Technology adoption and effective decision-making in financial institutions: Moderated-mediation analysis

Technology adoption and effective decision-making in financial institutions: Moderated-mediation analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Technology adoption and effective decision-making in financial institutions: Moderated-mediation analysis Isaac Okyere, Michelle Afrifah, Ethel Yiranbon Annor-Tenkorang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9227540/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Purpose: This study examines the influence of technology adoption in enhancing decision-making effectiveness within financial institutions, with a specific focus on the mediating role of risk management capabilities. It also investigates how managerial competence and digital culture moderate this relationship. Method: A cross-sectional survey design was used to collect data from 291 managerial staff in financial institutions, who were selected using a multi-stage sampling approach. The PROCESS macro was applied to analyse the hypothesised model. Findings : The study demonstrates that technology adoption significantly enhances effective decision-making in financial institutions, primarily through its impact on risk management capabilities. Furthermore, the moderating roles of managerial competence and digital culture were confirmed, with both factors amplifying the positive effects of technology adoption on decision-making through enhanced risk management capabilities. Originality/Value : This research provides a novel contribution to the understanding of how technology adoption influences decision-making in the financial sector. By focusing on managerial competence and digital culture as moderators and risk management capabilities as a mediator, the study offers practical insights for financial leaders to optimise decision-making through strategic technology integration. Technology adoption Risk management capabilities Decision-making Managerial competence Digital culture Financial institutions Figures Figure 1 Figure 2 Introduction In today’s dynamic and fast-paced financial environment, organisations face complex and ever-changing risks, ranging from market volatility to emerging cyber threats (Abikoye et al., 2024 ). The ability to make effective decisions in such uncertain conditions is crucial for ensuring long-term organisational success. Effective decision-making deals with the capacity to make choices that are not only informed and rational but also strategically aligned with an organisation’s objectives (Chikeleze and Baehrend, 2017 ). In financial institutions, where the stakes are high, adopting advanced technologies has become essential to supporting managerial decisions (Kou and Lu, 2025 ). Technology adoption refers to the process by which organisations incorporate new technologies to streamline operations, increase efficiency, and improve strategic decision-making (Ihejirika et al., 2025 ). While the adoption of technology has demonstrated potential in improving decision-making effectiveness, research on this relationship in an emerging context remains relatively underexplored. This study examines the nexus between technology adoption and effective decision-making in financial institutions. The financial sector is increasingly leveraging technologies to enhance decision-making processes. These technologies, including artificial intelligence (AI), machine learning, and big data analytics, have demonstrated a significant ability to improve how decision-makers evaluate, interpret, and mitigate financial risks. In this context, where uncertainty and risk are ever-present, technologies empower financial institutions by providing timely and relevant data, which leads to more accurate forecasts and better risk assessments (Chen, 2025 ). While much of the existing research on the impact of technology adoption on decision-making has focused on the direct effects, it has largely overlooked the mechanisms that explain these relationships. Contemporary scholars have begun investigating the factors influencing the direct association between technology adoption and decision-making (Ahmed et al., 2022 ; Hijazi et al., 2026 ). However, most studies have concentrated on the technical aspects of technology implementation, shaping the link between technology adoption and decision-making effectiveness (Adel, 2024 ; Kou and Lu, 2025 ), while neglecting the organisational processes. This research aims to address this gap by introducing employee risk management capabilities as a critical mediator. Specifically, this study suggests that as financial institutions integrate advanced technologies, they can enhance decision-making through improved risk management capabilities. Moreover, considering the mediation role of risk management capabilities alone does not guarantee improved decision-making outcomes. Numerous organisational mechanisms influence the linkage between technology adoption and effective decision-making through risk management capabilities. Building on Contingency Theory (Fiedler, 1971 ), this study explores the moderating roles of managerial competence and digital culture in shaping the association between technology adoption and effective decision-making through risk management capabilities. According to Contingency Theory, the effectiveness of leadership decisions is contingent upon the alignment between certain situational factors. In this context, managerial competence and digital culture are among the contingent factors that determine how effectively technology adoption translates into enhanced decision-making through risk management capabilities. The interaction between these leadership qualities and the organisational environment influences how technological advancements are leveraged to improve effective decision outcomes. Based on this, the study introduces managerial competence and digital culture as moderators that influence the mediating role of risk management capabilities in the linkage between technology adoption and decision-making processes. This study addresses these critical gaps in the literature by examining the mediation effect of risk management capabilities in the relationship between technology adoption and effective decision-making through the lens of TAM. Additionally, the study integrates Contingency Theory to investigate how organisational factors, such as managerial competence and digital culture, influence the strength and direction of the relationship between technology adoption and decision-making through risk management capabilities. This paper offers several important contributions to the field. First, it extends the TAM by highlighting an indirect pathway through which technology adoption affects decision-making, mediated by risk management capabilities. Second, it expands the understanding of managerial competence and digital culture by framing them as crucial boundary conditions that indirectly influence decision-making effectiveness through risk management processes. Lastly, through the integration of these two models, this study offers valuable insights into how financial institutions can strategically adopt technology to optimise their decision-making through risk management capabilities. Literature reviews Technology adoption and effective decision-making Technology adoption has become a crucial factor in enhancing effective decision-making within organisations. It refers to the decision to embrace and implement new technological tools to improve efficiency (Ihejirika et al., 2025 ). TAM explains that two main factors (perceived ease of use and perceived usefulness) influence the decision to adopt new technology (Davis, 1989 ). Perceived ease of use refers to the extent to which a technology is considered easy to operate, while perceived usefulness is the degree to which it is believed to enhance job performance (Addai, 2025 ). TAM suggests that when decision-makers find technology both easy to use and beneficial, they are more likely to adopt it, leading to better decision-making outcomes. The model proffers that technology adoption improves decision-making by increasing efficiency, accuracy, and the ability to process large volumes of data quickly. Technologies empower decision-makers by providing real-time insights and enhancing risk assessment capabilities, enabling more strategic and informed choices (Ahmed et al., 2022 ). Technology adoption influences effective decision-making by streamlining the decision process and reducing cognitive load on decision-makers (Gonçalves et al., 2024 ). By automating data collection and analysis, technologies enable leaders to focus on higher-level strategic thinking, rather than being bogged down in data processing tasks. Additionally, tools such as predictive modelling and data visualisation help decision-makers gain insights