From Branch to Digital: Modeling Customer Channel Preferences in Electronic Banking Services

Abstract

The digital transformation of financial services has changed how customers interact with banks through electronic channels, yet the factors influencing channel choice between branch-based and digital banking are not entirely understood, especially in emerging European markets. This study investigates banking channel preferences over three consecutive years (2023–2025) in Constanta County, Romania, quantifying how perceived bank technologization and sociodemographic characteristics are associated with the likelihood of digital banking adoption in the e-commerce context. Using repeated cross-sectional survey data from 785 respondents, we applied pooled and year-specific logistic regression models to evaluate temporal effects and estimate the predictive contribution of a composite perception measure (TechScore). Results show that although digital banking usage increased from 87.7% to 92.4%, time alone did not significantly predict adoption. Technologization perceptions consistently increased the odds of digital banking use, with stronger effects in 2025. Age and living environment were significant determinants, while gender and relationship length were not. As digital financial services mature, perceived bank technologization becomes increasingly influential in channel-use decisions. The study contributes to the electronic commerce and technology acceptance literature by demonstrating the importance of perception-based predictors in digital banking contexts and highlights how perception-based evaluation shapes channel choice in digital service platforms, offering insights applicable to electronic commerce contexts where providers compete across physical and digital channels.

1. Introduction

1.1. The Digital Transformation of Banking Services

Over the past two decades, the financial services sector has undergone substantial transformation, driven by technological innovation and evolving customer expectations [1,2,3]. Traditional branch-based banking, once the dominant mode of service delivery, now operates alongside digital channels, including internet banking, mobile applications, and contactless payment systems, positioning banks as e-commerce platforms for financial transactions [4,5]. This evolution reflects broader patterns in how consumers interact with financial institutions, as digital platforms offer more convenience, accessibility, and real-time functionality than physical branches do [6,7]. Digital banking (e-banking) represents a specific form of electronic commerce where financial services (including payments, lending, account management, and investment) are delivered through online and mobile channels, mirroring the transformation observed across retail and service sectors [8,9,10].

Digital banking adoption has accelerated globally, particularly following the COVID-19 pandemic, when both customers and financial institutions were pressured to rapidly adopt contactless service channels [11,12]. European banks have made significant investments in digital infrastructure to reduce operational costs and meet customer demand for flexible, multichannel access [13,14,15,16], positioning themselves as competitors in the broader e-commerce ecosystem [10]. However, adoption patterns differ across demographic segments, geographic areas, and national markets, indicating that the transition from branch to digital banking is not uniform [17,18,19,20]. In Romania, where digital banking infrastructure is rapidly advancing but still in its early stages, and where the role of non-bank financial institutions is relatively limited, leaving banks as the primary interface between consumers and financial services [21], the shift to digital channels has been observed more prominently among younger, urban customers, reflecting a turning point in the country’s financial services transformation [22,23]. This pattern reflects the broader e-commerce adoption dynamics where demographic and infrastructural factors create digital divides [24]. Moreover, empirical findings show that customer trust and satisfaction play an important role in shaping perceptions of banking reputation and the acceptance of new service channels [25], consistent with e-commerce literature emphasizing the role of trust in online transaction environments [26,27].

These contextual variations are consistent with the Diffusion of Innovations Theory [28], which explains how differences in access, perceived usefulness, and social influence create diverse adoption patterns among populations and market environments.

Understanding the determinants of banking channel preferences is very important for financial institutions that must allocate resources efficiently between physical infrastructure and digital platforms [6,7,29]. From an electronic commerce perspective, banking represents a regulated and increasingly platform-based service industry, where insights into channel choice and perception-driven adoption can inform broader discussions on digital service delivery, platform competition, and customer experience management. While descriptive studies document increasing digital adoption rates [30,31], few investigate the underlying factors that shape these preferences or how these relationships evolve over time [8,32], particularly from an e-commerce perspective that considers digital banking as online service delivery [33,34]. In this context, recent research highlights the significance of employee education and professional development in supporting responsible and customer-oriented innovation within the banking sector [35].

1.2. Technology Acceptance and Customer Behavior in Banking

Research on technology acceptance provides the theoretical basis for understanding digital banking adoption as a form of electronic commerce. The Technology Acceptance Model (TAM) establishes perceived usefulness and perceived ease of use as primary drivers of technology adoption decisions [36], and has been validated in the banking industry, where customers evaluate online services according to their functional benefits and simplicity of use [37,38], mirroring patterns observed in broader e-commerce contexts [27]. Extensions of TAM, particularly the Unified Theory of Acceptance and Use of Technology (UTAUT), incorporate additional predictors such as social influence and facilitating conditions, emphasizing how interpersonal expectations and support structures shape adoption decisions [39,40]. These models have been successfully applied to understand customer acceptance of online financial services as electronic service delivery channels [41,42]. In addition to these cognitive factors, research shows that trust and perceived security are essential in financial service contexts, as in other e-commerce domains. Customers must have confidence in the integrity and safety of online transactions in order to adopt them [43,44,45,46]. Recent empirical evidence confirms that trust in technology-mediated processes extends beyond traditional banking and plays a significant role in shaping users’ attitudes toward algorithm-driven services, such as AI-based decision systems in e-commerce, where perceived transparency and ethical use significantly influence acceptance [47]. Together, these frameworks highlight the significant role of technology perceptions in influencing channel-use preferences in digital financial services commerce.

In the context of electronic commerce, digital banking represents a specific application domain where traditional TAM and UTAUT constructs interact with financial service characteristics. The perception of bank technologization extends beyond general technology acceptance to encompass how customers evaluate banks’ digital commerce capabilities, including platform usability, transaction security, and multichannel integration. This perspective positions banks not just as financial intermediaries but as e-commerce service providers competing in digital marketplaces where user experience, technological sophistication, and good online service delivery determine competitive advantage [40,48,49].

In addition, customer satisfaction plays an important role in shaping digital engagement in online banking environments. As digital interfaces replace in-person contact, satisfaction depends not only on platform functionality but also on the perceived e-service quality delivered through digital channels [50,51] and responsiveness of financial institutions. Recent research underscores the role of customer satisfaction in the development and consolidation of modern banking services, strengthening trust and encouraging continued digital usage [52,53,54], with studies demonstrating that internet banking service quality, measured through e-commerce frameworks like E-SERVQUAL, significantly predicts customer loyalty [33].

Prior research has identified demographic and socioeconomic factors as important determinants of digital banking adoption. Age consistently emerges as a strong predictor, with younger customers demonstrating higher adoption rates than older groups [18,55,56]. Urban residents generally use digital channels more than rural populations, reflecting differences in infrastructure, connectivity, and literacy [17,20,57], factors that mirror broader patterns of digital commerce accessibility [58]. Income level also influences adoption patterns, with higher-income users being more likely to adopt online channels [59,60]. Gender effects appear to be varied and dependent on context, with some studies finding significant differences [45,61], while others report converging adoption rates in case of men and women [62,63].

Recent studies suggest that the determinants of digital banking adoption may change over time as markets mature and digital services become commonly used [8,32]. Most existing research employs cross-sectional designs that capture adoption patterns at a single point in time, making it difficult to distinguish temporal trends from perception-driven effects, particularly in understanding how e-commerce behavior in financial services evolves as digital channels become normalized.

