Interactional Value Formation in Digital Banking: Exploring Key Precursors and the Role of Customer Emotional Intelligence

Abstract

Bank interactions require frequent financial exchanges, and digitalization has brought new challenges like complex interfaces, low digital literacy, trust deficits, and spontaneous communication, all of which hinder value formation. To remain competitive, banks must prioritize co-creating value in digital interactions. This paper investigates the dynamics of interactional value formation (IVF) in digital banking, identifies its core precursors, and assesses the influence of customer emotional intelligence (EI) on these interactions. A quantitative study was conducted with 442 digitally active banking customers in southern India. The structural equation modeling technique SmartPLS 4 was employed for analysis. Additionally, a bootstrapping test and slope analysis was used to measure moderation effect. Results indicate that relationship quality and knowledge are strongly related to IVF, however, communication has no impact. EI strengthens the relationship-IVF link but weakens the knowledge-IVF connection, with no moderating effect on communication-IVF. The study is one among the first to test IVF in digital banking interactions in India and observe the moderating role of customers’ EI on the interactions. Future studies can explore the role of employee’s EI and training on interactional outcomes.

Keywords

relationship quality knowledge communication emotional intelligence interactional value formation digital interactions

Introduction

Technology has consistently influenced key aspects of business (Indriasari et al., 2022), particularly in banking services, where it has transformed areas like risk analysis, product design, and distribution channels (Omarini, 2018). Technological evolution extends to customer interactions, with changed customer behavior (Gupta et al., 2022)as they expect convenient, 24 × 7 access to services (Accenture, 2019), user-friendly and hassle-free experience (B. Kaur, Kiran, et al., 2021; S. J. Kaur, Ali, et al., 2021), and personalized experiences (Omarini, 2018). Banking interactions are moving from a “high contact” services model (Ustrov et al., 2022) due to digitalization as customers rely on online interactions for most of their financial requirements and visit bank branches only for critical financial decisions such as investment planning, higher education loans for their children, home loans, etc. Consequently, banks’ focus must shift to providing a high-quality digital interactional experience that is as close to face-to-face interaction as possible to stand out in a competitive market. However, digital interactions present unique challenges for value co-creation (VCC) such as diminished personal touch inherent in digital interactions (Agarwal et al., 2009; López-Miguens & Vazquez, 2017; Singh & Srivastava, 2018), difficulties related to digital literacy, and resistance to technological adoption (Pathak et al., 2019; Schreieck et al., 2017). Moreover, complexities arise from challenges in interpreting emotional cues and accessing comprehensive information, further complicating the digital banking experience (Audrin & Audrin, 2024; Järvi et al., 2020). These challenges hinder joint interactions between customers and service providers, making it essential to examine how banks can enhance co-creation in digital interactions. A value co-creating interaction involves the active participation of actors such as customers and employees (Vargo & Lusch, 2004). In interactional value formation (IVF), customers are viewed as active contributors sharing equal responsibility in value co-creation, rather than as passive recipients (Tauh & Basri, 2025). IVF has emerged as a critical focus, positioning the interactions between frontline employees and customers as pivotal sources of value (Prahalad & Ramaswamy, 2004). IVF underscores the centrality of service interactions and conceptualizes the customer as a co-creator of value (Payne et al., 2008) along with the service provider (Grönroos, 2011; Grönroos & Voima, 2023) a fundamental principle of Service-Dominant Logic (S-D Logic) (Vargo & Lusch, 2004). The conceptual foundation of value inherent in IVF and the interaction-oriented perspective exhibits a strong theoretical congruence with Holbrook’s (2006) definition of value, which mentions that the value lies in interactions and is jointly experienced. Most of the studies to date have examined service interactions through the value co-creation (VCC) concept; however, the focus is missing on value formation in interactions, as VCC is a broader term. While few researchers have used VCC and IVF interchangeably (P. Echeverri & Skålén, 2021; Goyal et al., 2021; Ple & Caceres, 2010; Saha & Goyal, 2019), addressed VCC as one of the dimensions of IVF. Our study addresses the limitations and attends to the nuances and critical factors that influence the IVF outcome in digital space.

