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Casino Loyalty: The Influence of Loyalty Program, Switching Costs, and Trust

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Customer loyalty has become a strategic goal to increase brand value and profitability. This study develops and tests a model of loyalty to understand the relative effects of loyalty program benefits (as positive barriers) and switching costs (as negative barriers) on emotional commitment and loyalty behaviors in the casino context. The findings showed that trust, perceived switching cost, and emotional commitment to the casino are more likely to influence relational or emotional outcomes such as word of mouth and voluntary partnership whereas the loyalty program is more likely to influence transactional outcomes such as repeat visitation and time spent in the casino. The emotional commitment served as a partial mediator in the model. The study has theoretical implications for understanding the loyalty process and practical implications for improving loyalty program effectiveness.
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DOI: 10.1177/1096348014550922
published online 24 September 2014Journal of Hospitality & Tourism Research
Seyhmus Baloglu, Yun Yin Zhong and Sarah Tanford
Trust
Casino Loyalty: The Influence of Loyalty Program, Switching Costs, and
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Journal of Hospitality & Tourism Research, 201X, Vol. XX, No. X, Month 2014, 1 –23
DOI: 10.1177/1096348014550922
© 2014 International Council on Hotel, Restaurant and Institutional Education
1
CASINO LOYALTY: THE INFLUENCE
OF LOYALTY PROGRAM, SWITCHING
COSTS, AND TRUST
Seyhmus Baloglu
Yun Yin Zhong
Sarah Tanford
University of Nevada, Las Vegas
Customer loyalty has become a strategic goal to increase brand value and profitability.
This study develops and tests a model of loyalty to understand the relative effects of
loyalty program benefits (as positive barriers) and switching costs (as negative
barriers) on emotional commitment and loyalty behaviors in the casino context. The
findings showed that trust, perceived switching cost, and emotional commitment to
the casino are more likely to influence relational or emotional outcomes such as
word of mouth and voluntary partnership whereas the loyalty program is more likely
to influence transactional outcomes such as repeat visitation and time spent in the
casino. The emotional commitment served as a partial mediator in the model. The
study has theoretical implications for understanding the loyalty process and practical
implications for improving loyalty program effectiveness.
KEYWORDS: frequency program; gaming; loyalty; switching costs; trust; commitment
INTRODUCTION
Emotional commitment has shown to be a core element that separates true
loyalty from “spurious” loyalty (Baloglu, 2002, Tanford & Baloglu, 2013). In
today’s highly competitive hospitality and travel industry, loyalty reward pro-
grams, as a relationship marketing tool, are extensively used by companies in
hope of cultivating customers’ emotional commitment and enhancing their life-
time value. According to the 2013 Colloquy Loyalty Census report, the U.S.
hospitality and travel industry had 881 million loyalty program memberships in
2012, a 14% growth compared with the previous year. Within this industry, the
gaming sector had more than 150 million memberships, the third largest cate-
gory after airline and hotel sectors. Between 2006 and 2012, the gaming sector
has seen a 94% membership increase. The growth trend is projected to sustain
550922JHTXXX10.1177/1096348014550922Journal Of Hospitality & Tourism ResearchBaloglu et al. / CASINO LOYALTY
research-article2014
Authors’ Note: This research was supported by a grant from the Caesars Foundation.
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2 JOURNAL OF HOSPITALITY & TOURISM RESEARCH
because of the accelerating economic recovery and gaming legalization in more
states (Berry, 2013).
Despite the proliferation of casino loyalty programs in recent years, existing
literature on hospitality and travel loyalty programs mostly focus on the hotel
industry (Dekay, Toh, & Raven, 2009; H. S. Hu, Huang, & Chen, 2010; Mattila,
2006; Melancon, Noble, & Noble, 2011; Tanford, 2013; Tanford, Raab, & Kim,
2011, 2013). Only a couple of studies have been done in the gaming industry
(Hendler & Latour, 2008; Palmer & Mahoney, 2005). Because the success of
loyalty programs greatly hinges on their design and service sector characteris-
tics, which can vary significantly across different industries (McCall & Voorhees,
2010; Meyer-Waarden & Benavent, 2009), it is reasonable to argue that the find-
ings on hotel loyalty programs might not generalize to the gaming sector.
Furthermore, even within the gaming industry, different visitor groups can have
very different perceptions of casino loyalty programs. A qualitative study found
that local patrons, as compared with tourists, are more emotionally connected to
the slot club, which is a common type of loyalty program in the gaming industry
(Hendler & Latour, 2008). The local segment (relatively a low-end market) has
attracted more attention from some Las Vegas casinos because high rollers (pre-
mium players) are found to be more costly to maintain as loyalty program mem-
bers (Hendler & Latour, 2008; Lucas, Kilby, & Santos, 2002). As a 2012 Las
Vegas Convention and Visitor Authority report showed, gaming is the third most
popular leisure activity for Las Vegas local residents, following eating-out and
movies. The significance of local casino patrons surely warrants further study to
understand their loyalty formation and the effectiveness of loyalty programs
among them.
While perceived loyalty program benefits are suggested to work as a positive
reinforcer to encourage customers’ repeat behaviors, switching costs can engen-
der a similar result based on customers’ perceived negative consequences asso-
ciated with switching providers (Henderson, Beck, & Palmatier, 2011). Such
costs can include customers’ search costs for service alternatives and learning
costs resulting from leaving their familiar service environment. The higher the
switching costs are, the more likely customers will stay with their existing ser-
vice providers (Jones, Mothersbaugh, & Beatty, 2000). In addition to perceived
loyalty program benefits and switching costs, customers’ trust of service provid-
ers has been shown to be a strong predictor of emotional commitment and loy-
alty behaviors (Bowen & Shoemaker, 1998; Morgan & Hunt, 1994; Sui &
Baloglu, 2003; Wilkins, Merrilees, & Herington, 2010). Most of the prior
research has investigated the effect of either one or two of the three predictors on
customer loyalty, but none has examined their joint effects on loyalty.
To fill the existing research gaps, this study investigates the effects of per-
ceived loyalty program benefits, switching costs, and trust on emotional com-
mitment and loyalty behaviors. In this respect, the study combines both
dependence- and dedication-based relationships on relational (word of mouth
and voluntary partnership) and transactional (frequency of visits and time spent
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Baloglu et al. / CASINO LOYALTY 3
in casino each visit) outcomes of loyalty. The results of this study contribute to
the gaming literature and industry practices in two major ways. First, the study
findings shed some light on the effectiveness of casino loyalty programs among
local patrons, an important but understudied segment for casinos. Second, by
incorporating trust, switching cost, and perceived loyalty program benefits, the
study provides a more comprehensive picture in depicting the key psychological
factors for local casino patrons’ loyalty formation.