Taken together, these theoretical perspectives provide a complementary explanatory framework for understanding banking channel choice in an electronic commerce context. Diffusion of innovation theory captures the temporal environment in which digital banking adoption occurs, describing how markets transition from early adoption to more mature stages. Within this evolving context, technology acceptance models such as TAM and UTAUT explain the perception-based mechanisms through which customers evaluate digital service channels. Therefore, channel preference emerges not only as a function of time or exposure, but also as the result of customers’ evolving assessments of banks’ technological capabilities.

1.3. Research Gaps and Study Motivation

Despite extensive research on digital banking adoption, several gaps remain in understanding how customers navigate the transition from traditional to digital channels in financial services e-commerce. First, most studies focus on adoption as a binary decision rather than examining primary channel preferences among customers who have access to both traditional and digital options, a distinction particularly important in omnichannel e-commerce environments where customers can switch between service delivery models. Second, while technology acceptance models highlight the role of perceptions, few studies quantify how perceived bank technologization specifically influences channel choice or how this relationship changes over time. Third, while digital adoption has been studied across different periods, multi-year comparative analyses are limited, which reduces the understanding of whether increases in digital usage reflect temporal trends or changes in underlying attitudes, especially in emerging e-commerce markets where digital financial services are rapidly evolving. These gaps indicate the need for an integrated approach that links temporal diffusion processes with perception-based evaluation mechanisms in order to explain how and why customers select digital versus branch-based service channels in evolving e-commerce banking environments.

This study addresses these gaps by examining banking channel preferences across three consecutive years (2023–2025) in the Romanian market context. Romania represents an emerging European market undergoing digital transformation, providing an opportunity to observe how customer behavior and perception–adoption relationships evolve during a transitional phase of financial services e-commerce development. The study employs a repeated cross-sectional design with logistic regression modeling to separate temporal effects from perception-based and demographic determinants of digital channel usage, contributing to both the e-commerce and financial services literature by examining how digital services delivery preferences develop in banking contexts.

Perceived bank technologization extends classical TAM constructs by capturing customers’ perceptions of how a bank’s digitalization affects their choice and ongoing relationship, offering a comprehensive perspective of how technologization shapes channel preferences in the digital financial services marketplace. This construct aligns with e-commerce research on platform quality and service delivery expectations, adapted for the financial services context.

Existing technology acceptance research mostly focuses on task-level perceptions such as perceived usefulness or ease of use, which capture how users evaluate specific systems or applications. However, in increasingly platform-based and competitive digital service markets, customers also form broader evaluations of providers’ overall technological capability. In the banking context, these evaluations reflect how customers perceive a bank’s digital sophistication, reliability, and ability to deliver services effectively through electronic channels. This study examines perceived bank technologization as an overall evaluation of a bank’s digital capability that goes beyond interaction with specific systems and reflects how customers assess banks as digital service providers in an evolving e-commerce environment.

1.4. Research Objectives and Questions

The primary objective of this study is to examine the evolution of customer preferences for banking access channels over the period 2023–2025, and to investigate the extent to which perceived bank technologization influences these preferences while controlling for demographic and socioeconomic factors in an emerging e-commerce banking environment. By combining temporal and explanatory perspectives, the study seeks to capture both what has changed in customer behavior and why these changes occur as digital financial services transition from innovative offerings to mainstream e-commerce channels.

Prior research on digital banking and electronic commerce has relied on cross-sectional designs, which often limit the ability to distinguish between temporal diffusion effects and perception-driven adoption mechanisms [8,32]. Studies grounded in technology acceptance theory emphasize the role of perceived usefulness, trust, and technologization in shaping digital service adoption [36,39,41], while demographic and socioeconomic factors such as age, income, and living environment remain important predictors across banking and e-commerce contexts [18,55,56]. However, relatively little attention has been paid to how these relationships evolve over time in emerging digital banking markets, or to whether increasing digital usage reflects simple temporal trends or changes in customer perceptions. In response to these gaps identified in the literature, the present study formulates the following research questions:

1

Building on studies examining diffusion processes and market maturation in digital banking and electronic commerce [8,32], to what extent has the preference for digital banking channels over branch-based access increased across the study period, and can this increase be attributed to temporal trends that are independent of individual characteristics in the context of expanding financial services e-commerce?

2

Consistent with technology acceptance and e-service quality literature emphasizing perception-based adoption drivers [36,39,49], how do customer perceptions of bank technologization influence the likelihood of using digital channels as primary mode of banking access, controlling for demographic and socioeconomic factors in online service delivery?

3

Extending prior research suggesting that adoption determinants evolve as digital markets mature [32,64], does the relationship between perceived technologization and digital channel adoption strengthen over time, suggesting that technology perceptions become more important as digital financial services markets mature within the broader e-commerce ecosystem?

These questions are operationalized through a pooled logistic regression model that incorporates year effects, a composite measure of technologization perceptions (TechScore), and an interaction term examining whether the perception–adoption relationship changes across years. Year-specific models complement the pooled analysis by allowing direct comparison of coefficient levels across survey years.

From an e-commerce management perspective, understanding these dynamics is critical for banks competing in digital marketplaces where customer channel preferences have a direct impact on service delivery costs, customer acquisition strategies, and competitive positioning against both traditional banks and fintech platforms.

The findings contribute to electronic commerce and technology acceptance literature by demonstrating how perception-based factors interact with temporal dynamics in shaping customer behavior in digital financial services contexts. For financial institutions operating in competitive e-commerce environments, the study provides evidence-based insights for resource allocation decisions regarding digital infrastructure investment and branch network management. By quantifying the relationship between perceived technologization and channel preferences, the analysis offers guidance for how banks can influence customer adoption patterns through strategic positioning of their technological capabilities as e-banking service providers.

2. Materials and Methods

2.1. Research Design and Data Collection

Our study employs a comparative repeated cross-sectional design to examine the preferences for the type of banking access channel across three consecutive years (2023–2025). The research design allowed for the analysis of temporal trends while capturing year-specific effects of technologization perceptions on customer behavior [65,66], while different respondents each year eliminated potential learning effects, enabling the examination of population-level trends in banking access channel preferences [65].

Data were collected through online questionnaires distributed annually between March and April from 2023 to 2025. Data collection method was consistent with best practices in e-commerce research for capturing digital user behavior [67]. The consistent timing of data collection allowed us to minimize seasonal variations that could interfere with temporal trend analysis [68]. The study focused on Constanta County, Romania, providing a regional perspective on banking digitalization trends in an emerging European market context.

The target population comprised customers of at least one of the ten major commercial banks operating in Romania. The respondents held at least one banking product (current account, deposit, loan, debit or credit card), thus ensuring that they had access to both traditional branch services and digital banking platforms offered by the financial institutions. Participants were recruited through online channels using convenience sampling methods, acknowledging the limitations of this approach for population generalizability, but considering this approach adequate for investigating hypothesized relationships between perceptions, demographics, and digital channel adoption [69,70]

The questionnaire was created using Google Forms and distributed online using different communication platforms. The full questionnaire items used for variable construction and statistical analyses are reported in Appendix A. All questions were designed as mandatory to eliminate incomplete responses and ensure data completeness. By doing so, we were able to obtain a clean dataset with no missing values, that facilitated robust statistical analysis without imputation procedures. All respondents provided informed consent for participation, and data collection procedures were in accordance with the European data protection regulations.

