A Model of Customer Loyalty: An Empirical Study of Indian Retail Banking Customer

Abstract

The present article proposes and tests an integrative model to examine the relationships among service quality (SQ), perceived value (PV), customer satisfaction (CS), corporate image (IMG) and customer loyalty (CL) in Indian retail banking context. The model is tested on the basis of data collected from 545 customers of Indian public sector banks (PSBs) using structural equation modeling (SEM) through path analysis. The finding exhibits that the present research model strongly predicts direct and/or indirect relationship of all constructs with CL phenomenon. However, the construct of PV was found to be insignificantly related to IMG in this integrative model. Moreover, the results also reveal that SQ exerts a strong influence on CS and CL; therefore, SQ is found as the most dominant construct in the model. The results of the model validate that SQ, PV, CS and IMG are the key antecedents of CL. Thus, integrative model of CL emerges as an ‘outcome’ in Indian context with special reference to selected PSBs.

Keywords

Service quality perceived value customer satisfaction corporate image customer loyalty structural equation modeling (SEM)

Introduction

The present Indian banking has evolved as one of the fastest growing marketplaces in the global banking industry due to liberalization and deregulation in the banking system of India; therefore, it is very close to turn into one of the third largest global banking industries following China and the USA by 2025 (Tripathi & Poddar, 2011). The entry of private sector banks and foreign banks has changed the rules of serving the customers in the bureaucratic nature of Indian banking system, and now customers have more choices to select their banks in order to satisfy their banking needs and demands in the present era of Indian banking system (Singh & Arora, 2011). Moreover, the cost of acquiring new customers is more than the costs of retaining the existing ones that enabled banking sectors to achieve higher profitability in competitive nature of Indian banking (Gupta & Dev, 2012; Kaura, 2013a; Lenka et al., 2009). Consequently, Indian banking sector is transforming rapidly into the free marketplace, where ‘high-touch’ banking plays a key role as a differentiating factor in the standardization of ‘high-tech’ banking in India (Choudhury, 2008). Therefore, such fierce competition made by new private and foreign players in the Indian banking sector has exaggerated competitive pressures on the nationalized or public sector banks (PSBs) for survival in the Indian market (Kaura et al., 2013). Hence, it is necessary for PSBs to understand the target customers’ needs and demands in order to better serve them via offering higher service quality (SQ) and customer value than their private peers, and therefore, it is necessary to have a system by which customer satisfaction (CS) and bank image can be continuously monitored to better enhance the Indian banking sector. In other words, CS is a subjective phenomenon just like perceived value (PV) which is evaluated by customer itself and cannot be determined objectively by the marketer or service provider (Carlos Fandos Roig et al., 2006; Ennew & Waite, 2013). In turn CS, driven by SQ and PV, encourages the favourable corporate image (IMG), repurchase intentions and loyalty in retail banking (Dick & Basu, 1994; Nguyen & LeBlanc, 1998) and thus provides greater source of a firms’ non-functional value proposition in the form of positive IMG as a key differentiating factor in the competitive landscape of the Indian banking market.

However, PSBs have shown lesser interest in the field of corporate branding rather than its foreign counterparts, such as Citigroup, HSBC, Standard Chartered Bank and so on, in the Indian market (Arora & Neha, 2016). Consequently, corporate reputation/image of bank is one of the major factors of switching customer to other banks in India (Vyas & Raitani, 2014).

Therefore, in order to design effective marketing strategy to enhance customer loyalty (CL) and positive image, it is imperative for PSBs to understand the significance of CS through SQ and PV as key antecedents of CL in the contemporary retail banking. In this backdrop, the present study endeavours to test and evaluate an integrative model of CL in the Indian retail banking context.

The structure of this article is as follows. Literature review is presented in the first section in order to provide a conceptual base for the study and hence to develop proposed empirical model that integrates SQ, PV, CS, IMG and CL in the banking context. In the second section, objectives and rationale of the study are presented followed by the ‘Methodology’ section adopted to evaluate the model. In the next section, a discussion of the results and their implications are given. Finally, the article concludes with limitations and possible directions for future research.

Literature Review

Customer Loyalty

Loyalty refers to a consumer’s commitment to repurchase a preferred product or service consistently in the forthcoming purchase despite some alternatives available in the market (Amin et al., 2013; Lenka et al., 2009). The initial stage of research for CL mostly deals with product-related loyalty or brand loyalty (Bloemer et al., 1998; Caruana, 2002), which consists of two major components: attitudinal component (i.e., customers’ positive attitude towards a product/service) (Auka, 2012; Dick & Basu, 1994; Lenka et al., 2009) and behavioural component (i.e., customers’ patronage to repurchase the product over the decades). In recent years, another dimension of loyalty, apart from behavioural and attitudinal dimensions of loyalty, has been explored by marketing researchers (Caruana, 2002) called ‘cognitive’ which is based on consumers’ purchase decision-making process with respect to alternative brands. Therefore, CL consists of three major dimensions of loyalty, namely, behavioural, attitudinal and cognitive components, in services marketing as well as physical product offerings.

