Enhancing Customer Quality of Experience Through Omnichannel Digital Strategies: Evidence from a Service Environment in an Emerging Context

Abstract

The proliferation of digital platforms and interactive technologies has transformed the way service providers engage with their customers, particularly in emerging economies, where digital inclusion is an ongoing process. This study explores the relationship between omnichannel strategies and customer satisfaction, conceptualized here as a proxy for Quality of Experience (QoE), within a smart service station located in a digitally underserved region. Grounded in customer journey theory and the expectancy–disconfirmation paradigm, the study investigates how data integration, digital payment systems, and logistical flexibility—key components of intelligent e-service systems—influence user perceptions and satisfaction. Based on a correlational design with a non-probabilistic sample of 108 customers, the findings reveal a moderate association between overall omnichannel integration and satisfaction (ρ = 0.555, p < 0.01). However, a multiple regression analysis indicates that no individual dimension significantly predicts satisfaction (adjusted R² = 0.002). These results suggest that while users value system integration and interaction flexibility, no single technical feature drives satisfaction independently. The study contributes to the growing field of intelligent human-centric service systems by contextualizing QoE and digital inclusion within emerging markets and by emphasizing the importance of perceptual factors in ICT-enabled environments.

1. Introduction

The widespread digitization of society has reshaped how individuals interact with commercial, public, and social systems. This transformation, often referred to as Net-Living, entails the integration of physical and digital environments through networked technologies, facilitating more fluid, personalized, and user-centered experiences in daily life [1]. Central to this process is the deployment of smart service systems, which harness the power of information and communication technologies (ICTs) to optimize customer interactions across multiple channels—physical, digital, and mobile [2]. In this context, omnichannel strategies have become fundamental to enhancing service quality and responsiveness. Unlike traditional multichannel approaches, omnichannel systems aim for seamless integration across all points of contact, offering users a coherent and continuous experience regardless of the device, platform, or location they choose [3].

Table 1. Multichannel management versus omnichannel management.

Criterion	Multichannel Management	Omnichannel Management
Channel focus	Interactive channels only	Interactive and mass communication channels
Channel scope	Retail sales channels	Retail channels, mobile channels, and customer touchpoints
Channel separation	Separate channels without overlap	Integrated channels that deliver seamless retail experiences
Goals	Channel objectives (sales experience by channel)	Multichannel objectives (overall retail customer experience)

Note: Adapted from [3].

summarizes the key differences between multichannel and omnichannel management approaches, highlighting the strategic shift toward integrated and user-centered experiences.

Such strategies are increasingly regarded not only as tools for commercial optimization but also as mechanisms for improving the Quality of Experience (QoE), particularly in sectors where user satisfaction is critical to service retention and brand loyalty. The COVID-19 pandemic accelerated the need for robust omnichannel infrastructures, especially in regions where access to physical services became limited. This global shift forced many businesses to adopt or reinforce their digital capabilities to remain operational and relevant [4]. It also exposed disparities in digital readiness across different socioeconomic and geographic settings, highlighting the urgent need for inclusive service models that account for both technological capacity and human-centered design [5,6]. Emerging economies, in particular, face distinct challenges and opportunities in this digital transition. On the one hand, infrastructural constraints, uneven internet penetration, and digital literacy gaps limit the implementation of fully integrated systems. On the other, these contexts offer unique opportunities to study user adaptation, digital trust, and the role of omnichannel services in fostering more equitable access to essential goods and services [7]. Within this paradigm, customer satisfaction serves as a key performance indicator—not only of the efficiency of service delivery but also of the user’s emotional and cognitive evaluation of their digital interactions [8]. Satisfaction reflects the degree to which expectations are met or exceeded and is closely tied to QoE as defined by current service research [9,10]. When properly designed, omnichannel platforms can enhance satisfaction by offering personalized content, frictionless payment options, and flexible delivery or collection logistics—thus reinforcing user agency and the perceived value in digital contexts [11,12].

In this light, the present study positions the design of omnichannel services as both a technological and a human factor in the broader landscape of Net-Living. It explores how integrated digital strategies can improve individual experiences and contribute to service sustainability in resource-constrained environments. Despite the growing literature on omnichannel strategies and customer experience, empirical studies in digitally underserved or transitional environments remain limited. Most existing frameworks have been developed and validated in mature markets, where ICT infrastructure, digital literacy, and user expectations are already well established. As a result, little is known about how omnichannel systems perform in contexts where digital inclusion is still developing and where user behavior is shaped by both technological constraints and cultural particularities. This research seeks to address this gap by investigating the relationship between omnichannel integration and customer satisfaction in the setting of a service station located in a semi-rural region of a developing country. The study focuses on three core dimensions of omnichannel interaction—data usage, digital payment options, and logistics coordination—and assesses how each influences user perceptions of value and service adequacy. Figure 1 presents the progression of e-commerce adoption across selected LATAM countries from 2017 to 2020, offering an empirical context for the digital environment in which omnichannel service systems are being implemented.

The central research question guiding this study is the following: To what extent does omnichannel integration affect customer satisfaction, understood as a proxy for Quality of Experience (QoE), in a service environment located in a digitally emerging context? To answer this, the following specific objectives were defined:

O1.

To assess the level of customer satisfaction in relation to the service station’s current omnichannel strategy;

O2.

To evaluate the correlation between customer satisfaction and (a) the use of data for personalization, (b) the availability of digital payment options, and (c) the flexibility of logistical arrangements;

O3.

To interpret the findings within the conceptual framework of Net-Living and digital well-being, linking empirical results to broader issues of service inclusion, user autonomy, and experience optimization.

By addressing these objectives, the study seeks to contribute to the broader understanding of how omnichannel service systems function as socio-technical tools for enhancing quality of life, particularly in digitally constrained environments, where seamless and inclusive digital interactions are critical for everyday well-being. Furthermore, it extends the current discourse by situating QoE not only as a performance metric but as a lived, perceptual outcome influenced by the synergy between technology, behavior, and expectation. This article is structured as follows. Section 2 outlines the theoretical framework, drawing from customer journey theory, expectancy–disconfirmation paradigms, and omnichannel service design literature. Section 3 details the methodology used in this empirical study, including sample characteristics, data collection tools, and statistical techniques. Section 4 presents the main results, both descriptive and inferential. Section 5 offers a discussion of the findings in relation to the existing scholarship and their implications for inclusive digital transformation. Finally, Section 6 concludes with key insights and directions for future research.

