The influence of customer brand identification on hotel brandevaluation and loyalty development

Author

So, Kevin, King, Ceridwyn, Sparks, Beverley, Wang, Ying

Published

2013

Journal Title

International Journal of Hospitality Management

DOI

https://doi.org/10.1016/j.ijhm.2013.02.002

Copyright Statement

© 2013 Elsevier. This is the author-manuscript version of this paper. Reproduced in accordancewith the copyright policy of the publisher. Please refer to the journal's website for access to thedefinitive, published version.

Downloaded from

http://hdl.handle.net/10072/55476

Griffith Research Online

https://research-repository.griffith.edu.au

The influence of customer brand identification on hotel brand evaluation and loyalty

development

Kevin Kam Fung So , Ceridwyn King , Beverley A. Sparks , and Ying Wang

Department of Tourism, Sport and Hotel Management

Griffith Business School, Griffith University

Parklands Drive, Southport, QLD, 4215, Australia

School of Tourism and Hospitality Management, Temple University

Philadelphia, Pennsylvania, USA

1810 North 13th Street

Speakman Hall 306

Philadelphia, PA 19122, USA

* Corresponding author

E-mail: kevinkamfungso@gmail.com

Acknowledgements: This research was funded by Griffith University’s Tourism, Sport and

Services Research Centre.

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The influence of customer brand identification on hotel brand evaluation and

loyalty development

ABSTRACT

Hotel firms have increasingly recognized that branding strategies constitute a strategic weapon to

secure a competitive edge in the global hotel industry. To extend current understanding of hotel

brand management, this study investigates the role of customer brand identification in the

formation of hotel brand loyalty. This study contributes to the literature by establishing that

customer brand identification is an indirect predictor of hotel brand loyalty through its three

known antecedents. Results suggest that while the customer may identify with a particular hotel

brand, hotel loyalty still depends on the customer’s positive evaluation of factors relating to

service experiences. However, as consumers’ identification with a hotel brand affects their

evaluation of these factors, hoteliers should leverage customers’ brand identification to engender

positive consumer evaluation of the hotel brand and, ultimately, increase brand loyalty.

Key Words: Identification; brand loyalty; service quality; perceived value; brand trust.

2

1. Introduction

In the highly competitive hotel industry, where products and services have reached

“commodity” status (Mattila, 2006), hoteliers are required to find ways to set their products and

services apart from others (Choi and Chu, 2001). This need has given rise to the use of branding

strategies as a source of differentiation (Pappu et al., 2005) and competitive advantage (Kim and

Kim, 2005), making branding one of the most dominant trends in the global hotel industry

(Kayaman and Arasli, 2007). Building strong hotel brands creates value for both the firm and the

customer. From the hotel’s perspective, a strong brand enhances the property’s market value

(O'Neill and Xiao, 2006), financial performance (Kim and Kim, 2005; Kim et al., 2003; Kwun

and Oh, 2007), and other key performance indicators such as average price, occupancy, revenue,

and return on investment (Forgacs, 2003). Research also indicates that branded hotels achieve

higher net operating income during economic recession (O'Neill and Carlback, 2011). From the

customer’s perspective, strong hotel brands reduce perceived risks and search costs (Kayaman

and Arasli, 2007) and provide a signal of quality assurance (Prasad and Dev, 2000), simplifying

the consumer’s pre-purchase evaluation of the service.

One commonly used indicator of brand strategy success is the strength of customers’

brand loyalty. For many years, the development and maintenance of brand loyalty has been the

ultimate goal of marketing activities of many organizations. The topic of brand loyalty has been

researched extensively, with studies largely focused on the examination of key marketing

concepts that serve as loyalty antecedents, such as service quality (e.g., Bloemer et al., 1999;

Kandampully et al., 2011), perceived value (e.g., Chen and Hu, 2010; Ryu et al., 2008;

Sirdeshmukh et al., 2002), customer satisfaction (e.g., Back and Lee, 2009; Back and Parks,

3

2003; Li and Petrick, 2008; Ryu et al., 2008), and brand trust (e.g., Chaudhuri and Holbrook,

2001; Han and Jeong, 2013).

While the findings of these studies contribute significantly to the current understanding

of how brand loyalty can be established from a customer’s perspective, investigators have given

little attention to brand loyalty development from a social identity perspective, which research

suggests could offer a more comprehensive understanding of customer brand relationships

(Bhattacharya and Sen, 2003; He et al., 2012). Customer brand identification (CBI) , originating

from social identity theory, can lead to a range of consumer outcomes, including brand loyalty

(He et al., 2012). Investigators also believe that the concept of CBI provides a richer

understanding of brand management (Kuenzel and Halliday, 2008), and propose that a strong

CBI can induce customers’ favorable evaluation of the brand (Ahearne et al., 2005; Donavan et

al., 2006; Underwood et al., 2001).

However, despite the recognized importance of CBI, its effects on the development of

hotel brand loyalty remain relatively unexplored. While several previous studies have

investigated the role of CBI in brand loyalty in various research settings, these studies have

produced conflicting results. For example, a study of cellular phone brands indicated that CBI

was not significant in explaining brand loyalty (Kim et al., 2001), whereas a study of car brands

showed that consumers' development of relationships through brand identification led to word-

of-mouth communication and intentions to repurchase the car brand of interest (Kuenzel and

Halliday, 2008). These inconsistent findings do not inform hotel practitioners as to whether they

should integrate such a relevant relationship factor into marketing strategies aimed at

strengthening customers’ brand loyalty. To date, no known studies have empirically tested the

influence of CBI on customer evaluation of hotel brands. Therefore, to address this gap in the

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literature, this paper investigates the influence of CBI on the formation of hotel brand loyalty and

on hotel brand evaluations.

Previous related empirical studies on CBI have predominantly investigated products or

services, many of which possess high levels of symbolic meaning and evoke consumer

commitment and emotional involvement, such as sports teams (Carlson et al., 2009), cars

(Kuenzel and Halliday, 2008), and cosmetics (Papista and Dimitriadis, 2012). While hotel

services are not considered symbolic to the degree of these products, the service identification

continuum suggests that consumers do develop a medium level of identification with hotel

services (Underwood et al., 2001). Furthermore, the proliferation of new hotel brands (Kim et al.,

2008; Olsen et al., 1998), as well as the extensive adoption of branding strategies in the hotel

industry (Forgacs, 2003; Prasad and Dev, 2000; So and King, 2010), requires a greater

understanding of the role CBI plays in creating a strong customer–brand relationship. This

insight is critical, because CBI represents a strong psychological attachment to the brand, which

has the potential to be enduring and indicative of future behavior.