In other words, loyal customer base leads to reduced costs and higher revenues; thus, this customer base can contribute to profitability of the organization (Ennew & Waite, 2013). The notion is that the cost of acquiring new customers is more than the costs of retaining the existing ones, and it is requisite to satisfy customers in Indian banking too (Gupta & Dev, 2012; Kaura, 2013b; Lenka et al., 2009). Therefore, CL is regarded unanimously the ultimate goal of any firm’s customer relationship programme in banking and financial service marketing (Caruana, 2002; Ennew & Waite, 2013; Ladhari et al., 2011). Concerning the aforementioned aspect of loyalty, academia and practitioners have shown extensive interest to gain better understanding about CL and its key predictor factors such as SQ, PV, satisfaction and image in different service settings including banking sector all over the world (Amin et al., 2013; Bloemer et al., 1998; Caruana, 2002; Hu, Kandampully, & Juwaheer, 2009; Lai et al., 2008; Nguyen & LeBlanc, 1998) and in India (Kaura, 2013a; Lenka et al., 2009). Therefore, CL plays a crucial role in preventing the switching behaviour of customers that directly affects the firm’s profitability and its market success.

Most of the scholars worldwide confirmed the direct and indirect relationship of SQ with CL through CS in their specific service context including banking domain (Gupta & Dev, 2012; Kaura, 2013b; Lenka et al., 2009; Nguyen & LeBlanc, 1998). Furthermore, Nguyen and LeBlanc (1998) found that SQ is an important source of higher CL. Therefore, high level of SQ will lead to greater loyalty via satisfaction and positive firm image, and thus, good corporate/brand image with higher satisfaction level can lead to higher CL in service marketing (Dick & Basu, 1994; Hu et al., 2009; Lai et al., 2008).

Moreover, the association between customer PV and CL has been considered and empirically tested by various authors. Their findings revealed that PV has a direct and positive influence on CL (Edward & Sahadev, 2011; El-Adly & Eid, 2016; Nguyen & LeBlanc, 1998), thereby determining the switching behaviour of customers in banking service. For instance, when PV declines, customers are more likely to switch to rival brands that may be caused by price or affective component of PV showing a diminishing loyal customer base (El-Adly & Eid, 2016). Likewise, in the Indian context, a few studies examined the effect of customers’ PV with CL directly and/or indirectly through satisfaction and found the significant relationship among them in non-financial service sectors like cellular or mobile services (Edward & Sahadev, 2011; Edward et al., 2010) in healthcare services (Chahal & Kumari, 2012).

The notion is that this relationship may be possible in other mass services in Indian market including financial and banking sector by offering superior service performance through higher SQ and superior customer value, treated as a key source of strong customer base and positive brand/IMG and CL in the form of repeat sales, word of mouth and customer patronage. Hence, four constructs are conceptualized in this study as key antecedents of CL in Indian retail banking context, namely SQ, PV, CS and IMG.

Antecedents of Customer Loyalty

Service Quality

In general, SQ has always been considered as a subjective phenomenon which is based on the overall customer’s perception of how well the service offering fulfils their needs and expectations, and they compare what the actual services offered with the expected service (Berry, Zeithaml, & Parasuraman, 1988; Ennew & Waite, 2013). Therefore, SQ is a subjective phenomenon which means an outcome of customers’ evaluation of their expectation in relation to services offered and their perception with respect to service performance (Grönroos, 1984; Parasuraman, Zeithaml, & Berry, 1985, 1988). The two popular SQ models were proposed and widely accepted to measure the construct of quality in service industry across the world (Ennew & Waite, 2013; Zeithaml et al., 2011). One is related to the gap model of SQ, that is, SERVQUAL (Service quality model) based on the comparison between customers’ expectations and customers’ perceptions (Grönroos, 1984; Parasuraman et al., 1985, 1988), originally developed by Parasuraman et al. (1985, 1988). As an operational criticism of SERVQUAL, the other measure is SERVPERF (Service performance model) proposed by Cronin and Taylor (1992, 1994) as a counterpart of the gap model of SERVQUAL due to its practicality at what time to measure it, either before or after getting the service (Al-hawari, 2015; George & Kumar, 2014), and therefore, operationally SERVQUAL should be addressed separately and then calculating the gap (Caruana, 2002). Apart from operational criticism, Sureshchandar, Rajendran, and Anantharaman (2002) assessed 22 items of SERVQUAL and found that the non-human elements were ignored (such as core service or service product, standardization of service delivery and the social responsibility). Thus, it can be said that SERVQUAL model only deals with the human elements of service quality based on employee–customer interaction) as reliability, assurance, responsiveness and empathy, however, the as tangibility dimension as tangible facets of service quality (i.e. related to service scapes) was nor properly considered in such a model. Therefore, such service measures are required to capture the consumer perception regarding the holistic approach of human, tangible and technical aspects of SQ (Lenka et al., 2009; Sureshchandar et al., 2002).