The study achieved sample sizes of 244 respondents in 2023, 330 in 2024, and 211 in 2025, totaling 785 observations across the three-year period. The final sample provides sufficient statistical power for logistic regression analysis with multiple predictors [71,72]. Detailed descriptive statistics of the sample are presented in Section 3.

2.2. Variables and Hypotheses

2.2.1. Dependent Variable

The dependent variable of our study, AccessChannel, indicates the main way customers access their banking products through different service delivery channels. The binary variable was coded as 0 for branch-based banking and 1 for digital channels (internet or mobile banking). While banking behavior is increasingly omnichannel, this binary operationalization captures customers’ primary channel preference, which is appropriate for analyzing dominant usage patterns rather than occasional or supplementary channel use. Using AccessChannel as the dependent variable for our model is in line with prior research that has modeled internet and mobile banking adoption as the main outcome variable in consumer behavior studies [6,37,61,73], treating digital banking as a form of electronic service delivery and e-commerce channel choice [74].

2.2.2. Main Independent Variables

The first independent variable that our model uses is Year, a temporal variable that captures the year-to-year comparative dimension of the study. Data collection occurred across three consecutive years, from 2023 to 2025. Including Year as a predictor allows us to assess population-level changes in adoption patterns, consistent with the use of repeated cross-sectional survey designs to capture temporal trends [65,66]. Prior studies on retail banking have also shown that customer reliance on digital channels increases over time, making temporal effects an important predictor of digital adoption [29], particularly as online financial services become normalized within broader e-commerce adoption patterns [12,19]. This variable enables the assessment of time-driven trends in digital adoption patterns and allows us to formulate the first research hypothesis:

Hypothesis H1:

Customer preference for digital banking channels over branch-based access increases across the 2023–2025 period, reflecting temporal trends in the adoption of e-banking independent of individual characteristics.

Perceived bank technologization (TechScore), the second independent variable, represents customers’ overall evaluation of how a bank’s level of digitalization influences both their initial choice of bank and their willingness to maintain an ongoing relationship. Unlike classical technology acceptance constructs, such as perceived usefulness or perceived ease of use, which focus on interaction with specific systems, TechScore reflects a broader assessment of a bank’s digital capabilities as a financial service provider in an electronic commerce environment.

TechScore is computed as the arithmetic mean of two items capturing customers’ perceptions of the influence of technology on bank choice and on relationship continuation. The two items used to construct TechScore were selected to capture distinct but complementary stages of customer evaluation in digital banking: initial bank choice and relationship continuation. Together, these items reflect how customers assess the relevance of technology not only at the point of adoption but also over the course of ongoing service use. Although the measure is based on only two items, this approach supports consistent comparison across survey periods and helps limit respondent fatigue in repeated cross-sectional surveys.

The wording of these items remained unchanged across the three survey periods, ensuring measurement’ consistency over time. The exact wording of the items used to construct the TechScore variable is provided in Appendix A.

Each component item was evaluated on a 1–5 scale, where higher values indicate greater perceived influence of technologization. This approach aligns with well-established theories of technology adoption, such as the Technology Acceptance Model [36], the Unified Theory of Acceptance and Use of Technology [39], and their applications in online and mobile banking context [30,41], as well as e-commerce services quality research examining how platform perceptions shape channel preferences [26].

By focusing on customers’ perceptions of how technology influences both bank selection and relationship continuation, TechScore complements established acceptance models rather than duplicating them. While TAM and UTAUT explain why users adopt specific technologies, TechScore captures how customers evaluate providers’ technological capabilities as a strategic attribute influencing channel choice. This distinction is even more relevant in emerging e-commerce environments, where customers begin to evaluate service providers not only on individual digital functions but also on their overall technological capability.

TechScore demonstrated acceptable to very good internal consistency across survey years, Cronbach’s alpha being 0.69 for 2023 (acceptable for exploratory research), 0.87 for 2024, and 0.85 for 2025, indicating reliable measurement of the underlying construct [75]. This leads to our second hypothesis:

Hypothesis H2:

Customers who perceive higher levels of bank technologization are more likely to use digital channels as their primary banking access mode.

To account for possible differences in item wording across survey years, we also performed a robustness check by standardizing TechScore within each year (z-scores). This ensures that results are not biased by year-specific variation in scale distribution. The standardized TechScore analysis produced nearly identical findings, with consistent positive effects on digital adoption, as reported in the Section 3 of the article.

To test our third research hypothesis, we included in the model the interaction term Year × TechScore, as a way to determine if the predictive effect of technologization varied across years. Interaction terms are often used in regression models to capture moderating effects [76,77], or in examining how perception–behavior relationship evolve as e-commerce markets mature [64]. This supports the formulation of our third hypothesis:

Hypothesis H3:

The relationship between perceived bank technologization and digital channel adoption strengthens over time.

2.2.3. Control Variables

Our model employs several demographic, socioeconomic, and banking relationship characteristics as control variables, that are consistent with e-banking research examining determinants of online service adoption [42].

Demographic characteristics include two variables known to influence banking channel preferences: gender and age. Gender was coded as 0 for male and 1 for female. Gender differences have been reported in internet banking attitudes and adoption studies, often reflecting broader patterns of financial behavior [45,78]. AgeGroup categorizes respondents into four harmonized groups across survey years: 1 (18–29 years), 2 (30–39 years), 3 (40–49 years), and 4 (50+ years). Prior studies have shown that age has a strong influence on online banking adoption, with younger consumers being more likely to use digital channels [18,55,56], a pattern that is observed in various e-commerce contexts where digital natives demonstrate higher adoption rates [79,80,81].

Socioeconomic factors used by our model include living environment and level of income. Research indicates that socioeconomic factors are important determinants of digital financial inclusion and internet banking behavior, reflecting broader digital divide patterns in e-commerce accessibility [58]. LivEnviron was coded as 0 for rural and 1 for urban living environments. Urban residents typically display higher adoption of digital channels than rural consumers [17,82], mirroring infrastructure disparities that affect all forms of electronic commerce [83,84,85]. Income represents household net monthly income and is divided into three categories: 1 (low income, below minimum wage), 2 (medium income, between minimum and medium wage), and 3 (high income, above medium wage). Income has also been demonstrated to influence banking behavior, as households with higher earnings are more likely to use online and mobile banking platforms [59,60], consistent with findings that economic resources facilitate participation in digital commerce [86].

Banking relationship characteristics are represented by RelLength, an indicator measuring the duration of the customer–bank relationship. Longer relationships are usually associated with stronger customer experience, trust, and loyalty, which may influence channel adoption behavior [48,49], factors that also play important roles in e-commerce loyalty and platform retention [87,88]. The variable is divided into four categories: 1 for less than 6 months, 2 for 6–12 months, 3 for 1–5 years, and 4 for more than 5 years. Relationship length serves as proxy for customer experience and loyalty, factors that may influence channel adoption patterns in digital financial services environments.

