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: rdant@ou.edu

Brinja Meiseberg Assistant Professor

Institute of Strategic Management Westfälische Wilhelms-Universität Münster

Leonardo-Campus 18 48149 Münster, Germany

Voice: +49 (251) 83-31959

Email: meiseberg@ism.uni-muenster.de

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 com-plexity 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 refer-ral. 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, custom-er satisfaction and reputation and by generating cross-national insights into their effects on cen-tral firm outcomes in terms of loyalty and word-of-mouth. Applying multigroup structural equa-tion 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 com-

plexity 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 practi-

tioners alike emphasize the need for relationship management practices to develop customer con-

tacts, acquire market knowledge, and make intelligent use of data and technology to enhance cus-

tomer 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 bene-

ficial to the firm, especially, loyalty and word-of-mouth referral. However, research on mecha-

nisms 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 auto-

matic 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 reputa-

tion. Accordingly, current marketing strategies frequently focus on developing and enhancing

customer satisfaction and firm reputation to increase loyalty, although the relative effects of hab-

it, 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 var-

ious culture-specific effects of satisfaction, habit and reputation on loyalty and WOM. We con-

centrate 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 coun-

tries 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 consum-

er 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 implica-

tions 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 pro-

vides 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 con-

tains 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 prod-

uct or service experience, the “judgment that a product or service feature, or the product or ser-

vice itself, provided (or is providing) a pleasurable level of consumption-related fulfillment, in-

cluding 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 An-

derson 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 long-

term 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 under-

stand what drives such loyalty has spawned numerous publications in marketing research (Jacoby

& Chesnut 1978; Ball et al. 2004; Chiou & Droge 2006; Evanschitzky & Wunderlich 2006; Gus-

tafsson 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 loyal-

ty 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 cus-

tomer loyalty, establishing linkages between satisfaction ratings and repurchase intentions

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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 in-

tentions (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; Macin-

tosh & 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 perspec-

tive, as opposed to image that can be short-term in nature (Barnett et al. 2006). We focus on cor-

porate reputation. The concept of reputation has been studied by researchers in the fields of eco-

nomics, 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 expe-

rience 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 perfor-

mance 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; Ngu-

yen & 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 ar-

gue 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 multidimen-

sional 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 com-

mand 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 inten-

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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 start-

ed 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, atti-

tude-behavior links do not necessarily depend on planned action, but on routine behaviors. Ac-

cording 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 defini-

tions 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 in-

stance, 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 be-

haviors, often characterised by frequent purchases, are prone to become habitual and thus auto-

matic over time, and are present particularly in the context of food and drink purchasing and con-

sumption (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’ behav-

ior 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 cus-

tomer 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 overestimat-

ing 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 econom-ic 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 ex-pected to naturally impose constraints on the principles governing the levels below, thereby establishing path de-pendence, 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 cross-

national 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; uncer-

tainty 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 val-

ues. 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 re-

sults. 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 like-

ly 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 10

countries, the sample is anchored in brands that meet the criteria of being well-known and well-

diffused 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 burg-

ers, fries, and soft drinks (e.g., with coffee and pastry), it also adapts to culturally diverse con-

sumer 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 oper-

ates 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 cur-

rently runs 727 outlets per BRIC state (compared with for example, only 104 in the former East-

ern 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 struc-

tured questionnaires to randomly chosen customers nearby or in the restaurants requesting to par-

ticipate in a short survey. Participants had to have patronized at least one of the chains in the re-

cent past (six months) to qualify to reply. The final dataset comprised 1128 respondents distribut-

ed 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 com-

parisons. 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 re-

sources. India and China have the world’s two largest populations with China expected to be as-

cendant 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 organiza-

tional 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 Ap-

pendix). 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 coeffi-

cients. 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 meas-

urement 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 in-

variant 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 com-

posite 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 signifi-

cantly different from zero. In the India-China group, six of the eight paths were significantly dif-

ferent from zero. Thus, we found strong significant support for our Unconstrained Model frame-

work 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 coun-

tries 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 con-

strained 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)

14

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 indi-

vidual models that were run prior to the multi-group models yielded coefficients closely approx-

imating the Multi-Group Partially Constrained Model in both signage and significance level. Be-

sides, 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. Acknowl-

edging 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 sat-

isfaction (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 individual-

country 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 mod-

erator, 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 Satisfac-

tion 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), re-

sults remained inconclusive. Moreover, to test for possible differences between genders, the re-

sponses 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 signifi-

cant differences emerged, indicating relative homogeneity of subsamples across these variables.

Assessment of Moderation Effects. Heeding Schoorman et al. (2007) who argue that future re-

search needs to focus on both the cultural environments and specify additional contextual varia-

bles 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 hav-

ing 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 con-

cerned 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 em-

beddedness 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 differ-

ences 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 infla-

tion 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 In-

dia/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 car-ried out to ferret out the sources of differences across the groups. Significant results from the two-group and the four-group 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 Satis-faction 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 de-

creases 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 culture-

specific 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 momen-

tum 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 individu-

alism (all score below), BR-countries score lower on masculinity than IC-countries, and substan-

tially higher on uncertainty avoidance. Whereas the dominant values in masculine societies, ac-

cording 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, perfor-

mance 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 trans-

ports 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 character-istic like “friendly” and “trustworthy” is most attributed to strong global brands in high uncertainty avoidance cul-tures (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 sta-

tus, 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 patron-

age 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 repu-

tation 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 uncer-

tainty and ambiguity and try to avoid these situations” (De Mooij & Hofstede 2010). High uncer-

