Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 6, 2946-2954 2025 Publisher: Learning Gate DOI: 10.55214/25768484.v9i6.8507 © 2025 by the authors; licensee Learning Gate © 2025 by the authors; licensee Learning Gate History: Received: 2 May 2025; Revised: 16 June 2025; Accepted: 20 June 2025; Published: 1 July 2025 * Correspondence: yurikoneshi@student.telkomuniversity.ac.id Indonesian consumers' intention to use digital bank services Yuriko Neshi1*, Maria Apsari Sugiat1, Fera Maria Silalahi1 1Faculty of Economics and Business, Telkom University, Indonesia; yurikoneshi@student.telkomuniversity.ac.id (Y.N.), mariasugiat@telkomuniversity.ac.id (M.A.S.), ferasilalahi@student.telkomuniversity.ac.id (F.M.S.). Abstract: Digital banks offer financial services through mobile apps rather than branches. Despite technological advancements, Indonesians rarely use digital banking. To promote the growth of digital banks, it is necessary to research customer preferences. This study aims to analyze consumer intentions to use digital banks based on the theory of planned behavior. The study examined six Indonesian digital banks founded after 2020. It used 487 responses from 69 million digital bank users. Structural equation modeling (SEM) with partial least squares was employed in this study. The SEM analysis showed that all construct variables significantly and positively affect digital bank intention. The coefficient of determination R² for the purchase intention (PI) construct is 0.703, indicating that 70.3% of the variance in the intention to utilize digital banking can be explained by the constructs of attitude (A), subjective norm (SN), and perceived behavioral control (PBC). A positive consumer attitude toward the product is the main predictor of purchase intention. Moreover, the F-square test results indicate that the path from perceived product value to attitude to purchase intention (PPV-A-PI) is the most significant pathway in the model. Consumer attitudes shaped by product perceptions are paramount in influencing decisions to utilize digital banks. Keywords: Digital bank, Consumer behaviour, Path analysis, Purchase intention, Structural equation model, Theory of planned behaviour. 1. Background Client-centric digitalisation has transformed the banking sector by changing client interactions, improving efficiency through new services, and increasing consumer reach through technology [1]. To provide easy financial services to tech-savvy consumers, banks must integrate their processes digitally [2]. Mobile banking, e-wallets, and QRIS (Quick Response Code Indonesian Standard) have made digital financial transactions easier, and smart branches have supported digital financial growth [3]. Digital banks, in collaboration with fintech and small enterprises, can provide digital financial solutions to meet individuals' financial needs, improving financial inclusion across deposit, lending, payment, settlement, and investment [4]. Digital banks offer financial services only through mobile apps, without branch facilities. They offer ease and encourage financial management without in-person encounters [5]. Digital banks must emphasize excellent digital capabilities based on a complete digital platform with an agile organizational architecture owing to the Fourth Industrial Revolution's impact on banking business operations. Banking digitalization encourages active money management, planning, and investing [6]. Virtual banks allow consumers to open accounts, transfer funds, invest, and make payments online, eliminating the need to visit branch offices [7]. Situational, influential, reactive, and sociodemographic factors drive digital transformation-induced changes in bank customer behaviour [8]. Despite banking technology advances, Indonesia has few digital bank users [9]. Consumers who prefer traditional banks' in-person services or struggle to adapt to online formats due to poor internet access or a lack of understanding of digital banking functionalities have not fully embraced the shift to https://orcid.org/0009-0001-1017-6785 https://orcid.org/0000-0003-2123-1150 https://orcid.org/0009-0002-3416-4903 2947 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 6: 2946-2954, 2025 DOI: 10.55214/25768484.v9i6.8507 © 2025 by the authors; licensee Learning Gate digital services [10]. Total digital bank assets represent 1.2% of commercial banks, indicating that digital banks are not the community's main financial funding and financing institutions. QRIS was utilized by over 51 million users by June 2024, a 38% increase from June 2023 [11]. Between 2016 and 2024, 79.5% of Indonesians were online [11]. This instance shows how digitization promises are not fulfilled by digital financial services. Customers choose digital banks due to brand perception, promotions, and deals. Price, quality, and other aspects impact customer expectations and shape brand impression [12]. The reputation of a corporation also influences consumers' technology-based financial service choices [13]. Due to globalization, consumers adopt technologies