Understanding BRISPOT Adoption in the SME Segment: The Role of Expectancy, Support, and Trust in a Digital Transformation Framework

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BRISPOT is a digital credit system used by relationship managers (RMs) in the SME segment of Bank Rakyat Indonesia (BRI). Methods Using the Unified Theory of Acceptance and Use of Technology (UTAUT) integrated with Social Cognitive Theory (SCT), this quantitative study surveyed 150 RMs. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results Performance expectancy, effort expectancy, and facilitating conditions significantly affected behavioral intention to use BRISPOT. Behavioral intention had a strong influence on actual usage. However, trust did not moderate the intention–use relationship. Conclusions The integration of UTAUT and SCT offers a robust explanation of digital adoption behavior among internal users. System usability and institutional support are critical for driving adoption. Trust, while conceptually important, may be context-dependent. 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F1000Research 2025, 14 :1037 ( https://doi.org/10.12688/f1000research.167883.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Understanding BRISPOT Adoption in the SME Segment: The Role of Expectancy, Support, and Trust in a Digital Transformation Framework [version 1; peer review: 1 approved, 1 approved with reservations] Priyastomo Priyastomo https://orcid.org/0009-0007-2581-961X 1 , Umar Nimran 1 , Arik Prasetya 1 , Teuku Noerman https://orcid.org/0000-0003-4842-4040 1 Priyastomo Priyastomo https://orcid.org/0009-0007-2581-961X 1 , Umar Nimran 1 , Arik Prasetya 1 , Teuku Noerman https://orcid.org/0000-0003-4842-4040 1 PUBLISHED 03 Oct 2025 Author details Author details 1 Faculty of Administrative Sciences, Universitas Brawijaya, Malang, East Java, Indonesia Priyastomo Priyastomo Roles: Conceptualization, Data Curation, Formal Analysis, Methodology, Writing – Original Draft Preparation Umar Nimran Roles: Investigation, Resources, Supervision Arik Prasetya Roles: Data Curation, Project Administration, Supervision, Validation Teuku Noerman Roles: Formal Analysis, Investigation, Resources, Supervision OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Innovations in Research Assessment collection. Abstract Background Digital transformation in banking is accelerating, yet internal adoption of key applications remains suboptimal. BRISPOT is a digital credit system used by relationship managers (RMs) in the SME segment of Bank Rakyat Indonesia (BRI). Methods Using the Unified Theory of Acceptance and Use of Technology (UTAUT) integrated with Social Cognitive Theory (SCT), this quantitative study surveyed 150 RMs. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results Performance expectancy, effort expectancy, and facilitating conditions significantly affected behavioral intention to use BRISPOT. Behavioral intention had a strong influence on actual usage. However, trust did not moderate the intention–use relationship. Conclusions The integration of UTAUT and SCT offers a robust explanation of digital adoption behavior among internal users. System usability and institutional support are critical for driving adoption. Trust, while conceptually important, may be context-dependent. READ ALL READ LESS Keywords UTAUT, Social Cognitive Theory, BRISPOT, behavioral intention, digital adoption, trust, banking technology Corresponding Author(s) Priyastomo Priyastomo ( [email protected] ) Close Corresponding author: Priyastomo Priyastomo Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2025 Priyastomo P et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Priyastomo P, Nimran U, Prasetya A and Noerman T. Understanding BRISPOT Adoption in the SME Segment: The Role of Expectancy, Support, and Trust in a Digital Transformation Framework [version 1; peer review: 1 approved, 1 approved with reservations] . F1000Research 2025, 14 :1037 ( https://doi.org/10.12688/f1000research.167883.1 ) First published: 03 Oct 2025, 14 :1037 ( https://doi.org/10.12688/f1000research.167883.1 ) Latest published: 03 Oct 2025, 14 :1037 ( https://doi.org/10.12688/f1000research.167883.1 ) 1. Introduction The rapid evolution of digital technologies in the era of Industry 4.0 has transformed the operational landscapes of various industries, with the banking sector at the forefront of this change. Industry 4.0, characterized by the integration of cyber-physical systems, artificial intelligence, cloud computing, and the Internet of Things (IoT), drives organizations toward automation, real-time data processing, and smart service delivery ( Schwab, 2016 ). In this context, the banking industry is under immense pressure to innovate and digitize its services to remain competitive and efficient. Bank Rakyat Indonesia (BRI), one of Indonesia’s largest state-owned banks, has responded to this challenge by developing BRISPOT, a digital credit application platform specifically designed to streamline loan processes in the Small and Medium Enterprise (SME) segment. The platform integrates various stages of the credit lifecycle from application to monitoring into a seamless digital process. However, despite the potential operational benefits, empirical evidence indicates that BRISPOT is not being utilized to its full potential. As of September 2023, only 30.4% of targeted relationship managers (RMs) were active users of the application, revealing a significant adoption gap that warrants further investigation. This underutilization is not merely a technical issue; rather, it reflects deeper behavioral, organizational, and contextual challenges. Understanding the factors that influence technology adoption among employees is thus crucial for achieving the intended outcomes of digital transformation. In addressing this issue, the Unified Theory of Acceptance and Use of Technology (UTAUT) provides a valuable framework. Developed by Venkatesh et al. (2003) , UTAUT identifies four primary constructs performance expectancy (PE), effort expectancy (EE), social influence, and facilitating conditions (FC) as critical predictors of behavioral intention (BI) and use behavior (UB) in technology adoption. Performance expectancy refers to the degree to which an individual believes that using the system will help improve their job performance. This construct is closely related to the perceived usefulness element in the Technology Acceptance Model (TAM) ( Davis, 1989 ). In the case of BRISPOT, PE captures how RMs perceive the system’s ability to simplify credit evaluation and enhance productivity. Effort expectancy denotes the ease of system usage, especially during early implementation phases. When a system is perceived as user-friendly, it is more likely to be adopted ( Venkatesh et al., 2003 ). Facilitating conditions, meanwhile, relate to the belief that the technical and organizational infrastructure exists to support system use. Although UTAUT provides a solid theoretical foundation, empirical studies have pointed out a persistent gap between behavioral intention