Research has argued several dimensions of IVF such as connection, trust, and commitment (Randall et al., 2011); customer citizenship and co-creation behaviors (Yi & Gong, 2013); co-production and value-in-use (Ranjan & Read, 2016) and antecedents such as relating, knowing, and communicating (Ballantyne and Varey, 2006; Neghina et al., 2015); fashion involvement (Frasquet-Deltoro et al., 2019) and willingness to participate (Thomas et al., 2020); customer fairness perception (Roy et al., 2020); attitude, perceived behavioral control and subjective norm (Zadeh et al., 2019). Though there are studies on antecedents of IVF, very few provide empirical evidence on online dyadic interactions at the micro level. Firstly, our study builds on relevant antecedentsidentified by Ballantyne and Varey (2006), Neghina et al. (2015), and Tauh and Basri (2025), incorporating key factors such as relationship quality (RQ) (Järvi et al., 2018; Osei-Frimpong et al., 2015; Ranjan et al., 2015), communication (Chathoth et al., 2020), and knowledge (Malar et al., 2019; Wu et al., 2023). Given that digital banking interactions handle highly sensitive information, relational elements like trust and connection serve as a foundation. At the same time, transparent and consistent communication reduces uncertainty and facilitates value co-creation (Tauh & Basri, 2025). The paper establishes a conceptual framework for digital banking interactions, supported by empirical evidence on three precursors, namely, RQ, communication, and knowledge. Secondly, it assesses the moderating role of emotional intelligence (EI) on the relationship between precursors and IVF. IVF in digital banking depends on effective customer-service exchanges, where EI plays a key role by helping individuals interpret emotional cues (Canale et al., 2022; S. Steinert & Dennis, 2022), enhancing communication, and fostering co-creation despite the absence of face-to-face interactions (Boadi et al., 2020; Majdalani & Maamari, 2016; Prentice, 2020). The perception of emotions differs between face-to-face and online platforms, as the virtual world presents unique challenges compared to the real world (Cebollero-Salinas et al., 2022), EI enables the understanding and use of emotions that help customers and service employees successfully navigate through interactions. Emotional contagion theory also resonates with the importance of emotions, where emotion scholars identified emotions as a key element of service interactions (Prentice, 2020), outlining the interrelation of customers’ emotions with the service provider and vice versa. Previous literature highlights the role of EI in online communication (Audrin & Audrin, 2024; Blunden & Brodsky, 2021; Daft & Langel, 1986; Kiesler et al., 1984), relationship quality (Chathoth et al., 2020; Delpechitre et al., 2018); knowledge sharing (Arakelian et al., 2013; Shariq et al., 2019) emphasizing that digitally emotionally intelligent can comprehend and respond to text messages, voice, and mail well (Van Rooy & Viswesvaran, 2004) and collaborate to co-create value.

The study took inspiration from Social Exchange Theory and S-D Logic and adopted a customer-centric perspective, examining how digitally active banking customers interact with frontline employees for value creation in online settings. Digital banking interactions, in this study, refer to customer engagement with banks through phone, email, and other remote digital channels such as chatbots. This research is the first to examine the precursors to digital banking interactions. It offers valuable insights for bank managers and frontline employees seeking to enhance digital service quality, optimize customer engagement strategies, and leverage emotional intelligence to improve value co-creation in online banking interactions.

Theoretical Foundation and Relevant Literature

Social Exchange Theory (SET)

SET emphasizes that as relationships progress, they foster greater credibility, commitment, and trust (Cropanzano & Mitchell, 2005). This principle requires adherence to specific rules, known as the rules of exchange. Gouldner (1960) identified three types of reciprocity rules: transactional, belief-based, and moral norms. Transactional reciprocity involves exchanges between interdependent parties and was further reinforced by Molm (1994), who argued that such reciprocal exchanges mitigate risks and facilitate cooperation. Individuals are distinguished between those with a high exchange orientation and those with a low exchange orientation, based on their reciprocity tendencies (Clark & Mills, 1979; Murstein et al., 1977). According to SET, repeated interactions between parties like customers and service providers generate mutual expectations to return the benefits and assistance exchanged (Blau, 1964). Thus, SET is based on the premise actor’s behavior or actions are dependent on other actors involved.

As we apply SET to the frame of IVF, it can be argued that customer reciprocation can be seen through customer citizenship behavior (Yan et al., 2016) such as when passengers in public transport greet the service provider as they feel valued and respected (P. Echeverri & Skålén, 2011). Digital banking, characterized as a high-interaction service, requires customers to share intricate financial information and participate actively. Building trust and effectively communicating emotions in non-face-to-face interactions with bank employees can present significant challenges and emotional burdens (Ahmed et al., 2023); IVF draws inspiration from SET and helps the service providers align with the interactional challenges identified in earlier literature. For example, customers helping other customers in finding out the solution to their banking queries or giving positive feedback online can be a few applications of SET theory in digital banking interactions.

Emotional Contagion Theory

According to the theory, emotions expressed by customers during the customer-service provider interaction will influence the employee response, and likewise, emotions expressed by employees will affect customer response (Prentice, 2020). In traditional service settings, this phenomenon occurs through facial expressions, tone, and body language; however, in digital interactions, emotional contagion can occur by mirroring the other parties’ voice notes, tone, messages (Hatfield et al., 1992; Liu et al., 2019), and emojis (Lee et al., 2020). In text-based interactions, emojis and punctuation enhance emotional clarity, reduce ambiguity, and influence customer perceptions of service warmth and empathy (Erle et al., 2022). Similarly, response speed in live chat and emails signals attentiveness, with delayed responses potentially amplifying customer frustration, while quicker replies contribute to more positive emotional contagion (Zhu et al., 2025). Studies on AI-powered chatbots and virtual assistants suggest that when chatbots mimic human conversational patterns—such as adjusting tone based on sentiment analysis or using emojis in appropriate contexts, customers report higher engagement and satisfaction (Khan et al., 2022). Conversely, AI systems that lack emotional adaptability may escalate negative emotions by providing rigid, impersonal responses, leading to customer dissatisfaction (Khan & Iqbal, 2020).

From a banking service perspective, emotional contagion is particularly relevant in customer complaints, dispute resolution, and financial advisory services as employees who mirror customer emotions empathetically foster trust and better service outcomes.

