How and when effective customer journeys drive brand loyalty: the role of consumer-brand identification

Abstract

Purpose

Effective customer journey design (ECJD) is considered a key variable in customer experience management and an essential source of brand meaning and pro-brand behavior. Although previous research has confirmed its importance for driving brand attitudes and loyalty, the role of consumer-brand identification as a social identity-based influence in this relationship has not yet been discussed. Drawing on construal level and social identity theories, this paper aims to investigate whether effective journeys and the resulting overall journey experience are equally powerful in driving brand loyalty among customers with different levels of consumer-brand identification.

Design/methodology/approach

The present article develops and tests a research model using data from the European and US service sectors (N = 1,454) to investigate how and when ECJD affects service brand loyalty.

Findings

Across two cultural contexts, four service industries and 33 service brands, the results reveal that ECJD is a crucial driver of service brand loyalty for customers with low consumer-brand identification. Moreover, the findings show that different aspects of journey effectiveness positively impact the valence of customers’ experience related to those journeys – a process that is ultimately decisive for their brand loyalty.

Originality/value

This study is unique because it generates theoretical and practical knowledge by combining the literature streams of customer journey design, customer experience and branding. Furthermore, this work demonstrates that consumer-brand identification is a critical boundary condition to be considered in the relationship between ECJD and brand loyalty in services.

Citation

Reitsamer, B.F., Stokburger-Sauer, N.E. and Kuhnle, J.S. (2024), "How and when effective customer journeys drive brand loyalty: the role of consumer-brand identification", Journal of Service Management, Vol. 35 No. 6, pp. 109-135. https://doi.org/10.1108/JOSM-08-2023-0374

1. Introduction

Strong brands are built on positive customer experiences that emerge from well-designed customer journeys (Lemon and Verhoef, 2016). In this way, brands add meaning and value to customer’s lives, simplify decision-making and increase brand loyalty (Kumar and Kaushik, 2020; Rather et al., 2022; Swaminathan et al., 2020). Service providers have thus begun to create personalized, meaningful experiences across the customer journey (Cocco and Demoulin, 2022; Dalla Pozza, 2022; Neslin, 2022), knowing that customer-centricity can make their brands up to 60% more profitable (Forbes, 2019). Airbnb, for instance, designed its brand-owned touchpoints (e.g. apps, online platforms and support services) to contribute to its promise of “making travelers feel they can belong anywhere in the world” (The Branding Journal, 2021). Likewise, at Disney, the company’s theme parks, movies, merchandise and customer service interactions are closely aligned with the brand promise of creating happiness through magical experiences (CEO Magazine, 2023).

However, in recent years, digitization, rising channel complexity and economic uncertainties have led to gaps between brand management, customer experience and customer journey design. In fact, many service firms struggle to align their brand promise with their customers’ needs because the brand and the customer experience are often managed as functional silos with little to no exchange between them (Kantar, 2023; Qualtrics, 2023). Due to the lack of customer-centricity, ineffective journeys and/or organizational issues, a customer’s experience and journey are often not aligned with a company’s brand strategy, specifically when the brand is unable to deliver excellent service to the customer at every available touchpoint (Jaakkola and Terho, 2021; Kuehnl et al., 2019). Thus, the current article seeks to bridge this gap and answer the overarching question, “How can brand management benefit from effective customer journey design (ECJD) to create positive customer experiences and brand loyalty in the service industry?”

Current marketing research suggests that a customer journey is considered effective if its touchpoints share the same brand/company theme (i.e. journey cohesion), are perceived uniformly in their design (i.e. journey consistency) and fit the situational context of the customer (i.e. journey context sensitivity; Kuehnl et al., 2019; Jaakkola and Terho, 2021). While a well-designed customer journey can drive pro-brand behavior (Jaakkola and Terho, 2021), it is important to consider that the cohesion, consistency and context sensitivity of touchpoints also influence customers’ overall experience related to those journeys (Ariely and Carmon, 2003; Chark et al., 2022; Trope and Liberman, 2010). Consequently, assessing how positive or negative customers evaluate their overall journey experience (OJX) as driver of customers’ probrand behavior (Wirtz et al., 2003) comes with significant advantages for both customers and management. When customers’ attention spans are short and immediate feedback is essential, simple measures provide a viable alternative to multidimensional measurement scales that require significant resources (Bergkvist and Rossiter, 2007; De Keyser et al., 2020). Similar developments have been observed with other marketing metrics, such as customer satisfaction and recommendation intentions, moving from multidimensional measures to simpler operationalizations (e.g. Fornell et al., 1996; Reichheld, 2003; De Keyser and Lariviere, 2014; Lervik Olsen et al., 2014). Therefore, the first objective of the current work is to investigate whether OJX mediates the influence of effective customer journeys on brand loyalty.

Further, although the service management literature has provided evidence that customers’ identification with a brand plays an important role in fostering pro-brand behavior (Homburg et al., 2009; Rather et al., 2022; Jin et al., 2023) and that its relationship with the customer experience is particularly worthwhile to investigate (Stokburger-Sauer et al., 2012; Coelho et al., 2018; Kantar, 2023), its exact role in the relationship between OJX and pro-brand behavior has yet to be clarified. Thus, examining the role of consumer-brand identification in this relationship enables us to identify the conditions when effective journey designs and their corresponding OJX matter for customers’ brand loyalty. Drawing on social identity theory (Tajfel and Turner, 1985), we argue that consumer-brand identification, understood as “a consumer’s perceived state of oneness with a brand” (Stokburger-Sauer et al., 2012, p. 407), modulates this effect, wherein stronger identification with a service brand should diminish the effect of OJX on brand loyalty. ECJDs and their corresponding OJXs should thus be particularly beneficial for customers with low levels of identification with a service brand, whereas the effect should be weaker for customers with strong brand identification (brand fans). Investigating this important boundary condition of consumer-brand identification is novel and the resulting insights should be of interest to customer experience scholars and managers alike. Hence, our second research objective is to examine whether consumer-brand identification moderates the relationship of ECJDs and their OJX on pro-brand behavior.

We begin this article with an integrative review of the literature on customer experience, customer journey design and pro-brand behavior. This literature review highlights overlaps and research gaps across these streams of literature and provides the basis for subsequent hypothesis development. We provide cross-cultural evidence for our research model across two studies using data from 1,454 customers of European and US service providers. The article concludes with a discussion of the results, implications for management and avenues for further research.

