With customer experiences and omnichannel retailing becoming increasingly fragmented, this study develops an innovative evaluation model for brand loyalty, integrating consumer-based brand equity (CBBE), customer journey, and the loyalty spiral framework. It conceptualizes brand resonance as an emotionally driven, multi-touchpoint spiral that evolves through consumers’ continuous engagement with the brand at various stages of interaction. It used a hybrid methodology that combined structural equation modeling and the fuzzy linguistic preference relations–analytic network process. Drawing on empirical data from the S Chain Coffee Shop, the proposed model identified four key dimensions—emotional resonance, behavioral continuity, touchpoint quality, and experience value—along with 12 critical subfactors. The findings indicated that emotionally rich experiences, such as joyful participation, personalized services, and community engagement, had a greater impact on brand loyalty than functional attributes. This study contributes to the literature on CBBE by presenting a cyclical, emotion-centered loyalty model that reflects current consumer behavior in digital environments. It also provides a practical three-phase roadmap for creating emotional value, comprising gamified engagement before purchase, value-added incentives during purchase, and loyalty recognition after purchase. Furthermore, it offers a replicable approach for mapping loyalty spirals across diverse industries, supporting sustainable competitive advantage through experiential innovation.
The COVID-19 pandemic significantly transformed consumer behavior, accelerating a shift toward e-commerce and increasing the demand for seamless online experiences (Chang & Meyerhoefer, 2021; Verma & Gustafsson, 2020). Although they do not introduce new path structures, mobile devices are essential consumer tools that facilitate the navigation of complex, multi-touchpoint shopping journeys. Loyalty drivers vary across consumer segments. Pragmatic shoppers prioritize product satisfaction, while omnichannel users, who move between online and offline experiences, value satisfaction and inspirational engagement (Herhausen et al., 2019). Interacting with brands through diverse channels and media enhances the social dimension of the consumer–brand experience (Edelman & Singer, 2015). However, limited research has examined how touchpoints operate synergistically to improve customer satisfaction. Addressing this gap, Baidya et al. (2023) identified four categories of value-driven touchpoints that contribute individually and collectively to customer experience, highlighting their interdependent influence. Similarly, Herhausen et al. (2019) segmented customer journeys by touchpoint usage and explored how satisfaction, inspiration, and loyalty emerged from these interaction patterns.
Previous research on customer experience management has emphasized the long-term evolution of customer journeys across service cycles (Homburg et al., 2017; Lemon & Verhoef, 2016). Numerous studies have advocated consistent and predictable journey designs, supported by streamlined technologies that simplify, personalize, and contextualize interactions to encourage repeat engagement and purchasing (Court et al., 2009; Kuehnl et al., 2019). Building on this concept, Calza et al. (2023) introduced the “sustainable experiential ecosystem” model, incorporating environmental sustainability and collaboration among multiple stakeholders into the customer journey. They concluded that coordinating efforts across diverse touchpoints, particularly in food delivery, addresses sustainability challenges. Companies must identify distinct journey segments and understand how experiential value is reassessed over time to effectively manage increasingly complex customer journeys (Anderl et al., 2016; Ballestar et al., 2018). Kuehnl et al. (2019) found that the effectiveness of customer journey design depends on whether consumers perceive touchpoints as cohesive, consistent, and context sensitive. This helps identify the key factors, such as product type, switching costs, and brand involvement, which influence these perceptions.
While previous research on consumer-based brand equity (CBBE) has primarily examined the causal relationships among its core dimensions, less attention has been paid to its role in fostering brand resonance. Previous studies have addressed this gap by applying CBBE across varied contexts. Londoño et al. (2016) developed the consumer-based brand–retailer–channel equity framework, revealing that awareness, perceived quality, and loyalty significantly influenced equity across brand and distribution levels. In the tourism sector, Kotsi et al. (2018) demonstrated that brand awareness, image, quality, and value positively affected travelers’ attitudinal loyalty, with brand image and perceived value being particularly influential in destination branding. Recently, Akdogan et al. (2025) investigated the impact of marketing resources and activities on business performance in the banking sector, considering the mediating role of CBBE. Their study integrated a resource-based view with the marketing productivity chain, presenting a comprehensive model linking marketing strategy, consumer response, and organizational performance. The findings emphasized CBBE’s strategic value in aligning marketing investments with business outcomes.
Recent studies have expanded the traditional view of CBBE by investigating how contextual variables, such as corporate social responsibility (CSR) communication and country image, influence brand equity perceptions. Muniz et al. (2019) found that while CSR messaging did not affect perceived product quality, it significantly influenced brand loyalty and had a nuanced impact on brand awareness. Their cross-national analysis in Australia, the United States, and Spain underscored the importance of aligning CSR strategies with brand-building efforts to strengthen immediate brand equity. In the retail context, Troiville et al. (2019) proposed a second-order reflective–formative model, comprising eight dimensions capturing how consumer value creation enhances retailer brand equity. Similarly, Martinelli and De Canio (2021) demonstrated that a positive affective country image enhances retail brand equity.
In contrast, cognitive image could negatively affect brand equity, underscoring the interplay between emotional and rational brand cues. Despite these advancements, brand equity research continues to adopt a linear view of consumer decision-making, structured around the pre-purchase, purchase, and post-purchase stages. Strong brands reduce risk and search costs, resulting in increased consumer willingness to pay and enhanced loyalty (Coelho et al., 2018). However, this sequential perspective overlooks the iterative and dynamic nature of real-world consumer journeys. In practice, brand interactions unfold as a spiral rather than a closed loop (Court et al., 2009; Fleming, 2016), with consumers continuously reevaluating, repurchasing, and reengaging across multiple channels, and loyalty intensifying over time through meaningful and consistent experiences. Brand resonance occurs when consumers form synchronized, emotionally charged relationships with brands. To reflect this complexity, research must move beyond static models and explore the nonlinear, recursive pathways through which brand equity and loyalty coevolve.
In today’s complex retail landscape, brands must deliver more than functional value—they must cultivate emotional connections and meaningful experiences across multiple touchpoints. Although CBBE has been widely studied, most previous research has adopted a linear, static lens, often isolating constructs, such as brand awareness or loyalty, without considering their evolution through dynamic customer experiences. Meanwhile, concepts such as customer journey and loyalty loop have gained traction; however, the interplay between these constructs and their cumulative impact on emotional attachment and behavioral loyalty remains underexplored. Existing models overlook the interaction of touchpoints to create a loyalty spiral that fosters a profound commitment over time. This study addresses these gaps by reconceptualizing brand equity as a recursive, emotionally driven process. Building on Keller's (2016) CBBE framework and the loyalty spiral model by Siebert et al. (2020), this study proposes an integrative approach that leverages strategies, including personalization, gamification, and value-based incentives, to enhance customer–brand relationships throughout the journey. Using a globally recognized coffee chain as the empirical context, it investigates how emotionally resonant strategies enhance brand resonance in omnichannel environments. By combining theoretical insights with applied evaluation, it enriches the academic discourse on brand equity and its practical applications in experience-driven retail settings.
Methodologically, recent studies have demonstrated the value of integrating symmetric and asymmetric techniques, such as structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA), to yield a more nuanced understanding of technology adoption and decision-making processes (Chen et al., 2023). Hence, this study combines SEM with the fuzzy linguistic preference relations–analytic network process (FLPR-ANP) to capture causal relationships and interdependencies among CBBE dimensions and omnichannel touchpoints. This dual-method design allows for a nuanced analysis of how emotional engagement and experiential factors jointly influence brand resonance and customer loyalty. This study bridges theory and practice to refine the conceptualization of brand equity and offer brand managers emotionally resonant, behaviorally effective, and adaptable actionable strategies for today’s digital and experience-driven marketplace.
