A Cross-National Comparison of the Role of Habit
in Linkages between Customer Satisfaction and Firm Reputation
and their Effects on Firm-Level Outcomes in Franchising
Rajiv P. Dant
Professor of Marketing
Helen Robson Walton Centennial Chair in Marketing Strategy
Division of Marketing and Supply Chain Management
Price College of Business
The University of Oklahoma
307 West Brooks
Norman, OK 73019-4001, USA
Voice: (405) 325-4675
Email: [email protected]
Brinja Meiseberg
Assistant Professor
Institute of Strategic Management
Westfälische Wilhelms-Universität Münster
Leonardo-Campus 18
48149 Münster, Germany
Voice: +49 (251) 83-31959
Email: [email protected]
Presented at the
Economics and Management of Networks Conference
(EMNet 2013)
(http://emnet.univie.ac.at/)
Robinson Hotel and University Ibn Zohr
Agadir, Morocco
November 21-23, 2013
Abstract
First, designing marketing strategies for global operations is a challenging task due to the complexity inherent in successful customer relationship management and brand image creation across
diverse markets. However, profiting from substantial economic growth outside the Western
economies requires firms to develop a more profound understanding of tackling these issues in
cross-national contexts. Second, much debate has been offered on strategies applicable to trigger
customer behavior beneficial to the firm, especially, customer loyalty and word-of-mouth referral. However, research on mechanisms that drive such behavior has mainly focused on intentional
processes, which ignores the fact that frequently performed behaviors become automatic over
time. Then, ignoring habit-persistency effects may result in systematically overestimating the
influence of other relevant drivers of customer behavior, e.g. of satisfaction or firm reputation.
Against this background, this study contributes to the literature by providing an extension to the
prevalent consumer loyalty theorizing through integrating the concepts of habit creation, customer satisfaction and reputation and by generating cross-national insights into their effects on central firm outcomes in terms of loyalty and word-of-mouth. Applying multigroup structural equation modeling, the analyses draw on two global fast food brands’ consumer data collected in the
BRIC and their domestic US market. The results document essentially diverging nomological
linkages among the concepts under study across nations and provide important intuitions on how
global brands strategize their brand positioning best.
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A Cross-National Comparison of the Role of Habit
in Linkages between Customer Satisfaction and Firm Reputation
and their Effects on Firm-Level Outcomes in Franchising
1. Introduction
The problem is when that fun stuff becomes the habit.
– Michelle Obama, 2011
First, designing marketing strategies for global operations is a challenging task due to the complexity inherent in successful customer relationship management and brand image creation across
diverse markets. However, profiting from substantial economic growth outside the Western
economies requires firms to develop a more profound understanding of tackling these issues in
cross-national contexts. Especially the BRIC economies (Brazil, Russia, India and China) draw
attention as they increasingly develop into key players in the global economy due to their rising
middle classes and sheer market size (40% of the world population). For environments like these
that are marked by high economic potential and increasing competition, researchers and practitioners alike emphasize the need for relationship management practices to develop customer contacts, acquire market knowledge, and make intelligent use of data and technology to enhance customer loyalty and organizational performance (Ganesan et al. 2009; Reinartz et al. 2004).
Second, much debate has been offered on strategies applicable to trigger customer behavior beneficial to the firm, especially, loyalty and word-of-mouth referral. However, research on mechanisms that drive such behavior has mainly focused on intentional processes, assuming that loyalty
starts with positive cognition and affect towards a product or brand and ends with intention and
commitment towards repurchase (Oliver 1997; Reichheld 1996). However, this assumption may
not be applicable to continued behaviors or behaviors characterized by frequent purchase (e.g.,
food consumption), as it ignores that frequently performed behaviors become habitual and automatic over time. Then, ignoring habit-persistency effects results in systematically overestimating
the influence of other relevant drivers of customer behavior, e.g. of satisfaction or firm reputation. Accordingly, current marketing strategies frequently focus on developing and enhancing
customer satisfaction and firm reputation to increase loyalty, although the relative effects of habit, satisfaction and reputation are not well understood, especially not in cross-national contexts.
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Against this background, this study contributes to the literature by providing an extension to the
prevalent loyalty theorizing through integrating the concepts of habit, satisfaction and reputation
and by generating cross-national insights into their relative effects on central firm outcomes (i.e.,
loyalty and word-of-mouth, WOM). Applying multigroup structural equation modeling, we draw
on consumer data from two global brands in the fast-food sector, McDonald’s and Burger King,
collected in the BRIC economies and their domestic US market. Focusing on a franchise context
ensures business format similarity and comparability of the studied settings across countries.
Our contributions are as follows: First, as a descriptive contribution, we document essentially
diverging nomological linkages among the concepts under study across nations and establish various culture-specific effects of satisfaction, habit and reputation on loyalty and WOM. We concentrate on the BRIC states, whose enormous market potential makes these countries particularly
interesting to study. Post-hoc tests contrast BRIC data with the US consumer market, the home
market of many global brands in the fast food industry. We focus on samples from several countries to enhance external study validity and test the model’s predictive power across different
cultural settings. Second, contributing to theory, we illustrate the relative effects of habit-based
vs. satisfaction- and reputation-related behavior on firm-level outcomes. Thereby, we generate
new knowledge concerning the conscious/strategic and unconscious/automatic nature of consumer loyalty and WOM. Third, contributing to practice, we provide insights for how global brands
should design marketing strategies in different marketplaces. We offer managerial guidance on
the advantageousness of fostering habit creation vs. satisfaction and reputation as well as implications on the relevance of reputation for performance in the presence of habit. In sum, our study
sheds light on customer loyalty in general and international franchising in particular, and provides important intuitions on how global brands strategize their brand positioning best.
The rest of the paper is organized as follows. We begin with a brief review of the theoretical and
conceptual arguments that provide a basis for studying linkages between satisfaction/reputation
and loyalty/WOM, the moderating role of habit and the significance of the cross-national context,
culminating in the derivation of our slate of hypotheses. Next, the methodology adopted in our
empirical investigation is described, followed by a report of our findings. The final section contains a discussion of the theoretical and managerial implications of those results.
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2. Theoretical Development
2.1 Effects of Satisfaction on Loyalty and WOM
Satisfaction is a consumer’s post-purchase evaluation and affective response to the overall product or service experience, the “judgment that a product or service feature, or the product or service itself, provided (or is providing) a pleasurable level of consumption-related fulfillment, including levels of under- or over-fulfillment” (Oliver 1997, p. 13). Repeated satisfaction with a
product, service or brand becomes aggregated over time and forms global satisfaction over the
course of a relationship with a firm (DeWulf et al. 2001; Johnson et al. 1995). According to Anderson and Srinivasan (2003) customer satisfaction management becomes a strategic imperative
for firms, because “a dissatisfied customer is more likely to search for information on alternatives
and more likely to yield to competitor overtures than is a satisfied customer”. Thus, experiencing
frequent satisfaction episodes is considered key to building and retaining a loyal base of longterm consumers (Limayem et al. 2007; Oliver 1999; Reinartz et al. 2005).
