Marketing Mix Influence on Service Brand Equity and Its Dimensions

Abstract

Service sector is growing across the globe including India. Despite the growing importance of the sector and services marketing, there is a paucity of literature in the area in general and branding literature in particular.

This study is carried out in the retail banking sector with loans as the product category. Retail banking sector is increasingly seen as an attractive market segment with opportunities for growth and profits. We have reviewed the service branding literature and it is observed that most of the available brand equity models are for goods. Based on the research gap, a service branding model has been proposed and empirically validated. The model represents service marketing mix influence on certain selected dimensions of brand equity, namely, brand image(s) and perceived service quality and thereby on brand equity. Structural equation modelling is used to test the hierarchical relationship.

The study has established significant relationships among the marketing mix elements and brand equity dimensions. It contributes significantly to the literature on service branding and provides valuable insights to practicing managers.

Keywords

Services Brand Equity Brand Image Perceived Service Quality Advertising Word of Mouth Physical Evidence

Introduction

Service marketing has been different from goods marketing and is said to be more challenging. As an area this is relatively new and there is a paucity of literature (Chernatony & Riley, 1999). The development of the field started in a structured manner during the 1990s though the starting was made in the 1970s (Berry & Parashuraman, 1993).

There have been numerous studies on brands and brand equity. Brands have been established to be a source of competitive advantage and have been forwarded as valuable asset for any organization (Aaker, 1991; Gray, 2006). The conventional fast-moving consumer goods approach to branding needs to be adjusted for the services sector (McDonald et al., 2001). All authors converge on the need for a concerted effort to study brand and brand equity in services.

The traditional marketing mix elements of product, price, place and promotion are inadequate in achieving the marketing objectives in services (Bitner, 1990). This study has examined the influence of marketing mix elements of advertising, word of mouth (WOM), physical evidence and employees on the brand equity dimensions of user brand image, corporate brand image and service brand image as well as perceived service quality and thereby on brand equity in a hierarchical relationship.

Brand image has been considered as a single construct in almost all studies but recently the realization that has been gaining ground is that brand image is essentially a composite of three sub-dimensions namely user, corporate and service brand image. Though these three have been forwarded by Biel (1992) and carried forward as a concept by many, these have not been empirically tested as separate and independent construct in any study in the context of service brands.

The service brand equity study has been carried out in the retail banking sector. Retail banking has a very high growth rate in the country thus offering immense managerial benefits.

The Service Sector in the Country

With a compounded annual growth rate of 9 per cent for 2001–2012, the service sector plays an important role in the progress and development of Indian economy. One of the key indicators of growth of the services sector is its contribution to the gross domestic product (GDP) of the country. Services contribute about 56.9 per cent share in GDP (2012). With share of services in employment being 28.1 per cent in 2012, share of services to total exports in 2013 being 32.8 per cent and an export growth of 4.8 per cent (2013), our country is ranked 10th in terms of overall GDP and 12th in terms of services GDP in 2012 (Economic Survey, 2014).

The Banking Sector

In services sector, banking service has emerged as a growth area offering immense business opportunities. The Indian commercial banking sector offers an enormous opportunity (Business Monitor International, 2011). The entry of foreign banks and marketing-oriented approach of the new private sector banks has intensified competition. This has made the banking scene different in the country (Das, 2009). Claessens et al. (2001) have studied effect of the presence of foreign banks in domestic markets and have said that the banking market across the globe is becoming increasingly internationalized and integrated. The retail banking market is getting transformed from the buyer’s market to the seller’s market and thus marketing and branding initiatives have become all the more relevant (Gopinath, 1995). Faced with this kind of a situation, for the domestic banks, the shift of focus to retail banking operations is a business necessity.

In this study, we have picked up loan as product category in the retail banking sector to test assumptions regarding marketing mix elements, brand equity dimensions and brand equity.

Literature Review

Two of the major benefits of brand equity are financial and behavioural (Keller, 1993). The marketer should understand the antecedents of brand equity so that brand equity can be built more effectively and the benefits are realized.

Studies relating to brand equity and its antecedents are many. The notable studies in this regard are by George and Barksdale (1974), Bharadwaj et al. (1993), Mc Enally and Chernatony (1999), Yoo et al. (2000), Yoo and Donthu (2000), Berry (2000), Chernatony et al. (2004), Shanker and Fuller (2008), Chang et al. (2008), Chattopadhyay et al. (2010), Ha et al. (2010), Mukherjee and Shivani (2013) etc. Except for the study by Bharadwaj et al. (1993), Mc Enally and Chernatony (1999), Berry (2000), Chernatony (2004), Chang et al. (2008), Ha et al. (2010) and Mukherjee and Shivani (2013), all of the above studies are focused on goods. This leads to the identification of service branding as an area where a significant gap in the literature exists. We have traced a big gap regarding influence of extended service marketing mix elements on the dimensions of brand equity.

This study has considered two dimensions of brand equity namely brand image and perceived quality in evaluating the role of marketing mix elements.

Biel (1992) has described brand image as consisting of three contributing sub-images namely that of the product/service, of the company (organization) and that of the user. The concept that brand image is a composite of distinct sub-dimensions and that these are of significant importance have been stated by Lasser et al. (2005), Pappu et al. (2007), Hayes et al. (2008), Bravo et al. (2010), Kim and Hyun (2011) etc. More recently, Chang et al. (2008) and Sierra et al. (2010) have empirically validated the impact of brand image on brand equity with these three sub-dimensions of user, corporate and service brand image in the service sector. Though there are studies that have focused on the image sub-dimensions, as discussed above, we could not trace literature where these dimensions have been considered as independent constructs. We in this study have considered the three image dimensions of user, corporate and service as independent latent constructs and have tried to evaluate the effect of these on brand equity. We have also tried to evaluate the effect of the marketing mix elements on each of these dimensions.