This study introduces the concept of CBI into the hotel brand loyalty development

equation, testing its relationships with brand loyalty as well as with several established loyalty

antecedents, including service quality, perceived value, and brand trust. While customer

satisfaction is also a commonly used antecedent of loyalty, this study specifically includes

service quality rather than customer satisfaction. Service quality summarizes “the consumer’s

judgment about a service’s overall excellence or superiority” (Zeithaml, 1988, p. 3), which is

considered a more direct measure of the service offering. In contrast, customer satisfaction is

generally accepted as an overall evaluative judgment that takes into account service quality and

5

perceived value. Therefore, a more detailed examination is warranted to gain insight into what

specifically contributes to hotel brand loyalty.

The study makes a number of contributions to the hospitality literature. First, it tests and

demonstrates that CBI plays a significant indirect role in the development of hotel brand loyalty.

Second, it tests and supports, for the first time, hotel CBI’s power to engender positive customer

evaluations of the hotel brand as indicated in consumers’ enhanced perceptions of service

quality, perceived value, and brand trust. The favorable brand evaluations in turn provide an

important basis upon which loyalty is established.

The structure of this paper is as follows. The next section provides a theoretical

foundation for this study by reviewing the relevant literature on brand loyalty and its commonly

identified antecedents, as well as the literature on CBI. This review is followed by a description

of the research procedures adopted to test the proposed hypotheses. Subsequently, the results

section presents the assessment of both the measurement and structural model, followed by a test

of mediation effects of service quality, perceived value, and brand trust through the examination

of the four competing models. The final section discusses the research findings along with

limitations and directions for future research.

2. Literature review

2.1 Brand loyalty

The concept of brand loyalty has been approached from three perspectives: behavioral,

attitudinal, and composite loyalty. Researchers holding a behavioral view assume that repeat

transactions represent the loyalty of a consumer toward the brand (e.g., Ehrenberg et al., 2004).

While the behavioral approach provides a more realistic picture of how well the brand is

performing in relation to its competitors (O’Malley, 1998), behavioral measures as the sole

6

indicator of loyalty have been criticized as being unable to distinguish between true loyalty and

spurious loyalty (Dick and Basu, 1994; Odin et al., 2001). In contrast, attitudinal loyalty is often

viewed as comprising stated preferences, commitment, or purchase intentions of the consumer,

thus emphasizing the psychological element of brand loyalty (Bennett and Rundle-Thiele, 2002;

Mellens et al., 1996). While consideration of the attitudinal aspects of loyalty allows the

researcher to distinguish brand loyalty from repeat buying, it focuses on consumer declarations

rather than on actual purchases and thus may not be an accurate representation of reality

(Mellens et al., 1996; Odin et al., 2001). For example, a positive attitude toward a brand may not

lead to actual purchase behavior.

In contrast, the composite approach considers loyalty to be a biased behavioral purchase

practice that results from a psychological process (Jacoby, 1971). This approach suggests that the

evaluation of a consumer’s loyalty to a particular brand requires simultaneous consideration of

attitudes and purchase behavior (e.g., Day, 1969; Dick and Basu, 1994). Because the composite

view provides a holistic understanding of the loyalty concept, it has been examined and

supported in several brand loyalty studies (e.g., Harris and Goode, 2004; Li and Petrick, 2008)

and is consequently adopted here.

To gain insight into the development of brand loyalty, scholars have consistently

documented the contribution of service quality, perceived value, and brand trust. Service quality

and perceived value have been considered as evaluative judgment variables (Butcher et al., 2001)

or service evaluation (Lai et al., 2009), depending on the customer’s actual service experience.

While brand trust has been considered to be a relational variable (Sirdeshmukh et al., 2002), the

process by which a consumer attributes a trust image to the brand is based primarily on his/her

experience with that brand (Delgado-Ballester and Munuera-Alemán, 2001). Thus, consumer

7

evaluation of these factors depends largely on the service transaction, and, the current

understanding of brand loyalty formation suggests that consumer brand loyalty in a hotel context

develops through the enhancement of the service experience. However, a growing body of

literature suggests that psychological factors such as CBI can also affect the dynamic

relationships between a customer and the brand (e.g., He et al., 2012; Kim et al., 2001).

2.2 Customer brand identification

The concept of identification originates from social identity theory, which maintains that

the self-concept comprises a personal identity, consisting of idiosyncratic characteristics such as

abilities and interests, and a social identity, encompassing salient group classifications (Ashforth

and Mael, 1989; Tajfel and Turner, 1985). Identification is essentially a perceptual construct

(Mael and Ashforth, 1992), implying identity fit and identity matching. Individuals tend to go

beyond their self-identity to develop a social identity by classifying themselves and others into

various social categories (e.g., organizational membership and sport clubs) (Mael and Ashforth,

1992). Identification occurs when an individual sees him- or herself as psychologically

intertwined with the characteristics of the group.

From a consumer perspective, identification is an individual’s “perceived oneness with or

belongingness to an organization ” (Bhattacharya et al., 1995, p. 46). In an attempt to determine

why and under what conditions consumers enter into strong, committed, and meaningful

relationships with certain companies, investigators have proposed that strong consumer–

company relationships are based on consumers’ identification with the companies that help them

satisfy one or more important self-definitional needs (Bhattacharya and Sen, 2003). In addition,

such consumer–company identification is active, selective, and volitional on consumers’ behalf

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and motivates them to engage in favorable as well as potentially unfavorable company-related

behaviors (Bhattacharya and Sen, 2003).

2.3 The effect of customer brand identification on hotel brand loyalty

Social identity may influence individuals’ perceptions, cognitions, and evaluations of

issues and events, and consumers’ increased identification with a product offering or brand can

lead to enhanced customer outcomes, such as stronger loyalty to the brand (Underwood et al.,

2001). While research concerning customer and hotel brand identification is very limited,

parallel understanding can be drawn from other related research settings. For example, sports

teams may create a strong level of brand loyalty on the part of fans despite the strengths and

weaknesses of the organization (Parker and Stuart, 1997), possibly because of the strong

identification between the fan and the sports team. Research also indicates that customer–

company identification increases product utilization (Ahearne et al., 2005) as well as repurchase

frequency (Bhattacharya et al., 1995). Similarly, customers who identify with a brand

community are more likely to recommend the brand (Algesheimer et al., 2005). Empirical

research also supports the effect of CBI on brand loyalty measures, including word-of-mouth

intentions (Kuenzel and Halliday, 2008; Tuskej et al., 2013), purchase intention (Kuenzel and

Halliday, 2008), and consumer commitment (Tuskej et al., 2013), as well as the brand loyalty

construct (He and Li, 2011; He et al., 2012; Homburg et al., 2009; Kuenzel and Halliday, 2010).