Literature reviewConsumer-based brand equity (CBBE)CBBE refers to the differential effect of brand knowledge on consumer responses to marketing activities (Keller, 1993). Drawing on cognitive psychology and strategic marketing, CBBE is widely recognized as a multidimensional construct comprising brand awareness, associations, perceived quality, and loyalty (Aaker, 1991; Keller, 1993). As a strategic intangible asset, CBBE fosters long-term customer relationships, thereby achieving a competitive advantage. Keller (2001) investigated CBBE through five brand dimensions: performance, image, judgment, feelings, and resonance. These dimensions encompass cognitive (i.e., performance and judgment) and affective aspects (i.e., image and feelings) of brand evaluation. They highlight how consumers form and internalize brand meanings through memory-based associations.
Several empirical studies on CBBE have examined how its dimensions affect consumer loyalty and brand value, enhancing the theoretical understanding. Kotsi et al. (2018) found that, in tourism, brand awareness, image, and value significantly shape attitudinal loyalty, whereas brand quality has a limited effect. This highlights the distinct roles of different brand components. Furthermore, Alvarado-Karste and Guzmán (2020) revealed that consumers’ cognitive styles influence how they respond to brand messaging—analytical individuals prefer rational appeals, while intuitive individuals are drawn to emotional narratives. Furthermore, social influence and alignment with brand value enhance perceived brand value, providing strategic insights for segmentation and positioning. Algharabat et al. (2020) emphasized the impact of social media-driven brand engagement on CBBE formation. They identified consumer involvement, participation, and interaction with self-expressive brands as key antecedents that enhance brand loyalty, awareness, associations, and perceived quality, particularly in the telecommunications sector. These findings highlight the strategic role of social platforms in fostering consumer–brand relationships and enhancing brand equity. Conversely, Hajdas et al. (2023) questioned the adequacy of conventional CBBE models when accounting for ideological alignment. Their study compared materialistic and material-resistant consumer cultures, exemplified by Black Friday and Buy Nothing Day, concluding that brand activism enhances equity only when grounded in cultural relevance and perceived authenticity.
In the CBBE framework, brand performance measures how effectively a brand meets consumers’ functional needs, such as utility, aesthetics, and economic value. Keller (2001) identified five key attributes that contribute to brand performance: product reliability and serviceability, service effectiveness, style and design, pricing strategy, and product features. Brand image also addresses consumers’ psychological and social needs, reflecting their perceptions of a brand’s external attributes. Camarero et al. (2010) found that a positive brand image enhances perceived quality and builds trust among consumers. Regarding private-label brands (PLBs), Girard et al. (2017) found that perceived quality and brand associations increase perceived value and decrease perceived risk, strengthening brand loyalty and equity. Furthermore, store loyalty is a key factor influencing PLB equity, underscoring the close relationship between retailer image and brand performance.
Brand judgment refers to consumers’ overall evaluation of a brand, shaped by their perceptions of quality, credibility, consideration, and superiority (Keller, 2001). These dimensions inform rational assessments and influence a brand’s perceived competitive advantage. In contrast, brand feelings refer to the emotional responses derived from interacting with a brand, capturing sensory and experiential dimensions (Aaker, 1996; Keller, 2001). Keller identified six key emotions associated with brands: warmth, joy, excitement, security, social approval, and self-respect. Building on this, Augusto and Torres (2018) found that brand attitude and electronic word-of-mouth significantly affect consumers’ willingness to pay a premium. This relationship is mediated by consumer–brand identification and CBBE, stressing the interplay between emotional attachment and digital engagement in shaping brand value and loyalty.
Brand resonancePositioned at the top of the CBBE model (Keller, 2001), brand resonance refers to the intense psychological connection between consumers and a brand, characterized by deep cognitive identification and emotional attachment, leading to repeat purchase, active engagement, and brand advocacy (Ande et al., 2017). When all the elements of the CBBE model are successfully established, consumers demonstrate loyalty and contribute to the brand community by sharing positive experiences (Keller, 2001). Brand resonance results in two important marketing outcomes: lasting loyalty and a heightened emotional connection with the brand. González-Morales et al. (2020) examined brand resonance in ecological branding, emphasizing its role in building loyalty and emotional ties through neuromarketing strategies. Their study highlights the importance of incorporating subconscious consumer responses into branding, providing a neuroscientific foundation for enhancing ecological brand equity.
Keller (2001) identified the four dimensions of brand resonance: behavioral loyalty, emotional attachment, sense of community, and active engagement. These capture the depth of consumer–brand relationships, extending beyond transactional behavior to reflect enduring emotional and cognitive alignment (Keller & Swaminathan, 2019). Brand resonance, a central mechanism in cultivating long-term brand relationships, integrates rational evaluations and affective responses (Keller, 2012; Shieh & Lai, 2017). Empirical evidence has confirmed its strategic importance in diverse sectors. For instance, Anselmsson et al. (2017) validated the resonance-based loyalty model in the retail sector, while Donvito et al. (2020) highlighted its cross-industry applicability. In luxury branding, Husain et al. (2022) and Kang et al. (2022) underscored brand resonance as a critical driver of emotional loyalty, particularly among millennials in emerging markets. Owing to their strong orientation toward digital experiences, emotional brand engagement, and social sharing behaviors, millennials are a key demographic in brand resonance research (Husain et al., 2022; Kang et al., 2022). Hence, their studies revealed how brand prestige, country-of-origin cues, and peer influence enhance resonance through emotional and cognitive identification. Millennials’ digital nativity, emotional engagement, and social sharing behaviors strengthen resonance across omnichannel and multimedia touchpoints. Building on the CBBE framework, this study identifies key factors that influence loyalty enhancement (Table 1).
Factors influencing the enhancement of brand loyalty based on CBBE.
| Dimension | Definition | References |
|---|---|---|
| Brand performance | The product fulfils consumers' functional needs; that is, within its category, the brand satisfies consumers' needs in terms of utility, aesthetics, and economics. Brand performance has five constituent attributes: product reliability, durability, and serviceability; product service effectiveness, efficiency, and empathy; product style and design format; product pricing strategy; and the main components and other auxiliary characteristics. | Keller (2001); Keller and Lehmann (2006); Datta et al. (2017) |
| Brand image | The external characteristics of the product include how the brand attempts to meet the psychological or social needs of consumers, as well as consumers' impressions of the manufacturer's products. Brand image has four connotations: user image, purchasing and usage situations, brand personality and values, and historical heritage and experience. | Keller (2001); Camarero et al. (2010); Zhang (2015) |
| Brand judgment | Consumers' personal opinions and evaluations of a brand can be summed up in four major areas for overall brand judgment: brand quality, brand credibility, brand consideration, and brand superiority. | Keller (2001; 2003); Keller and Swaminathan (2019) |
| Brand feeling | Brands create emotional benefits that consumers perceive through their purchasing and usage experience. There are six types of emotional responses that can be used to measure these brand feelings: warmth, joy, excitement, security, social approval, and self-respect. | Aaker (1996); Keller (2001; 2003) |
| Brand resonance | The synchronicity and intensity of the relationship between consumers and the brand, and the strength and depth of the psychological connection between consumers and the brand are vital aspects to consider. The measurement attributes of brand resonance include behavioral loyalty, emotional attachment, a sense of community, and active participation. | Raut et al. (2020); Keller (2001; 2003); Keller and Swaminathan (2019) |
Customer journey involves a series of touchpoints that influence consumer experiences pre-purchase, mid-purchase, and post-purchase (Becker et al., 2020; Lemon & Verhoef, 2016; Voorhees et al., 2017). Identifying key touchpoints is a challenging yet crucial task, as they greatly affect consumer perceptions and evaluations (Berman, 2020; Hu & Tracogna, 2020). Throughout their journey, customers adjust their goals and expectations; thus, firms must understand the behaviors and decision-making contexts of the different stages of the journey (Åkesson et al., 2014; Stein & Ramaseshan, 2016). Integrating mobile technologies adds to this complexity, as user experiences are increasingly digital and context-dependent (Debasa et al., 2023). In omnichannel retailing, Tueanrat et al. (2021) highlighted the role of customer behavior, cocreation, and experiential values in enhancing customers’ journey satisfaction. Nonetheless, how information is searched for and reflected upon after purchases varies. Frasquet et al. (2024) illustrated that channel novelty and inspiring engagement in online and offline settings improved journey continuity, increased customer loyalty, and fostered greater engagement.