LITERATURE REVIEW
Behavioral Loyalty and Emotional Commitment
The value of customer loyalty lies in the notion of “customer lifetime
value,” which measures the net present value as a result of the projected spend-
ing over the life of purchase with a company minus the costs to retain and
provide service to the customer (Shoemaker & Lewis, 1999). The calculation
of customer lifetime value involves the estimation of customers’ retention rate,
spending, costs, and discount rate (Shoemaker & Kapoor, 2008; Shoemaker &
Lewis, 1999). In a much cited study, Reichheld and Sasser (1990) reported that
a 5% increase in customer retention would result in 25% to 125% profit
increase for companies. The potential substantial profit growth is not only
attributed to loyal customers’ transactional behaviors including repeated pur-
chase, cross-buying, and insensitivity to price increases but also their non-
transactional behaviors such as referral behaviors (Jain & Singh, 2002;
Shoemaker & Lewis, 1999). Kumar et al. (2010) explicitly pointed out that the
calculation of lifetime value of loyal customers should include, in addition to
the financial value of customers’ purchase behaviors, the value of their referral
behaviors (positive word-of-mouth [WOM] driven by extrinsic motives),
influencer behaviors (positive WOM behaviors driven by intrinsic motives),
and knowledge behaviors via feedback. Shoemaker and Kapoor (2008) posited
that besides economic exchanges, customers and companies also trade intan-
gible resources in their relationship. While customers reveal their preferences
and give feedback to companies, the latter can provide more personalized ser-
vice to the former and even integrate solutions to customer problems into their
service delivery systems. Such an advanced structural bond between custom-
ers and companies can help create a competitive advantage that cannot be
easily imitated by competitors (Shoemaker & Kapoor, 2008).
Despite the undebatable importance of customer loyalty, it is a complex con-
struct to measure. Historically, loyalty was measured using behavioral indicators
such as repeat purchase, repeat visit, share of wallet, positive WOM, partnership
behaviors, and the like (Mattila, 2006; Meyer-Waarden & Benavent, 2009; Sui
& Baloglu, 2003; Wirtz, Mattila, & Lwin, 2007). Some researchers further cat-
egorized these behavioral indicators into transactional behaviors (e.g., repeat
purchase) and relational behaviors (e.g., positive WOM), as they argued that a
customer’s lifetime value goes beyond his or her financial value, and that
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4 JOURNAL OF HOSPITALITY & TOURISM RESEARCH
citizenship behaviors are a very important part of customer equity (Lacey, 2009;
Melancon et al., 2011). While transactional behaviors are mainly concerned
with customers’ financial exchanges with a company, relational behaviors are
“the non-financial, social behaviors toward the organization that result from
relational exchanges” (Melancon et al., 2011, p. 345). In this study, visit fre-
quency and number of hours per visit are included as indicators for transactional
behaviors (Sui & Baloglu, 2003), and WOM and partnership are included as
relationship behaviors (Lacey, 2009; Melancon et al., 2011). Although spend-
ing-related variables such as average spending or share of wallet are also impor-
tant indicators for transactional behaviors (Leenheer, Van Heerde, Bijmolt, &
Smidts, 2007; Meyer-Waarden & Benavent, 2009; Wirtz et al., 2007), they are
not reported in this study for two reasons: (a) research has suggested that casino
visitors are reluctant to reveal their gaming budget and spending, making spend-
ing-related variables less desirable self-report indicators (Hendler & Latour,
2008) and (b) though customers’ share of gaming budget data were collected in
this study, the estimation was rendered inaccurate because of too many missing
values.
Customer loyalty measured simply as behaviors has its limitations. The major
criticism of the behavioral measurement of loyalty is that it does not distinguish
loyal customers who genuinely bond with a brand from those who stay in the
relationship because of inertia or lack of alternatives (Li & Petrick, 2008).
Emotional commitment is often considered as a key component of attitudinal
loyalty, which separates true loyalty from spurious loyalty (Baloglu, 2002;
Tanford & Baloglu, 2013). Emotional commitment, also referred as affective
commitment, is defined as “the extent to which a customer identifies with and
feels a positive attachment for a partner” (Fullerton, 2011, p. 92). Emotional
commitment is a subdimension of commitment, which is defined as a custom-
er’s enduring desire to commit in a valued relationship (Mattila, 2001, 2006;
Morgan & Hunt, 1994). It is distinguished from calculative commitment, another
subdimension of commitment, which focuses on customers’ cognitive benefit
perceptions (Mattila, 2001, 2006). Emotional commitment has been found to be
a significant predictor of customers’ loyalty behaviors in various service con-
texts including financial service, auto repair service, hotels, restaurants, and
casinos (Barsky & Nash, 2002; Bowen & Shoemaker, 1998; Fullerton, 2011;
Mattila, 2001, 2006; Sui & Baloglu, 2003; Tanford et al., 2013). Bowen and
Shoemaker (1998) found that emotional commitment positively affected cus-
tomers’ product use and voluntary partnership behaviors in the luxury hotel con-
text. Emotional commitment was negatively associated with defection for
customers who stayed at both full-service and limited-service hotels in research
by Tanford et al. (2013). The relationship between emotional commitment and
loyalty was also demonstrated in the restaurant industry (Mattila, 2001) as well
as other service industries (e.g., Fullerton, 2011). In the casino context, Sui and
Baloglu (2003) concluded that emotional commitment is a positive determinant
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Baloglu et al. / CASINO LOYALTY 5
of multiple behavioral loyalty variables, including positive WOM, cooperation
behaviors, time spent in casino, proportion of visits, and ancillary product usage.
In summary, the literature consistently shows a positive relationship between
emotional commitment and loyalty, leading to the following hypothesis:
Hypotheses 1a to 1d: Emotional commitment is positively related to loyalty behav-
iors (positive word of mouth, voluntary partnership behaviors, visit frequency, and
time spent per visit).