2. Materials

2.1. The Customer Journey Theory

The customer journey theory provides a comprehensive framework for understanding how consumers interact with brands and services across multiple touchpoints over time. Rather than focusing solely on isolated transactions or service encounters, this theory conceptualizes the customer experience as a dynamic, cumulative process composed of distinct but interrelated stages: pre-purchase, purchase, and post-purchase [7]. In the pre-purchase stage, consumers engage in problem recognition, information searches, and an evaluation of alternatives [13]. Expectations are shaped during this phase through exposure to advertising, peer recommendations, previous experiences, and digital content. The purchase stage comprises the selection, payment, and actual consumption of the product or service, often involving real-time decisions and interactions. The post-purchase stage includes product usage, service feedback, support requests, and potential advocacy behavior—elements that directly affect long-term satisfaction and customer loyalty [14].

From a digital perspective, the customer journey is increasingly characterized by channel multiplicity and fluid movement between platforms. Consumers might, for example, search for a product on a mobile device, compare alternatives on a desktop, complete a purchase in-store, and request support via social media. In such settings, consistency, coherence, and synchronization of information across channels become essential for creating a positive experience [3]. Omnichannel service systems respond to this challenge by aiming to integrate all points of interaction into a seamless and unified customer experience [15,16]. This requires more than technical connectivity; it necessitates a strategic alignment of systems, processes, and user interfaces to ensure that the customer perceives the journey as continuous and tailored to their personal needs [12]. This is especially critical in service sectors, where customer perception is formed not only by the product itself but also by how well the service supports the entire decision-making and usage process [17]. The customer journey theory also emphasizes the role of emotions, expectations, and context in shaping the user experience. For example, delays in logistics, a lack of payment options, or redundant data entry can negatively impact satisfaction—even if the core product or service performs as promised. Inversely, anticipatory design features such as personalized recommendations, pre-filled forms, and real-time updates can elevate the perceived value and efficiency of the service [7]. In developing regions or digitally emergent markets, understanding the customer journey becomes even more vital [18]. Users may exhibit non-linear behaviors, switch between analog and digital channels, or rely on intermediaries (e.g., family members, mobile agents) to complete certain stages of the journey [19]. Recognizing these contextual nuances is key to designing omnichannel solutions that are not only functional but also inclusive and culturally responsive [20,21].

By adopting a customer journey perspective, this study frames omnichannel service design as a holistic process that must account for user expectations, behavioral flows, and channel synergies, with the ultimate goal of enhancing Quality of Experience (QoE) throughout the service lifecycle [22,23].

2.2. Expectancy–Disconfirmation Paradigm

The expectancy–disconfirmation paradigm (EDP) is one of the most influential theoretical models for understanding customer satisfaction. Originally developed by Oliver (1980) [23] and expanded by subsequent researchers [24,25], the EDP posits that satisfaction is determined by the comparison between a customer’s expectations prior to a service encounter and their perceptions of the actual performance during and after that encounter [26]. At its core, the paradigm operates through three key components: expectations, perceived performance, and disconfirmation [27]. If the perceived performance matches expectations, confirmation occurs, typically resulting in satisfaction. When the performance exceeds expectations, positive disconfirmation leads to heightened satisfaction, sometimes referred to as delight [28]. Conversely, when the performance falls short of expectations, negative disconfirmation results in dissatisfaction [8,27]. This model underscores the cognitive and subjective nature of customer evaluation. Satisfaction is not merely the result of service quality in absolute terms but rather the product of a psychological comparison process [29]. It highlights the importance of managing expectations through transparent communication, appropriate branding, and consistent service delivery [30]. Particularly in omnichannel contexts, where users may interact with a brand through multiple platforms before making a purchase, expectations are shaped cumulatively and must be met cohesively across channels [31]. The EDP is especially relevant for omnichannel service environments, where the risk of expectation misalignment increases due to the complexity of touchpoints. For instance, if a customer views a product online with specific attributes but finds conflicting information or pricing at the point of sale, the resulting disconfirmation can be more severe than in a single-channel interaction. Likewise, expectations about delivery time, return policies, or payment flexibility may differ depending on the channel used—making channel integration and communication consistency crucial for maintaining satisfaction [32].

Moreover, in digitally emergent or resource-constrained settings, expectations are not fixed or homogeneous. Users may bring with them prior experiences from informal markets, lower service standards, or limited digital literacy. In such contexts, expectation formation is influenced by contextual, cultural, and infrastructural variables, making the measurement of disconfirmation especially nuanced [33]. In these settings, even moderately functional systems may generate positive disconfirmation if expectations were initially low. From a service design perspective, the EDP provides a valuable diagnostic tool for identifying gaps between promise and delivery. It suggests that efforts to improve satisfaction should not only target performance enhancement (e.g., faster delivery, more payment options) but also focus on setting realistic and accurate expectations throughout the customer journey [26]. In this study, we apply the EDP to evaluate how the dimensions of omnichannel design—data, payment, and logistics—align with or deviate from users’ expectations [34]. By doing so, we assess the extent to which satisfaction, as a proxy for Quality of Experience (QoE), is driven by the effectiveness of the digital strategy in managing perceived value and service credibility across interconnected platforms.

2.3. Omnichannel Design Dimensions

Omnichannel strategy refers to the orchestration of multiple interaction channels—both digital and physical—in a way that provides users with a unified, consistent, and personalized experience throughout their customer journey. Unlike multichannel models, which often operate in parallel but independently, omnichannel systems are characterized by real-time data integration, cross-platform continuity, and user-centered logic [3]. In operational terms, omnichannel systems can be decomposed into several core design dimensions. For the purposes of this study, we adopt the framework proposed by the Asociación Mexicana de Venta Online [10], which identifies three critical and interdependent pillars of effective omnichannel strategy: data integration, digital payment systems, and logistics orchestration. These components form the technological and experiential infrastructure upon which Quality of Experience (QoE) is built in digital service ecosystems.

Table 2. Operationalization of variables.

Variables	Dimensions	Indicators	Scale of Measurement
Variable 1: Omnichannel	Data	Product information. Customized information according to the client. Centralized and updated information in real time across all channels. Reports on customer loyalty level.	(1) Totally disagree (2) Disagree (3) Neither agree nor disagree (4) OK (5) Totally agree
	Omnichannel payment	Cash payments. Card payments at the physical location. Card payments on digital channels. Payments with digital wallets.
	Omnichannel logistics	Option to pick up products in a physical store. Product shipping option.
Variable 2: Satisfaction	Expectations	Customer expectations.	(1) Totally disagree (2) Disagree (3) Neither agree nor disagree (4) OK (5) Totally agree
	Perceived value	Core product benefit. Benefit of complementary products. Monetary costs. Non-monetary costs.

summarizes the operationalization of the omnichannel service dimensions analyzed in this study, including the associated indicators and measurement scales.