2. Conceptual background

The brand and its brand meaning develop from a customer’s experience throughout the entire customer journey. Given that little previous research has explored the intersection of customer experience, customer journey and pro-brand behavior in services, the following literature review briefly examines the extant research on these three domains, with a special focus on research that exemplifies and bridges any two of the three domains (indicated by the overlapping areas in Figure 1).

2.1 Customer experience

Customer experience is one of the most important concepts in customer journey research and management and can be defined as “cognitive, emotional, behavioral, sensorial, and social responses to a firm’s offerings during the customer’s entire purchase journey” (Lemon and Verhoef, 2016, p. 71). In contemporary business contexts, companies have shifted their focus from mere service provision to the creation of memorable and meaningful customer experiences (Jani and Han, 2015; Kandampully et al., 2018). Notably, the service marketing literature corroborates the growing sentiment among researchers that customers place higher value on experiences rather than tangible aspects (Bilgihan et al., 2014), with the servicescape being a critical factor in enabling them to evaluate and immerse themselves in the service experience (Bitner, 2008). Service and experience are thus closely linked, forming an inseparable connection from a customer’s perspective. Their interdependence is apparent as each element possesses restricted value when considered in isolation (Kandampully et al., 2023).

2.2 Customer journey and effective customer journey design

Customer experiences are shaped by all the interactions between a brand and its customers throughout the entire journey (Becker and Jaakkola, 2020). Previous research has conceptualized the customer journey as consisting of distinct stages, ranging from the prepurchase stage (i.e. the initial consideration and search for a product or service) to the actual purchase stage (i.e. interactions with the product or service and its environment during the purchase process) and the post-purchase stage (i.e. use, evaluation, service requests, future engagement and potential repurchase; Lemon and Verhoef, 2016; Voorhees et al., 2017). The customer journey approach extends research on service blueprinting (Shostack, 1982) by relying on visualization techniques to crystallize the points of interaction with customers (i.e. touchpoints) involved at each stage (e.g. customer journey mapping; Vakulenko et al., 2019). Current service research emphasizes the role of service quality (Halvorsrud et al., 2016; Van Vaerenbergh et al., 2019) and co-creation within journeys (e.g. Bolton et al., 2014), channel management (Barwitz and Maas, 2018; Palazón et al., 2022) and customer response behavior, particularly customer engagement (Cocco and Demoulin, 2022; Hollebeek et al., 2019; Kumar et al., 2019). Importantly, recent literature uses the customer journey approach as a conceptual basis to combine the concepts of servicescape and experiencescape, demonstrating that a collective focus on physical, technological, social, natural and cultural components across the entire customer journey can create lasting memories (Dedeoglu et al., 2018) and enhance value for the customer (Kandampully et al., 2023).

A customer journey is considered effective if its touchpoints are perceived as cohesive, consistent and context-sensitive (Kuehnl et al., 2019). Here, thematic cohesion refers to the extent to which customers perceive brand-owned touchpoints as sharing the same brand theme and meaning (Homburg et al., 2017; Massi et al., 2023). A customer’s journey in the airline industry, for example, is based on the common theme of travel and mobility. Consistency captures the extent to which customers perceive brand-owned touchpoints as uniform in their design, communication and process (Kao et al., 2020; Suay-Pérez et al., 2022). Airlines, for instance, use their corporate design consistently through all kinds of touchpoints, such as their website, ticketing, airport terminals, lounges, frequent-flyer programs and aircraft cabins. Context sensitivity illustrates how customers perceive brand-owned touchpoints to match their specific (life) goals, situational contexts, channel preferences and current activities (Barwitz and Maas, 2018; Lambillotte et al., 2022; Tong et al., 2020). The airline, for example, depending on customers’ preferences, offers them the possibility of purchasing flight tickets online via a third-party app, a travel agency or directly at the airport.

2.3 Pro-brand behavior and consumer-brand identification

Of major relevance to the current study is research that is at the intersection of customer experience and pro-brand behavior. Thus far, only a few papers (Brakus et al., 2009; Baxendale et al., 2015; Kumar and Kaushik, 2020; Rather et al., 2022) have investigated the impact of customer experiences on brand building. Some empirical evidence supports the notion that delivering a compelling positive customer experience yields various benefits, including enhanced brand consideration (Baxendale et al., 2015), customer-brand relationships (Hammedi et al., 2015) and increased brand loyalty (Brakus et al., 2009). Thus, assessing the influence of customer experiences and customer journeys on brand outcomes is important as it addresses a crucial theoretical gap in the existing literature.

The little previous service management research dedicated to exploring consumers’ interactions with brands and brand evaluations has provided several findings. Brakus et al. (2009, p. 54); for example, proposed a comprehensive theoretical framework of brand experience to encompass “sensations, feelings, cognitions, and behavioral responses evoked by brand-related stimuli,” revealing a positive correlation between brand experiences and crucial brand outcomes, such as satisfaction and loyalty. Baxendale et al. (2015) also provided evidence regarding the relevance of both touchpoint frequency and touchpoint positivity (similar to affective brand experience) to brand consideration. Kumar and Kaushik (2020) related Brakus et al.’s (2009) brand experience framework to service (and product) brand identification in the context of consumer-brand relationships and show positive relationships specifically for the sensory and affective brand experience dimensions. Brand identification has been found to be of major relevance in this regard. Similar findings were proposed by Rather et al. (2022).

Identifying with a brand is the expression of consumers’ search for identity-fulfilling meaning in the marketplace of brands (Stokburger-Sauer et al., 2012). Sometimes, this search results in becoming a brand fan, with a fan being “a person with a relatively deep, positive emotional conviction about someone or something famous [ …] driven to explore and participate in fannish practices” (Duffett, 2013, p. 18). Brand fandom is fostered through brand communities by facilitating shared customer experiences and multiway interactions (McAlexander et al., 2002; Muniz and O’Guinn, 2001). Belonging to a group of brand fans and acting in ways consistent with the shared brand identity result from customers’ experiences and ultimately drive pro-brand behavior (Kumar and Kaushik, 2020; Rather et al., 2022; Stokburger-Sauer et al., 2012).

The modest literature on the importance of customer experience for pro-brand behavior is complemented by little research on the role of customer journeys in pro-brand outcomes. To date only one study has empirically compared the relevance of effective journeys to brand experience for driving brand attitudes. It reports that ECJD is a stronger driver of utilitarian brand attitudes, while brand experience more strongly influences hedonic brand attitudes (Kuehnl et al., 2019).