On this basis, we propose the following hypothesis:

Hypothesis 1. Customer hotel brand identification has a positive association with hotel brand

loyalty.

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Establishing brand loyalty towards service brands is considered to be more challenging

than brands associated with goods. This is because the intangible nature of service brands is

associated with increases in consumers’ perceived risk of purchasing a service. To address this

concern, brand cues are suggested as a way to enhance the brand image which, in turn,

influences service purchase decisions (Brady et al., 2005). As extrinsic cues such as advertising

and personal referrals have been shown to be significant influences in hotel purchase decisions

(Brady et al., 2005), it is reasonable to suggest that a level of identification with the brand is the

result of such brand cues. Specifically, Kuenzel and Halliday (2008) demonstrate that corporate

communication, in addition to the perceptions of others that the brand is well regarded has a

significant influence on customer brand identification. Therefore, with the potential for CBI

formation occurring prior to a hotel purchase established, consideration is also given to the

effects of such identification in the brand loyalty formation equation. In particular, consistent

with cognitive dissonance theory (Festinger, 1957), which suggests that consumers often

rationalize their choice by enhancing the positive aspects of the chosen brand and by suppressing

its negative aspects (Mazursky et al., 1987; Sheth and Parvatiyar, 1995), it is suggested that CBI

affects the traditional loyalty antecedents of service quality, perceived value and brand trust.

2.4 The effect of customer brand identification on service quality

Consumers’ increased identification with a product offering or brand can lead to a range

of favorable customer outcomes, such as stronger perceptions of quality. Interestingly, prior

studies appear to support two opposite predictions concerning the relationship between service

quality and CBI. On the one hand, scholars posit that characteristics such as the physical facility

of the service environment could assist consumers in developing social identification

(Underwood et al., 2001). When consumers perceive the brand as having high quality they are

10

more likely to identify strongly with the brand (He and Li, 2011; Lam et al., 2011). However,

within the hotel industry, where products have been described as a commodity (Mattila, 2006),

superior service quality is considered as necessary but insufficient to establish strong CBI. From

an alternative perspective, scholars argue that identification may be the key underlying

psychological variable that affects customers’ product judgments, positive responses, and

positive product evaluations (Ahearne et al., 2005). Individuals who identify with a brand are

more likely to engage in favorable actions toward the brand (Donavan et al., 2006).

In the context of hotel services, the purchase and consumption of a branded offering is

considered to be a visible activity which is able to provide distinct social meaning and product

symbolism associated with the purchase (i.e. brand identification) (Wilkins et al., 2010). When

evaluating a hotel brand, customers are more likely to be satisfied with the brand when brand

identification enhances their positive image within social groups or contributes to their sense of

belonging to a social group (Nam et al., 2011). Such an effect is empirically supported not only

in the tourism and hospitality literature (Nam et al., 2011), but also in the broader business

domain (He et al., 2012). Therefore, the influence of CBI on customer satisfaction, a concept that

is related to service quality is highlighted. While the potential for service quality to enhance CBI

cannot be discounted, as an antecedent, CBI for a service brand would seem to be more

consistent in its influence on evaluative judgments given its initial formation occurring prior to

consumption, rather than being influenced by those same customer evaluations. We therefore

propose that:

Hypothesis 2. Customer hotel brand identification has a positive association with service

quality.

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Perceived service quality, representing “the consumer’s judgment about a product’s

overall excellence or superiority” (Zeithaml, 1988, p. 3), in turn directly determines the level of a

customer’s loyalty to a product or brand (e.g., Aydin and Ozer, 2005; Zeithaml et al., 1996). The

relationship can be explained by the Model of the Behavioral Consequences of Service Quality

(Zeithaml et al., 1996), which posits that high assessments of service quality lead to customers’

favorable behavioral intentions, such as loyalty to the company. This effect occurs because

enhanced service quality helps consumers cultivate a favorable attitude toward a service provider,

thus developing preference loyalty (de Ruyter et al., 1998). Empirical evidence supports service

quality’s influence on repurchase intention (Rauyruen and Miller, 2007), willingness to

recommend (de Ruyter et al., 1998), and service loyalty (Caruana, 2002). On this basis, the

following hypothesis is proposed:

Hypothesis 3. Service quality has a positive association with hotel brand loyalty.

2.5 The effect of customer brand identification on perceived value

While the perceived value of a service is determined primarily by its price (Brodie et al.,

2009; Oh, 2000; Petrick, 2004) and its quality (Harris and Goode, 2004; Kim et al., 2008; Petrick,

2004; Sweeney et al., 1997), an enhanced level of CBI may also affect consumers’ perceptions of

value. For example, in the organizational literature, where the concept of identification is rooted,

scholars argue that an individual’s identification with an organization enhances his/her support

for it (Ashforth and Mael, 1989). Analogously, from a consumer perspective, the greater the

identification with an organization or a brand, the more likely the customer is to be content with

the organization’s products (Papista and Dimitriadis, 2012). More specifically, CBI represents a

consumer’s attachment to the brand, and therefore customers with enhanced levels of

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identification are more likely to favorably evaluate the value of an exchange relationship with the

brand of interest (He et al., 2012). Accordingly, we hypothesize that

Hypothesis 4. Customer hotel brand identification has a positive association with perceived

value.

Most conceptual definitions of perceived value are grounded on the description of value

as “the consumer’s overall assessment of the utility of a product based on perceptions of what is

received and what is given” (Zeithaml, 1988, p. 14). Value is a superordinate consumer goal that

regulates consumer actions at the level of behavioral intentions of loyalty (Sirdeshmukh et al.,

2002). Previous research suggests that perceived value influences revisit intent (Kim et al., 2008;

Oh, 1999; Petrick, 2004), commitment (Pura, 2005), and brand loyalty (e.g., Chen and Hu, 2010;

Sirdeshmukh et al., 2002). For this reason, we also propose that

Hypothesis 5. Perceived value has a positive association with hotel brand loyalty.

2.6 The effect of customer brand identification on brand trust

In addition to having the ability to engender favorable perceived value, CBI is

conceptually related to the concept of brand trust. On the one hand, research suggests that trust is

an antecedent of an identified relationship, because consumers tend to identify with trustworthy

organizations or brands to express their self-definition as well as to enhance their self-esteem

(Keh and Xie, 2009). Conversely, the attachment characterized by brand identification may

provide a platform for brand trust development (Dunn and Schweitzer, 2005; Williams, 2001).