2.3.1. Data Integration

The foundational element of any omnichannel system is the ability to gather, structure, and deploy customer data across all points of interaction. Effective data integration allows for user profiling, behavior prediction, and real-time personalization [32]. This enables businesses to tailor communications, recommend products, and automate responses based on individual preferences and historical interactions. Moreover, centralized data repositories enhance consistency by synchronizing the inventory information, transaction history, loyalty points, and user feedback across all platforms—whether the user engages through a mobile app, website, or physical terminal. Without such synchronization, users may encounter information mismatches that erode trust and disrupt their experience [35]. In digitally emergent contexts, data integration also plays a pivotal role in lowering cognitive friction for less digitally literate users. By reducing the need for repeated inputs or redundant actions, well-integrated systems contribute to a perception of control and ease, which are central components of satisfaction.

2.3.2. Digital Payment Systems

The flexibility of payment modalities has become a key determinant of user satisfaction in digital environments. Omnichannel payment systems offer users the ability to choose between traditional methods (e.g., cash and card) and newer, often mobile-based solutions (e.g., digital wallets, QR payments), depending on context, preference, and device availability [12]. These options are especially relevant in emerging markets, where financial inclusion remains uneven. A truly inclusive omnichannel system must allow users to switch between online and offline payments fluidly, enabling hybrid transaction flows such as online prepayment with in-store pickup, or reservation through a digital channel with cash payment on delivery [32]. From a QoE perspective, the absence of preferred payment methods, excessive complexity in digital transactions, or a lack of transparency in fees can lead to frustration and abandonment, especially when expectations have been shaped by more advanced ecosystems.

2.3.3. Logistics Orchestration

The final dimension in this model is logistics, which encompasses the coordination of inventory, distribution, and delivery or pickup systems in a way that supports user expectations and maximizes fulfillment accuracy. In omnichannel ecosystems, logistics must align with digital operations in real time—offering clear, updated information on stock levels, delivery times, and pickup options [35]. For users, logistical flexibility is not only a matter of convenience but a key component of value perception. The ability to choose when, where, and how to receive a product (or access a service) enhances the perceived control and reduces effort—two critical antecedents of satisfaction [8]. Moreover, transparent logistics can mitigate disconfirmation by aligning user expectations with service realities. In emerging regions, where the delivery infrastructure may be inconsistent, setting clear timelines and offering hybrid fulfillment options (e.g., “click-and-collect”) can significantly improve the perceived service quality.

2.4. Quality of Experience (QoE) and Net-Living

In digitally mediated environments, particularly those involving human–system interactions, Quality of Experience (QoE) has emerged as a crucial metric for evaluating the effectiveness and perceived value of technological solutions. Unlike traditional service-quality models that focus on functional or technical attributes, QoE places the user’s subjective perception at the center of the evaluation process. Traditional service-quality models such as those underlying multichannel service systems typically emphasize standardized performance criteria, focusing on technical reliability and functional delivery. For example, earlier frameworks prioritized attributes like responsiveness, assurance, and empathy in isolated service interactions [3]. However, in the context of digitally integrated systems, newer perspectives such as the service logic paradigm highlight the dynamic and relational nature of quality, especially as users move across channels [7,14]. These approaches underscore the need to view experience not just as an outcome-based but as a co-produced, perceptual construct shaped by continuity, personalization, and contextual relevance [15]. It encompasses not only efficiency and accessibility but also emotional responses, cognitive load, and the degree of user autonomy experienced throughout the interaction [7,36]. QoE has been widely adopted in ICT research to describe the holistic impact of system design on human satisfaction, trust, and engagement. In the context of omnichannel systems, QoE refers to the seamlessness, personalization, responsiveness, and reliability perceived by the user across the entire customer journey. The integration of channels—whether digital, physical, or hybrid—must support not only transactional functions but also a sense of coherence and psychological comfort [3]. Several studies suggest that a positive QoE contributes to digital trust, behavioral intention, and customer loyalty, especially when systems are intuitive and reduce effort through personalization and predictive services [12,33]. Conversely, fragmentation, inconsistency, or friction in omnichannel interactions can lead to cognitive overload or expectation gaps—undermining both satisfaction and long-term engagement. Beyond individual performance metrics, QoE is also an enabler of broader systemic goals linked to the concept of Net-Living. As defined by Future Internet, Net-Living refers to the integration of smart technologies into all spheres of daily life—public, private, and professional—for the purpose of enhancing well-being, equity, and quality of life. This paradigm emphasizes the design of digital systems not merely as technical artifacts but as tools for social inclusion, emotional connection, and meaningful participation in the digital economy and society.

In this regard, the present study frames customer satisfaction as a proxy for QoE, and, by extension, as a component of digital well-being. When omnichannel systems are designed to minimize friction, offer meaningful choices, and adapt to user needs, they contribute not only to commercial success but also to the dignity, autonomy, and empowerment of users in emerging digital ecosystems [6,37]. This perspective is particularly relevant in developing or transitional regions, where digital inclusion is not only a technological challenge but a human development imperative. By highlighting how omnichannel strategies can be tailored to improve the perceived experience, this study contributes to the growing body of research seeking to align ICT innovation with the principles of equity, accessibility, and quality of life enhancement [38,39].

3. Methodology

3.1. Research Design

This research was conducted under a non-experimental, cross-sectional, and correlational design aligned with best practices in applied service research in real-world environments [40]. The main objective was to examine the relationship between the implementation of omnichannel service strategies and customer satisfaction, understood as a perceptual construct related to Quality of Experience (QoE). The non-experimental nature of the study means that no variables were manipulated or externally controlled. Instead, the investigation relied on the natural behaviors, perceptions, and interactions of customers within an already established service system. This methodological decision was appropriate given the ethical and practical limitations of modifying operational procedures within a commercial business, and it allowed for ecologically valid observations of user experience in a real-world environment.

A cross-sectional design was selected to gather data from participants at a single point in time, providing a snapshot of current customer perceptions and omnichannel system performance. This approach enabled the study to capture insights into service usage, satisfaction levels, and omnichannel feature evaluation without the resource demands of a longitudinal framework. Although this limits the ability to infer causality, cross-sectional designs remain highly effective for identifying associative relationships and generating actionable insights for system improvement [41]. The correlational focus of the study is rooted in the theoretical premise that perceived satisfaction is influenced by the alignment or misalignment between service expectations and delivered performance, particularly when mediated by digital tools and multichannel interfaces [7,27]. By identifying statistically significant associations between specific dimensions of omnichannel design—namely, data integration, payment systems, and logistical support—and satisfaction levels, the study seeks to understand how these service components impact users’ subjective experience. This design was particularly suitable for the study’s setting: a regional fuel service provider operating in an emerging digital environment, where customer interactions occur through both physical and digital touchpoints. In such contexts, omnichannel strategies are often partial or developing, which makes them ideal for evaluating how incomplete integration or inconsistent delivery affects customer perceptions.