The effect of selected marketing mix elements like advertising, WOM, physical evidence and people (employee) on the dimensions of brand image and perceived quality have been examined. The models of brand equity that have been proposed by Aaker (1991) and Keller (1993) form the basis of our study. Our study is inspired by the service marketing triangle concept proposed by Gronroos (1984) and is an extension of the service branding model by Berry (2000).

In context of above, the following model is proposed which has been empirically validated (Figure 1).

Figure 1.

Source: Author’s own.

The Constructs

Perceived Service Quality

Perceived quality as a determinant of brand equity has been proposed by many, notably Gronroos (1982), Parashuraman et al. (1985), Zeithaml (1988), Aaker (1991, 1996), Michell et al. (2001) and more recently by Ching and Tseng (2010).

The quality dimension in an offering has been more prevalent in the goods sector but quality in goods is required to be looked up differently than in services. Perceived quality is highly subjective in what the consumer judges of the product’s overall superiority. The consumer judgements are subjective and are influenced by personal experience of the consumer, unique needs and consumption situations etc. (Zeithaml, 1988). The consumers experience is largely a function of the employee performance and physical evidence or the environment in which the service is delivered. Service quality and the factors that influence it has been studied by Zeithaml and Berry (1993) who have supported the proposition that WOM has an influence in shaping it. Parashuraman et al. (1985) have also highlighted certain factors instrumental in influencing consumer perception, namely, WOM, personal needs and past experience of the consumer. Ching and Tseng (2010) in their study of customer-based brand equity in airlines have empirically proved the proposition that perceived quality positively influences brand equity. Brand acts as a heuristic cue that effectively reduces the discrepancies associated with all of the different perception and delivery elements and thus largely homogenize the quality perception to the benefit of the marketer (Gupta, 2011).

In measuring service quality, both the SERVQUAL scale forwarded by Parashuraman et al. (1985) and the SERVPERF approach forwarded by Cronin and Taylor (1992) have been widely used. In this study, we have used the SERVPERF approach. This approach is convenient to administer and it reduces the measurement by half and has thus received widespread acceptance by many notably Babakus and Boller (1992), Finn and Lamb (1991), Sureshchander et al. (2002) etc.

Advertising

Advertising is a very strong tool of external brand communication. It is a very strong element of the promotion mix element and has definitive influence on brand equity (Villarejo & Manuel, 2005). External brand communication or external marketing is one of the pillars of service marketing and one of the three components of the service marketing triangle (Gronroos, 1984).

Advertising and WOM both influence the consumers’ attitude and aroused feelings. The advertisement that is perceived favourably leads to a positive feeling towards the advertised brand and thus a positive service consumption experience (O Cass et al., 2004). Advertising influences the consumers’ perception towards products and trials and may significantly minimize the negative feelings that consumers have towards brand (Kempf & Smith, 1998). The role of advertising in dissemination of realistic expectations has been highlighted by Cobb et al. (1995) who, through their study, forwarded that advertising disseminates realistic expectations of the service experience to consumers in the pre-purchase stage thereby inducing positive reactions and feelings on consumption. The role of advertising in shaping the expectation of consumers in the context of service and in view of its intangibility dimension has also been emphasized upon by Gotlieb et al. (2000).

Brand image encompassing brand personality is an important factor that leads to brand associations and subsequently brand equity (Hayes et al., 2008). The authors have stated that advertising is the most important factor that shapes brand image and that brand image is an important factor leading to brand associations and subsequently brand equity. The impact of advertisement on perceived quality and equity has been strongly forwarded by many authors notably Aaker et al. (1994).

In measuring the influence of advertising, we have adopted the scale used by O Cass et al. (2004) with minor changes in the context of our study.

Word of Mouth

Word of mouth is a very powerful behaviour and purchase influencer for goods and services (Bansal & Voyer, 2000; File et al., 1994; Murray, 1991; O’Cass & Grace, 2004 etc.). It is the most powerful communication channel that can influence the consumers (Keller, 2007). In the context of the banking sector, WOM has been vindicated to be more important than advertising in shaping consumer minds and influencing the image of the brands (O’Cass & Grace, 2004).

The personal nature of WOM dissemination makes it more effective in influencing the attitude, perception and behavioural intentions of people. It is trusted by people over advertising or other sponsored and marketer-controlled disseminations (Buttle, 1998). Similar opinion has been expressed by Bansal and Voyer (2000) who have said that WOM being a personal source commands greater trust and influence over purchase decisions than advertising which is more of an organizational and formal source. They have also said that higher the perceived risk, higher is the relevance of WOM as the consumers would be actively seeking information from personal and credible sources. The unique service characteristics particularly intangibility and heterogeneity makes WOM more important as a source of influence and in consumption of service, the consumers seek more information from friends and relatives than a sponsored and marketer controlled media (Murray, 1991).

To examine the impact of WOM on the dimensions of brand equity, we have adopted the scale used by O’Cass and Grace (2004).