While the marketing literature suggests that trust develops predominantly from past experience

with the brand (Delgado-Ballester and Munuera-Alemán, 2001; Delgado-Ballester et al., 2003;

Ravald and Gronroos, 1996), organizational research has introduced the notion of

13

“identification-based trust,” which is motivated through individuals’ identification with the

social entity rather than through past interactions or experienced benefits (Kramer et al., 1996).

Thus CBI provides a more favorable basis for customers to assess brand performance experience

as compared to prior expectation (He and Li, 2011). When brand performance expectation is

confirmed or exceeded, identified customers are reassured of their psychological attachment with

the brand, which in turn helps the customers to preserve their self-esteem.

While the previous tourism and hospitality research has not investigated the direct effect

of CBI on trust, parallel support can be drawn from the linkage between image congruence and

trust. For example, research shows that luxury restaurant customers who perceive high social

image congruence are more likely to trust the firm (Han and Hyun, 2012). Analogously, such

relationship may extend to the process of brand identification. Therefore, we propose the

following hypothesis:

Hypothesis 6. Customer hotel brand identification has a positive association with brand trust.

While brand trust is enhanced by the hotel customer’s strong identification with the brand,

trust also serves as a significant determinant of brand loyalty (Aydin et al., 2005; Flavián et al.,

2006; Garbarino and Johnson, 1999). Trust leads to brand loyalty and commitment because it

creates exchange relationships that the customer values highly (Morgan and Hunt, 1994).

Therefore, loyalty and commitment underlie the ongoing process of continuing and maintaining

a valued and important relationship that trust has created (Chaudhuri and Holbrook, 2001).

Theoretical reasoning for the relationship between trust and loyalty has identified three

ways in which trust enhances an individual’s commitment to a relationship (Ganesan and Hess,

1997). First, trust reduces the level of perceived risk associated with opportunistic behaviors by

the partner. Second, trust increases the confidence of the partner that short-term inequities will be

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resolved over a long period. Finally, trust reduces the transaction costs in an exchange

relationship. On this basis we advance the following hypothesis:

Hypothesis 7. Brand trust has a positive association with hotel brand loyalty.

The preceding discussion articulates the hypothesized relationships among the five

constructs investigated in this study. As previous literature has identified, established brand

loyalty antecedents such as service quality, perceived value, and brand trust summarize the

consumer’s experience with the hotel brand and lay an important foundation for cultivating

brand loyal relationships. The significance of the service consumption experience is supported

by research in the domains of service (Berry, 2000) and hotel brand management (So and King,

2010), which suggests that once consumers have had experience with the brand, this experience

becomes the overwhelming determinant for subsequent brand evaluations. As the previous

discussion suggests, factors inherent in the service offering—including price, service

environment, and service delivery—primarily determine the consumer’s assessment of these

service constructs.

However, the social identity and CBI literature (Ahearne et al., 2005; Donavan et al.,

2006; He et al., 2012; Papista and Dimitriadis, 2012) suggests that CBI also affects consumers’

judgment and evaluation of the brand. For this reason, consumer evaluation of the service

experience-related factors is proposed to be enhanced by consumers’ strong attachment to the

brand as a result of CBI. In addition, CBI is a psychological factor that signifies the intertwined

relationship between the consumer and the brand (Mael and Ashforth, 1992) and is therefore

expected to influence brand loyalty directly. Therefore, on the basis of the previous discussion,

as well as Baron and Kenny’s (1986) logic of mediation, we propose the following hypothesis:

15

Hypothesis 8. Service quality, perceived value, and brand trust partially mediate the effect of

customer hotel brand identification on hotel brand loyalty.

3. Method

The quantitative method used to test the research hypotheses included the development of

a survey questionnaire to measure customers’ perceptions of hotel brands. The rationale for the

selection of the survey method was three-fold. First, survey research involves a structured and

pre-designed questionnaire that is effective in eliciting specific and primary information from

respondents (Malhotra et al., 2008). Second, the use of the survey method facilitates examination

of factors and relationships that are not directly measurable (Hair et al., 2003) and that are the

focus of this study (e.g., CBI and perceived value). Finally, prior researchers have extensively

used the survey method to examine consumer brand loyalty (e.g., Brodie et al., 2009; Chaudhuri

and Holbrook, 2001; Sirdeshmukh et al., 2002), making the method appropriate for this study.

The survey instrument was compiled using measurement items generated from the

literature. Table 1 presents the source and description of the scales. These measurement items

had been validated in previous studies producing high Cronbach’s alpha and, therefore, were

considered appropriate for this study. Item wording was slightly modified to reflect the context

of this study. The use of existing scales ensured the reliability and validity of the measurement.

To measure the five constructs of interest, the sample of this study was drawn from a

panel of consumers who had expressed interest in participating in research projects. The national

database contains demographic, lifestyle, and purchasing data on consumers from Australia and

is a comprehensive online membership portal with over 500,000 members. In drawing the

sample, we used a qualifying question to ensure that only individuals who had traveled

domestically or internationally in the past 12 months participated in the survey. This study

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adopted a systematic random sampling method, whereby the market list firm was instructed to

calculate a sample interval to result in a list of 2,500 potential respondents. An invitational e-

mail with a click-through survey link was distributed to these respondents to encourage them to

participate in the survey. Respondents who agreed to participate were asked to indicate a hotel

brand that they had most recently used. The hotel brand name was then auto-populated to each

question for the respondents to indicate the extent to which they agreed or disagreed with the

items with respect to the indicated brand on a seven-point Likert-type scale (1 = strongly

disagree, 7 = strongly agree) as well as a semantic differential scale. Over a two-week data

collection period, 252 of the 2,500 potential respondents completed the survey, resulting in a

response rate of approximately 10%. Forty five cases were eliminated from the sample owing to

incomplete responses, leaving a total of 207 usable surveys. The sample size was above the

acceptable level for SEM models containing five or fewer constructs (Hair et al., 2006), and

therefore was considered appropriate.

4. Results

Female respondents accounted for 65% of the sample, while male respondents

represented 32%, and the remaining 3% did not indicate their gender. Ten percent of the

respondents were under the age of 30, with 39% between the ages of 30 and 50, and 51% over

the age of 50. In terms of annual income, 20% of the sample earned under AUD20,000, 37%

earned between AUD20,000 and AUD50,000 and 43% earned over AUD50,000. All hotel

brands indicated by the 207 respondents were classified into various categories on the Global

Hotel Chain Scales of Smith Travel Research (2012), with 48.79% being luxury or upper upscale

hotel brands (e.g., Shangri-La, Marriott, Hilton, Sheraton, Sofitel), 33.33% being upscale and

17

upper midscale (e.g., Holiday Inn, Mercure, Rydges), and the remaining 17.87% being midscale

and economy (e.g., Best Western, Quality Inn).