into potential outcomes and trends, making it easier to anticipate challenges and opportunities (Sarker, 2021 ). As decision-makers feel more confident in the reliability and speed of technology, they can make decisions more quickly and with greater accuracy. In essence, the adoption of technology provides the necessary support for timely, data-driven decision-making, enhancing both the quality and efficiency of decisions across various organisational settings (Ahmed et al., 2022 ). Studies have consistently shown that integration of technologies helps management make more strategic and confident decisions (Anwar et al., 2026 ; Sarker, 2021 ). Based on this, it is postulated that: H1. Technology adoption has a significant positive relationship with effective decision-making. Risk managerial capabilities as a mediator Risk management capabilities refer to an organisation’s ability to identify, assess, and mitigate risks using appropriate tools and strategies (Scott and Few, 2016 ). These capabilities include the processes, skills, and strategies that enable an organisation to anticipate, manage, and respond to risks proactively and effectively. Previous research has demonstrated that the adoption of technology enables organisations to make informed decisions more efficiently (Tian et al., 2025 ). However, the pathways through which technology adoption improves decision-making remain inadequately examined. This study suggests that risk management capabilities act as a crucial intermediary, enabling technology adoption to influence the quality of decisions made by managers, particularly in uncertain and high-risk environments. As organisations develop more robust risk management systems through technology, decision-makers are better equipped to navigate risks and make choices that align with the institution's strategic goals (Monazzam and Crawford, 2024 ). Studies have demonstrated a positive relationship between technology adoption and risk management capabilities, suggesting that the integration of advanced technologies enhances an organisation’s ability to identify, assess, and mitigate potential risks more effectively (Slassi-Sennou and Elmouhib, 2025 ). These technologies provide real-time data, predictive analytics, and automated decision-making tools, which improve the accuracy of risk assessments and enable proactive risk management strategies. Additionally, risk management capabilities have been found to predict effective decision-making, as they enable organisations to make informed, timely, and strategic decisions based on precise risk evaluations (Meesook et al., 2025 ). However, the role of risk management capabilities as a mediating factor linking technology adoption with effective decision-making has been insufficiently explored. Organisations with advanced risk management tools can leverage data analytics, predictive models, and decision-support systems to enhance both the quality and speed of their decision-making processes (Ahmed et al., 2022 ). By incorporating risk management frameworks into decision-making, technology not only streamlines the process but also ensures that decisions are both well-informed and strategically sound. Given the significant relationships between these constructs, the researchers hypothesise that (Fig. 1 ): H2 Risk management capabilities mediate the association between technology adoption and effective decision-making within financial institutions. Conditional moderation of managerial competence and digital culture The Contingency Theory provides an important framework for understanding how contextual factors influence the impact of technology adoption on effective decision-making through the lens of risk management capabilities. According to this theory, the effectiveness of an organisation’s strategies depends on how well they align with specific situational factors (Fiedler, 1971 ). This theory indicates that there is no one-size-fits-all approach to management; instead, the optimal strategy or structure is contingent upon the internal and external environments in which the organisation operates (Hijazi et al., 2026 ). This perspective suggests that organisational success in adopting technology depends not only on the technology itself but also on how well the organisation’s environment supports its integration. In the context of this study, two key factors (managerial competence and digital culture) are identified as contingency factors in which technology adoption influences effective decision-making via risk management capabilities. From an organisational perspective, managerial competence refers to the collective skills, knowledge, and capabilities of an organisation's leadership team that drive successful technology adoption and integration (Aliu et al., 2025 ). Organisations with competent managers ensure the appropriate selection of technological tools and integrate them effectively into existing risk management frameworks to enhance decision-making. In organisations with strong managerial competence, leaders align technology adoption with strategic goals, facilitating smoother transitions and amplifying the overall impact on organisational processes (Hijazi et al., 2026 ). When managerial competence is high, technology integration into risk management processes is more effective, leading to improved decision-making. Conversely, organisations lacking managerial competence may face challenges with technology misalignment, resulting in inefficiencies in risk management and undermining the potential benefits of technology adoption (López-González et al., 2024 ). Similarly, digital culture within an organisation plays a pivotal role in determining how well technology is embraced and utilised by employees. Digital culture refers to the shared values, behaviours, and attitudes regarding the use of digital technologies within the organisation (Giannini and Bowen, 2019 ). A strong digital culture is characterised by openness to innovation, a willingness to adapt to technological changes, and a collective understanding of the importance of digital tools in enhancing business outcomes. Organisations with a positive digital culture foster an environment where employees view technology as an enabler of success, which in turn facilitates the adoption of new systems and tools (Cao et al., 2025 ). This culture encourages employees to adopt technology for risk management, contributing to effective decision-making. Conversely, organisations that lack a supportive digital culture may experience resistance to change, leading to underutilisation or improper application of technological tools. In such contexts, the potential benefits of technology adoption in improving risk management capabilities and consequently decision-making may be diminished (Giannini and Bowen, 2019 ). Based on the foregoing discussion, the researchers postulate the following hypotheses (Fig. 1 ): H3 Managerial competence positively moderates the relationship between technology adoption and effective decision-making through risk management capabilities. H4 Digital culture positively moderates the relationship between technology adoption and effective decision-making through risk management capabilities. Method Design, procedure and data analysis A quantitative, cross-sectional survey design was utilised to investigate the influence of technology adoption on effective decision-making in financial institutions, with a particular focus on the moderated mediation roles of managerial competence and digital culture through risk management capabilities. The cross-sectional approach was chosen because it allows for data collection at a single point in time (Capili, 2021 ). Financial institutions were used due to their critical reliance on technology for risk management and decision-making processes. Managerial staff were selected for this study because they are the key decision-makers within financial institutions. Managerial staff also possess firsthand knowledge of the factors affecting decision-making and risk management within the organisation (Kozioł-Nadolna and Beyer, 2021 ). In this study, a multi-stage sampling technique was employed to select the sample. The first stage identified five regions in Ghana from which financial institutions were selected. These regions were chosen strategically to ensure diverse representation of financial institutions across the country. In the second stage, financial institutions within these regions were selected, with a total of 30 institutions chosen using a cluster sampling technique. The third stage focused on selecting respondents from these institutions. A sample frame comprising all employees from the 30 selected financial institutions across the five regions was constructed. Convenience sampling was then used to select 350 respondents. To calculate the appropriate sample size, the method proposed