Recent advancements in customer experience research have broadened the understanding of customer interactions, from single encounters to ongoing experiences encompassing multiple service cycles (Kranzbühler et al., 2018). Initial and recurring journeys have different dynamics, necessitating distinct conceptual frameworks to comprehend how customer expectations evolve. By leveraging big data across touchpoints, companies can identify friction points and enhance customer journey management using data-driven insights (Campbell et al., 2020). For instance, Lecoeuvre et al. (2021) demonstrated that perceptions related to the age of service personnel significantly influenced B2B customer expectations and perceptions, underscoring the importance of managing early-stage interactions to improve customer experience. Lemon and Verhoef (2016) conceptualized a dynamic customer experience comprising direct and indirect interactions, during which customers continuously reassess the perceived value. This value perception fluctuated across stages of the journey and was closely linked to how effectively customer expectations were met (Brakus et al., 2009; Lin et al., 2020; Lee et al., 2017). Delivering high experiential value enhances satisfaction, stimulates positive word-of-mouth, and strengthens competitive positioning (Becker & Jaakkola, 2020).
An effective customer experience design aligns with customer beliefs, emotions, and values (Roggeveen & Rosengren, 2022). Touchpoints facilitate ongoing interactions and information exchange throughout the experience (Baxendale et al., 2015), reinforcing the strategic importance of managing experiences across all purchasing stages (Edelman & Singer, 2015; Lemon & Verhoef, 2016). Satisfied customers are more likely to advocate for the brand, underscoring the importance of customer experience strategies that promote short-term outcomes and long-term loyalty (Homburg et al., 2017). Hamilton and Price (2019) described the consumer journey as a comprehensive framework within which individuals interact with brands, technologies, and services in pursuit of broader life goals. By highlighting consumer motivations and contextual factors, this viewpoint transcended traditional customer journey models. Neslin (2022)) found that integrating online and offline channels created cohesive and seamless interactions across touchpoints, improving customer satisfaction and enhancing brand loyalty.
Loyalty spiralThe loyalty spiral begins with the loyalty loop. In the digital marketplace, consumers increasingly seek emotionally resonant brand experiences, prompting firms to prioritize long-term customer–brand relationships. Edelman and Singer (2015) proposed a revised customer journey model that transitioned from a linear purchase funnel to a cyclical loyalty loop. Their framework begins with traditional stages of awareness, interest, consideration, intent, evaluation, and purchase and builds on it by adding enjoyment, advocacy, and bonding, emphasizing post-purchase engagement and sustained loyalty. In the enjoyment stage, experiences exceed customer expectations, fostering loyalty. With this, customers are likely to enter the advocacy stage, sharing positive experiences with others. Subsequently, in the bonding stage, emotional attachment is formed with the brand, reinforcing long-term commitment. This cyclical process resets with each repurchase, gradually deepening the customer–brand relationship. In competitive markets, the loyalty loop illustrates how post-purchase experiences drive recurrent engagement and repurchase intentions.
Siebert et al. (2020) developed the loyalty spiral model, combining the stable loyalty loop and the sticky involvement spiral. The stable loyalty loop is a cyclical pattern in which initial brand experiences, bolstered by advertising, content marketing, and promotions, result in repeat purchases and long-term loyalty. Here, companies enhance customer experience by improving services, anticipating customer needs, and providing timely engagement. However, if negative events occur, customer loyalty may decline, leading them to switch to a competing brand. In contrast, in the sticky involvement spiral, deeper engagement is achieved by introducing novelty and unpredictability into the customer journey. This fosters long-term involvement and emotional attachment with the brand.
In the initial stage of the involvement spiral, brands spark consumer curiosity by offering free or low-cost products, encouraging a quick entry into the cycle. As consumers progress, brands vary their services by creating novelty and excitement, encouraging ongoing engagement through one-time purchases or upgrades. However, negative experiences disrupt consumer satisfaction, resulting in them exiting the spiral, either suddenly or gradually. By integrating the loyalty loop and involvement spiral, the customer journey framework encompasses multiple service cycles. Depending on the service type—utilitarian or hedonic—Siebert et al. (2020) proposed six design strategies (labeled A–F) to enhance customer engagement (Fig. 1).
Sustaining customer journeys in multiservice systems (Siebert et al., 2020).
S Chain Coffee Shop, a globally recognized specialty coffee enterprise, is the study’s focal case for investigating brand loyalty mechanisms. S Chain Coffee Shop has established a formidable global footprint. As of late 2024 and early 2025, the company’s operations had grown to over 40,199 stores worldwide. Data from September 2024 indicated a total of 40,199 locations, with a significant concentration in the United States (16,935 stores) and China (7594 stores). The company is present in over 80 countries, underscoring its status as the largest global coffeehouse chain. The workforce supporting this expansive network included approximately 361,000 employees in fiscal year 2024. The corporation’s financial performance underscores its market leadership. For the fiscal year ending in October 2023, it reported a consolidated net revenue of $35.98 billion, an 11.55 % increase from the previous year. Revenues in the subsequent fiscal year of 2024 grew to $36.18 billion. This consistent revenue growth in a competitive market indicated the company’s strong consumer base and effective business strategies.
Interviews were conducted with loyal customers who frequented the stores, both online and offline, to gain in-depth insights. S Chain Coffee Shop positions its retail locations as a “third place” beyond home and work, where a cozy and inviting environment fosters emotional attachment. The brand curates its sensory landscape, including interior design, ambient lighting, coffee aroma, background music, and tactile experiences, thereby aligning with the cultural and stylistic nuances of each store’s locale, enhancing the overall customer experience, and strengthening brand associations. Additionally, it emphasizes personalized services to promote emotional engagement. Customized beverages, cup designs featuring personal greetings, and courteous service rituals are designed to evoke warmth, friendliness, and joy, key emotional attributes that contribute to memorable consumer experiences. These affective touchpoints are a foundation for brand attachment and loyalty. In its “Restaurants 25 2024″ report, Brand Finance named S Chain Coffee Shop as the world’s most valuable restaurant brand, a position it has held for eight consecutive years. The brand’s value was estimated at $60.7 billion, a 14 % increase from the previous year. This valuation was attributed to its successful reinvention, comprising expansion, employee support, and innovative store concepts. In the Interbrand “Best Global Brands 2024” ranking, it was listed at #52. This ranking considered financial performance, the role the brand played in purchase decisions, and its competitive strength, with its 2024 report indicating a brand value of $15.3 billion for S Chain Coffee Shop.
Furthermore, S Chain Coffee Shop integrates relational and digital strategies to reinforce customer identification and foster community belonging. It utilizes loyalty programs and mobile applications to facilitate repeat purchases, foster a sense of connection, and solidify brand commitment. It leverages these tools to cultivate transactional relationships, alongside emotional and communal bonds. With accelerating technological advancements, S Chain Coffee Shop is evolving toward a “fourth place” paradigm, seamlessly blending online and offline brand encounters. Through active engagement on social media platforms, such as Facebook, X (previously Twitter), and Instagram, it amplifies its community-oriented culture and extends brand interactions beyond the physical store. This omnichannel strategy improves the continuity and richness of the customer journey, offering a holistic and immersive brand experience.
Hence, S Chain Coffee Shop is a vast and expanding international retail network, with increasing annual revenues and a powerful, globally recognized brand. Its ability to maintain its position as the leading coffee shop chain worldwide is possible due to a significant workforce and a strategic focus on enhancing the brand and customer experience. Although the exact market share percentage varies per different market reports, its revenue and brand valuation figures establish it as a dominant player in the global coffee and restaurant industries. It exemplifies experiential branding and digital integration, making it an ideal subject for analyzing the development and evolution of brand loyalty across consumer touchpoints.
