From Customer Experience to Purchase Intention: The Moderating Role of Trust in Hybrid Banking Decision Environments

The banking sector is undergoing a profound transformation driven by digital technologies, regulatory pressures, and changing customer behavior, which are reshaping how financial services are delivered, evaluated, and integrated into customer decision processes [1]. In particular, the digitization of financial services has put traditional business models under increasing pressure [2]. Digital technologies enable customers to access banking services regardless of time and place, while online banking, mobile applications, and digital advisory services increasingly shape how customers evaluate financial options and develop behavioral intentions [3]. Customers increasingly expect seamless, transparent, and consistent interactions across digital and in-branch channels, particularly when financial decisions involve uncertainty, risk, and the need for reliable information [4]. At the same time, competition in the banking sector has intensified significantly. In addition to traditional banks, FinTech companies and digital platform providers are increasingly emerging as new competitors [2]. These companies are often characterized by innovative technologies and a strong customer focus, thereby setting new standards for service quality and customer experience. Against this backdrop, customer experience has become increasingly important in banking because it shapes how customers interpret service interactions and evaluate the reliability of financial service providers. Companies are increasingly recognizing that long-term economic success is not determined solely by products or prices, but is significantly influenced by the quality of the customer experience [5]. Customer experience describes the totality of customers’ perceptions and evaluations across the customer journey and encompasses both direct and indirect interactions with a company [6]. In the banking sector, these experiences are particularly complex because customers move between digital and in-person touchpoints and expect consistent, reliable, and decision-supportive interactions [7]. A positive customer experience can help reduce uncertainty, build trust, and strengthen the relationship between the customer and the bank in the long term [8]. Trust represents a critical mechanism through which customers reduce uncertainty and evaluate reliability in banking environments characterized by information asymmetry and perceived risk [9]. Customers expect banks to act reliably and responsibly when managing their financial resources. Trust thus serves as a fundamental prerequisite for stable and long-term customer relationships and significantly influences the decision-making processes of bank customers [10]. Furthermore, purchase intention represents a central behavioral outcome in customer decision-making. It describes the subjective probability that a customer will use a product or service and is considered an important predictor of actual purchasing behavior [11]. The significance of purchase intention as a behavioral variable can also be explained by established theoretical approaches such as the Theory of Planned Behavior. This theory assumes that behavioral intentions are significantly influenced by individual attitudes as well as perceived control and subjective norms [12]. In the context of financial services, trust can be interpreted as a central factor that reduces uncertainty and thus promotes the formation of positive behavioral intentions [10]. Despite the growing importance of customer experience in banking decision environments, existing empirical research remains strongly context-specific and provides limited insight into how customer experience translates into behavioral intention under different trust conditions. Several studies have examined the relationships between customer experience, trust, and purchase intention in banking-related contexts, yet these relationships are often treated as linear associations rather than context-dependent behavioral relationships. For instance, previous studies have emphasized the role of customer experience in digital and hybrid banking environments [2], [3], while other studies have highlighted the importance of interpersonal trust in stationary banking interactions [13], [14]. However, a large share of the existing literature focuses on digital contexts, such as online banking or e-commerce settings, where customer experience is frequently examined in relation to behavioral outcomes [15], [16]. In these studies, trust is predominantly conceptualized as a mediating mechanism that explains why customer experience influences purchase intention, whereas less attention has been paid to whether trust changes the strength of this relationship. While this mediation perspective has substantially advanced the understanding of underlying causal mechanisms, it provides only limited insight into whether and under which contextual conditions the effect of customer experience on behavioral outcomes becomes stronger or weaker. In contrast, studies that explicitly consider hybrid banking environments, where digital and in-branch interactions are combined, remain limited. Moreover, the role of trust as a moderating variable has received only marginal attention in prior research, despite initial evidence suggesting that trust can shape the strength of relationships between key constructs [17], [18].

Against this backdrop, a clear research gap emerges. Existing studies tend to focus on isolated relationships or specific service contexts, while an integrated decision-oriented analysis of customer experience, trust, and purchase intention that explicitly incorporates the moderating role of trust in hybrid banking settings remains limited. In hybrid banking environments, where customers continuously switch between technology-based and interpersonal service encounters, trust may not only emerge as an outcome of customer experience but also function as a boundary condition that shapes how strongly interaction experiences are translated into behavioral intentions. The present study addresses this gap by providing an integrated empirical analysis of these relationships and by explicitly modeling trust as a moderating variable within a hybrid banking context.

Specifically, the study is guided by the following research question: How does customer experience influence bank customers’ purchase intention under different levels of trust in hybrid banking environments?

By addressing this question, the study contributes to existing research by extending prior mediation-based explanations and by offering a more differentiated understanding of how customer experience translates into behavioral intentions under different trust conditions in hybrid banking environments. In doing so, the study contributes not only to customer experience and trust research but also to the understanding of behavioral decision processes in digitally integrated banking settings, by showing that trust can function as a contextual amplifier of customer experience effects beyond established mediation-based models.

In recent years, customer experience has emerged as a central construct in service management and customer decision research [5], [8]. While traditional marketing approaches often focused on individual transactions or product features, the customer experience approach adopts a holistic perspective on how customers evaluate and interpret interactions across multiple service touchpoints [4]. This development is closely linked to fundamental shifts in the understanding of marketing, which have resulted in particular from increasing market saturation, rising competitive intensity, and changing customer demands [19]. In many industries, products are becoming increasingly interchangeable, and pricing strategies alone are no longer sufficient to differentiate a company from the competition in the long term [19]. At the same time, customers can compare offers in a short amount of time, which raises expectations for personalized, consistent, and decision-relevant interaction experiences across all touchpoints [20]. Against this backdrop, marketing has evolved from a transaction-oriented approach to a relationship-oriented and, ultimately, experience-oriented understanding [4].