and actual use behavior. One critical factor that may help explain this gap is trust. Trust, defined as the belief in the reliability, security, and effectiveness of a system, plays a significant role in technology acceptance, especially in environments characterized by uncertainty and digital risks ( Gefen et al., 2003 ; Pavlou, 2003 ). In financial services, where data sensitivity and regulatory compliance are paramount, trust becomes an essential moderating variable that can strengthen or weaken the relationship between intention and behavior. This study incorporates trust as a moderating variable in the relationship between behavioral intention to use and actual use behavior. It is expected that even if RMs have a high intention to use BRISPOT, the absence of trust in the system whether due to perceived technical issues, data security concerns, or organizational factors can inhibit actual usage. Conversely, high levels of trust may reinforce intention and translate it into consistent system use. To enrich the analytical lens, the study also draws upon the Social Cognitive Theory (SCT) as a grand theory to explain psychological and environmental influences on behavior. SCT emphasizes self-efficacy, observational learning, and outcome expectations ( Bandura, 1986 ), which are highly relevant in understanding how RMs adopt and use digital applications. For instance, RMs who observe their peers successfully using BRISPOT, and who believe in their own capability to do so, are more likely to adopt the application. This complements UTAUT’s constructs and offers a more holistic understanding of user behavior in digital transformation initiatives. This research is conducted in the context of the SME segment of BRI, which holds strategic importance due to its large loan portfolio contribution. The study investigates the influence of PE, EE, and FC on BI, the effect of BI on UB, and the moderating role of trust. The goal is to identify actionable insights that can guide BRI and similar financial institutions in enhancing the effectiveness of their digital tools and achieving higher adoption rates. The novelty of this study lies in its integration of SCT and UTAUT with trust as a moderating factor, which has not been widely explored in the context of banking applications in developing countries. Moreover, the focus on internal users (i.e., RMs) rather than customers adds depth to the literature, as most digital adoption studies in banking emphasize consumer behavior rather than employee adoption. In sum, this research addresses a critical gap in digital banking implementation by exploring the behavioral and contextual determinants of technology acceptance in an operational banking environment. It aims to contribute both theoretically by extending UTAUT with trust and SCT and practically by offering recommendations for enhancing the adoption and effectiveness of BRISPOT and similar systems. 2. Theoretical framework and hypothesis development 2.1 Theoretical framework This study integrates the Unified Theory of Acceptance and Use of Technology (UTAUT) ( Venkatesh et al., 2003 ) with Social Cognitive Theory (SCT) ( Bandura, 1986 ) to understand technology adoption behavior among internal users of a digital banking system. The research aims to explain how three primary constructs Performance Expectancy (PE), Effort Expectancy (EE), and Facilitating Conditions (FC) influence Behavioral Intention (BI) to use BRISPOT, and how BI affects Use Behavior (UB). Furthermore, this study incorporates Trust as a moderating variable between BI and UB to address the commonly observed intention–behavior gap in digital platform usage. UTAUT provides a robust base to predict technology acceptance in organizational contexts. Performance Expectancy (PE) is considered the strongest predictor, referring to the perceived benefits of using the system in enhancing job performance. Effort Expectancy (EE) relates to the degree of ease associated with using the technology. Facilitating Conditions (FC) represent the extent to which users believe that an adequate organizational and technical infrastructure is available to support system use. These three constructs are theorized to influence Behavioral Intention (BI), which in turn predicts actual Use Behavior (UB). However, the behavioral intention–use behavior linkage often fails to hold due to intervening contextual factors. In response, Trust is incorporated into the framework as a moderating variable that could strengthen or weaken this relationship ( Pavlou, 2003 ; Gefen et al., 2003 ). Trust is particularly important in financial services, where perceived risks, system integrity, and information security significantly affect user behavior. Social Cognitive Theory (SCT) complements UTAUT by offering a lens through which individual cognition (e.g., self-efficacy) and social influence (e.g., peer modeling) contribute to behavior change. Employees with high self-efficacy and observational learning are likely to find the system easier to use and more beneficial, which directly supports PE and EE. 2.2 Hypothesis development 2.2.1 Performance expectancy and behavioral intention Performance Expectancy (PE) is defined as the degree to which an individual believes that using the system will help achieve job-related goals ( Venkatesh et al., 2003 ). In BRISPOT’s context, PE reflects the system’s perceived ability to accelerate the loan process, reduce paperwork, and improve customer service delivery. Prior research consistently confirms that PE significantly influences the intention to use technology ( Alalwan et al., 2017 ; Dwivedi et al., 2019 ). H1: Performance Expectancy has a positive effect on Behavioral Intention to use BRISPOT. 2.2.2 Effort expectancy and behavioral intention Effort Expectancy (EE) refers to the ease of use associated with the system. If users perceive the system as easy to navigate, they are more likely to develop favorable attitudes and intentions toward its usage. For first-time users or employees less familiar with digital interfaces, perceived ease plays a critical role in adoption ( Venkatesh et al., 2003 ; Williams et al., 2015 ). H2: Effort Expectancy has a positive effect on Behavioral Intention to use BRISPOT. 2.2.3 Facilitating conditions and behavioral intention Facilitating Conditions (FC) encompass the availability of resources, training, and support infrastructure to enable system use. Though traditionally linked with actual usage, in this study FC is expected to influence intention, given that the presence of support systems shapes user perceptions before adoption ( Rogers, 1995 ; Venkatesh et al., 2012 ). H3: Facilitating Conditions have a positive effect on Behavioral Intention to use BRISPOT. 2.2.4 Behavioral intention and use behavior Behavioral Intention (BI) is a key determinant of actual technology usage. A strong intention to use a system generally results in higher engagement and frequency of use. However, this linkage is influenced by both internal (e.g., confidence) and external (e.g., trust, organizational constraints) factors ( Ajzen, 1991 ; Venkatesh et al., 2003 ). H4: Behavioral Intention has a positive effect on Use Behavior of BRISPOT. 