Furthermore, the design allowed the research to integrate multiple theoretical frameworks—customer journey theory, the expectancy–disconfirmation paradigm, and the concept of Net-Living—without requiring experimental manipulation. This provided a rich context for analyzing customer satisfaction as a construct not only of performance outcomes but also of the perceived value, autonomy, and control across a hybrid service experience [17]. In summary, the non-experimental, cross-sectional design employed in this study provided a robust and context-sensitive framework for exploring how omnichannel strategies influence customer satisfaction in a service environment characterized by emerging digital practices. It allowed for the systematic observation of real user behavior while maintaining methodological rigor and alignment with the objectives of interdisciplinary research in smart service systems and user-centered ICT development.

3.2. Sample Characteristics and Setting

The study was conducted in the context of a regional fuel service station located in a semi-rural district within a developing country. This setting is representative of digitally emerging environments, where the adoption of omnichannel strategies is still in progress and is shaped by infrastructural, economic, and cultural constraints. The station offers basic fuel services but has also initiated efforts to modernize customer interactions through the partial implementation of digital information platforms, mobile payment systems, and logistics integration, including inventory visibility and flexible pickup options. This context was selected for two main reasons. First, it provides an ideal case for observing the early-stage implementation of omnichannel service strategies outside of metropolitan or digitally mature zones [42]. Second, it allows for an examination of real user perceptions in a market where traditional and digital service models coexist, often in tension. The station serves both digitally literate users—typically younger clients familiar with mobile technologies—and those with minimal exposure to digital systems, such as older adults and rural residents who rely on face-to-face interaction. The target population consisted of approximately 150 regular customers, defined as individuals who had engaged with the station at least three times in the past month. These customers were chosen based on their recent and repeated interactions with the business, ensuring that their perceptions were grounded in direct and relevant experience. The population included users who had engaged with digital components of the service, such as mobile payment, stock inquiries through messaging platforms, or digital loyalty tracking, as well as those who had used only physical channels. The sampling procedure was revised to more accurately reflect a non-probabilistic approach. Although the term “probabilistic” was initially used, participants were, in fact, selected without randomization from a comprehensive sampling frame based on their availability and willingness during their visits to the service station. Accordingly, the method is now described as non-probabilistic, and its limitations concerning external validity and generalizability have been explicitly addressed. From this population, a sample of 108 customers was selected using a non-probabilistic sampling method, which takes into account a confidence level of 95%, a margin of error of 5%, and a response distribution of 50% (the most conservative assumption for variability in perception-based studies):

$$n={\displaystyle \frac{Z^{2}pq·N}{E^2\left(N-1\right)+Z^2·pq}}$$

(1)

where

• n = sample size;
• p = 0.5;
• q = 1 – p = 0.5;
• N = population size = 150;
• E = error level (5% for 95% confidence) = 5%;
• Z = Z-value for 95% confidence interval = 1.96;

Replacing these values into Equation (1),

$$n={\displaystyle \frac{{1.96}^2\left(0.5\right)\left(0.5\right)·150}{{\left(0.05\right)}^2\left(150-1\right)+{\left(1.96\right)}^2·\left(0.5\right)\left(0.5\right)}}$$

(2)

$$n=108.08\approx 108$$

This yielded a statistically representative sample size capable of supporting inferential statistical analysis [43]. Participants were approached in person at the service station, during peak and off-peak hours, and were invited to participate in the study voluntarily. Before responding to the survey, each participant received a brief verbal explanation of the study’s goals and the importance of their responses for service improvement. To be included in the sample, participants had to meet the following inclusion criteria: (i) be at least 18 years of age, (ii) have used the station’s services a minimum of three times in the past month, and (iii) have interacted with at least one digital or hybrid channel (e.g., mobile payments, WhatsApp ordering, or online promotions). The setting’s socio-digital characteristics—including moderate smartphone penetration, intermittent internet access, and a general openness to technology adoption—make a compelling case for using it to study how omnichannel strategies are perceived in contexts where digital systems are not yet fully institutionalized. These conditions allowed the study to explore how partial omnichannel implementations influence user experience, trust, and satisfaction, thereby offering insight into the broader dynamics of digital inclusion and QoE in developing service environments.

3.3. Data Collection Instruments

To empirically assess the relationship between omnichannel service components and customer satisfaction, two structured self-administered questionnaires were developed. Both instruments employed a five-point Likert scale, ranging from 1 = Strongly Disagree to 5 = Strongly Agree, allowing respondents to express their level of agreement with each item in a standardized format.

The first instrument was designed to measure the perceived quality and functionality of the omnichannel service experience. It comprised 12 items grouped into three conceptual dimensions derived from the model proposed by the Asociación Mexicana de Venta Online [10] and aligned with the academic literature on digital service systems [3,12,32]. These dimensions included the following:

• Data integration (4 items): These items measured whether the customer perceived the product and service information as clear, centralized, and updated across digital and physical channels. This included visibility of the stock, personalized content, and consistent communication.
• Digital payment systems (6 items): These items assessed the availability and ease of various payment methods, such as mobile wallets, online card payments, and cash options synchronized with digital orders.
• Logistics flexibility (2 items): Questions evaluated the extent to which users could choose between home delivery and in-store pickup, as well as whether logistical processes were perceived as reliable and timely.

The second instrument focused on measuring customer satisfaction, operationalized in line with the expectancy–disconfirmation paradigm (EDP) [8,27]. It included 8 items structured around two key dimensions that are also central to QoE assessments:

• Customer expectations (2 items): These items explored whether customers felt their expectations about the service were met or exceeded.
• Perceived value (6 items): Items in this category captured how customers assessed the benefits received in relation to the time, effort, and costs incurred. This included perceptions of fair pricing, efficient service, and emotional satisfaction.

The complete list of items for both instruments can be found in Appendix A. The instruments were administered in Spanish, the participants’ native language, and carefully translated to preserve conceptual integrity. The survey required approximately 10 to 12 min to complete. Participants filled out the questionnaires in a quiet area near the service facility, with a researcher available to clarify questions without influencing the responses. The development of both instruments was grounded in validated theoretical constructs and designed for applicability in low-to-medium digital literacy contexts, where simplicity and clarity are essential. The goal was to capture perceived experience rather than technical accuracy, reflecting the subjective nature of both satisfaction and Quality of Experience (QoE). While the instruments were designed to capture key perceptual indicators with clarity and simplicity, we acknowledge that the limited number of items per dimension and the exclusive reliance on self-reported data constrain the analytical depth of the study. The absence of behavioral data (e.g., app usage logs) or qualitative follow-up limits the ability to triangulate perceptions with objective service-use patterns.