People (Employees)

The speed and efficiency of service may be very important when consumers are time stressed (Keller, 1993). The changing way of life and the consequent time crunch for the consumers suggests that the perceived speed and efficiency of service delivery which is largely a function of the employees is very important. Attempts to link brands with people have been done effectively by Keller (2003) in a generalized manner. He presented the concept of secondary associations and linked people to brands presenting it as an important dimension of brand knowledge, an important brand equity dimension. Chernatony et al. (2003) have forwarded the positive role that employees/staff play in service delivery and in shaping consumer perception. The interaction of the employees with the consumers at the point of service delivery has a dominant impact on the consumers’ perception or image of service brands (Chernatony et al., 2004). The role of employees in service delivery has also been highlighted by Ioanna Papasolomou et al. (2006) and Constantinides (2006).

In measurement of the role of employees and employee performance in services, we have picked up items relating to employees from the scale used by Hightower (2010).

Physical Evidence

The general elements of physical evidence include all aspects of an organizations physical facility that includes servicescape and other forms of tangible communication (Zeithaml & Bitner, 2000, p. 253). The existing literature on servicescape does not explicitly present the influence of servicescape on brand equity and the consequent consumer choice decision. However, there have been references made to atmospherics/physical environment in a number of studies. One of the first studies relating to the influence of atmospherics on brand equity has been carried out by Kotler (1973). Atmospherics and its elements have been later christened as servicescape by Bitner (1992).

Bitner (1990) evaluated the effect of physical surroundings on employee response and studied how the different service marketing mix elements influence consumer satisfaction. Zeithaml et al. (1985) and Zeithaml and Berry (1993) have also forwarded that physical environment has a distinct impact on the behavioural outcome of consumers and the image that it creates for the service organization. The role of servicescape in image creation and perception has also been supported by Levitt (1981). Lin (2004) arguing in favour of servicescape talks about the importance of servicescape by saying that it helps in communicating and formation of a complete image amongst consumers. More the time people spend in a service premise, more is the likelihood of the influence (Parish et al., 2008; Wall & Berry, 2007). In the case of our study, the loan seekers might be required to visit banks quite a number of times and might have substantial waiting time and are therefore likely to be strongly influenced by the servicescape elements. In context of studies in the country, Paninchukannath (2009) validated the influence of physical evidence particularly the interior service settings in interpersonal service organizations.

Physical evidence could also have a positive relationship with WOM. In this regard, the study carried out by Ezeh and Lloyd (2007) is notable. Their study has forwarded that the approach and avoidance towards the service is influenced by the servicescape. They added that in services which are high in credence quality, the dissonance is likely to be substantially reduced if there is a promising servicescape. This would be highly relevant because this suggest a way through which the WOM communication can be influenced.

The influence of WOM on consumers has been established through numerous studies and if WOM could be influenced purposefully and in a planned way, it could pay substantial dividends to the service firm.

Physical evidence, though apparently a unified concept, should for all practical reasons be looked as a composite of different dimensions or levels. Hightower (2010) in extension of his previous work has forwarded that consumers think of servicescape at three different levels, namely, the overall level, the dimension level and the sub-dimension level, and has broken them down as the ambient, social and design factors as per these dimensions. In this study, we have considered the above dimensions and framework in measurement.

Brand Image(s)

Brand image has been defined as perceptions about a brand as reflected by the brands associations held in the consumer memory. It is thus meaning of the brand as perceived by the consumer that holds importance here.

Brand associations significantly contribute to brand image formation. Brand associations help the consumer in organizing and retrieving the information which influences the purchase decision and is one of the major factors that cause brand equity (Keller, 2003). The marketer could create these associations through a planned use of the marketing mix elements. One of the strongest ways of creating association is to focus on consumer experience. In a study by Faircloth et al. (2001), different brand association variables were manipulated to find out the impact of association on brand equity. The study revealed a positive relationship of brand image and brand association to brand equity. They have inferred that marketers should strategize to influence the associations which will lead to a change in equity.

Biel (1992) has described brand image as consisting of three contributing sub–images, namely, that of the product/service, that of the company (organization) and that of the user. The concept of brand image and that it consists of other dimensions within have also been stated by Lasser et al. (2005) where they have emphasized on the social image as the dominating component of brand image and stated that it is an important dimension of brand equity. The image dimension particularly the country image dimension was studied by Pappu et al. (2007) where they have specifically considered perceived quality and brand image as brand equity constructs. The role of corporate brand image has been highlighted by Kim and Hyun (2011), where in a study of the Korean IT software sector, corporate image has been established to play a mediating role between the marketing elements and the dimensions of brand equity. Corporate brand image studies specific to the banking sector have been carried out by Bravo et al. (2010), who have expressed corporate brand image as what relates to the image associated with an organization’s name. They have forwarded that corporate image contributes to loyalty and perceptions of consumers and play a significant role in the financial sector. The authors have included servicescape as an influencing dimension of corporate brand image in their literature. The study by Biel (1992) has been extended by Hayes et al. (2008), who in their experimental study on sunglasses tried to assess the influence of three types of brand associations namely corporate association, product attributes and user imagery that cumulatively result in brand personality. Sierra et al. (2010) and Chang et al. (2008) have empirically validated the impact of brand image on brand equity with these three sub-dimensions of user, corporate and service brand image in the service sector. Though there are studies which have focused on these image sub-dimensions, as discussed above, we could not trace literature regarding these dimensions being considered as independent constructs. We in this study have considered the three image dimensions of user, corporate and service as independent latent constructs and have tried to evaluate the effect of these on brand equity as also have tried to evaluate the effect of the marketing mix elements on each of these dimensions.