Fuzzy linguistic preference relations–analytic network process (FLPR-ANP)This study combined SEM and FLPR-ANP to strengthen the model evaluation. SEM validated the measurement structure and confirmed the interrelationships among key constructs. A hierarchical structure was established based on the SEM results. Subsequently, FLPR-ANP performed pairwise comparisons, created a comparison matrix, and determined the relative weights of the dimensions. This integrated approach enabled a comprehensive assessment of the S Chain Coffee Shop’s brand loyalty performance, supporting the development of targeted strategies to improve customer loyalty.
In multicriteria decision-making (MCDM), pairwise comparisons calculate eigenvalues that determine the relative importance of various factors. However, as the number of criteria increases, the process becomes increasingly complex and susceptible to inconsistencies (Mikhailov & Tsvetinov 2004). While the consistency ratio (CR) can identify these inconsistencies, correcting them requires multiple revisions, which is time-consuming and degrades decision-making efficiency. Wolfslehner et al. (2005) observed that the exponential increase in computational complexity was a major limitation of traditional MCDM approaches. Additionally, subjective judgments and semantic ambiguities typically arise during managerial evaluations. Qahtan et al. (2022) introduced the probabilistic hesitant fuzzy weighted zero-inconsistency criterion (P-H-FWZIC), alongside multiobjective optimization by ratio analysis, to address these challenges. These approaches strengthened decision-making under uncertainty, providing a transferable framework for sustainable evaluations in complex, multidimensional contexts.
The FLPR method provided significant advantages in addressing inconsistencies in pairwise comparisons, simplifying evaluations of multiple elements, and clarifying semantic ambiguities. Introduced by Wang and Chen (2011), this approach built upon Herrera-Viedma et al.’s (2004) consistent fuzzy preference relations framework. It enhanced analytical precision by integrating fuzzy linguistic variables. Although relatively new, it has been widely adopted in decision-making research. Applications include evaluating strategic retail alliances (Hsu & Tang, 2019), assessing mobile app stickiness in retail (Hsu & Tang, 2020), examining brand attachment in the automotive industry (Hsu et al., 2021), analyzing partnerships in telecommunications (Tang & Hsu, 2022), and studying the impact of mobile advertising on purchase intentions (Hsu et al., 2023). Developed by Saaty in 1996, the ANP extends the analytic hierarchy process (AHP) by incorporating interdependencies and feedback among criteria. Unlike the AHP, which assumes attribute independence, the ANP accounts for complex interrelationships, making it more suitable for real-world decision-making (Asadabadi et al., 2019). The ANP has been increasingly adopted in recent years, owing to its capability to model these interactions (Kheybari et al., 2020; Zebardast, 2022). To further address semantic ambiguity in expert judgments, researchers have integrated FLPR into ANP, leading to the FLPR-ANP method. This hybrid approach enhances the robustness and interpretability of the MCDM (Tang & Hsu, 2018).
The rationale for integrating SEM, FLPR, and ANP lies in their complementary analytical strengths. SEM provides confirmatory power to test the hypothesized causal relationships among CBBE constructs, ensuring that the measurement model is reliable and valid before proceeding to prioritization. The ANP extends the traditional AHP by capturing interdependencies among factors, essential in brand loyalty research where constructs, such as emotional attachment, service quality, and touchpoint experiences, mutually influence each other. The FLPR enhances the ANP by accommodating linguistic uncertainty and minimizing inconsistency in expert judgments, enabling accurate and context-sensitive weighting of factors. By integrating these methods sequentially, the loyalty spiral framework is built on validated constructs (via SEM) and optimized through robust MCDM (via FLPR-ANP). This methodological synthesis is theoretically rigorous and managerially relevant, prioritizing loyalty drivers in complex omnichannel environments.
Empirical studyThis study developed and validated a brand loyalty evaluation model tailored to a global coffee chain using a mixed-methods approach. The research comprised five sequential stages. First, a comprehensive literature review and case analysis were conducted to identify four core dimensions and 23 influencing factors of brand loyalty, forming the basis of a preliminary framework. Second, this framework was empirically validated through confirmatory factor analysis (CFA) using SEM via AMOS 25.0. The analysis drew on 286 valid responses from S Chain Coffee Shop customers collected via social media platforms. Third, the study employed FLPR-ANP to determine the relative weights of the influencing factors. A fuzzy pairwise comparison questionnaire was administered to 30 loyal customers selected based on app usage and purchase history. Fourth, an evaluation of brand performance was conducted based on 12 key factors identified through CFA, which were rated on a five-point Likert scale to reflect customer perceptions. Fifth, the study developed a loyalty spiral strategy by integrating the FLPR-ANP-derived weights with the performance evaluation results.
Additionally, the sequential integration of SEM and FLPR-ANP maximized methodological robustness and practical applicability. SEM verified the structural soundness of the brand loyalty model, ensuring that only statistically sound and theoretically relevant factors were retained. This minimized the risk of including weak or irrelevant variables in the prioritization process. The validated constructs were the input criteria for FLPR-ANP, which ranked the importance of these factors and accounted for the complex, bidirectional influences present in consumer–brand relationships. By embedding FLPR within ANP, the method accommodated human judgment expressed linguistically, which was particularly suitable for capturing experiential and emotional dimensions of brand loyalty. This hybrid approach bridged the gap between statistical validation and decision optimization, making the results academically rigorous and actionable for brand managers intending to enhance loyalty in real-world contexts. Together, the five stages constituted a systematic empirical model for diagnosing and enhancing brand loyalty. Each component of the methodology is elaborated as follows:
Step 1: Constructing the Brand Loyalty Assessment Dimensions
This study developed a theoretical foundation to assess brand loyalty. An extensive literature review was conducted, drawing upon seminal and current research on CBBE, loyalty formation, and customer experience management. The study identified four overarching dimensions of brand loyalty formation: brand value perception, brand relationship experience, brand identification and emotion, and behavioral engagement. Within these dimensions, 23 influencing factors were defined, encompassing cognitive and affective drivers, such as trust, satisfaction, emotional connection, perceived quality, and repurchase intention.
Step 2: Establishing the Brand Loyalty Evaluation Model
The study empirically validated the theoretical brand loyalty framework developed in Step 1 by applying CFA within an SEM approach. This confirmed the factorial structure, assessed internal consistency, and verified the construct validity of the brand loyalty evaluation model. Data were collected through a structured questionnaire survey of consumers of S Chain Coffee Shop. The survey was administered online via major social media platforms, including Facebook groups, LINE communities, and Instagram, to ensure a diverse and representative sample. This approach was consistent with established consumer behavior and brand equity research, where social media recruitment was used effectively to reach active and engaged brand communities (Chu & Kim, 2011; Dolan et al., 2016; Wright et al., 2024). Of the 300 responses gathered, 286 were deemed valid after data cleaning, including screening for missing values and response biases. The sample size was adequate according to SEM literature. Following Hair (2011), a sample size of 200 or more is considered acceptable for CFA when model complexity is moderate. Similarly, Kline (2023) noted that a ratio of at least 10 respondents per estimated parameter was advisable. Hence, the present sample of 286 respondents exceeded these thresholds, ensuring robust statistical power and stability of the parameter estimates.
Variables with low standardized factor loadings were systematically reviewed and removed, improving the accuracy and efficiency of subsequent FLPR-ANP analysis. The objective was to retain the three most representative and statistically robust indicators for each of the four core brand loyalty dimensions. After each deletion, the measurement model was recalibrated to assess changes in structure and fit. The revised measurement model demonstrated no estimate violations, and tests for model fit, construct validity, and reliability analysis fell within acceptable ranges. The results confirmed the integrity of the revised model structure, supporting its use in the subsequent weighting and prioritization using FLPR-ANP.
Step 3: Application of FLPR-ANP to Assess the Weight of Brand Loyalty-Influencing Factors
FLPR-ANP evaluated the relative importance of the influencing factors identified through CFA. For details of the computational steps, please refer to the Appendix. Using this MCDM approach, complex interdependencies and subjective evaluations expressed through linguistic variables were analyzed, addressing potential inconsistencies in expert judgment. A fuzzy pairwise comparison questionnaire was developed using the 12 retained influencing factors across the four brand loyalty dimensions. Strict selection criteria were applied to identify qualified respondents to ensure the validity and reliability of the expert judgments. Participants were required to have used the S Chain Coffee Shop mobile app for more than one year and demonstrated high-frequency purchasing behavior, defined as averaging a minimum of four transactions per month across online and offline channels.