Though SERVQUAL model is a generic instrument for assessing SQ across the world, it has been subjected to criticism and raises the question of its common effectiveness and applicability on account of the differences of industrial, cultural, socio-demographical and geographical settings of world over time. Similarly, the effectiveness of such a standard scale is also questionable and, thus, required modified SQ dimensions in a specific banking service context. With this similar argument, several scholars, such as Johnston (1997), Bahia and Nantel (2000), Olorunniwo and Hsu (2006), Sureshchandar et al. (2002), Choudhury (2008), Lenka et al. (2009), Gupta and Dev (2012), Kaura (2013a) and Choudhury (2014), have used different dimensions of SQ (i.e., modified version of perceived SQ) developed as niche perceived SQ in banking contexts apart from standardized model of SERVQUAL and SERVPERF to assess SQ. On the basis of the aforementioned arguments of SQ model particularly in financial and banking domain, the present study employed the modified performance based on SQ approach using five SQ dimensions, namely Tangibility (TAN), Reliability (REL), Assurance (ASSU), Responsiveness (RES) and Empathy (EMP), in order to empirically validate the perspective of its relevancy and effectiveness in Indian setting with special emphasis on PSBs.

Perceived Value

Zeithaml (1988) defined PV as the customers’ overall judgement about the utility of a product based on their perception of what is received and what is given. Similarly, Monroe (1979) defines customer-PV as the trade-off between perceived benefits (i.e., combination of physical attributes, service attributes and technical support regarding products/services) and perceived sacrifice (i.e., purchase price made by customer for product/services).

Moreover, Fandoms Roig, García, and Moliner Tena (2009), Kotler et al. (2009), and Zeithaml et al. (2011) viewed ‘PV’ as a multidimensional construct, which consists of both the dimensions: one is concerning a functional component (i.e., related to product quality and price, etc.) and the other is emotional or affective (i.e., one’s internal feelings and a social influence, relating to the social impact of the purchase); however, such a multidimensional construct is not much explored in the service literature (Chahal & Kumari, 2012). In other words, customer PV is a collective evaluation of costs (i.e. price) versus benefits (i.e. attributes including SQ) made by customers relating to product/service offerings, as such subjective phenomenon varies customer to customer (Auka, 2012; Ennew & Waite, 2013; Parasuraman et al., 1985; Ravald & Grönroos, 1996). In addition, the customer PV is directly and indirectly associated with the other subjective measures like quality, satisfaction and image (Nguyen & LeBlanc, 1998). Consequently it provides greater source of a firms’ competitive leverage in service industry, which in turn encourage repurchase intentions, positive word of mouth and CL (Tam, 2004).

The notion is that customer PV is a subjective and personal evaluation between benefits (functional and emotional) versus costs (monetary and non-monetary) with respect to services offered by retail banks in terms of transaction value (value for money) and attitudinal value. Based on the available literature, the subjective measure of PV cannot be evaluated decisively from the perspective of service provider (Ennew & Waite, 2013; Fandos Roig et al., 2009).

Customer Satisfaction

In marketing literature, CS phenomenon has been treated as customers’ post-purchase evaluation of firms’ offerings taking into consideration their expectations and argued as a subjective and personal phenomenon (Oliver, 1997; Woodside, Frey, & Daly, 1989; Zeithaml et al., 2011). Therefore, it is increasingly based on organization’s capacity to satisfy the needs and wants of target market via superior customers’ offerings (Kotler & Armstrong, 2012). In other words, CS is a collective outcome of perception, evaluation and emotional reactions to the consumption experience in relation to a product/service made by customer (George & Kumar, 2014). Furthermore, satisfied/delighted customers lead to repurchase, recommendations and positive word of mouth with favourable image, and CL in turn builds greater market share and profitability (Caruana, 2002; Sureshchandar et al., 2002). The notion is that CS is a subjective and individual phenomenon, primarily concerned with overall customers’ actual evaluation of satisfaction and dissatisfaction in relation to service encounters with the banks’ touch points over the different periods of time. Hence, such subjective phenomenon is treated as a key strategic factor in achieving differentiation in the standardized nature of evolving global banking arena, which ultimately turns into the base of customer retention and loyalty (Ennew & Waite, 2013; Reichheld, 1996).