2.3. Model Description

2.3.1. Pooled Model with Temporal Dimension

Given the binary nature of our dependent variable (AccessChannel), we employ logistic regression analysis to model the relationship between customer banking channel preferences and the set of predictors [89,90]. Using logistic regression over linear probability models can have several advantages, including bounded predicted probabilities between 0 and 1 and the natural interpretation of coefficients as log-odds ratios [91]. This approach is adequate for studies of technology adoption in which the outcome is a choice between traditional and digital channels [28,39].

Our primary analytical model incorporates the temporal dimension (Year), technologization perceptions (TechScore), and their interaction across the 2023–2025 period (Year × TechScore), along with the control variables (Gender, AgeGroup, LivEnviron, Income, RelLength). The complete model is as follows:

$$\begin{array}{c}\mathrm{l}\mathrm{o}\mathrm{g}\mathrm{i}\mathrm{t}\left(\mathrm{P}\left({\mathrm{A}\mathrm{c}\mathrm{c}\mathrm{e}\mathrm{s}\mathrm{s}\mathrm{C}\mathrm{h}\mathrm{a}\mathrm{n}\mathrm{n}\mathrm{e}\mathrm{l}}_{\mathrm{i}}=1\right)\right)\hfill \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}={\mathrm{\beta }}_0+{\mathrm{\beta }}_1{\mathrm{Y}\mathrm{e}\mathrm{a}\mathrm{r}}_{\mathrm{i}}+{\mathrm{\beta }}_2{\mathrm{T}\mathrm{e}\mathrm{c}\mathrm{h}\mathrm{S}\mathrm{c}\mathrm{o}\mathrm{r}\mathrm{e}}_{\mathrm{i}}+{\mathrm{\beta }}_3\left({\mathrm{Y}\mathrm{e}\mathrm{a}\mathrm{r}}_{\mathrm{i}}\times {\mathrm{T}\mathrm{e}\mathrm{c}\mathrm{h}\mathrm{S}\mathrm{c}\mathrm{o}\mathrm{r}\mathrm{e}}_{\mathrm{i}}\right)\hfill \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{\displaystyle \sum _{\mathrm{j}}}{\mathrm{\gamma }}_{\mathrm{j}}{\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{s}}_{\mathrm{i}\mathrm{j}}\hfill \end{array}$$

(1)

where P(AccessChannel_i = 1) represents the probability that respondent i primarily uses digital banking channels. In the pooled model, Year is treated as a categorical factor (dummy-coded with 2023 as the reference) to allow for non-linear differences across waves. To ensure robustness, we also estimated year-specific models where Year is implicit as a categorical factor, enabling us to detect potential non-linear or irregular shifts in adoption patterns across the period. The interaction term (Year × TechScore) tests hypothesis H3, a significant interaction coefficient indicating that the effect of TechScore differs across years, with the trajectory of the relationship changing as time progresses.

The coefficient β₁ measures the log-odds change per unit increase in Year, testing H1 about the temporal trend in digital adoption while holding other variables constants. The coefficient β₂ represents the baseline effect of TechScore at the first year of observation and directly tests H2, that higher values of TechScore are associated with an increased likelihood of digital channel use. The coefficient β₃ captures how the TechScore effect changes across time, testing H3. The control variable coefficients γ_j represent log-odds changes for demographic, socioeconomic, and relationship characteristics. Odds ratios are obtained by exponentiation of the coefficients (OR = e^β), which allows intuitive interpretation as multiplicative effects on the odds of digital channel use [92].

2.3.2. Year-Specific Models

To examine the temporal evolution of variable relationships and provide additional evidence for our hypotheses, we also estimate separate year-specific models for each year. These models exclude the Year variable and the interaction term, allowing us to assess how the effect of technologization perceptions changes across the analyzed period. The year-specific models are:

$$\mathrm{l}\mathrm{o}\mathrm{g}\mathrm{i}\mathrm{t}\left(\mathrm{P}\left({\mathrm{A}\mathrm{c}\mathrm{c}\mathrm{e}\mathrm{s}\mathrm{s}\mathrm{C}\mathrm{h}\mathrm{a}\mathrm{n}\mathrm{n}\mathrm{e}\mathrm{l}}_{\mathrm{i}\mathrm{t}}=1\right)\right)={\mathrm{\beta }}_{0\mathrm{t}}+{\mathrm{\beta }}_{1\mathrm{t}}{\mathrm{T}\mathrm{e}\mathrm{c}\mathrm{h}\mathrm{S}\mathrm{c}\mathrm{o}\mathrm{r}\mathrm{e}}_{\mathrm{i}}\mathrm{t}+\sum _{\mathrm{j}}{\mathrm{\gamma }}_{\mathrm{j}\mathrm{t}}{\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{s}}_{\mathrm{i}\mathrm{j}\mathrm{t}}$$

(2)

where t represents the survey year. This approach enables direct comparison of β_1t across years, providing empirical evidence for H3, also allowing us to capture potential non-linear changes in variable relationships that might not be fully represented by the pooled interaction model.

Model estimation employs maximum likelihood methods, which provide asymptotically efficient estimates given our substantial sample size of 785 observations [93]. We assess model assumptions by examining variance inflation factors to detect multicollinearity issues (VIF < 5), conducting residual analysis for outliers, and applying the Hosmer–Lemeshow goodness-of-fit test to evaluate model adequacy [94]. The independence assumption is satisfied by our repeated cross-sectional design with different respondents each year, which eliminates potential autocorrelation concerns that may arise in longitudinal panel data.

All statistical computations and modeling procedures were carried out using R statistical software (version 4.4.3).

3. Results

3.1. Descriptive Statistics

The study analyzed a total of 785 valid responses, with annual distributions of 244 in 2023 (31.1%), 330 in 2024 (42.0%), and 211 in 2025 (26.9%). The variation in annual sample size reflects the naturalistic data collection approach and potential differences in response rates across years [67].

The sample exhibited consistent demographic patterns across years. Female respondents constituted the majority, representing 66.8% in 2023, 67.9% in 2024, and 68.7 in 2025, resulting in an overall female representation of 67.7%. Urban residents predominated across all survey periods, though showing a slight declining trend from 76.6% in 2023 to 73.6% in 2024 and 69.2% in 2025, with an overall urban representation of 73.4%. The age distribution was concentrated in the younger segments, with respondents aged 18–29 years representing 61.1% of the total sample. Medium-income respondents formed the largest group at 49.2%, followed by high-income respondents at 29.7% and low-income respondents at 21.1%.

Banking relationship duration showed that most respondents maintained established relationships with their banks, with 39.9% reporting relationships lasting 1–5 years and 35.7% maintaining relationships exceeding five years. Only 9.7% were relatively new customers with relationships shorter than six months. The full demographic and socioeconomic profile of the sample is presented in

Table 1. Sample profile.

Variable	Frequency (n)	% of Sample
Gender
Male	253	32.3%
Female	532	67.7%
Age group
18–29 years	480	61.1%
30–39 years	134	17.1%
40–49 years	106	13.5%
50+ years	65	8.3%
Living environment
Rural	209	26.6%
Urban	576	73.4%
Income
Low	166	21.1%
Medium	386	49.2%
High	233	29.7%
Relationship length
<6 months	76	9.7%
6–12 months	116	14.8%
1–5 years	313	39.9%
>5 years	280	35.7%

Source: authors’ calculations.