Customer experience (CX) describes the totality of a customer’s perceptions, emotions, and experiences during their interaction with a company throughout the entire customer journey [6]. These experiences arise across various touchpoints and encompass both digital and in-person interactions [4]. In the banking sector, this is particularly important, as customers use both digital services and in-person advice and expect a consistent, high-quality interaction [7]. In contrast to related constructs such as service quality and customer satisfaction, customer experience adopts a broader and more holistic perspective. While service quality primarily focuses on the functional evaluation of service performance and satisfaction reflects a post-consumption evaluation, customer experience captures the broader process through which customers evaluate interactions across multiple touchpoints, including cognitive, emotional, and contextual responses [9] [10]. This distinction is particularly relevant in the banking sector, where complex and multi-stage interactions extend beyond single service encounters. The theoretical framework of customer experience can be explained through various approaches. Within the context of Service-Dominant Logic, perceived value arises not from the product itself, but from its use and the interaction between the customer and the company [21]. Customer experience can thus be understood as the result of a co-creation process in which customers are actively involved in shaping their own experience [7].

Furthermore, CX encompasses both cognitive and affective components. While cognitive aspects relate to the functional evaluation of an offering, affective components reflect emotional reactions that play a key role in differentiation [22], [23]. Particularly in the banking sector, where services are intangible and require explanation, these emotional and trust-related dimensions play a central role [6]. The impact of customer experience on behavioral intentions can be explained by various theoretical approaches. Expectation-Confirmation Theory posits that positive experiences lead to the confirmation of expectations and thereby influence future behavioral decisions [24]. Complementing this, the Theory of Planned Behavior shows that positive experiences influence evaluative judgments and behavioral intentions toward a company and thus contribute to the formation of behavioral intentions [12]. In this context, customer experience can be interpreted as a central antecedent of both cognitive evaluations and affective responses, which subsequently shape behavioral intentions.

Against this backdrop, it becomes clear that customer experience shapes long-term evaluative judgments and behavioral intentions in banking decision contexts. Positive customer experiences reduce uncertainty, reinforce the perception of value, and increase the likelihood of future purchasing decisions. Particularly in the banking sector, which is characterized by high uncertainty and complex decision-making structures, customer experience plays a central role in influencing behavioral intentions. Based on these theoretical considerations, the following hypothesis is formulated:

H1: Customer experience has a significant positive influence on purchase intention.

Trust is a central component of the relationship between companies and customers, particularly in the banking sector, which is characterized by uncertainty, information asymmetries, and long-term commitments [9], [10]. Customers must be able to trust that their bank acts reliably, competently, and in their best interests. This particular importance of trust in the banking sector stems from the role of banks as custodial and risk-bearing institutions, to which customers entrust financial resources and expect responsible management [10].

Against this backdrop, customer experience plays a decisive role in building trust. Despite their close relationship, customer experience and trust represent conceptually related but analytically distinct constructs. While customer experience captures customers' immediate, situation-specific, and emotionally embedded perceptions of service encounters across different touchpoints throughout the customer journey, trust reflects a more stable, cumulative, and evaluative belief regarding the reliability, integrity, and competence of a company that develops over repeated interactions over time [10]. In this sense, customer experience primarily reflects how customers perceive and process individual interactions, whereas trust reflects how these experiences are cognitively integrated into broader expectations about future interactions. In particular, dimensions such as "peace of mind" within customer experience refer to momentary feelings of security during interactions, whereas trust represents a cumulative evaluative judgment that develops over time across multiple experiences.

In the banking sector, CX is shaped throughout the entire customer journey and is significantly influenced by both in-person and digital touchpoints [4], [7]. Positive experiences in these interactions influence perceptions of security, transparency, and reliability. From the perspective of Service-Dominant Logic, positive interactions signal competence and reliability and thus contribute to building trust [21]. Furthermore, the relationship between customer experience and trust can be explained through approaches from trust research. Trust is based primarily on the dimensions of competence, integrity, and benevolence [10]. Positive customer experiences reinforce the perception of these qualities by demonstrating that a company acts reliably, communicates transparently, and responds to customers' needs. Accordingly, customer experience can be interpreted as an antecedent that provides evaluative cues that help reduce uncertainty, which are subsequently integrated into the formation of trust. Thus, customer experiences serve as a concrete foundation upon which customers develop and stabilize trust [7].

Expectation-confirmation theory also provides an explanation for this relationship. When expectations are confirmed or exceeded by positive experiences, this leads not only to satisfaction but also to a more stable assessment of a company's reliability. This allows trust to be built over the long term [24]. In the brick-and-mortar banking sector, personal interaction plays a particularly central role in building trust. Competent advice, empathetic communication, and a trusting atmosphere help customers perceive the bank as credible and responsible [13], [14]. At the same time, digital touchpoints are becoming increasingly important, so that trust is established at both the interpersonal and technological levels [17].

In summary, it can be stated that customer experience exerts a significant influence on the perception of trust by reducing uncertainties and promoting positive evaluations regarding competence, integrity, and reliability. Based on these theoretical considerations, the following hypothesis is formulated:

H2: Customer experience has a significant positive influence on trust.