Concerning the relationship between ‘SQ’ and ‘CS’, a wide range of marketing literature emphasized on both phenomena; SQ and CS are conceptually distinct but closely related constructs (Caruana, 2002; Sureshchandar et al., 2002), and studies found a causal relationship between these two, and thus perceptions of SQ influence the satisfaction or dissatisfaction which, in turn, influence future purchase behaviour (Ennew & Waite, 2013; Hurley & Estelami, 1998; Zeithaml et al., 2011). In other words, perceived SQ is a key predictor of CS, and therefore higher SQ leads to higher CS and vice versa (Cronin & Taylor, 1992) in the modern era of financial and banking market (Ennew & Waite, 2013; Gupta & Dev, 2012; Lenka et al., 2009; Siddiqi, 2011; Sureshchandar et al., 2002). In such a context, Sureshchandar et al. (2002) argued that human, non-human and tangible aspects are the key predictors of CS and therefore suggested the five critical factors of perceived SQ and their relationship with CS in the Indian context. Furthermore, Lenka et al. (2009) also proposed human, technical and tangible aspects of SQ that influence CS which in turn promote and enhance CL in the Indian banking sector.

In addition, a variety of scholars worldwide validate the relationship of CS with other constructs, such as PV, loyalty and so on, through modified SQ model in their specific banking settings over the different periods of time, namely Johnston (1997), Bahia and Nantel (2000), Olorunniwo and Hsu (2006) in the Western context, whereas Sureshchandar et al. (2002), Choudhury (2008), Lenka et al. (2009), Gupta and Dev (2012), Kaura (2013b), Choudhury (2014) in the Indian context.

Corporate Image

The phenomenon of image has always been a focal point of academic and corporate researchers in the form of brand image of product marketing and IMG in the case of service domain, consistently treated as a differentiation factor of strategic marketing over different periods of time (Aaker, 1991; Keller, 1993; Keller & Lehmann, 2006). However, such a construct has been less investigated with the perspective of financial marketing rather than other mass service contexts such as medical, tourism and hospitality and so on. Many scholars such as Ladhari et al. (2011), Hu et al. (2009) and Baisya (2013) emphasized that favourable image stipulates corporate positioning and maintains relationships with customers and ultimately increases profitability and market share. In addition, IMG acts as a filter which is primarily concerned with customers’ perception of behavioural and psychological aspects of SQ, and the overall image of the service firm is influenced by SQ, customer value and satisfaction (Caruana & Malta, 2002; Grönroos, 1984, 1988; Hu et al., 2009). Particularly in retail banking, IMG is operationalized based on the dimensions of image attributes, and such attributes relate to branch outlets, quality of service delivery, interest rates, investment returns and so on (Ennew & Waite, 2013).

Therefore, it can be argued that satisfied customer base promotes positive IMG through word of mouth; furthermore, such favourable image can be an important source of customers’ repurchase behaviour in the form of patronage or loyalty and word of mouth (Dick & Basu, 1994; Nguyen & LeBlanc, 1998). Moreover, Vyas and Raitani (2014) recognized that bank reputation/image is one of the major factors of customer switching to other banks in India. Furthermore, several scholars confirmed that perceived SQ and customer value and satisfaction are the major predictors of favourable IMG in a banking context (Amin et al., 2013; Bloemer et al., 1998; Caruana & Malta, 2002; Ladhari et al., 2011; Onyancha, 2013). IMG is, therefore, viewed as a cumulative component of quality, value and satisfaction that is modified at each and every level of service encounter experienced by customers (Nguyen & LeBlanc, 1998).

Objectives of the Study

In light of aforesaid arguments concerning literature gap in the Indian context, the study attempts to operationalize the relationship of CL with its key predictors such as SQ, PV, CS and IMG in order to assess and empirically validate the integration model of CL by using structural equation modeling (SEM) through path analysis in the Indian retail banking context. Hence, the present study strives to extend the existing literature of service marketing by fulfilling the aforementioned research gap in the Indian retail banking with special emphasis on PSBs.

Rationale of the Study

Several scholars in the Western context have paid little attention to the interrelationship of the phenomenon of CL with its key predictor constructs, namely, SQ and customer value, CS and IMG in service marketing literature including retail banking sector (Bloemer et al., 1998; Hu et al., 2009; Ladhari et al., 2011; Lai et al., 2008; Nguyen & LeBlanc, 1998). However, fewer studies examined the relationship of SQ and/or PV with CS and CL directly and indirectly in the telecom sector (Edward & Sahadev, 2011; Edward et al., 2010), healthcare services (Chahal & Kumari, 2012) and banking sector (Lenka et al., 2009) of Indian market. Thus, the relationships among SQ, PV, CS, IMG and CL as an integration model were totally absent and not validated in Asian and Indian retail banking context so far, especially in nationalized or PSBs. Therefore, it is vital for PSBs to understand the significance of CL model, to enhance loyal customer base and to avoid switching existing customers towards its private peers in the present competitive Indian market.