Interactional Value Formation (IVF)

The term IVF delves into the intricacies of value creation within interpersonal exchanges. Interactions are defined as the dynamic exchange and evolution of resources through dialogue and cooperation (Gummesson & Mele, 2010). Scholarly discourse drew a clear distinction between noninteractive and interactive value production as distinct phenomena. While Grönroos (1982) underscored functional quality, Lehtinen and Lehtinen (1991) emphasized interactive quality, both recognizing interactions as pivotal in assessing service quality. The term interactional value formation combines interaction and value formation. Customer–provider interactions represent the primary locus of value creation (Grönroos, 1982; Prahalad & Ramaswamy, 2004; Shostack, 1977), which are frequently conceptualized as reciprocal exchanges (Ballantyne & Varey, 2006).

Building on this foundation, introduced SET, which highlights the significant role of employee-customer interactions in value formation processes. S-D Logic, introduced by Vargo and Lusch (2004), brought about a substantial theoretical reorientation by underscoring customer interaction as the focal point of marketing and service experiences. Through this interactional orientation, the focus shifted away from static transactions toward the ongoing, joint creation of value. Prahalad and Ramaswamy (2004) expanded on this idea by developing a co-creation framework, identifying interaction, resource access, shared responsibility, and clarity as essential pillars. Their work emphasized that the co-creation process requires committed participation from both customers and firms.

When studying joint actions between customers and employees, it is crucial to focus on everyday actions and practices followed. Practice theory argues that social order is made up of practices, and behavior is only feasible and understood in connection to shared and standard practices (Bourdieu, 1977; Giddens, 1984). M. Echeverri (2011) applied practice theory from marketing research to elucidate the mechanics of IVF. Their empirical study in Swedish public transport identified five interactional practices, analyzing elements such as processes, agreements, and engagements. Observations of commuter and driver interactions revealed how actors collaborate to either cocreate or diminish value.

To summarize, IVF places interactions as the centerpiece of value co-creation. While SET explains the relational foundations of interactions, S-D Logic positions interaction as the central mechanism of value creation. Taking inspiration from SET and S-D logic, we observe the customer-frontline employee digital interactions at a micro level. Given the scarcity of empirical research on the precursors of IVF in digital contexts, it is essential to examine how they shape outcomes from the customer’s perspective.

Precursors of IVF

Relationship Quality

Social exchanges, as highlighted by Ahmed et al. (2023), form the basis of relationships, as illustrated by Cropanzano and Mitchell (2005) in their depiction of interdependent exchanges. It is crucial to distinguish between the relational aspect and the transactional process, given their potential interchangeability. Neghina et al. (2015) argue that customer experiences in service interactions are more influenced by relationships than byproducts/services. Cambra-Fierro et al. (2019) support relational value cocreation, while Mukherjee and Nath (2003) highlight trust in online banking. Various subconstructs, such as commitment (Fawcett et al., 2021; Randall et al., 2011; Wang et al., 2020), trust (Arica et al., 2023; Randall et al., 2011; Read et al., 2014), interpersonal connection (Randall et al., 2011), and personalization (Kim & Slotegraaf, 2016), impact interaction quality. Tabaeeian et al. (2022) found that hedonic and personalized tourism influence B2B relationship quality. Mostafa and Ibrahim (2020) find minimal trust impacts customer co-creation in Egyptian banking. Shamim et al. (2017) stress the importance of the connection between parties in influencing customer behavior and value co-creation, noting that customers with a keen sense of interdependence are more likely to participate actively. Additionally, Assiouras et al. (2023) confirm the importance of trust in the tourism industry, with participants deeming it essential when interacting with service providers.

H1: Relationship quality positively affects IVF.

Communication

Value co-creation hinges on bidirectional communication, fostering dialogue (Chathoth et al., 2020). Communication effectiveness, measured by frequency, dialogue, and content, is pivotal (Neghina et al., 2015). Dialogue’s centrality in cocreation is emphasized by Prahalad and Ramaswamy (2004) and underscored by Gustafsson et al. (2012), who stress the importance of frequency and content for innovative service co-creation. Interestingly, Keeling et al. (2020) observe that power inequality in service interactions, such as in healthcare or financial services, does not deter value cocreation; instead, it can influence value cocreation positively. Assiouras et al. (2023) recognized the role of bidirectional communication in generating value between participants. They examined mega-disruption scenarios such as COVID-19 when physical interactions practically came to a halt, which intensified the need for value creation in interactions. In online banking and digital education, poor communication results in value co-destruction (Malar et al., 2019), while effective communication clarifies tasks and expectations and boosts participation. Building on Mohr and Nevin’s (1990) model, frequency and content play a more significant role than direction in innovative services for the creation of value (Gustafsson et al., 2012).

H2: There is a direct relationship between communication and IVF.

Knowledge

S-D logic suggests that value creation occurs when knowledge and skills interact with systems and organizational environments (P. Echeverri & Skålén, 2011). Knowledge management is crucial in information-driven sectors such as finance and healthcare (Payne et al., 2008). However, knowledge asymmetry challenges effective communication (Aarikka-Stenroos & Jaakkola, 2012;Keeling et al., 2015; Osei-Frimpong et al., 2015). Payne et al. (2008) argue that organizations often possess knowledge but lack effective application, co-destroy value, especially in customer-facing banking roles (Kashif & Zarkada, 2015; Malar et al., 2019). The knowledge dimensions outlined by Prahalad and Ramaswamy (2004) consist of information acquisition, dissemination, and feedback. Studies show that employee knowledge-seeking behavior (Chathoth et al., 2020) and customer knowledge-sharing in healthcare (Wu et al., 2023) co-create value. However, improper communication co-destroys value (Jarvi et al., 2018). Tourism interactions often suffer from knowledge imbalances, which contribute to co-destruction (Guan et al., 2022). Although most IVF studies highlight the positive role of knowledge, Osei-Frimpong et al.’s (2015) research indicates that in healthcare interactions, knowledge can sometimes have negative consequences by shaping patient attitudes in ways that produce divergent outcomes.

