimension was found to be more significant.

Agathee (2010) who aimed to determine the relationship between service quality and customer satisfaction in the Mauritian Banking sector, found a huge gap between customer’s expectation and perception for reliability and responsiveness using Servqual approach. Tangibles score highest on both expectations and perceptions while responsiveness and reliability score among the lowest on perceptions.

FA will be reviewed in the subsequent chapter.

2.12 Conclusion

Chapter 2 reviewed the relevant literature on service quality, customer perceptions and customer expectations and SERVQUAL model. The importance of assessing service quality in the banking industry has been studied and related studies are looked into. In the next chapter, the research methodology will be defined, how respondents will be selected for the survey, limitations and methods for analyzing the result of the survey will be spelt out.

CHAPTER 3: METHODOLOGY

3.0 Introduction

This chapter involves presenting the choice of method of collecting data, questionnaire design, questionnaire administration, method for analyzing data- paired sample tests and FA. Constraints that the researcher faced during the collection of data have been mentioned.

3.1 Questionnaire design

The questionnaire (Appendix 4) has been designed into three sections and consists of two sets of 22-items. The first part of the questionnaire sought personal questions from the customers. In the second part, the respondents are required to rate a set of items that handles their expectations of banks’ services on the level of importance on a predefined five-point Likert Scale where 1 stands for strongly disagree, 2 stands for disagree, 3 stands for neutral, 4 for stands for agree and 5 stands for strongly agree. The third part of the questionnaire required the respondent to rate a set of corresponding items that refer to their perceptions about the services being provided. Those statements were divided into five different dimensions -tangibles, reliability, assurance, responsiveness and empathy. All the items measuring service quality dimensions were standard questions developed by Parasuraman et al. (1988) except one question that the researcher added in the tangibility to have 22 items in each part. The question that the researcher added was formulated from Yavas et al., (1997) definition of tangibility.

3.2 Sample design

Hinson et al., (2006) in their study compare service quality across top-three performing banks when ranked by total operating assets, share of industry deposits, net advances, profit margins and representations from their profit and loss statements.

The population that the researcher targeted is all the individual retail customers of the two selected banks Mauritius. The two banks selected are the MCB and SBM which holds more than two thirds of the Mauritian market (Padachi et al., 2008).

Bellou and Andronikidis (2008) carried out their study in a city where at least one branch of all major banks operate and all employees were randomly chosen. The researcher carried out the data collection in two towns Rose-hill and Quatre-Bornes where the two selected banks have a branch. The researcher has chosen these two towns for convenience purpose and due to time constraint. The researcher selected the respondents from the two branches randomly. The researcher carried out the survey in the 4 branches during two slot of time – from 9 a.m to 11.30 a.m and from 1 p.m to 3 p.m. The systematic sampling technique was very difficult to use since the banks were not willing to provide a list of all their customers due to confidentiality. Also, the researcher would not have been able to contact all the selected respondents and collect all the questionnaires in due time.

The Table 3.1 shows the formula used to calculate the sample size for the survey:

Table 3.1: Sample Size Formula

N = P% × Q% × Z2

E%

Source : Abdool., (2006)

Where:

N= The minimum sample size

P% = The proportion belong to specified category

Q%= The proportion not belonging to specified category

Z = The Z-value corresponds to level of confidence required

E%= The margin of error required

The sample size used by the researcher is 230 at a margin of error of 6.3% (Appendix 5). The researcher has proportionately allocated the sample size to MCB and SBM. 170 questionnaires were distributed to MCB and 72 questionnaires to SBM (Appendix 5).

3.3 Pilot study

The questionnaire was initially pilot tested to 10 respondents at two branches of the two banks selected –MCB and SBM- to verify that the questions were clear and not ambiguous. Few questions were reviewed as a result of the pilot study. The researcher removed a question in the empathy part as the respondents found that two questions in the initial questionnaire resembled so much that it causes confusion.

3.4 Questionnaire Administration

The questionnaires were self-administered basis at the two branches found in Rose-hill and Quatre-bornes for both MCB and SBM to customers present at the banking hall and transacting business with the banks. The questionnaires were administered during the period 10 December to 20 December 2012 and the period 7 January to 15 January. All data collection procedures were designed to ensure the anonymity of participants. The respondents were requested by the researcher to answer the items of expectations before service is delivered and to measure the items of perceptions after the service has been delivered. Most respondents follow suit except 12 respondents due to time constraint.

3.5 Limitations

Few people complained that the questionnaire was too long and refused to fill the questionnaire. But, most respondents selected were easy to convince to answer the questionnaire. The researcher asked in the same questionnaire about the expectations and perceptions following Parasuraman et al. (1988) example which created a bit of confusion to a few respondents. Due to these few constraints, the researcher has had to invalidate 10 questionnaires.