Customer loyalty is important as not only is the cost of retaining a customer less than the cost
of acquiring a new customer, but also existing customers cost less to maintain than newly
acquired customers (Reichheld 1996). Defining loyalty in a variety of ways, the need to understand what drives such loyalty has spawned numerous publications in marketing research (Jacoby
& Chesnut 1978; Ball et al. 2004; Chiou & Droge 2006; Evanschitzky & Wunderlich 2006; Gustafsson et al. 2005; Yi & La 2004). In this literature, Oliver’s (1999) model has been cited most,
which suggests that consumers go through different phases from cognitive and affective loyalty
(satisfaction) before committing to loyalty intention or loyalty behavior. Here, we focus on loyalty intention. Loyalty intention is a deeply held commitment to repurchase a product, service or
brand, and is often used as a substitute for loyalty behavior as the ultimate dependent variable in
satisfaction-loyalty studies (Chiou & Droge 2006; Evanschitzky & Wunderlich 2006; Han et al.
2008; Oliver 1999). According to Sheth and Park (1974), customers are truly loyal when they
resist pressures to switch to other brands, particularly if purchase behavior is repeated frequently
(Newman & Werbel 1973; Nguyen & Leblanc 2001; Woodside et al. 1980).
Although much of the marketing literature argues that essentially, satisfaction drives customer loyalty, establishing linkages between satisfaction ratings and repurchase intentions
5
or behavior has not been easy for firms (Mittal & Kamakura 2001). The literature also suggests
that satisfaction is not only a strong predictor for loyalty but also for positive word-of-mouth intentions (Anselmsson 2006; Darian et al. 2001; Eggert & Ulaga 2002; Gomez et al. 2004; Lin &
Wang 2006; Martenson 2007; Oliver 1980; Theodoridis & Chatzipanagiotou 2009; Westbrook &
Oliver 1991; Wong & Sohal 2003). WOM intentions denote customers’ willingness to refer a
firm to other stakeholders (Anderson 1998; Reynolds & Beatty 1999). Then, through social ties,
such information can travel between groups and get disseminated into the broader social system,
influencing consumers’ attitudes and behaviors towards that firm (Brown & Reingen 1987).
H1a: Customer satisfaction increases customer loyalty intentions.
H1b: Customer satisfaction increases customer WOM intentions.
2.2 Effects of Reputation on Loyalty and WOM
A second central antecedent of customer loyalty, aside from satisfaction, is corporate image or
reputation (Anderson & Weitz 1989; De Wulf et al. 2001; Orth & Green 2009). However, while
studies offer empirical support for a positive direct influence of satisfaction on loyalty (Ball et al.
2004; Bloemer & de Ruyter 1998; Bolton 1998; Garbarino & Johnson 1999; Lessig 1973; Macintosh & Lockshin 1997; Mazursky & Jacoby 1986; Osman 1993; Rust & Zahorik 1993), evidence
for image or reputation effects on loyalty so far remains highly fragmented (Green & Orth 2009).
Corporate image and corporate reputation are generally considered as two distinct, yet strongly
related, constructs. Whereas some researchers use image and reputation as substitutes, others
such as Fombrun (1996) describe reputation as the esteem that a firm has in a long-term perspective, as opposed to image that can be short-term in nature (Barnett et al. 2006). We focus on corporate reputation. The concept of reputation has been studied by researchers in the fields of economics, organization and marketing. Economists analyse reputation in relation to product quality
and price (Shapiro 1983; Wilson 1985). Organizational researchers see reputation as an intangible
resource that significantly contributes to firm performance (Fombrun & Shanley 1990; Hall 1993;
Rao 1994). Marketing scholars study reputation under the notion of “brand equity” that grants
credibility to firms (Aaker 1996; Herbig et al. 1994). Although the vocabulary differs, consensus
has emerged that reputation results from a firm’s past actions in terms of direct and indirect experience and information stakeholders have concerning the firm (Fombrun & Shanley 1990; Ruth &
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York 2004; Weigelt & Camerer 1988; Yoon et al. 1993). Accordingly, Bennett and Rentschler
(2003) define reputation as a concept “that refers to value judgments among the public about an
organization’s qualities, formed over a long period, regarding its consistency, trustworthiness and
reliability.” In sum, corporate reputation provides the referential barometer for a firm’s performance in the eyes of stakeholders and serves to communicate information to its target groups
regarding the quality of a firm’s products or services in comparison with those of its competitors
(Chen et al. 2009; Davidson & Worrell 2006; Hoffer et al. 1988; Jarrell & Peltzman 1985; Nguyen & Leblanc 2001; Podolny 1993; Rhee & Haunschild 2006).
Accordingly, enhancing and maintaining corporate reputation is critical for firm performance
(Brown et al. 2006; Fombrun 1996; Fombrun & Shanley 1990; Lange et al. 2011; MacMillan et
al. 2005). Corporate reputation is difficult to accumulate, imitate, substitute, or transfer (Rao
1994; Surroca et al. 2010) and is related to customer behavior that is beneficial to the firm, as
consumers generally consider firm reputation before undertaking a purchase decision (Argarwal
et al. 2009; Barnett et al. 2006; Bello 2005; Brown et al. 2006; Caruana & Ewing 2010; Grewal et
al. 1998; Rindova et al. 2005; Shrum & Wuthnow 1988; Weigelt & Camerer 1988; Zeithaml
2000). Yet, empirical findings concerning its “true” effects on firm performance vary. Some argue that reputation offers “insurance” effects that reduce consequences of negative incidents like
product recall or unethical organizational conduct (e.g., Godfreyet al. 2009; Jones et al. 2000;
Schnietz & Epstein 2005), others report contradictory results (Brooks et al. 2003; Rhee &
Haunschild 2006; Sutton & Galunic 1996; Wade et al. 2006). Since reputation is a multidimensional construct, its effects on performance also vary with the specific performance aspect under
study. However, studies suggest that a good corporate reputation grants the opportunity to command premium prices, increase sales and market share (Shapiro 1982), establish and maintain
loyal customer relationships (Andreassen & Lindestad 1998; Nguyen & Leblanc 2001; Robertson
1993; Yoon et al. 1993), and garner more positive WOM (Martenson 2007).
H2a: Corporate reputation increases customer loyalty intentions.
H2b: Corporate reputation increases customer WOM intentions.
2.3 Effects of Habitual Behavior
Research on psychological mechanisms that drive loyalty has mainly focused on deliberate inten7
tions and goal-directedness (Oliver 1999; Pritchard et al. 1999). That is, most models assume that
the behavior of individuals is directly preceded by deliberate planning and intention regarding the
subsequent course of action. Due to this assumptional limitation, such models cannot correctly
explain consumers’ actions, as in reality, many actions are simply resumptions of activities started some time before, which in turn makes forming specific intentions in the first place simply
unnecessary in very many situations. Hence, consistent with modern psychological theories, attitude-behavior links do not necessarily depend on planned action, but on routine behaviors. According to script theory, individual behavior largely falls into patterns called “scripts” (named
that way as by providing a program for action, they function analogously to scripts). In script
theory, the basic unit of analysis, a “scene”, is a sequence of events linked by the affects triggered
during the experience of those events. Tomkins (1987) introduced script theory as a development
of his affect theory, suggesting that affective experiences fall into patterns that individuals group
according to various criteria (such as the types of persons and places involved or the degree of
intensity of the experience), which constitute the scripts that inform our behavior in an effort to
maximize positive and minimize negative affect. 1 In consequence, individuals often act based on
habit, driven by the automatic, implicit patterns stored in their memory (Verplanken & Aarts
1999). Then, differences between intentional loyalty and habit are particularly intriguing given
the similarity in manifested behavior, such as high purchase frequency, low brand switching, and
high share of wallet (Liu-Thompkins & Tam 2013).