H3: Knowledge positively influences IVF.

Emotional Intelligence as the Moderator

The basic challenge in online interactions is to estimate the emotions of the actors involved in the interaction (Goldenberg & Gross, 2020). Banking interactions require the parties to understand the emotional state of each other and respond suitably for a value-creating outcome. In face-to-face banking interactions, customers and employees rely on facial expressions, tone, and body language to convey emotions. In contrast, digital banking removes non-verbal richness, requiring emotionally intelligent individuals to navigate text-based, voice-based, and AI-mediated communication more effectively (Blunden & Brodsky, 2021; Canale et al., 2022). EI helps bridge this gap by enhancing digital communication clarity, interpreting customer sentiment, and managing service recovery situations (Boadi et al., 2020). The elucidation of these constructs underscores the ability to discern, examine, and regulate one’s own and others’ emotions, an imperative skill set in banking services. Given the necessity to comprehend and respond to emotions in an online interaction, a comprehensive understanding of emotional intelligence is important.

Studies have established a connection between EI and digital communication (Blunden & Brodsky, 2021; Goldenberg & Gross, 2020). Building on this, the research emphasizes the importance of digital emotional intelligence (Audrin & Audrin, 2023), highlighting it as an advanced form of EI that is essential for fostering positive outcomes in digital interactions. The notion that strong EI traits facilitate co-creative banking interactions is widely accepted; however, empirical studies on EI in the digital banking customer domain are limited. Research has focused on observing service providers’ EI in IVF (Chathoth et al., 2020; Delpechitre et al., 2018; Osei-Frimpong et al., 2015; Ogunbodede et al., 2021; Zhu et al., 2023), whereas limited attention has been paid to observing the impact of customers’ EI on IVF (Buzova et al., 2023; Frau et al., 2023). Positive and negative emotions exert distinct influences on co-creation, particularly in emotionally charged exchanges. Various EI sub-constructs, such as social skills, empathy, and neurotic traits, have been associated with positive outcomes in IVF (Chathoth et al., 2020; Delpichitre et al., 2018; Ogunbodede et al., 2021; Osei-Frimpong et al., 2015; Tauh & Basri, 2025), whereas few studies scrutinized customers’ online sharing of negative emotions, revealing their association with value co-destruction. EI fosters empathetic communication, aiding conflict resolution by understanding emotional triggers, especially in digital (non-face-to-face) interactions. Few studies highlight EI’s moderating role on communication in online contexts, reporting a positive impact (Chaudhary et al., 2022; Warrier et al., 2021), whereas Zahed-Babelan and Moenikia (2010) explored EI as a mediating factor in digital learning, identifying it as a significant predictor of co-creation. Studies have further explored emotional intelligence as an antecedent across different contexts, including interpersonal relationships (Dykstra, 2020; Schutte et al., 2001). Greater employee EI increases customers’ willingness to interact (Delpechitre et al., 2018), while customer EI strengthens these exchanges by cultivating trust and confidence. Extensive research has examined the effects of the antecedents, such as relationships (Garanti, 2023; Gyllenhammar et al., 2023; Osei-Frimpong et al., 2018; Tari Kasnakoglu, 2016; Teng & Tsai, 2020), communication, knowledge, and emotional intelligence on interactional value formation. However, a gap was observed in digital banking studies that investigate the influence of EI interactional outcomes (Figure 1).

H4: EI moderates the relationship between relationship quality and IVF.

H5: EI moderates the relationship between communication and IVF.

H6: EI moderates the relationship between knowledge and IVF.

Figure 1.

Conceptual model.

Materials and Methods

Research Design

This research utilized a cross-sectional survey method, drawing on deductive reasoning anchored in IVF theories. The post-positivist philosophy was adopted to interpret the results through the objective approach. It aimed at gathering customers’ perceptions in virtual banking interactions. This descriptive-analytical study used quantitative methods to understand the link between antecedents and IVF through the customers’ EI.

This study did not involve any procedures requiring formal ethical approval, as it relied solely on non-invasive survey responses from adult participants. The research process adhered to recognized ethical standards governing social science studies. The participants were briefed on the aims of the research and signed a consent form before participation. Participant identity and privacy were protected for the entire duration of the survey.

Tools of Data Collection

Data collection, conducted over 8 months via email, involved a validated structured questionnaire featuring scales on relationship quality, communication, and knowledge as developed by Neghina et al. (2015). The EI scale was adopted from Wong and Law’s (2002) scale, comprising emotion identification, utilization/facilitation, understanding of emotions, and their management. Customer IVF was measured using a multidimensional scale that comprises customer participation behavior. Customer participation behavior is defined through responsible behavior, which refers to when customers treat themselves responsibly, like partial employees (Ennew & Binks, 1999), and follow the service employees’ directions; personal interaction, which denotes personal relationships between customers and frontline employees (Ennew & Binks, 1999), when they interact online.