Methodology

Sample Design and Data Source

The research is conducted in the national capital region of India. The survey responses have been collected from Delhi (South Delhi, New Delhi), Haryana (Gurgaon, Rewari), Rajasthan (Alwar) and Uttar Pradesh (Noida, Ghaziabad). A convenient sample design has been used for collecting the data. Customers are chosen from different socio-economic backgrounds, and data were collected using a structured questionnaire. The questionnaire was pretested with pilot survey, and 120 PSB customers were contacted to obtain an understanding regarding the clarity of items of the questionnaire to respondents, and minor corrections were made in the questionnaire before the finalization of the questionnaire. Further, Cronbach’s α test is applied for the confirmation of scale reliability, and results showed that Cronbach’s α value was above 0.7 for all and signified an acceptable level of reliability (Nunnally & Bernstein, 1994) of the scale used in the study. Eventually, 545 respondents of the 8 PSBs of India with the largest market share were selected as the target population after pilot survey. Among these respondents of the study, most were male (63.79%). Youth population (43%) (age group of 20–35) was targeted to give response. With regard to education, 33.9 per cent of respondents were graduates, while 28 per cent of respondents were postgraduates. Therefore, majority of the respondents (61.9%) of the current study were either graduates or postgraduates. This shows that respondents were quite mature and understood the value of their responses; however, with regard to occupation, 41.9 per cent of respondents belonged to business class and 35.9 per cent of respondents to service class. On the other hand, a very small proportion of respondents belonged to professional and other occupations in the study. A total of 800 questionnaires were distributed to the customers of selected eight PSBs. The selected PSBs were: (a) State Bank of India, (b) Bank of Baroda, (c) Punjab National Bank, (d) Bank of India, (e) Canara Bank, (f) IDBI Bank Ltd., (g) Union Bank of India and (h) Central Bank of India. These banks were selected on the basis of total assets, as shown in Table 1. All respondents maintain at least one active account in a particular selected bank. A total of 545 (68.125%) questionnaires were found appropriately filled by the customers. The field work was carried out from July 2015 to January 2016. The questionnaires were filled by visiting the selected branches and distributing the questionnaires to customers outside the banks and nearby ATMs. Only those respondents were selected who visited the sampled banks at different times (day time and on various days of the week or month). Customers were assured about the confidentiality of their responses. Structured and sieve questions were put in place to ensure the quality of the responses.

Measures

Five major constructs using multiple-item scales were involved in this model, and these constructs are derived from previous studies. A 7-point Likert scale was used to measure the customer perceptions of SQ, PV, CS, image and their loyalty towards the banks, ranging from ‘strongly disagree (1)’ to ‘strongly agree (7)’. The language used in data collection was Indian English as it is an official language of India. Twenty items of ‘SQ’ construct were taken into consideration. The study included four items of ‘tangibility’ adopted from the studies of Ladhari et al. (2011) and Kashif, Wan Shukran, Rehman, and Sarifuddin (2015); four items of ‘responsiveness’ adopted from the studies of Ladhari et al. (2011) and Sanjuq (2014); four items of ‘empathy’ adopted from the study of Sanjuq (2014); four items of ‘assurance’ drawn from the studies of Kashif et al. (2015); and four items of ‘reliability’ taken from the study of Sanjuq (2014). Moreover, three items of ‘PV’ taken from the studies of Carlos Fandos Roig et al. (2006), Korda and Snoj (2010) and Auka (2012); on the other hand, three items of CS adopted from the studies of Kashif et al. (2015), and three items of Image adopted from the studies of Bloemer et al. (1998) and Ladhari et al. (2011). Finally, three items of CL were drawn from the studies of Lenka et al. (2009) and Kaura (2013b) to understand the picture of the relationships of the constructs in Indian retail banking context.

Empirical Model

Based on the available literature, the empirical model is proposed by linking four key components, namely SQ, PV, CS and IMG, as the antecedents of CL in the study. In addition, CS and IMG play a mediating role between SQ and PV with CL in this integrative model. Therefore, the phenomenon of CL emerges as an ‘outcome’ in the Indian context with special reference to selected PSBs (Figure 1).

Table 1.

Name of Selected Public Sector Banks on the Basis of Total Assets

Total Assets 2012–2013
Top Eight Public Sector Banks		Total Assets (in millions)
1	State Bank of India	₹15,686,991.9
2	Bank of Baroda	₹5,471,354.5
3	Punjab National Bank	₹4,789,654.8
4	Bank of India	₹4,526,027.3
5	Canara Bank	₹4,126,741.3
6	IDBI Bank Ltd.	₹3,228,174.6
7	Union Bank of India	₹3,126,699.9
8	Central Bank of India	₹2,683,424.8

Source: Prowess database (September 2014).

Analysis

Reliability and Validity of the First-order Measurement Model

According to the recommendation of Gerbing and Anderson (1988), exploratory factor analysis (EFA) was applied using principal component analysis under the restriction that the eigen value of each generated factor is more than one. Statistical package for social sciences (SPSS) was used to identify the underlying factors following the guidelines of Gerbing and Anderson (1988). The final result identified 32 important variables on 9 meaningful factors. Kaiser–Meyer–Olkin value was found to be 0.931, which was more than the recommended value of 0.6 for sample adequacy. Bartlett’s test of sphericity was also significant (p = 0.000), shown in Table 3, supporting the factorability of the correlation matrix. Table 2 depicts the communalities of the variables. The factors with values greater than one were considered for the study. All the 32 variables were found with their communalities greater than 0.50 and were retained for the analysis and communalities of these 32 measures ranging from 0.735 to 0.918. The factors with eigen values greater than 1 were considered for the study (Hair, Black, Babin, Anderson, & Tatham, 2015). This step has resulted in nine factors explaining 87.339 per cent of the variance after varimax rotation, as shown in Table 3, after that psychometric property (reliability, convergent and discriminant validity) of 32 factors was assessed.