The Role of Trust
Customers do not establish emotional commitment with a company in a vac-
uum. Morgan and Hunt (1994) pointed out that trust is key determinant of com-
mitment in relationship marketing. They argued that “Because commitment
entails vulnerability, parties will seek for trustworthy partners” (Morgan &
Hunt, 1994, p. 24). Trust, also referred to as confidence, is defined as customers’
confidence in service providers’ integrity, reliability, and quality (Bowen &
Shoemaker, 1998; Morgan & Hunt, 1994; Paul, Henning-Thurau, Gremler,
Gwinner, & Wiertz, 2009). There is ample empirical support for the positive
relationship between trust and emotional commitment. For example, Bowen and
Shoemaker (1998) concluded that trust is a strong predictor of affective commit-
ment in the luxury hotel context. This finding was corroborated in casino (Sui &
Baloglu, 2003) and general service (Fullerton, 2011) contexts.
In addition, trust can be a direct antecedent to loyalty behaviors. Sui and
Baloglu (2003) found that trust positively affected casino patrons’ positive
WOM and cooperation behaviors. Trust was a significant predictor of intentions
to revisit and spread positive WOM among luxury restaurant patrons in research
by Han and Jeong (2013). Brand trust has been shown to be a significant driver
of hotel brand loyalty, either directly (Lee & Back, 2010) or mediated through
brand attitude (Wilkins et al., 2010). It has also been contended that trust (confi-
dence) is a key relationship factor that positively contributes to customers’
repeat purchase intentions (Paul et al., 2009).
In summary, the literature indicates that trust is related to both emotional
commitment and loyalty behaviors, leading to the following hypotheses:
Hypothesis 2: Trust is positively related to emotional commitment.
Hypotheses 3a to 3d: Trust is positively related to loyalty behaviors (positive word of
mouth, voluntary partnership behaviors, visit frequency, and time spent per visit).
The Role of Loyalty Programs
In line with previous research (H. S. Hu et al., 2010; Leenheer et al., 2007),
loyalty program here is defined as a firm’s systematic marketing efforts to provide
incentives in hope of increasing customers’ attitudinal and behavioral loyalty.
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6 JOURNAL OF HOSPITALITY & TOURISM RESEARCH
Shoemaker and Lewis (1999) made specific distinction between a frequency pro-
gram and a loyalty program, holding that the former aimed at generating repeated
business transactions while the latter focused on fostering customers’ emotional
commitment.
Loyalty programs in different industries may vary drastically based on their tier
structure and reward structure including reward type, frequency, magnitude, and
framing (McCall & Voorhees, 2010). In the gaming sector, loyalty programs are
more than a complex discount scheme merely for boosting repeat visits. Casinos
commonly use customer relationship marketing tools to customize membership-
based loyalty programs, which focus on personalized communications and pro-
vide a mixture of hard and soft benefits to a group of preidentified customers
(Hendler & Latour, 2008; Lacey, 2009). While hard benefits are those tangible and
economic rewards such as free play, point accumulations, and free rooms or meals,
soft benefits are more socially and emotionally oriented and intend to provide
customers with a sense of recognition and special treatment (Bridson, Evans, &
Hickman, 2008). The soft benefits included in a casino loyalty program can be
exclusive events and tournaments as well as special amenities and services.
The effect of loyalty programs on emotional commitment is inconclusive in
existing literature. Leenheer et al. (2007) argued that loyalty programs can
enhance emotional commitment through psychological and sociological mecha-
nisms. Loyalty program members can have a strong group identity and feel pre-
ferred and special. Social exchange theory suggests that customers are likely to
reciprocate with service providers as a result of their appreciation of the rewards
and special treatment. Through such a process, the emotional bond between cus-
tomers and their service providers is eventually built (Henderson et al., 2011).
However, Melancon et al. (2011) empirically demonstrated that the impact of
loyalty programs on emotional commitment is contingent on the nature of
rewards they offer. Social rewards (soft benefits), which mainly promote cus-
tomers’ intrinsic enjoyment, significantly affect emotional commitment, while
economic rewards (hard benefits) do not. Research on hotel loyalty programs
suggests that emotional commitment is not strongly developed until members
reach higher tiers in the loyalty program (Tanford, 2013), and that lower tier
members may not differ from nonmembers in the degree of emotional commit-
ment (Tanford et al., 2011).
In the casino context, the extant two studies regarding loyalty programs show
mixed results. Hendler and Latour (2008) used the Zaltman Metaphor Elicitation
Technique (ZMET) to disclose casino patrons’ perception of the loyalty program
of a mega casino resort in Las Vegas. They found that local customers, as com-
pared with tourists, are more emotionally bonded with the casino loyalty program
(the slot club), though they tend to view the program benefits in an economic
light. Palmer and Mahoney (2005), on the other hand, discovered that all seg-
ments of loyalty program members have relatively low relationship commitment
(less than 4 points on a 7-point Likert-type scale). They therefore concluded that
casino loyalty programs have limited impact on customers’ commitment.
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Baloglu et al. / CASINO LOYALTY 7
Despite these inconclusive results, we hypothesize in this study that per-
ceived loyalty program benefits are positively related to emotional commit-
ment. This stance is taken because (a) soft benefits such as special treatment
and exclusive events are common elements in casino loyalty programs, and soft
benefits have shown to be a significant antecedent for emotional commitment
(Melancon et al., 2011) and (b) casinos have made substantial investment in
building sophisticated information databases, and are adept at customizing ben-
efits to their patrons based on the highly accurate information collected (Hendler
& Latour, 2008).
Hypothesis 4: Perceived loyalty program benefits are positively related to emotional
commitment.
In addition, prior research has shown that loyalty programs have a small posi-
tive yet significant effect on various loyalty behaviors including share of wallet,
visit frequency, average spending, and recommendation (Leenheer et al., 2007;
Mattila, 2006; Meyer-Waarden & Benavent, 2009; Wirtz et al., 2007). In the
hotel context, Mattila (2006) found that value-added benefits, rather than point
accumulations, can encourage customer loyalty behaviors such as positive
WOM and business referral. A more recent study by H. S. Hu et al. (2010) sug-
gested that the perceived value of hotel loyalty programs positively affects cus-
tomer loyalty behavior intention through the mediating effect of program loyalty.
The effect of loyalty program benefits on loyalty behaviors is based on the prin-
ciple of reinforcement (Bridson et al., 2008). Loyalty program benefits work as
an external motivation for customers to initiate their purchase intention and
behaviors (Henderson et al., 2011). Customers’ desirable behaviors are then
repeatedly rewarded and reinforced so that they eventually develop a strong
habitual pattern and a biased service preference:
Hypotheses 5a to 5d: Perceived loyalty program benefits are positively related to
loyalty behaviors (positive word of mouth, voluntary partnership behaviors, visit
frequency, and time spent per visit).