Triandis (1980) defines habit as situation-behavior sequences that have become automatic, so that
they can occur even without awareness or self-instruction (Wood & Neal 2009). In the context of
buying decisions, the habit construct is typically conceived as past frequency or consecutive
product purchase (Jolley et al. 2006; Ouellette & Wood 1998; Seetharaman 2004). Current definitions associate habit with repeated behavior that has gained automatic qualities, performed under
relatively stable conditions, with a minimal focus of attention (Ouellette & Wood 1998). For instance, Verplanken and Aarts (1999) describe habit as a learned sequence of acts that has become
an automatic response to specific cues, and is functional in obtaining certain goals or end states.
1
A common example is if visiting a restaurant individuals automatically behave according to a “restaurant script”:
finding a free seat, having the order taken, eating the meal. That is, the guest does not have to “convince” the staff to
provide a meal, but all behave according to their repective role. Knowing the script for a particular situation therefore
results in reduced need for planning, decision-making and related mental activity (Schank 1986).
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Wood and Neal (2009) argue that habit formation is originally an intention-dependent process
where goals provide the initial outcome-oriented impetus for response repetition. Continued behaviors, often characterised by frequent purchases, are prone to become habitual and thus automatic over time, and are present particularly in the context of food and drink purchasing and consumption (Limayem et al. 2007; Ouellette & Wood 1998). Here, consumers tend to buy the same
brand across shopping episodes, the same amount at a given store across repeat visits, and tend to
consume similar types of meals across days (Wood & Neal 2009). Therefore, when behavior is
performed in stable contexts and for low-involvement or frequent purchases, consumers’ behavior can be initiated and executed without needing the person’s conscious intent and guidance
(Webb & Sheeran 2006; Ouellette & Wood 1998). Accordingly, habit will alter effects of customer satisfaction or corporate reputation on purchase decisions, and thus, on firm outcomes like
loyalty and WOM. In consequence, ignoring habit effects will lead to systematically overestimating loyalty being driven by firm strategies targeted towards fostering satisfaction or reputation.
H3a: The linkage between customer satisfaction and loyalty intentions is moderated by habit.
H3b: The linkage between customer satisfaction and WOM intentions is moderated by habit.
H3c: The linkage between corporate reputation and loyalty intentions is moderated by habit.
H3d: The linkage between corporate reputation and WOM intentions is moderated by habit.
2.4 Effects of the Cultural Context
Culture is defined as “a set of shared values and beliefs that characterize national, ethnic, moral
and other group behavior” (Adapa 2008; Craig & Douglas 2006; Faure & Sjostedt 1993; Parra
2001). Thereby, the cultural environment spans formal and informal forces, including regulative,
cognitive, and normative structures that affect human and organizational behavior (Holtbrügge &
Baron 2013; Meyer & Rowan 1977). 2 Consensus holds that the cultural context in which firms
operate has a direct influence on outcomes of market strategies (Brouthers 2002; Brouthers &
Hennart 2007; Henisz 2000; Meyer & Nguyen 2005). Despite the enthusiasm for increased global
2
The mechanisms that connect culture and the institutional context in societies are mostly terra incognita in economic and marketing theory. Williamson (2000) advanced a 4-stage model aimed at explaining their potential linkages.
In his model, Level 1 comprised of informal institutions that reflect norms, customs, traditions, religions etc. Level 2
consisted of formal legal rules including constitutions, law, and property rights. Governance structures (e.g., of
firms) and marginal analyses (e.g., of business outcomes) constituted Levels 3 and 4. And since each level was expected to naturally impose constraints on the principles governing the levels below, thereby establishing path dependence, institutions were expected to “have a lasting grip on the way a society conducts itself” (Licht et al. 2005).
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interaction and economic exchange, many companies find that cultural differences challenge their
ability to efficiently conduct business in different markets due to their lack of understanding of
these differences (Tu 2011).
Concerning cross-cultural psychology and challenges in international management, especially
Hofstede’s framework has proven valuable for understanding consumer behavior in crossnational contexts, and has become a paradigm in research and practice (Calantone et al. 2006; De
Mooij & Hofstede 2010; Dow 2005; Okazaki et al. 2006; Wong & Merrilees 2007). Hofstede’s
theory of cultural dimensions describes effects of a society’s culture on its members’ values and
on how the latter relate to behavior. Hofstede (1994) explains that cultures and societies differ
with respect to (originally, four) dimensions: focus on individualism versus collectivism; uncertainty avoidance versus willingness to accept risks; power distance, i.e., the strength of social
hierarchy; and masculinity-femininity, refering to task orientation versus person orientation. For
example, regarding the individualism index, there is a clear gap between developed and Western
countries on the one hand, and less developed and Eastern countries on the other. North America
scores highest on individualism, whereas Asia and Latin America tend to hold collectivistic values. Uncertainty avoidance scores are high in Latin America, but low for Anglo and Chinese
countries. Power distance is very high in Latin and Asian countries, and the orientation towards
masculinity is relatively high in the Anglo world, whereas Latin countries show contrasting results. As previous research in international marketing argues, these underlying orientations make
customers behaving in culture-specific ways (De Mooij 2004 2010; De Mooij & Hofstede 2002;
Hofstede 2001; Hofstede & Hofstede 2005). In consequence, the linkages studied here most likely vary systematically across societies.
H4: The linkages proposed above are culture-specific.
The conceptual model implicit in our slate of hypotheses is summarized in Figure 1.
[Insert Figure 1 about here]
3. Data, Variables and Methods
3.1 Sample
Brand Selection. As we intend to study linkages in an international context and across various
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countries, the sample is anchored in brands that meet the criteria of being well-known and welldiffused global brands. We focus on two iconic US brands: McDonald’s and Burger King. As fast
food chains have been used to investigate franchising ontology in international settings before
(Dant & Schul 1992; Grünhagen et al. 2012; Kaufmann & Lafontaine 1994; Pizanti & Lerner
2003), the setting is based on previous studies and findings add to the literature in the field.
McDonald’s is recognized as running one of the most successful brand strategies in the world
with over 31,000 restaurants in more than 120 countries, and employing over 1.5 m people
(www.mcdonalds.com). While the company has broadened its original product portfolio of burgers, fries, and soft drinks (e.g., with coffee and pastry), it also adapts to culturally diverse consumer markets (e.g., by choice of meat; seafood, vegetarian or rice dishes; or seasoning styles).