The above discussion clearly indicates that some studies have attempted to bridge any two of the three research areas. At the same time, the review highlights a significant research gap in terms of investigations covering all three research areas (i.e. customer journey design, customer experience and pro-brand behavior) in the context of service brands. This is where the current research steps in as it aims to examine whether ECJD and its holistic assessment (OJX) drive brand loyalty, and whether consumer-brand identification plays a role in this relationship.

3. Hypotheses development

3.1 Effective customer journey design, overall journey experience and service brand loyalty

This paper differentiates between the concrete evaluation of a journey’s effectiveness (ECJD) and the more abstract (valence of) customers’ OJX. This distinction is important because OJX does not fully correspond to individual touchpoint experiences (e.g. Kuehnl et al., 2019; Jaakkola and Terho, 2021) but is driven by it. Consequently, different aspects of journey effectiveness (e.g. cohesion, consistency and context sensitivity during information search, booking, check-in, boarding, flight and luggage pick-up) should influence customers’ overall experience related to their journeys (e.g. a positive travel experience with an airline; Ariely and Zauberman, 2003; Chark et al., 2022).

Although the concept of customer experience is a complex construct often suggested to consist of several dimensions (e.g. sensorial, cognitive, affective, physical and social; Brakus et al., 2009; Gahler et al., 2023), customers’ differential responses towards a journey “can also be related to the unidimensional construct of affective valence or positivity” (Baxendale et al., 2015, p. 238; Kahneman and Krueger, 2006). This is because affective markers remain in episodic memory and drive subsequent brand-related cognitions and outcomes (Baumeister et al., 2007). Recent research by Gahler et al. (2023) demonstrated that “affective customer experience” is the most powerful dimension driving customer loyalty. In this paper, we follow this notion and that of Baxendale et al. (2015), who used the concept of touchpoint positivity as a holistic, affect-driven assessment of customers’ touchpoint experiences and consider OJX as a consequence of ECJD (that further drives brand-related outcomes).

Theoretically, the relationship between ECJD and OJX can be explained by construal level theory (Trope and Liberman, 2010). According to Trope and Liberman (2010, p. 2): “Moving from a concrete representation of an object to a more abstract representation involves retaining central features and omitting features that, by the very act of abstraction, are deemed incidental. […] An abstract representation is selected according to its relevance to one’s goals.” While the abstract construal in terms of OJX is more self-centered, the more concrete construal in terms of ECJD mainly investigates the journey itself. More specifically, Kuehnl et al. (2019, p. 556) note that “as concrete construal […], an effective CJD stresses the feasibility over the desirability aspects.” On the other hand, more abstract representations (e.g. Kuehnl et al. (2019) used the example of brand experience) are involved with the assessment of touchpoints when it comes to their potential to satisfy desires and induce stimulations and emotions. The (valence of) OJX represents such an abstract, holistic and more affective evaluation of these individual journey experiences. ECJD can thus be viewed as a concrete evaluation of the customer journey with a focus on feasibility considerations that leads to OJX, a more abstract and self-centered evaluation of the customer journey, with a focus on emotions and desirability. Following this line of argumentation, we propose the following hypothesis:

H1.

An effective customer journey design increases the valence of a customer’s overall journey experience.

Research from both cognitive psychology and service management suggests that customers aggregate their perceptions when retrospectively processing past experiences (Ariely and Carmon, 2003; Chark et al., 2022). Importantly, this holistic evaluation of experiences across customers’ journeys is decisive for their future decision-making, such as the intention to repeat and recommend an event (Ariely and Carmon, 2000; Fredrickson and Kahneman, 1993). Marketing research confirms that customers’ retrospective assessments of their experiences significantly influence their subsequent repurchase intentions (Montgomery and Unnava, 2009), satisfaction (Westbrook and Oliver, 1991), spending (Arnold and Reynolds, 2009) and word-of-mouth (WOM) behaviors (Reitsamer et al., 2020). As such, we assume a similar pattern for the effect of customers’ OJX on their brand loyalty. In our conceptualization of brand loyalty, we refer to behavioral brand loyalty (sometimes also named brand purchase intention, e.g. Yoo and Donthu, 2001) and define it as the future-directed willingness “to be more likely to buy a brand again […] and less likely to buy an alternative brand” (Brakus et al., 2009, p. 63). Thus, we propose:

H2.

The valence of a customer’s overall journey experience positively impacts on service brand loyalty.

Smooth customer journeys should create loyalty loops due to predictable and effective touchpoints (c.f., Siebert et al., 2020). Previous studies have confirmed this positive and direct influence of effective journeys on brand loyalty (Kuehnl et al., 2019). When remembering their journeys, however, we argue that customers’ OJX is likely to mediate this effect (the “how” of effective journeys as drivers of success metrics), because people tend to rely on abstract representations when assessing previous experiences with greater psychological distance (Ariely and Carmon, 2003). Having such a positive OJX in mind (i.e. an abstract, high-level construal, Trope and Liberman, 2010) will thus not only impact customers’ loyalty but also determine their receptivity for future brand-owned touchpoints (Lambillotte et al., 2022). Consequently, the more effective a customer journey has been perceived, the more positive customers’ OJX, which finally increases their loyalty to the service brand. Thus, we propose the following hypothesis:

H3.

The valence of a customer’s overall journey experience positively mediates the relationship of effective customer journey design and service brand loyalty.

3.2 The moderating role of consumer-brand identification

In today’s market environments, customers interact with service brands across multiple touchpoints and channels. Importantly, these interactions determine their affection towards brands and can result in all kinds of brand relationships (Fournier, 1998; Lombart and Louis, 2016; Kumar and Kaushik, 2020). Previous research finds that customers who have an expressive brand relationship more easily form brand loyalty (Carroll and Ahuvia, 2006; Oliver, 2010), have a higher tendency to purchase products at a price premium (Chaudhuri and Holbrook, 2001) and more often engage in positive WOM (Rahman et al., 2021). Although previous research on customer journey design has given some attention to brands (Kuehnl et al., 2019), the role of customer-brand relationships in the context of ECJD remains largely unclear. Considering that consumer-brand identification is an important driver of a service brand’s long-term competitive advantage (Nyffenegger et al., 2014; Stokburger-Sauer et al., 2012), customers’ satisfaction and well-being (Sato et al., 2023), the relevance of consumer-brand identification should also be investigated in the context of customer journey experiences. Specifically, whether the level of consumer-brand identification modulates the positive effect of customers’ OJX on brand loyalty (the “when” of effective journeys as driver of success metrics) needs to be tested.