2.2.5 The moderating role of trust Trust refers to the belief that the system is reliable, secure, and capable of performing tasks as intended. In the context of BRISPOT, trust influences how confidently users convert their intentions into actual usage. Trust mitigates perceived risk and enhances willingness to rely on the system ( Gefen et al., 2003 ; Pavlou, 2003 ). H5: Trust positively moderates the relationship between Behavioral Intention and Use Behavior of BRISPOT. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and Social Cognitive Theory (SCT), this study develops a comprehensive theoretical model that integrates behavioral and cognitive perspectives to explain the adoption of BRISPOT. The model highlights how performance expectancy, effort expectancy, and facilitating conditions influence behavioral intention, which in turn drives use behavior. Furthermore, trust is introduced as a moderating variable to capture the contextual and psychological factors that may strengthen or weaken the intention–behavior relationship. This integrated framework provides a nuanced understanding of internal technology adoption in a banking environment, emphasizing both system-level enablers and individual-level perceptions. By bridging the gap between intention and actual use, the model aims to generate practical insights for enhancing the effectiveness of digital transformation initiatives in the financial services sector. The hypothesized research model is illustrated in Figure 1 . Figure 1. Hypothesized research model integrating Unified Theory of Acceptance and Use of Technology (UTAUT) and Social Cognitive Theory (SCT), with Trust as a moderating variable between Behavioral Intention and Use Behavior. 3. Methodology 3.1 Research design This study adopts a quantitative, explanatory research design to examine the influence of Performance Expectancy, Effort Expectancy, and Facilitating Conditions on Behavioral Intention, and the subsequent effect on Use Behavior of the BRISPOT application, with Trust as a moderating variable. The research is grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT) and enriched by the Social Cognitive Theory (SCT), providing a robust framework for analyzing technology adoption behavior among internal banking professionals. 3.2 Population and sample The population of this study consists of relationship managers (RMs) from Bank Rakyat Indonesia (BRI) who are assigned to handle Small and Medium Enterprise (SME) credit segments and are authorized users of the BRISPOT digital application. The unit of analysis is individual RMs. Using the Hair et al. (2010) guideline for Partial Least Squares Structural Equation Modeling (PLS-SEM), a minimum sample size of 10 times the largest number of structural paths directed at a particular construct is recommended ( Solimun, et al., 2021 ). As five structural paths are tested, the minimum sample size is 50. To enhance generalizability and validity, a total of 150 valid responses were collected using purposive sampling, targeting only active SME loan officers with at least 6 months of BRISPOT experience. 3.3 Data collection Primary data were collected using a structured online questionnaire, distributed via internal email and communication platforms over a period of two weeks in early 2025. All questionnaire items were adapted from validated scales in prior studies and measured using a five-point Likert scale (1 = strongly disagree to 5 = strongly agree). The survey consisted of six main constructs: PE, EE, FC, BI, UB, and Trust. To ensure clarity and validity, the instrument was pre-tested with 10 RMs and refined based on feedback regarding language, terminology, and structure. Extended data, including the questionnaire items and anonymized dataset, are available in the Figshare repository ( https://doi.org/10.6084/m9.figshare.30029947 ) ( Priyastomo et al., 2025 ). 3.4 Measurement of variables Performance Expectancy (PE): Adapted from Venkatesh et al. (2003) , measured using four items related to perceived usefulness of BRISPOT in job performance. • Effort Expectancy (EE): Four items measuring perceived ease of use, drawn from UTAUT and Davis (1989) . • Facilitating Conditions (FC): Three items assessing availability of support and infrastructure. • Behavioral Intention (BI): Three items representing willingness to continue using BRISPOT. • Use Behavior (UB): Measured through self-reported frequency and extent of BRISPOT usage. • Trust: Adapted from Gefen et al. (2003) and Pavlou (2003) , comprising four items evaluating perceived security, system reliability, and integrity. All items were adapted and translated to Indonesian using back-translation procedures to maintain construct equivalence. 3.5 Data analysis The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS 4.0. PLS-SEM was selected due to its suitability for predictive, theory-building research, especially when working with complex models and small-to-medium sample sizes ( Hair et al., 2019 ; Ubaidillah, et al., 2022 ). The analysis followed a two-step approach: • Measurement Model Evaluation: Validity and reliability were assessed using Composite Reliability (CR), Cronbach’s Alpha (CA), Average Variance Extracted (AVE), and outer loadings. Discriminant validity was evaluated using the Fornell–Larcker criterion and Heterotrait-Monotrait (HTMT) ratio. • Structural Model Evaluation: Hypothesis testing was performed by examining path coefficients (β), t-values, and p-values using bootstrapping (5,000 subsamples). Coefficient of determination (R 2 ), effect size (f 2 ), and predictive relevance (Q 2 ) were also reported. • Moderation analysis was conducted by creating an interaction term between Behavioral Intention and Trust to test the moderating effect on Use Behavior. 3.6 Common method bias and ethical consideration To address common method bias (CMB), Harman’s single-factor test was employed and revealed no dominant factor. Procedural remedies included ensuring anonymity, randomization of item order, and assuring participants that there were no right or wrong answers. All ethical protocols were followed, and informed consent was obtained from participants. No identifying information was collected, and the data were analyzed in aggregate. 