TV advertising is adapted to cultural preferences as well, e.g., campaigns show successful soccer
players in the U.K., but the company advertises in newspapers and magazines in China, as TV
campaign have proven less effective in East Asia (Vignali 2001). In contrast, Burger Kind operates 12,000 restaurants in 73 countries (www.bk.com). Like McDonald’s, the company adjusts to
local tastes, e.g. by offering halal or kosher products in the Middle East, yet Burger King applies
a standardized marketing strategy (“have it your way”) that rather than diversifying the menu
allows customizing meals with preferred ingredients.
Country Selection. For the analyses, we focus on the BRIC economies (Brazil, Russia, India,
China). These countries are underrepresented in previous research, yet are forecasted to emerge
as key players in the global economy in the 21st century (Holtbrügge & Baron 2013), making
them a particularly interesting subject of study. Both rapid growth prospects and the sheer size of
the BRIC markets promise exceptional opportunities for Western companies. McDonald’s currently runs 727 outlets per BRIC state (compared with for example, only 104 in the former Eastern Bloc). Burger King operates 71 outlets (compared with 26). The sampling frame was drawn
from the population of fast-food customers. Trained interviewers distributed identically structured questionnaires to randomly chosen customers nearby or in the restaurants requesting to participate in a short survey. Participants had to have patronized at least one of the chains in the recent past (six months) to qualify to reply. The final dataset comprised 1128 respondents distributed equally across countries, with 57% anchored to McDonald’s, 43% to Burger King. Response
rates were high, ranging from 68% to 87% across countries and brands, which mitigates concerns
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of non-response bias. In addition, we managed to collect initial samples from the US (n = 65) and
South Africa (as the fifth state in the BRICS framework, n = 30) for exploratory post-hoc comparisons. Where questionnaires had to be translated into local languages prior to administration,
the familiar translation back-translation regimen was followed using Anglophones and native
speakers to ensure that the content and the thrust of the questions remained unchanged.
3.2 Variables and Methods
Variables. All variables are based on measures from previous research. Sources, items, reliability
statistics for latent constructs (Satisfaction, Reputation, WOM), and the metrics (Habit, Loyalty)
can be found in the Appendix. Table 1 displays descriptive statistics and correlations.
Psychometric Assessment. We test our model using a two-group structural equation modeling
framework. We formed two country groups based on the BRIC’s Hofstede characteristics (see the
Appendix): Brazil/Russia and India/China. These groups are also in line with the literature which
commonly groups the BRIC states likewise (Borker 2012). Brazil and Russia are large land mass
countries with relatively low populations that are rich in exploitable and exportable natural resources. India and China have the world’s two largest populations with China expected to be ascendant in manufacturing due to its strong industrial infrastructure, and India expected to expand
in the service sector. Also, Russia and Brazil have greater similarities to one another than to India
and China on various matters of business conduct, e.g., disclosure (Borker 2012). Most important
for our study focus, concerning Hofstede’s (1980) dimensions, these pairings exhibit the same
patterns of high versus low scores on the four cultural dimensions supposed to affect organizational and consumer behavior. Thus, Russia and Brazil, and China and India, are the two groups.
Fornell and Larker (1981) note that any assessment of a structural model must be preceded by a
rigorous evaluation of the measurement model that demonstrates satisfactory levels of validity
and reliability. Thus, before assessing any structural relationships, we evaluate the measurement
model using confirmatory factor analysis (CFA) (Yiu & Lau 2008). A separate CFA was run for
each country group to ensure construct unidimensionality and to eliminate potentially unreliable
items. We commenced this scrutiny with running exploratory factor analyses, both one construct
at a time (to check unidimensionality) and constructs simultaneously to check for two factor
structures. Both sets of analyses yielded results supporting our construct formation. All items
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showed high item-to-construct loadings in both groups (all loadings were larger than .80) and
were attended by high fit indices: the comparative fit index (CFI > .97), Bentler and Bonett’s
normed fit index (NFI > .88), Tucker and Lewis’s non-normed fit index (NNFI > .88), Bollen’s
incremental fit index (IFI > .96), and root mean square error of approximation (RMSEA ~ .05).
Scale reliability was assessed by computation of composite reliabilities (CR). Reliabilities were
calculated on the basis of individual country, individual brand; individual-country-, two-brand;
country-group, individual-brand; as well as country-group, two-brands (for the latter, see the Appendix). Results indicated scale reliability throughout. Coefficient α values all ranged well above
the conventional benchmark of 0.70 (Nunnally & Bernstein 1994).
When factor analyzed, all factor loadings were found to be highly significant on their respective
constructs and there are no cross-loadings larger than .30, which indicates convergent validity
(Bagozzi et al. 1991; Homburg et al. 2008). Convergent validity was also assessed by computing
the average variance extracted (AVE) estimates. Fornell and Larcker (1981) suggest that AVE
estimates of 0.50 or larger are indicative of convergent validity (see Appendix, all estimates are
> .50). Discriminant validity of the constructs was evaluated by comparing AVE with squares of
inter-trait correlations (Fornell & Larcker 1981). Discriminant validity is demonstrated when the
square of the correlations is less than the AVE or when the square root of the AVE is larger than
the correlations. Our smallest square root of AVE is .781 which exceeds all correlation coefficients. Finally, following Podsakoff et al. (2003), we checked for common method biases using
Harman’s single factor tests, which suggested absence of the threat of common methods variance.
When conducting cross-national research, researchers must also identify what exactly constitutes
“equivalent” phenomena across the countries in question. Douglas and Craig (1983) note that
researchers must ensure that they are actually measuring the same construct when using the same
items across different countries. Our questionnaire translation procedure of academic translating
and back-translating sought to ascertain that we were in fact examining equivalent phenomena.
All constructs were composed of identical items in the two groups. The equivalence of the measurement models across the two samples was tested using multi-group CFA (Bollen 1989). Factor
loadings for the two country groups were set to be invariant for all items as factor patterns and
factor loadings were expected to be equal for both the Brazil-Russia and the India-China samples
if in fact their measurement properties were identical. Our results indicated that factor patterns
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were indeed identical: Critical ratios/z-score tests revealed no difference between the groups for
12 of the 13 factor loadings, which, in line with the literature, allows one to accept the premise of
identical measurement properties (Calantone et al. 1996). Fit indices for the two-group CFA
model were highly satisfactory (CFI = .98). Since the measurement models were found to be invariant across the country groups and given the pedigree of the latent scales as well as the above
demonstrated psychometric properties, the individual scale items were collapsed to create composite construct scores for use in all subsequent inferential analyses.
Inferential Analyses. Turning to the assessment of the structural model (Figure 1), we initially
estimated identical models for both country groups individually. Bollen (1989) argued that the
most demanding test of comparability of models across different groups (here, cultural settings)
is when the models have the same form (i.e. same constructs or measures and relationships
among those). We next evaluated a model that we dub “Multi-Group Unconstrained Model” (see
Table 2). Both individual models converged well. Re-running the model where path coefficients
were freely estimated, we found that in the Brazil-Russia group, seven model paths were significantly different from zero. In the India-China group, six of the eight paths were significantly different from zero. Thus, we found strong significant support for our Unconstrained Model framework for both country groups. These were also accompanied by much better fit statistics (e.g.,
CFI = .97; see Table 2 for other diagnostic indices).