Table 2.

Communalities

Communalities
	Initial	Extraction		Initial	Extraction
TAN1	1.000	0.900	RES1	1.000	0.804
TAN2	1.000	0.918	RES2	1.000	0.890
TAN3	1.000	0.911	RES3	1.000	0.885
TAN4	1.000	0.890	RES4	1.000	0.853
REL1	1.000	0.892	IMG1	1.000	0.900
REL2	1.000	0.911	IMG2	1.000	0.865
REL3	1.000	0.906	IMG3	1.000	0.905
REL4	1.000	0.859	CS1	1.000	0.894
ASSU1	1.000	0.848	CS2	1.000	0.905
ASSU2	1.000	0.895	CS3	1.000	0.871
ASSU3	1.000	0.895	CL1	1.000	0.890
ASSU4	1.000	0.875	CL2	1.000	0.911
EMP1	1.000	0.844	CL3	1.000	0.874
EMP2	1.000	0.889	PV1	1.000	0.735
EMP3	1.000	0.876	PV2	1.000	0.784
EMP4	1.000	0.848	PV3	1.000	0.825

Source: The authors.

Note: Extraction method: principal component analysis.

Table 3.

Rotated Component Matrix, Eigen value, Total Variance, Kaiser–Meyer–Olkin Measure of Sampling Adequacy and Bartlett’s Test of Sphericity for Public Sector Banks

Rotated Component Matrix
	Component
	REL	ASSU	TAN	EMP	RES	IMG	CL	CS	PV
TAN1			0.805
TAN2			0.853
TAN3			0.848
TAN4			0.824
REL1	0.879
REL2	0.876
REL3	0.874
REL4	0.845
ASSU1		0.848
ASSU2		0.893
ASSU3		0.872
ASSU4		0.858
EMP1				0.814
EMP2				0.845
EMP3				0.828
EMP4				0.829
RES1					0.722
RES2					0.802
RES3					0.78
RES4					0.775
IMG1						0.899
IMG2						0.893
IMG3						0.91
CS1								0.873
CS2								0.862
CS3								0.836
CL1							0.887
CL2							0.897
CL3							0.88
PV1									0.835
PV2									0.874
PV3									0.89
Eigen value	12.682	3.144	2.329	2.13	2.046	1.671	1.616	1.291	1.039
% of variance	39.633	9.826	7.277	6.656	6.394	5.223	5.049	4.033	3.248
Total Variance						87.339
Kaiser–Meyer–Olkin measure of sampling adequacy						0.931
Bartlett’s test of sphericity						Approx. chi-square		18260.977
						df		496
						Sig.		0.00

Source: The authors.

Note: Extraction method: principal component analysis; rotation method: varimax with Kaiser normalization.

Structural equation model is implemented using AMOS for data analyses. The fitness of model depends on chi-square (χ2/df; where df = degree of freedom). As the sample size is greater than 200, the chi-squared value and p value are neglected as the chi-square is very sensitive to the size of the sample (Nath, Bhal, & Kapoor, 2013). Other factors which are important for the validity of model fit are: goodness-of-fit index (GFI), incremental fit index (IFI), normed fit index (NFI) and comparative fit index (CFI) and root mean square error of approximation (RMSEA) (Malhotra & Dash, 2014). The result of the model fit showed satisfactory results which meets up to the recommended level, that is, chi-square/df = 1.29, GFI = 0.943, AGFI = 0.929, IFI = 0.993, NFI = 0.970, CFI = 0.993, RMSEA = 0.023 (Hair et al., 2015; Malhotra & Dash, 2014).

The composite reliability (CR) is shown in Table 4 and constructs ranging from 0.855 to 0.964 are acceptable. Cronbach’s α coefficients are calculated for each of the multi-item measures and results showed the internal consistency of the constructs as the alpha value of all the constructs ranged from 0.852 to 0.963, which is above the threshold of 0.70 as recommended by Hair et al. (2015) and Malhotra and Dash (2014). Convergent validity of the constructs is demonstrated by using factor loading and average variance extracted (AVE) as suggested by Hair et al. (2015). Results showed that standardized factor loading of all the measures in the model are greater than 0.50 which ranged from 0.742 to 0.946, whereas the values of AVE were found acceptable, which ranged from 0.663 to 0.870 and proved convergent validity (Hair et al., 2015; Malhotra & Dash, 2014).