tainty 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 uncer-

tainty 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 es-

sential 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 crea-

tion, 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, indi-cating 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 perfor-

mance. Yet, previous research has rarely addressed how global brands can achieve such relation-

ships across diverse international markets. Besides, research on the processes that drive loyalty

behavior has mainly focused on intentional mechanisms, assuming that loyalty starts with posi-

tive 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 be-

haviors 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 be-

havior, 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 con-

sumer 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 docu-

ment the effects of satisfaction, habit and reputation on loyalty and WOM intentions. To summa-

rize 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 satis-

faction decreases if customers have formed a habit of patronizing a particular brand. Positive ef-

fects 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 glob-

al 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 per-

formance. 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 Wit-

teloostuijn 2007; Pan et al. 1999; Slangen & Hennart 2008). Moreover, prior research adopts al-

most 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 domi-

nant 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 con-

sumption behavior (and practitioners in food marketing and potentially, other fast-moving con-

sumer 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 ap-

proach in social psychology (Ajzen 1991), suggesting that intention is the main causal mecha-

nism behind the enactment of behaviour, so even if habit is considered, its role is usually sup-

posed 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 ac-

tive, 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 business-

es, 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 sat-

isfying 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 ob-

tained by fostering habit-based loyalty through providing stable contexts, cumulative satisfaction

and frequent consumption opportunities. Thus, marketing strategies should focus on enticing con-

sumers to quickly develop the habit of patronizing the desired brand (Limayem et al. 2007). Be-

sides, 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 ongo-

ing debates surrounding relevant attributes in global marketing strategies.

Of course, the present study faces several limitations. We employ cross-sectional data, there-

fore, causal effects can only be inferred. According to Jaccard and Blanton (2005), for under-

standing 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-of-

home, own country versus abroad purchase behavior). Hence, we invite other scholars to under-

take 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

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BR IC

Variable Mean Std. Dev. Mean Std. Dev. (1) (2) (3) (4) (5)

(1) Satisfaction 5.25 1.26 5.25 0.97 1.000 0.524 *** 0.504 *** 0.609 *** 0.356 ***

(2) Reputation 5.38 1.00 5.32 0.98 0.463 *** 1.000 0.338 *** 0.339 *** 0.281 ***

(3) Loyalty 5.88 1.44 5.00 1.54 0.649 *** 0.535 *** 1.000 0.771 *** 0.326 ***

(4) WOM 4.33 1.57 4.89 1.34 0.618 *** 0.274 *** 0.471 *** 1.000 0.443 ***

(5) Habit 17.50 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 Multi-Group

Unconstrained Model Multi-Group

Partially Constrained Model BR IC BR IC BR IC

H1 H1a Satisfaction Loyalty .24*** .21*** .27*** .21*** .25*** .22***

H1b Satisfaction WOM .21*** .19*** .24*** .20*** .21*** .21***

H2 H2a Reputation Loyalty .19*** .13*** .23*** .16* .23*** .14*

H2b Reputation WOM .13** .13** .14** .15* .14** .13*

H3 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

Model Fit Indices

CFI: .81 NFI: .71 IFI: .77 RMSEA = .07 NNFI: .63 CMIN/d.f.: 2.72, p < .05

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

Notes: Standardized coefficients reported. Significance levels: ***p < 0.01; **p < 0.05; *p < .01. The partially constrained model displays culture-specific path-coefficients (H4) shaded and in bold italics.

TABLE 2. MODEL RESULTS FOR THE UNCONSTRAINED, CONSTRAINED AND PARTIALLY CONSTRAINED MULTI-GROUP MODELS

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Comparison of Means

MANOVA F (15, 958) = 13.829 p < .001

Variables Country Mean F p-value ANOVA Satisfaction Brazil-Russia 5.25

11.91 <0.001 India-China 5.25 South Africa 5.86 USA 6.08 ANOVA Reputation Brazil-Russia 5.38

2.63 <0.05 India-China 5.32 South Africa 5.85 USA 5.29 ANOVA Loyalty Brazil-Russia 5.88

11.80 <0.001 India-China 5.00 South Africa 5.83 USA 6.17 ANOVA WOM Brazil-Russia 4.33

21.52 <0.001 India-China 4.89 South Africa 5.80 USA 5.69 ANOVA Habit Brazil-Russia 17.50

13.46 <0.001 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

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APPENDIX A. MEASURES EMPLOYED Construct

Items (Anchored with 7-point Likert-type scales 1 = ”I strongly disagree” to 7 = ”I strongly agree”)

CR BR

CR IC

AVE BR

AVE

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 fast-food 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?

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APPENDIX B. BRICS SCORES ON HOFSTEDE DIMENSIONS5 Power Distance (PDI)Individualism versus Collectivism (IDV)Masculinity versus Femininity (MAS)Uncertainty Avoidance (UAI)

PDI IDV MAS UAI PDI H/L IDV H/L MAS H/L UAI H/L

Mean values* 57,5 48,5 50 60Country code

SPSS

Brasil 69 38 49 76 Hi Lo Lo Hi 16Russia 93 39 36 95 Hi Lo Lo Hi 10India 77 48 56 40 Hi Lo Hi Lo 12China 80 20 66 30 Hi Lo Hi Lo 13South Africa 49 65 63 49 Lo Hi Hi Lo 17

*Hofstede's Dimensionstheoretical min

theoretical max

actual range

(min-max) mean value

PDI -90 120 11-104 57,5UAI -150 230 8-112 60IDV 6-91 48,5MAS 5-95 50Source: Hofstede (1983) 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 > meanLo represents all values <= meanObtained 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.

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