Purchase intention describes the subjective probability that a consumer will purchase a product or service and is considered a key predictor of actual purchasing behavior [11]. In the context of the banking sector, purchase intention specifically refers to the customer's willingness to use additional financial products or services offered by their existing bank, such as loans, investment products, or insurance solutions, rather than a general intention to use banking services. In the banking sector, purchase intention is closely linked to the perception of trust, as financial services are often characterized by high uncertainty, information asymmetries, and limited controllability [9] [10]. Given the long-term and risk-sensitive nature of financial decisions, customers are more likely to engage in cross-buying behavior when they perceive their bank as trustworthy. Trust reduces these uncertainties and thus facilitates decision-making processes. Customers who trust their bank are more likely to use additional products or services. Accordingly, purchase intention in this study reflects a behavioral intention directed toward deepening the existing customer relationship rather than initiating a new one. Trust thus functions as a key mechanism through which customers evaluate uncertainty and form behavioral intentions in banking environments. This is particularly evident in complex and long-term financial decisions, where trust serves as the foundation for decision-making. Furthermore, trust stabilizes attitudes during the decision-making process and increases the willingness to engage in complex and long-term financial decisions [15], [25].

The theoretical basis for this relationship can be explained by several approaches. In addition to the Theory of Planned Behavior, which describes behavioral intentions as the result of attitudes, subjective norms, and perceived control [12], trust can be interpreted as a key influencing factor that reduces uncertainty and thus increases perceived behavioral control. Thus, trust contributes to positive attitudes toward a bank being more likely to translate into concrete behavioral intentions. Furthermore, trust research shows that trust arises particularly when customers perceive a company as competent, ethical, and benevolent [10]. In the brick-and-mortar banking sector, trust often arises through personal interactions in which customers perceive the competence and empathy of advisors [14]. At the same time, moral aspects such as fairness and a sense of responsibility play a decisive role in building trust [13]. This form of trust has a direct impact on purchase intention, as it increases the willingness to engage with offers.

Based on these theoretical considerations, the following hypothesis is formulated:

H3: Trust has a significant positive influence on the purchase intention of bank customers.

While Hypotheses H1 through H3 examine direct relationships between customer experience, trust, and purchase intention, the following hypothesis broadens the perspective to include a moderating effect. In previous research, trust is often viewed as a mediating mechanism that explains why positive customer experiences lead to increased purchase intention [13], [25]. This perspective is plausible, since positive customer experiences can foster perceptions of reliability, integrity, and benevolence, which in turn may increase customers’ willingness to purchase additional financial products. However, recent approaches suggest that customer experience cannot be understood as a linear influencing factor but rather depends heavily on the customer’s subjective interpretation [26].

Against this backdrop, trust may not only represent an outcome of customer experience, but also a contextual condition that shapes the extent to which customer experience translates more strongly into behavioral intentions. A mediation perspective explains why customer experience affects purchase intention, whereas a moderation perspective helps explain when and under which conditions this effect becomes stronger or weaker. Trust acts as a mechanism for reducing perceived risks and uncertainties and thus influences the evaluation of experiences [9], [10]. This is particularly relevant in banking, where even positive experiences may not be sufficient to trigger purchase intentions if customers do not perceive the bank as trustworthy. Conversely, when trust is already high, even moderately positive experiences may be interpreted more favorably and translated more strongly into purchase intentions. Theoretically, this effect can be explained, among other things, by the Theory of Planned Behavior. Trust can be interpreted as a stabilizing factor that reinforces the translation of attitudes into behavioral intentions [12]. Empirical studies show that trust can significantly influence the strength of the relationship between key attitudinal variables and purchase intention [18]. Accordingly, trust is not conceptualized here merely as an intervening mechanism, but as a variable that conditions the effectiveness of customer experience. This leads to the assumption that customer experience exerts its influence on purchase intention particularly when a high level of trust is also present. When trust is low, even positive experiences may be less likely to translate into behavioral intentions, whereas when trust is high, even moderate experiences can trigger a stronger purchase intention. Trust thus functions as a reinforcing mechanism that determines the extent to which customer experience influences behavioral intentions more strongly [17], [18]. Based on these theoretical considerations, the following hypothesis is formulated:

H4: The influence of customer experience on purchase intention is moderated by trust, such that the influence is stronger when trust is higher.

The following section contextualizes the constructs derived theoretically above within the banking sector. The banking sector has played a central role in modern economies for decades. Its key functions include financial intermediation, payment processing, and lending, which contribute significantly to the stability and functionality of the financial system [27]. At the same time, the banking sector is undergoing an ongoing transformation process shaped by technological, regulatory, and market-related developments [1].

Traditionally, the German banking sector has been characterized by the three-pillar model, consisting of private commercial banks, public-law institutions, and cooperative banks. Particularly in brick-and-mortar banking, personal advice, individualized support, and long-term customer relationships formed the basis of customer loyalty [1]. Although this structure is specific to Germany, it reflects broader characteristics of traditional banking systems, in which relationship-based interactions and personalized advisory services play a central role. Accordingly, the findings of this study can be applied to other banks operating in similar environments. With the advance of digitalization and the market entry of fintechs and neobanks, this model is coming under increasing pressure. New competitors offer financial services digitally, efficiently, and often at lower cost, which has fundamentally changed customer expectations [2]. Traditional differentiators such as price or branch density are no longer sufficient, meaning that relational and decision-related factors are becoming increasingly important. This transformation is particularly relevant for the present study, as it highlights the growing importance of customer experience and trust across both digital and physical touchpoints. In line with the empirical setting, which captures customer perceptions across different interaction channels, the analysis adopts a hybrid perspective that integrates both digital and in-branch banking experiences.