4. Results 4.1 Measurement model To ensure the robustness of the measurement model within the PLS-SEM framework, this study conducted a comprehensive assessment of construct validity, which encompasses both convergent and discriminant validity, as well as internal consistency reliability. This assessment employed key metrics including factor loadings (FL), average variance extracted (AVE), Cronbach’s alpha (CA), and composite reliability (CR), in accordance with established methodological guidelines ( Ahmed, 2024 ). As shown in Table 1 , all items exhibited standardized factor loadings above the recommended threshold of 0.70, indicating that each observed variable demonstrates substantial contribution to its corresponding latent construct. Furthermore, all constructs reported AVE values exceeding 0.50, confirming satisfactory levels of convergent validity. Reliability indicators also met the acceptable criteria, with both CA and CR values surpassing the 0.70 benchmark, thereby indicating internal consistency and construct reliability ( Ahmed, 2024 ; Sarstedt et al., 2019 ). Table 1. Measurement model. Variabel Indikator Loading factor CA CR AVE Performance Expectancy (X1) X1.1 0.947 0.870 0.902 0.787 X1.2 0.977 X1.3 0.975 X1.4 0.970 X1.5 0.945 X1.6 0.873 X1.7 0.970 Effort Expectancy (X2) X2.1 0.957 0.885 0.915 0.712 X2.2 0.932 X2.3 0.972 X2.4 0.989 X2.5 0.950 Facilitating Condition (X3) X3.1 0.838 0.895 0.927 0.758 X3.2 0.970 X3.3 0.976 Behavioral Intention to Use (Y1) Y1.1 0.971 0.900 0.930 0.796 Y1.2 0.979 Y1.3 0.981 Y1.4 0.971 Y1.5 0.860 Y1.6 0.949 Trust (Y2) Y2.1 0.991 0.945 0.960 0.730 Y2.2 0.984 Y2.3 0.994 Use Behavior (Y3) Y3.1 0.969 0.875 0.910 0.855 Y3.2 0.985 Y3.3 0.898 Following the confirmation of convergent validity and internal consistency, the discriminant validity of the constructs was examined using the Fornell-Larcker criterion. The results, summarized in Table 2 , demonstrate that the square root of each construct’s AVE is greater than its correlations with other constructs, thereby providing empirical evidence of discriminant validity. Table 2. Fornell–Larcker criterion. Variabel X1 X2 X3 Y1 Y2 Y3 Performance Expectancy (X1) 0.887 Effort Expectancy (X2) 0.520 0.844 Facilitating Condition (X3) 0.601 0.512 0.871 Behavioral Intention to Use (Y1) 0.720 0.620 0.865 0.892 Trust (Y2) 0.530 0.510 0.643 0.578 0.854 Use Behavior (Y3) 0.650 0.576 0.748 0.960 0.590 0.924 To reinforce these findings, the heterotrait-monotrait ratio of correlations (HTMT) was also employed as an additional criterion, which is considered a more rigorous test for discriminant validity in variance-based SEM ( Hair et al., 2017 ; Henseler et al., 2015 ). As presented in Table 3 , all HTMT values fall below the conservative threshold of 0.90, indicating that the constructs are empirically distinct from one another. Table 3. Heterotrait–Monotrait (HTMT) ratio of correlations. Variabel X1 X2 X3 Y1 Y2 Y3 Performance Expectancy (X1) 0.792 Effort Expectancy (X2) 0.724 0.764 Facilitating Condition (X3) 0.701 0.743 0.738 Behavioral Intention to Use (Y1) 0.691 0.705 0.720 0.726 Trust (Y2) 0.664 0.642 0.714 0.705 0.714 Use Behavior (Y3) 0.608 0.576 0.683 0.678 0.640 0.703 Collectively, these results confirm that the measurement model satisfies the necessary conditions for convergent and discriminant validity, as well as internal consistency reliability. Therefore, the constructs are deemed valid and reliable, and are suitable for subsequent structural model analysis. 4.2 Structural model The results of hypothesis testing are summarized in Table 4 , which presents both direct and indirect effects among constructs. Table 4. Structural model results for direct and indirect effects. Direct effect Independent Dependent Path Coefficient T-Statistic P-Value Decision Performance Expectancy (X1) Behavioral Intention to Use (Y1) 0.720 9.181 0.000 Accepted Effort Expectancy (X2) Behavioral Intention to Use (Y1) 0.669 7.921 0.000 Accepted Facilitating Condition (X3) Behavioral Intention to Use (Y1) 0.228 3.069 0.000 Accepted Behavioral Intention to Use (Y1) Use Behavior (Y3) 0.857 14.732 0.000 Accepted Trust (Y2) Use Behavior (Y3) 0.005 0.528 0.597 Rejected Indirect effect Independent Mediation Dependent Path Coefficient P-Value Decision Performance Expectancy (X1) Behavioral Intention to Use (Y1) Use Behavior (Y3) 0.617 0.000 Accepted Effort Expectancy (X2) Behavioral Intention to Use (Y1) Use Behavior (Y3) 0.573 0.000 Accepted Facilitating Condition (X3) Behavioral Intention to Use (Y1) Use Behavior (Y3) 0.352 0.000 Accepted Trust (Y2) Behavioral Intention to Use (Y1) Use Behavior (Y3) 0.005 0.551 Rejected The analysis of direct relationships in this study reveals that Performance Expectancy (X1), Effort Expectancy (X2), and Facilitating Conditions (X3) exert positive and statistically significant effects on Behavioral Intention to Use (Y1). These findings are supported by path coefficients of 0.720 (p = 0.000), 0.669 (p = 0.000), and 0.228 (p = 0.000), respectively, all with t-statistics above the 1.96 threshold and p-values below 0.05, thus confirming the acceptance of the proposed hypotheses. This suggests that the greater the perceived performance benefits and ease of system use, along with the availability of technical and organizational support, the stronger an individual’s intention to adopt the technology. Furthermore, Behavioral Intention to Use (Y1) is shown to have a robust and significant direct effect on Use Behavior (Y3), with a path coefficient of 0.857 and a p-value of 0.000. This finding underscores behavioral intention as a principal determinant of actual system usage, in line with the core tenets of the Unified Theory of Acceptance and Use of Technology (UTAUT) as introduced by Venkatesh et al. (2003) . In contrast, Trust (Y2) does not exhibit a statistically significant direct effect on Use Behavior, as reflected by its minimal path coefficient of 0.005, a t-statistic of 0.528, and a p-value of 0.597. This suggests that trust in the system, while conceptually relevant, may not independently drive actual usage behavior in the absence of strong performance and usability perceptions. The analysis of indirect effects further validates the mediating role of Behavioral Intention to Use (Y1) in the relationships between the three exogenous variables (X1, X2, X3) and Use Behavior (Y3). Specifically, Performance Expectancy indirectly affects Use Behavior via Behavioral Intention, with a significant path coefficient of 0.617 (p = 0.000). Similar indirect effects are observed for Effort Expectancy and Facilitating Conditions, with significant coefficients of 0.573 (p = 0.000) and 0.352 (p = 0.000), respectively. These results reinforce the critical function of behavioral intention as a psychological mechanism that translates user perceptions into actual system usage. However, Trust (Y2) again shows no significant indirect influence on Use Behavior through Behavioral Intention, as evidenced by a low path coefficient (0.005) and a p-value of 0.551. This non-significance implies that trust may serve as a baseline or hygiene factor important for ensuring acceptance, yet insufficient on its own to motivate usage behavior without complementary factors such as performance benefits or system usability. Overall, the findings provide empirical support for the UTAUT framework, which emphasizes the importance of Performance Expectancy, Effort Expectancy, and Facilitating Conditions in shaping behavioral intention, which subsequently drives technology use behavior. Moreover, the results highlight that the integration of Trust into the UTAUT model should be contextually evaluated, as trust may not be a universally salient factor across all technological adoption scenarios. From a managerial standpoint, these insights suggest that system developers and implementers should prioritize enhancing users’ perceptions of performance, ease of use, and institutional support mechanisms to foster sustainable and effective system adoption. 