As the majority of the indicated brands were international luxury and upscale hotel

brands, the examination of CBI was deemed appropriate given the potential for higher levels of

symbolic meaning with respect to luxury and upscale hotel brands. The research data were

analyzed through structural equation modeling (SEM) according to the two-step procedure

recommended by Anderson and Gerbing (1988), with an initial examination of the measurement

model followed by testing the hypothesized structural relationships among the five constructs. In

addition, analysis tested the mediating effects of service quality, perceived value, and brand trust.

To assess nonresponse bias, we compared early respondents (top 5%) with late

respondents (bottom 5%) on the demographic variables (e.g., age, gender, and income) and the

measurement items (Armstrong and Overton, 1977). The chi-square tests indicate no significance

differences (α = .05) between early and late respondents in terms of respondent characteristics. In

addition, the t tests results show that all measured items were not significantly different (α = .05)

between early and late respondents. Therefore, nonresponse bias was not evident in this study.

4.1 Measurement model

To evaluate the performance of the measurement model, we conducted a confirmatory

factor analysis (CFA) with the five constructs measured in this study using AMOS 18.0 through

maximum likelihood estimation. As this estimation method relies on data normality, the

distribution of the collected data was examined. Normality is attributed to both skewness and

kurtosis. While skewness tends to impact analysis of means, it is kurtosis that severely influences

tests of variances and covariances (Byrne, 2009; DeCarlo, 1997), which is the basis for SEM.

Therefore, the kurtosis of all items was evaluated. According to West et al. (1995), a rescaled

18

value of greater than 7 is indicative of early departure from normality. Using this threshold as a

guide, an inspection of the kurtosis values produced by AMOS suggests that no item to be

substantially kurtotic, therefore satisfying the assumption of maximum likelihood estimation of

SEM. The measurement model resulted in a significant chi-square value of 316.65 (df =179, p

< .05), which is highly sensitive to sample size. However, the ratio of the chi-square to degrees

of freedom (χ2/df = 1.77) is below the recommended cutoff point of 3 (Bagozzi and Yi, 1988).

Overall the measurement model achieved good fit (GFI = .87, CFI = .97, TLI = .96, NFI = .93,

and RMSEA = .06), as Table 1 shows. To check for item cross-loadings, we examined the

modification indices of the measurement model and the results indicate that no substantial cross-

loadings were evident.

Insert Table 1 about here

In addition, the validity and reliability of each scale were examined. Convergent validity

was evidenced with statistically significant (p < .01) item factor loadings (Anderson and Gerbing,

1988). As indicated in Table 1, standardized factor loadings for all 21 items achieved the

suggested threshold of .70 (Hair et al., 2006). The t-values for all standardized factor loadings

were greater than 2.57 (Netemeyer et al., 2003), suggesting that they are significant indicators of

their respective constructs (p < .01) and providing support for convergent validity.

Discriminant validity of the measured constructs was tested in two ways. First, the test

suggested by Fornell and Larcker (1981) was conducted to compare the correlations of the

factors with the square root of the average variance extracted for each of the factors.

Discriminant validity can be established if the square root of the average variance extracted for

each one of the factors is greater than the correlations among the factors. As Table 2 shows, the

square root of the average variance extracted for each factor is greater than its correlations with

19

other factors, providing evidence for discriminant validity. Second, all pairs of constructs were

analyzed in two-factor CFA models (Anderson and Gerbing, 1988), where each model was

estimated twice, with one constraining the correlation between the constructs to be one and the

other allowing free estimation of the parameter. This procedure resulted in ten comparisons of

the constrained and unconstrained measurement models. For each combination, a chi-square

difference test was performed to check whether the constrained model is significantly worse than

unconstrained model. According to Bagozzi and Phillips (1982), discriminant validity is

achieved if a significantly lower chi-square value is obtained for the model in which the

correlation is not constrained to unity. As Table 3 indicates, the analysis shows that all

combinations resulted in a significantly higher value (χ2 > 3.84 at α = 5%) for the constrained

model, providing evidence of discriminant validity (Jöreskog, 1971).

Insert Table 2 about here

Insert Table 3 about here

Scale reliability was evaluated with Cronbach’s coefficient alpha and average variance

extracted (AVE). As Table 1 indicates, all five factors achieved the recommended level of

construct reliability (α > .70) (Hair et al., 2006), with Cronbach’s alpha values ranging from .91

to .94. Furthermore, the AVE of all constructs achieved the .50 cutoff recommended by Fornell

and Larcker (1981), demonstrating sufficient indicator reliability. Overall, the preceding

statistical tests provide strong empirical support that scales were valid and reliable measures of

their respective constructs.

As this study collected information via the same method (i.e., self-administered online

surveys), common method variance may introduce spurious relationships among the constructs.

Various techniques have been proposed to assess common method variance (e.g., Harman's

20

single-factor test, CFA test, and the marker variable technique), each having its advantages and

limitations (cf. Malhotra et al., 2006). In this study, a CFA test was used to examine whether a

single factor can account for all of the variance in the data (e.g., Baldauf et al., 2009; Mossholder

et al., 1998). The analysis was conducted in a CFA with all 21 items loading onto a single

common factor. Using a chi-square difference test, we compared the results of the common

factor model with the CFA results of the proposed measurement model, which included the five

latent factors. The results show that the proposed measurement model fits significantly better

than the common factor model (Δ χ2 = 1713.028, df = 10, p < .0001). The results of the analysis

indicate that common method variance was not a major issue in this study.

4.2 Structural model

The overall structural model was then tested using AMOS 18.0 with maximum likelihood

estimation, with the three service evaluation constructs assumed to be correlated because they

summarize various interrelated aspects of hotel consumer evaluation of a service offering. The

results presented in Table 4 indicate a good model fit (χ² = 316.65, p < .05, df = 179, χ²/df = 1.77,

GFI = .87, CFI = .97, NFI = .93, TLI = .96, and RMSEA = .06). The structural path coefficients

suggest that of the seven hypothesized paths tested, only one path was not significant (i.e., H1:

CBI→BL). Thus, with the exception of H1, the seven paths are supported. Table 4 presents

results of hypotheses testing with beta weights of the hypothesized paths and model fit statistics.

Insert Table 4 about here

4.3 Testing rival models

While the literature review argues for the relationships hypothesized in this study, it also

acknowledges alternative perspectives. Therefore, although the proposed structural model

indicates a good model fit, we also examined rival models. Testing theoretically rival or

21

competing models is recommended to rule out equivalent or even better fitting models

(MacCallum and Austin, 2000; Thompson, 2000). This approach is a stronger test than a slight

modification of a single theory and is particularly relevant in SEM, where a model can

demonstrate acceptable fit but where acceptable fit alone is not sufficient to show that another

model will not fit equally well or better (Hair et al., 2006).