by Tabachnick and Fidell ( 1996 ) was applied, using the formula: N > 50 + 8(p), where p represents the number of variables. Given that five variables were included in the study, the minimum sample size was calculated to be 90. Therefore, 350 respondents were chosen to ensure sufficient statistical power and representativeness. This multi-stage sampling approach ensured a broad and varied sample, providing a solid foundation for analysing the relationship between technology adoption and effective decision-making in financial institutions (Ahmed, 2024 ). A total of 350 questionnaires were distributed to respondents across the selected institutions, and 291 completed responses were returned, yielding a response rate of 83.1%. Among the respondents, 47.4% were male, and 53.6% were female, with ages ranging from 39 to 53 years. The sample included 60.3% middle management and 39.7% senior management. The collected data were analysed using the PROCESS Macro for SPSS to test both mediation and moderation effects in the proposed model. The PROCESS Macro was chosen because of its ability to handle complex relationships, including testing direct, indirect, and interaction effects (Hayes, 2022 ). This tool is widely recognised for its statistical power and efficiency in analysing how risk management capabilities mediate the association between technology adoption and effective decision-making, while also testing the moderating roles of managerial competence and digital culture. Measures The instruments for data collection utilised a five-point Likert scale, ranging from 1 “strongly disagree” to 5 “strongly agree.” Technology adoption was measured using the 8-item Technology Adoption Scale by Ratchford and Barnhart ( 2012 ). The scale was adapted to the context of financial institutions to capture how organisations adopt new technologies. This scale exhibited strong reliability, with a Cronbach's alpha of 0.90, confirming its consistency and robustness in the context of financial institutions. A sample item is: “Our organisation regularly adopts new technologies to improve efficiency.” Risk management capabilities were assessed using an adapted scale from the works of Gama et al. ( 2020 ). This scale consists of seven items, capturing both reactive and proactive risk management actions. The scale demonstrated satisfactory reliability with a Cronbach's alpha of 0.87. A sample item from the scale reads: “The organisation has effective measures in place to identify emerging risks in real-time.” Managerial competence was assessed using an adapted version of Gunawan et al. ( 2019 ) Managerial Competency Scale. This scale comprises five items that evaluate managerial skills in areas such as strategic thinking, leadership, and the ability to leverage technology for organisational goals. With a Cronbach's alpha of 0.89, the scale showed strong reliability in the context of this study. A sample item is: “The managers in this organisation effectively guide and support their teams in achieving organisational goals.” The Digital Culture Scale used in this study was developed by Hautala-Kankaanpää ( 2022 ). The scale assesses the organisational environment’s openness to technology and innovation. This scale includes six items that measure the extent to which an organisation fosters a culture of innovation and adaptability to digital change. A high Cronbach’s alpha of 0.87 was reported, ensuring the scale’s robustness and reliability in assessing digital culture within financial institutions. A sample item is: “The organisation promotes continuous learning to enhance digital skills among employees.” The effectiveness of decision-making was measured with a customised 6-item scale developed by Bennett et al. ( 2010 ). The scale evaluates decision quality, timeliness, and alignment with organisational goals. The scale had an excellent reliability, with a Cronbach’s alpha of 0.91. A sample item is: “The decisions made by my organisation are well-informed and effectively contribute to the organisation’s success.” In addition to the primary constructs, control variables were included to account for individual demographic factors that might influence technology adoption and decision-making. These included age and level of management (middle management and senior management). These demographic characteristics were controlled because prior research has indicated that they significantly impact technology adoption and decision-making practices (Addai, 2025 ; Cruz-Cárdenas et al., 2019 ). By including these variables, the study aimed to control for their potential influence, ensuring that the results are not biased by external factors. Pilot testing To ensure the reliability and validity of the measurement scales, a pilot study was conducted with 30 managerial staff members from various financial institutions not included in the main study. The purpose of the pilot test was to assess the clarity, relevance, and applicability of the survey items in the context of Ghanaian financial institutions. Feedback gathered from the pilot study was used to refine the questionnaire, with minor adjustments made to the wording of certain items for improved clarity and cultural relevance, ensuring alignment with local practices. After these revisions, the final questionnaire exhibited strong reliability, with a Cronbach's alpha of 0.86 for all major constructs, indicating high internal consistency (Cheung et al., 2024 ). Results Assessment of the measurement model An initial exploratory factor analysis (EFA) was performed to assess the factor structure of the constructs: technology adoption, effective decision-making, risk management capabilities, managerial competence, and digital culture. The factor loadings of all items exceeded the critical threshold of 0.70, indicating strong alignment with their respective constructs (Hair et al., 2017 ). The results confirmed that the model exhibited single-dimensionality, meaning the items collectively capture their intended purpose (see Table 1 ). Furthermore, goodness-of-fit indices demonstrated that the model fit the data well, supporting the sturdiness of the measurement model in capturing the relationships between the underlying variables. Table 1 Factor loadings and psychometric properties of the measures Measures Loadings Variance Exp. (%) α CR AVE Technology adoption (TA) 27.149 0.902 0.912 0.660 TA2 0.918** TA8 0.904** TA1 0.885** TA6 0.872** TA3 0.855** TA5 0.837** TA7 0.830** TA4 0.811** Risk management capabilities (RMC) 16.053 0.866 0.849 0.627 RMC1 0.877** RMC3 0.860** RMC7 0.853** RMC6 0.851** RMC5 0.838** RMC4 0.822** RMC2 0.820** Managerial competence (MC) 13.077 0.892 0.895 0.608 MC3 0.906** MC5 0.891** MC2 0.868** MC4 0.850** MC1 0.827** Digital culture (DC) 10.471 0.870 0.883 0.595 DC3 0.883** DC4 0.870** DC1 0.855** DC6 0.838** DC5 0.824** DC2 0.817** Effective decision-making (EDM) 8.315 0.891 0.913 0.610 EDM4 0.911** EDM1 0.883** EDM6 0.870** EDM2 0.866** EDM5 0.854** EDM3 0.837** Note(s) : KMO = 0.815; Bartlett’s test of sphericity: χ2 = 1907.359, p < 0.001 Source(s) : Field survey (2025) Suitability for factor analysis and factor structure The appropriateness of the data for factor analysis was assessed using Bartlett’s Test of Sphericity and the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy. A KMO value of 0.815 was obtained, indicating high sampling adequacy and confirming that the sample size was sufficient for the analysis (Kaiser, 1974 ). Additionally, Bartlett’s Test was significant (χ² = 1907.359, p < .001), supporting the appropriateness of the data for factor extraction. Principal Component Analysis (PCA) revealed that the factors accounted for 75.065% of the total variance, indicating that the selected variables were highly explanatory and well-represented within the data. Reliability and validity assessment Reliability was examined using Composite Reliability (CR), with values for all constructs exceeding the threshold of 0.70. Specifically, the constructs demonstrated the following composite reliability coefficients: technology adoption (CR = 0.912), risk management capabilities (CR = 0.849), managerial competence (CR = 0.895), digital culture (CR = 0.883), and effective decision-making (CR = 0.913). The Average Variance Extracted (AVE) was computed to assess convergent validity for each construct. All AVE values were greater than the 0.50 threshold. This confirms that the items within each construct effectively measured the intended concept (Cheung et al., 2024 ). Discriminant validity was established through the square root of AVE, which was found to be higher than the inter-construct correlations. This confirms that the constructs were distinct and not overly correlated. Moreover, to assess multicollinearity, the Variance Inflation Factor (VIF) values were examined, with all scores well below the threshold of 