5. Discussion 5.1 The effect of performance expectancy on behavioral intention to use The analysis reveals that performance expectancy exerts a strong and statistically significant influence on behavioral intention to use BRISPOT. This finding corroborates the original proposition of the UTAUT framework ( Venkatesh et al., 2003 ), which posits performance expectancy as the most influential determinant of technology adoption intention. In the context of this study, relationship managers (RMs) are more likely to adopt BRISPOT when they perceive that the system effectively supports their performance targets, such as accelerating credit evaluation, reducing paperwork, and enhancing service delivery for SME clients. The strength of this relationship suggests that perceived instrumental value is a critical motivator in organizational settings, aligning with prior empirical evidence in financial services ( Alalwan et al., 2017 ; Dwivedi et al., 2019 ). Therefore, improving the alignment between BRISPOT’s capabilities and RMs’ job-related performance expectations is essential to enhance adoption rates. 5.2 The effect of effort expectancy on behavioral intention to use The results also demonstrate that effort expectancy has a significant positive impact on behavioral intention. This finding is consistent with prior research emphasizing the importance of ease of use in technology acceptance models ( Davis, 1989 ; Venkatesh et al., 2003 ). Within the operational dynamics of BRI, ease of system navigation, intuitive interfaces, and minimized complexity are key factors influencing adoption decisions. RMs are more inclined to use BRISPOT when the learning curve is perceived as low and when system interactions do not hinder their workflow. The implication is that BRISPOT’s usability especially for users who may not be highly digitally literate should be continuously improved through user-centered design and targeted training interventions. This aligns with SCT’s emphasis on self-efficacy, where individuals with greater perceived control over system use are more motivated to adopt it ( Bandura, 1986 ). 5.3 The effect of facilitating conditions on behavioral intention to use The empirical findings confirm that facilitating conditions significantly influence behavioral intention, underscoring the role of organizational and technical support in shaping user intentions even prior to actual system use. This is particularly relevant in BRI’s operational environment, where infrastructure readiness, system availability, and responsive technical assistance are essential enablers. While UTAUT traditionally associates facilitating conditions with actual use behavior, this study demonstrates their anticipatory effect on intention formation, consistent with Rogers’ (1995) theory of perceived support during the adoption decision process. It suggests that efforts to improve BRISPOT’s infrastructure such as stable internet access, system uptime, and helpdesk support can indirectly but substantially elevate user intention. 5.4 The effect of behavioral intention to use on use behavior The study finds strong empirical support for the relationship between behavioral intention and use behavior, in line with the foundational assumptions of UTAUT and Theory of Planned Behavior ( Ajzen, 1991 ). This indicates that RMs who express high intention are indeed more likely to engage with BRISPOT frequently and meaningfully. The high path coefficient and statistical significance suggest minimal friction between motivation and action, provided other conditions remain favorable. However, this linkage also highlights the importance of monitoring behavioral metrics (e.g., login frequency, transaction completion) to assess whether expressed intentions translate into consistent usage, which is a critical metric for digital transformation success. 5.5 The moderating role of trust in the relationship between behavioral intention and use behavior Contrary to expectations, trust does not significantly moderate the relationship between behavioral intention and use behavior. This finding deviates from previous studies in e-commerce and financial technology contexts ( Gefen et al., 2003 ; Pavlou, 2003 ), where trust often strengthens the translation of intention into action. In the case of BRISPOT, the lack of significant moderation may reflect high baseline trust in internal systems provided by a reputable institution like BRI, thereby reducing variance in the trust construct across users. Alternatively, the results may suggest that performance-driven and structural factors (such as job requirements or system mandates) overpower the effect of psychological confidence in the system. This underscores the context-specific nature of trust and its influence, suggesting that future studies could explore trust’s role more deeply through qualitative inquiry or by distinguishing between trust in system versus trust in data accuracy or organizational intention. 6. Conclusion, Limitation, and Further research 6.1 Conclusion This study investigates the key determinants influencing the adoption of BRISPOT, a digital loan application platform developed by Bank Rakyat Indonesia (BRI), within the context of relationship managers (RMs) serving the SME segment. Drawing upon the Unified Theory of Acceptance and Use of Technology (UTAUT) and enriched by Social Cognitive Theory (SCT), the findings empirically validate that performance expectancy, effort expectancy, and facilitating conditions significantly affect behavioral intention to use the system. Furthermore, behavioral intention is found to have a strong and positive effect on actual use behavior, reinforcing the predictive strength of intention in digital adoption models. However, contrary to theoretical expectations, trust does not moderate the relationship between behavioral intention and use behavior, suggesting that its influence may be context-dependent or mediated by other latent variables in organizational settings. The study offers theoretical contributions by extending UTAUT with a contextualized moderating variable (trust), while also integrating SCT to acknowledge the cognitive and environmental dynamics of internal system adoption. From a practical standpoint, the findings underscore the importance for banking institutions to invest not only in the functional and technical quality of digital platforms but also in the structural support and user training that shape adoption behavior. A user-centric approach that enhances perceived usefulness and ease of use, while ensuring operational readiness, is imperative for driving meaningful digital transformation in the banking sector. 