The Role of Switching Costs
Switching costs are one type of negative switching barrier (Han, Back, &
Kim, 2011). Switching barriers can be defined as “any factor which makes it
more difficult or costly for consumers to change providers” (Jones et al., 2000,
p. 261). While positive barriers such as loyalty program benefits focus on the
gains that customers get out of their relationship with their current service pro-
vider, negative switching barriers such as switching costs lock customers in
based on their perception of loss (Han et al., 2011). There are different types of
switching costs, including procedural (e.g. time and effort), financial, and rela-
tionship switching costs (Burnham, Frels, & Mahajan, 2003).
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8 JOURNAL OF HOSPITALITY & TOURISM RESEARCH
The link between switching costs and emotional commitment as well as loyalty
behaviors has been theoretically and empirically substantiated. Examining cus-
tomer loyalty in the cruise line industry, Li and Petrick (2008) explained the posi-
tive relationship between switching costs (referred as “investment size” in their
study) and emotional commitment applying the investment model in social psy-
chology. They empirically demonstrated that the more customers invested in their
service relationship, in other words, the more they could lose if a service relation-
ship ends, and the more likely they will be loyal to their service provider. Many
other empirical studies yielded similar results. Sui and Baloglu (2003) found that
switching costs (i.e., time and effort) significantly affected casino patrons’ emo-
tional commitment, and were also direct antecedents of patrons’ cooperation behav-
iors and proportion of visits to the casino of interest. In the general service context,
relationship switching cost and lost benefits significantly affected customers’ emo-
tional commitment in banking and hairstyling services (Jones, Reynolds,
Mothersbaugh, & Beatty, 2007). In addition, switching costs have been shown to be
significant determinants of customers’ intention to stay with the incumbent service
providers (Burnham et al., 2003; Vázquez-Carrasco & Foxall, 2006).
In summary, the literature shows that switching costs are related to both emo-
tional commitment and behavioral loyalty, leading to the following hypotheses:
Hypothesis 6: Switching costs are positively related to emotional commitment.
Hypotheses 7a to 7d: Switching costs are positively related to loyalty behaviors
(positive word of mouth, voluntary partnership behaviors, visit frequency, and
time spent per visit).
Loyalty Models
Several of the studies reviewed above combined one or more of the variables
of interest in structural models of customer loyalty. Only one of these (Sui &
Baloglu, 2003) involved casino loyalty. In that model, trust and switching costs
were antecedents of emotional attachment as well as direct predictors of loyalty
behaviors. Our model most closely follows this set of proposed relationships,
with the addition of a loyalty program variable. In the hotel domain, Bowen and
Shoemaker (1998) found that trust, switching costs, and hotel benefits influ-
enced behavioral loyalty (product use and voluntary partnership) through their
effects on emotional commitment. Brand trust influenced behavioral loyalty via
its impact on brand attitude, while service quality was an antecedent of both trust
and satisfaction in Wilkins et al.’s (2010) hotel study. In a model to predict
switching behavior, Tanford et al. (2013) found that affective (emotional) com-
mitment and switching costs mediated the effects of several hotel attributes on
likelihood to defect. Reward program membership influenced defection through
perceived reward program value for limited-service but not full-service hotels.
Perceived reward program value influenced program loyalty, which in turn
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Baloglu et al. / CASINO LOYALTY 9
predicted customer loyalty for hotels in research by H. S. Hu et al. (2010), but
that study did not consider trust, emotional commitment, or switching costs.
Proposed Model
Based on the above literature discussion, we propose that trust, switching
costs, and casino loyalty program benefits positively affect customers’ emo-
tional commitment, which mediates the effects of the three independent vari-
ables on loyalty behaviors defined as both relational (i.e., WOM, voluntary
partnership behaviors) and transactional (i.e., frequency of visits, hours per vis-
its) outcomes. Furthermore, we propose that the three independent variables will
have a direct and positive effect on loyalty behaviors. The conceptual model is
shown in Figure 1, on which the proposed hypotheses are indicated.
It is worth noting that the three independent variables are expected to be corre-
lated for two reasons. First, there is evidence suggesting that correlations exist
among trust, switching costs, and perceived loyalty programs. For example, Mattila
(2006) reported an association between apparent switching cost and hotel loyalty
programs, albeit low. Leenheer et al. (2007) also maintained that preexisting trust
of a brand can bias customers’ loyalty program enrollment and perception. Second,
the apparent correlations among independent variables need to be specified in order
to increase the structural model’s precision (Hair, Black, Babin, & Anderson, 2010).
METHODOLOGY AND DATA COLLECTION
Subjects and Procedure
The sample consisted of 262 casino customers at a local Las Vegas casino.
The clientele are mostly lower class African American and Hispanic residents
who live nearby. The casino has a hotel, restaurant, slots and table games, and a
Figure 1
Conceptual Model
Hours per Visit
Trust
Switching
Costs
Emoonal
Commitment
Frequency of
Visits
Loyalty
Program
(+) H1a-d
(+) H2
(+) H3a-d
(+) H5a-d
(+) H4
(+) H6
(+) H7a-d
Voluntary
Partnership
Word of
Mouth
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10 JOURNAL OF HOSPITALITY & TOURISM RESEARCH
sports book. Respondents were recruited by employees 24 hours a day during a
1-month period throughout the property and at a special event held for loyalty
program members. To ensure anonymity, surveys were placed in a sealed box
immediately after completion and collected weekly by the researchers.
Instrument
English and Spanish versions of the questionnaire were created, using a
back-translation process to establish equivalence. Six multi-item constructs
were measured with 28 survey items. The loyalty program measure consisted
of ratings of seven loyalty program attributes (e.g., room and meal offers, free
slot/table play) on a scale from 1 (poor) to 7 (excellent), adapted for casinos
from a scale used by Mattila (2006) and Tanford et al. (2011) to measure per-
ceived value of hotel loyalty programs. Loyalty antecedents included mea-
sures of emotional commitment (e.g., “I am emotionally attached to <casino
name>”), switching costs (e.g., “The cost in time and effort of changing to a
different casino are high for me”), and trust (e.g., “I trust the management of
<casino name>”), rated on 7-point Likert-type scales from strongly disagree to
strongly agree. Behavioral intentions were measured with ratings of WOM
(e.g., “I would recommend <casino> to other people”) and voluntary partner-
ship (e.g., “If I saw an idea that I liked at another casino, I would share that
idea with the <casino’s> management or employees”), also on 7-point Likert-
type scales. All the loyalty antecedents were taken from Baloglu’s (2002)
study of casino loyalty. The switching cost measure was supplemented with
items adapted for the casino context from Jones et al. (2007) to capture social
and lost benefit switching costs in addition to procedural. There were two
behavioral indices: frequency of visitation per month in 5-day intervals, and
hours spent in the casino per visit in an open-ended format from 1 to 24. The
survey included categorical questions on loyalty program membership, casino
game preferences, and demographics.