Following social identity theory (Tajfel and Turner, 1985; Ashforth and Mael, 1989), it can be argued that consumers develop a perceived state of oneness with a brand if it represents the self-concept they want to acquire or maintain (Johnson et al., 2011; Stokburger-Sauer et al., 2012). Consumers with a high level of consumer-brand identification are sometimes referred to as “brand fans” (Kozinets, 2001; Lim and Brown-Devlin, 2023). Brand fans are known to “follow with purpose, with drive and with the goal of wanting to show support to those they admire” (Forbes, 2022). Consequently, aside from defending the brand (Ilhan et al., 2018), they are likely to overlook its potential shortcomings (Bhattacharya and Sen, 2003; Lam et al., 2010; Lim and Brown-Devlin, 2023), such as negative eWOM (Ho-Dac et al., 2013; Wilson et al., 2017). This so-called “halo effect” (Leuthesser et al., 1995) most likely makes such customers resistant to customer journeys with lower perceived effectiveness. On the other side of the continuum, nonfans are conscious of the existence of a brand but do not develop a further psychological connection with it (Funk and James, 2001; McDonald et al., 2024). This is because they are either new to a brand or a market and have not yet had sufficient interaction with the brand, or because of slight experiences or outcomes in the past (McDonald et al., 2024). We argue that, for such individuals, the consistency, cohesion and context sensitivity of a journey’s touchpoints can serve as viable means to increase their interaction with the respective brand, grow their loyalty and eventually transform them into brand fans (Jahn and Kunz, 2012). Consequently, we propose that the impact of customers’ OJX on their service brand loyalty should be stronger for those with low identification than for those who strongly identify with a brand. For the latter, the mere effectiveness of the customer journey will not play a crucial role in influencing their loyalty to the service brand. We propose:

H4.

The mediation effect of a customer’s overall journey experience in the relationship of effective customer journey design and service brand loyalty is moderated by consumer-brand identification such that higher levels of consumer-brand identification weaken the effect of the valence of a customer’s overall journey experience on their service brand loyalty.

Figure 2 presents our conceptual framework.

4. Methodology

4.1 Study 1

4.1.1 Study design

Study 1 involved an online survey of customers in the US and European service sectors (US: N = 425, M_age = 36, 45.2% female; Europe: N = 485, M_age = 37.5, 59% female). The participants were recruited using Mechanical Turk and Clickworker, two established crowdsourcing data collection platforms in the field of social sciences (Goodman and Paolacci, 2017). Both samples were merged as the Levene’s test for equality of variances was insignificant, indicating that both datasets did not show significant differences regarding their homogeneity of variances (N = 910, M_age = 38, 52.5% female). In two pretests (US: N = 82, M_age = 37; 34.2% female; Europe: N = 99, M_age = 36.5, SD_age = 11.04; 39% female), we explained the concept of ECJD and asked the participants to think about two brands they considered as either strong or weak in terms of their customer journey design for four selected industries (i.e. communication, retail, financial services and transportation) as part of an open association task. The two strong and two weak brands mentioned most often were selected for the main study to achieve a considerable amount of variance in our data. This procedure resulted in 32 service brands that were transferred to the main study (US: Amazon.com, American Airlines, AT&T, Bank of America, BestBuy, Capital One, Comcast, Delta Airlines, DHL, FedEx, JPMorgan Chase, T-Mobile, Target, Verizon, Walmart and Wells Fargo; Europe: 1&1, Amazon, Commerzbank, Deutsche Bank, DHL, DPD, Lufthansa, INGDiBa, Netto, O2, Penny, Postbank, Rewe, Ryanair, Telekom, and Vodafone). For the main study, each new participant was randomly assigned to one of the 32 service brands and then asked to indicate their brand familiarity (Kent and Allen, 1994) on a seven-point Likert scale (1 = “strongly disagree,” 7 = “strongly agree”). If the average mean of the brand familiarity score with the respective brand was below four, the participant was redirected to another brand (Kuehnl et al., 2019).

4.1.2 Measurements

All constructs were measured with established scales from the literature using seven-point Likert scales ranging from 1 (“strongly disagree”) to 7 (“strongly agree”) (see Appendix). ECJD was operationalized as a reflective second-order construct using the three-dimensional scale with twelve items by Kuehnl et al. (2019), including thematic cohesion (α = 0.898; e.g. “The touchpoints of X have a clear thematic philosophy”), consistency (α = 0.922; e.g. “X conveys a uniform impression across different touchpoints”), and context sensitivity (α = 0.915; e.g. “Different touchpoints of X are well aligned to my personal circumstances”). The valence of a customer’s OJX was measured with a single item from Baxendale et al. (2015) (i.e. “How did the customer journey make you feel about X?”), ranging from “very negative” to “very positive.” Consumer-brand identification was captured with five items by Stokburger-Sauer et al. (2012) (α = 0.963; e.g. “I identify strongly with brand X”). Brand loyalty was operationalized with three items following Zeithaml et al. (1996) (α = 0.900; e.g. “I will use brand X again”).

4.1.3 Results of study 1

Consistent with the two-step approach (Anderson and Gerbing, 1988), we first assessed internal consistency using Cronbach’s α, item-to-total correlations, average variance extracted, and factor loadings as first-generation criteria. The model parameters were subsequently assessed using second-generation criteria in confirmatory factor analysis (CFA). The measurement model (comparative fit index (CFI) = 0.96, Tucker–Lewis index (TLI) = 0.95, root mean square error of approximation (RMSEA) = 0.054, standardized root mean square residual (SRMR) = 0.052, χ² = 875.027, df = 240, p < 0.001) indicates an acceptable fit. Furthermore, all construct measures show high convergent validity, clearly above the threshold values recommended in the literature (Anderson and Gerbing, 1988). Discriminant validity was fulfilled as the intercorrelations between pairs of constructs were less than the square root of the average variance extracted estimates of the two constructs (Fornell and Larcker, 1981; see Table 1). To rule out common method bias in our results, we used procedural and statistical approaches (Podsakoff et al., 2024). First, we inspected all scales to reduce ambiguity, separated the measurement of predictor and outcome variables in our questionnaire, and ensured anonymity and privacy to our respondents (Malhotra, 2019). Second, we applied Harman’s single-factor test, comparing a single-factor model where all manifest variables are explained through one common method factor to the multifactor measurement model. The results reveal that the fit of the single-factor model is significantly worse than that of the measurement model in both studies (Δχ2 (8df) = 2738.21, p < 0.001), indicating that the correlations between observed variables cannot be adequately explained by one common method factor.