3.6 Reliability

The reliability test was conducted using the SPSS version 20.0 and based on 20 respondents that the researcher contacted on a personal approach. Reliability indicates the accuracy or precision of the measuring instrument (Norland, 1990). The alpha coefficient for the twenty-two items for the expectation part is 0.875 (Table 3.2) while the alpha coefficient for the twenty- two items for the perception part is 0.948 suggesting that the items have relatively high internal consistency (Table 3.3).  According to Nunnaly (1978), the coefficient of the alpha should be greater than 0.7 for the measure to be deemed reliable (Agathee, 2010).

Table 3.2: Reliability Test for Expectation

Reliability Statistics for Expectation

Cronbach's Alpha

Cronbach's Alpha Based on Standardized Items

N of Items

.875

.879

22

Table 3.3: Reliability Test for Perception

Reliability Statistics for Perception

Cronbach's Alpha

Cronbach's Alpha Based on Standardized Items

N of Items

.948

.949

22

3.7 Validity

After the pilot test and after doing a few changes to the initial draft, the researcher contacted four persons who are knowledgeable to the subject for face validity, criterion validity and construct validity. The four persons were a lecturer, a bank manager, a marketing manager and a training manager. The views and suggestions of those persons were considered and necessary changes were made.

3.8 Data analysis techniques

The researcher recorded all the responses in SPSS version 20 in order to analyse the data. The researcher will use the Servqual Approach, paired sample test and FA to analyse the data and will then compare the results obtained from each techniques.

paired sample test

3.8.1 Factor Analysis

FA provides the tools for analysing the structure of the interrelationships (correlations) among a large number of variables by defining sets of variables that are highly interrelated, known as factors (Hair et al., 2010).

For standardized variables, factor model represented as follows:

Xi= Ai1F1 + Ai2F2 + … + AimFm + ViUi

Where

Xi = ith standardised variable

Aij = standardized multiple regression coefficients of variable i on common factor j

F = common factor

Vi = standardized regression coefficients of variable i on unique factor j

Ui = unique factor of variable i

M = number of common factor

The unique factors are uncorrelated with each other and with the common factors. The common factors can be expressed as linear combinations of the observed variables.

Fi = Wi1X1 + Wi2X2 + … + WikXk

where

Fi = estimator of the ith factor

Wi = weight of factors score coefficients

k = number of variables

3.8.1.1 FA Decision Process

Stage 1: Objectives of FA

The general purpose of FA techniques is to find a way to summarise the information contained in a number of original variables into a set of new, composite dimensions or factors with a minimum loss of information-that is, to search for and define the fundamental constructs or dimensions assumed to underlie the original variables (Hair et al., 2010). In meeting its objectives, FA is keyed to four issues: specifying the unit of analysis, achieving data summarization and/or data reduction, variable selection, and using analysis results with other multivariate techniques (Hair et al., 2010). The researcher has selected variables as unit of analysis for FA.

Stage 2: Designing a FA

When a study is being designed to assess a proposed structure, the researcher should ensure to include five or more variables that may represent each proposed factor (Hair et al., 2010). Hair et al., (2010) also stated that as a general rule to factor analyse a sample, the minimum sample is to have at least five times as many observations as the number of variables to be analysed.

Stage 3: Assumptions in FA

Any departure from linearity, homoscedasticity, normality should be carefully examined since any departure should apply only to the extent that they diminish the observed correlations (Hair et al., 2010). Some degree of multicollinearity is desirable because the objective is to identify interrelated sets of variables (Hair et al., 2010).

Different methods are used to determine the suitability for applying FA to a data set. The researcher must ensure that the data matrix has sufficient correlations to justify the application of FA (Hair et al., 2010). Fidell and Tabachnick (2001) adverted that a matrix that is factorable should include several sizeable correlations. The authors added that if no correlation exceeds 0.3, use of FA is questionable because there is probably nothing to factor analyse.

Bartlett test of sphericity is a statistical test for the presence of correlations among the variables and it provides the statistical significance that the correlations matrix has significant correlations among at least some of the variables (Hair et al., 2010). It tests the null hypothesis that the correlation matrix is an identity matrix and FA is performed if the null hypothesis is rejected. Kaiser-Meyer-Olkin (KMO) is a measure of sampling adequacy and a researcher should always have a KMO above 0.5 before proceeding with the FA.

Stage 4: Deriving factors and assessing overall fit

Decisions about the method of extracting the factors and the number of factors selected to represent the underlying structure in the data.

Method of extracting the factors

There are two major methods of extracting the factors from a set of variables: exploratory FA (EFA) and confirmatory FA (CFA). In EFA, one seeks to describe and summarise data by grouping together variables that are correlated (Fidell and Tabachnick, 2001). EFA provides the researcher with information about the how many factors are needed to best represent the data (Hair et al., 2010). In confirmatory FA, variables are carefully and specifically chosen to reveal underlying processes.

EFA is associated with theory development while confirmatory is associated with theory testing.