In the measurement of brand image(s), the scale is the one based on Biel’s proposition partially modified by Chang et al. (2008) in their study.

Brand Equity

The scale for measuring this is adopted from the study by Cheng and Tseng (2010).

Hypotheses

Based on the review of literature, the following hypotheses are proposed and tested through an empirical study.

H 1: Perceived service quality significantly influences brand equity in retail banking services.

H 2: Favourability towards advertising is positively related to and significantly contributes to perceived service quality.

H 3: Word of mouth (WOM) is positively related to perceived service quality and significantly contributes to it.

H 4: People (employees) have a positive relationship with perceived service quality and significantly contribute to it.

H 5: Physical evidence/servicescape/atmospherics has a positive relationship with perceived service quality and significantly contributes to it.

H 6: User brand image is positively related to and significantly contributes to brand equity.

H 7: Corporate brand image is positively related to and significantly contributes to brand equity.

H 8: Service brand image is positively related to and significantly contributes to brand equity.

H 9: Advertising favourability is positively related and significantly contributes to user brand image.

H 10: Advertising favourability is positively related and significantly contributes to corporate brand image.

H 11: Advertising favourability is positively related and significantly contributes to service brand image.

H 12: WOM is positively related to and significantly contributes to user brand image.

H 13: WOM is positively related to and significantly contributes to corporate brand image.

H 14: WOM is positively related to and significantly contributes to service brand image.

H 15: People (employees) are positively related to and significantly contribute to user brand image.

H 16: People (employees) are positively related to and significantly contribute to corporate brand image.

H 17: People (employees) are positively related to and significantly contribute to service brand image.

H 18: Physical evidence/servicescape/atmospherics has a positive relationship and significantly contributes to user brand image.

H 19: Physical evidence/servicescape/atmospherics has a positive relationship and significantly contributes to corporate brand image.

H 20: Physical evidence/servicescape/atmospherics has a positive relationship and significantly contributes to service brand image.

Research Methodology

In our study we have considered State Bank of India, a public sector bank, which has the largest network of branches and has the largest spread/presence across the country. In all circles and states, it has a very diverse user portfolio from different demographic groups and thus any circle or state is a true representative of the retail consumer sample universe.

In retail banking products, loan involves a reasonable degree of risk perception in its consumption and this makes it an ideal choice of product category to test our hypothesized relationships.

The bank officially categorizes its branches as urban, semi-urban, rural and metro. The criterion for such categorization is the population within the area as per the Government of India, 2001 census. We have used this categorization and considered only the urban and semi-urban branches in our sample design. The state does not have any metro branch. Rural branches were not considered part of the study because during the pilot study respondents in the rural category were finding it extremely difficult to understand the questions and the concepts related to the study in the questionnaire. Attempts to translate the questions and explain the concepts were proving to be leading and influencing the respondents’ opinion.

The quotas or strata in our study have been created on the basis of whether the bank branch is urban or semi-urban. From the strata thus created, proportional allocation technique has been adopted in picking up the number of branches from each stratum from the list of branches in districts. The branches are our sample units and loan seekers our sample elements. Within the different strata, 20 per cent of branches have been picked up from the total number of branches per district, decimals rounded off to nearest integers. This percentage arrived at is based on suggestions by different managers of the branches in districts. After the number of branches per category and district was arrived at, the selection of the specific branches has been done as per our convenience and judgement. The criterion herein was the ease of approach to the bank branches. Based on inputs from managers of different branches, 20 consumers (loan seekers) were decided to be connected per branch which cumulated to a targeted sample size of 900. In our study, we tried to connect with 900 respondents across 28 semi-urban branches and 17 urban branches, a total of 45 bank branches. Out of 900 potential respondents who were tried to be connected, 564 people agreed to respond which translated to a response rate of 62.66 per cent. Of the valid response from 564 people, only 348 data points were usable. The data have been collected through a single structured questionnaire which has been prepared on the basis of literature study and insights from the pilot study. The constructs identified form the basis of the proposed model, and questionnaire is based on the constructs and the hypothesized relationships. The questionnaire has been administered on loan seekers and the mall intercept survey technique was adopted. We have used the five-point Likert scale against which response has been sought from the respondents with response values ranging from 1 to 5. The scales used in the study are standard scales, which have been accepted and used previously. The scales have, however, been tested for validity and reliability to ascertain its effectiveness in the context of the study. Based on Cronbach’s alpha, the scale items have been retained or removed and the final refined scale arrived at.

Approach to Data Analysis

This study focuses on examining the causal relationships between different constructs. The effort is to find the influence of the marketing mix elements on service brand equity and its dimensions. In carrying out the research, data entry and descriptive analysis of data as well as test of reliability have been done with SPSS 20, statistical package. For analysis and establishment of causal relationship, structural equation modelling has been adopted. The specific package used is LISREL 8.80 (Karl Joreskog and Dag Sorbom; Scientific Software International Inc., July 2006).

The data have been tested for skewness and kurtosis and both are within the acceptable range of +2 to –2 (George & Mallery, 2008, p. 99) and suggest that the data are normally distributed.

Response Character

The demographic details of the sample have revealed certain distinct trends.