In this context, customer experience, trust, and purchase intention are coming into sharp focus, as they are key determinants of banks’ long-term success [5], [8]. Despite digitalization, brick-and-mortar banking operations remain relevant, particularly for decisions that require extensive advice and are based on trust [3]. Branches serve as central touchpoints for building trust and personal loyalty [23]. The importance of customer experience stems in particular from the characteristics of financial services, which are intangible, complex, and require explanation [6]. Accordingly, perceived quality is largely determined by experiences along the customer journey. In the brick-and-mortar context, CX is particularly evident in personal interactions and the quality of advice, with positive experiences fostering long-term emotional bonds [7].

Trust represents a central psychological dimension, as financial services are characterized by uncertainty and information asymmetries. It serves as a mechanism for reducing complexity and risk [9]. In brick-and-mortar banking, trust is built particularly through personal interactions in which customers perceive competence, integrity, and empathy [10], [14]. With increasing digitalization, trust in technological systems is also gaining importance, as customers expect secure and reliable applications [17].

Purchase intention is a key behavioral metric in the banking sector, as it reflects the willingness to use financial products. Due to the complexity and long-term nature of financial decisions, it is largely based on the perception of service quality, customer experience, and trust [18], [28]. In particular, personal interactions during the advisory process positively influence purchase intention when they are perceived as competent, transparent, and empathetic [8], [29].

This study is based on a quantitative research design aimed at empirically examining the relationships among customer experience, trust, and purchase intention in hybrid banking environments. Structural equation models enable the analysis of directed relationships between variables, which, when supported by a sound theoretical foundation, can be interpreted as causal [28], [30].

A standardized online questionnaire is used for data collection. This approach enabled the efficient collection of standardized responses from a comparatively large sample of banking customers. The survey targets bank customers with experience in using financial services. The goal is to achieve a sample that is as heterogeneous as possible in order to capture diverse banking experiences and customer evaluations. The questionnaire is distributed through various channels, including personal networks and online platforms. The survey period spans several weeks. After cleaning the data, for example, by excluding incomplete records and conspicuous response patterns, a final sample of 371 valid records is obtained. The resulting sample size was considered sufficient for the planned PLS-SEM analysis. The sample consists primarily of young adults with a median age of 32, ranging from 18 to 81 years old. This indicates a generally heterogeneous age structure, with a clear emphasis on younger respondents. With regard to gender distribution, there is an imbalance in favor of female participants. Overall, 62.8\% of respondents are female, while 37.2% are male. In terms of educational attainment, the sample exhibits a high overall level of education. A large proportion of the respondents hold a college degree (45.82%) or a high school diploma (24.26%). Lower educational qualifications, on the other hand, are significantly less common. Overall, the sample can therefore be classified predominantly as having a medium to high level of education. The sociodemographic characteristics of the sample are presented in Table 1.

The constructs are operationalized using established measurement instruments that have been validated in the literature. The goal is to measure the constructs of customer experience, trust, and purchase intention in a reliable and valid manner. All items are measured on a five-point Likert scale ranging from “strongly disagree” to “strongly agree.”

Customer experience was measured using an established Experience Quality (EXQ) scale [8]. This model conceptualizes customer experience as a multidimensional construct and encompasses the dimensions of Peace of Mind, Moments of Truth, Outcome Focus, and Product Experience. The Peace of Mind dimension describes the feeling of security and trust during interaction with a company. Moments of Truth refer to critical touchpoints that have a particularly strong influence on the perception of the customer experience. Outcome Focus captures the perception of goal achievement from the customer’s perspective, while Product Experience describes the perception of the products and services offered. The EXQ model thus represents an established approach to the holistic measurement of customer experience and is frequently applied, particularly in the service sector [8]. The original items are adapted to the context of the banking sector to ensure a substantive fit with the study.

To measure the construct of trust, established scales from trust research are used, particularly those based on the work of previous study [10]. Trust is understood as a multidimensional construct comprising the dimensions of ability, integrity, benevolence, and predictability. The ability dimension describes the perceived competence of the bank to provide services reliably and professionally. Integrity refers to the perception that the bank acts honestly and reliably. Benevolence describes the perception that the bank acts in the customer’s best interest. Predictability describes customers’ expectation that a company acts consistently, transparently, and in compliance with regulations. These dimensions represent central components of trust research and enable a nuanced assessment of trust within the context of complex service relationships [9], [10].

Although the underlying measurement approach was originally developed in digital and e-commerce contexts, its transferability to the banking sector is theoretically justified. Trust in both contexts is fundamentally based on the reduction of uncertainty and perceived risk in situations characterized by information asymmetry and limited controllability. These conditions are not only present in online environments but are even more pronounced in financial services, where decisions are complex, long-term, and associated with significant financial consequences. Accordingly, the dimensions of ability, integrity, benevolence, and predictability can be interpreted as context-independent trust-building mechanisms that capture core aspects of trust across different service settings. In the banking sector, these dimensions are reflected, for example, in the perceived advisory competence of bank employees (ability), transparent and fair behavior (integrity), customer-oriented decision-making (benevolence), and consistent and reliable service processes (predictability) [9], [10]. The items are adapted to the context of the banking sector to ensure a context-appropriate assessment of customer perceptions. This context-specific adaptation ensures that the measurement instrument reflects both interpersonal and technology-based trust components that are characteristic of hybrid banking environments.