Last, we imposed model constraints on the eight structural paths shown in Figure 1 by setting
path coefficients equal across the two samples (see Table 2). Following the usual approach to
identify similarities and differences between samples in multi-group structural equation modeling
(Bollen 1989), we intended to examine whether these constraints actually held or whether model
fit could be improved by loosening some of the constraints. This procedure tests the assumption
that the general form of the measurement and structural models underlying both of the two countries were the same, but that the values of the specific path parameters could differ across the two
samples. A comparison of the results and the respective fit of the unconstrained and the fully constrained models suggested that the model parameters for Brazil-Russia were different from those
for India-China. Nested chi-square tests and critical ratios/z-score tests were used to identify
which paths differed. The results were identical and indicated that three constraints should be
released: (1) the path from Reputation to Loyalty, (2) the path from Reputation to WOM, and (3)
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the path indicating a moderating effect of Habit on the Reputation-Loyalty linkage. Thus, we
released the equality constraints on these three paths which yielded our “Multi-Group Partially
Constrained Model” (Table 2, last column). Comparing this “partially constrained” model (with
the above constraints released), with other models where other equality constraints were released
showed a deterioration in the model fit statistics. Thus, we found that five of the eight structural
parameter estimates are invariant across the two country groups, but the above three linkages
differed across the samples (lending support to H4). The Multi-Group Partially Constrained
Model also yielded the best fit statistics of the three models evaluated as presented in Table 2
(i.e., CFI = .98, NFI = .91, IFI = .98, NNFI= .91, and RMSEA = .07) (Garson 2010). The individual models that were run prior to the multi-group models yielded coefficients closely approximating the Multi-Group Partially Constrained Model in both signage and significance level. Besides, the two sample sizes being equal was analytically beneficial since there is little systematic
knowledge on effects of largely uneven sample sizes in the context of multi-group modeling. We
therefore conclude our results are robust.
3.3 Additional Analyses
Three types of additional analyses were executed to get a more incisive peek at the data patterns:
(1) evaluation of rival models, (2) assessment of moderation effects, and (3) multivariate analysis
of variance (MANOVA) comparisons.
Evaluation of Rival Models. “Founding fathers” of structural equation modeling like Haavelmo
(1943) or Duncan (1975) have argued that SEM was a tool for drawing causal conclusions from a
combination of observational data and theoretical assumptions. SEM does not establish ultimate
causation per se, which requires careful manipulative experiments (Chin 1998). However, SEM
does allow interpreting parameters as causal effects based on scientific reasoning and previously
conducted research (Pearl 2012). Besides, Iacobucci et al. (2007) criticize the state-of-art in SEM
where studies rarely mention, and much less test, any competing models. They point out that rival
causal-effects models could often be equally plausible from a theoretical perspective. Acknowledging the aforementioned causality gap and as the theoretical linkages in our model might be
argued in alternative ways, we tested several rival models as well.
First, based on previous reseach, we started with the premise that loyalty could be an antecedent
15
of satisfaction, so that theoretically, reputation and loyalty together could lead to satisfaction and
WOM, moderated by habit. However, both in the individual country structural models, as well as
in the multi-group models, model fit decreased significantly, and most paths were insignificant.
Next, we considered the possibility that loyalty and habit may simply have a direct effect on satisfaction (instead of moderation effects). However, again, model fit decreased significantly, both
in the individual-country structural models, as well as in the multi-group models (CFI < .68 and
less). Third, we changed the model so that satisfaction and reputation together lead to WOM and
WOM lead to loyalty (as well as the other way round, with loyalty preceding WOM). However,
model fit once again deteriorated substantially due to these alterations, both in the individualcountry structural models, as well as in the multi-group models (CFI < .69 and less). We also
included habit as a mediator, and as an outcome variable in addition to loyalty and WOM as well
as as an effect of loyalty and WOM; we further modeled satisfaction as a mediator, and as a moderator, reputation and habit as antecedents, and loyalty and WOM as outcomes; however, these as
well as other theoretically justifiable rival models did not achieve any comparably substantial
model fit. We take these results as an additional indicator of the robustness of our findings (i.e.,
that the Partially Constrained Model as shown in Table 2 provides the optimal fit for our data).
In addition, we used socio-demographic data obtained through the survey in terms respondents’
age, gender and education, which we applied as antecedents to the model constructs of Satisfaction and Reputation and the Habit metric. However, although the data showed similar patterns
across countries (as regards the country-based as well as brand-based quotas of men and women
respondents, their ages, and the amount of post-highschool education; e.g., χ
2
tests across the
gender versus country groups and brand groups crosstabulations were non-significant suggesting
that gender distribution across country groups or brand groups were statistically equivalent), results remained inconclusive. Moreover, to test for possible differences between genders, the responses of women were compared to the responses of men using MANOVA analyses across
Habit, Loyalty and the latent constructs. The same analysis was run on a brand-basis. No significant differences emerged, indicating relative homogeneity of subsamples across these variables.
Assessment of Moderation Effects. Heeding Schoorman et al. (2007) who argue that future research needs to focus on both the cultural environments and specify additional contextual variables to more fully understand causal links, we included several context-specific variables (i.e., the
16
measures for respondents’ age, gender and education) to tease out potential moderating effects.
Following the literature, we modeled these moderators in two distinct ways: (1) by forming all
possible product terms between the items of the respective latent constructs and metrics and having them load on a moderator latent construct, and (2) by forming product terms for all possible
pairs of single items (Chin 1998; Jonsson 1998; Kenny & Judd 1984). We did not use the third
option of creating and utilizing dichotomous moderator groups in our multi-group analysis to test
moderation effects as our model was already split into the two-groups-dataset and we were concerned about maintaining adequate sample sizes for further analyses. The results substantiated
that only the moderating effects of habit on the linkages were significant.
MANOVA Comparisons. For gaining some additional insights into the influence of cultural embeddedness on levels of satisfaction, reputation, loyalty, WOM and habit, we employ MANOVA
procedures. MANOVA is useful when there are (1) multiple metrically measured outcome and
(2) one or more categorical predictor variables (here, the two country groups). The test for differences across the predictor groups in MANOVA is based on statistics convertible into equivalent
multivariate F-ratios (Cooley and Lohnes 1971). The procedure also safeguards against the inflation in the experiment-wise error rate that would occur if a series of t-tests were mounted instead
of the MANOVA-ANOVA-multiple paired comparisons routine. Brazil/Russia customers score
significantly higher (p < .001) on Habit (almost 18 visits per year as opposed to nine in the India/China group; see Table 1 for mean values) and Loyalty (p < .001) and lower on WOM (p <
.001). These differences support the proposition that linkages depend on the cultural context at
hand. Other differences were not statistically significant across the BR-IC-samples. 3
[Insert Tables 1-3 about here]
3
Table 3 presents MANOVA results for the BRICS and the US data. When there are three or more groups in a
MANOVA-ANOVA analysis, it is customary to carry out simultaneous multiple paired comparisons that contain the
experiment-wise error to a pre-specified level (usually, 0.05). Since we included South Africa and the US in the
analyses for post-hoc tests there were four groups, so Tukey’s simultaneous multiple paired comparisons were carried out to ferret out the sources of differences across the groups. Significant results from the two-group and the fourgroup MANOVAs show significant differences not only across the BRIC, but particularly, in comparison with the
brands’ domestic US market (which is intuitive) and also with South Africa, the fifth BRICS state. In terms of Satisfaction and Habit scores, South Africa comes closest to the US, whereas it is similar to the Brazil-Russia group in
Loyalty scores, and interestingly, scores highest of all countries on Reputation and WOM, indicating the relevance of
cross-cultural research into the linkages under study.