Age Group of Respondents

Majority of the respondents are from the age group 30–39 years, closely followed by the 20–29 years age groups (refer Table 1). Collectively both of these age groups comprise about 55 per cent of the total respondents, which is an interesting finding. This in a way reflects the appetite of a specific population age group towards retail loan consumption. The demographic trends of the country and the huge young Indian population suggest a huge potential that lies in this segment. A key aspect driving the growth of major sectors in the country is favourable population demographics—50 per cent of the population is less than 25 years (Ernst & Young, 2010). The power of youth today is evident in its large numbers, tendency to consume and in its ability to influence larger household decisions. India’s population is also urbanizing at a rapid pace with the urban Indian population projected to increase from 28 per cent to 40 per cent of the total population by 2020 (Nielson–CII, 2012).

Table 1.

Sample Characteristic as Per Age

Age Group of Respondents (years)	Frequency	Per cent
Less than 20	21	6.0
20–29	120	34.5
30–39	127	36.5
40–49	28	8.0
50 and above	52	14.9
Total	348	100.0

Source: Primary data analysis output.

Age and Loan Cross-tabulation

The age and loan cross-tabulation was carried out to determine how preference for loan type is distributed among the different age groups. With a huge population of the country categorized as young, it is worth exploring if there exists any specific relation between age and preference for specific loan types. The cross-tabulation has forwarded some distinct revelations. It suggests that the highest aspiration for loans is among the youth in the age group of 20–39 years and the most sought after loan is for cars and two wheelers (refer Table 2). The growing appetite for consumption among the younger population has resulted in many companies positioning their products targeted at this demographic segment (Kotler et al., 2011. p. 192). Thus specific positioning and segmentation exercise targeted at this group should be attempted.

Table 2.

Age and Loan Cross-tabulation

Age (years)		Loan Type								Total
Age (years)		Car Loan	Personal Loan	Housing Loan	Gold Loan	Pensioners Loan	Against LIC Policy	Two–wheeler Loan	Student Loan	Total
Age (years)	Less than 20	0	0	0	0	0	0	3	18	21
	20–29	23	10	8	0	0	0	64	15	120
	30–39	54	24	37	1	0	0	11	0	127
	40–49	2	1	22	2	0	0	0	1	28
	50 and above	1	8	17	5	1	6	0	14	52
	Total	80	43	84	8	1	6	78	48	348

Source: Primary data analysis output.

Table 3.

Gender and Loan type Cross-tabulation

		Loan Type								Total
		Car Loan	Personal Loan	Housing Loan	Gold Loan	Pensioners Loan	Loan against LIC Policy	Two-wheelerLoan	Student Loan	Total
Sex	Male	50	28	80	1	1	6	57	46	269
	Female	30	15	4	7	0	0	21	2	79
Total		80	43	84	8	1	6	78	48	348

Source: Primary data analysis output.

Gender and Loan Type Cross-tabulation

Car, housing and two wheeler loans are the most sought by men. For the women, a nearly similar trend is seen. The women also have a reasonable appetite for car, two wheeler and personal loans. Thirty-eight per cent of the total car loans and 35 per cent of the total personal loan sought are from the women (refer Table 3). This reflects their consumption appetite and independent decision-making mindset. We consider this to be reasonably high particularly in reference to a state like Jharkhand which still has a lot to move ahead in gender equality.

Women mostly are still not the sole decision makers in the households, yet their contribution to decision making in purchases is increasing (Nielson—CII, 2012). The Nielson survey reveals that women are equal participants to decision making alongside men and are key influencers in purchases involving high amounts. The Nielson survey specifically points to the need for marketing companies to address this trend and accordingly structure their marketing strategies. We infer that this is a major trend evolving towards consumption and retail lending has to rise up to this new trend.

Measure of Scale Reliability

The scale has been subjected to reliability test based on the pilot study carried out. Cronbach’s alpha score has been used to assess the suitability of the constructs and the items. The final Cronbach’s alpha score of the items and the constructs show an acceptable value.

The Cronbach’s alpha score for all constructs reveal a high internal consistency as all the scores are above the threshold level of 0.60 (Malhotra & Dash, 2010, p. 279). Coefficient alpha values of our constructs have a range of 0.64 and 0.84. (Refer Table 4) Values above 0.6 indicate an acceptable level of scale reliability for theory testing research (Nunnally & Bernstein, 1994).

Table 4.

Reliability (Cronbach’s alpha) of Constructs

Construct	No. of Items	Cronbach’s Alpha
Brand equity	5	0.847
Perceived quality
Tangibility	4	0.853
Reliability	5	0.830
Responsiveness	4	0.854
Assurance	4	0.851
Empathy	5	0.820
User brand image	3	0.816
Corporate brand image	2	0.738
Service brand image	2	0.680
Advertisement	3	0.727
Word of mouth	5	0.845
Physical evidence
Physical evidence ambience	3	0.651
Physical evidence social	5	0.760
Physical evidence design	2	0.642
Employee	3	0.648

Source: Primary data analysis output.

Table 5.

Construct Reliability

Constructs	Construct Reliability
Brand equity	0.86
User brand image	0.81
Corporate brand image	0.82
Service brand image	0.86
Advertisement	0.78
Word of mouth	0.88
Physical evidence	0.88
Perceived service quality	0.91
Employees	0.74

Source: Primary data analysis output.

Construct Reliability

Cronbach’s alpha value alone does not reflect unidimensionality. Unidimensional measures mean that a set of variables/indicators have only one underlying construct and it becomes particularly important when more than two constructs are involved, as in the present case.