6.2 Limitation Despite its contributions, this study has several limitations. First, the cross-sectional nature of the data limits the ability to capture changes in user perceptions or behaviors over time. Longitudinal data could provide richer insights into how behavioral intention and actual usage evolve, particularly in response to system updates or organizational policies. Second, the study focuses exclusively on relationship managers within the SME segment of a single state-owned bank, which may limit the generalizability of the findings across other employee roles, segments, or financial institutions. Third, the study’s reliance on self-reported measures of use behavior may be subject to social desirability bias or overestimation, which could affect the accuracy of reported adoption levels. In addition, although trust was theorized to moderate the relationship between intention and behavior, the operationalization of trust may not have fully captured its multidimensional nature (e.g., trust in system, data, management, or external regulations), which might explain its non-significant effect. Future studies may consider a more granular approach in measuring trust or examining its mediating rather than moderating role. 6.3 Further research Building on these limitations, future research could adopt a longitudinal research design to examine dynamic shifts in adoption behavior as users gain experience and as digital platforms evolve. Expanding the scope to include different banking roles (e.g., credit analysts, branch managers) or customer-facing staff in various regions would enhance the external validity of the findings. Moreover, future studies should explore additional constructs from the technology acceptance and behavioral sciences literature, such as digital literacy, organizational culture, leadership support, or resistance to change, to enrich the explanatory power of the model. Furthermore, the role of trust in digital system adoption warrants deeper investigation. Qualitative or mixed-methods research could uncover the nuanced perceptions and experiences that shape trust-related decisions in organizational technology use. Finally, comparative studies across different banks or sectors (e.g., insurance, fintech, government services) may provide valuable insights into contextual variations in technology adoption drivers, contributing to the development of more robust and adaptive theoretical models. Ethical approval This study was conducted in accordance with the Declaration of Helsinki. Prior to data collection, ethical approval was obtained from the Ethics Committee of the Faculty of Administrative Sciences, Universitas Brawijaya, Indonesia (Approval Number: 112/KEPK/FIA/UB/2024). All protocols complied with national and international ethical standards for research involving human participants. Informed consent Written informed consent was obtained from all participants prior to their involvement in the study. Participants were informed about the study’s purpose, procedures, and their right to withdraw at any time. All respondents were adults and voluntarily agreed to participate. Data availability statement Underlying data The data presented in this study are confidential and cannot be publicly shared due to confidentiality agreements with the participants and restrictions imposed by the institution where the research was conducted. To protect participant privacy, data access is strictly controlled. Researchers who wish to access the data must submit a formal request to the corresponding author. This request should include the purpose of their research, the specific data needed, intended use, and measures to ensure data security and participant confidentiality. While data sharing is restricted, we are open to considering requests on a case-by-case basis. If feasible, we will provide anonymized data to minimize privacy concerns. All requests will be reviewed by our Institutional Review Board (IRB) to ensure compliance with ethical standards. To initiate a request or for further inquiries, please contact [email protected] . Extended data Figshare: Dataset and questionnaire for “Understanding BRISPOT Adoption in the SME Segment: The Role of Expectancy, Support, and Trust in a Digital Transformation Framework”, DOI: 10.6084/m9.figshare.30029947 (Priyastomo, P., et al., 2025 ). Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). References Ahmed RR: PLSSEM Vs. CB-SEM Modeling: A Comparative Analysis of Two Multivariate Approaches. 2024. Ajzen I: The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 1991; 50 (2): 179–211. Publisher Full Text Alalwan AA, Dwivedi YK, Rana NP, et al. : Examining factors influencing Jordanian customers’ intentions and adoption of internet banking: Extending UTAUT2 with risk. J. Retail. Consum. 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Publisher Full Text Williams MD, Rana NP, Dwivedi YK: The unified theory of acceptance and use of technology (UTAUT): A literature review. J. Enterp. Inf. Manag. 2015; 28 (3): 443–488. Publisher Full Text Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 03 Oct 2025 ADD YOUR COMMENT Comment Author details Author details 1 Faculty of Administrative Sciences, Universitas Brawijaya, Malang, East Java, Indonesia Priyastomo Priyastomo Roles: Conceptualization, Data Curation, Formal Analysis, Methodology, Writing – Original Draft Preparation Umar Nimran Roles: Investigation, Resources, Supervision Arik Prasetya Roles: Data Curation, Project Administration, Supervision, Validation Teuku Noerman Roles: Formal Analysis, Investigation, Resources, Supervision Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (1) version 1 Published: 03 Oct 2025, 14:1037 https://doi.org/10.12688/f1000research.167883.1 Copyright © 2025 Priyastomo P et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Priyastomo P, Nimran U, Prasetya A and Noerman T. Understanding BRISPOT Adoption in the SME Segment: The Role of Expectancy, Support, and Trust in a Digital Transformation Framework [version 1; peer review: 1 approved, 1 approved with reservations] . F1000Research 2025, 14 :1037 ( https://doi.org/10.12688/f1000research.167883.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 03 Oct 2025 Views 0 Cite How to cite this report: Shaheen WA. Reviewer Report For: Understanding BRISPOT Adoption in the SME Segment: The Role of Expectancy, Support, and Trust in a Digital Transformation Framework [version 1; peer review: 1 approved, 1 approved with reservations] . F1000Research 2025, 14 :1037 ( https://doi.org/10.5256/f1000research.185027.r432512 ) The direct URL for this report is: https://f1000research.com/articles/14-1037/v1#referee-response-432512 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 06 Jan 2026 Wasim Abbas Shaheen , Quaid-i-Azam University, Islamabad, Pakistan Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.185027.r432512 The integration of UTAUT with Social Cognitive Theory and the inclusion of trust as a moderator is a clear strength, offering a nuanced framework for understanding internal technology adoption. The focus on relationship managers in a state-owned ... Continue reading READ ALL The integration of UTAUT with Social Cognitive Theory and the inclusion of trust as a moderator is a clear strength, offering a nuanced framework for understanding internal technology adoption. The