Participants meeting these criteria were classified as “loyal members,” that is, consumers who had sustained brand engagement and experience across multiple touchpoints. These participants actively used the brand’s official communication platforms, such as the LINE official account. The fuzzy pairwise comparison questionnaires were distributed and collected through digital channels, including Facebook, LINE, and Gmail. This targeted recruitment aligned with MCDM research practices, where smaller but highly qualified expert or loyal-consumer panels were recommended to ensure judgment validity (Hsu & Tang, 2020; Liu & Yang, 2025; Mikhailov & Tsvetinov, 2004). Thirty valid responses were obtained from loyal members, providing the basis for generating the fuzzy comparison matrices. The linguistic assessments were converted into fuzzy numerical values and analyzed using FLPR-ANP to compute the global and local weight values of each influencing factor. This ensured that the weight estimation reflected the cognitive evaluations and experiential familiarity of loyal consumers, enhancing the model’s contextual relevance and practical robustness.
Step 4: Evaluating the Performance of the Brand Loyalty Model
A performance evaluation of the brand loyalty model was conducted by applying the 12 influencing factors. A structured questionnaire assessed consumers’ perceptions of S Chain Coffee Shop’s brand performance. Each factor was measured using a five-point Likert scale, and the respondents (n = 30) rated the extent to which the brand fulfilled their expectations per real usage experiences. The evaluation comprised the key loyalty constructs of brand recognition and association, perceived quality, customer trust and attachment, and brand resonance and engagement. This survey was distributed to a broader consumer group, distinct from the FLPR-ANP decision panel, improving the generalizability of the performance assessment.
This stage combined consumer evaluations with the FLPR-ANP-derived factor weights, providing a comprehensive diagnostic framework to identify performance strengths and weaknesses. Key gaps between importance and performance were identified using the weighted analysis, providing a foundation for strategic brand improvement. The performance results recommendations included identifying underperforming yet high-priority factors, realigning brand initiatives with consumer expectations, and formulating data-driven interventions to strengthen brand loyalty. These findings directly informed the development of loyalty enhancement strategies in the subsequent step.
Step 5: Developing a Loyalty Spiral Plan
Building upon the findings of the FLPR-ANP analysis and the brand loyalty performance evaluation, this study designed a strategic loyalty spiral plan to deepen consumer engagement and improve long-term brand attachment. This was grounded in the loyalty spiral framework, emphasizing iterative consumer–brand interactions that evolve from initial satisfaction to advocacy and bonding. A third-stage questionnaire, integrating insights from behavioral science, experiential marketing, and value-based brand management, was utilized to capture consumer expectations and construct an ideal loyalty enhancement strategy.
The questionnaire comprised three sections. The first section recontextualized the three highest-weighted influencing factors, identified through FLPR-ANP, into colloquial, consumer-friendly language, consistent with S Chain Coffee Shop’s brand tone, facilitating emotional resonance and intuitive comprehension. Focusing on the company’s core value proposition and market positioning, the second section elicited feedback on whether current brand practices aligned with customer expectations and experiential value. The third section was designed according to the peak–end rule, which posited that consumer evaluations were shaped predominantly by the most emotionally intense and final moments of experience. This section probed how the S Chain Coffee Shop could create “peak” experiences and meaningful “endings” that reinforced loyalty and stimulated repurchase intentions.
Drawing on the responses, the study outlined tailored customer journey maps and identified critical touchpoints across pre-purchase, mid-purchase, and post-purchase. These insights were synthesized into actionable loyalty strategies that aligned with the stages of the loyalty spiral: enjoyment, advocacy, and bonding. The proposed loyalty spiral plan systematically linked empirical findings with consumer-expressed ideals, providing a robust, customer-centric roadmap for sustaining engagement, enhancing emotional connection, and cultivating enduring brand loyalty in the highly competitive specialty coffee market.
ResultsThis study assessed the structural relationships between brand loyalty factors and evaluated their relative weights by employing a combination of SEM and FLPR-ANP. The SEM results revealed that emotional connection, consistent service experience, and satisfaction significantly influenced brand loyalty. This was consistent with previous studies that confirmed the role of brand experience and customer satisfaction in fostering long-term loyalty (Brakus et al., 2009; Carlson et al., 2015; Lemon & Verhoef, 2016). The findings further confirmed that different touchpoints along the customer journey made distinct contributions to loyalty development, echoing Baxendale et al. (2015) and Roggeveen and Rosengren (2022), who noted that customers’ evolving perceptions were affected by contextual and emotional interactions.
FLPR-ANP identified the most influential loyalty factors across multiple dimensions by integrating expert judgments and addressing ambiguity in semantic information. The results confirmed that brand image consistency, emotional attachment, and service responsiveness were the top three loyalty-influencing factors. These findings were consistent with research findings that experiential consistency across channels (Frasquet et al., 2024) and emotional bonding (Brakus et al., 2009) were critical determinants of loyalty. The prominence of emotional attachment further confirmed the role of sensory and affective cues highlighted in the multisensory experience literature (Hamilton & Price, 2019; Lemon & Verhoef, 2016). Additionally, integrating fuzzy linguistic preferences strengthened decision-making in subjective and semantically ambiguous contexts, aligning with Hsu and Tang (2019) and Hsu et al. (2023). According to the nuanced outcome weighting, fostering customer loyalty in omnichannel environments must consider cognitive evaluations (such as consistency and informativeness) and affective aspects (such as novelty and arousal). This aligned with Tueanrat et al. (2021), who emphasized that different stages of the customer journey contributed equally to the overall satisfaction and loyalty development.
Dimensions and factors of the brand resonance evaluation model and CFAThis study defined the evaluation dimensions and influencing factors through a literature review and case studies (Table 2). A CFA of the SEM was conducted using Amos 25.0 software to establish the reliability and validity of the brand resonance evaluation model. As indicated in Table 3, the estimation diagnostics checklist demonstrated that the standardized factor loadings of the measurement model ranged from 0.5 to 0.9. All t-values exceed 1.96, confirming statistical significance (Henseler & Schuberth, 2020; Heredia et al., 2022). Moreover, the standardized coefficients were below 0.95, with no substantial standard errors, and all error variances were acceptable, confirming that no estimation violations occurred. Therefore, it was appropriate to proceed with the overall model fit test.
Definition of dimensions and factors for the brand loyalty evaluation model.
Estimation checklist of dimensions and factors for the brand loyalty evaluation model.
In terms of Regarding model evaluation, a higher fit indicated greater model usability. Based on the judgment criteria (Table 4), the brand loyalty evaluation model demonstrated a good fit, as all the values met the specified standards. Therefore, it was appropriate to proceed with the validity tests. The study established the criteria for determining whether a model passed the evaluation. The test results indicated that all the values met the required standards; thus, the research model exhibited a good fit. Construct validity could be divided into two types: convergent and discriminant. According to Fornell and Larcker’s (1981) convergent validity standards, composite reliability (CR) should exceed 0.6, and the average variance extracted (AVE) should be greater than 0.5. In this study, all CR values surpassed 0.6; however, the AVE for brand effectiveness did not reach 0.5. Nonetheless, as noted by Fornell and Larcker (1981) and Lam (2012), an AVE value of 0.4 may be acceptable, provided the CR remains above 0.6. Consequently, the model’s convergent validity was considered adequate, and this study fell within the acceptable range (Table 5). Overall, the model exhibited strong construct validity.
Goodness of fit checklist for the brand loyalty evaluation model.
Construct validity of the brand loyalty evaluation model.