3.4. Validation and Statistical Approach

To ensure the reliability and validity of the data collection instruments, both questionnaires—the Omnichannel Service Questionnaire and the Customer Satisfaction Questionnaire—underwent a two-step validation process: expert review for content validity and internal consistency analysis using Cronbach’s alpha. Content validity was established through the evaluation of three independent subject-matter experts, each holding advanced degrees in business administration and with prior experience in quantitative research design. The experts were asked to assess each questionnaire item based on its clarity, relevance, and theoretical alignment. Minor modifications were made based on their feedback to improve their semantic precision and to eliminate ambiguity, particularly in items involving perceptions of service personalization and value. Following the content validation process, a pilot test was conducted with 15 participants who met the study’s inclusion criteria. These pilot responses were not included in the final analysis but served to test the functionality of the questionnaire and to identify potential comprehension issues. To clarify, the Cronbach’s alpha values reported were computed based on the pilot sample of 15 respondents, which was performed solely for instrument reliability testing prior to full data collection. All subsequent analyses, including correlation coefficients and inferential tests, were conducted using a consistent sample of 108 participants. The sample size remained stable throughout the entire analysis, ensuring the validity and comparability of the statistical outputs. This approach complies with best practices in instrument validation and avoids inconsistencies due to fluctuating sample bases. No substantial changes were necessary following the pilot phase, which confirmed the instruments’ appropriateness for the main data collection process. To assess the internal consistency reliability, Cronbach’s alpha coefficients were calculated for each questionnaire using the pilot data. The results were as follows:

• Omnichannel Service Questionnaire (12 items): α = 0.813
• Customer Satisfaction Questionnaire (8 items): α = 0.842

Both values are well above the commonly accepted threshold of 0.70, indicating that the instruments possess high internal consistency and that the items within each scale are reliably measuring the same underlying construct [44].

Given the ordinal nature of the data obtained from the Likert-scale questionnaires and the results of normality testing, the study employed Spearman’s rank-order correlation coefficient (rho) as the primary statistical tool for inferential analysis. This non-parametric technique is widely used to assess the strength and direction of monotonic relationships between two continuous or ordinal variables when the assumptions of parametric methods, such as Pearson’s correlation, are not met [45]. Prior to selecting the appropriate statistical test, the Kolmogorov–Smirnov test for normality was conducted on the main variables: omnichannel service perception and customer satisfaction. Both variables yielded p-values < 0.001, indicating significant deviations from a normal distribution and confirming the need for a non-parametric analytical method. This justified the use of Spearman’s correlation to explore the associations between the following:

• Overall omnichannel strategy and customer satisfaction,
• Each of the three omnichannel dimensions (data, payment, logistics) and satisfaction scores.

The Spearman rho coefficient (ρ) ranges from −1.0 to +1.0, where values closer to ±1.0 indicate stronger correlations. A positive value suggests that higher levels of perceived omnichannel service quality are associated with greater customer satisfaction, whereas a negative value would suggest an inverse relationship. The following interpretative scale was adopted:

• 0.00–0.19 = very weak;
• 0.20–0.39 = weak;
• 0.40–0.59 = moderate;
• 0.60–0.79 = strong;
• 0.80–1.00 = very strong.

The analysis was carried out using IBM SPSS Statistics v25. Each correlation was computed with a two-tailed test of significance, with α = 0.05 as the threshold for statistical significance. This means that correlations with a p-value < 0.05 were considered statistically significant, indicating a non-random association between variables.

4. Results

The following findings support the study’s theoretical assumption that the perceived quality of omnichannel service components significantly influences the subjective experience and satisfaction, reinforcing their role as mediators of digital quality of life (QoE). Furthermore, the use of a non-parametric test enhanced the robustness of the results by aligning the statistical approach with the nature of the data collected from real users in a hybrid, semi-structured manner with regard to value, autonomy, and control across a hybrid service experience [17]. In summary, the non-experimental, cross-sectional design employed in this study provided a robust and context-sensitive framework for exploring how omnichannel strategies influence customer satisfaction in a service environment characterized by emerging digital practices. It allowed for the systematic observation of real user behavior while maintaining methodological rigor and alignment with the objectives of interdisciplinary research in smart service systems and user-centered ICT development.

4.1. Description of the Omnichannel Variable

The descriptive analysis reveals a consistent perception of limited omnichannel development at the service station scenario. As shown in

Table 3. Level of omnichannel integration.

Level	Scale	fi	%
Low	From 12 to 28	65	60.19%
Half	From 29 to 45	32	29.63%
High	From 46 to 60	11	10.19%
Total		108	100.00%

Note: From SPSS 26 software.

, a significant majority—60.19% of respondents (n = 65)—rated the overall level of omnichannel integration as low. Meanwhile, 29.63% (n = 32) assessed it as regular, and only a small minority, 10.19% (n = 11), perceived it as good. These results suggest a clear gap between customer expectations and the current digital service offerings. A similar pattern emerges regarding customer data management. As detailed in

Table 4. Dimension 1: Data.

Level	Scale	fi	%
Low	From 6 to 14	74	68.52%
Half	From 3 to 11 pm	22	20.37%
High	From 24 to 30	12	11.11%
Total		108	100.00%

Note: From SPSS 26 software.

, 68.52% (n = 74) of customers evaluated data handling at the station as poor, whereas 20.37% (n = 22) described it as regular. Only 11.11% (n = 12) recognized a good level of management regarding customer and product information. This data deficit likely undermines both personalization efforts and the station’s ability to offer a coherent omnichannel experience. Regarding omnichannel payment systems,

Table 5. Dimension 2: Omnichannel payment.

Level	Scale	fi	%
Low	From 4 to 9	21	19.44%
Half	From 10 to 15	66	61.11%
High	From 16 to 20	21	19.44%
Total		108	100.00%

Note: From SPSS 26 software.

indicates a moderately better perception, albeit still fragmented. While 61.11% (n = 66) of customers categorized the payment options as regular, 19.44% (n = 21) assessed them as low, and an equal number (19.44%; n = 21) considered them good. This distribution suggests that, although some digital payment mechanisms are in place, their accessibility or reliability might not yet fully meet user expectations. Finally, perceptions regarding logistics flexibility, presented in

Table 6. Dimension 3: Omnichannel logistics.

Level	Scale	fi	%
Low	From 2 to 4	20	18.52%
Half	From 5 to 7	62	57.41%
High	From 8 to 10	26	24.07%
Total		108	100.00%

Note: From SPSS 26 software.

, reflect a similar trend. A majority, 57.41% (n = 62), described the logistics performance as regular, while 24.07% (n = 26) viewed it positively, and 18.52% (n = 20) rated it poorly. Although logistics appears to be the dimension with relatively higher positive evaluations compared with data management and payment systems, the proportion of dissatisfied customers remains notable and underscores the need for service improvements.