Discriminant validity of the constructs is shown in Table 5 where average variance extract for each construct is found to be larger than their squared correlation and hence showed high level of discriminant validity. The result declared that variables are common in association with the latent construct than with other latent construct (George & Kumar, 2014), hence proving the discriminant validity of the constructs.

Table 4.

First Model Standardized Regression Weights, Composite Reliability, Average Variance Extracted and Cronbach’s α

Standardized Regression Weights				CR	AVE	Cronbach’s α
TAN1	←	TAN	0.934	0.964	0.870	0.963
TAN2	←		0.942
TAN3	←		0.936
TAN4	←		0.918
RES1	←	RES	0.851	0.943	0.806	0.940
RES2	←		0.916
RES3	←		0.928
RES4	←		0.894
EMP1	←	EMP	0.881	0.947	0.816	0.945
EMP2	←		0.927
EMP3	←		0.919
EMP4	←		0.886
ASSU1	←	ASSU	0.888	0.953	0.834	0.952
ASSU2	←		0.921
ASSU3	←		0.932
ASSU4	←		0.912
REL1	←	REL	0.923	0.959	0.855	0.959
REL2	←		0.942
REL3	←		0.938
REL4	←		0.894
CS1	←	CS	0.907	0.938	0.834	0.937
CS2	←		0.938
CS3	←		0.894
IMG1	←	IMG	0.931	0.937	0.831	0.936
IMG2	←		0.871
IMG3	←		0.932
CL1	←	CL	0.911	0.939	0.837	0.939
CL2	←		0.946
CL3	←		0.887
PV1	←	PV	0.742	0.855	0.663	0.852
PV2	←		0.801
PV3	←		0.893

Source: The authors.

Table 5.

Discriminant Validity Results of the First-Order Measurement Model

	CL	TAN	RES	EMP	ASSU	REL	CS	IMG	PV
CL	(0.915)
TAN	0.410	(0.933)
RES	0.308	0.641	(0.898)
EMP	0.333	0.548	0.596	(0.903)
ASSU	0.294	0.470	0.578	0.491	(0.913)
REL	0.213	0.550	0.537	0.524	0.423	(0.924)
CS	0.502	0.398	0.438	0.435	0.334	0.338	(0.913)
IMG	0.325	0.293	0.433	0.22	0.252	0.238	0.402	(0.912)
PV	0.192	0.151	0.112	0.197	0.110	0.160	0.265	0.186	(0.814)

Source: The authors.

Notes: The diagonals represent the square root of AVE and the lower cell represents the squared correlation among constructs.

Reliability and Validity of the Second-order Measurement Model

The second-order measurement model is used to validate the main construct, namely ‘SQ’, which has five dimensions, that is, tangibility, reliability, empathy, responsiveness and assurance. Table 6 shows that second-order measurement model fulfils all the requirements as the CR of all the constructs (ranging from 0.853 to 0.939 which is greater than 0.70 standards) along with AVE (ranging from 0.540 to 0.837), and showed more than the recommended value of 0.50. All the AVE estimates for each construct are greater than the corresponding inter-construct squared correlation estimates (Table 6). The result of the second-order measurement model fit also showed satisfactory results which meets up to the recommended level, that is, chi-square/df = 1.40, GFI = 0.936, AGFI = 0.924, IFI = 0.990, NFI = 0.966, CFI = 0.990 and RMSEA = 0.027 (Hair et al., 2015; Malhotra & Dash, 2014).

Structural Equation Modeling

After scrutinizing the satisfactory fit of model, structural equation model is used to examine the hypothesis through path coefficient beta weight (β) which defines the relationship between the dependent and independent variables (Nath et al., 2013). Figure 2 shows that most of the constructs are strongly supported, except PV which is found insignificant related to IMG (β = 0.071). SQ is found to be statistically significant and has positive impact on CS (β = 0.517, p < 0.01). The result showed that SQ exerted a stronger influence on CS, and this finding is found to be consistent with the study of Sureshchandar et al. (2002) and Choudhury (2014). On the basis of the standardized regression weight, it is found that PV has significant and positive impact on CS (β = 0.170, p < 0.01), which is consistent with Hu et al. (2009) and Edward and Sahadev (2011). The path between SQ and IMG is found to be significant which shows positive impact (β = 0.264, p < 0.01) and strongly supported the model. However, it is observed that the relationship between PV and IMG is not significant in this study (β = 0.071, p > 0.01); therefore, such finding differs from the results validated in Western financial and banking context (Nguyen & LeBlanc, 1998). Furthermore, the relationship between CS and IMG is found to be significant (β = 0.241, p < 0.01) which is consistent with the studies of Ladhari et al. (2011) and Amin et al. (2013) in Asian banking context. On the other hand, SQ construct dominated the strong influence on CL and significantly impacted on CL (β = 0.367, p < 0.01), and it is consistent with the findings of Caruana (2002) and Lenka et al. (2009). Therefore, such a finding implies that customer faith increases when customers believe that they are getting effective quality of service by the PSBs. Moreover, the proposed relationship between PV and CL is found to be significant (β = 0.099, p < 0.05), which is consistent with the findings of Nguyen and LeBlanc (1998) and Edward and Sahadev (2011); thus, PV has a positive impact on CL. However, the structural equation model analysis showed that there is a significant impact of IMG on CL (β = 0.159, p < 0.01). Therefore, such result was found to be consistent with the studies of Nguyen and LeBlanc (1998) and Lai, Griffin, and Babin (2009) in the Western setting of financial service.