An Excel spreadsheet was used to consolidate data from 30 respondents, employing calculation formulas to analyze the weights of various dimensions and factors. Valuable insights were gained from ranking the strengths and weaknesses of each factor (Table 6). The dimension that stood out for S Chain Coffee Shop’s coffee brand loyalty was brand feeling. The top three consumer sentiment factors were enthusiasm, joy, and service quality, underscoring the importance of affective engagement in brand loyalty. This highlighted the essential elements, which drove brand success.
Importance evaluation of brand loyalty dimensions and factors for S Chain Coffee Shop.
The above results aligned with previous research emphasizing emotional attachment and consumer–brand interaction in brand equity formation. Algharabat et al. (2020) highlighted how consumer interaction and involvement in self-expressive brands strengthened emotional responses and brand loyalty. The strong performance of service-related indicators aligned with Kotsi et al. (2018), where experiential factors, such as brand image and value, were more influential than quality in determining attitudinal loyalty. Therefore, S Chain Coffee Shop’s emphasis on service quality and emotional cues aligned with contemporary CBBE research. Additionally, the ranking of brand resonance dimensions mirrored the pattern observed by Husain et al. (2022) and Kang et al. (2022), who found that behavioral loyalty and emotional attachment dominated brand strength among millennials, especially with high digital engagement.
This study evaluated performance by multiplying the weight value by the current performance metrics. As shown in Table 7, “joy” had the second-highest weight and ranked tenth in performance, indicating that the current performance of the enterprise in this area was below expectations and had significant room for improvement. Overall, the performance evaluation was close to 4, suggesting that the coffee brand’s resonance was above average. The analysis revealed that “brand judgment” performed the best, followed by “brand efficacy” and “brand feelings.” However, the rankings derived from the performance evaluation and the weight value analysis differed; hence, to strengthen brand loyalty, the S Chain Coffee Shop should actively improve “brand feelings.”
Performance evaluation of brand loyalty factors for S Chain Coffee Shop.
This study demonstrated the analytical strength of combining SEM and FLPR-ANP for evaluating brand loyalty mechanisms. While SEM provided confirmatory insights into the interdependence of brand experience constructs, FLPR-ANP added strategic depth by prioritizing key interventions under uncertainty (Qahtan et al., 2022). These tools captured the causal relationships and managerial levers across the loyalty spiral (Siebert et al., 2020). The findings contributed to the evolving discourse on customer journey management by offering a practical framework for mapping loyalty loops with empirical accuracy (Åkesson et al., 2014; Roggeveen & Rosengren, 2022).
Recent studies have highlighted the importance of customer experience dynamics, especially those influenced by mobile technologies and multisensory retail environments. These studies advocate for integrated omnichannel strategies and the cocreation of experiences (Debasa et al., 2023; Hamilton & Price, 2019). Consequently, the loyalty spiral is viewed as a post-purchase reinforcement cycle and a long-term engagement model applicable to hedonic (pleasure-seeking) and utilitarian (practical) contexts (Frasquet et al., 2024; Lemon & Verhoef, 2016).
Theoretical and management implicationsThe study explored how loyalty spirals operated across service touchpoints, contributing to the theoretical and practical understandings of the CBBE framework. By decomposing loyalty into granular constructs, validated through SEM, and strategically prioritizing them through FLPR-ANP, it clarified how brand meaning and attachment evolved in digitally mediated environments. Additionally, integrating the loyalty spiral with fuzzy network modeling advanced customer journey theories by recognizing nonlinear, emotion-driven decision-making. The model recognized that loyalty was a recursive and experiential process influenced by rational evaluations and hedonic perceptions.
Theoretical implicationsRegarding brand management, this study found that brands must adopt a holistic approach to customer experience, integrating emotional engagement, personalized communication, and digitally mediated interactions. The findings indicated that brand loyalty was no longer shaped solely by cognitive evaluations of brand attributes; instead, it was influenced by affective, experiential, and context-sensitive elements embedded throughout the customer journey. This perspective challenged traditional linear models of loyalty and supported a dynamic, cyclical understanding of consumer–brand relationships. Particularly, the study advanced the following theoretical insights:
- (1)
Reconceptualizing Brand Feeling in the Brand Equity Context: This study confirmed the role of brand feeling in cultivating brand loyalty, expanding traditional brand equity, which predominantly emphasized functional and cognitive brand attributes (Aaker, 1991, 1996; Keller, 1993). The findings revealed that affective dimensions, particularly enthusiasm and joy, were significantly more influential in fostering loyalty than rational evaluations of brand performance. These insights were consistent with recent developments in experiential and multisensory branding literature (Brakus et al., 2009; Hamilton & Price, 2019), suggesting that emotional resonance and experiential interactions constituted the foundation of brand–consumer relationships. Theoretically, this requires brand equity models to account for dynamic emotional processes and context-dependent experiences as core drivers of brand loyalty.
- (2)
Integrating Customer Journey Touchpoints into an Expanded Brand Equity Model: This study examined strategic touchpoints, including online interactive games, price incentives, and post-purchase gifts, across pre-purchase, purchase, and post-purchase, proposing an integrated and dynamic view of brand equity. In contrast with traditional static models, the findings highlighted that brand–consumer interactions unfolded through experiences in multiple stages, which required consistent and emotionally engaging brand stimuli (Hamilton et al., 2021). Hence, brand equity must be conceptualized as a multi-touchpoint construct capturing the evolving nature of consumer engagement across physical and digital environments. This research contributed to the theoretical expansion of customer journey frameworks (Åkesson et al., 2014; Roggeveen & Rosengren, 2022) by situating brand equity within a temporally and experientially integrated model.
- (3)
Advancing the Loyalty Spiral Perspective in Consumer–Brand Relationship: Utilizing the loyalty spiral framework (Siebert et al., 2020), this study presented a cyclical view of how brand loyalty developed over time. This shift in perspective challenged the traditional loyalty ladder concept and suggested that loyalty was strengthened through repetitive engagement loops, where emotional responses and brand experiences accumulated and intensified. According to the loyalty spiral model, sustained loyalty was achieved through recurring interactions, which were emotionally meaningful and evolved with consumer preferences and behaviors. This promoted a dynamic and process-oriented understanding of consumer–brand relationships, aligning with current discussions in experiential marketing and emotional branding.
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Contextualizing the Strategic Role of Marketing Interventions in Loyalty Formation: The findings provided a theoretical basis for implementing phase-specific marketing interventions. It highlighted the effectiveness of loyalty-building strategies that were relevant to the context and aligned with emotions. This study showed how emotional engagement shifted throughout the customer journey, addressing criticisms of traditional CBBE models, which ignored cultural authenticity and emotional significance (Hajdas et al., 2023). Hence, this study highlighted that brand strategies should consider situational factors, cultural contexts, and emotional triggers to effectively foster brand attachment. This entails moving away from standardized value propositions toward more adaptive and emotionally resonant branding that reflects real consumer experiences.
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Methodological Contributions to the Evaluation of Brand Loyalty Drivers: Integrating SEM and FLPR-ANP was a significant methodological contribution. This hybrid approach allowed a comprehensive assessment of complex, multidimensional brand loyalty constructs, addressing causal relationships and the ambiguity inherent in expert judgment. Furthermore, it improved the precision of factor prioritization within brand strategy development. Theoretically, this methodological innovation advanced the analytical toolkit of brand researchers by encouraging the use of fuzzy logic, weighted criteria models, and dynamic evaluation frameworks, which accurately capture the nuances of consumer decision-making and brand attachment.
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Bridging CBBE with Service-Dominant Logics: By emphasizing cocreated brand value through customer participation and experiential involvement, this study provided a theoretical bridge between CBBE and the service-dominant logic (SDL) framework. The use of interactive elements, such as gamified engagement and feedback incentives, reflected the shift from firm-centric brand control to consumer-driven brand meaning-making. These findings supported the proposition that brand equity was continuously shaped through contextualized and participatory consumer experiences. Theoretically, this contributed to the ongoing integration of SDL principles into the branding literature, providing a more relational and dynamic understanding of how brand equity was developed and sustained in omnichannel environments.