Different Likert scales were employed to reduce response bias. To check for potential nonresponse bias, a comparison between the responses from the initial and latter half was done, revealing no notable variation. Pilot testing revealed that all items achieved Cronbach’s alpha coefficients and demonstrated significant correlations, confirming the reliability of the scales (Bagozzi & Yi, 2011).

Sampling Design

First, five Indian banks were recruited for the study, representing the private sector, the public sector, and the small finance bank. The retail branches of the selected banks in the Karnataka region were shortlisted. The study’s benefits were outlined, and access to results was offered to encourage participation. Initially, digitally active customers were identified through direct engagement with retail bank branches. These participants then provided referrals, facilitating the expansion of the sample through snowball sampling while maintaining diversity in digital banking experiences.

Given its effectiveness in reaching otherwise inaccessible groups (Dusek et al., 2015), snowball sampling was applied to recruit digitally active banking customers. While snowball sampling effectively reaches digitally engaged banking customers, it risks selection bias, as referrals may disproportionately share similar demographics and banking behaviors. To enhance sample representativeness, a stratified sample selection from public, private, and small finance banks was conducted, ensuring diversity was represented. A subset of referred participants underwent independent follow-up checks to safeguard against self-selection bias and enhance sample integrity.

To check the acceptability and ensure that the questionnaire was easy to understand, a pilot study was conducted with 43 customers through offline and online modes. These respondents were subsequently asked to refer to other customers. These respondents were asked to give the lead by suggesting other users. A total of 526 customers participated in the study. Sixty-one digitally inactive customer responses and 26 incomplete responses were removed and a final set of 442 customers was used for data analysis. The demographic profile indicated an average customer age of 38 years, with males representing 61% and females 39% of the sample.

Data Analysis

PLS-SEM method was implemented via SmartPLS version 4.0. Hair and Alamer (2022) highlights it as a preferable alternative to SEM for analyzing composite models. This approach is particularly appropriate because it functions well with smaller datasets and facilitates the estimation of complex interrelations among latent constructs, including moderation and mediation analyses, thereby validating our choice. The measurement model evaluated whether the constructs were reliable and valid, whereas the significance of hypothesized links was assessed through the structural model. Moreover, hypothesis testing was carried out via bootstrapping (Henseler, 2018; Henseler & Sarstedt, 2013), with slope analysis applied to evaluate the strength of moderation.

To calculate the moderation results, a two-stage approach was adopted (Becker et al., 2022). This method utilizes the latent variable scores derived from the main effects model (Relationship quality → IVF; Communication → IVF; Knowledge → IVF), excluding the interaction term, for the latent predictor and latent moderator variables (EI → IVF). These scores were retained and employed to generate the product indicator for the subsequent analysis stage (Hair & Alamer., 2022). This stage includes the interaction term alongside the predictors and moderator variable (Relationship quality → × EI IVF); (Communication × EI → IVF); and (Knowledge × EI → IVF).

Results

Measurement Model

All constructs were reliable and valid as per the measurement model. With composite reliability (CR) values expected to exceed .7 to demonstrate internal consistency, the present study’s CR values, which ranged from .888 to .958, were well within acceptable limits. Additionally, Cronbach’s alpha values for all constructs were above .7 (refer to Table 1), indicating satisfactory internal consistency. The average variance extracted (AVE) values surpass 0.5 (Hair et al., 2021), confirming convergent validity.

Table 1.

Construct Reliability and Convergent Validity.

Constructs	Composite reliability	Cronbach’s alpha	AVE
Relationship quality	.874	.785	0.698
Communication	.874	.812	0.634
Knowledge	.915	.892	0.606
IVF advocacy	.901		0.648
IVF personal interaction	.884		0.717
IVF relationship behavior	.942		0.844
IVF tolerance	.927		0.865

Note. IVF = interactional value formation.

The second stage analysis incorporated the latent scores of IVF constructs into the dataset. Bootstrapping with 5,000 subsamples was used to validate the formative HOC model. The outer weights were first tested for significance, and because IVFT1 and IVFT2 fell below 0.5, their outer loadings were checked instead. With loadings above 0.5, both were deemed significant. Outer weights for all second-order constructs were significant. EI’s AVE values were above the acceptable range. As shown in Table 2, the HTMT values for EI were 0.810 with communication, 0.817 with knowledge, and 0.696 with relationship quality. Discriminant validity is established as all the values are below the 0.90 threshold (Hair & Alamer, 2022).

Table 2.

HTMT Values.

Constructs	RQ	C	K
C	0.798
K	0.776	0.872
EI	0.726	0.825	0.805

Note. RQ = relationship quality; C = communication; K = knowledge; EI = emotional intelligence.

Bootstrapping results (refer to Table 3) highlight that the path coefficients are statistically significant. Knowledge (K) demonstrates a greater impact on IVF than relationship quality, whereas communication has a non-significant effect on IVF.

Table 3.

Bootstrapping Results: Moderation Effect of EI.

Constructs	Path coefficient	Standard deviation	t Statistics	p Values
C → IVF	−0.050	0.074	0.682	.495
EI → IVF	0.362	0.052	6.917	.000
K → IVF	0.270	0.062	4.373	.000
RQ → IVF	0.233	0.048	4.806	.000
EI × RQ → IVF	0.223	0.052	4.290	.000
EI × C → IVF	−0.001	0.062	0.021	.983
EI × K → IVF	−0.183	0.057	3.211	.001

Note. RQ = relationship quality; C = communication; K = knowledge; EI = emotional intelligence; IVF = interactional value formation.