The study hypotheses were tested using structural equation modeling (SEM) applying Mplus 7.4 with maximum likelihood estimation. The structural model (CFI = 0.96, TLI = 0.95, RMSEA = 0.060, SRMR = 0.052, χ² = 783.853, df = 182) confirms our predicted main effects, indicating a positive influence of ECJD on customer’s OJX (β = 0.674, p < 0.001; support for H1), which subsequently exerts a positive effect on brand loyalty (β = 0.274, p < 0.001; support for H2). A bootstrap estimation with 5,000 resamples (CFI = 0.95, TLI = 0.95, RMSEA = 0.075, SRMR = 0.052, χ² = 1105.557, df = 182) further shows a significant mediation of OJX on the path of ECJD and brand loyalty (β = 0.185, p < 0 0.001). As the direct effect is also significant (β = 0.175, p < 0.001), a partial mediation results (i.e. partial support for H3; see Table 4).

For H4, we predicted that consumer-brand identification moderates the effect of customers’ OJX on their brand loyalty. Specifically, higher levels of consumer-brand identification should weaken the latter effect. We ran a latent moderated structural equation procedure (LMS) to test this hypothesis (Cheung and Lau, 2017; Hou et al., 2023). Given that the common model fit indices cannot be estimated when specifying latent interaction terms in Mplus (Sardeshmukh and Vandenberg, 2017), we compared a restricted model without the interaction effect to a second (full) model, including the latent interaction (OJX ✕ consumer-brand identification). We assessed the model fit using the Akaike information criterion (AIC), Bayesian information criteria (BIC) and log-likelihood (LL). When ranking models by AIC and BIC, the best approximating model is the one with the lowest AIC and BIC values (c.f., Burnham and Anderson, 2002; Symonds and Moussalli, 2011). The results indicate that AIC decreases from 54,219.942 (restricted model) to 54,192.961 (full model) and BIC decreases from 54,556.883 (restricted model) to 54,534.716 (full model). Moreover, we compared the two models using the LL ratio test [LR (df = 1) = 28.98, p < 0.001], which is significant. We thus conclude that the full model shows a better fit. The hypothesized latent interaction reveals that consumer-brand identification negatively moderates the effect of OJX on brand loyalty (β = −0.112, p < 0.001; support for H4). Therefore, the more customers identify with a service brand, the weaker the effect of their OJX on brand loyalty. The results are summarized in Table 3.

To account for potential cultural differences in our dataset, we conducted a latent-class analysis with two known classes (0 = US, N = 425, M_age = 36, 45.2% female; 1 = Europe, N = 485, M_age = 37.5, 59% female). We opted for a latent class analysis because Mplus does not support the multigroup command in models with latent interactions (Muthén and Muthén, 1998–2012). First, we compared an unconstrained model (AIC = 55,239.486; BIC = 55,643.815; LL = −27,535.743) with a restricted model (AIC = 55,204.767; BIC = 55,613.910; LL = −27,517.383). The significant change in chi-square (χ² = 18.36, df = 1, p < 0.001) indicates that both classes are different at the model level and that path coefficients can be compared across classes (Millsap, 2012). The results reveal that ECJD is a strong predictor of OJX in both subsamples (US: β = 0.655, p < 0.001; Europe: β = 0.624, p < 0.001; support for H1), which subsequently drives brand loyalty (US: β = 0.308, p < 0.001; Europe: β = 0.624, p < 0.001; support for H2). However, the direct effect of ECJD on brand loyalty is insignificant in the European class (β = 0.075, p > 0.05), implying a full mediation for OJX between ECJD and brand loyalty (β = 0.186, p < 0.001; full support for H3). In comparison, for the US class, the direct effect is significant (β = 0.268, p < 0.001), indicating a partial mediation (β = 0.202, p < 0.001; partial support for H3). Most importantly, we find a significant negative moderation of consumer-brand identification on the b-path in both classes (US: β = −0.106, p < 0.001; Europe: β = −0.099, p < 0.001; support for H4). Overall, the results remain considerably stable across both cultural classes.

4.1.4 Discussion of study 1

Study 1 confirms our predictions that effective journeys positively influence customers’ OJX, which subsequently drives their brand loyalty. Although the mediation effect is found to be partial (except for the European subsample), the indirect effect adds explanatory power and helps explain the mechanism of “how” ECJD drives brand loyalty by demonstrating that different aspects of journey effectiveness positively influence customers’ OJX (c.f. Ariely and Carmon, 2003; Chark et al., 2022), which subsequently drives their later retention behavior. Consumer-brand identification presents a critical boundary condition in the latter effect, as it has been shown that ECJD and OJX are a crucial means to increase the loyalty of customers with low levels of brand identification, while factors other than ECJD account for brand fans’ loyalty toward a service brand.

4.2 Study 2

4.2.1 Study design and construct measures

In Study 2, we tested our predictions in an online survey in cooperation with a European insurance provider and its clients. Financial service brands, such as insurance providers, are particularly suitable for our study objectives because they deal with sensitive customer data that make reliable encounters and effective touchpoint design critical. A prestudy (N = 25) was set up to enhance the clarity and content validity of our questionnaire. For participation in the main study, it was crucial that all subjects were current customers of the insurance provider. To incentivize participation, an optional prize draw was included at the end of the questionnaire. A total of 6,657 invitations were sent out, of which 544 questionnaires were completed (M_age = 48.5; 37% female). All constructs were measured with identical scales as in Study 1 and showed high convergent validity and reliability (Hair et al., 2018; see Table 1). Again, common method bias is not considered to be of concern, as Harman’s single-factor test indicates that the fit of the single common factor model is significantly worse compared to the measurement model (Δχ2 (8df) = 1422.37, p < 0.001).

4.2.2 Results of study 2

As in Study 1, the measurement model (CFI = 0.98, TLI = 0.98, RMSEA = 0.040, SRMR = 0.029, χ² = 326.937, df = 181, p < 0.001) shows acceptable fit, and all construct measures demonstrate high convergent and discriminant validity (Anderson and Gerbing, 1988; Fornell and Larcker, 1981; Hair et al., 2018; see Tables 1 and 2).