like digital banking for convenience, usability, and security [2]. When embracing mobile banking, users consider simplicity of use, perceived usefulness, and service [14]. The planned behaviour model that drives customers to buy (purchase intention) is heavily influenced by ease of use, brand perception, and product cost [15]. Consumer purchase intention is influenced by convenience, social influence, habits, hedonic motivation, security, and personal innovation [16]. The experience of using electronic banking significantly impacts the perception of quality and value [17]. Perceived quality is the substantial value of perceived value and includes ease of transactions, access, and performance. Economic value, convenience of use, social impact, corporate reputation, features, and rewards strongly influence the purchase intention of digital- only banking [18]. According to Kasmo, et al. [19] digital bank consumers value convenience, efficiency, and confidence. Besides economic efficiency, which is the value of the benefits consumers get from using digital banks in terms of saving time, energy, and money, security risk from personal financial data leaks increases the perceived risk of using digital banks. Perceived value mediates various complex factors that influence the intention to adopt digital-only banks, including convenience, economic efficiency, functional risk, security risk, critical mass, number of services, trust, and environmental concern [20]. Positive product benefit assessments boost purchase intentions and positive behaviour [21]. According to the background phenomena, the push for technological innovation in banking products and services, combined with increased internet usage, the dynamics of social and economic life, and the push for financial inclusion, necessitate a more in-depth understanding of the factors that influence consumer behaviour when selecting digital banks. To promote the development of digital banks, research is required to determine the elements that impact customer inclinations to use these services. This study aims to analyse consumer intentions to use digital banking based on the theory of planned behaviour. 2. Theoretical Framework Since 1935, the consumer behaviour model for deciding which goods or services to buy has changed. The study of consumer behaviour was greatly advanced in 1980 when Ajzen and Fishbein introduced the Theory of Planned Behaviour. This model makes it easier to understand behavioural determinants and enables users to take into account factors that were previously only taken into consideration separately [22]. According to Reina and Rodríguez [22] consumer behaviour theory made a lot of progress around 1990. These advances improved the behavioural model by creating the idea of social and behavioural integration, proving that there is a link between behavioural beliefs, attitudes, subjective norms, and behavioural control, and using this theory in many different areas of life. They also said that the way different factors combine still doesn't give a better picture of how people's goals and actions are affected. The Theory of Planned Behaviour was looked at again by Ajzen [23] who added the idea of perceived behavioural control to it. This was thought to give a new perspective and help control some behaviours. The theory of planned conduct is an extension of the theory of reasoned action, which maintains that the primary driver of behaviour is personal intention. It holds that the more strongly one intends to participate in a behaviour, the more likely one is to carry it out [23]. The theory of planned conduct, 2948 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 6: 2946-2954, 2025 DOI: 10.55214/25768484.v9i6.8507 © 2025 by the authors; licensee Learning Gate according to Ajzen [23] is theoretically based on a number of attitudes—attitude toward behaviour, subjective norm, and perceived behaviour control—that can forecast human behaviour in identifying an intention. By changing one of the variables, namely product price, to product perceived value, the study's framework makes reference to earlier research by Tai, et al. [15] which used the theory of planned behaviour as a basis to explain the factors that influence consumer intentions to purchase digital banking products and services. This allows consumers to see the benefits of the value of digital banking products and services. Additionally, Wang and Butkouskaya [21] describes how product pricing affects how much buyers think a product or service is worth. Therefore, in order to determine consumer purchase intention in adopting digital banking, this study will evaluate the link between the construct factors in the theory of planned behaviour and the variables of ease of use, product perceived value, and brand perception (Figure 1). This study identifies ease of use (EU), product perceived value (PPV), and brand perception (BP) as independent variables, while purchase intention (PI) serves as the dependent variable. Ease of use (EU) denotes the degree to which the interface between the