Purchase intention was operationalized using an established behavioral intention scale adapted to the banking context [11]. The scale measures consumers’ willingness to use a company’s products or services in the future. In the context of this study, the items refer to the intention of bank customers to use additional financial products or services offered by their bank. Purchase intention is understood here as a key predictor of actual future behavior [12]. This classification aligns with established approaches in consumer behavior research, in which behavioral intentions are interpreted as the immediate precursor to actual behavior. The items are also adapted to the specific context of the banking sector. This ensures that the measurement adequately accounts for the unique characteristics of financial services, particularly their complexity and long-term orientation.

Figure 1 shows the underlying research model of the study. This model encompasses the direct relationships between customer experience, trust, and purchase intention, as well as the moderating effect of trust. The relationships depicted form the basis for the empirical testing of the hypotheses.

Partial Least Squares Structural Equation Modeling (PLS-SEM) is used to analyze the collected data. The analysis is conducted using RStudio with the “plspm” package.

PLS-SEM was selected as the most appropriate analytical approach due to the specific objectives and structural characteristics of the proposed research model. The present study follows a prediction-oriented research logic by examining not only the direct relationships between customer experience, trust, and purchase intention, but also the conditional influence of trust on this relationship. In particular, the inclusion of a latent interaction term to test the moderating role of trust increases the complexity of the structural model and requires an estimation approach capable of handling interaction effects robustly. PLS-SEM is chosen over covariance-based SEM (CB-SEM) because it is particularly suitable for variance-based modeling and for estimating complex predictive relationships involving moderation effects. In contrast to CB-SEM, which focuses primarily on global model fit and theory confirmation, PLS-SEM is particularly appropriate for exploratory and variance-based analyses, where the primary objective is to maximize the explained variance of endogenous constructs. Furthermore, PLS-SEM is less restrictive with regard to distributional assumptions and is well suited for models that include moderating effects, as is the case in the present study [31].

A bootstrapping procedure with 5,000 resamples is used to test the significance of the path coefficients. Statistical significance is assessed based on \(t\)-values (\(t \geq 1.96\) for \(p < 0.05\)) and bias-corrected confidence intervals [30]. Various quality criteria are used to evaluate the measurement model. These include Cronbach's alpha to assess internal consistency, composite reliability to evaluate reliability, and Average Variance Extracted (AVE) to assess convergent validity. Threshold values of \(\geq0.70\) for Cronbach's alpha and composite reliability, and \(\geq 0.50\) for AVE are applied as recommended in the literature [32].

In addition, discriminant validity is assessed using both the Fornell-Larcker criterion and cross-loadings. While the Heterotrait-Monotrait ratio (HTMT) is recommended in recent PLS-SEM research, it was not explicitly calculated in the present study. Therefore, discriminant validity is primarily evaluated based on traditional criteria. This methodological choice should be interpreted with caution, as the absence of HTMT limits the ability to detect more subtle overlaps between conceptually related constructs. In particular, given the theoretical proximity between customer experience and trust, the possibility of construct overlap cannot be fully excluded. As a result, the discriminant validity of the measurement model should be regarded as supported, but not conclusively established according to current methodological standards.

Multicollinearity is examined conceptually, as the variance inflation factor (VIF) was not explicitly calculated. Given the observed correlations between constructs, potential multicollinearity cannot be entirely ruled out. The absence of VIF statistics limits the ability to empirically assess whether shared variance between predictor constructs may have influenced the stability of the estimated path coefficients or the observed interaction effect. Consequently, the structural relationships identified in this study should be interpreted with caution, particularly with regard to the magnitude and precision of the estimated effects. The structural model is evaluated based on the coefficient of determination (\(R^2\)). Although additional evaluation criteria such as effect sizes (\(f^2\)) and predictive relevance (\(Q^2\)) are recommended in contemporary PLS-SEM research, these were not explicitly computed in the present study. Consequently, the assessment of effect sizes and predictive relevance remains limited.

The results of the reliability and convergent validity analyses are presented in Table 2. The Cronbach’s alpha and composite reliability values for all constructs exceed the recommended threshold of 0.70, indicating a high level of internal consistency in the measurement instruments [30].

The AVE values for most constructs exceed the recommended threshold of 0.50 [30]. Only for Customer Experience is the value slightly below this threshold. However, due to the high composite reliability and the theoretical foundation of the construct, Customer Experience is nevertheless retained in the model [30]. The discriminant validity of the constructs is examined using the Fornell-Larcker criterion. These results are presented in Table 3. The results show that the square root of the AVE values on the diagonal is greater than the correlations between the constructs in each case. However, relatively high correlations between customer experience and trust suggest a potential conceptual proximity between these constructs. Therefore, limitations regarding discriminant validity cannot be fully excluded [30]. This interpretation is further strengthened by the absence of HTMT values, which are currently regarded as a more sensitive criterion for identifying discriminant validity issues in PLS-SEM research. Consequently, although the traditional criteria support discriminant validity, a certain degree of conceptual overlap between these constructs cannot be ruled out entirely.

Overall, the results confirm the suitability of the measurement instruments used to assess the constructs of customer experience, trust, and purchase intention. Thus, the measurement models meet the key quality criteria of reliability and validity and form a suitable basis for the subsequent analysis of the structural model. At the same time, the absence of additional diagnostic indicators such as HTMT and VIF implies that both construct distinctiveness and the stability of structural estimates should be interpreted as indicative rather than definitive.

In the following, the structural model is analyzed to test the formulated hypotheses. The results of the structural model are presented in Table 4. The significance of the path coefficients is determined using bootstrapping with 5,000 resamples [30]. Overall, the results indicate that all hypothesized relationships are statistically significant. The estimated path coefficients, corresponding \(t\)-values, \(p\)-values, and confidence intervals are reported below.