The study hypotheses were tested using structural equation modeling (Mplus 7.4) with maximum likelihood estimation. The structural model (CFI = 0.98, TLI = 0.98, RMSEA = 0.040, SRMR = 0.030, χ² = 330.416, df = 182) confirms our earlier findings, revealing a positive influence of ECJD on customer’s OJX (β = 0.564, p < 0.001; support for H1), and a subsequent positive effect from OJX on brand loyalty (β = 0.268, p < 0.001; support for H2). A bootstrap estimation with 5,000 resamples (CFI = 0.97, TLI = 0.97, RMSEA = 0.055, SRMR = 0.030, χ² = 457.966, df = 182) confirms the mediation of OJX on the effect of ECJD and brand loyalty (β = 0.145, p < 0.001). As the direct effect is also significant (β = 0.385, p < 0.001), a partial mediation results (i.e. partial support for H3; see Table 4).

Next, we conducted a latent moderated structural equation procedure (Cheung and Lau, 2017; Hou et al., 2023) to test for the latent interaction of OJX and consumer-brand identification on brand loyalty. As in Study 1, we compared a restricted model without the interaction effect to a second (full) model, including the latent interaction (OJX ✕ consumer-brand identification; Sardeshmukh and Vandenberg, 2017). The results confirm that AIC decreases from 28,574.103 (restricted model) to 28,565.878 (full model), and BIC decreases from 28,869.125 (restricted model) to 28,868.115 (full model). Moreover, we compared the two models using the LL ratio test [LR (df = 1) = 115.22, p < 0.001], yielding a significant result. We thus conclude that the full model shows a better fit. The hypothesized latent interaction reveals that consumer-brand identification negatively moderates the effect from OJX to brand loyalty (β = −0.106, p < 0.01), thus supporting H4 (see Table 3).

4.2.3 Discussion of study 2

Conducted in cooperation with an insurance provider and its clients, Study 2 provides sector-specific empirical field evidence of the mediating effect of customers’ OJX on the relationship between ECJD and service brand loyalty. Most importantly, the study validates that stronger identification with a service brand leads to a decrease in the effect of OJX on brand loyalty. Table 3 summarizes the fit indices and standardized coefficients across all studies.

5. Discussion

5.1 Summary and theoretical implications

The present study examines the conditions for how and when ECJDs are considered beneficial for customers’ brand loyalty. Across two cultural contexts, four service industries, and 33 service brands, we demonstrate that different aspects of journey effectiveness positively impact the valence of customers’ experience related to those journeys (Ariely and Zauberman, 2003; Chark et al., 2022), which is decisive for their later service brand loyalty (Ariely and Carmon, 2000; Chark et al., 2022). Drawing on construal level theory (Trope and Liberman, 2010), we show that abstract representations of the customer journey (in the form of the success metric “overall journey experience”) can indeed add significant power in explaining the mechanism of how ECJD drives brand loyalty. At the same time, our results reveal that for some service industries (e.g. finance; c.f. Study 2), the mere effectiveness of the customer journey is sufficient to considerably drive brand loyalty.

Moreover, we demonstrate that customers’ identification with a service brand modulates this effect. ECJD particularly influences brand loyalty when consumer-brand identification is low, thus providing an answer to the question of when ECJD matters for service brand loyalty. Considering the theoretical foundation of social identity theory (Tajfel and Turner, 1985), the social identity-based influence seems to have more explanatory power for consumers with high levels of consumer-brand identification, whereas for individuals with lower levels of consumer-brand identification, effective customer journeys can be decisive for brand success. This finding represents a significant contribution to the intersection of service and branding research. For new customers or individuals who have not yet had sufficient interaction with a brand, consistent, cohesive and context-sensitive touchpoints can serve as viable means to build up their loyalty. By contrast, individuals who have already developed strong consumer-brand identification (e.g. brand fans) tend to overlook perceived journey effectiveness along with the potential shortcomings of the brand (Bhattacharya and Sen, 2003; Lam et al., 2010; Leuthesser et al., 1995). Strong identification with a service brand can thus act as a buffer for negative experiences to (negatively) impact brand loyalty. By increasing brand identification, organizations can expand customers’ “zone of tolerance” (e.g. in case of service failures) and keep them inside the loyalty loop (Siebert et al., 2020). Likewise, service firms can leverage the increased tolerance level of brand fans for developing both their brand and customer experience further by purposefully integrating them into product testing, technological innovations and brand development initiatives.

5.2 Managerial implications

The current research extends our theoretical and practical knowledge by demonstrating that customers’ OJX and their brand identification are key variables to consider in designing effective customer journeys. While high levels of service quality (Parasuraman et al., 2005), a well-designed servicescape (Bitner et al., 2008), and a memorable service experience (e.g. Voorhees et al., 2017) can build the foundation for pro-brand behavior, ECJD represents a particularly crucial service design element for introducing customers with low levels of brand identification into a loyalty loop with service brands (e.g. Tueanrat et al., 2021; Siebert et al., 2020). Likewise, managers must consider that ECJD influences how positive or negative customers evaluate their experience along the journey and that this abstract evaluation will significantly drive their future behavior (c.f., Chark et al., 2022; Wirtz et al., 2003).

Consequently, apart from offering consistent, coherent and context-sensitive touchpoints, service firms are advised to take initiatives to enhance customers' recall of their journey with a service brand, as this will facilitate the formation of a positive OJX and drive brand loyalty. Managers could ease this process by using personalized reminders of people’s peak moments or endings in their journey, as these moments are found to have a particularly strong effect on customers’ memories (c.f., Redelmeier and Kahneman, 1996). Measuring customers’ OJX can also serve as a reliable indicator to map the valence of a journey in retrospect. Considering that both management and customers favor fast and simple feedback, this single-item measure can easily be implemented in follow-up mails or app-based notifications.

Moreover, managers need to be aware that for some service industries (e.g. finance; c.f. Study 2 and Table 4), effective journeys per se can sufficiently drive customer loyalty. Banks or insurances, for example, are advised to make a special effort to personalize their interactions with clients (e.g. by using their preferred communication channels or offering tailored solutions) and strive for consistency and cohesion across all touchpoints and channels (e.g. by using uniform design and language across their app, mail and personal conversations or providing reliable information about financial transactions). Considering the findings from Study 2, these efforts should account for a good degree of brand loyalty.