user and the technology facilitates interaction, learning, retention, efficiency, and accuracy [24]. Product Perceived Value (PPV) is a product's perceived value that directly affects financial value and represents consumer price perception [25]. Emerging market consumers' digital transaction intentions depend on brand perception [26]. Table 1. List of Research Variables. Constructs Indicators (Manifest Variables) Constructs Indicators (Manifest Variables) Ease of Use (EU) Easy to download (EU1) Attitude (A) Good (A1) Easy to operate (EU2) Useful (A2) Easy to understand (EU3) Pleasant (A3) Efficient (EU4) Important (A4) Productive (EU5) Beneficial (A5) Doing by itself (EU6) Subjective Norms (SN) Peer (SN1) Using manual or online help (EU7) Superior (SN2) Product Perceived Value (PPV) Quality (PPV1) Family (SN3) Price (PPV2) Personal (SN4) Status of product & service (PPV3) Perceived Behaviour Control (PBC) Independent decision (PBC1) Emotional response (PPV4 & PPV5) Financial capability (PBC2) Brand Perception (BP) Foreign brand (BP1) Complete information (PBC3) Best quality brand (BP2) Availability (PBC4) Unknown brand (BP3) Buying decision (PBC5) Best-selling brand (BP4) Purchase Intention (PI) Consider (PI1) Well-known brand (BP5) Recommend (PI2) Brand influence (BP6) Buy (PI3) Brand of product (BP7) Delay (PI4) The intervening variables consist of the components of the theory of planned behaviour: attitude (A), subjective norm (SN), and perceived behavioural control (PBC), which collectively constitute planned behaviour. The indicators, or manifest variables, for each construct variable are the directly measured variables that comprise the raw data. Specifically, the seven constructs (latent variables) are measured by 32 formative indicators and 4 reflective indicators that have been derived from literature (Tabel 1). Furthermore, by examining the link between construct variables as illustrated by the research framework in Figure 1, this study will examine the following twelve hypotheses: i. H1: Ease of use (EU) has a positive and significant effect on attitude (A). ii. H2: Ease of use (EU) has a positive and significant effect on subjective norms (SN). iii. H3: Ease of use (EU) has a positive and significant effect on perceived behavioural control (PBC). 2949 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 6: 2946-2954, 2025 DOI: 10.55214/25768484.v9i6.8507 © 2025 by the authors; licensee Learning Gate iv. H4: Product perceived Value (PPV) has a positive and significant effect on attitude (A). v. H5: Product perceived Value (PPV) has a positive and significant effect on subjective norms (SN). vi. H6: Product perceived Value (PPV) has a positive and significant effect on perceived behavioural control (PBC). vii. H7: Brand perception (BP) has a positive and significant effect on attitude (A). viii. H8: Brand perception (BP) has a positive and significant effect on subjective norms (SN). ix. H9: Brand perception (BP) has a positive and significant effect on perceived behavioural control (PBC). x. H10: Attitude (A) has a positive and significant effect on purchase intention (PI). xi. H11: Subjective norms (SN) has a positive and significant effect on purchase intention (PI). xii. H12: Perceived behavioural control (PBC) has a positive and significant effect on purchase intention (PI). Figure 1. Research Framework. 3. Data and Methods Six digital banks that have emerged in Indonesia since 2020 were the subject of the study, and they are detailed in four of the six online banks that are publicly traded on the Indonesia Stock Exchange. This study sampled 487 respondents from a total of 69 million users across six digital banking applications. The structural equation modelling-partial least squares (PLS-SEM) approach is used in this study, and questionnaires were used to gather data from respondents. PLS-SEM tests hypothetical relationships between constructs by using path coefficients and assessing their statistical significance. The method first estimates the scores of each construct, then calculates path coefficients to represent the relationships between them. These coefficients, along with their p-values, are used to determine the significance and direction of the relationships, effectively testing the hypotheses [27]. The relationships of the variables examined when SEM is applied are visually depicted in Figure 2, which illustrates the research path model of customers' intention to use digital bank services. After preparing the data, the next steps in creating and analysing the structural equation model are to set up the measurement model, set up the structural model, and then estimate, bootstrap, and 2950 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 6: 2946-2954, 2025 DOI: 10.55214/25768484.v9i6.8507 © 2025 by the authors; licensee Learning Gate summarize the model [27]. Specifying the measurement model involves defining the relationships between latent variables (constructs) and manifest variables (indicators or observed variables). This step explains how indicators measure latent variables. Path diagrams define latent construct relationships in the structural model. This model shows the research framework's causal or predictive relationships. Identifying constructs (latent variables), specifying path direction, and preventing circular relationships are crucial. Estimation, bootstrapping, and summarization are crucial in PLS-SEM. Model estimation begins with PLS algorithms. We perform bootstrapping to calculate standard errors and confidence intervals and assess estimate stability. Summarizing the results helps interpret the model's path coefficients, loadings, and other statistics [28]. Figure 2. The Path Model of Consumers' Intention to Use Digital Bank Services. 