For Hypothesis H1, the results show that customer experience has a significant positive effect on purchase intention (\(\beta = 0.369\), \(t = 4.83\), \(p < 0.001\), 95% CI [0.20; 0.54]). Based on the magnitude of the path coefficient, the effect can be interpreted as moderate. For Hypothesis H2, customer experience exhibits a strong and highly significant effect on trust (\(\beta = 0.859\), \(t > 10.00\), \(p < 0.001\), 95% CI [0.80; 0.91]). This represents a large effect, although the high coefficient may also reflect conceptual proximity between the constructs. For Hypothesis H3, trust has a significant positive effect on purchase intention (\(\beta = 0.370\), \(t = 4.68\), \(p < 0.001\), 95% CI [0.16; 0.48]). The effect can be interpreted as moderate. For Hypothesis H4, the interaction effect between customer experience and trust on purchase intention is also significant (\(\beta = 0.097\), \(t = 2.18\), \(p < 0.05\), 95% CI [0.01; 0.18]). Although statistically significant, the effect size is small.

Taken together, the results confirm that customer experience influences purchase intention directly while trust additionally acts as a moderating variable.

In addition to the direct effects, the moderating influence of trust on the relationship between customer experience and purchase intention was also examined. The results of Hypothesis H4 show that trust significantly moderates the relationship between customer experience and purchase intention (\(\beta = 0.097\), \(t = 2.19\), \(p < 0.05\)).

Although the interaction effect is statistically significant, its magnitude is relatively small, indicating a limited substantive impact. Accordingly, the moderating role of trust should be interpreted with caution. The results suggest that customer experience does not exert a uniform effect on purchase intention, but that this relationship varies depending on the level of trust. However, the size of the interaction indicates that trust acts as a contextual refinement rather than a dominant driver of this relationship. Specifically, the findings indicate that the positive effect of customer experience on purchase intention is somewhat stronger at higher levels of trust. In contrast, at lower levels of trust, positive experiences are less consistently translated into behavioral intentions, as perceived uncertainty and risk remain relevant. To further illustrate this interaction, a graphical representation of simple slopes can be used to compare the effect of customer experience on purchase intention at different levels of trust, for example, low trust versus high trust. Such an analysis typically shows steeper slopes under high-trust conditions, thereby visualizing the conditional nature of the relationship. Overall, these findings support the assumption that trust not only acts as a direct predictor of purchase intention but also functions as a contextual boundary condition that slightly amplifies the effect of customer experience. However, given the relatively small effect size, the moderating role of trust should be interpreted as complementary rather than central within the model.

Figure 2 shows the results of the structural model and visualizes the significant relationships between the constructs examined using standardized path coefficients. The figure illustrates that the strongest relationship within the model is observed between customer experience and trust \(\left(\beta = 0.859\right)\), indicating that positive interaction experiences represent an important foundation for trust formation in the banking context. In addition, both customer experience \(\left(\beta = 0.369\right)\) and trust \(\left(\beta = 0.370\right)\) show moderate and comparatively similar direct effects on purchase intention, suggesting that both constructs independently contribute to customers' behavioral intentions.

The dashed interaction path \(\left(\beta = 0.097\right)\) represents the moderating effect of trust on the relationship between customer experience and purchase intention. Although this effect is smaller in magnitude compared to the direct effects, the figure demonstrates that trust slightly strengthens the positive association between customer experience and purchase intention. In practical terms, this indicates that positive customer experiences are more likely to translate into purchase intentions when customers already perceive a higher level of trust toward the bank. The visual contrast between the strong direct paths and the comparatively smaller interaction path further supports the interpretation of trust as a contextual boundary condition rather than a dominant explanatory mechanism within the model.

To further evaluate the structural model, the explained variance of the endogenous constructs was examined. Customer experience explains a substantial portion of the variance in trust ($R^2$ = 0.737), indicating a high level of explanatory power [29]. For purchase intention, the model shows moderate explanatory power ($R^2$ = 0.454) [30]. Overall, the results suggest that the model provides a strong explanation for trust and a moderate explanation for purchase intention. The comparatively high R² value for trust warrants a cautious interpretation. While it reflects strong predictive relationships within the model, it may also indicate potential issues related to common method bias or conceptual proximity between the constructs. Given that both customer experience and trust are measured based on self-reported perceptions and partially rely on similar evaluative mechanisms (e.g., perceived reliability, security, and emotional evaluation), a certain degree of conceptual overlap cannot be fully excluded. This is particularly relevant in the context of the EXQ dimension “Peace of Mind,” which captures affective states closely associated with feelings of security and confidence. Nevertheless, the results of the discriminant validity assessment (e.g., Fornell-Larcker criterion and cross-loadings) indicate that the constructs can be empirically distinguished [30]. Therefore, despite potential conceptual proximity, the measurement model provides sufficient evidence for discriminant validity.

Overall, the high explanatory power is interpreted as an indication of a strong substantive relationship between customer experience and trust, while acknowledging potential methodological influences.

Future research could address these limitations by employing multi-source data, longitudinal designs, or alternative operationalizations to reduce potential common method bias and to further disentangle the conceptual boundaries between customer experience and trust.

The results of the present study suggest that customer experience and trust in the banking sector are closely interrelated and jointly associated with the purchase intention of bank customers.