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Applying the Peak–End Rule to Enhance Theoretical Understanding of Brand Memory and Loyalty: This study offered a new perspective on how specific high-emotion moments in the customer journey significantly impacted overall brand evaluation and future consumer behaviors by incorporating the peak–end rule into brand loyalty modeling. The research demonstrated that post-purchase strategies, such as giving memorable tokens, provided emotionally impactful endings that strengthened long-term brand attachment. This theoretical insight into loyalty frameworks suggested that consumer memory was shaped by intense emotional peaks and final impressions. The findings opened up new avenues for future research into how emotional elements during the customer journey could be strategically deployed to influence brand outcomes.
Hence, these findings provided valuable theoretical insights for academics studying brand loyalty, customer loyalty, and the customer journey in the retail sector, especially in the context of chain coffee businesses. They enhanced academic analysis of brand equity by providing a framework for understanding the complex relationships between consumers and brands, particularly in the rapidly changing retail environment and shifting consumer preferences.
Management implicationsThe findings offered multiple actionable insights for the S Chain Coffee Shop to enhance brand loyalty by strategically managing customer experiences, emotional engagement, and omnichannel interactions. Drawing on the integrated use of SEM and FLPR-ANP, this section outlines eleven managerial implications to guide brand optimization across consumer touchpoints and international markets.
- (1)
Prioritize Affective Connection over Functional Attributes: Brand feeling was the most critical loyalty driver for the S Chain Coffee Shop, with enthusiasm and joy identified as the top two influential emotions. This empirical evidence supported a necessary strategic shift in brand management, from focusing on functional brand performance to prioritizing investment in cultivating affective connections. This aligned with and extended the evolution of CBBE, which progressively recognized the centrality of affective and experiential components (Brakus et al., 2009) beyond the foundational cognitive attributes (Keller, 1993). Brand managers should perform strategic audits of resource allocation, shifting investments from purely product-centric or price-based initiatives toward a holistic emotional journey. This includes training frontline employees in emotional intelligence to act as affective touchpoints, curating the multisensory in-store environment (e.g., music, aroma, and lighting) to evoke warmth and joy, and leveraging brand storytelling across communications to build a relatable, humanized brand persona that fosters genuine emotional attachment (Algharabat et al., 2020).
- (2)
Design the Customer Journey as a Reinforcing Loyalty Spiral, not a Linear Funnel: This study empirically validated the effectiveness of designing phase-specific marketing interventions across pre-purchase, purchase, and post-purchase. This provided a practical blueprint for implementing the loyalty spiral model (Siebert et al., 2020), on the premise that loyalty was built through iterative, reinforcing engagement loops rather than a traditional, linear purchase funnel. The findings demonstrated how a sequence of distinct touchpoints, from gamified engagement to price incentives to post-purchase gifts, transformed a series of transactions into a deeper relational cycle. Brand managers must shift their mindset from optimizing individual touchpoints to designing a seamless, interconnected customer journey spiral by creating a journey map designed for re-engagement. The strategies validated in this research include using sticky involvement spirals (e.g., unpredictable games) to spark initial engagement and leveraging stable loyalty loops (e.g., value-based incentives and rewarding feedback mechanisms) to encourage repeat behavior (Lemon & Verhoef, 2016).
- (3)
Ensure Seamless Omnichannel Integration for a Cohesive Brand Experience: The research was set in the context of “increasingly fragmented customer experiences and omnichannel retailing.” The loyalty-building strategies—spanning online games, in-store service, and mobile app interactions—were intended to achieve an integrated omnichannel strategy. A brand’s emotional appeal could be destroyed in an instant if the experience is disjointed across channels. As previous studies have shown, consistency and seamlessness across online and offline touchpoints are crucial for strengthening customer satisfaction and loyalty (Kuehnl et al., 2019; Neslin, 2022). To provide customers with a unified brand experience, brand managers must actively dismantle internal barriers between digital, marketing, and in-store operations teams. This requires integrating data across platforms to provide a comprehensive view of customers’ journeys, maintaining a consistent emotional tone and service standards across all interactions on social media, mobile apps, or with a barista in a physical store, and utilizing technology to facilitate seamless transitions, such as mobile order-ahead features that improve the in-store experience.
- (4)
Apply the Peak–End Rule to Correct Emotional Underperformance: Despite its importance, “joy” received a low rating in brand performance. This discrepancy between performance and importance suggested that consumers did not experience much joy during their interactions. This aligned with the peak–end rule (Hamilton & Price, 2019), which stated that the emotional intensity at the peak and end of an experience disproportionately impacted consumer memory. This was consistent with the loyalty spiral model (Siebert et al., 2020), where enjoyment was crucial to progress to advocacy and bonding. Brand managers can create meaningful peak moments and emotionally resonant conclusions to bridge existing gaps. Examples of this include surprise gifts for high spenders, augmented reality drink experiences, and personalized thank-you notes at checkout. These small, meaningful moments act as memory anchors, enhancing emotional recall and fostering long-term attachment. Additionally, the interactive online games identified in this study were excellent examples of how to incorporate joy and enthusiasm into the early stages of the purchasing journey. By identifying and addressing these emotional–performance gaps, managers can ensure that their most valuable resources are invested in initiatives that strengthen brand resonance.
- (5)
Engineer “Peak” and “End” Moments to Solidify Brand Memory: This research emphasized the strategic effectiveness of a seemingly simple gesture: gifting a small present post-purchase. This approach directly applies the peak–end rule, a well-established psychological principle, which states that an individual’s memory of an experience is heavily influenced by its most emotionally intense moment (the peak) and final moment (the end). Therefore, the conclusion of a customer interaction is a powerful yet underutilized tool for strengthening long-term brand memory. Brand managers should actively “engineer” memorable endings for key customer journeys. Rather than viewing payment or departure as a transactional conclusion, these moments should be turned into opportunities to create a positive emotional peak. This can be implemented through simple, scalable actions, such as offering a surprise and delight mechanism at checkout, sending a personalized thank-you notification in the mobile app, or providing a small, unexpected token that reinforces the brand’s generosity and evokes a feeling of delight. Brands can use these endings to significantly improve the customer’s overall experience, increasing the likelihood of advocacy and ensuring a smooth re-entry into the next loyalty cycle.
This research has several limitations. It focused on the S Chain Coffee Shop, potentially limiting the generalizability of the findings to other brands, industries, or cultural contexts. Utilizing the SEM and FLPR-ANP methodologies may have affected the applicability of the findings. Additionally, relying on quantitative methods and consumer perceptions gathered at a single point in time might not fully capture the dynamic nature of brand loyalty.
To maintain brand loyalty and connection, this study emphasized the importance of integrating customer journey touchpoints with emotional engagement strategies. Future research should consider several directions, such as expanding the study to include other brands and sectors, exploring additional factors that affect brand loyalty, and using longitudinal designs to track changes over time. Additionally, they could examine the scalability of the research model across different industries, the influence of cultural differences on brand loyalty, and the incorporation of newer technologies and social media platforms in consumer behavior research.
ConclusionsThis study concluded that the S Chain Coffee Shop’s initiatives throughout the customer journey, including online interactive games, price incentives, and feedback gifts, have effectively maintained and strengthened brand loyalty. Since its customers valued their service experience, “brand feeling” was the leading factor for improving brand loyalty, supported by strategic customer journey touchpoints. This study further confirmed the effectiveness of the brand’s marketing efforts throughout the customer journey, including prepurchase interactive online games, mid-purchase price incentives, and post-purchase feedback gifts. These strategies successfully sustained the brand loyalty cycle. This comprehensive approach to evaluating and enhancing brand loyalty through customer experience touchpoints offered significant insights for marketing strategies focused on building and sustaining customer loyalty and brand connection.
Drawing on the FLPR-ANP analysis and performance evaluation results, this study investigated how to develop an ideal loyalty program for the S Chain Coffee Shop. This led to the third-stage questionnaire, the first part of which assessed the three most influential factors: enthusiasm, joy, and service quality. These factors were rephrased in a conversational style to align with the brand’s identity. The second part captured the brand’s value proposition, along with its current business status. The third part drew on the peak–end rule, exploring how the brand could create memorable moments that provided enjoyment to the consumers. The keywords were compiled and categorized per the interviewee responses.