Data Analysis
Data analysis consisted of several stages. Structural equation modeling, using
AMOS 20, was conducted to test the proposed measurement and structural
model simultaneously. Multiple measures were used to assess the fit between
model and data, such as normed chi-square (χ2/df), Tucker–Lewis index (TLI),
critical function index (CFI), and root mean square error of approximation
(RMSEA) that have been suggested for single-group analysis in the literature
(Browne & Cudeck, 1993; Byrne, 1998; Hair et al., 2010; L. Hu & Bentler,
1995). Fit indices and modification matrices were used to improve the fit of the
model. To establish internal consistency and validity of the constructs, compos-
ite reliabilities and validity measures were obtained, and nomological, conver-
gent, and discriminant validity of the constructs were assessed.
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Baloglu et al. / CASINO LOYALTY 11
RESULTS AND DISCUSSION
Sample Profile
As summarized in Table 1, nearly all respondents (97.3%) were reward club
members. Most respondents have either become a member in the past 6 months
(29.9%) or have been members for more than 2 years (45.4%). Fifty-seven per-
cent were male and most respondents were married (49.2%), followed by singles
(40.6%). The majority belonged to middle-age categories (30-50 years old) and
the $39,000-$45,000 income group. Most respondents had education of high
school or less (49.4%), some college (23.2), or college degree (14.6%).
Measurement Model, Reliability, and Validity
Table 2 displays the complete text of all multi-item construct scales, as well
as the standardized regression coefficients, which are also shown in Figure 2
along with the structural model results All individual t values were significant
with no offending estimates in the measurement model, such as negative error
variances, standardized coefficients exceeding or very close to 1.0, or very large
standard errors associated with any estimated coefficients (Reisinger & Turner,
1998). In addition, we did not detect any sign of multicollinearity that would
adversely affect the accuracy of results, such as absence of offending estimates,
input covariance matrix being not positive definite, and positive correlations
turning into significant negative coefficients (“wrong” signs of the coefficients)
because of the presence of suppression and/or multicollinearity (Grewal, Cote,
& Baumgartner, 2004).
The initial run of the model showed good fit indices (normed χ2 = 2.76; TLI =
0.91; CFI = 0.92; RMSEA = 0.082) except for RMSEA as it was slightly above
the suggested threshold of 0.08. The modification indices were examined and
two of the suggestions were judged to be reasonably justified. First, there was a
strong error covariance between “I am emotionally attached to the casino” and
“I feel like a part of the family as a customer of the casino,” both measuring
emotional commitment. Another error covariance was added between the two
items of switching cost, “inconvenient to go to other casinos” and “high cost of
switching to another casino.” After these modifications, the fit indices were
acceptable based on the suggested threshold values (normed χ2 = 2.60; TLI =
0.92; CFI = 0.93; RMSEA = 0.078). It should be noted that these respecifica-
tions did not change significance and magnitude of the structural coefficients or
relationships.
The study assessed convergent and discriminant validity based on the guide-
lines provided by Jöreskog and Sörbom (1996) and Hair et al. (2010). The multi-
item constructs—trust, switching cost, emotional commitment, WOM, and
voluntary partnership—all had very high composite reliability coefficients and
excellent convergent properties. The average variance extracted (AVEs) for trust
(0.84), switching cost (0.63), emotional commitment (0.76), WOM (0.90), and
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12 JOURNAL OF HOSPITALITY & TOURISM RESEARCH
voluntary partnership (0.77) were all good and above the 0.50 threshold. Some
of the AVE estimates slightly exceeded the squared correlations between pairs of
constructs, particularly between independent and dependent variables (Table 3).
To assess the discriminant validity statistically, confirmatory factor analysis was
conducted by treating these constructs as one construct, that is, setting the cor-
relations among them to unity. The unity model exhibited very poor fit indices.
A chi-square discriminant validity test was also performed by comparing
Table 1
Demographic Profile of Respondents
Variables Categories Valid NFrequency Percentage
Club membership No 261 7 2.7
Yes 254 97.3
Length of membership Less than 6 months 251 75 29.9
6 months to 1 year 23 9.2
1-2 years 39 15.5
More than 2 years 114 45.4
Gender Female 138 57.0
Male 242 104 43.0
Age (years) 21-30 256 39 15.2
31-40 61 23.8
41-50 82 32.0
51-60 45 17.6
61-70 22 8.6
>70 years 7 2.7
Marital status Single 254 103 40.6
Married 125 49.2
Divorced/widowed/
separated
26 10.2
Ethnicity African American 252 92 36.5
Caucasian 63 25.0
Hispanic 76 30.2
Other 21 8.3
Education Some high school 233 40 17.2
High school grad 75 32.2
Trade/technical 20 8.6
Some college 54 23.2
College degree 34 14.6
Graduate degree 10 4.3
Income ($) <15,000 250 54 21.6
15,000-30,000 57 22.8
39,000-45,000 70 28.0
46,000-60,000 31 12.4
>60,000 38 15.2
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Baloglu et al. / CASINO LOYALTY 13
Table 2
Confirmatory Factor Analysis Results and Complete Text of Scale Items
Scale/Item
Standardized
Regression
Weights
Rated on a 7-point scale from 1 (strongly disagree) to 7 (strongly agree):
Word of Mouth (WOM; Baloglu, 2002)
WOM2: I would recommend (casino) to other people .967
WOM1: I tell other people positive things about (casino) .966
WOM3: I take pride in telling people about my experiences at (casino) .907
Voluntary Partnership (VP; Baloglu, 2002; Bowen & Shoemaker, 1998)
VP1: I am more likely to tell management or employees about problems that
occur in (casino) than other casinos
.888
VP2: If I saw an idea I liked at another casino, I would share that idea with
(casino’s) management
.868
Emotional Commitment (EC; Baloglu, 2002)
EC5: Although there are other casinos nearby, I still like coming to (casino) .950
EC3: I enjoy visiting (casino) .927
EC2: I feel like a part of the family as a customer of (casino) .873
EC4: Time spent at (casino) is important to me .863
EC1: I am emotionally attached to (casino) .739
Trust (TR; Baloglu, 2002; Bowen & Shoemaker, 1998)
TR4: When employees at (casino) say they will do something, I am sure it will
get done
.947
TR3: If I ask a manager or employee a question, I feel they will tell me the
truth
.947
TR2: I am sure the service that I get at (casino) will be the same every time
I visit
.915
TR1: I trust the management of (casino) .896
TR5: The communication I receive from (casino) is believable
Switching Costs (SC; Baloglu, 2002; Jones, Reynolds, Mothersbaugh, & Beatty, 2007)
SC4: If I switched to another casino, I might not get the personal recognition
that I get at (casino)
.885
SC6: If I switched, I might not receive the service I am accustomed to .863
SC3: If I switched to another casino, I might not get the same benefits I get
at (casino)
.861
SC2: The cost in time and effort of changing to a different casino are high
for me
.747
SC5: If I switched, I might lose my current reward club status .705
SC1: It would be very inconvenient for me to go to other casinos .689
Reward program benefits rated on a scale from 1 (poor) to 7 (excellent):
Loyalty Program (LP; Mattila, 2006; Tanford, Raab, & Kim, 2011)
LP2: Ability to earn points that can be redeemed for purchases at the casino .890
LP4: Tournaments and special events .869
LP1: Free slot/table play .860
LP3: Free room and meal offers .842
LP5: Drawings, raffles and giveaways .811
LP6: Point multipliers and bonus points .788
LP7: Special amenities and services .732
Note: The name of the casino was inserted for (casino). All coefficients are significant at p < .001.