5, signifying that multicollinearity was not a concern (Hair et al., 2017 ) (see Table 2 ). Common method bias (CMB) and the normality of data Given the cross-sectional nature of the data, the potential for CMB was carefully considered. To mitigate this risk, the Harman’s Single Factor Test was employed. The results showed that the single-factor model did not adequately fit the data (CFI = 0.582, SRMR = 0.194, RMSEA = 0.471) compared to the five-factor model (CFI = 0.829, SRMR = 0.081, RMSEA = 0.073). This indicates that CMB was not a significant issue. Additionally, the study employed multiple data collection methods with varied response channels to reduce the likelihood of bias in the responses. The use of well-designed questionnaires further minimised self-report biases, ensuring that the relationships identified in the model accurately reflect the constructs of interest. The normality of the data was assessed using both Kolmogorov-Smirnov and Shapiro-Wilk tests, both of which indicated a normal distribution (p > 0.05). Additionally, correlation coefficients were examined to verify the linkages between the constructs. The values were found to be below the 0.70 threshold, indicating no concern with multicollinearity (see Table 2 ). Table 2 Descriptives and intercorrelation between the variables (n = 291) Measures Intercorrelations 1 2 3 4 5 1 Technology adoption - 2 Risk management capabilities 0.30 ** - 3 Managerial competence 0.39 ** 0.40 ** - 4 Digital culture 0.51 ** 0.27 ** 0.26 ** - 5 Effective decision-making 0.47 ** 0.38 ** 0.44 ** 0.38 ** - MSV 0.17 0.32 0.29 0.44 0.30 VIF 3.08 4.16 1.93 2.70 1.68 Mean 2.71 2.33 3.95 2.84 3.16 Stand. Deviation 1.04 2.72 0.96 1.84 1.15 **p < 0.01 Hypotheses Testing To investigate the relationships proposed in H1 and H2, which explore the connections between technology adoption and effective decision-making, and the mediating role of risk management capabilities, data analysis was conducted using Model 4 of the PROCESS macro. The results confirmed a statistically significant positive association between technology adoption and effective decision-making (β = 0.207, t = 12.176, LLCI = 0.174, ULCI = 0.240, p < 0.001), supporting H1. Additionally, the analysis confirmed that risk management capabilities partially mediate the association between technology adoption and effective decision-making (β = 0.319, t = 3.396, LLCI = 0.134, ULCI = 0.503, p < 0.001) since the direct relationship was significant, thus validating H2. In assessing the moderated mediation effects postulated in H3 and H4, Model 7 of the PROCESS macro was utilised. The results revealed that managerial competence significantly moderates this relationship between technology adoption and effective decision-making through risk management capabilities (β = 0.291, t = 9.387, LLCI = 0.230, ULCI = 0.352, ΔR² = 0.171, p < 0.01) (Fig. 2 ). Specifically, the interaction term indicated that higher managerial competence strengthens the positive impact of technology adoption on decision-making by improving the effectiveness of risk management capabilities, supporting H3. Similarly, the analysis for H4 indicated that digital culture also moderates the nexus between technology adoption and effective decision-making through risk management (β = 0.185, t = 7.115, LLCI = 0.134, ULCI = 0.236, ΔR² = 0.106, p < 0.001). This shows that financial institutions with a strong digital culture experience greater benefits from technology adoption, as employees within such a culture are more proficient at integrating technology into their decision-making and risk management processes. This finding affirms H4 (Fig. 2 ). Discussion This study examined the direct and indirect effects of technology adoption on effective decision-making in financial institutions via risk management capabilities, with a focus on managerial competence and digital culture as moderators. The first hypothesis, which proposed a significant association between technology adoption and effective decision-making, was supported. The results suggest that financial institutions adopting advanced technologies are better equipped to make informed and timely decisions. This finding aligns with the TAM, which posits that technology adoption is influenced by its perceived usefulness and ease of use (Davis, 1989 ). In decision-making, technological tools such as predictive modelling provide valuable insights and enhance decision quality. This is consistent with the work of Chen ( 2025 ) and Ahmed et al. ( 2022 ), who argue that technology improves decision-making by providing real-time data and advanced risk assessments, thereby empowering leaders to make strategic, data-driven decisions. By adopting technology, institutions can reduce uncertainty, speed up their decision-making processes, and better align decisions with organisational goals. The second hypothesis, which suggested that risk management capabilities mediate the relationship between technology adoption and effective decision-making, was also supported. The results revealed that integrating technology into risk management processes significantly enhanced decision-making within financial institutions. This mediation effect suggests that while technology adoption provides the tools for risk assessment and mitigation, it is the strengthened risk management capabilities that act as the mechanism through which technology influences decision-making. This finding aligns with the growing body of literature emphasising the importance of risk management in shaping organisational decision-making (Anderson and Fraser, 2020). By improving risk management capabilities, technology adoption helps financial institutions minimise uncertainties and enhance their decision-making processes. The third and fourth hypotheses explored the moderating effects of managerial competence and digital culture on the relationship between technology adoption and effective decision-making through risk management capabilities. Both hypotheses were supported by the results, which highlighted the critical roles these organisational factors play in the successful integration of technology in decision-making processes. First, managerial competence was found to positively moderate the relationship between technology adoption and effective decision-making through risk management capabilities. This finding suggests that financial institutions with skilled and knowledgeable managers are better positioned to leverage technological tools for enhanced decision-making within risk management frameworks. From the perspective of Contingency Theory, which underscores the importance of context-specific factors, this result emphasises the vital role of managerial expertise in the successful integration of technology (Tushman and O'Reilly, 1996). Similarly, digital culture was found to moderate the relationship between technology adoption and effective decision-making through risk management capabilities. This suggests that institutions with a strong digital culture, characterised by openness to change, adaptability, and a proactive approach to technology, experience more significant benefits from technology adoption. A robust digital culture fosters an environment where employees embrace technological innovations, integrate them into their daily work, and use them effectively in decision-making processes. Implications This study makes a significant contribution to the body of knowledge by exploring the direct effects of technology adoption on decision-making processes within financial institutions. It particularly advances the understanding of how risk management capabilities mediate this relationship and how managerial competence and digital culture serve as moderators in the context of technology adoption. By investigating these dynamics, this research fills a notable gap in the literature, particularly within the financial sector, where technology adoption is essential for enhancing decision-making. The findings suggest that technology adoption not only enhances risk management capabilities but also improves decision-making by providing managers with the tools necessary to analyse and mitigate risks effectively. Furthermore, the study reveals the critical role of organisational factors, such as managerial competence and digital culture, in ensuring that technology adoption translates into tangible improvements in decision-making. This study broadens the scope of knowledge on the intersection of technology, risk management, and decision-making in organisations. Theoretically, this study contributes to the development of the TAM and Contingency Theory. By applying TAM to examine the association between technology adoption and effective decision-making, this research supports and extends existing literature by demonstrating how the perceived usefulness and ease of use of technology enhance organisational