.

Figure 1 illustrates the evolution of access channel preferences across the period. Digital banking (internet and mobile banking) was the dominant access mode throughout the study period, increasing from 87.7% in 2023 to 88.5% in 2024 and 92.4% in 2025. Overall, 89.3% of respondents reported digital channel as their primary mode of access, while 10.7% relied primarily on branch-based services. Although digital channel users constituted the majority of the sample, the size of the branch-user subsample was sufficient for stable logistic regression estimation, in line with events per variable (EPV) guidelines [71,72].

TechScore values showed temporal variation, with an overall sample mean of 3.92 (SD = 1.11), and scores ranging from 1 to 5. In 2023, the mean TechScore was 4.17 (SD = 0.85), then dropped to 3.67 (SD = 1.21) in 2024, and rose again to 4.03 (SD = 1.13) in 2025. Median values were high in all years (4.5 in 2023 and 2025, 3.75 in 2024) indicating generally high perceived importance of technologization (

Table 2. TechScore summary by year.

Year	Mean	Std. Dev.	Min	Max	Median
2023	4.17	0.85	1.0	5.0	4.50
2024	3.67	1.21	1.0	5.0	3.75
2025	4.03	1.13	1.0	5.0	4.50

Source: authors’ calculations.

).

The observed variation in TechScore across years suggests temporal fluctuation in perceptions of bank technologization. Despite these temporal variations, the distribution provided sufficient variance for regression analysis, with respondents covering the entire range of the technologization perception scale (Figure 2).

These descriptive patterns provide the empirical foundation for testing the hypothesized relationships between temporal trends, technologization perceptions, and digital banking adoption in the proposed analytical models.

3.2. Logistic Regression Results

3.2.1. Model Fit and Diagnostics

The pooled model demonstrated reasonable fit characteristics with a McFadden pseudo-R² of 0.232, indicating that the included variables explain approximately 23% of the variance in channel choice behavior [95]. The Hosmer–Lemeshow goodness-of-fit test yielded a non-significant result (X² = 6.954, p = 0.542), suggesting adequate model calibration across probability ranges [89]. The adjusted GVIF values (3.42 and 3.38, respectively) fall within acceptable ranges, suggesting that while some collinearity exists, it does not severely compromise the model estimates [96].

Year-specific models showed different but overall good fit characteristics, with improvements in explanatory power over time. McFadden pseudo-R² values increased from 0.189 in 2023 to 0.222 in 2024 and 0.442 in 2025, indicating higher explanatory power in later survey years [95]. All year-specific models passed the Hosmer–Lemeshow goodness-of-fit test (2023: X² = 12.18, p = 0.143; 2024: X² = 10.05, p = 0.261; 2025: X² = 3.75, p = 0.879), indicating adequate calibration across probability ranges for each year [89]. Classification accuracy ranged from 87.7% in 2023 to 90.0% in 2024 and 95.3% in 2025 [97].

3.2.2. Pooled Model Analysis

The pooled logistic regression model (Equation (1)) was estimated by using maximum likelihood methods on the complete set of 785 observations.

Table 3. Pooled logistic regression results (n = 785).

Variable	OR	95% CI	p-Value
Year2024 (vs. 2023)	2.53	(0.27–22.78)	0.412
Year2025 (vs. 2023)	1.41	(0.10–19.09)	0.794
TechScore	1.82	(1.11–3.00)	0.018
Gender Female (vs. Male)	1.08	(0.60–1.89)	0.795
Age 30–39 (vs. 18–29)	0.29	(0.14–0.61)	0.001
Age 40–49 (vs. 18–29)	0.21	(0.10–0.45)	<0.001
Age 50+ (vs. 18–29)	0.06	(0.03–0.14)	<0.001
LivEnviron Urban (vs. Rural)	2.70	(1.58–4.60)	<0.001
Income Medium (vs. Low)	1.14	(0.58–2.20)	0.696
Income High (vs. Low)	3.15	(1.35–7.58)	0.009
RelLength 6–12 m (vs. <6 m)	0.98	(0.35–2.74)	0.972
RelLength 1–5 y (vs. <6 m)	1.12	(0.45–2.60)	0.794
RelLength >5 y (vs. <6 m)	1.24	(0.49–2.95)	0.637
Interaction: 2024 × TechScore	0.85	(0.47–1.53)	0.591
Interaction: 2025 × TechScore	1.26	(0.62–2.62)	0.535

Source: authors’ calculations.

presents the complete results of the pooled logistic regression model, including odds ratios, confidence intervals, and significance tests for all variables.

The pooled model results provide evidence regarding the proposed hypotheses. For H1, the temporal trend effects were not statistically significant when Year was treated as a categorical variable. Neither Year2024 (OR = 2.53, p = 0.412) nor Year2025 (OR = 1.41, p = 0.794) showed significant differences compared to 2023, indicating no statistically significant temporal differences relative to 2023.

The TechScore index provides strong support for H2, indicating a statistically significant association between perceived technologization and digital channel adoption (OR = 1.82, p = 0.018). Each unit increase in TechScore increases the odds of digital channel use by 82%, indicating that customers’ perceptions of bank technologies are an important predictor of access channel preferences.

The interaction term (Year × TechScore) did not achieve statistical significance, therefore not supporting H3 in the pooled model. Neither the 2024 interaction (OR = 0.85, p = 0.591) nor the 2025 interaction (OR = 1.26, p = 0.535) demonstrated significant effects. This indicates that the relationship between technologization perceptions and digital channel adoption did not exhibit a statistically significant change over time in the pooled analysis. To explore potential temporal variation beyond the pooled interaction, H3 was examined using year-specific models.

Regarding the control variables, the most pronounced effect on channel choice can be observed for age where all older age groups showed significantly lower digital adoption rates compared to the 18–29 age group, with the 50+ group demonstrating a 94% reduction in digital banking odds (OR = 0.06, p < 0.001). Another significant predictor was living environment, with urban residents being 2.7 times more likely to use digital channels than rural residents (OR = 2.70, p <0.001). Also, high income customers showed higher digital adoption rates compared to low-income customers (OR = 3.15, p = 0.009). Gender and the length of the customer–bank relationship were not significant predictors.

To further investigate the temporal patterns that may not be fully captured by the pooled interaction model, we proceeded to the separate analyses of each survey year.

3.2.3. Year-Specific Model Comparison

The year-specific analyses (Equation (2)) provide additional information about the temporal patterns and potential non-linear relationships that may not be fully captured by the pooled interaction model. In

Table 4. Year-specific logistic regression results.