The construct reliability values calculated through confirmatory factor analysis (CFA) values from the measurement model in SEM (refer Table 5) meet the recommended criteria of 0.7 (Fornell & Larcker, 1981) and thus the consistency and reliability of the measured variables are established.

Structural Equation Modelling

Measurement Model

The measurement theory can be represented with a model that shows how the measured variables come together to represent constructs. Through confirmatory factor analysis, we have tried to test how well the measured variables represent the constructs.

The study has a total of nine latent constructs. The constructs like perceived service quality and physical evidence are highly diversified. We have considered the standard classification of Parashuraman et al. (1991) in adopting the five dimensions of service quality, namely, tangibility, reliability, responsiveness, assurance and empathy.

Similarly, the physical evidence construct consisting of three subdimensions of ambience, social and design have been considered in the study. The constructs are diversified and have quite a large number of items. In view of this diversity, second order factor analysis was carried out to determine the strength of the items in describing variance. Item parcelling has been done during the formulation of the measurement model (Refer Table 7). When the items are many and diversified in nature item parcelling is a suitable approach (Cattell, 1956). These parcels can then be used as indicators when the total number of items pertaining to a specific area is too many (Bandalos, 2002).

We have adopted the second-order CFA results and considered physical evidence as a latent construct with three summated or parcelled items. The results (refer Table 6) point to the acceptance of the second-order factor approach. The approach also has a strong theoretical base. Item parcelling has also been adopted in the case of perceived service quality.

Five first-order factors and one second-order factor have a strong theory base, and the calculated statistics also are within the recommended values (Bagozzi, 1988; Browne & Cudeck, 1993; Hair et al., 2006, p. 775; Hooper et al., 2008). We thus adopt the model to be part of the overall measurement model.

Table 6.

Competing CFA Models for Physical Evidence

Model	Chi-square (df)	Chi-square (df)	GFI	AGFI	RMSEA
One first-order factors	142(35)	4.057	0.92	0.88	0.094
Three first-order factors	112.64(32)	3.52	0.94	0.90	0.085
Three first-order factors and one second-order factor	112.64(32)	3.52	0.94	0.90	0.085

Source: Primary data analysis output.

Table 7.

Competing CFA Models for Perceived Quality

Model	Chi-square (df)	Chi-square (df)	GFI	AGFI	RMSEA
One first-order factor	1708.78(209)	8.17	0.69	0.63	0.144
Five first-order factors	389.96(199)	1.95	0.91	0.88	0.053
Five first-order and one second-order factor	411.91(204)	2.01	0.90	0.88	0.054

Source: Primary data analysis output.

Parameter Estimates for the Measurement Model

The analysis results for this study indicate that all items were loaded highly on their corresponding construct and the t-value of those items was greater than 2.0 which is an acceptable statistic (Segars & Grover, 1993). The analysis of the squared multiple correlations (refer Table 8) demonstrate that, except for a few items, most of the items met the recommended criteria of 0.40. This means, overall, that the items shared substantial variance with their hypothesized constructs (Taylor & Todd, 1995).

Fit Statistics for the Measurement Model

The measurement model is supported based on the statistics except the chi-square value (refer Table 9). However, the higher chi-square value criteria is not accepted as a suitable criterion particularly in research studies and data analysis where the sample size is large (Browne & Cudeck, 1993). Normed Fit Index (NFI) value of 0.96 is also above the recommended value of 0.90. Therefore, it could be articulated that the measurement model of this study has an acceptable level of fitness. Other fitness indices met the recommended minimum values as well: chi-square (χ²)/df of 1.91, GFI of 0.92, AGFI of 0.87, CFI of 0.98, RMR of 0.033 and RMSEA of 0.051 (refer Table 9).

Table 8.

Parameter Estimates of Measurement Model*

Constructs	Items	Standardized Loadings	T-values	Squared Multiple Correlations
Brand equity	Brand equity 1	0.70	14.20	0.49
	Brand equity 2	0.80	17.07	0.64
	Brand equity 3	0.71	14.50	0.51
	Brand equity 4	0.75	15.63	0.57
	Brand equity 5	0.66	13.00	0.43
User brand image	User image 1	0.87	19.23	0.76
	User image 2	0.72	14.88	0.56
	User image 3	0.66	13.21	0.44
Corporate brand image	Corporate image 1	0.77	15.14	0.59
	Corporate image 2	0.79	15.70	0.63
Service brand image	Service image 1	0.88	18.48	0.77
	Service image 2	0.82	16.84	0.67
Physical evidence	Items parcelled
(Ambience)	Ambience 1
	Ambience 2
	Ambience 3	0.60	12.20	0.36
(Social)	Social 1
	Social 2
	Social 3
	Social 4	0.98	24.71	0.95
	Social 5
(Design)	Design 1
	Design 2	0.63	12.91	0.39
Perceived service quality	Items parcelled
Tangibility	Perceived quality 1
	Perceived quality 2
	Perceived quality 3
	Perceived quality 4	0.82	17.83	0.67
Reliability	Perceived quality 5
	Perceived quality 6
	Perceived quality 7
	Perceived quality 8	0.81	17.78	0.66
	Perceived quality 9
Responsiveness	Perceived quality 10			0.47
	Perceived quality11
	Perceived quality12	0.69	13.96
	Perceived quality13
Assurance	Perceived quality14
	Perceived quality15
	Perceived quality16	0.62	12.17	0.38
	Perceived quality17
Empathy	Perceived quality 18
	Perceived quality 19
	Perceived quality 20
	Perceived quality 21	0.70	14.44	0.50
	Perceived quality 22
Employees	Employee 1	0.62	13.37	0.39
	Employee 2	0.67	14.46	0.45
	Employee 3	0.56	12.23	0.32
Advertising	Advertising 1	0.73	13.70	0.53
	Advertising 2	0.66	12.23	0.44
	Advertising 3	0.67	12.49	0.45
Word of mouth	WOM 1	0.74	15.25	0.55
	WOM 2	0.75	15.43	0.56
	WOM 3	0.72	14.75	0.52
	WOM 4	0.74	15.26	0.55
	WOM 5	0.66	13.12	0.44

Source: Primary data analysis output.