decision-making outcomes. This study further extends the original framework of TAM, showcasing its relevance in the financial sector, where decision-making is heavily reliant on accurate, timely data and advanced risk assessments. Moreover, the study’s use of Contingency Theory in analysing the moderated mediation effects adds depth to our understanding of how contextual factors, such as managerial competence and digital culture, influence the effectiveness of technology adoption. By linking these theoretical frameworks, the study provides a more holistic understanding of how technology interacts with organisational variables to shape decision-making and risk management outcomes. From a managerial perspective, the findings offer practical, actionable recommendations for improving decision-making and risk management in financial institutions. First, the positive direct relationship between technology adoption and effective decision-making underscores the importance of investing in advanced technologies. Financial institutions should prioritise the integration of technological tools that enhance their decision-making processes by providing more accurate and real-time data. Managers should advocate for technological upgrades that improve data-driven decision-making, enabling quicker, more informed choices and reducing the uncertainty inherent in complex financial environments. Second, the mediation effect of risk management capabilities suggests that organisations need to ensure that their risk management frameworks are equipped to handle the influx of technological tools. Managers must focus on building robust risk management strategies that integrate new technologies, allowing them to optimise decision-making. Lastly, the moderating effects of managerial competence and digital culture highlight the essential role of leadership and organisational culture in the success of technology adoption. To maximise the positive influence of technology, financial institutions should invest in leadership development programs that focus on enhancing managerial skills and digital literacy. Additionally, fostering a digital culture within the organisation will further strengthen the positive association between technology and decision-making through risk management capabilities. By prioritising these actionable strategies, managers can enhance their institution's ability to leverage technology for better risk management and more effective decision-making. Limitations and Research Suggestions This study comes with several limitations that need to be addressed in future research. First, the cross-sectional design limits the ability to make causal inferences between technology adoption, risk management capabilities, and effective decision-making, as it only captures a snapshot of these constructs at one point in time. A longitudinal approach could provide deeper insights into the temporal dynamics and causal pathways of these relationships. Second, the study's exclusive focus on the financial institutions may limit the generalisability of the findings to other industries with different technological needs and organisational contexts. Expanding the scope to include other sectors such as education, healthcare, or manufacturing would offer a broader understanding of how technology adoption impacts decision-making across diverse settings. Finally, the reliance on questionnaires as the sole data collection method may introduce self-report bias and limit the depth of insight into the complex phenomena studied. Future research could benefit from employing a mixed-methods approach, integrating qualitative interviews or case studies, and supplementing self-reported data with objective performance metrics to enhance the validity and comprehensiveness of the findings. Declarations DATA AVAILABILITY STATEMENT The data supporting the findings of the study are available upon reasonable request by contacting the corresponding author. ETHICAL STATEMENT Funding : Not applicable. Competing interests : The author declares that he has no conflicts of interest that influence this research. Ethical approval : The research adhered to the ethical standards outlined by the journal. Furthermore, prior to commencement, approval was granted by the Institutional Review Board, ensuring compliance with ethical protocols. Informed Consent : Before engaging in the research, all participants received a comprehensive overview of the study's objectives and provided their informed consent. Author Contribution Authors' Contributions: All the authors conceived the study and performed data analysis. I.O. brought the idea, wrote the methodology, and supervised the research. M.A. and E.Y.A. wrote the draft; P.A. performed the analysis and wrote the results section. All authors read and approved the final manuscript for submission. References Abikoye BE, Umeorah SC, Adelaja AO, Ayodele OF, Ogunsuji YM (2024) Regulatory compliance and efficiency in financial technologies: Challenges and innovations. World J Adv Res Reviews 23(1):1830–1844. https://doi.org/10.30574/wjarr.2024.23.1.2174 Addai P (2025) Leading with intelligence: How AI leadership, innovation culture, AI acceptance, and digital maturity transform talent management in public service. 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Springer. https://doi.org/10.1007/978-3-319-97457-6_1 Gonçalves AR, Pinto DC, Shuqair S, Dalmoro M, Mattila AS (2024) Artificial intelligence vs. autonomous decision-making in streaming platforms: A mixed-method approach. Int J Inf Manag 76:102748. https://doi.org/10.1016/j.ijinfomgt.2023.102748 Gunawan J, Aungsuroch Y, Fisher ML, McDaniel AM (2019) Development and psychometric properties of managerial competence scale for first-line nurse managers in Indonesia. Sage Open Nurs 5:2377960819831468. https://doi.org/10.1177/2377960819831468 Hair JF, Hult GTM, Ringle CM, Sarstedt M (2017) A primer on partial least squares structural equation modelling (PLS-SEM), 2nd edn. Sage Hautala-Kankaanpää T (2022) The impact of digitalization on firm performance: Examining the role of digital culture and the effect of supply chain capability. Bus Process Manage J 28(8):90–109. https://doi.org/10.1108/BPMJ-03-2022-0122 Hayes AF (2022) Introduction to mediation, moderation, and conditional process analysis: A regression-based approach, 3rd edn. Guilford Press Hijazi AA, Das P, Li Y, Moehler RC, Maxwell DW (2026) A contingency approach to platform implementation: Platform types, attributes, and value chain integration. Building Res Inform 54(1):4–23. https://doi.org/10.1080/09613218.2025.2510270 Ihejirika BC, Isichei EE, Agbaeze EK (2025) Technology adoption and employees' performance: The moderating role of coronaphobia, African Journal of Economic and Management Studies , ahead-of-print. https://doi.org/10.1108/AJEMS-04-2024-0242 Kaiser HF (1974) An index of factorial simplicity, Psychometrika , Vol. 39 No. 1, pp. 31–36. https://doi.org/10.1007/BF02291575 Kou G, Lu Y (2025) FinTech: A literature review of emerging financial technologies and applications. Financial Innov 11., Article 1. https://doi.org/10.1186/s40854-024-00668-6 Kozioł-Nadolna K, Beyer K (2021) Determinants of the decision-making process in organizations. Procedia Comput Sci 192:2375–2384. https://doi.org/10.1016/j.procs.2021.09.006 López-González J, Martínez JM, Lomboy M, Expósito L (2024) Study of emotional intelligence and leadership competencies in university students. Cogent Educ 11:1. https://doi.org/10.1080/2331186X.2024.2411826 Meesook K, Imjai N, Usman B, Vongchavalitkul B, Aujirapongpan S (2025) The influence of AI literacy on risk management skills and the roles of diagnostic capabilities and prognostic capabilities: Empirical insight from Thai Gen Z accounting students. Int J Inform Manage Data Insights 5(1) Article 100341. https://doi.org/10.1016/j.jjimei.2025.100341 Monazzam A, Crawford J (2024) The role of enterprise risk management in enabling organisational resilience: A case study of the Swedish mining industry. J Manage Control 35:59–108. https://doi.org/10.1007/s00187-024-00370-9 Ratchford M, Barnhart M (2012) Development and validation of the technology adoption propensity (TAP) index, Journal of Business Research , Vol. 65 No. 8, pp. 1209–1215. https://doi.org/10.1016/j.jbusres.2011.07.001 Sarker IH (2021) Data science and analytics: An overview from data-driven smart computing, decision-making, and applications perspective. SN Comput Sci 2(5) Article 377. https://doi.org/10.1007/s42979-021-00765-8 Scott Z, Few R (2016) Strengthening capacities for disaster risk management I: Insights from existing research and practice. Int J Disaster Risk Reduct 20:145–153. https://doi.org/10.1016/j.ijdrr.2016.04.010 Slassi-Sennou S, Elmouhib S (2025) Managing financial and operational risks through digital transformation: The mediating influence of information