R ² of IVF .586. The f-square effect sizes for RQ, communication, and knowledge were 0.021, 0.001, and 0.039, respectively.

Moderation Analysis

This paper observed the moderating role of EI on RQ, communication, knowledge, and IVF. The study assessed whether the interaction effect is significant using the output given in Table 3. The analysis yields t-values of 4.290 and 3.211 for the paths linking RQ × EI and K × EI, respectively, with IVF. Similarly, the 97.5% bootstrap confidence intervals of the effect of EI are (0.328, 0.124) and (−0.052, −0.281). EI has a significant moderate influence on the links between RQ and IVF and between knowledge and IVF, thus validating H5 and H7. This shows that with an increase in EI, RQ strengthens IVF. H7 is not supported, as emotional intelligence attenuates the relationship between knowledge and IVF. EI has a non-significant influence on the C-IVF relationship. The path shows t-values of 0.021 and a 97.5% bootstrap confidence interval for the effect of EI (0.115, 0.133). This finding does not support H2. Even H4 is accepted since no significant relationship between EI and IVF was noted.

As shown in the slope (Figure 2), the middle line reflects the relationship between the average level of EI. The other two lines represent the RQ and IVF relationships for higher levels of EI. The positive nature of the RQ–IVF relationship is evident across all plotted lines (high and medium), given their upward slopes. The sharper slope observed for high EI reveals that RQ exerts a much stronger influence on IVF at elevated EI levels than at lower ones. The line is flat at a low level of EI, showing that at a lower EI level, the increase in RQ does not lead to a change in IVF. H5 is therefore supported empirically.

Figure 2.

Slope analysis of the two-way interaction effect RQ × EI on IVF.

Regarding the relationship between communication and IVF, the middle line shows the relationship for an average level of EI, the upper line shows at elevated levels, and the low line shows the relationship at low levels (Figure 3). EI has no moderating effect on the communication-IVF relationship. Thus, H6 did not receive empirical support.

Figure 3.

Slope analysis of the two-way interaction effect of communication × EI on IVF.

In Figure 4, the slope for low EI is much sharper than at high EI levels. For high EI, we have a weaker relationship between K and IVF (flatter line) than we have for low EI, where the slope is steeper. This shows that at lower EI, knowledge has a greater role in determining IVF than at higher levels of EI. The line for high EI tends to flatten, reflecting that at higher levels of EI, an increase in knowledge does not lead to an equivalent change in IVF. Thus, H6 receives empirical support.

Figure 4.

Slope analysis of the two-way interaction effect of knowledge × EI on IVF.

Discussion

This study examined the antecedents of IVF in virtual banking, focusing on the customer perspective. As banking interactions increasingly move online, traditional co-creation mechanisms are evolving (Brett, 2021). Understanding how relationship quality, communication, and knowledge contribute to IVF is essential for shaping effective digital service experiences. While previous research has discussed IVF dimensions and antecedents, uncertainties remain about their exact roles. Randall et al. (2011) identified commitment and connection as IVF dimensions, while communication and knowledge were considered as antecedents of relationship quality by Neghina et al. (2015). This study expands on their work by empirically validating the roles of these antecedents in IVF within digital banking interactions. The current research responds to the call for further investigation into IVF in digital service systems or interactions (Audrin & Audrin, 2024). This need has grown due to significant technological advancements in the banking industry, particularly the challenges banks face in delivering co-created interaction experiences to customers (Brett, 2021), as most banking interactions now occur online. We argue that these developments require examining key constructs and variables that may influence digital interactions through IVF.

Though the research has discussed the dimensions and precursors of IVF, clarity is lacking. This underscores the necessity of addressing the role ambiguity of these constructs and raises a need for empirical evidence. Banking interactions differ from other services as they involve trust, financial information, and security aspects. Digital banking customers expect convenient, updated solutions coupled with a personalized touch. Bringing the digital interactional experience closer to the in-person experience is a challenge for the banks as the competition rises high in the financial industry. The study has taken the first step in defining key precursors of IVF in digital banking interactions and forms the basis of further empirical research.

Concerning RQ, the results reveal that RQ positively and significantly influences IVF. When relationships are characterized by trust and commitment, they generate security, and participant connectedness further raises the chances of higher involvement in digital interactions. The results corroborate the previous research by Garanti (2023) and Sandhu et al. (2024). However, relationships do not always result in co-creation, such as public insurance interactions, as it can compromise the disbursement process and hinder co-creation (Gyllenhammar et al., 2023). Building relationships in digital interactions would prepare customers to be more tolerant and willing to accommodate in case of any delay or service shortfall. When customers with different levels of EI were compared, customers with high EI influence were better able to influence the RQ → IVF relationship. This skill enhances relationship quality by fostering deeper connections and stronger emotional bonds. It can be said that customers with higher EI develop connections and facilitate better collaboration with service providers, leading to improved relationships and value formation.