Variable	2023			2024			2025
	OR	95% CI	p-Value	OR	95% CI	p-Value	OR	95% CI	p-Value
(Intercept)	0.95	(0.07–12.50)	0.971	1.96	(0.35–10.83)	0.439	0.03	(0.00–1.14)	0.059
TechScore	1.86	(1.14–3.02)	0.012	1.52	(1.10–2.10)	0.010	3.37	(1.57–7.22)	0.002
Gender Female (vs. Male)	1.03	(0.40–2.66)	0.945	0.95	(0.40–2.25)	0.920	1.71	(0.34–8.56)	0.508
Age 30–39 (vs. 18–29)	0.48	(0.14–1.63)	0.24	0.22	(0.08–0.64)	0.005	0.08	(0.00–1.05)	0.055
Age 40–49 (vs. 18–29)	0.38	(0.1–1.47)	0.16	0.11	(0.03–0.34)	<0.001	0.26	(0.03–2.01)	0.201
Age 50+ (vs. 18–29)	0.16	(0.04–0.53)	0.003	0.03	(0.01–0.15)	<0.001	0.03	(0.00–0.27)	0.002
LivEnviron Urban (vs. Rural)	2.14	(0.85–5.36)	0.105	2.55	(1.13–5.78)	0.024	10.29	(2.13–49.72)	0.004
Income Medium (vs. Low)	0.70	(0.22–2.20)	0.549	1.39	(0.54–3.59)	0.491	1.49	(0.19–11.64)	0.702
Income High (vs. Low)	2.44	(0.56–10.54)	0.231	3.82	(1.06–13.77)	0.04	3.51	(0.37–33.04)	0.272
RelLength 6–12 m (vs. <6 m)	1.91	(0.10–13.14)	0.886	0.80	(0.18–3.43)	0.768	1.30	(0.12–13.56)	0.822
RelLength 1–5 y (vs. <6 m)	0.585	(0.08–4.28)	0.598	0.85	(0.26–2.79)	0.794	7.37	(0.73–73.80)	0.089
RelLength >5 y (vs. <6 m)	0.519	(0.06–3.88)	0.523	1.20	(0.32–4.41)	0.781	5.44	(0.63–46.95)	0.124

Source: authors’ calculations.

we present the complete results of the logistic regression for each survey year.

By using the year-specific model comparison we were able to identify important temporal patterns in the TechScore effect that allowed us to further evaluate H2 and H3. For H2, TechScore remained a significant predictor in each year, the odds ratio progressing from 1.86 (p = 0.012) in 2023 to 1.52 (p = 0.010) in 2024 and 3.37 (p = 0.002) in 2025. For H3, the year-specific models indicate variation in the magnitude of the TechScore effect across years. While the association was modest in 2023 and 2024, it became much stronger in 2025, suggesting that by 2025, technologization perceptions were more strongly associated with channel choice, providing partial support to H3.

The control variables exhibited varying patterns of significance across years. Age effects were most pronounced in 2024, with respondents aged 30–39 and 40–49 showing significantly lower odds of digital channel adoption compared to the youngest group, while the oldest age group (50+) displayed consistently lower adoption odds across all three years. Living environment showed increasing association strength over time, becoming statistically significant in 2024 and 2025 (2023: OR = 2.14, p = 0.105; 2024: OR = 2.55, p = 0.024; 2025: OR = 10.29, p = 0.004). Income effects varied across survey years, with high-income respondents exhibiting a significant positive association only in 2024 (OR = 3.82, p = 0.040), while medium income showed no significant effects. Gender and relationship length were not statistically significant predictors in any year.

The following observations summarize year-specific patterns descriptively and are discussed in greater detail in Section 4.

The observed instability of several control variables across survey years suggests that their effects are not structurally fixed but context-dependent. Variations in significance and across years may reflect differences in sample composition, changes in external conditions, or modifications in the ways in which specific demographic and socioeconomic groups engage with digital banking services during different phases of market development. These patterns suggest that control variables should be interpreted as contingent influences rather than stable predictors of digital channel preference.

Across survey years, perceived bank technologization was consistently associated with digital channel preference (H2), while evidence regarding temporal strengthening effects (H3) remain mixed. Year-specific analyses indicate variation in the strength of the association across survey periods, but the pooled model does not provide statistical support for a systematic strengthening of this relationship over time. Similarly, temporal trends in digital channel adoption (H1) do not appear to operate independently of individual-level characteristics, suggesting that changes in adoption patterns are more closely associated with perception-based and demographic factors than with time alone.

4. Discussion

Before interpreting the empirical findings, it is important to reiterate the conceptual scope of the perceived bank technologization (TechScore) construct. TechScore captures a provider-level evaluation of banks’ digital capabilities, reflecting how customers assess banks as digital service platforms rather than how they interact with specific technologies or applications. In contrast to classical technology acceptance constructs—such as perceived usefulness or perceived ease of use—which focus on task-level system interaction, TechScore represents a broader perception of technologization that shapes channel choice in electronic commerce contexts.

4.1. Hypotheses and Theoretical Implications

This study examined how the perception of technologization, demographic, and socioeconomic factors are associated with banking channel preferences in an emerging e-commerce banking environment. Using pooled and year-specific logistic regression models, we tested three hypotheses: temporal trends in adoption (H1), the role of perceived technologization (H2), and its evolving importance over time (H3).

The analysis provides insights into the factors influencing banking channel preferences across the 2023–2025 period. The findings indicate that digital banking adoption within this sample is more closely associated with individual-level factors rather than with temporal trends alone, with technologization perception having an important role in shaping customer behavior.

The lack of significant temporal effects (H1) in the pooled model suggests that the increase in digital adoption rates from 87.7% in 2023 to 92.4% in 2025 is not just a result of time passing. While this contrasts with studies documenting strong temporal effects during earlier diffusion phases [40,42], it is consistent with research suggesting that digital banking adoption approaches saturation in more advanced digital banking contexts [31,48]. Two contextual factors likely contributed to this pattern. First, the sample—predominantly young adults, with medium income, from urban areas—incorporates demographic segments that are more likely to exhibit high levels of digital banking adoption [55,56,98]. Second, the study period followed the COVID-19 pandemic, which had already accelerated digital channel adoption globally, creating a high baseline from which further temporal growth was limited [11,12], similar to patterns observed across e-commerce sectors where the pandemic permanently shifted consumer behavior toward digital channels. As a result, the limited temporal variation observed in the pooled model likely reflects the characteristics of a digitally advanced sample rather than the absence of broader adoption dynamics at the population level.

The strong support for H2 demonstrates that customers’ perceptions of bank technologization are strongly associated with digital channel preference in the financial services context. In both pooled and year-specific models, TechScore was a strong and consistent predictor of digital adoption. The observed increase in digital banking odds associated with higher TechScore values aligns with technology acceptance research emphasizing the role of perceived usefulness in adoption and maintenance decisions [36,37,39], as well as with e-commerce literature demonstrating that platform quality perceptions drive online service adoption [99]. These findings extend prior technology acceptance research by capturing how customers evaluate banks using criteria similar to those applied to other digital service providers operating in competitive online marketplaces [100,101].

With respect to H3, the pooled interaction term between Year and TechScore was not statistically significant, suggesting no systematic strengthening of the perception–adoption relationship over time. However, year-specific models reveal variation in the magnitude of the TechScore association, with the strongest effect observed in 2025. Therefore, these patterns should be interpreted as indicative rather than as evidence of a systematic strengthening effect over time. This aligns with recent studies on digital transformation in banking stating that customer perceptions of digital capabilities become progressively more critical as banks mature digitally [102], while determinants of digital banking adoption also change over time, with perceptions of digital features gaining more importance [8]. Similarly, studies have shown that the strength of adoption predictors varies across adoption stages [32], a pattern consistent with e-commerce literature on technology adoption lifecycle effects. Taken together, these findings support a prudent interpretation that perceived technologization plays a central role in digital channel choice, while its temporal dynamics remain context-dependent.