As the literature suggests that identification may form as a result of prior consumer

associations with the brand, such as service transactions (Underwood et al., 2001), service

quality (He and Li, 2011), and trust (Keh and Xie, 2009), a theoretically logical possibility is that

service quality, perceived value, and brand trust could be modeled as antecedents to CBI. Thus, a

rival model was estimated with the three traditional loyalty antecedents affecting CBI, which in

turn affects brand loyalty. This model also indicates an acceptable model fit (χ2 = 419.61, df =

182, χ2/df = 2.31, GFI = .84, CFI = .94, NFI = .90, TLI = .93, and RMSEA = .08). However, the

results show that service quality and perceived value are not significant in predicting CBI.

To further compare the two competing models, we conducted a chi-square difference test,

which indicated that the rival model fits significantly worse than the originally proposed model

(Δ χ2 = 102.412, df = 3, p < .001). Furthermore, parsimony fit measures such as the Akaike

information criterion (AIC) (Akaike, 1987) and the Browne–Cudeck criterion (BCC) (Browne

and Cudeck, 1989) were used to assess model parsimony and fit (Rust et al., 1995). In the

original model, AIC is 420.648 and BCC is 433.083, while in the rival model AIC is 517.606 and

BCC is 529.323, suggesting that the original model is preferable over the rival model. On the

basis of these statistical tests, as well as the theoretical argument presented in the literature

review, the rival model is rejected in favor of the proposed structural model.

22

4.4 Testing for mediation

To test the mediation effects of SQ, PV, and BT hypothesized as linking the independent

variable (i.e., CBI) and dependent variable (i.e., BL), four alternative structural models were

estimated following the test procedures outlined by James, Mulaik and Brett (2006) and

subsequently adopted by Grace and Weaven (2010) and Baldauf et al. (2009). Prior to the

examination of a mediating effect, investigation of the four conditions under which the existence

of mediation can be supported is essential. The first condition is satisfied if the independent

variable (i.e., CBI) directly influences the mediators (SQ, PV, and BT). The second condition is

met if the mediators directly influence the dependent variable (BL). The results of Model 1

(Table 5) indicate that both conditions have been satisfied. The third condition suggests that the

independent variable (CBI) must significantly influence the dependent variable (BL). In line with

prior research (Baldauf et al., 2009; Grace and Weaven, 2010), this condition was investigated in

a model with a direct path from the independent variables (CBI) to the dependent variable (BL),

without the presence of mediators (i.e., Model 2). As Table 5 indicates, the path was significant

(p < .001), therefore satisfying this condition. The fourth condition is met if, after including the

paths from the independent variables (CBI) to the mediators (SQ, PV, and BT), the direct paths

from the independent variable (CBI) to the dependent variable (BL) become nonsignificant (full

mediation) or reduce their strength (partial mediation). Using the results presented in Table 5, a

comparison of Model 2 and Model 4 indicates that, after the inclusion of the mediators (SQ, PV,

and BT), the direct path from the independent variable (CBI) to the dependent variable (BL)

became nonsignificant, thus satisfying the fourth condition.

The final test for full mediation involves testing whether the full mediation model (Model

1, with paths from CBI going through SQ, PV, and BT to BL) produces a better fit than the no-

mediation model, where the paths from SQ, PV, and BT to BL were not included, thus

23

eliminating any indirect effect (Model 3). A chi-square difference test was conducted to

determine which model achieves the best fit. The results indicate that the no-mediation model

(Model 3) was significantly worse than the full mediation model (Δχ2

= 100.3, Δ df = 2, p < .001),

lending support for the full mediation model (Model 1). To test for partial mediation, the full

mediation model (Model 1) was compared with the partial mediation model that includes both

direct and indirect paths (Model 4). The results show that Model 4 is not significantly better than

Model 1 (Δχ2

= 2.66, Δ df = 1, p > .05). As the path from CBI to BL was found not significant

after including SQ, PV, and BT, the full mediation model was supported. Figure 1 graphically

depicts the results of the final model.

Insert Table 5 about here

Insert Figure 1 about here

5. Discussion and implications

This study contributes to the hospitality management literature by demonstrating that CBI

has an indirect effect on hotel brand loyalty through customer judgments of service quality,

perceived value, and brand trust. These findings suggest that when customers identify with a

hotel brand, they tend to have a more favorable judgment of the brand’s overall service

excellence or superiority (i.e., service quality) and overall assessment of the utility (i.e.,

perceived value), and to exhibit a greater level of willingness to rely on that brand (i.e., brand

trust). Such positive hotel brand evaluation in turn determines customers’ loyalty level with the

brand. While effective management and delivery of the service encounter, as well as other

factors such as pricing and customer expectations, are thought to primarily influence enhanced

levels of hotel brand evaluations, the results of this study show that CBI also enhances customer

evaluations of the brand. When customers identify with the brand psychologically, they develop

24

a strong attachment to the brand, which in turn results in favorable evaluation of the brand and its

offerings.

While the testing of rival models in this study suggests that the consumer’s positive

evaluation of service quality and perceived value mediates CBI’s relationship to brand loyalty, a

reasonable expectation is that CBI, as an exogenous variable, can also be cultivated through

experience with a product or brand. That is, CBI may result from initial product use. In this sense,

identification develops or consolidates over time through multiple encounters or experiences that

reinforce continued loyalty to the brand.

Although researchers have noted that customers in the sports context exhibit loyalty to a

sports team regardless of its weaknesses and strengths (e.g., sports fans continue to support a

team despite its performance on the field) (Parker and Stuart, 1997), the results of our study

suggest that in a hotel environment, brand loyalty is significantly influenced by factors relating

to consumer evaluation of service experience. Specifically, after including the three loyalty

antecedents of service quality, perceived value, and brand trust, we found the direct effect of CBI

on brand loyalty to be insignificant. Such a contrast in findings could be a result of the study

context. For example, supporting a sports team is a more personal relationship that often starts at

a very early age and is often associated with family, friends, the community within which one

lives, and general daily life during the sporting season. These referent groups, or external

influences, continually reinforce the relationship, thereby strengthening loyalty to the team.

Furthermore, opportunities to support another sports team or brand may be limited. In contrast, a

relationship with a hotel brand can be considered less enduring, and it is not continually

reinforced by close referent groups. In addition, alternative brand relationships (i.e., competition)

are plentiful in a hotel context. As a result, hotel consumers’ loyalty is predicated on factors that

25

are linked to the performance of a service offering. The results of our study do suggest that when

consumers positively identify with the hotel brand, their evaluations of the hotel brand are

enhanced, which contributes to formation of loyalty.