4. Result The testing outcomes of the structural model indicate that the majority of relationships between constructs are statistically significant and exhibit a positive directional association (Table 2). The results of the hypothesis test in Table 2 indicate that nearly all hypotheses were validated, with the exception of hypothesis two (H2). The ease of use (EU) has demonstrated no significant impact on subjective norms (SN). All validated hypotheses exhibited moderate effects, with the exception of hypothesis 7 (H7) and hypothesis 9 (H9). The relationship between EU and A and PBC was found to be significant and robust, suggesting that the ease of use of digital banking services fosters positive attitudes and perceptions of control over 2951 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 6: 2946-2954, 2025 DOI: 10.55214/25768484.v9i6.8507 © 2025 by the authors; licensee Learning Gate service usage behaviour. In contrast, the correlation between perceived ease of use (EU) and social norms (SN) is negligible, suggesting that perceived ease lacks sufficient strength to affect social pressure or subjective norms regarding the utilization of digital banking. This finding indicates that personal perceptions of technological ease significantly affect individual judgment and self-regulation more than social influence does. The PPV construct consistently demonstrates a significant and robust impact on A, PBC, and SN. This reinforces the notion that consumer perceptions of product value, encompassing benefits, price, and quality, are essential elements in cultivating favourable attitudes, a sense of agency in decision-making, and adherence to social norms. Simultaneously, BP exerts a considerable impact on the three mediating variables, albeit the magnitude of its influence is categorized as weak to moderate. This suggests that brand perceptions influence opinions and behavioural regulation, yet are not the primary determinant in consumer choices regarding digital banking services. Table 2. Results of Hypothesis Testing. Path Coefficient T Statistics P Values Interrelation of Construct Pathways EU -> A 0.329 6.130 0.000000 Positive, Significant, Moderate EU -> SN 0.059 1.094 0.274000 Positive, Not Significant, Weak EU -> PBC 0.330 6.613 0.000000 Positive, Significant, Moderate PPV -> A 0.450 8.629 0.000000 Positive, Significant, Moderate PPV -> PBC 0.364 6.182 0.000000 Positive, Significant, Moderate PPV -> SN 0.329 5.289 0.000000 Positive, Significant, Moderate BP -> A 0.147 3.155 0.002000 Positive, Significant, Weak BP -> PBC 0.227 4.973 0.000000 Positive, Significant, Weak BP -> SN 0.405 9.042 0.000000 Positive, Significant, Moderate A -> PI 0.460 6.810 0.000000 Positive, Significant, Moderate SN -> PI 0.176 4.693 0.000000 Positive, Significant, Moderate PBC -> PI 0.289 4.518 0.000000 Positive, Significant, Moderate Regarding the direct correlation to intention, attitude (A) is the construct exerting the most significant influence on PI, evidenced by the highest path coefficient value (0.460). This indicates that a more favourable consumer attitude towards digital banking services correlates with an increased intention to utilize them. SN and PBC also make substantial contributions to PI, albeit with diminished intensity. Consequently, attitude serves as a primary predictor in this model, whereas social norms and perceived control assume supportive roles (Figure 3). The R² value indicates that the exogenous and mediating constructs account for 70.3% of the variance in usage intention (PI), 69.6% in attitude (A), 66.6% in perceived behavioural control (PBC), and 50.4% in subjective norm (SN). The values are classified as strong for PI, A, and PBC, and moderate for SN, signifying that the model possesses substantial explanatory power. Nevertheless, SN appears to require supplementary variables to enhance its explanatory power, including cultural factors, the digital social environment, or the impact of influencers. Effect size (F²) analysis corroborates prior findings by demonstrating that the PPV → A, BP → SN, and A → PI pathways exert moderate influences on their corresponding dependent variables. Alternative pathways indicate minimal contributions, affirming that while all constructs are theoretically pertinent, only a select few exert significant practical impact. Consequently, attitudes shaped by perceptions of