17
4. Inferences on Hypotheses
As proposed by H1, customer satisfaction strongly increases loyalty and WOM intentions in both
country groups. Corporate reputation is positively releated to loyalty and WOM (supporting H2),
yet the effect is stronger for loyalty, and is much more pronounced in the Brazil-Russia than in
the India-China sample. As regards the moderation analyses, we find that habit significantly decreases the effect of satisfaction on loyalty (H3a) and WOM intentions (H3b) across country
groups. That is, in the presence of habit, achieving customer satisfaction is less relevant for firm
outcomes. However, the positive effect of reputation on firm outcomes (H3c, d) is intensified in
the presence of habit, but only for loyalty (not WOM), and only in the Brazil-Russia sample, i.e.
habit does not affect reputation-outcome linkages in the India-China sample. Concerning culturespecific effects, we find strong differences concerning results related to the reputation variable.
That is, H4 is partly supported as reputation has stronger positive effects on firm outcomes in
Brazil-Russia than in India-China, and these positive effects in Brazil-Russia even gain momentum if habit is present. Hence, there are both culture-specific effects in the linkages among key
constructs (reputation to loyalty and WOM), and the moderating effect of habit differs as well.
Focusing on Hofstede’s dimensions, the finding may be explained as follows. First, although the
BRICs do not differ markedly in terms of power distance (all score above average) and individualism (all score below), BR-countries score lower on masculinity than IC-countries, and substantially higher on uncertainty avoidance. Whereas the dominant values in masculine societies, according to Hofstede, are achievement and success, feminine societies strive for caring for others
and life quality (De Mooij & Hofstede 2002; De Mooij 2010). In masculine societies, performance and achievement must be demonstrated, and status brands are important means to show
one’s success (De Mooij & Hofstede 2002; De Mooij 2010). Hence, firm reputation that transports either message – quality versus achievement – is more appealing depending on the cultural
context, and given the strength of the reputation variable in the BR-states, both US brands seem
to meet consumer expectations for messages linking the companies to life quality orientations
(which may also match McDonald’s move towards “health-conscious” products). 4 Given that
4
Besides, cross-cultural brand studies indicate that brand reputation in fact varies with culture-specific attributes,
even if firms strive to produce a globally consistent image (Crocus 2004; De Mooij 2010); for example, a characteristic like “friendly” and “trustworthy” is most attributed to strong global brands in high uncertainty avoidance cultures (like the BR-states), whereas “prestigious” is attributed in high power distance cultures. In both low power
18
Indian and Chinese consumers understand patronizing iconic US brands as a proof of social status, in line with our results, habit will play a smaller role in purchase decisions than intentional
demonstration effects. The latter fits in also with the substantially higher patronage frequency in
BR-countries compared with IC-countries, as demonstrating achievement through brand patronage in IC-countries would be unlikely if visits to the brands’ outlets were an ordinary “everyday”
experience (i.e., nothing rare or “exclusive”). Besides, in China, a visit to McDonald’s costs
about twice as much as a meal elsewhere, so the experience is expensive. Hence, effects of reputation on outcomes may be weaker as affording the visit is difficult for many consumers anyway.
Second, uncertainty avoidance is defined as “the extent to which people feel threatened by uncertainty and ambiguity and try to avoid these situations” (De Mooij & Hofstede 2010). High uncertainty avoidance cultures are generally health-conscious by focusing on purity in food and drink
(De Mooij & Hofstede 2002; De Mooij 2010). Here, well-established US brands may promise
better quality than what is generally perceived as local standard. Moreover, people of high uncertainty avoidance are less open to change and innovation than people of low uncertainty avoidance
cultures, which explains e.g., differences in innovation adoption (Yaveroglu & Donthu 2002;
Yeniurt & Townsend 2003; Tellis et al. 2003). For them (i.e., in the BR-states), in line with our
results, reputation and habit can play a strong role to reduce consumption risks, either through the
signalling function that a good reputation has or by personal experience from frequent patronage.
5. Discussion
Due to expectations of rapid economic development and growth, forming a better understanding
of business opportunities in markets outside the developed economies becomes increasingly essential for both scholars and practitioners. Based on a rising middle class and sheer market size
(40% of the global population), BRIC countries currently emerge as key players in the global
economy. However, Western brands entering these markets encounter complex difficulties in
business conduct, particularly with respect to customer relationship management and image creation, which renders the development of effective marketing strategies a truly challenging task.
distance and low uncertainty avoidance settings, people attributed “innovative” and “different” to these brands, indicating that consumers project their own personality preferences on to brands (de Mooij & Hofstede 2010). Thus,
different cultural conditions lead consumers to different brand evaluations (Koçak et al. 2007).
19
Particularly those strategies that build loyal customer relationships are central to firm performance. Yet, previous research has rarely addressed how global brands can achieve such relationships across diverse international markets. Besides, research on the processes that drive loyalty
behavior has mainly focused on intentional mechanisms, assuming that loyalty starts with positive cognition and affect towards a product or brand, and ends with intention and commitment
directed towards repurchase. However, this assumption may not be applicable to continued behaviors or behaviors characterized by frequent purchase (e.g., food consumption), as it ignores
that frequently performed behaviors become habitual and automatic over time. In consequence,
research may systematically overestimate the influence of other relevant drivers of customer behavior, e.g., effects of satisfaction or firm reputation. In consequence, the drivers and the relative
effects of habit, satisfaction and reputation are not well understood, particularly, as they apply to
formulating marketing strategies in a cross-national context.
Therefore, this study contributes to the literature by providing an extension of the prevalent consumer loyalty theorizing through integrating the concepts of habit, customer satisfaction and firm
reputation and by generating cross-national insights into the drivers and relative effects of these
concepts on firm outcomes in terms of loyalty and word-of-mouth intentions. The analyses draw
on consumer data collected from two well-known, globally diffused brands from the fast-food
sector, McDonald’s and Burger King, in the BRIC economies (and for further comparisons, in
their domestic market USA and in South Africa).
Based on multi-group structural equation modeling and MANOVA analyses, our results document the effects of satisfaction, habit and reputation on loyalty and WOM intentions. To summarize the key findings, first as argued in the literature for various settings, satisfaction is beneficial
for firm outcomes, also in case of BRIC economies. However, the importance of customer satisfaction decreases if customers have formed a habit of patronizing a particular brand. Positive effects on firm outcomes also originate from high corporate reputation. The reputation effects are
stronger in the BR-countries than the IC-countries. Habit strength increases the importance of
reputation, so that supporting habit formation is particularly beneficial for firms that have a
strong brand image; however, this effect holds only in the BR-countries.