Consistent with our second research objective, our results suggest that different marketing programs are required for customers with low vs. high levels of consumer-brand identification. For individuals with low identification (e.g. new customers or people valuing swift service delivery without building closer relationships with the brand), effective journey designs matter and are a convincing reason to remain loyal to the brand. In financial services such as banking, ensuring consistency and coherence across platforms and touchpoints (e.g. bank branch, online services and mobile apps), together with personalized services, can serve as a sufficient driver for low identifiers to remain loyal. By contrast, due to their strong relationship and previous interactions with the brand, brand fans might regard journey effectiveness as a precondition, but not a key argument for remaining loyal. Thus, drivers rewarding their loyalty (e.g. premium support, special offers and dedicated events for long-term customers) should be considered, and corresponding marketing programs must be designed specifically for this group.

Finally, although our results suggest that brand fans tend to have a larger “zone of tolerance” when it comes to negative experiences along the customer journey, this does not mean that recovery efforts are unimportant for them. While service firms should generally minimize service failures, they are advised to be fully responsive and compensate brand fans with potentially different, but meaningful offerings, refunds or rewards. At Disney, for example, compensation for loyal customers particularly involves experiential benefits such as free passes to their theme parks or free dinners. Likewise, the sports brand Reebok mixes transactional and experiential benefits for loyal customers as part of their “Unlocked program”. In exchange for brand interactions, customers not only earn points, but also receive early access to products, training sessions, invitations to launch events and additional personalized offers.

5.3 Limitations and future research

The present study has several limitations that present directions for future research summarized in Table 5. First, our findings apply only to smooth customer journeys that follow a cyclical pattern of predictable experiences to build customer loyalty over time (Siebert et al., 2020), and to brand-owned touchpoints (Lemon and Verhoef, 2016) that are created and managed by the organization. Our research design did not include customer-owned, partner-owned or external/social touchpoints, which might also play a significant role in influencing customers’ OJX and their pro-brand behavior. Future research could broaden the perspective here by investigating how different touchpoint types contribute to customers’ OJX.

Second, although we find a robust mediation of customers’ OJX in the effect of ECJD on brand loyalty in both studies, the mediation is partial in nature. Notably, previous studies have also found a direct effect of ECJD on brand loyalty (Jaakkola and Terho, 2021; Kuehnl et al., 2019), positing that effective journeys evoke a concrete, low-level construal with rich details about a journey and its feasibility driving customers’ loyalty. When retrospectively processing their journeys, however, there is widespread consensus in retrospection research (c.f., Ariely and Carmon, 2003) that customers rely on an abstract, high-level construal (such as OJX), which influences their subsequent decision-making and behaviors. Our results, particularly the stronger indirect effect in Study 1, confirm the latter reasoning. Nevertheless, we suggest future research to delve into the exact mechanism by which customers form overall journey evaluations and examine which moments of their journey are particularly decisive in this regard.

Third, it would be interesting to study how the role and importance of ECJD develops over a customer’s entire lifecycle with a service brand. While effective journeys are a feasible means of introducing customers into a loyalty loop with brands, our results show that for brand fans, effective journeys are not a decisive driver of their loyalty. Thus, future research could clarify which other factors, if not effective journeys, account for the long-term loyalty of brand fans.

Fourth, studies could apply a more diverse set of empirical methods when studying the consequences of effectiveness across a customer’s journey. Although self-report surveys are a suitable means for capturing customers’ experiential perception, the measurement could be complemented by real-time tracking of their experiences (e.g. by electrophysiological methods) and the use of corporate or app data.

Finally, although we found consistency in our effects across two different cultural settings (US and Europe), we strongly recommend that future studies test our model in other cultural contexts (e.g. Asia, Pacific Rim) to determine whether the results remain stable. Likewise, replicating our model on company level (c.f. Study 2) would be beneficial, as even service firms operating in the same industry can strongly differ in their ECJD perceptions.

Figures

Figure 1

Research focus

Figure 2

Conceptual framework

Table 1

Descriptive statistics and correlations

	Construct	Study 1						Study 2
		M	SD	1	2	3	4	M	SD	1	2	3	4
1	Effective Customer Journey Design	4.92	1.20	0.922a				5.17	1.29	0.946a
2	Valence of Overall Journey Experience	4.98	1.26	0.423b	n.a.			6.09	1.11	0.327b	n.a.
3	Consumer-Brand Identification	3.96	1.73	0.537	0.361	0.841		3.91	1.72	0.491	0.212	0.770
4	Brand Loyalty	4.74	1.55	0.537	0.494	0.684	0.757	5.70	1.36	0.489	0.340	0.397	0.784

Note(s): The theoretical scale range is 1–7 for all variables. For more details on the measures, see Appendix

^aAverage variance extracted

^bInter-construct squared correlations

Source(s): Table by the authors

Table 2

Measurement model

Constructs	Items	Study 1			Study 2
		SFL	CR	AVE	SFL	CR	AVE
ECJD (Second Order)	COH	0.978	0.973	0.922	0.959	0.981	0.946
	CON	0.983			0.999
	CST	0.918			0.959
Consumer-Brand Identification	CBI01	0.931	0.964	0.841	0.892	0.944	0.770
	CBI02	0.898			0.851
	CBI03	0.917			0.882
	CBI04	0.910			0.863
	CBI05	0.928			0.899
Brand Loyalty	LOY01	0.939	0.903	0.757	0.864	0.916	0.784
	LOY02	0.813			0.883
	LOY03	0.856			0.908
Global Fit		CFI = 0.96, TLI = 0.95, RMSEA = 0.054, SRMR = 0.052, χ2 = 875.027 (df = 240, p < 0.001)			CFI = 0.98, TLI = 0.98, RMSEA = 0.040, SRMR = 0.029, χ2 = 326.937 (df = 181, p < 0.001)

Note(s): SFL = Standardized Factor Loading; CR = Construct Reliability; AVE = Average Variance Extracted CFI = Comparative Fit Index; TLI = Tucker–Lewis Index; RMSEA = Root Mean Square Error of Approximation SRMR = Standardized Root Mean Square Residual; ECJD = Effective Customer Journey Design; COH = Coherence CON = Consistency; CST = Context-sensitivity

Source(s): Table by the authors

Table 3

Fit indices and standardized coefficients for all structural models

	Study 1		Study 2
	Baseline	Interaction	Baseline	Interaction
	N = 910	N = 910	N = 544	N = 544
Model Fit
χ2	783.853		330.416
df	182		182
Log Likelihood	−27,039.971	−27,025.481	−14,271.051	−14,213.439
Estimated Paths	70	71	70	71
CFI	0.955		0.979
TLI	0.948		0.976
RMSEA	0.060		0.040
SRMR	0.052		0.030
AIC	54,219.942	54,192.961	28,574.103	28,568.878
BIC	54,556.883	54,534.716	28,869.125	28,868.115
Effects
ECJD → OJX	0.674***	0.673***	0.564***	0.575***
ECJD → LOY	0.175***	0.164***	0.408***	0.386***
OJX → LOY	0.274***	0.241***	0.268***	0.189***
CBI → LOY	0.539***	0.569***	0.250***	0.285***
OJX ✕ CBI → LOY		−0.112***		−0.106**