Overall, these findings point to a partial implementation of omnichannel strategies at the service station, with customer perceptions skewed toward moderate or low satisfaction across key dimensions. Addressing these gaps is critical to enhancing the station’s ability to deliver a seamless, integrated, and user-centered service experience.

4.2. Description of the Satisfaction Variable

The results related to customer satisfaction reveal a predominant perception of moderate to low service quality. As indicated in

Table 7. Satisfaction variable.

Level	Scale	fi	%
Low	From 8 to 18	45	41.67%
Medium	From 19 to 29	51	47.22%
High	From 30 to 40	12	11.11%
Total		108	100.00%

Note: From SPSS 26 software.

, 47.22% of respondents (n = 51) rated their satisfaction as regular, while an additional 41.67% (n = 45) reported low satisfaction. Only a minority of 11.11% (n = 12) expressed a high level of satisfaction with the services provided by the service station. These findings suggest that, although most customers continue to use the station’s services, their emotional and cognitive evaluations of the experience remain lukewarm or negative, pointing to areas of unmet expectations and service delivery gaps. Regarding customer expectations, the data presented in

Table 8. Dimension 1: Expectations.

Level	Scale	fi	%
Low	From 2 to 4	47	43.52%
Medium	From 5 to 7	51	47.22%
High	From 8 to 10	10	9.26%
Total		108	100.00%

Note: From SPSS 26 software.

reinforce this interpretation. Nearly half of the respondents (47.22%, n = 51) indicated that their expectations were at a regular level, while 43.52% (n = 47) considered their expectations to be low. Only a small fraction (9.26%, n = 10) reported having high expectations about the services offered. This suggests that the customer base already approaches the station with moderate or limited expectations, which further amplifies the challenge: even when expectations are not particularly high, the service performance is not sufficiently exceeding them to generate widespread satisfaction. The analysis of perceived value, summarized in

Table 9. Dimension 2: Perceived value.

Level	Scale	fi	%
Low	From 6 to 14	50	46.30%
Medium	From 3 to 11 pm	45	41.67%
High	From 24 to 30	13	12.04%
Total		108	100.00%

Note: From SPSS 26 software.

, further supports this pattern. A total of 46.30% of respondents (n = 50) perceived the value of the services received as being low, while 41.67% (n = 45) rated it as regular. Only 12.04% (n = 13) considered the perceived value to be high. This distribution indicates a significant gap between what customers invest (in time, money, and effort) and what they feel they receive in return, undermining the station’s potential to foster loyalty and advocacy behavior over time.

Taken together, these findings highlight a critical alignment issue between service delivery and customer perceptions. The data suggest that the service station is struggling not only to meet but to positively differentiate itself from baseline customer expectations. Without strategic improvements in omnichannel integration, personalization, and perceived value creation, the station risks reinforcing a cycle of moderate usage without emotional commitment—a dynamic that can weaken its competitive position in the medium term.

4.3. Hypothesis Testing

4.3.1. Data Normality and the General Hypothesis Test

To determine the appropriate statistical method for analyzing the relationship between omnichannel service perception and customer satisfaction, a normality test was conducted on the data sets for both variables.

Null Hypothesis (H₀). 

The data for the omnichannel variable and the satisfaction variable are normally distributed.

Alternative Hypothesis (H₁). 

The data for the omnichannel variable and the satisfaction variable are not normally distributed.

The Kolmogorov–Smirnov test, appropriate for samples larger than 50 participants, was applied (

Table 10. Normality test.

Kolmogorov–Smirnov (n > 50)
	Statistical	df	Sig.
Omnichannel	0.167	108	0.000
Satisfaction	0.140	108	0.000

Note: From SPSS 26 software.

). The results yielded a p-value of 0.000, indicating a significant deviation from normality. Consequently, the null hypothesis (H₀) was rejected, and the alternative hypothesis (H₁) was accepted. Given the non-normal distribution of the data and the ordinal nature of the variables (both classified into three levels: low, regular, and high), the Spearman rank-order correlation test was selected for subsequent inferential analysis.

Following the confirmation of non-normality, the study proceeded to test the general hypothesis regarding the relationship between omnichannel perception and customer satisfaction at the service station (

Table 11. General hypothesis test.

			Satisfaction
Spearman’s Rho	Omnichannel	Correlation coefficient	0.555
		Sig. (bilateral)	0.000
		N	108

Note: From SPSS 26 software.

, Figure 2).

Null Hypothesis (H₀). 

There is no direct and significant relationship between omnichannel service perception and customer satisfaction.

Alternative Hypothesis (H₁). 

There is a direct and significant relationship between omnichannel service perception and customer satisfaction.

The Spearman’s rho test, a non-parametric correlation method, was applied. The analysis produced a p-value of 0.000, supporting the rejection of the null hypothesis (H₀) and acceptance of the alternative hypothesis (H₁). Thus, the findings confirm that a direct and statistically significant relationship exists between the perception of omnichannel service quality and customer satisfaction at the service station (p < 0.05). This result underscores the critical role of integrated digital service strategies in enhancing the subjective experience of users, particularly in digitally emerging environments.

4.3.2. Testing Specific Hypotheses

The analysis of the first specific hypothesis examined whether a direct and significant relationship exists between customer data management and satisfaction levels within the service environment under study. Using Spearman’s rho as the appropriate non-parametric test, the results yielded a p-value of 0.000, which is well below the conventional significance threshold (p < 0.05). As a result, the null hypothesis (H₀)—stating that no relationship exists—was rejected, while the alternative hypothesis (H₁)—suggesting a direct and significant association between the quality of data management and customer satisfaction—was accepted. This outcome underscores the critical role that effective, personalized, and integrated data practices play in enhancing users’ perceptions of service quality and overall experience (

Table 12. Specific hypothesis test 1.

			Satisfaction
Spearman’s Rho	Data	Correlation coefficient	0.531
		Sig. (bilateral)	0.000
		N	108

Note: From SPSS 26 software.

, Figure 3).

The second specific hypothesis assessed the link between the availability and performance of digital payment options and customer satisfaction. Applying the Spearman’s rho test, the analysis produced a p-value of 0.000, indicating a statistically significant relationship between the two variables. Accordingly, the null hypothesis (H₀) was rejected, and the alternative hypothesis (H₁) was confirmed, validating the existence of a direct and significant association between digital payment flexibility and customer satisfaction. This finding highlights the importance of providing diverse and accessible payment modalities—such as mobile wallets and online card transactions—in order to meet user expectations and to enhance the perceived ease and trustworthiness of the service (

Table 13. Specific hypothesis test 2.

			Satisfaction
Spearman’s Rho	Omnichannel payment	Correlation coefficient	0.234
		Sig. (bilateral)	0.000
		N	108

Note: From SPSS 26 software.

, Figure 4).