4.2. Demographic and Socioeconomic Influences

The strong age effects observed in our study are consistent with established patterns in digital banking research, which show higher adoption rates for young customers. The 94% reduction in digital banking odds for customers aged 50+ reflects persistent generational differences in technology adoption, in line with previous studies identifying age as one of the strongest predictors of digital banking adoption [18,55,56], and e-commerce platforms usage [79,80,81,103].

Living environment was also an important influence factor, with urban residents showing ten times higher odds of digital adoption by 2025. This pattern suggests uneven development of digital banking infrastructure and adoption across geographic areas, widening the urban–rural gap [20]. Similar disparities are widely documented in e-commerce research, where infrastructure limitations, internet connectivity, and digital literacy contribute to persistent digital divides between urban and rural populations [83,85,104].

The temporal variation in income effects on banking channel adoption is more complex than is generally observed in digital banking research. While previous studies document positive associations between income and digital channel adoption [38,105,106], the inconsistent patterns observed across survey years in this study suggest that income effects may be context-dependent. These variations may reflect changing market dynamics, or shifts in perceived costs, benefits, and system characteristics of digital banking, consistent with evidence that adoption decisions are influenced by both behavioral and technical factors [41,61,107]. This finding is even more relevant in emerging e-commerce markets, where adoption dynamics remain fluid.

Taken together, the unstable effects observed for age categories, income, and living environment across survey years indicate that demographic and socioeconomic determinants of digital banking adoption do not operate uniformly over time. These fluctuations do not reflect model inconsistency, instead, they point to the influence of contextual dynamics, such as market saturation, technological standardization, or changes in customer expectations. In emerging e-commerce markets, where digital financial services are still evolving, the relevance of traditional predictors may vary as different customer segments adjust at different speeds to technological and institutional changes.

Gender showed no significant effects on channel choice in either the pooled or year-specific models. While this contrasts with earlier studies identifying gender differences in banking technology adoption [45,61], it aligns with research suggesting that gender gaps in digital banking are diminishing as technology becomes more available and user-friendly [56,62,63]. This pattern is also consistent with broader trends in e-commerce adoption, where initial gender disparities in online shopping and digital service use have narrowed as platforms have become more intuitive and socially normalized [108]. The absence of gender effects may further reflect the predominantly young sample and the normalization of digital banking and electronic commerce under EU-wide standards and post-pandemic digital inclusion efforts in Romania.

Similarly to gender, bank–customer relationship length did not exhibit a significant effect on channel choice once other factors were controlled. This finding is in contrast with research emphasizing the role of relationship quality, communication, and trust built over time in banking behavior [109,110,111], but suggests that, in contemporary digital banking environments, relationship duration provides limited explanatory power beyond demographic characteristics and perception-based factors. Instead, transactional considerations and evaluations of platform quality appear to play a more prominent role, consistent with findings from competitive digital marketplaces where service quality and technological capabilities matter more than tenure-based loyalty [112,113,114]. Both gender and relationship length lack of influence suggest that basic access barriers have generally been overcome in this market context. Channel choice decisions are primarily driven by age, location, income, and perceptions of technology.

5. Conclusions

This study examined how customers’ preferences for banking channels evolve as they interact with digital financial services in an e-commerce context and other platform-mediated service industries, analyzing 785 observations across three consecutive years (2023–2025) in the Romanian banking market. Our logistic regression framework revealed complex relationships between technologization perceptions, socio-demographic factors, and digital banking adoption patterns as online financial services evolved from innovative offerings to mainstream channels. The empirical findings provide differentiated support for our hypotheses. While descriptive statistics showed increasing digital adoption rates from 87.7% to 92.4%, the pooled logistic model revealed that this progression cannot be attributed to temporal effects alone (H1 not supported), but rather to individual-level factors that shape channel choice in digital commerce environments. Technologization perceptions emerged as the primary drivers of channel choice, with the TechScore variable demonstrating consistent predictive power across all specifications. Each unit increase in TechScore was associated with an 82% increase in digital banking odds (H2 supported), indicating that customers’ perception of bank technological capabilities significantly influences their preference for online service delivery channels. The year-specific models revealed variation in the magnitude of the TechScore effects, with TechScore odds ratios increasing from 1.86 in 2023 to 3.37 in 2025. While this pattern suggests that technologization perceptions may become more relevant over time, it should be interpreted cautiously, as the pooled interaction effect was not statistically significant (H3 partially supported).

The study makes three contributions to the electronic commerce literature. First, it provides multi-year evidence from an emerging European market, separating perception-driven adoption mechanisms from simple temporal diffusion effects. Second, it introduces and empirically validates a provider-level construct of perceived bank technologization that complements established technology acceptance models by capturing how customers evaluate digital service platforms rather than isolated technologies. Third, by combining pooled and year-specific models, the study demonstrates that the relevance of demographic and socioeconomic predictors depends on the context, highlighting the need for dynamic rather than static modeling approaches in digital commerce research.

5.1. Methodological Contributions

This study contributes to the electronic commerce and banking behavior literature through its repeated cross-sectional modeling approach across a three-year period, capturing how customer preferences evolve as digital banking becomes the normal way for banks to deliver financial services. The combination of pooled and year-specific logistic regression models allowed for nuanced examination of temporal trends that single-periods studies cannot capture [65], enabling the analysis of how perception–behavior relationships change as online financial services mature. The development and validation of the TechScore composite measure, with Cronbach’s alpha values ranging from 0.69 to 0.87, provides a reliable instrument for quantifying customer perceptions of bank technologization in digital commerce contexts, which can be adapted for comparative studies across different e-banking markets or extended to include additional dimensions of technological perception relevant to online service delivery.

Although the interaction term was not significant in the pooled model, the year-specific analyses revealed meaningful variation in predictor effects over time. This research strategy provides a template for examining non-linear temporal patterns in e-commerce and technology adoption research [76,77], particularly in understanding how customer attitudes toward digital financial services evolve during market maturation phases. The finding that the strength of variable effects fluctuates across years poses a challenge to the assumption of temporal stability in behavioral models, suggesting that periodic recalibration of models may be necessary in rapidly evolving digital financial services markets.

5.2. Theoretical Implications

From a theoretical perspective, the study demonstrates how diffusion-based and perception-based frameworks can be combined to explain channel choice behavior in digital service markets. While diffusion theory captures the broader temporal context of adoption, technology acceptance models explain the evaluations that influence individual decisions within that context. By linking these perspectives, the findings highlight that customer channel preferences are shaped less by time alone and more by how technological capabilities are perceived.