Source of items provided in parentheses.
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14 JOURNAL OF HOSPITALITY & TOURISM RESEARCH
unconstrained and constrained model, which was found to be very distinct. To
provide more rigorous support, the unity tests were performed on one pair of the
constructs at a time, which included eight separate tests. Each unity model
showed worse fit measures compared with the unconstrained model and their
chi-square indifference tests were significantly worse (p < .0001). These results
provided support for discriminant validity of the measures in the model.
The findings also showed strong support for nomological validity, which is
confirmation of significant correlations and paths between the constructs in the-
oretically predicted ways (Malhotra, 1999; Smith & Barclay, 1997). Most path
coefficients were significant (p < .05) and in the expected direction on theoreti-
cal grounds.
Structural Model and Hypotheses Testing
Most proposed relationships appear tenable as summarized in Figure 2. Both
trust (0.70, p < .001) and switching cost (0.27, p < .001) were positively related
to emotional commitment (Hypotheses 2 and 6), and the effect of trust was much
greater than that of switching cost. The perceptions of the loyalty program did
Figure 2
Model Results
.91
.97
.97
.89
.87
.89
.86
.71
.86
.75
.69
.89
.95
.95
.92
.90
.73
.79
.82
.87
.84
.89
.86
.28***
.17**
.31***
.14*
.19*
.17**
.50***
.41***
.27***
.70***
TR
SC
EC
LP
WOM
VP
Visit
Time
.44***
LP7
LP1 LP2 LP3 LP4 LP5 LP6
SC1
TR1
WOM1
VP1
TR2
TR3
TR4
TR5
SC2
SC3
SC4
WOM3
WOM2
VP2
SC5
SC6
EC1 EC2
1
EC3 EC4 EC5
.74 .87 .93 .86.95
Note: All coefficients are standardized coefficients. TR = Trust; SC = Switching Cost;
EC = Emotional Commitment; LP = Loyalty Program; WOM = Word of Mouth; VP =
Voluntary Partnership; Visit = Visit Frequency; Time = Time Spent in Casino Each Visit.
*p < .05. **p < .01. ***p < .001.
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Baloglu et al. / CASINO LOYALTY 15
not have a significant impact on emotional commitment, but positively influ-
enced frequency of casino visits (0.17, p < .01; Hypothesis 5c) and number of
hours spent in the casino (0.28, p < .001; Hypothesis 5d). Trust had a direct and
positive impact on WOM (0.44, p < .001; Hypothesis 3a) and voluntary partner-
ship (0.19, p < .05; Hypothesis 3b). Likewise, switching cost was directly and
positively related to WOM (0.14, p < .01; Hypothesis 7a) and voluntary partner-
ship (0.31, p < .001; Hypothesis 7b). The effects of emotional commitment on
WOM (0.41, p < .001; Hypothesis 1a), voluntary partnership (0.50, p < .001;
Hypothesis 1b), and frequency of casino visits (0.17, p < .001; Hypothesis 1c)
were significant and positive.
Emotional Commitment as Mediator
Direct, indirect, and total effects of exogenous (independent) variables on
endogenous (dependent) variables were examined to delineate the overall pat-
tern of the model. Summary results appear in Table 4. Trust had a larger indirect
effect (0.35) than its direct effect (0.19) on voluntary partnership whereas the
indirect effect of switching cost (0.11) was smaller than its direct effect (0.14) on
voluntary partnership. After taking the indirect effects of trust on voluntary part-
nership through emotional commitment (0.35), the total effect of trust (0.54)
became as strong as that of emotional attachment (0.50) in influencing partner-
ship. On the other hand, based on their total effects, trust (0.73) influenced
WOM more strongly than emotional commitment (0.40). The findings suggest
that emotional commitment partially mediates the effects of trust and switching
cost on behavioral outcomes of loyalty in the model tested in this study. Unlike
trust and switching cost, it influenced both relational (WOM and voluntary
Table 3
Reliability and Validity Results
CR AVE Trust
Emotional
Commitment
Word of
Mouth
Voluntary
Partnership
Switching
Cost
Loyalty
Program
Trust 0.964 0.843 0.843
Emotional
Commitment
0.944 0.772 0.828** 0.772
Word of Mouth 0.963 0.897 0.861** 0.856** 0.897
Voluntary
Partnership
0.871 0.771 0.785** 0.852** 0.893** 0.771
Switching Cost 0.913 0.638 0.613** 0.679** 0.677** 0.757** 0.638
Loyalty
Program
0.940 0.693 0.025* 0.027* 0.032* 0.034* 0.128** 0.693
Note: The diagonal values are average variance extracted (AVE) values (in bold). The values below
diagonal are squared correlations. The unity and chi-square indifference tests were conducted for
the underlined squared correlations as they slightly exceeded their associated AVE value(s). CR =
composite reliability.
*p < .05. **p < .001.