and communication technologies’ adoption and resistance to change. J Risk Financial Manage 18(3):128. https://doi.org/10.3390/jrfm18030128 Tabachnick BG, Fidell LS (1996) Multivariate analysis, 3rd edn. HarperCollins College Tian K, Zhu Z, Mbachu J, Ghanbaripour A, Moorhead M (2025) Artificial intelligence in risk management within the realm of construction projects: A bibliometric analysis and systematic literature review. J Innov Knowl 10(3):100711. https://doi.org/10.1016/j.jik.2025.100711 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9227540","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":613056202,"identity":"db30d914-1505-4607-b7a9-ed6522c2ed39","order_by":0,"name":"Isaac Okyere","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYBACNiAEATkonxmIeYjTYgxRTYwWBqiWxAaitfCxt6VJfNxjkz7f/fwxCYYK68QG/rUH8NvBc+yY5IxnabkbzySzSTCcSU9skHiXgF+LRHqbNM+Bw7kbZzCzSTC2HQZqOWNAWMufA//TDcFa/hGlJe2YNMOBAwnyEiAtDUAt/D0EtPAcS7bsOZBsuIEn2dgi4Vi6cZsED34t8u1thjd+HLCTl28/+PDGhxpr2X5+Ag4DAhYJEGlwAEgkgJ2aQEgHA/MHsHUNMD7/AYJaRsEoGAWjYGQBAA0MQdz3QOztAAAAAElFTkSuQmCC","orcid":"","institution":"Ghana Communication Technology University","correspondingAuthor":true,"prefix":"","firstName":"Isaac","middleName":"","lastName":"Okyere","suffix":""},{"id":613056203,"identity":"e670d668-934b-44bb-a0df-aac42eff65af","order_by":1,"name":"Michelle Afrifah","email":"","orcid":"","institution":"Ghana Communication Technology University","correspondingAuthor":false,"prefix":"","firstName":"Michelle","middleName":"","lastName":"Afrifah","suffix":""},{"id":613056204,"identity":"79c3b3bc-e749-436e-976b-77950bac0f64","order_by":2,"name":"Ethel Yiranbon Annor-Tenkorang","email":"","orcid":"","institution":"Ghana Communication Technology University","correspondingAuthor":false,"prefix":"","firstName":"Ethel","middleName":"Yiranbon","lastName":"Annor-Tenkorang","suffix":""},{"id":613056205,"identity":"88645dce-f332-4a9a-95a6-228ffd050eee","order_by":3,"name":"Prince Addai","email":"","orcid":"","institution":"Ghana Communication Technology University","correspondingAuthor":false,"prefix":"","firstName":"Prince","middleName":"","lastName":"Addai","suffix":""}],"badges":[],"createdAt":"2026-03-25 23:38:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9227540/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9227540/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105576026,"identity":"d19894ca-a58b-4229-a6a2-3e1e99268205","added_by":"auto","created_at":"2026-03-27 13:42:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":5350,"visible":true,"origin":"","legend":"\u003cp\u003eHypothesised model\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9227540/v1/a4f511c950c2027f30fd6fc2.png"},{"id":105576396,"identity":"458164d6-15cc-4431-a9c8-9dcda55e2773","added_by":"auto","created_at":"2026-03-27 13:44:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":6172,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eEmpirical results\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9227540/v1/e5f37e00e6f200ad38ff07b0.png"},{"id":105576792,"identity":"e9c1fb36-9bfe-4f6f-ab9c-4c1043a62962","added_by":"auto","created_at":"2026-03-27 13:45:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":944399,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9227540/v1/a78b46c3-3919-4eda-955d-bfb45b775852.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Technology adoption and effective decision-making in financial institutions: Moderated-mediation analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn today\u0026rsquo;s dynamic and fast-paced financial environment, organisations face complex and ever-changing risks, ranging from market volatility to emerging cyber threats (Abikoye et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The ability to make effective decisions in such uncertain conditions is crucial for ensuring long-term organisational success. Effective decision-making deals with the capacity to make choices that are not only informed and rational but also strategically aligned with an organisation\u0026rsquo;s objectives (Chikeleze and Baehrend, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In financial institutions, where the stakes are high, adopting advanced technologies has become essential to supporting managerial decisions (Kou and Lu, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Technology adoption refers to the process by which organisations incorporate new technologies to streamline operations, increase efficiency, and improve strategic decision-making (Ihejirika et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). While the adoption of technology has demonstrated potential in improving decision-making effectiveness, research on this relationship in an emerging context remains relatively underexplored. This study examines the nexus between technology adoption and effective decision-making in financial institutions.\u003c/p\u003e \u003cp\u003eThe financial sector is increasingly leveraging technologies to enhance decision-making processes. These technologies, including artificial intelligence (AI), machine learning, and big data analytics, have demonstrated a significant ability to improve how decision-makers evaluate, interpret, and mitigate financial risks. In this context, where uncertainty and risk are ever-present, technologies empower financial institutions by providing timely and relevant data, which leads to more accurate forecasts and better risk assessments (Chen, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). While much of the existing research on the impact of technology adoption on decision-making has focused on the direct effects, it has largely overlooked the mechanisms that explain these relationships. Contemporary scholars have begun investigating the factors influencing the direct association between technology adoption and decision-making (Ahmed et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hijazi et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). However, most studies have concentrated on the technical aspects of technology implementation, shaping the link between technology adoption and decision-making effectiveness (Adel, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kou and Lu, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), while neglecting the organisational processes. This research aims to address this gap by introducing employee risk management capabilities as a critical mediator. Specifically, this study suggests that as financial institutions integrate advanced technologies, they can enhance decision-making through improved risk management capabilities.\u003c/p\u003e \u003cp\u003eMoreover, considering the mediation role of risk management capabilities alone does not guarantee improved decision-making outcomes. Numerous organisational mechanisms influence the linkage between technology adoption and effective decision-making through risk management capabilities. Building on Contingency Theory (Fiedler, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1971\u003c/span\u003e), this study explores the moderating roles of managerial competence and digital culture in shaping the association between technology adoption and effective decision-making through risk management capabilities. According to Contingency Theory, the effectiveness of leadership decisions is contingent upon the alignment between certain situational factors. In this context, managerial competence and digital culture are among the contingent factors that determine how effectively technology adoption translates into enhanced decision-making through risk management capabilities. The interaction between these leadership qualities and the organisational environment influences how technological advancements are leveraged to improve effective decision outcomes. Based on this, the study introduces managerial competence and digital culture as moderators that influence the mediating role of risk management capabilities in the linkage between technology adoption and decision-making processes.\u003c/p\u003e \u003cp\u003eThis study addresses these critical gaps in the literature by examining the mediation effect of risk management capabilities in the relationship between technology adoption and effective decision-making through the lens of TAM. Additionally, the study integrates Contingency Theory to investigate how organisational factors, such as managerial competence and digital culture, influence the strength and direction of the relationship between technology adoption and decision-making through risk management capabilities. This paper offers several important contributions to the field. First, it extends the TAM by highlighting an indirect pathway through which technology adoption affects decision-making, mediated by risk management capabilities. Second, it expands the understanding of managerial competence and digital culture by framing them as crucial boundary conditions that indirectly influence decision-making effectiveness through risk management processes. Lastly, through the integration of these two models, this study offers valuable insights into how financial institutions can strategically adopt technology to optimise their decision-making through risk management capabilities.