Communication, in this study, has been found to have no relationship with IVF in digital banking interactions. The results are not in line with previous studies that communication positively influences IVF. Moreover, customers need constant bidirectional communication in technology-led interactions, such as an interactive tourism study by Kirova (2020). However, retail banking customers do not attribute communication as an antecedent to co-creation; the plausible explanation for the finding is their association of frequent interactions as a sign of delay in service resolution or being pushy for sales. The customers feel annoyed with multiple messages or calls from the bank and associate them with an intrusion into their privacy (Sunikka & Bragge, 2009) or find them too complex to understand (McMahon & Naylor, 2023). Customers view digital banking as a source of quicker and more convenient solutions at their available time, and feeling heard or a timely response is not considered to be exceptional, but a basic requirement. The results of the moderation analysis (communication × EI → IVF) showed that EI has no impact at higher or lower levels. The hypothesis was not supported; however, the findings resonate with Buzova et al. (2022), who showed that in the context of tourists, EI is better positioned as an antecedent to communication rather than a moderating variable. In contrast to previous studies (Michael et al., 2009) that found a positive influence of EI on intercultural communication, our study did not observe a strong EI influence on the C → IVF relationship. The plausible explanation could be that emotionally intelligent customers possess strong critical thinking skills and may prefer to solve their issues by themselves, reducing their dependency on communication for co-creation. Another reason could be the digitally savvy customer who trusts and understands digital solutions well or receives superior customer services from the bank, would co-create value without relying on communication. In some cases, frequent communication is considered intrusive, and emotionally intelligent customers might prefer minimalistic communication or only when it is necessary.

Concerning the knowledge construct, this study reveals that knowledge exerts the most positive and significant influence on IVF in digital banking interactions. For customers, effective banking interactions depend on the essential practices of collecting and exchanging information. Furthermore, consistent with prior research, knowledge is identified as essential to IVF. As banking knowledge is constantly evolving, customers increasingly expect employees to remain knowledgeable about the newest digital approaches to financial problems. While feedback serves to coordinate interactions, the extent of information shared, whether solicited or voluntarily provided, demonstrates the customers’ commitment to co-creating value (Neghina et al., 2015). However, knowledge can also yield divergent outcomes. For instance, in some cases, increased knowledge may negatively impact the interaction, leading to shifts in customer attitudes. EI functions as a significant negative moderator between knowledge and IVF, meaning that when customers possess higher EI, they depend less on knowledge in their banking interactions to achieve value creation. This suggests that as customers enhance their EI trait, their reliance on cognitive skills reduces, and they create value through their emotional awareness during digital interactions. It signifies the importance of emotional factors over knowledge, where interactional outcomes are more influenced by emotional insights for value formation. Digitally competent customers with high EI are more adept at navigating self-service banking platforms, reducing their dependence on knowledge-based interactions. Higher EI enables them to better manage frustration management, allowing customers to engage with digital banking services efficiently without requiring additional guidance. The article contributes to ongoing research by addressing the need to explore how EI shapes interactions in digital environments (Audrin & Audrin, 2024).

Managerial and Theoretical Implications

A crucial step toward designing strategies for co-creating interactions lies in understanding the drivers of digital banking interactions, as service experiences represent a major source of competitive edge in the banking sector. Theoretically, the study extends the conceptual framework given by Neghina et al. (2015) to provide empirical evidence in the digital context. The work contributes to IVF literature by proposing a framework aimed at examining customers’ views of IVF. The findings support theoretical linkages between EI and IVF outcomes, underscoring the importance of relationship quality and knowledge in determining the outcomes of digital banking interactions (Neghina et al., 2015). This research advances S–D Logic (Vargo & Lusch, 2004) by demonstrating that co-creation in digital banking is driven more by relationship quality and self-service knowledge than by direct human interaction. By illustrating how trust and commitment substitute for physical presence in digital banking interactions, this work refines SET and highlights its centrality in IVF. It also extends the Emotional Contagion Theory (Prentice, 2020) by suggesting that emotionally intelligent customers are self-sufficient and engage in co-creation through adaptive, independent problem-solving rather than communicative exchanges. This study provides evidence-based implications by capturing customers’ perceptions of technology-mediated banking exchanges. By examining the foundations of IVF, the study pinpoints critical aspects that support value co-creating exchanges in banking. As digitalization accelerates and customer behavior evolves, the findings stress that trust and connection remain central to customer expectations. Consequently, banks must prioritize relational strategies when engaging with clients in virtual environments. It can be done by hiring frontline employees with the desired characteristics. Results suggest that robust relational ties between customers and banks diminish the ambiguity inherent in online exchanges.

Another key implication pertains to the knowledge aspects of these interactions. The research demonstrates that customers place significant importance on seeking and sharing accurate information when engaging with bankers online. Based on these findings, banks must conduct regular training sessions focused on product knowledge, bank processes, and customer engagement. Such training can enhance employees’ financial expertise and improve their ability to serve customers through remote channels. Digital interactions pose significant challenges, as remote encounters often lack opportunities for seeking additional information and soliciting service feedback. The implementation of a robust feedback mechanism in bank organizations, post each interaction, can facilitate the collection of customer insights, providing a comprehensive understanding of service delivery, which is otherwise difficult to ascertain in remote interactions.

Finally, this article raises awareness of EI traits in digital interactions. Digital banking interactions hinge upon the establishment of trust, relational bonds, and rapport cultivated between customers and bank employees. Banks can take cues from the findings and focus on fostering the EI of their staff as customers with greater EI collaboration (Schröder-Abe & Schutz, 2011) with more EI employees, resulting in co-creation. Emotionally intelligent customers prioritize seamless service experiences over extensive explanations, meaning clarity in digital design may replace the need for direct knowledge-sharing interactions. Consequently, this study suggests that the EI factor is critical to customers as well also shows the influence of EI on the causal link between knowledge and IVF.