The two most frequently mentioned keywords identified key touchpoints and provided input for creating a customer journey map for the brand (Fig. 2). This map illustrates insights into the preferred services and activities at various purchase stages, along with diverse consumer experiences and emotions. It discusses how the brand strengthens customer satisfaction and loyalty through personalized services and the integration of digital platforms to create a seamless online and offline experience. This highlighted the effectiveness of its loyalty programs in promoting customer retention and the strategic use of social media to engage customers and strengthen brand loyalty. These strategies significantly contributed to S Chain Coffee Shop’s sustained market dominance and customer loyalty, underscoring the importance of continuous innovation in customer service and engagement practices to maintain brand loyalty.
According to the six design strategies proposed by Siebert et al. (2020) (Fig. 1), the S Chain Coffee Shop falls under Type B during pre-purchase and Type A during purchase and post-purchase (Fig. 3). During pre-purchase, it employed a strategy of providing online interactive lottery games. This design concept created new, unpredictable participation spirals. It improved the online interactive experience, allowing consumers to feel warmth, enthusiasm, and surprise while stimulating their curiosity about the game content and rewards. This quickly engages them in new participation spirals, resulting in an exciting gaming experience. At the end of the game, consumers received a reward in the form of a discount for their next purchase.
During purchase, S Chain Coffee Shop implemented a pricing and discount strategy to trigger adjacent loyalty cycles while maintaining existing ones. They offered online or in-store discount promotions during special holidays or new product launches to entice consumers to make purchases. Additionally, the proactive promotion of discounted items by service staff during purchase surprised consumers and supported their decision-making. This interaction evoked emotions across four dimensions. Several consumers are sensitive to S Chain Coffee Shop’s prices, making promotional activities essential for encouraging repeat purchases. During post-purchase, the coffee shop employed a strategy of “gifting a small present,” mirroring the purchase design concept. As the consumption process neared conclusion, customers who met a certain spending threshold received a gift, creating a “peak” moment at the end of their journey. This strategy maintained the loyalty cycle by leaving the customer with a lasting impression. This study presented three loyalty spiral proposals throughout the customer journey to help the S Chain Coffee Shop foster consumer loyalty and offer strategic solutions to strengthen brand resonance.
This study proved Siebert et al.’s (2020) loyalty spiral model, which combined the stable loyalty loop with the sticky involvement spiral. The findings indicated that the S Chain Coffee Shop encouraged repeat engagement through various touchpoints at different stages of the customer journey. This highlighted the importance of emotional attachment, experiential value, and novelty. The results aligned with recent literature emphasizing the strategic importance of managing emotions and expectations throughout the customer journey (Frasquet et al., 2024; Lemon & Verhoef, 2016). Interactive games, discount strategies, and post-purchase gifts aligned with spiral involvement strategies, as they created memorable experiences, stimulated repurchases, and maintained engagement. The study further addressed the critique made by Hajdas et al. (2023) of traditional CBBE models overlooking cultural authenticity. The findings demonstrated that emotionally resonant and contextually tailored experiences, such as themed environments and surprise gifts, enhanced perceived value, and social bonding and connection. Overall, the strategic integration of customer journey design, emotional touchpoints, and digital interaction contributed to developing brand loyalty, as emphasized by recent studies on consumer experience and engagement (Alvarado-Karste & Guzmán, 2020; Becker et al., 2020).
This study contributed to evolving understandings of brand loyalty by integrating SEM and FLPR-ANP to model the loyalty spiral in the context of a global coffee chain. The empirical findings underscored the role of emotional resonance, service consistency, and personalized interactions in driving sustained customer engagement. This supported existing literature that considered customer experience as central to competitive differentiation and long-term relationship building (Becker & Jaakkola, 2020; Homburg et al., 2017; Hamilton & Price, 2019). Identifying key loyalty factors provided managers with insights for tailoring customer journeys and optimizing engagement strategies at critical touchpoints. FLPR-ANP addressed the subjectivity and linguistic vagueness inherent in expert judgments, yielding a reliable prioritization framework for brand loyalty strategy. This is especially relevant for industries where customer experiences are emotionally charged and mediated by digital and physical touchpoints, consistent with the service-dominant logic of value co-creation literature (Lemon & Verhoef, 2016; Roggeveen & Rosengren, 2022). Additionally, integrating consumer-centered design elements, such as the peak–end rule and personalized messaging, reflects the increasing emphasis on creating memorable service moments that catalyze loyalty spirals (Siebert et al., 2020). In conclusion, the research framework addressed the “what” (key loyalty drivers) and “how” (strategic prioritization), contributing to theory development and brand management.
CRediT authorship contribution statementJia-Wei Tang: Writing – review & editing, Writing – original draft, Methodology, Formal analysis, Data curation, Conceptualization. Pei-Hsuan Tsai: Writing – review & editing, Writing – original draft, Methodology, Formal analysis. Ming-Chia Hsieh: Writing – review & editing, Formal analysis, Data curation.
The Computational Procedure of Fuzzy Linguistic Preference Relations–Analytic Network Process
This study adopted the Fuzzy Linguistic Preference Relations method (Fuzzy LinPreRa) proposed by Wang and Chen (2008). It integrated it into the Analytic Network Process (ANP) framework to develop the Fuzzy LinPreRa–ANP approach for computing factor weight values. The following was the analytical procedure, based on Tang and Hsu (2018).
1. Constructing the Fuzzy Pairwise Comparison Matrix
Suppose the set of evaluation factors isX1,X2,...,Xn. Pairwise comparisons are conducted sequentially (i.e., comparing X1 with X2, X2 with X3, …, and finally Xn−1 with Xn), A total of n−1 preference relation values are needed. Let aijk denote the preference relation value of the decision-maker k when comparing XiwithXj. Following Wang and Chen (2008), the fuzzy preference relationships are evaluated using fuzzy numbers within the range [9˜−1,9˜], and their corresponding fuzzy linguistic terms to indicate the importance of each factor.
The fuzzy pairwise comparison matrix A˜k for decision-maker k is expressed as:
The symbol “ × ” in the matrix denotes that the decision-maker does not need to provide preference information.
2. Converting to the Fuzzy Preference Relation Matrix
Using Eqs. (1)-(5), each a˜ijk is converted into p˜ijk, where p˜ijk=(pijL,pijM,pijR), pijL is the left bound, pijM is the middle value, and pijR is the left bound of the triangular fuzzy number. The fuzzy preference relation matrix (p˜ijk)n×n is thus obtained for the decision-maker k(k=1,2,3,...,m). The transformation formulas are as follows:
To ensure that each triangular fuzzy number lies within [0,1], the following normalization functions, Eqs. (6)-(8), are applied, where c is the minimum absolute value among all triangular fuzzy numbers in (p˜ijk)n×n.
3. Aggregating Decision-Makers’ Evaluations to Obtain Fuzzy Preference Weights
The arithmetic mean method (Tang & Hsu, 2018; Wang & Chen, 2008) is widely used to integrate evaluators’ opinions. This study adopts the arithmetic mean to compute aggregated fuzzy preference relation values across m by applying Eq. (9).
The mean fuzzy preference relation value for factor i is obtained by Eq. (10).
The fuzzy preference relation weight W˜i for factor i is obtained by Eq. (11), and then the defuzzified weight wi is computed as Eq. (12).
4. Establishing the Fuzzy Preference Relation Interdependence Matrix and Final Weights
The fuzzy preference relation interdependence matrix is constructed under the assumption that factors influence each other. The wij denotes ni × nj interdependence submatrix representing the effect of factors in cluster Ci on those in cluster Cj. The value wij is the defuzzified preference relation value considering inter-factor influence, while wi is the factor weight without considering such interdependencies. Multiplying the two yields, the adjusted weight Wi, as follows, Eq. (13).