The findings related to Hypothesis H1 indicate that customer experience is positively associated with purchase intention. This suggests that customer experiences in the banking sector are not merely perceived but may also be reflected in customers’ behavioral intentions. Of particular relevance is that customer experience functions as a holistic construct that integrates multiple touchpoints along the customer journey and thus captures cumulative experiential evaluations rather than isolated interactions. The results for Hypothesis H2 further suggest a strong positive association between customer experience and trust. The high path coefficient indicates that customer experiences may represent an important foundation for the development of trust. This finding is consistent with theoretical perspectives that emphasize the role of repeated and consistent interaction experiences in shaping trust perceptions. Accordingly, trust appears to be closely linked to customers’ evaluations of concrete interaction experiences rather than being solely based on abstract brand perceptions. With regard to Hypothesis H3, the findings indicate that trust is positively associated with purchase intention. This suggests that trust may function as a stabilizing factor in customers’ decision-making processes, particularly in contexts characterized by uncertainty. In the banking sector, where decisions are often complex and long-term, trust appears to be an important condition under which customers are more likely to translate positive evaluations into behavioral intentions. A key extension of previous research is reflected in the moderating effect of trust (H4). The results suggest that the relationship between customer experience and purchase intention varies depending on the level of trust. Specifically, the association between customer experience and purchase intention appears to be somewhat stronger at higher levels of trust. However, given the relatively small effect size, this moderating influence should be interpreted with caution and understood as a subtle contextual refinement rather than a dominant effect. Several explanations may account for the comparatively limited magnitude of this effect. First, within the banking sector, trust may represent a fundamental relational prerequisite rather than a strong differentiating factor, as customers generally expect banks to operate reliably, securely, and responsibly. Under such conditions, trust may already be embedded in customers’ baseline expectations, thereby limiting its incremental moderating influence [3].

Second, prior research suggests that the role of trust is highly context-dependent. Meta-analytic evidence from digital commerce contexts demonstrates that trust exerts different behavioral effects depending on the specific trust object and interaction setting, indicating that its influence varies across relational and institutional environments [17]. This supports the interpretation that, in hybrid banking environments, trust may function less as a primary amplifier and more as a contextual boundary condition shaping how customer experience translates into behavioral intentions.

Third, the strong direct relationship observed between customer experience and trust in the present model suggests that both constructs are closely interconnected. As a result, part of the explanatory variance associated with trust may already be captured through customer experience itself, reducing the additional variance explained by the interaction term.

Taken together, the relatively small moderation effect appears theoretically plausible rather than unexpected. It suggests that trust primarily acts as a contextual stabilizer that enhances the translation of positive experiences into behavioral intentions under specific conditions, rather than serving as the dominant explanatory mechanism within the model.

Against the backdrop of advancing digitalization, these findings gain additional relevance. The integration of digital and in-person touchpoints implies that customer experience is increasingly shaped across multiple channels. The results indicate that the perceived consistency of these experiences may play an important role in the development of trust and its association with behavioral intentions.

In summary, the study provides empirical evidence for associations between customer experience, trust, and purchase intention, while also highlighting the conditional role of trust. Given the cross-sectional and self-reported nature of the data, the findings should be interpreted as indicative of relationships rather than definitive causal effects. Nonetheless, the results contribute to the existing literature by offering a more nuanced understanding of how trust may influence the strength of the relationship between customer experience and purchase intention.

The empirical findings of this study provide several practical insights that can be interpreted as indicative guidance for strategic decisions in the banking sector. The results regarding Hypothesis H1 suggest that customer experience is positively associated with purchase intention. From a practical perspective, this may indicate that banks could benefit from considering customer experience as an important element in their strategic orientation. The deliberate design of positive customer experiences throughout the customer journey may contribute to increasing customers’ willingness to use additional financial products.

In particular, the consistency of the customer experience across various touchpoints appears to be relevant. As the customer journey in the banking sector encompasses both digital and in-person interactions, an integrated design of touchpoints may support more consistent customer perceptions. More specifically, banks may benefit from systematically aligning digital channels such as mobile banking applications, online advisory tools, appointment booking systems, and service chat functions with in-branch advisory processes. For example, information entered by customers in digital pre-consultation tools could be made directly available to branch advisors in order to reduce information asymmetries, avoid repeated data entry, and create a more seamless transition between digital and personal interactions.

The results regarding Hypothesis H2 further indicate a strong association between customer experience and trust. This suggests that the design of customer experiences may be linked to the development of trust. Trust does not arise in isolation, but rather appears to be associated with repeated interaction experiences. Accordingly, banks may consider continuously evaluating and improving customer interactions over time. Digital touchpoints are particularly relevant in this context, as a large share of interactions takes place via digital channels. The findings suggest that user-friendly, transparent, and reliable digital applications may play a role in shaping trust-related perceptions. In operational terms, this may include reducing application complexity, improving response times in digital support channels, providing transparent real-time status updates for financial requests, and ensuring that customers receive consistent information across digital and in-person channels.

Furthermore, the results of Hypothesis H3 indicate that trust is positively associated with purchase intention. From a managerial perspective, this may imply that measures aimed at fostering trust, such as transparent communication, reliable services, and customer-oriented advice, could be relevant for strengthening customer relationships and encouraging the use of additional products. Banks may also consider implementing employee training programs focused on advisory consistency, digital service competence, and customer communication, as interpersonal trust remains particularly relevant in complex financial decision-making situations such as investment planning, financing, or retirement advisory services.