Table 6.

Convergent and Discriminant Validity Results of the Second-Order Measurement Model

	CR	AVE	PV	CS	IMG	CL	SQ
PV	0.855	0.663	(0.814)
CS	0.938	0.834	0.265	(0.913)
IMG	0.937	0.832	0.186	0.402	(0.912)
CL	0.939	0.837	0.192	0.502	0.324	(0.915)
SQ	0.853	0.540	0.191	0.531	0.410	0.425	(0.735)

Source: The authors.

Notes: The diagonals represent the square root of AVE and the lower cell represents the squared correlation among constructs.

Conclusion and Managerial Implications

The present study contributes a model that shows the contribution of SQ and PV on CS, IMG and CL. The results shown in Figure 2 confirm that all constructs are strongly supported and found to be significantly related to CL directly and/or indirectly, except PV which is found to have no direct effect on IMG in the integrated model of CL in the current study. Moreover, the findings also explored that SQ exerts a stronger impact on CS and CL; therefore, SQ was found to be the most dominant construct in the model. Furthermore, the study showed SQ as a key antecedent for CS and CL, while PV is also considered as a main determinant of satisfaction and loyalty of bank customers.

Therefore, the results validated the integrative model of SQ, PV, CS, IMG and CL with special reference to PSBs in India. However, in this study, the results showed that PV did not have significant impact on IMG; therefore, such low emotional or attitudinal value is perceived by customers towards the image of PSBs, and it may be the reason for low attention of customers towards each level of service offerings made by PSBs in comparison with their private counterparts, and this is one of the main causes of customer switching behaviour in India and Asian bank market (Vyas & Raitani, 2014). Hence, PV is an important source of positive word of mouth and customers’ psychological value which encourage repeated purchases (Tam, 2004). In turn, such value provides competitive leverage to the firm; therefore, it should be addressed at priority level by Indian PSBs in order to retain the existing customer base and future bank population, that is, young segment of India in the present era of stiff financial and banking global market.

Furthermore, IMG also acts as a filter which is primarily concerned with customers’ perception of behavioural and psychological aspects of SQ (Caruana, 2002). The result implies that SQ and CS have significant impact on IMG; at the same time, IMG showed positive effect on CL.

The notion is that improvement in SQ and customer value will directly enhance the CS and image of the firm, and thus satisfied customer base with their favourable firm’s image plays a vital role in improving their loyalty. In turn, higher CL leads to reduce costs and increase revenues leading to repeat sales, positive word of mouth and customer patronage that directly contribute to the profitability of service firm (Caruana, 2002; Ennew & Waite, 2013). Therefore, CL is an outcome of higher SQ, superior customer value, strong IMG and CS (Nguyen & LeBlanc, 1998). Hence, loyalty is treated as competitive leverage in the present era of digitalized and standardized financial and banking market in India.

However, the results disclose that in the modern era of competition, CS is not sufficient, while other aspects like loyalty and IMG help in managing the competitive advantage for PSBs in India. Higher competitiveness requires high level of SQ and improved PV which overall combines an image structure and helps in customer retention for longer period of time. SQ creates greater competitive leverage not just in satisfying customers but also in retaining the customer over an extensive period of time with the banks. Thus, the study tried to fill the gap of service literature through presenting a unique combination of overall banking service evolution approach on previously untested relationship specifically in Indian and Asian cultural contexts. In a nutshell, the findings of the study confirmed that the CL model in Indian retail banking service is very much consistent with the findings of scholars in the Western financial and banking context in most of the cases. This study will help policymakers and planners of PSBs to better understand the significance of CL in a special context to retail banking of India, and applicability of other constructs of this model will certainly play a remarkable role in redesigning the existing policies and services offered by the PSBs.

Limitations and Future Research

The present research article bestows a comprehensively integrated model for understanding the dynamic connections among service-related factors such as SQ, PV, CS, IMG, and CL. The model is tested in the Indian retail banking context with emphasis on nationalized banks or PSBs, excluding its private peers; therefore, findings of the study cannot be generalized on overall Indian banking context, and such findings may not be fully applicable in other service settings in Indian and Asian perspectives. Therefore, further research should lay emphasis on private sector banks and other service settings in national and international contexts in order to test the validity of the present integrative research model of loyalty.

Footnotes

Acknowledgement

The authors are grateful to the anonymous referees of the journal for their extremely useful suggestions to improve the quality of the article. Usual disclaimers apply.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

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