EFA is in fact made up of two methods:

Common Factor Analysis (Principal Axis Factoring-PAF)

Common FA considers only the common or shared variance that both the unique and error variances are not of interest in defining the structure of the variables (Hair et al., 2010).

Principal Component Analysis (PCA)

PCA considers the total variance and derives factors that contain small proportions of unique variance and, in some instances, error variance (Hair et al., 2010).

PCA will be embraced for the purpose of the study since the researcher aims to reduce data and PCA is more appropriate than PAF since according Hair et al., (2010), the latter is best in well-specified theoretical applications. PCA by focusing on the minimum number of factors needed to account for the maximum portion of the total variance represented in the original set of variables, is more suitable when data reduction is aimed.

Criteria for the number of factors to extract

Latent root criterion

Latent root criterion also known eigenvalue-one criterion or the Kaiser criterion, is a commonly used technique to determine the number of factors or components to include. Only the factors having eigenvalues greater than 1 are considered significant and factors with eigenvalues less than 1 are insignificant and are disregarded (Hair et al., 2010).

Percentage of Variance Criterion

It is an approach based on achieving a specified cumulative percentage of total variance extracted by successive factors (Hair et al., 2010). The purpose is to ensure practical significance for the derived factors by ensuring that they explain at least a specified amount of variance (Hair et al., 2010). Hair et al., (2010) argued that it is not uncommon to consider a solution that accounts for 60% of the variance (and in some instances even less) as satisfactory.

Scree Test Criterion

The scree test is derived by plotting the latent roots against the number of factors in their order of extraction and the shape of the resulting curve is used to evaluate the cut-off point (Hair et al., 2010). The point at which the curve first begins to straighten out is considered to indicate the maximum number of factors to extract (Hair et al., 2010). Scree test tends to indicate one or two more factors that are indicated by the latent root criteria.

Stage 5: Interpreting the factors

After the factors are extracted, a table gives communalities which measure the percent of variance in a given variable explained by all the factors. The researcher should view the communalities to assess whether the variables meet acceptable levels of explanation and all variables with communalities less than 0.5 are considered as not having sufficient explanation (Hair et al., 2010).

Component matrix are coefficients (factor loadings) to express variables in terms of components. Before rotation, each factor correlates with several variables. So, we need to rotate the factors for better interpretation.

Rotation of factor

The goal of any rotation is to obtain some theoretically meaningful factors and, if possible, the simplest factor structure (Hair et al., 2010).

The researcher will use the orthogonal rotation in which axes are retained at 90 degrees instead of oblique rotation because the analytical procedures for performing oblique rotations are not as well developed and are subject to some controversies (Hair et al., 2010) and also because the orthogonal rotation is the preferred method when the aim is to reduce data to a small number of variables.

The researcher will perform the orthogonal rotation using Varimax. Varimax rotation centers on simplifying the columns of the factor matrix and it maximises the sum of variances of required loadings of the factor matrix. With this rotational approach, some high loadings (that is, close to -1 or +1) are likely, as are some loadings near 0 in each column of the matrix. The logic is that interpretation is easiest when the variable-factor correlations are close to either +1 or -1, thus indicating a clear positive or negative association between the variable and the factor or close to 0, indicating a clear lack of association.

Assessing Significance

To be statistically significant, a small loading is needed given either a large sample size or a large number of variables being analysed and a larger loading is needed given a factor solution with a larger number of factors (Hair et al., 2010). The authors also stated that the larger the absolute size of the loading, the more important the loading in interpreting the factor structure. The statistical significance of factor loadings depends on sample size as shown in Table 3.4.

Table 3.4: Guidelines for identifying significance factor loadings based on sample size

Source: Hair et al., 2010

However, in practice it may be found that one or more variables each has significant loadings on several factors or a variable has no significant loadings. Thus, the job of interpreting the factors is much more difficult. When a variable is found to have more than one significant loading, it is termed as cross loadings. Eventually, the objective is to make each variable associate with only one factor.

According to Hair et al., (2010), the researcher can ignore those problematic variables and interpret the solution as it is, which is appropriate if the objective is solely data reduction but then in the factor solution, the variables are poorly represented. Each variable can otherwise be evaluated for possible deletion.

When an acceptable factor solution has been obtained in which all variables have a significant on a factor, the researcher attempts to assign some meaning to the pattern of factor loadings.

Variables with high loadings are considered more important and have greater influence on the name or label selected to represent a factor.

The researcher then assigns a name to a factor that accurately reflects the variables loading on that factor. Finally, the researcher computes the factor scores for creating a smaller set of variable to replace the original set. Factor score represents the degree to which each individual score high on the group of items with high loadings on a factor (Hair et al., 2010).

3.9 Conclusion

Chapter 3 gives an idea which population the researcher has targeted, the sampling technique used, the collection of data, the problems faced by the researcher. Chapter 3 reviewed paired sample tests and FA which will be used by the researcher to analyse the data collected in chapter 4.