Training programs that focus on comprehending emotional cues and responding to customers must be conducted to have better service interactions. Banks can use behavioral interview techniques while recruiting to assess the candidates’ emotional intelligence capabilities, such as the ability to manage difficult customers, communicate effectively, and build people skills.

Bank managers must note that while knowledge remains important, emotionally intelligent customers contribute independently and make decisions during digital interactions. It demands a shift in focus from mere product knowledge to relationships and developing an emotionally aware environment in bank organizations. Leveraging bank technologies that support empathetic interactions, such as artificial intelligence-based customer-oriented tools, can enhance interactional quality. Adaptive user interfaces that respond to emotional cues can enhance customer satisfaction and improve digital banking interactions.

To enhance IVF, managers should prioritize value-driven communication over frequent interactions, ensuring that customer engagement efforts are perceived as beneficial rather than disruptive. Employee training programs should focus on delivering relevant and insightful information, rather than simply increasing the volume of customer contact. Additionally, self-service technologies can be leveraged to facilitate seamless knowledge-sharing and improve customer engagement, reducing the dependency on direct communication while still providing meaningful interactions.

A key takeaway is the growing preference for self-directed problem-solving. Banks must transition from frequent customer touchpoints to strategically delivering relevant, timely knowledge. Employee training should focus on acting as knowledge facilitators rather than information providers, equipping staff with financial expertise, emotional intelligence, and digital engagement skills. Investments in AI-driven chatbots, self-service financial tools, and intuitive advisory platforms can empower customers while reducing reliance on direct human interactions.

Findings extend beyond banking to fintech, e-commerce, and telehealth, where digital service experiences increasingly rely on self-service capabilities and personalized interactions. Businesses should shift from excessive communication strategies to relationship-building and knowledge-sharing mechanisms that foster independent engagement.

Conclusion and Future Research

The first contribution is the identification of key precursors to IVF as perceived by customers in digital banking interactions. Previous studies have not illustrated the precursors of IVF in digital banking interactions (Buzova et al., 2022; Chathoth et al., 2020; P. Echeverri & Salomonson, 2017; Gyllenhammar et al., 2023;), making it a novel contribution to IVF literature. Relationship quality and knowledge significantly influence value formation in digital interactions. Second, the study observes the moderating role of the customer’s EI on the precursor-IVF relationship. The study showed that customers with high EI can collaborate better and develop bonds while they interact online with frontline bankers, which results in co-creation. No moderating effect was found on the communication-IVF relationship. Literature has focused primarily on the service provider’s EI traits; however, the study adds evidence that trait EI is crucial for customers as well and can moderate IVF outcomes.

Despite yielding useful findings, the study is subject to several limitations. Firstly, while the dataset spans different banks, its restriction to India’s financial sector may narrow the relevance of the conclusions for broader regional or international applications. A further limitation arises from the cross-sectional research design, which prevents definitive causal conclusions between independent and dependent variables. Thirdly, the research adopts a perceptual approach based on survey-based responses, which may carry the risk of individual perceptual bias and the influence of social desirability, potentially affecting the reliability of the responses. Fourth, while IVF is fundamentally a dyadic phenomenon involving both customers and service providers, the present study captures only the customers’ perspectives. Future research should aim to incorporate the views of frontline employees to enable a holistic approach service interactions. Finally, the interpretation of findings is grounded in established theoretical frameworks and prior empirical studies, which may influence the contextual framing and limit the scope for novel theoretical contributions.

Although the current paper has tried to observe the critical constructs of IVF in shaping digital interactions, many other factors could affect this relationship, such as situational and technological factors. Information and communication technology lay the foundation for digital interactions; however, technology acceptance and usage might lead to a few challenges. Further studies may examine how ICT influences IVF in digital contexts, thereby enriching theoretical and practical understanding of value formation. It would be worth investigating the influence of cultural differences on IVF, as banking customers come from diverse backgrounds. The sample in this study was also constrained to customers; however, both parties (bank employees and customers) contribute equally to IVF outcomes, and future studies can explore the employee perspectives on IVF outcomes. Although we tried gathering data from a diverse sample of the Indian private sector, public sector, and small finance banks to enhance our ability to generalize the results, digital interactions in other industries, such as education and tourism, vary substantially. Another research area could be increasing the sample to international boundaries and observing the influence of cultural differences on interactions.

Footnotes

ORCID iD

Savitha Basri

Ethical Considerations

Ethical approval was obtained from the ethics committee of the Manipal Academy of Higher Education, Manipal, Karnataka, India. General ethical principles of research were applied while providing consent and ensuring the confidentiality of the participants. General ethical principles of research were applied while providing consent and ensuring the confidentiality of the participants. The information sheet was shared with survey participants that explicitly stated the study objectives, expected outcomes and responsibilities of the researchers in safeguarding the privacy and confidentiality of the data. It also provided information on the use of data for research and publication purposes. The participants were asked to give their consent in writing if they voluntarily agreed to participate in the survey. Anonymity and confidentiality were strictly maintained throughout the survey.

Author Contributions

Conceptualization: Ramya Tauh and Basri Savitha. Methodology, data analysis and interpretation: Ramya Tauh and Basri Savitha. Writing – Original draft preparation: Ramya Tauh. Writing – review & editing: Ramya Tauh and Basri Savitha.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data can be shared if requested.

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