Within this integrated framework, perceived bank technologization contributes by redirecting the analytical focus from system-level technology acceptance to provider-level technological evaluation. Rather than capturing how customers interact with specific digital applications, this construct reflects how banks are assessed as digital service providers competing within electronic commerce environments. This perspective extends established acceptance models by emphasizing integrated evaluations of digital capability that become increasingly relevant as digital financial services expand and diversify. Beyond banking, this provider-level perspective is relevant for electronic commerce research examining how customers evaluate competing digital service platforms, where overall technological capabilities can influence channel choice, changing behavior, and long-term engagement.

Our findings advance both electronic commerce studies and technology acceptance theory by demonstrating that perception-based factors can take precedence over temporal and demographic predictors in emerging digital financial services markets. The increasing impact of TechScore across years supports the concept that perceived technological capabilities become stronger predictors once digital banking transforms from an innovative offering to a standard service [36,39,101]. This pattern reflects broader dynamics observed in e-commerce adoption, where platform perceptions increasingly drive channel preferences as markets mature [12].

The non-significant effects of relationship length and gender suggest that, in post-pandemic markets characterized by widespread access and high digital literacy, traditional relational and demographic barriers have diminished in digital financial services usage. The finding is in contrast with relationship marketing literature emphasizing tenure effects [48,109], but aligns with diffusion theory’s later-stage adoption patterns, where individual perceptions and contextual accessibility are more important than social or relational determinants [28]. These results are also in line with studies reflecting the greater importance of convenience and platform quality over relationship-based factors in maturing e-commerce environments [114]. Future theoretical framework should consider how digitalization reshapes the relative importance of relationship versus transactional factors in online financial services delivery, drawing insights from broader e-commerce research on platform switching and multi-channel customer behavior.

5.3. Practical Implications

These findings can have strategic implications for service providers operating in digital commerce environments, particularly financial institutions navigating digital transformation and platform-based competition. As banking business models evolve toward omnichannel approaches—where online presence dominates physical infrastructure—or shift entirely to digital-only operations, understanding the drivers of customer channel preferences becomes critical for strategic resource allocation and competitive positioning. Banks should acknowledge that customer acquisition and retention in digital channels depend more on perceived technological capabilities than on market maturity or temporal progression [102]. The strengthening relationship between TechScore and digital adoption indicates that investments in visible technological improvements are associated with higher returns over time, particularly as customers evaluate banks’ digital services using benchmarks established by fintech providers and e-commerce platforms, expecting seamless interfaces and instant transactions comparable to retail e-commerce experiences. Financial institutions might benefit from emphasizing technological features—such as security, reliability, ease of access, and integration across platforms—in their communication strategies, ensuring that customers are aware of their digital innovations [49], and treating their digital offerings as competitive e-commerce products rather than mere extensions of traditional services.

The persistent age effects, with customers over 50 years of age showing 94% lower odds of digital adoption, suggest that banks should target their actions on older demographics by providing differentiated service models that consider these generational preferences while gradually facilitating digital transition [55]. The observed urban–rural gap highlights the need for specific digital inclusion strategies in less connected regions, including simplified interfaces, enhanced customer support, or hybrid service models that combine digital features with personal assistance, acknowledging infrastructure disparities common across e-commerce adoption. The diminishing role of relationship duration implies that customer retention in the digital age depends less on tenure and more on continuous value creation through intuitive interfaces and responsive service design, mirroring loyalty dynamics in broader e-commerce markets where platform quality and user experience drive retention.

5.4. Study Limitations

Several limitations should be acknowledged when interpreting these results. First, the convenience sampling approach, while adequate for testing hypothesized relationships, limits generalizability to the broader population [69,70]. The sample’s demographic composition—predominantly young, urban, and digitally engaged—may exaggerate the overall rate of digital adoption and minimize the barriers faced by other segments, a common challenge in e-commerce and digital services research focusing on early-to-mainstream adoption phases. This sample composition may also contribute to the observed instability of control variables effects across years, as small changes in respondent profiles or external conditions can have an uneven effect on the coefficient estimates in repeated cross-sectional designs. Future research employing probability sampling methods could validate these findings across more representative populations, including underserved groups whose digital financial services adoption patterns may differ from the general population.

The regional focus on Constanta County provides valuable insights into an emerging European e-banking market but may not reflect patterns in other geographic or economic contexts where digital financial services infrastructure and competitive landscape vary. Cross-national studies could examine whether the dominance of perception-based over temporal factors holds across different stages of digital banking maturity [31], particularly comparing markets with varying levels of fintech competition and regulatory frameworks. The binary operationalization of channel preference simplifies the multichannel nature of contemporary banking and omnichannel e-commerce behavior but remains appropriate for analyzing primary channel choice. Self-reported perceptions may also introduce response biases that future research could address through behavioral or transactional data, such as actual usage logs from mobile banking applications. They might also employ multinomial or ordered logistic models to capture more nuanced channel usage patterns [91], reflecting how customers navigate between multiple service delivery options in practice.

In addition, the TechScore construct is based on a limited number of items, which limits its ability to capture the full complexity of perceived technological capability. While this approach was suitable for repeated cross-sectional analysis, future research could extend this construct by incorporating additional dimensions related to digital service quality, platform integration, or user experience.

The study period captured a post-pandemic phase when digital adoption had already accelerated, creating a high baseline that may obscure earlier stages of e-banking adoption. Longer observation periods could reveal whether the strengthening effect of technologization perceptions represents a temporary phenomenon driven by pandemic-induced behavior changes or a sustained trend as digital financial services become permanently embedded in consumer routines.

5.5. Future Research Directions

Building on these findings, several research directions deserve further exploration in the context of evolving digital financial services. Qualitative studies could investigate the specific technological features that shape customer perceptions and drive channel preferences in competitive fintech environments, while experimental designs could establish causal relationships between perception formation and adoption behavior. The development of more nuanced TechScore measures, potentially incorporating dimensions such as security perceptions, user experience quality, platform integration capabilities, or innovation frequency, could enhance its predictive accuracy.

Longitudinal panel designs that monitor individual customers over time would complement our repeated cross-sectional approach by revealing individual-level change processes as customers transition between banking channels [66]. Advanced analytical techniques such as artificial neural networks could identify non-linear relationships and interaction effects not captured by traditional logistic models, particularly useful for modeling complex e-commerce behavior patterns. Integration of actual usage data from banking systems could validate self-reported preferences and reveal differences between stated and revealed preferences, providing insights into how customers actually navigate omnichannel financial services ecosystems.

Future research should examine the temporal stability of demographic and socioeconomic predictors in digital banking adoption, distinguishing between structural effects and context-driven fluctuations, particularly in emerging e-commerce markets undergoing rapid transformation. Such research would also benefit from more balanced and representative samples in order to separate real temporal dynamics from changes driven by sample composition in repeated cross-sectional designs.

The rapidly evolving financial technologies, including artificial intelligence integration, offer opportunities for extending this research framework. Understanding how these emerging technologies reshape the perception–adoption relationship will be very important for banks, as digital banking tends to become the universal way to provide financial services in many markets.

This study demonstrates that successful digital transformation in banking requires more than technological development. It demands careful attention to customer perceptions, demographic realities, and evolving market dynamics. The logistic regression models developed in this study provide a foundation for evidence-based decision-making in an increasingly competitive digital financial services landscape, where understanding customer channel preferences determines strategic positioning against both traditional banks and fintech competitors.