Note: *All values significant at level 1.

Testing the Structural Model

The causal relationships stated in the form of hypothesis have been tested with structural equation modelling and the summary of the estimates and t-values are as given in Table 10.

Testing of the Hypotheses

Each of the hypotheses was tested by using an analysis of indirect effects of marketing mix variables on brand equity (Bollen, 1991; Yoo et al., 2000). In the structural model, no direct path between the marketing mix variables and brand equity was specified. Instead, brand equity was conceptualized to be indirectly affected through the mediating brand equity dimensions. The assumed relationships were tested using LISREL 8.

Table 9.

Fit Statistics for the Measurement Model*

Fit Statistic	Recommended Value	Value Calculated	Conclusion
Chi-square	P >= 0.05	761.14(p = 0.00)	Not fit
Chi-square/df	<=3	761.14/398 = 1.91	Fit
GFI	>=0.9	0.92	Fit
AGFI	>=0.8	0.87	Fit
NFI	>=0.9	0.96	Fit
CFI	>=0.9	0.98	Fit
RMR	<=0.9	0.033	Fit
RMSEA	<=0.1	0.051	Fit

Source: Primary data analysis output.

Note: *All *Values significant at level 1.

Table 10.

Results of Hypotheses Testing

Hypothesis	From	To	Standardized Coefficient	T-value	Result
H1	Perceived service quality	Brand equity	0.19	2.30	Supported
H2	Advertising	Perceived service quality	0.18	2.46	Supported
H3	Word of mouth	Perceived service quality	0.29	4.20	Supported
H4	Employee	Perceived service quality	0.09	2.52	Supported
H5	Physical evidence	Perceived service quality	0.39	6.32	Supported
H6	User brand image	Brand equity	0.29	3.18	Supported
H7	Corporate brand image	Brand equity	0.0059	–0.07	Not supported
H8	Service brand Image	Brand equity	0.29	4.00	Supported
H9	Advertising	User brand image	0.32	4.38	Supported
H10	Advertising	Corporate brand image	0.18	2.00	Supported
H11	Advertising	Service brand image	0.22	2.58	Supported
H12	Word of mouth	User brand image	0.24	3.51	Supported
H13	Word of mouth	Corporate brand image	0.41	4.76	Supported
H14	Word of mouth	Service brand image	0.30	3.62	Supported
H15	Employees	User brand image	0.03	0.95	Not supported
H16	Employees	Corporate brand image	0.37	0.92	Not Supported
H17	Employees	Service brand image	0.08	2.02	Supported
H18	Physical evidence	User brand image	0.38	6.23	Supported
H19	Physical evidence	Corporate brand image	0.25	3.39	Supported
H20	Physical evidence	Service brand image	0.20	2.81	Supported

Source: Primary data analysis output.

Findings and Managerial Significance

As with the measurement model, the fit indices (refer Table 11) suggest a fit for the structural model with all fit statistics complying with the recommended criterion except the chi-square value. The misfit and noncompliance of the chi-square value to the recommended standard value is explained on the basis of the large sample size.

The standard coefficient values and the corresponding t-values suggest a validation of the conceptual model that we have proposed.

Our proposition that perceived service quality significantly and positively influences brand equity has been proved correct. The standardized coefficient value between perceived quality and brand equity is 0.19 with a t-value of 2.30 (refer Table 10). Most of the studies relating Perceived Service Quality (PSQ) to brand equity have been carried out in the goods sector. Our study empirically validates the existing propositions of PSQ to brand equity relationship in the context of retail banking service in India. Perceived service quality as a composite of five dimensions of tangibility, reliability, responsiveness, assurance and empathy proposed by Parashuraman (1991) is proved true and highly relevant in the context of our country and retail banking.

The coefficient value of advertising to perceived service quality is 0.18 with a t-value of 2.46 (refer Table 10). Thus, our proposition that assumes a positive and significant relationship of advertising with perceived quality is supported. The findings here is in line with the finding of Villarejo and Manuel (2005) and Moorthy and Zhao (2000), where they have empirically established the direct relationship of advertising with brand equity dimensions including perceived quality. The impact of advertisement on perceived quality and brand equity has also been highlighted by Aaker et al. (1994). Positive and significant relationship of advertisement with perceived quality holds a lot of significance for the marketer as advertising is within the control of the marketer.

Table 11.

Parameter Estimates of the Structural Model*

FitStatistic	Recommended Value	Value Calculated	Conclusion
Chi-square	P >= 0.05	845.51/p = 0.00	Not fit
Chi-square/df	<=3	845.51/408 = 2.07	Fit
GFI	>=0.9	0.90	Fit
AGFI	>=0.8	0.87	Fit
NFI	>=0.9	0.96	Fit
CFI	>=0.9	0.98	Fit
RMR	<=0.9	0.036	Fit
RMSEA	<=0.1	0.056	Fit

Source: Primary data analysis output.