\u003c/p\u003e\n\u003ch3\u003eLiterature reviews\u003c/h3\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eTechnology adoption and effective decision-making\u003c/h2\u003e \u003cp\u003eTechnology adoption has become a crucial factor in enhancing effective decision-making within organisations. It refers to the decision to embrace and implement new technological tools to improve efficiency (Ihejirika et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). TAM explains that two main factors (perceived ease of use and perceived usefulness) influence the decision to adopt new technology (Davis, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). Perceived ease of use refers to the extent to which a technology is considered easy to operate, while perceived usefulness is the degree to which it is believed to enhance job performance (Addai, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). TAM suggests that when decision-makers find technology both easy to use and beneficial, they are more likely to adopt it, leading to better decision-making outcomes. The model proffers that technology adoption improves decision-making by increasing efficiency, accuracy, and the ability to process large volumes of data quickly. Technologies empower decision-makers by providing real-time insights and enhancing risk assessment capabilities, enabling more strategic and informed choices (Ahmed et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTechnology adoption influences effective decision-making by streamlining the decision process and reducing cognitive load on decision-makers (Gon\u0026ccedil;alves et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). By automating data collection and analysis, technologies enable leaders to focus on higher-level strategic thinking, rather than being bogged down in data processing tasks. Additionally, tools such as predictive modelling and data visualisation help decision-makers gain insights into potential outcomes and trends, making it easier to anticipate challenges and opportunities (Sarker, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As decision-makers feel more confident in the reliability and speed of technology, they can make decisions more quickly and with greater accuracy. In essence, the adoption of technology provides the necessary support for timely, data-driven decision-making, enhancing both the quality and efficiency of decisions across various organisational settings (Ahmed et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Studies have consistently shown that integration of technologies helps management make more strategic and confident decisions (Anwar et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2026\u003c/span\u003e; Sarker, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Based on this, it is postulated that:\u003c/p\u003e \u003cp\u003eH1. Technology adoption has a significant positive relationship with effective decision-making.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRisk managerial capabilities as a mediator\u003c/h3\u003e\n\u003cp\u003eRisk management capabilities refer to an organisation\u0026rsquo;s ability to identify, assess, and mitigate risks using appropriate tools and strategies (Scott and Few, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These capabilities include the processes, skills, and strategies that enable an organisation to anticipate, manage, and respond to risks proactively and effectively. Previous research has demonstrated that the adoption of technology enables organisations to make informed decisions more efficiently (Tian et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, the pathways through which technology adoption improves decision-making remain inadequately examined. This study suggests that risk management capabilities act as a crucial intermediary, enabling technology adoption to influence the quality of decisions made by managers, particularly in uncertain and high-risk environments. As organisations develop more robust risk management systems through technology, decision-makers are better equipped to navigate risks and make choices that align with the institution's strategic goals (Monazzam and Crawford, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStudies have demonstrated a positive relationship between technology adoption and risk management capabilities, suggesting that the integration of advanced technologies enhances an organisation\u0026rsquo;s ability to identify, assess, and mitigate potential risks more effectively (Slassi-Sennou and Elmouhib, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These technologies provide real-time data, predictive analytics, and automated decision-making tools, which improve the accuracy of risk assessments and enable proactive risk management strategies. Additionally, risk management capabilities have been found to predict effective decision-making, as they enable organisations to make informed, timely, and strategic decisions based on precise risk evaluations (Meesook et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, the role of risk management capabilities as a mediating factor linking technology adoption with effective decision-making has been insufficiently explored. Organisations with advanced risk management tools can leverage data analytics, predictive models, and decision-support systems to enhance both the quality and speed of their decision-making processes (Ahmed et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). By incorporating risk management frameworks into decision-making, technology not only streamlines the process but also ensures that decisions are both well-informed and strategically sound. Given the significant relationships between these constructs, the researchers hypothesise that (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e):\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH2\u003c/strong\u003e \u003cp\u003eRisk management capabilities mediate the association between technology adoption and effective decision-making within financial institutions.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eConditional moderation of managerial competence and digital culture\u003c/h3\u003e\n\u003cp\u003eThe Contingency Theory provides an important framework for understanding how contextual factors influence the impact of technology adoption on effective decision-making through the lens of risk management capabilities. According to this theory, the effectiveness of an organisation\u0026rsquo;s strategies depends on how well they align with specific situational factors (Fiedler, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1971\u003c/span\u003e). This theory indicates that there is no one-size-fits-all approach to management; instead, the optimal strategy or structure is contingent upon the internal and external environments in which the organisation operates (Hijazi et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). This perspective suggests that organisational success in adopting technology depends not only on the technology itself but also on how well the organisation\u0026rsquo;s environment supports its integration. In the context of this study, two key factors (managerial competence and digital culture) are identified as contingency factors in which technology adoption influences effective decision-making via risk management capabilities.