This study has several implications for hotel brand loyalty management. From a

theoretical perspective, this research extends the current understanding of hotel brand loyalty by

testing the role of CBI in the process of loyalty development. Results provide evidence that the

social identity perspective of brand loyalty can be incorporated into the service consumption

experience approach to provide a more complete picture of hotel brand loyalty. While the results

indicate that a strong CBI is insufficient to establish hotel brand loyalty in isolation, CBI does

represent a significant factor that exerts an indirect influence on brand loyalty through

customers’ brand evaluation, highlighting the significance of CBI in the enhancement of hotel

brand evaluation and, subsequently, hotel brand loyalty development.

From a practical point of view, this study’s results suggest that in building and

maintaining strong customer loyalty, hotel brand managers must create positive customer

perceptions of the service consumption experience. These brand management aspects represent

the essential functional elements that hotels must satisfy to meet customer expectations.

However, the primary insight this study provides is afforded to marketers of hotel brands.

Introducing CBI into the hotel brand loyalty discussion emphasizes the importance of consumer

identification with a brand to consumers’ evaluation of the hotel experience, which ultimately

drives hotel brand loyalty. In this study, the evaluation of CBI was relatively low, suggesting that

consumers currently do not identify strongly with hotel brands. This finding is not surprising

given the recent explosion of global hotel brands, which has led to confusion in the marketplace

resulting from a lack of differentiation (King et al., 2011). Therefore, the results of this study

26

may demonstrate the need to develop brands that communicate distinctly different brand

experiences that consumers find meaningful, especially as every incremental improvement in a

consumer’s CBI potentially results in a subsequent improvement of the service evaluation,

thereby strengthening loyalty towards the hotel.

From this perspective, in addition to striving for service excellence hotel marketers must

make a concerted effort to develop a distinct hotel brand image or brand identity that resonates

with customers but also clearly distinguishes that brand from its competitors. Identity

distinctiveness can attract customers to develop identification with the brand. In developing

marketing programs and campaigns that foster strong CBI, marketers strengthen brand loyalty as

a result of CBI’s influence on customer service evaluation. Creating a clear, unique identity that

target customer segments desire allows a sustainable differentiation of the offering and helps to

enhance customers' identification with the brand (Baumgarth and Schmidt, 2010). The rise of the

boutique hotel, which is often described as unique and personalized, suggests that hotel

consumers are looking for a point of differentiation in their hotel selection. Further evidence of

this phenomenon is the emergence of global hotel brands in the boutique market to capture more

market share. Examples include Starwood’s Element or W Hotels and Marriott’s partnership

with Ian Schrager to develop Edition Hotels.

While customer relationship management (CRM) has been promoted in the hospitality

literature as an interactive relationship between the customer and firm (Piccoli et al., 2003), the

hotel industry's operationalization of CRM practices seems to entail mainly loyalty programs that

offer incentives, which are considered more transactional than relationship-based. These pricing

incentives are arguably the lowest level of relationship marketing practices and are vulnerable to

competitor promotions (Berry, 1995). They do not fully deliver on the CRM proposition, and

27

hotel firms should consider adopting a higher level of relationship marketing practices that foster

social bonding, such as regular communications with customers (Berry, 1995) and socially

oriented programs (Rust et al., 2000). The results of this study highlight the need for CRM

strategies in hotels to be not activity-based (i.e. loyalty program) but an organizational

philosophy that champions uniqueness and distinctiveness, both of which are important to CBI

development (Bhattacharya and Sen, 2003; Stokburger-Sauer et al., 2012).

6. Limitations, future research, and conclusion

This study contributes to the tourism and hospitality literature by demonstrating the direct

effect of CBI in enhancing customer evaluation of the consumption experience with hotel brands,

as well as its indirect effect on the development of hotel brand loyalty. However, an evaluation

of the findings must acknowledge several limitations. First, like previous related studies, this

research uses cross-sectional data, which means that the results can suggest only an association

between the constructs under investigation rather than a causal relationship. Second, the

relatively low response rate may affect the validity of the study’s findings. Third, while the

survey items were intended to measure hotel customers’ perceptions of the five constructs at the

brand level, some customers may have had difficulty in making a clear distinction between the

brand level and property level, resulting in measurement error. Fourth, as the sample products

were not all in the luxury or upper upscale categories, the lower category hotels may have

affected the results because a customer is less likely to have high CBI with an economy hotel

brand. Finally, it is not clear whether the same findings will emerge if survey respondents were

differentiated according to frequency of travel/hotel stay or usage, which should be the subject of

future investigations.

28

The study suggests a number of possible areas for future research. First, qualitative

research methods can also be used to investigate CBI with hotel brands and explore customers’

experiences with hotel brands that they identify with. For example, in-depth interviews could

offer further insight into the reason CBI does not directly engender brand loyalty in a hotel

setting yet does so in cars. We have provided a possible explanation—that products with high

symbolic value are more likely to generate CBI—but qualitative evidence is needed for

verification. Similarly, focus group interviews with hotel customers may clarify how identified

customers differ from less identified customers in their evaluations of the hotel brand. Second,

future research could test the conceptual model across business and leisure travelers, as well as

investigate the effect of frequency of stay on CBI levels. Results could expand brand managers’

comprehension of the conditions under which CBI is more likely to occur. Third, because the

current study considers brand loyalty as a unidimensional construct comprising both attitudinal

and behavioral aspects, future research might investigate the effects of CBI and hotel evaluations

on different aspects of brand loyalty (i.e., cognitive, affective, conative, and action-oriented),

thereby offering additional insight into the impacts of CBI on different facets of the loyalty

construct. Fourth, future research should investigate CBI for higher end brands such as Ritz

Carlton, where consumer association with the brand relates to an individual’s self-concept.

Finally, a worthwhile undertaking for future investigation would be to identify determinants of

hotel brand identification.

In conclusion, this study investigates the role of CBI in establishing loyal hotel customers.

Results provide an important step in the advancement of customer relationship management

knowledge from both theoretical and practical perspectives. From a theoretical perspective, this

study has addressed a relatively unexplored area in the brand management literature and has

29

provided empirical evidence of CBI’s influence on customer evaluation of hotel brands, which

contributes to brand loyalty. From a practical point of view, the findings of this study suggest

that, in addition to maintaining an operational focus, hotel firms must develop strong customer

identification with the brand to further enhance brand loyalty.