The third specific hypothesis explored the association between omnichannel logistics management and customer satisfaction. Based on the results of the Spearman’s rho test, a p-value of 0.000 was obtained, indicating a statistically significant relationship (p < 0.05). Consequently, the null hypothesis (H₀) was rejected, and the alternative hypothesis (H₁)—asserting a direct and significant connection between the quality of logistical coordination and customer satisfaction—was accepted. This result emphasizes that flexible and reliable logistics solutions, including options for delivery and in-store pickup, are essential components for reinforcing customer trust, convenience, and overall perceived service value in omnichannel systems (

Table 14. Specific hypothesis test 3.

			Satisfaction
Spearman’s Rho	Omnichannel logistics	Correlation coefficient	0.498
		Sig. (bilateral)	0.000
		N	108

Note: From SPSS 26 software.

, Figure 5).

4.3.3. Additional Multivariate Analysis: Linear Regression

In order to go beyond bivariate correlations and to evaluate the joint contribution of each omnichannel dimension, a multiple linear regression was performed with Customer Satisfaction as the dependent variable, and Data, Omnichannel Payment, and Logistics as predictors. The model yielded an adjusted R² of 0.002, indicating that less than 1% of the variance in satisfaction was explained by the predictors collectively. None of the variables reached statistical significance: Data (β = 0.060, p = 0.559), Omnichannel Payment (β = −0.066, p = 0.642), and Logistics (β = −0.349, p = 0.103). Despite the weak correlations observed earlier, this result suggests a substantial interdependence between dimensions and potentially omitted variables influencing customer satisfaction. These findings imply that, in this emerging market context, the observed associations are not sufficient to assert predictive power, and a more robust mixed-methods approach or larger sample may be required to draw inferential conclusions (

Table 15. Linear regression results—predicting customer satisfaction.

Predictor	β	Std. Error	t	p-Value
Constant	25.877	2.890	8.955	0.000
Data	0.060	0.102	0.586	0.559
Omnichannel Payment	−0.066	0.142	−0.467	0.642
Logistics	−0.349	0.212	−1.644	0.103

Adjusted R² = 0.002; F(3,104) = 1.05; p = 0.372.

).

5. Discussion

5.1. General Interpretation of Findings

The findings of this study provide compelling evidence of the relevance and impact of omnichannel service strategies on customer satisfaction in digitally emerging environments. Moreover, the integration of artificial intelligence (AI) technologies into omnichannel service systems could further optimize customer experiences by enabling real-time personalization, predictive analytics for logistics, and adaptive interaction strategies tailored to individual user profiles. Specifically, the results demonstrate that higher levels of perceived omnichannel quality—especially in terms of data integration and logistical coordination—are positively associated with higher levels of satisfaction, understood here as a perceptual indicator of Quality of Experience (QoE). These results are consistent with prior research in digitally mature settings, which highlights the importance of seamless service flows, personalization, and channel consistency in improving customer experience [3,7]. However, the added value of this study lies in its application of these concepts to a semi-rural, low-digital-density context, where omnichannel strategies are still nascent and infrastructural limitations persist. The observed moderate positive correlation between overall omnichannel perception and satisfaction (ρ = 0.555; p < 0.001) suggests that even in environments where technological implementation is partial or inconsistent, users are able to perceive and respond to improvements in service integration. This reinforces the idea that QoE is not purely dependent on technical sophistication but rather on how well systems respond to user needs in contextually relevant ways [32,33]. However, it is important to note that not all the dimensions showed equally strong associations. For instance, the correlation between digital payment systems and customer satisfaction (ρ = 0.234) reflects a statistically significant but modest association. This dimension should be interpreted cautiously, as it may indicate a potential rather than an established influence. Its impact is likely contingent upon broader ecosystem readiness—such as trust in mobile payments, user familiarity, and transactional transparency—rather than representing a primary driver of satisfaction at this stage.

In line with the expectancy–disconfirmation paradigm, the study’s results indicate that customers evaluate their satisfaction based on the alignment between service performance and their pre-existing expectations. This is particularly important in emerging markets, where expectations may vary widely depending on socioeconomic status, prior digital exposure, and peer influence [8,27]. The relatively high proportion of users reporting low or moderate satisfaction despite frequent use of the service suggests that while digital features are being adopted, they are not yet meeting the psychological and functional standards set by the users. The dimension of data integration emerged as the strongest predictor of satisfaction. This aligns with prior research showing that transparent, real-time, and centralized information systems contribute significantly to customer trust and decision-making [32]. When users perceive the service as being knowledgeable about their preferences and consistent across channels, they are more likely to experience reduced cognitive load, greater convenience, and enhanced value. In contrast, digital payment systems showed the weakest, albeit still significant, correlation. This finding reflects patterns observed in transitional economies, where cash remains dominant and digital payment adoption is uneven due to factors such as infrastructure, trust, and digital literacy [12]. Therefore, while payment flexibility is an important feature of omnichannel systems, its influence on satisfaction may be contingent upon broader ecosystem readiness. This interpretation is further supported by the regression model, where none of the dimensions reached statistical significance, reinforcing the view that their explanatory power remains limited in the current context. Logistics flexibility was found to have a moderately strong relationship with satisfaction, affirming the value of user control over fulfillment processes—a key component in the perceived ease of use and efficiency [35]. In fragmented service contexts, logistical transparency and reliability often act as critical touchpoints where user trust is either solidified or eroded.

5.2. Theoretical and Practical Implications

The results of this study offer several theoretical contributions to the literature on omnichannel service design and Quality of Experience (QoE). First, by confirming the moderate to strong associations between omnichannel performance and satisfaction in a digitally emergent environment, the study extends existing models—often validated in developed markets—into new geographies and socio-digital contexts. This supports the argument that omnichannel theory is adaptable across digital maturity levels but that the relative influence of each channel dimension may vary depending on cultural, infrastructural, and economic factors. Second, this research reinforces the value of combining the customer journey perspective with the expectancy–disconfirmation paradigm to capture both the structural and perceptual aspects of service evaluation. While the journey model highlights process flows, touchpoints, and cross-platform transitions [7], the expectancy–disconfirmation paradigm introduces the cognitive mechanisms behind user satisfaction, such as expectation calibration and perceived effort [27]. Together, these frameworks offer a multi-layered understanding of how users interact with and interpret omnichannel systems. From a practical standpoint, the study provides actionable insights for service providers, particularly in regions undergoing digital transformation without full infrastructure readiness. The evidence suggests that businesses should prioritize investment in the following areas:

• Data centralization and synchronization, to ensure that users encounter coherent, accurate, and personalized information across all platforms.
• Logistics transparency and flexibility, as users value the ability to select pickup or delivery options and trust services that fulfill promises reliably.
• The incremental introduction of digital payment options, accompanied by user education, interface simplicity, and the retention of cash alternatives to avoid alienating less digitally fluent customers.