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16 JOURNAL OF HOSPITALITY & TOURISM RESEARCH
partnership) and transactional (frequency of visits) behavioral outcomes of loy-
alty in the model.
The results supported the proposed partial mediation model. The results
empirically confirm the proposition of Bendapudi and Berry (1997) and the
findings of Sui and Baloglu (2003) that dependence, trust, and emotional
attachment would have different effects on various behavioral outcomes of
loyalty. This study, on the other hand, finds that the loyalty program would
have such a differential effect as well. It appears that trust, perceived switch-
ing cost, and emotional commitment to the casino are more likely to influence
relational or emotional outcomes such as WOM and voluntary partnership,
whereas the loyalty program is more likely to influence transactional outcomes
such as repeat visitation and time spent in the casino. The main goal of the
casinos is to have customers pay repeat visits to their casinos. In this respect,
emotional commitment and loyalty program influence the frequency or repeat
visitation to the same extent, suggesting that loyalty programs are as important
as emotional commitment in increasing the number of customer visits to local
casinos.
As the dependence on and trust to the casino increases, the emotional com-
mitment of customers to the casino augments. Therefore, maintaining or devel-
oping emotional attachment requires casinos to deal with and cultivate both trust
and dependence. This finding was consistent with previous research (Geyskens,
Steenkamp, Scheer, & Kumar, 1996; Sui & Baloglu, 2003) even after including
the effects of loyalty program in the model. In addition, trust appears to be more
influential than the dependence relationship. Therefore, casinos should aim
much of their effort on building trust, which will ultimately enhance emotional
commitment.
Table 4
Standardized Direct and Indirect Effects
From To Direct Indirect Total
Trust Emotional Commitment .702 .702
Word of Mouth .444 .286 .730
Voluntary Partnership .192 .352 .544
Frequency of Visits .120 .120
Switching Cost Emotional Commitment .273 .273
Word of Mouth .143 .111 .254
Voluntary Partnership .307 .137 .444
Loyalty Program Frequency of Visits .174 .174
Hours Spent in Casino .281 .281
Emotional Commitment Word of Mouth .408 .408
Voluntary Partnership .502 .502
Frequency of Visits .172 .172
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Baloglu et al. / CASINO LOYALTY 17
Interestingly, the loyalty program had no impact on WOM or willingness to
help the casino. Its influence on visit frequency and time spent in the casino,
unlike positive WOM and willingness to help the casino, can easily be broken by
competitive tactics. The casino customers are more likely to recommend and
spread positive comments when they have higher levels of trust and emotional
commitment to the company. When indirect effects were taken into account,
WOM and cooperation (voluntary partnership) were more strongly influenced
by emotional commitment and trust compared with switching costs. These
results show that trust is more important than switching cost in terms of influ-
encing long-term strategic behavioral outcomes of loyalty (i.e., WOM and coop-
eration) and future business. This suggests that casinos should augment
emotional commitment and, therefore, a feeling of trust toward their
organizations.
The findings demonstrate that loyalty programs, as compared with switching
costs, are more likely to affect customers’ transactional behaviors rather than
relational behaviors. Loyalty program’s perceived performance was the only
variable influencing length of time spent in the casino. It seems that in-house
activities to reward points and recognition help keep customers on the premises.
Though loyalty programs offer both financial and social benefits, customers
were not emotionally affected by them. This finding lends support to Hendler
and Latour’s (2008) observation that customers tend to view loyalty program
benefits in the economic perspective. Switching costs, including procedural,
relational, and financial costs, significantly affect emotional commitment and
relational behaviors. Yet switching costs is not as strong a predictor as trust in
predicting emotional commitment, which is able to predict both transactional
and relational loyalty behaviors. That said, it is concluded that emotional com-
mitment of customers to the casino is the most critical attitudinal dimension of
relationship marketing. Casinos need to cultivate trust and customers’ depen-
dence to the casino (switching costs). Simply relying on loyalty programs to
foster true loyalty might not be the most effective strategy for casinos.
CONCLUSION
This study investigated the antecedents and consequences of relationship
marketing for casinos by integrating both constraint-based (switching cost, loy-
alty program) and dedication-based (trust, emotional commitment) customer
relationship variables. The study makes contributions to existing literature from
several perspectives. The study finds that emotional commitment of customers
to the casino plays a critical role as a partial mediator between other attitudinal
variables and behavioral outcomes of relationship marketing. It is the most
important dimension of relationship marketing as it directly influenced both
relational and transactional outcomes of loyalty. The study provided further sup-
port for external validity of some empirical linkages found for casinos and
extended previous loyalty or relationship marketing models by integrating
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18 JOURNAL OF HOSPITALITY & TOURISM RESEARCH
perceived performance of loyalty or frequent player programs into a theoretical
framework and empirical model of service loyalty and relationship marketing.
The study also provides practical implications to casinos. The findings dem-
onstrate that emotional attachment of customers to the casino is the most critical
attitudinal dimension of relationship marketing as it positively influenced all
behavioral outcomes of loyalty. In addition, it appears that the loyalty program
is particularly significant to financial benefit because of its impact on casino
visit frequency and time spent in the casino, which are critical to increase the
spending on other revenue centers and revenue growth of a casino.
As compared with mega casino resorts, which mainly cater to tourists, emo-
tional commitment may be even more important for local casinos, which rely on
repeat customers. In practice, local casinos intuitively recognize this with their
marketing and loyalty programs. For example, Station Casinos uses the slogan
“We love locals” and posts it on billboards and has it embossed on t-shirts, caps,
and mugs. They changed the name of their players’ card from “Boarding Pass”
to “my Boarding Pass” and use terms such as “my offers” and “my rewards” for
loyalty program offers and redemption. Boyd Gaming has a B-Connected social
network that provides opportunities for reward program members to communi-
cate via social media, thereby building a community that extends beyond the
casino experience. Building a sense of community serves to increase attachment
to the program, which has been identified as a key element of reward program
success (McCall & Voorhees, 2010). The customized recognition practices by
casinos and individualized recognition by employees also attempt to cultivate
emotional commitment.
Considering local residents are more “savvy” players who are knowledgeable
of different reward structures and game payouts, together with the intense com-
petition landscapes of gaming operations in Las Vegas (Hendler & Latour,
2008), these segments of customers could easily free themselves from depen-
dency relationships by going to competitors offering better or similar deals. If
casinos only focus on loyalty programs and switching costs to maintain relation-
ships, customers can easily be lost to the competition. In this respect, the trust
and emotional commitment of customers, which are harder to change by the
competition, are more influential than their dependence on the casino. Building
trust and emotional commitment seems to be more effective to take advantage of
the long-term benefits of relationship marketing.