Note: *All values significant at level 1.

Values from structural analysis regarding our assumption of a strong positive and direct relationship between WOM and perceived quality are also proved correct. The coefficient value is 0.29 and the t-value is 4.20 (refer Table 10). WOM is an element that is not entirely within the control of the marketer but its importance suggests that marketers should find ways through which it can be effectively influenced. The study carried out with the antecedents of WOM by De Matos & Rossi (2008) where the different antecedents of WOM were empirically examined could form a basis on which the marketers could focus their attention. A comparison between the influence of advertising and the influence of WOM on perceived quality suggests that WOM has a greater influence on PSQ compared to advertising. The advertising to PSQ coefficient value is 0.18 compared to 0.29 for WOM to PSQ (refer Table 10). The findings are similar to the study by O Cass and Grace (2004), where they have stated that in case of banking services, WOM is more important than advertising.

Assumption regarding employee to perceived service quality relationship is supported with a coefficient value of 0.09 and t-value of 2.52 (refer Table 10). Though the relationship is not very strong but being supported is in line with general findings of most researchers in this area. The findings are in line with the study by Hays and Hill (2001) wherein it is suggested that employees have a positive and significant relationship with PSQ, particularly in services because of the interaction of the customer with the frontline service firm employee.

The assumption regarding the positive influence of physical evidence (servicescape) on perceived quality has been proved correct with a coefficient estimate of 0.39 and t-value of 6.32 (refer Table 10). Thus, the positive relationship between physical evidence and perceived quality is strongly established. We take this as a significant finding because this again is an area that is within the control of the marketer.

The dimensions of brand image, namely, user brand image, corporate brand image and service brand image, have been suggested to be validated in past research by a number of researchers notably Chang et al. (2008) and Biel (1992) etc., but existing literature does not reveal any such validation in the retail banking sector. In this study, we have primarily attempted to address this gap. In our study, user brand image and service brand image have emerged as having a positive relationship with brand equity with a coefficient value of 0.29 and t-value of 3.18 and 4.00, respectively. The relationship of corporate brand image to brand equity with a coefficient value of 0.0059 and a t-value of –0.07 has not been established (refer Table 10). This is in contrast to what has been forwarded by a number of authors notably Kim and Hyun (2011), Onkvisit & Shaw (1989), Riley and De Chernatony (2000), McDonald et al. (2001) etc. We understand that this is an immense opportunity for the marketers where they may correct themselves by not being excessively focussed on building corporate brand image. Compared to advertising relation to user brand image, WOM has a stronger relationship with user brand image (refer Table 10). This is in contrast to the popular perception that corporate brand image can be built most effectively through advertising. We consider this as a significant finding.

Bravo et al. (2010) have expressed corporate brand image as the image associated with an organization’s name. Though the relationship of advertising, WOM and physical evidence to corporate brand image is validated, the relation of corporate brand image to brand equity is not established (refer Table 10). The positive and significant relationship of user and service brand image to brand equity and the positive relationship of advertising, WOM and physical evidence to user brand image has been established and is a notable finding. Similarly, the relationships of advertising, WOM, physical evidence and employees to service brand image have also been established. In fact, compared to advertising relation to corporate brand image WOM has a stronger relationship with corporate brand image (refer Table 10). This is in contrast to the popular perception that corporate brand image can be built most effectively through advertising. We consider this as a significant finding. it is thus inferred that since the marketing mix elements positively influence user and service brand image and since these two image constructs positively and reasonably strongly influence brand equity, marketers have the suggestion of a new way through which they can create strong and positive image and brand equity.

Physical evidence has a positive and significant influence on all brand image dimensions and perceived quality thus revealing an enormous opportunity for the service marketers because this is what the marketers can effectively control. Another noticeable finding is the positive and significant relationship of WOM with the brand image dimensions and perceived quality. WOM thus emerges as a very strong marketing element and forwards the need for understanding and finding ways through which WOM could be influenced.

Though not strongly supported, the employee to service brand image positive relationship suggests an opportunity for the marketer because employee is the element that the service firms can control and improve through monitoring and training.

Limitations and Future Scope

Though the proposed model is validated, the study is not without its limitations. Loan as a product category has been picked up in retail banking because of our assumption that it is high in risk perception and experience quality. However, if the tangibility spectrum of services is considered, there are many other sectors where there is higher degree of abstractness and where the service is higher in credence quality and risk perception. The appropriateness of the study in other sectors and other category of service products could be examined in future studies.

We have considered the country’s largest public sector bank the State Bank of India. However, there are numerous other foreign, public sector and private banks in the country and the study could be extended to these.

The sample population was restricted to the state of Jharkhand. Though state bank offers a great variability in its user base, there could still be a high discrepancy in demographics across the regions of the country and to effectively address these, the study very well could be extended to include a sample drawn from across the country.

Regarding the marketing mix elements that act as key influence on dimensions of brand equity, process as an element could be evaluated further.

It was lately suggested and also observed that the outcome of a loan application, that is, its approval or rejection, could have a strong influence on the respondents opinion and perception about the service and this as a factor could be included in future studies.

WOM has emerged as a key antecedent of service brand equity and future studies should be directed at trying to examine the specific influence pattern of WOM on dimensions of brand equity. The study carried out by Celso and Rossi (2008) focused on the antecedents of WOM could prove to be a reference point regarding this.

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