The moderating effect of trust also suggests that the relationship between customer experience and purchase intention may be influenced by the level of trust. However, given the relatively small effect size, this finding should be interpreted with caution and should not be overemphasized in practical applications. It may nevertheless indicate that customer experience and trust-building are closely interrelated and could be considered jointly rather than in isolation. Rather than treating digitalization and relationship management as separate strategic initiatives, banks may benefit from integrating both perspectives into a unified customer journey management approach.

At the same time, the practical implications of this study should be interpreted in light of its methodological limitations. The findings are based on cross-sectional self-reported data and may therefore not be fully generalizable to all banking contexts. Consequently, the implications outlined above should be understood as indicative rather than prescriptive and may require further validation in different contexts or through longitudinal and experimental research designs.

In summary, the results suggest that a focus on customer experience and trust may be associated with stronger customer relationships. However, these insights should be applied with consideration of contextual factors and the inherent limitations of the data.

Despite the findings, this study has several limitations that must be taken into account when interpreting the results. A major limitation concerns the sampling design used. The data were collected using a convenience sample, which means it cannot be ruled out that certain population groups are over- or underrepresented. This may limit the generalizability of the results. In particular, the sample is characterized by a relatively high level of education and a concentration of younger participants, which may influence the perception of digital and hybrid banking services. Individuals in these demographic groups often show higher levels of digital familiarity, greater confidence in using technology-based services, and lower perceived uncertainty when interacting with digital banking channels. As a result, customer experience evaluations may be systematically more positive, and trust may be established more easily compared to broader customer populations. This may have contributed to the relatively strong relationships observed between customer experience and trust, while potentially reducing the incremental moderating effect of trust, as a certain baseline level of trust in digital financial services may already be present within this sample.

In addition to the composition of the sample, the context of data collection should also be considered a limitation. Since the survey was conducted online, there is a possibility that participants had varying levels of motivation or interpreted the questions differently. This may result in potential biases in the collected data. Furthermore, the exclusive use of self-reported data introduces the risk of common method bias, as all constructs were measured using the same survey instrument at a single point in time. This may inflate observed relationships between variables. Closely related to this, self-report bias and socially desirable responding cannot be fully excluded. Respondents may have provided answers that reflect socially acceptable attitudes rather than their actual perceptions or intentions. Another limitation stems from the study design. The present study is based on a cross-sectional analysis, which allows for the identification of correlations between the variables under investigation but does not permit clear conclusions regarding direct relationships or long-term trends. This limitation is particularly relevant given the strong relationships observed between customer experience and trust, which may partly reflect shared perceptual foundations rather than purely causal effects. Furthermore, the study is limited to the context of the banking sector. The results are therefore not readily transferable to other industries. Different industries may have specific contextual factors that influence the importance of customer experience, trust, and purchase intention in different ways. Future research could take the form of a longitudinal study to analyze the long-term development of the relationships examined. In addition, experimental or multi-source research designs could help to reduce common method bias and provide stronger evidence regarding direct relationships. Furthermore, additional factors, such as social, cultural, and financial influences, could be integrated into the model to facilitate a more comprehensive understanding and to test the generalizability of the results. Future studies could also further disentangle the conceptual relationship between customer experience and trust, for example by applying alternative measurement approaches or separating affective and cognitive components more explicitly.

The aim of this study was to empirically examine the influence of customer experience on trust and purchase intention in the banking sector, as well as to analyze the moderating effect of trust.

The results of the study provide consistent evidence for positive associations between customer experience, trust, and purchase intention. In particular, the strong relationship between customer experience and trust as well as the moderate relationships with purchase intention can be regarded as the most robust findings of this study. These results suggest that customer experience and trust are closely interrelated constructs that are systematically linked to customers’ behavioral intentions within the banking context. At the same time, the findings should be interpreted with caution. While the moderating effect of trust was found to be statistically significant, its magnitude is relatively small. This indicates that the conditional role of trust represents a context-dependent effect rather than a central driver of customer behavior. Moreover, due to the cross-sectional and self-reported nature of the data, the results do not allow for definitive causal conclusions. The observed relationships may partly reflect shared perceptual structures or method-related influences, particularly given the conceptual proximity between customer experience and trust. In addition, the use of a convenience sample limits the generalizability of the findings to broader populations or different banking contexts. Against this background, the present study should be understood as providing indicative rather than conclusive evidence regarding the interplay of customer experience, trust, and purchase intention. From a theoretical perspective, the study contributes to existing research by integrating customer experience, trust, and purchase intention within the context of the banking sector and by empirically examining the moderating role of trust. In doing so, it extends prior research that has predominantly conceptualized trust as a mediating mechanism, by demonstrating that trust may also operate as a boundary condition that influences the strength of the relationship between customer experience and purchase intention. This finding broadens the current theoretical understanding of trust by showing that its role is not limited to explaining behavioral outcomes through indirect effects, but may also shape the contextual conditions under which customer experience translates into behavioral intentions, particularly in hybrid banking environments characterized by both digital and interpersonal interactions. Although the observed moderating effect is comparatively small, its significance suggests that trust captures a distinct explanatory mechanism beyond established mediation-based models.

From a practical perspective, the findings suggest that customer experience and trust may represent relevant factors in shaping customer behavior. However, these implications should be interpreted with consideration of the study’s methodological limitations and may require further validation in different contexts and research designs.

In summary, the study highlights the importance of customer experience and trust as interconnected constructs in the banking sector, while demonstrating that trust may not only emerge as an outcome of customer experience, but can also influence when and to what extent customer experience affects purchase intention. At the same time, the strength and nature of these relationships remain subject to contextual and methodological conditions.