30

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Table 1

Results of the measurement model

Construct/Item SL CR SR AVE

Customer-Brand Identification (CBI), adapted from Mael and Ashforth (1992) .93 .72

7-point agreement scale anchored by 7 (strongly agree) and 1 (strongly disagree)

Mean = 3.60, SD = 1.35

Thinking about [insert brand name], please indicate how much you agree or disagree with

each of the following statements.

1. When someone criticizes this brand, it feels like a personal insult. .79 NA

2. I am very interested in what others think about this brand. .72 11.30

3. When I talk about this brand, I usually say we rather than they. .86 14.18

4. This brand’s successes are my successes. .94 15.99

5. When someone praises this brand, it feels like a personal compliment. .91 15.25

Service Quality (SQ), adapted from Cronin, Brady, and Hult (2000) .94 .84

Mean = 5.43, SD = 1.05

As a customer, how would you rate the level of service quality you receive from [insert brand

name]?

1. “Poor” 1 2 3 4 5 6 7 “Excellent” .86 NA

2. “Inferior” 1 2 3 4 5 6 7 “Superior” .93 19.51

3. “Low Standards” 1 2 3 4 5 6 7 “High Standards” .96 20.51

Perceived Value(PV), adapted from Sirdeshmukh et al. (2002) .93 .77

Mean = 5.22, SD= .98

Please evaluate [insert brand name] on the following factors:

1. For the prices you pay for staying with this hotel, would you say staying at this hotel is a

“Very poor deal” 1 2 3 4 5 6 7 “Very good deal” .86 NA

2. For the time you spent in order to stay with this hotel, would you say staying at this hotel is

“Highly unreasonable” 1 2 3 4 5 6 7 “Highly reasonable” .86 16.25

3. For the effort involved in staying with this hotel, would you say staying at this hotel is

“Not at all worthwhile” 1 2 3 4 5 6 7 “Very worthwhile” .91 17.69

4. How you would rate your overall experience with this hotel?

“Extremely poor value” 1 2 3 4 5 6 7 “Extremely good value” .87 16.74

Brand Trust (BT), adapted from Chaudhuri and Holbrook (2001)

7-point agreement scale anchored by 7 (strongly agree) and 1 (strongly disagree) .91 .73

Mean = 5.44, SD = .95

Thinking about [insert brand name], please indicate how much you agree or disagree with

each of the following statements.

1. I trust this brand. .83 NA

2. I rely on this brand. .76 12.68

3. This is an honest brand. .92 16.78

4. This brand is safe. .90 16.47

Brand Loyalty (BL), adapted from Zeithaml et al. (1996)

7-point agreement scale anchored by 7 (strongly agree) and 1 (strongly disagree) .94 .76

Mean = 5.13, SD = 1.08

Thinking about [insert brand name], please indicate how much you agree or disagree with

each of the following statements.

45

1. I would say positive things about this brand to other people. .92 NA

2. I would recommend this brand to someone who seeks my advice. .97 27.04

3. I would encourage friends and relatives to do business with this brand. .91 21.69

4. I would consider this brand my first choice to buy services. .77 14.83

5. I would do more business with this brand in the next few years. .77 14.86

Goodness-of-fit statistics: χ² = 316.65 (p < .05, df = 179), χ²/df = 1.77, GFI = .87, CFI = .97, NFI = .93, TLI = .96 and

RMSEA = .06.

Notes: SL = standardized loadings; CR = critical ratio; SR = scale reliability; AVE = average variance extracted.

46

Table 2

Discriminant validity analysis from CFA

CBI SQ PV BT BL

CBI .85

SQ .31 .92

PV .39 .67 .88

BT .50 .65 .68 .85

BL .41 .62 .63 .65 .87

The bold diagonal elements are the square root of the variance shared between the constructs and

their measures. Off diagonal elements are the correlations between constructs.

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Table 3

Discriminant validity analysis from chi-square difference tests

Comparisons Unconstrained Model Constrained Model Chi-Square Difference Discriminant Validity

χ² df χ² df Δχ² Δdf

CBI SQ 21.37 19 55.18 20 33.81 1 Yes

PV 33.01 26 55.13 27 22.12 1 Yes

BT 57.22 26 81.35 27 24.13 1 Yes

BL 101.83 34 120.03 35 18.20 1 Yes

SQ PV 31.95 13 44.99 14 13.04 1 Yes

BT 32.36 13 57.17 14 24.81 1 Yes

BL 62.22 19 75.85 20 13.63 1 Yes

PV BT 33.23 19 52.27 20 19.04 1 Yes

BL 83.18 26 93.57 27 10.39 1 Yes

BT BL 82.08 26 99.92 27 17.84 1 Yes

48

Table 4

Structural model results – Overall model

Dependent Variables Independent Variables Hypotheses Beta Weight Result

Brand Loyalty Customer Brand Identification H1 .10 N/S

Brand Loyalty Service Quality H3 .26** Sig.

Brand Loyalty Perceived Value H5 .22** Sig.

Brand Loyalty Brand Trust H7 .28** Sig.

Service Quality Customer Brand Identification H2 .31*** Sig.

Perceived Value Customer Brand Identification H4 .39*** Sig.

Brand Trust Customer Brand Identification H6 .50*** Sig.

Goodness-of-fit statistics: χ² = 316.65 (p < .05, df = 179), χ²/df = 1.77, GFI = .87, CFI = .97, NFI = .93, TLI

= .96, and RMSEA = .06

* Significant p < .05.

** Significant p < .01.

*** Significant p < .001.

49

Table 5

Mediation analysis results

Fit Estimates χ² df Δχ² Δdf CFI GFI TLI NFI RMSEA

Model 1 319.31 180 Base comparison .97 .87 .96 .93 .06

Model 2 101.83 34 .96 .91 .95 .95 .10

Model 3 419.61 182 100.3 2 .94 .84 .93 .90 .08

Model 4 316.65 179 2.66 1 .97 .87 .96 .93 .06

Model 1,

Full Mediation

Model 2,

IV affects DV

Model 3,

No Mediation

Model 4,

Partial Mediation

CBI → SQ .31*** – .32*** .31***

CBI → PV .39*** – .40*** .39***

CBI → BT .50*** – .51*** .50***

CBI → BL – .40*** .43*** .10 8

SQ → BL .25** – – .26**

PV → BL .23** – – .22**

BT → BL .34*** – – .28**

R²

SQ .09 – .10 .09

PV .15 – .16 .15

BT .25 – .26 .25

BL .52 .16 .18 .52

Two-tailed significance testing.

*Significant p < .05.

** Significant p < .01.

*** Significant p < .001.

50

Figure 1. Results for Final Structural Model