To build trust, casinos should enlist customers in their planning and market-
ing efforts. Trust was directly related to both WOM and voluntary partnership.
By providing opportunities to engage in WOM through endorsements and testi-
monials, this relationship can be strengthened. Many casinos use the traditional
method of customer surveys to gather feedback, but this falls short of voluntary
partnership. Casinos could form an advisory board or conduct focus groups con-
sisting of program members representing all tier levels (not just the highest tier)
to increase the level of trust. Reward program benefits could be provided for
these activities to increase the switching costs that are related to both voluntary
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Baloglu et al. / CASINO LOYALTY 19
partnership and WOM. Trust can also be built by empowering employees to
respond to customer requests or issues directly rather than having to get permis-
sion from management. For example, employees could be authorized to provide
complimentary items (meals, show tickets, etc.) to customers who are dissatis-
fied for some reason. According to the service–profit chain, providing employ-
ees with the tools to serve customers will increase employee satisfaction and
productivity, leading to customer satisfaction and ultimately loyalty (Heskett,
Jones, Loveman, Sasser, & Schlesinger, 2008).
This study investigated an important and neglected population of casino-
goers. The sample contained mostly African American and Hispanic custom-
ers in lower income brackets. Overall, their spending per visit was low,
producing an average of $25 in win (i.e., revenue) for the casino. They had a
high level of trust (mean = 5.55 on the 7-point scale), which translated into
WOM but not visitation. It is possible that they lacked the means to visit fre-
quently even if they possessed attitudinal loyalty. Therefore, the loyalty pro-
gram benefits were an effective incentive because they had financial value
(free meals, slot play, etc.). However, emotional commitment is an essential
element in the loyalty equation, since competitors could provide equally
valuable rewards. One way to build emotional commitment would be to pro-
vide events, entertainment, and activities that appeal to cultural/ethnic char-
acteristics of casino guests. This particular casino caters to its prevalent
demographic by hosting events such as weekly Latin Karaoke and Saturday
Soul Sessions. Trust can be established by providing excellent customer ser-
vice and a safe environment, which Yi and Busser (2008) identified as deter-
minants of repatronage and recommendation among Las Vegas locals. Since
lower income customers cannot afford many luxuries, making them feel pam-
pered through superior service and personal recognition will also help build
emotional commitment.
Most participants were working-class residents who lived and worked in the
neighborhoods near the casino. Although their budgets were limited, they visited
regularly and close to half had been loyalty program members for more than 2
years. When designing loyalty programs for local casinos, it is more important
than ever to consider customer lifetime value. This may require a different busi-
ness model than casinos that serve tourists who visit infrequently but spend
more per visit. When assigning reward tiers, management of local casinos should
use measures other than spend per visit, which does not capture the true value of
long-term customers with modest budgets. Casinos often “demote” players who
have been less active or spend less for a period of time. This could erode emo-
tional attachment among regular customers who may be temporarily unable to
visit because of job or financial constraints. Management should evaluate cus-
tomers’ activity over an extended time period, and reach out to those customers
who become inactive. Providing personal communication accompanied by an
offer could strengthen the emotional bond and motivate the customer to return to
the casino.
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20 JOURNAL OF HOSPITALITY & TOURISM RESEARCH
This study has several limitations. First, it was conducted at a local Las Vegas
casino, whose major clientele were lower class local residents. Such a sample
frame represents both a strength and weakness in this study. Local residents are
a major source for local casinos and an important supplement market to tourist
casinos in filling gaps in low-demand seasons. Their perceptions and behaviors
are expected to be very different from those of tourists. For example, local cus-
tomers, usually with lower socioeconomic class, may place more weight on the
economic benefits of loyalty programs. Hendler and Latour (2008) also pointed
out that as compared with tourists, local residents are well versed with payouts
of various games and reward structures of different loyalty programs. Despite
the importance and uniqueness of this particular local segment, few studies have
tried to understand its members’ perceptions of casino loyalty program and their
loyalty behaviors. This study sheds unique insights on the psychological mecha-
nisms with this particular segment, though the results might not be generalizable
to other customer segments or other geographic locations. Nevertheless, it serves
as a starting point for future research in comparing this segment with other
important segments such as the more affluent high rollers or nonmembers of
loyalty programs. Second, this study only focused on loyalty program cardhold-
ers. The findings only depicted the effects of loyalty program benefits, as com-
pared with trust and switching costs, on affective commitment and loyalty
behaviors among this group of customers. Future research can investigate the
moderating effect of loyalty program membership on the relationship between
switching costs/trust and customer loyalty. Third, although gaming budget ques-
tions to reflect “share of wallet” were included in our survey, they were nonus-
able due to excessive missing data. Fourth, the findings are limited to
unidirectional influences among the variables in the model (a recursive
modeling).
Given the importance of trust in the model, future research should focus on
antecedents of trust in the hotel casino context. For example, security is impor-
tant for casinos and could be considered an antecedent of trust. With the advent
of online gaming, the importance of this dimension is magnified for casino oper-
ations. Loyalty for online gaming venues is an untapped area for future research,
where trust in website security may prove to be a critical element. Evaluating
this and other antecedents would provide strategic and practical insights for
hotel casinos to cultivate trust and directly and indirectly influence other rela-
tional and behavioral outcomes. These outcomes would enable operators to
manage relationship marketing programs more effectively and build customer
loyalty.
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Submitted January 25, 2014
Accepted July 17, 2014
Refereed Anonymously
Seyhmus Baloglu, PhD (e-mail: seyhmus.baloglu@unlv.edu), is Professor and Harrah
Distinguished Chair, in the William F. Harrah College of Hotel Administration at the
University of Nevada, Las Vegas. Yun Yin Zhong (zhongy3@unlv.nevada.edu) is a
PhD candidate in the William F. Harrah College of Hotel Administration at the University
of Nevada, Las Vegas. Sarah Tanford, PhD (e-mail: sarah.tanford@unlv.edu), is
Associate Professor and Director of MS in Hotel Administration Program in the William
F. Harrah College of Hotel Administration at the University of Nevada, Las Vegas.
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The content of this 3rd edition marketing research textbook is practical and up to date and is based on an applied and managerially focused approach. Australian an New Zealand research and examples have been thoroughly intergrated into every chapter. <br /