product value are the paramount factor in elucidating the intention to utilize digital banking services. Despite the model's robust explanatory capability, the outcomes of the predictive relevance assessment (PLSpredict) indicate that the out-of-sample predictive efficacy remains comparatively low. The majority of indicators exhibit RMSE and MAE values that are comparable to or exceed those of the reference linear regression model, suggesting that this model is more suitable for elucidating theoretical relationships than for making practical predictions on new data. While the structural relationship 2952 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 6: 2946-2954, 2025 DOI: 10.55214/25768484.v9i6.8507 © 2025 by the authors; licensee Learning Gate between constructs is robust and significant, its efficacy in forecasting the actual behaviour of new users necessitates further refinement, including the incorporation of additional constructs such as trust, digital literacy, or perceived risk. These findings substantiate the extension of the TPB as a robust framework for elucidating digital banking behaviour. The practical implications of this study's results suggest that digital banks must prioritize the development of consumer-perceived product value and foster positive attitudes through responsive, secure, and value-enhancing services. Although brand perception is significant, communication strategies that influence users' attitudes and behavioural regulation will more effectively enhance usage intentions. This research presents additional prospects for incorporating external and emotional variables into forthcoming digital consumer behaviour models. Figure 3. The Path Model of "Consumer Intentions in Using Digital Banking Services". 5. Conclusion This study concludes that consumer intention to utilize digital banking services is significantly affected by attitude, subjective norm, and perceived behavioural control, as determined by SEM-PLS analysis using SmartPLS 4 software. Attitude is the construct that most significantly affects usage intention, primarily shaped by consumer perceptions of the product's perceived value. The usability of digital banking services has been demonstrated to foster favourable attitudes and perceptions of control, although it does not directly influence social norms. The perception of the brand exerts a beneficial effect, albeit with a comparatively minor impact. The model demonstrates strong theoretical explanatory capability; however, its out-of-sample predictive accuracy remains comparatively low, highlighting the necessity for additional model enhancement. Digital banks should prioritize enhancing the perceived value of their products by highlighting functional advantages, security, accessibility, and cost-effectiveness. Marketing strategies must focus on enhancing favourable consumer perceptions via digital education, customer testimonials, and tailored 2953 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 6: 2946-2954, 2025 DOI: 10.55214/25768484.v9i6.8507 © 2025 by the authors; licensee Learning Gate user experiences. It is essential to enhance subjective norms via community strategies, endorsements, or campaigns that leverage social influencers or online communities. 6. Discussion Despite the model's robust explanatory capacity, the results from the predictive relevance assessment (PLSpredict) indicate that the out-of-sample predictive efficacy remains comparatively low. The majority of indicators exhibit RMSE and MAE values that are comparable to or exceed those of the reference linear regression model, suggesting that this model is more suitable for elucidating theoretical relationships than for making practical predictions of new data. While the structural relationship between constructs is robust and significant, its efficacy in forecasting the actual behaviour of new users necessitates further refinement, including the incorporation of additional constructs such as trust, digital literacy, or perceived risk. These findings substantiate the extension of the TPB as a robust framework for elucidating digital banking behaviour. The practical implications of this study's results suggest that digital banks must prioritize the development of consumer-perceived product value and foster favourable attitudes through responsive, secure, and value-enhancing services. Although brand perception is significant, communication strategies that influence users' attitudes and behavioural regulation will be more efficacious in enhancing usage intentions. This study presents additional opportunities to incorporate external and emotional factors into future digital consumer behaviour models. Transparency: The authors confirm that the manuscript is an honest, accurate, and transparent account of the study; that no vital features of the study have been omitted; and that any discrepancies from the study as planned have been explained. This study followed all ethical practices during writing. Copyright: © 2025 by the authors. 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