Theoretical Implications. Few studies have examined whether and how theories and concepts in
relationship management apply across cultural contexts. Although many scholars have called for
20
an emic perspective to such research for a more nuanced understanding of how cultural context
influences the implicit nomological networks and how successful exchanges may depend on their
embeddedness in nations and societies (Homburg et al. 2009; McFarland et al. 2008; Sheng et al.
2011), most relevant investigations have adopted an etic approach to theory testing. Similarly,
studies on firm reputation have mainly investigated single country settings while using a sample
of global firms (Barnett, Jermier, and Lafferty 2006; Rindova et al. 2005; Weigelt & Camerer
1988). Our study sheds light on the literature by examining customer behavior towards two global brands across BRIC countries, showing how generalizations drawn from single country studies
might lead to erroneous conclusions and might jeopardize our understanding of global firm performance. Besides, despite the increasing importance of the BRIC countries, only a few studies
have addressed the question of how strategizing in BRIC markets by foreign brands is related to
firm outcomes (Canabal & White 2008; Holtbrügge & Baron 2013; Morschett et al. 2010). The
few studies that focus on the BRIC countries are usually limited to single-country analyses (e.g.,
Deng 2001; Johnson & Tellis 2008) or single aspects of market entry (e.g., Dikova & van Witteloostuijn 2007; Pan et al. 1999; Slangen & Hennart 2008). Moreover, prior research adopts almost exclusively transaction cost theory to explain market strategies in the BRIC countries (e.g.,
Brouthers 2002; Luo 2001; Meyer 2001; Yiu & Makino 2002) and is thus focused on the analysis
of firm- and industry-specific factors while the effects of country-specific contextual factors are
neglected. We focus on cross-cultural effects, drawing from relationship marketing, script theory
and habit research.
Besides, with regard to studying the role of habits in understanding consumer loyalty, the dominant approach in a business-to-consumer context has been to see loyalty behavior as a planned,
conscious process (Oliver 1999). However, this study advocates that scholars evaluating consumption behavior (and practitioners in food marketing and potentially, other fast-moving consumer goods) should conceive loyalty as governed by two distinctive phenomena: intentions
when referring to loyalty formation and habits when evaluating loyalty persistence. Most studies
still focus on perspectives is in accordance with the traditional attitude-intention-behaviour approach in social psychology (Ajzen 1991), suggesting that intention is the main causal mechanism behind the enactment of behaviour, so even if habit is considered, its role is usually supposed to be of minor relevance; in line with traditions in the marketing literature where most
21
studies neglect the possibility that long-practised behaviour may no longer be under motivational
conscious control, but rather influenced by antecedents other than intention (Chiou & Droge
2006; Evanschitzky & Wunderlich 2006; Han et al. 2008; Verplanken & Orbell 2003). Moreover,
habit is usually considered to be a negative construct both in daily life, e.g. in the context of bad
habits or addiction (Lindblad & Lyttkens 2002) as well as in parts of the loyalty literature (Dick
& Basu 1994). The reason is the assumption that habit is passive, convenient, a non-conscious
form of retention associated with “spurious loyalty” and different from “true loyalty” as an active, planned and conscious decision-making process (Dick & Basu 1994; Verplanken & Orbell
2003). However, habit can represent a positive trait or outcome for both consumers and businesses, saving cognitive effort and time (Wood & Neal 2009). Without habits, people would be
doomed to plan, guide and monitor every single action (Neal et al. 2006). Thus, buying a brand
out of habit may be something companies actually want to encourage.
Managerial Implications. In highly competitive markets with products of comparable quality satisfying similar goals, businesses must be keenly interested in strategies designed to strengthen
consumer loyalty for an incumbent brand, or to break consumers’ loyalty to competing brands.
Our results stress that approaches to identifying loyal consumers or customers should focus on
both intention and habit-dependent views of loyalty. Accordingly, first, building loyalty in the
initial phases of product adoption requires fostering awareness and beliefs about the brand as well
as satisfactory consumption experiences. Second, maintaining loyalty for a product can be obtained by fostering habit-based loyalty through providing stable contexts, cumulative satisfaction
and frequent consumption opportunities. Thus, marketing strategies should focus on enticing consumers to quickly develop the habit of patronizing the desired brand (Limayem et al. 2007). Besides, changing the physical surroundings in which consumers purchase habitually (e.g. product
placement, store displays, introducing a different packaging design) carries the risk to break the
habit by suppressing the link between the context and purchasing the brand in the memory (Wood
& Neal 2009).
Additionally, societal culture, regulation, politics, professionalization, business networks and
corporate culture are all relevant attributes in which business activity is culturally embedded, and
firms must decide whether strategies can and should be standardized versus adapted to respective
cultural contexts. In sum, we therefore provide new insights for how global brands may strategize
22
their brand positioning in different marketplaces by sheding light on customer loyalty in general
and in international franchising in particular, and providing new nuanced insights into the ongoing debates surrounding relevant attributes in global marketing strategies.
Of course, the present study faces several limitations. We employ cross-sectional data, therefore, causal effects can only be inferred. According to Jaccard and Blanton (2005), for understanding stable behaviours, cross-sectional data can be just as informative as longitudinal data;
yet we recommend longitudinal studies to verify and validate the present findings. Second, we
cannot observe factual behavior; rather, hypotheses build on individuals’ self-reported behavior.
Future studies may focus on combining different methodologies (quantitative and qualitative) and
objective data (experiments or neural system techniques) to understand consumers’ habits and
loyalty behaviour in different contexts in more detail (e.g., including also in-home versus out-ofhome, own country versus abroad purchase behavior). Hence, we invite other scholars to undertake similar multi-country investigations to avoid falling into an etic trap, which definitionally
would result in narrow phenomenological understanding of our precepts.
23
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Figure and Tables
FIGURE 1. PROPOSED MODEL
34
BR
IC
Variable
Mean
Std. Dev.
Mean
Std. Dev.
(1) Satisfaction
5.25
1.26
5.25
0.97
(2) Reputation
5.38
1.00
5.32
(3) Loyalty
5.88
1.44
(4) WOM
4.33
(5) Habit
17.50
(1)
(2)
(3)
(4)
(5)
1.000
0.524***
0.504***
0.609***
0.356***
0.98
0.463***
1.000
0.338***
0.339***
0.281***
5.00
1.54
0.649***
0.535***
1.000
0.771***
0.326***
1.57
4.89
1.34
0.618***
0.274***
0.471***
1.000
0.443***
26.27
8.83
11.82
0.142*
0.134*
0.097
0.13*
1.000
Significance levels: ***p < 0.01; **p < 0.05; *p < .01 (two-tailed). Brazil and Russia (BR) data below the diagonal, India and China (IC) above.