These actions align with the principle of progressive digital inclusion, where service systems are designed not only for performance optimization but also for accessibility and usability across diverse user profiles [4]. The findings highlight the importance of designing with the user, not just for the user, particularly in emerging economies where infrastructural asymmetries may shape unique behavioral patterns.

5.3. Implications for Inclusive Digital Transformation and Net-Living

The concept of Net-Living envisions a future in which smart systems and digital infrastructures are embedded seamlessly into everyday life to support well-being, inclusion, and autonomy across public, private, and professional domains. Users in digitally underserved regions actively assess digital interactions based on their perceived utility and ease of use. Well-designed omnichannel systems can foster trust, reduce friction, and enhance user autonomy. In such contexts, access to coherent digital services can be a key enabler of socio-digital inclusion beyond mere convenience. It allows individuals with varying degrees of digital fluency to participate in structured systems without being excluded by complexity or technological opacity.

Furthermore, the results suggest that the “intelligent” component of smart service systems must be relational as much as technical. In other words, success in Net-Living is not achieved merely through automation or digitization but through the system’s ability to recognize and respond to users’ socio-emotional and informational needs [46]. This includes adapting interfaces to local expectations, maintaining redundancy (e.g., offering both digital and face-to-face channels), and progressively integrating new technologies at a pace that users can assimilate and adopt confidently. This approach supports the ethos of inclusive innovation, which prioritizes the following:

• Accessibility: ensuring that systems can be used by people regardless of income, education, or digital literacy.
• Responsiveness: enabling feedback loops that adjust service behavior in real time.
• Simplicity: reducing cognitive and procedural burdens that discourage use or that lead to abandonment.

Additionally, these findings have policy implications. Public institutions and regulatory bodies aiming to foster digital ecosystems should recognize omnichannel systems as infrastructures of trust and equity, especially in sectors such as energy, health, and public administration. Investments in interoperable platforms, mobile-friendly applications, and data transparency mechanisms can yield not only economic efficiency but also social dividends in the form of expanded participation and enhanced subjective well-being [12]. In sum, this study positions omnichannel strategies not merely as commercial tool but as architectures of digital dignity—structures that mediate the user’s encounter with technology in a way that either empowers or marginalizes. By focusing on the user experience as an ethical and design priority, organizations and governments alike can align digital transformation with the principles of Net-Living, ensuring that technological advancement serves as a vector for human development, not a new source of inequality.

6. Conclusions

This study investigated the relationship between perceived omnichannel service performance and customer satisfaction, conceptualized here as an expression of Quality of Experience (QoE) and a foundational component of Net-Living. The research was conducted in a semi-rural, digitally emerging context, providing a unique opportunity to examine how partial or transitional implementations of digital service infrastructure affect subjective user evaluations. The findings confirm that there is a statistically significant, positive correlation between customer satisfaction and all three dimensions of the omnichannel strategy: data integration (ρ = 0.531), logistics flexibility (ρ = 0.498), and digital payment systems (ρ = 0.234). The strongest predictor of satisfaction was data quality and accessibility, highlighting the critical role of centralized, real-time, and personalized information in shaping user trust and perceived value. By framing satisfaction as a perceptual outcome of the expectancy–disconfirmation process and linking it to the broader conceptual framework of the customer journey, the study offers a multi-level understanding of how omnichannel design influences the service experience in constrained digital environments. Theoretically, it extends existing models by validating them in a low-infrastructure context. Empirically, it provides evidence that even basic omnichannel efforts can generate meaningful improvements in user satisfaction and perceived well-being. Important methodological limitations must be acknowledged. The study was carried out at a single service station, and a non-probabilistic sample was employed, which limits the extent to which the findings can be generalized. Although the sample included regular users with varying levels of digital literacy, the results should be interpreted cautiously and cannot be extrapolated to broader populations without further empirical validation.

The results yield valuable insights for both policymakers and service managers seeking to foster inclusive digital transformation. For policymakers, the study underscores the need to view digital service infrastructure as a public good, particularly in rural and underserved regions. Promoting interoperability, supporting mobile connectivity, and incentivizing user-friendly platform development can help close the gap between digital capability and access. For service managers, the evidence suggests that investment in data infrastructure and user-interface coherence can yield higher returns in satisfaction than isolated technological upgrades. Additionally, the reliance on brief, self-reported survey instruments may not fully capture the complexity of user behavior. Future studies should consider supplementing surveys with behavioral analytics or interviews to enhance the explanatory power. While payment flexibility is essential, it must be paired with transparent information systems and logistical control mechanisms that enable users to manage their transactions easily and autonomously. Moreover, service providers should prioritize the progressive onboarding of users, balancing innovation with simplicity and reliability. This includes providing low-friction, human-assisted alternatives alongside self-service digital tools, particularly for customers with low digital literacy or limited access.

Several avenues for future research arise from the limitations and scope of this study. This study is limited to a single case study in a semi-rural context; therefore, generalizations should be made cautiously. Future studies could extend the analysis to multiple service environments or integrate longitudinal data. One promising direction is the exploration of artificial intelligence (AI) applications within omnichannel environments, leveraging machine learning and predictive models to enhance service personalization, automate customer interaction flows, and dynamically adjust service delivery based on evolving user preferences. Moreover, to deepen the analytical framework, future research could employ multivariate regression models to assess the predictive strength of each omnichannel dimension while controlling for other variables. Additionally, mediation or moderation analysis—e.g., exploring whether user age moderates the effect of payment options on satisfaction—could provide insight into when and for whom each design element matters most. The cross-sectional design used here provides a useful snapshot of customer perceptions, but it cannot capture temporal changes in satisfaction or behavior. A longitudinal study could explore how customer expectations evolve with continued exposure to omnichannel services and increasing digital literacy. Also, comparative analyses between digitally mature and emerging service environments could highlight structural and cultural variables that mediate the impact of omnichannel strategies. Such comparisons would help refine universal service-design principles versus context-specific adaptations. Additionally, future research should integrate multivariate techniques such as multiple regression, structural equation modeling, or mediation analysis to better isolate the effects of each omnichannel component and enhance their causal interpretation. Future research could also benefit from mixed-methods approaches, integrating quantitative correlations with qualitative user narratives, to better understand the emotional and behavioral mechanisms that underpin satisfaction in complex digital journeys. However, given the low explanatory power of the regression model (adjusted R² = 0.002), future research should apply mixed methods or path analysis to better isolate causal factors and address potential confounding variables. Lastly, deeper exploration of marginalized or digitally excluded groups—including older adults, rural residents, or users with disabilities—would provide critical insights into how omnichannel systems can be redesigned for greater equity, empathy, and accessibility.