Note(s): ***p < 0.001, **p < 0.01, CFI = Comparative Fit Index; TLI = Tucker–Lewis Index; RMSEA = Root Mean Square Error of Approximation; SRMR = Standardized Root Mean Square Residual; AIC = Akaike Information Criterion; BIC = Bayesian Information Criterion; ECJD = Effective Customer Journey Design; OJX = Valence of Overall Journey Experience; CBI = Consumer-Brand Identification; LOY = Brand Loyalty

Source(s): Table by the authors

Table 4

Mediation effects

		Study 1	Study 2
Effect	Variables	Std. Estimate	Std. Estimate
Direct	ECJD → LOY	0.175***	0.385***
Indirect	ECJD → OJX → LOY	0.185***	0.145***
Total	ECJD → LOY + ECJD → OJX → LOY	0.360***	0.530***
Model Fit		CFI = 0.95, TLI = 0.95, RMSEA = 0.075, SRMR = 0.052, χ2 = 1105.557, df = 182	CFI = 0.97, TLI = 0.97, RMSEA = 0.055, SRMR = 0.030, χ2 = 457.966, df = 182

Note(s): ***p < 0.001; CFI = Comparative Fit Index; TLI = Tucker–Lewis Index; RMSEA = Root Mean Square Error of Approximation; SRMR = Standardized Root Mean Square Residual; ECJD = Effective Customer Journey Design; OJX = Valence of Overall Journey Experience; LOY = Brand Loyalty

Source(s): Table by the authors

Table 5

Avenues for customer journey research

Theme	Questions for future research
Effective Customer Journey Design	⁃ Are effective journey designs equally decisive for different types of customer journeys (e.g. smooth vs. sticky journeys)? ⁃ What role do channel complexity and channel preferences play for customer journey effectiveness? ⁃ Are ECJDs equally powerful for functional vs. hedonic services? ⁃ Can deviations from effective journey designs be desirable?
Customer Experience	⁃ How do customers integrate their experiences along the journey to form retrospective assessments? ⁃ Which moments of customers’ experiences along the journey are particularly decisive for their retrospective assessments? ⁃ How can service firms alter/adjust customers’ overall journey experiences during post-consumption (e.g. in case of service failures)?
Pro-Brand Behavior	⁃ What other aspects (if not their effectiveness) of the customer journey are decisive for brand fans to keep up their pro-brand behavior? ⁃ What role do brand communities play for consumer-brand identification along the customer journey? ⁃ Can consumer-brand identification serve as segmentation variable for service brands and their subsequent, segment-specific customer journey designs and marketing-mix strategies? ⁃ How does the role of effective journeys and their overall journey experience change in the entire lifecycle of a customer-brand relationship? ⁃ Which organizational structures and processes are beneficial for aligning customer experience and brand management in service organizations?
Empirical Investigation	⁃ How can visual design tools such as customer journey mapping be expanded to better incorporate a journey’s effectiveness? ⁃ How can self-report measures during and after the customer journey be combined with new technologies (e.g. electrophysiological methods) and corporate data? ⁃ How can existing measures for ECJD and pro-brand behavior be adjusted and simplified for mobile use?
Contextual Factors	⁃ How does the proposed model of ECJD and its impact on pro-brand behavior hold up in different cultural contexts? ⁃ What sector-specific characteristics need to be considered when designing ECJDs to create pro-brand behavior? ⁃ What role do partner-owned/social/external touchpoints play for customers’ overall journey experience and their resulting pro-brand behavior in services?

Source(s): Table by the authors

Table A1

Reliability and validity of measurement items (EFA)

Construct		Item	Study 1			Study 2
			α	FL	ITTC	α	FL	ITTC
Effective Customer Journey Design Kuehnl et al. (2019)	Cohesion of touchpoints	The touchpoints of brand X are thematically rooted	0.898	0.894	0.802	0.936	0.830	0.841
		The touchpoints of brand X have a clear thematic philosophy		0.888	0.791		0.860	0.867
		Brand X pursues a thematic concept		0.873	0.769		0.844	0.853
		Brand X stands for a specific theme and campaigns for it		0.849	0.735		0.825	0.836
	Consistency of touchpoints	Brand X conveys a uniform impression across different touchpoints	0.922	0.909	0.834	0.938	0.917	0.850
		Brand X is consistent across different touchpoints		0.900	0.819		0.910	0.840
		The presentation of brand X’s various touchpoints emits a homogeneous image		0.893	0.809		0.923	0.860
		Different touchpoints of brand X are designed in a concerted way		0.898	0.816		0.923	0.861
	Context sensitivity of touchpoint	When I encounter brand X, it takes my specific activities, interests or needs into account	0.915	0.878	0.783	0.909	0.856	0.748
		Different touchpoints of brand X are well aligned to my personal circumstances		0.908	0.831		0.906	0.871
		I have the impression that different touchpoints of brand X fit well into my daily routines		0.894	0.806		0.888	0.882
		The connection between different touchpoints of brand X allows me simple and fast activities		0.890	0.801		0.895	0.878
Consumer- Brand Identification Stokburger-Sauer et al. (2012)		I feel a strong sense of belonging to brand X	0.963	0.943	0.909	0.943	0.914	0.862
		I identify strongly with brand X		0.922	0.878		0.883	0.817
		Brand X embodies what I believe in		0.935	0.897		0.908	0.853
		Brand X is like a part of me		0.929	0.889		0.890	0.828
		Brand X has a great deal of personal meaning for me		0.941	0.907		0.919	0.870
Brand Loyalty Brakus et al. (2009)		In the future, I will be loyal to brand X	0.900	0.936	0.849	0.914	0.920	0.820
		I will use brand X again		0.894	0.766		0.923	0.825
		Brand X will be my first choice in the future		0.911	0.799		0.931	0.842

Note(s): α = Cronbach’s Alpha; EFA = Exploratory Factor Analysis; FL = Factor Loading; ITTC = Item-to-total-correlation

Source(s): Table by the authors