focus on relationship managers in a state-owned bank in Indonesia adds valuable context to the literature, which often emphasizes customer adoption in developed markets. The non-significant moderating role of trust, while unexpected, opens meaningful discussion about contextual factors in organizational adoption. The use of PLS-SEM is appropriate given the sample size and predictive aims. However, reporting is incomplete: key statistics such as R² values for endogenous constructs, effect sizes (f²), predictive relevance (Q²), and model fit indices (e.g., SRMR) are missing. Additionally, details on how the interaction term for moderation was calculated (e.g., mean-centering) are not provided. These omissions limit the ability to assess model quality and replicate the study. While the sample size meets PLS-SEM guidelines, the response rate and sampling frame are not described, raising concerns about selection bias. Demographic details of respondents (e.g., age, digital literacy, tenure) are absent but could enrich the interpretation of results. Furthermore, the data availability statement contradicts itself—claiming confidentiality while also citing a Figshare repository. This must be clarified to align with open data policies. Convergent and discriminant validity are adequately demonstrated via factor loadings, AVE, and Fornell-Larcker/HTMT. However, the operationalization of “trust” may be overly generic. In a banking context, trust could be multidimensional (e.g., trust in data security, system reliability, or organizational intent). The non-significant moderation might stem from measurement limitations rather than true absence of effect. The discussion logically interprets the significant findings, linking them to prior literature and practical steps for BRI. However, the non-significant moderation by trust is only briefly explained. A deeper exploration—perhaps referencing institutional trust in state-owned enterprises or mandatory usage contexts—would strengthen the contribution. The managerial recommendations are useful but could be more specific (e.g., tailored training, interface redesign). The limitations section appropriately notes the cross-sectional design and single-bank focus. However, it should also acknowledge the reliance on self-reported usage data, which may be biased. Future research suggestions are relevant but could be sharper—for example, proposing mixed methods to explore why trust did not moderate, or examining the role of digital literacy as a control variable. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Yes References 1. Tariq M, Maryam S, Shaheen W: Cognitive factors and actual usage of Fintech innovation: Exploring the UTAUT framework for digital banking. Heliyon . 2024; 10 (15). Publisher Full Text 2. Abdul Malik, Wasim Abbas Shaheen, Waqas: Cognitive Bias Asymmetry and Heuristic-Driven Market Anomalies: A Neurofinancial Noise Trading Analysis of Prospect Theory Elasticity in the Pakistan Stock Exchange (PSX). Social Sciences Spectrum . 2025; 4 (2): 108-141 Publisher Full Text 3. Maryam S, Saleem M, Ahmad A, Shaheen W: Leveraging information systems for competitive advantage: a study of Islamic financial institutions’ innovativeness and agility for Fintech financing. Journal of Islamic Marketing . 2025; 16 (12): 3711-3735 Publisher Full Text Competing Interests: No competing interests were disclosed. Reviewer Expertise: Business, Finance and Economics I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Shaheen WA. Reviewer Report For: Understanding BRISPOT Adoption in the SME Segment: The Role of Expectancy, Support, and Trust in a Digital Transformation Framework [version 1; peer review: 1 approved, 1 approved with reservations] . F1000Research 2025, 14 :1037 ( https://doi.org/10.5256/f1000research.185027.r432512 ) The direct URL for this report is: https://f1000research.com/articles/14-1037/v1#referee-response-432512 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Subbarao A. Reviewer Report For: Understanding BRISPOT Adoption in the SME Segment: The Role of Expectancy, Support, and Trust in a Digital Transformation Framework [version 1; peer review: 1 approved, 1 approved with reservations] . F1000Research 2025, 14 :1037 ( https://doi.org/10.5256/f1000research.185027.r429163 ) The direct URL for this report is: https://f1000research.com/articles/14-1037/v1#referee-response-429163 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 22 Nov 2025 Anusuyah Subbarao , Multimedia University, Cyberjaya, Malaysia Approved VIEWS 0 https://doi.org/10.5256/f1000research.185027.r429163 This paper addresses an important practical problem and employs appropriate methodology. The core findings regarding the UTAUT constructs are sound and contribute to understanding organizational technology adoption in banking. However, the paper requires major revisions before it can be ... Continue reading READ ALL This paper addresses an important practical problem and employs appropriate methodology. The core findings regarding the UTAUT constructs are sound and contribute to understanding organizational technology adoption in banking. However, the paper requires major revisions before it can be considered scientifically sound: 1. Statistical Reporting (Critical) Must add: R² values for Behavioral Intention and Use Behavior Effect sizes (f²) for all paths Predictive relevance (Q²) values Complete moderation analysis results in a table Path coefficient and statistics for the Trust × BI interaction term 2. Data Availability (Critical) Must resolve: Clarify the contradiction between confidentiality claims and Figshare availability Either provide public access to anonymized data or clearly explain restrictions Ensure compliance with F1000Research's open data policy 3. Method Details (Essential) Must add: Response rate and sampling frame details Respondent demographic characteristics (table) How the moderation term was computed and centered Key questionnaire items in an appendix (even if full survey is on Figshare) 4. Results Presentation Must improve: Add a table or figure showing moderation analysis Present model fit indices (SRMR at minimum) With these revisions, this would be a valuable contribution to the digital banking adoption literature, particularly regarding internal user adoption in developing economy contexts. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Digital Transformation, Big Data, Cloud Computing I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Subbarao A. Reviewer Report For: Understanding BRISPOT Adoption in the SME Segment: The Role of Expectancy, Support, and Trust in a Digital Transformation Framework [version 1; peer review: 1 approved, 1 approved with reservations] . F1000Research 2025, 14 :1037 ( https://doi.org/10.5256/f1000research.185027.r429163 ) The direct URL for this report is: https://f1000research.com/articles/14-1037/v1#referee-response-429163 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 03 Oct 2025 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 Version 1 03 Oct 25 read read Anusuyah Subbarao , Multimedia University, Cyberjaya, Malaysia Wasim Abbas Shaheen , Quaid-i-Azam University, Islamabad, Pakistan Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Shaheen W. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 06 Jan 2026 | for Version 1 Wasim Abbas Shaheen , Quaid-i-Azam University, Islamabad, Pakistan 0 Views copyright © 2026 Shaheen W. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The integration of UTAUT with Social Cognitive Theory and the inclusion of trust as a moderator is a clear strength, offering a nuanced framework for understanding internal technology adoption. The focus on relationship managers in a state-owned bank in Indonesia adds valuable context to the literature, which often emphasizes customer adoption in developed markets. The non-significant moderating role of trust, while unexpected, opens meaningful discussion about contextual factors in organizational adoption. The use of PLS-SEM is appropriate given the sample size and predictive aims. However, reporting is incomplete: key statistics such as R² values for endogenous constructs, effect sizes (f²), predictive relevance (Q²), and model fit indices (e.g., SRMR) are missing. Additionally, details on how the interaction term for moderation was calculated (e.g., mean-centering) are not provided. These omissions limit the ability to assess model quality and replicate the study. While the sample size meets PLS-SEM guidelines, the response rate and sampling frame are not described, raising concerns about selection bias. Demographic details of respondents (e.g., age, digital literacy, tenure) are absent but could enrich the interpretation of results. Furthermore, the data availability statement contradicts itself—claiming confidentiality while also citing a Figshare repository. This must be clarified to align with open data policies. Convergent and discriminant validity are adequately demonstrated via factor loadings, AVE, and Fornell-Larcker/HTMT. However, the operationalization of “trust” may be overly generic. In a banking context, trust could be multidimensional (e.g., trust in data security, system reliability, or organizational intent). The non-significant moderation might stem from measurement limitations rather than true absence of effect. The discussion logically interprets the significant findings, linking them to prior literature and practical steps for BRI. However, the non-significant moderation by trust is only briefly explained. A deeper exploration—perhaps referencing institutional trust in state-owned enterprises or mandatory usage contexts—would strengthen the contribution. The managerial recommendations are useful but could be more specific (e.g., tailored training, interface redesign). The limitations section appropriately notes the cross-sectional design and single-bank focus. However, it should also acknowledge the reliance on self-reported usage data, which may be biased. Future research suggestions are relevant but could be sharper—for example, proposing mixed methods to explore why trust did not moderate, or examining the role of digital literacy as a control variable. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Yes References 1. Tariq M, Maryam S, Shaheen W: Cognitive factors and actual usage of Fintech innovation: Exploring the UTAUT framework for digital banking. Heliyon . 2024; 10 (15). Publisher Full Text 2. Abdul Malik, Wasim Abbas Shaheen, Waqas: Cognitive Bias Asymmetry and Heuristic-Driven Market Anomalies: A Neurofinancial Noise Trading Analysis of Prospect Theory Elasticity in the Pakistan Stock Exchange (PSX). Social Sciences Spectrum . 2025; 4 (2): 108-141 Publisher Full Text 3. Maryam S, Saleem M, Ahmad A, Shaheen W: Leveraging information systems for competitive advantage: a study of Islamic financial institutions’ innovativeness and agility for Fintech financing. Journal of Islamic Marketing . 2025; 16 (12): 3711-3735 Publisher Full Text Competing Interests No competing interests were disclosed. Reviewer Expertise Business, Finance and Economics I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Shaheen WA. Peer Review Report For: Understanding BRISPOT Adoption in the SME Segment: The Role of Expectancy, Support, and Trust in a Digital Transformation Framework [version 1; peer review: 1 approved, 1 approved with reservations] . F1000Research 2025, 14 :1037 ( https://doi.org/10.5256/f1000research.185027.r432512) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-1037/v1#referee-response-432512 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Subbarao A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 22 Nov 2025 | for Version 1 Anusuyah Subbarao , Multimedia University, Cyberjaya, Malaysia 0 Views copyright © 2025 Subbarao A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This paper addresses an important practical problem and employs appropriate methodology. The core findings regarding the UTAUT constructs are sound and contribute to understanding organizational technology adoption in banking. However, the paper requires major revisions before it can be considered scientifically sound: 1. Statistical Reporting (Critical) Must add: R² values for Behavioral Intention and Use Behavior Effect sizes (f²) for all paths Predictive relevance (Q²) values Complete moderation analysis results in a table Path coefficient and statistics for the Trust × BI interaction term 2. Data Availability (Critical) Must resolve: Clarify the contradiction between confidentiality claims and Figshare availability Either provide public access to anonymized data or clearly explain restrictions Ensure compliance with F1000Research's open data policy 3. Method Details (Essential) Must add: Response rate and sampling frame details Respondent demographic characteristics (table) How the moderation term was computed and centered Key questionnaire items in an appendix (even if full survey is on Figshare) 4. Results Presentation Must improve: Add a table or figure showing moderation analysis Present model fit indices (SRMR at minimum) With these revisions, this would be a valuable contribution to the digital banking adoption literature, particularly regarding internal user adoption in developing economy contexts. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Digital Transformation, Big Data, Cloud Computing I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Subbarao A. Peer Review Report For: Understanding BRISPOT Adoption in the SME Segment: The Role of Expectancy, Support, and Trust in a Digital Transformation Framework [version 1; peer review: 1 approved, 1 approved with reservations] . F1000Research 2025, 14 :1037 ( https://doi.org/10.5256/f1000research.185027.r429163) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-1037/v1#referee-response-429163 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions Adjust parameters to alter display View on desktop for interactive features Includes Interactive Elements View on desktop for interactive features Competing Interests Policy Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. 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