TABLE 1. DESCRIPTIVE STATISTICS
Multi-Group
Constrained Model
H1
H2
H3
Multi-Group
Unconstrained Model
Multi-Group
Partially Constrained Model
BR
IC
BR
IC
BR
IC
H1a
Satisfaction  Loyalty
.24***
.21***
.27***
.21***
.25***
.22***
H1b
Satisfaction  WOM
.21***
.19***
.24***
.20***
.21***
.21***
H2a
Reputation  Loyalty
.19***
.13***
.23***
.16*
.23***
.14*
H2b
Reputation  WOM
.13**
.13**
.14**
.15*
.14**
.13*
H3a
Satisfaction x Habit  Loyalty
-.11*
-.12*
-.09*
-.08*
-.11**
-.11**
H3b
Satisfaction x Habit  WOM
-.13*
-.12*
-.10**
-.09**
-.11**
-.10**
H3c
Reputation x Habit  Loyalty
.10
.05
.11**
.03
.10*
.02
H3d
Reputation x Habit  WOM
.08
.06
.06
.01
.05
.03
CFI: .81
NFI: .71
IFI: .77
Model Fit Indices
RMSEA = .07
NNFI: .63
CMIN/d.f.: 2.72, p < .05
Notes: Standardized coefficients reported. Significance levels: ***p < 0.01; **p < 0.05; *p < .01.
35
The partially constrained model displays culture-specific path-coefficients (H4) shaded and in bold italics.
CFI: .97
NFI: .88
IFI: .96
RMSEA = .06
NNFI: .87
CMIN/d.f.: 1.63, p < .05
CFI: .98
NFI: .91
IFI: .98
RMSEA = .07
NNFI: .91
CMIN/d.f.: 1.45, p < .05
TABLE 2. MODEL RESULTS FOR THE UNCONSTRAINED, CONSTRAINED AND PARTIALLY CONSTRAINED MULTI-GROUP MODELS
Comparison of Means
F (15, 958) = 13.829
MANOVA
ANOVA
ANOVA
ANOVA
ANOVA
ANOVA
Variables
Country
Mean
Satisfaction
Brazil-Russia
5.25
India-China
5.25
South Africa
5.86
USA
6.08
Brazil-Russia
5.38
India-China
5.32
South Africa
5.85
USA
5.29
Reputation
Loyalty
WOM
Habit
Brazil-Russia
5.88
India-China
5.00
South Africa
5.83
USA
6.17
Brazil-Russia
4.33
India-China
4.89
South Africa
5.80
USA
5.69
Brazil-Russia
17.50
India-China
8.83
South Africa
1.27
USA
2.80
Significance levels: ***p < 0.01; **p < 0.05; *p < .01.
TABLE 3. MANOVA RESULTS
36
p < .001
F
p-value
11.91
<0.001
2.63
<0.05
11.80
<0.001
21.52
<0.001
13.46
<0.001
APPENDIX A. MEASURES EMPLOYED
Items
(Anchored with 7-point Likert-type scales 1 = ”I strongly disagree” to 7 = ”I strongly agree”)
CR
BR
CR
IC
Brand Satisfaction
Adapted from Crosby et al.
(1990) and Ganesan (1994).
(1) I am satisfied with this franchised fast-food restaurant.
(2) I am pleased with this franchised fast-food restaurant.
(3) I am favorably disposed toward this franchised fast-food restaurant.
(4) My experiences with this brand have been positive.
.84
.87
.63
.66
Brand Reputation
Adapted from the five-item
reputation scale developed
by Fombrun et al. (2000)
and Wang et al. (2006).
(1) My overall perceptions of total experience with this franchise system are very good.
(2) My perceptions of this franchise system compared to its competitors are very good.
(3) I believe in the good long-term future for this franchise system.
(4) I believe that the market standing of this franchise system is good.
(5) The market visibility of this franchise system in the marketplace is high.
.82
.84
.61
.64
Loyalty Intentions
Adapted from Hellier et al.
(2003).
All things considered, it is highly likely that I will actually dine at this brand of franchised fast-food
restaurant again.
WOM Intentions
Adapted from File et al.
(1992).
(1) I would recommend to other people that they should dine out at this brand of franchised fastfood restaurant.
(2) I would recommend this franchise system to other people interested in dining out.
(3) I would gladly talk about my experiences with this brand of restaurants to other people.
(4) I would like to seek out different franchised fast-food restaurants to patronize. (reverse-coded)
.81
.80
.63
.61
Habit
Adapted from Jolley et al.
(2006); Ouellette & Wood
(1998); Seetharaman (2004)
How frequently do you eat at this franchised restaurant chain?
Construct
37
AVE AVE
BR
IC
APPENDIX B. BRICS SCORES ON HOFSTEDE DIMENSIONS 5
Power Distance (PDI)
Individualism versus Collectivism (IDV)
Masculinity versus Femininity (MAS)
Uncertainty Avoidance (UAI)
PDI
IDV
MAS
UAI
PDI H/L
IDV H/L
57,5
48,5
Mean values*
Brasil
Russia
India
China
South Africa
*Hofstede's Dimensions
PDI
UAI
IDV
MAS
Source: Hofstede (1983)
69
93
77
80
49
38
39
48
20
65
49
36
56
66
63
76
95
40
30
49
Hi
Hi
Hi
Hi
Lo
Lo
Lo
Lo
Lo
Hi
MAS H/L UAI H/L
50
Lo
Lo
Hi
Hi
Hi
60
Hi
Hi
Lo
Lo
Lo
Country code
SPSS
16
10
12
13
17
actual
range
theoretical theoretical
(min-max) mean value
min
max
-90
-150
120
230
11-104
57,5
8-112
60
6-91
48,5
5-95
50
Hofstede provides the formulas and ranges of obtained values for each country.
The mean values are the arithmetical mean of the actual min and max values (obtained range).
The mean values do not consider the theoretical min or max values.
Hi represents all values > mean
Lo represents all values <= mean
Obtained values are congruent with the table presented by Hodgetts & Luthans (1993).
5
However, depending on the specific strategic goals and the countries studied, other forms of clustering may also be possible. Ardichvili et al. (2012) find significant similarities between the US and Brazil
in perceptions of ethical business conduct in large business organizations, and respondents from India and Brazil held more favorable assessments of ethical cultures of their organizations than respondents
from China and Russia. Ardichvili et al. (2010) suggest pairing countries of continental Europe, Mexico, China, and Brazil versus India as a separate cluster with Anglo countries including the US. Moreover,
the BRIC countries also differ from one another with respect to regional heterogeneity, political systems, and legal enforcement due to different paths, sequences, and speed of their economic and institutional
reforms. Of 183 countries analyzed for different aspects that constitute economic freedom, Russia is ranked 143rd, China 135th, India 124th, and Brazil 113th (Heritage Foundation 2011). Russia is ranked
154th of 178 countries in the Corruption Perceptions Index, followed by India (87th), China (78th), and Brazil (69th) (Transparency International 2010; Holtbrügge & Baron 2013). Economic reforms in
China started in 1979 while India and Russia followed 12 years later. China and Russia have a socialist heritage while Brazil has a long history as a market economy. India is a former colony with a British
legal system, while the other three countries have been independent for the longest time in history (Holtbrügge & Baron 2013). Moreover, institutional differences exist between provinces and states within
the BRICs (Azzoni 2001; Démurger 2001; Holtbrügge & Friedmann 2012), so favorable entry strategies and determinants of market success can vary within countries, too.
38