\u003c/p\u003e \u003cp\u003eFrom an organisational perspective, managerial competence refers to the collective skills, knowledge, and capabilities of an organisation's leadership team that drive successful technology adoption and integration (Aliu et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Organisations with competent managers ensure the appropriate selection of technological tools and integrate them effectively into existing risk management frameworks to enhance decision-making. In organisations with strong managerial competence, leaders align technology adoption with strategic goals, facilitating smoother transitions and amplifying the overall impact on organisational processes (Hijazi et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). When managerial competence is high, technology integration into risk management processes is more effective, leading to improved decision-making. Conversely, organisations lacking managerial competence may face challenges with technology misalignment, resulting in inefficiencies in risk management and undermining the potential benefits of technology adoption (L\u0026oacute;pez-Gonz\u0026aacute;lez et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSimilarly, digital culture within an organisation plays a pivotal role in determining how well technology is embraced and utilised by employees. Digital culture refers to the shared values, behaviours, and attitudes regarding the use of digital technologies within the organisation (Giannini and Bowen, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). A strong digital culture is characterised by openness to innovation, a willingness to adapt to technological changes, and a collective understanding of the importance of digital tools in enhancing business outcomes. Organisations with a positive digital culture foster an environment where employees view technology as an enabler of success, which in turn facilitates the adoption of new systems and tools (Cao et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This culture encourages employees to adopt technology for risk management, contributing to effective decision-making. Conversely, organisations that lack a supportive digital culture may experience resistance to change, leading to underutilisation or improper application of technological tools. In such contexts, the potential benefits of technology adoption in improving risk management capabilities and consequently decision-making may be diminished (Giannini and Bowen, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBased on the foregoing discussion, the researchers postulate the following hypotheses (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e):\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH3\u003c/strong\u003e \u003cp\u003eManagerial competence positively moderates the relationship between technology adoption and effective decision-making through risk management capabilities.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH4\u003c/strong\u003e \u003cp\u003eDigital culture positively moderates the relationship between technology adoption and effective decision-making through risk management capabilities.\u003c/p\u003e \u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eDesign, procedure and data analysis\u003c/h2\u003e \u003cp\u003eA quantitative, cross-sectional survey design was utilised to investigate the influence of technology adoption on effective decision-making in financial institutions, with a particular focus on the moderated mediation roles of managerial competence and digital culture through risk management capabilities. The cross-sectional approach was chosen because it allows for data collection at a single point in time (Capili, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Financial institutions were used due to their critical reliance on technology for risk management and decision-making processes. Managerial staff were selected for this study because they are the key decision-makers within financial institutions. Managerial staff also possess firsthand knowledge of the factors affecting decision-making and risk management within the organisation (Kozioł-Nadolna and Beyer, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, a multi-stage sampling technique was employed to select the sample. The first stage identified five regions in Ghana from which financial institutions were selected. These regions were chosen strategically to ensure diverse representation of financial institutions across the country. In the second stage, financial institutions within these regions were selected, with a total of 30 institutions chosen using a cluster sampling technique. The third stage focused on selecting respondents from these institutions. A sample frame comprising all employees from the 30 selected financial institutions across the five regions was constructed. Convenience sampling was then used to select 350 respondents. To calculate the appropriate sample size, the method proposed by Tabachnick and Fidell (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) was applied, using the formula: N\u0026thinsp;\u0026gt;\u0026thinsp;50\u0026thinsp;+\u0026thinsp;8(p), where p represents the number of variables. Given that five variables were included in the study, the minimum sample size was calculated to be 90. Therefore, 350 respondents were chosen to ensure sufficient statistical power and representativeness. This multi-stage sampling approach ensured a broad and varied sample, providing a solid foundation for analysing the relationship between technology adoption and effective decision-making in financial institutions (Ahmed, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). A total of 350 questionnaires were distributed to respondents across the selected institutions, and 291 completed responses were returned, yielding a response rate of 83.1%. Among the respondents, 47.4% were male, and 53.6% were female, with ages ranging from 39 to 53 years. The sample included 60.3% middle management and 39.7% senior management.\u003c/p\u003e \u003cp\u003eThe collected data were analysed using the PROCESS Macro for SPSS to test both mediation and moderation effects in the proposed model. The PROCESS Macro was chosen because of its ability to handle complex relationships, including testing direct, indirect, and interaction effects (Hayes, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This tool is widely recognised for its statistical power and efficiency in analysing how risk management capabilities mediate the association between technology adoption and effective decision-making, while also testing the moderating roles of managerial competence and digital culture.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMeasures\u003c/h2\u003e \u003cp\u003eThe instruments for data collection utilised a five-point Likert scale, ranging from 1 \u0026ldquo;strongly disagree\u0026rdquo; to 5 \u0026ldquo;strongly agree.\u0026rdquo; Technology adoption was measured using the 8-item Technology Adoption Scale by Ratchford and Barnhart (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The scale was adapted to the context of financial institutions to capture how organisations adopt new technologies. This scale exhibited strong reliability, with a Cronbach's alpha of 0.90, confirming its consistency and robustness in the context of financial institutions. A sample item is: \u0026ldquo;Our organisation regularly adopts new technologies to improve efficiency.\u0026rdquo;\u003c/p\u003e \u003cp\u003eRisk management capabilities were assessed using an adapted scale from the works of Gama et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This scale consists of seven items, capturing both reactive and proactive risk management actions. The scale demonstrated satisfactory reliability with a Cronbach's alpha of 0.87. A sample item from the scale reads: \u0026ldquo;The organisation has effective measures in place to identify emerging risks in real-time.\u0026rdquo;\u003c/p\u003e \u003cp\u003eManagerial competence was assessed using an adapted version of Gunawan et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) Managerial Competency Scale. This scale comprises five items that evaluate managerial skills in areas such as strategic thinking, leadership, and the ability to leverage technology for organisational goals. With a Cronbach's alpha of 0.89, the scale showed strong reliability in the context of this study. A sample item is: \u0026ldquo;The managers in this organisation effectively guide and support their teams in achieving organisational goals.\u0026rdquo;\u003c/p\u003e \u003cp\u003eThe Digital Culture Scale used in this study was developed by Hautala-Kankaanp\u0026auml;\u0026auml; (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The scale assesses the organisational environment\u0026rsquo;s openness to technology and innovation. This scale includes six items that measure the extent to which an organisation fosters a culture of innovation and adaptability to digital change. A high Cronbach\u0026rsquo;s alpha of 0.87 was reported, ensuring the scale\u0026rsquo;s robustness and reliability in assessing digital culture within financial institutions. A sample item is: \u0026ldquo;The organisation promotes continuous learning to enhance digital skills among employees.\u0026rdquo;\u003c/p\u003e \u003cp\u003eThe effectiveness of decision-making was measured with a customised 6-item scale developed by Bennett et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The scale evaluates decision quality, timeliness, and alignment with organisational goals. The scale had an excellent reliability, with a Cronbach\u0026rsquo;s alpha of 0.91. A sample item is: \u0026ldquo;The decisions made by my organisation are well-informed and effectively contribute to the organisation\u0026rsquo;s success.\u0026rdquo;\u003c/p\u003e \u003cp\u003eIn addition to the primary constructs, control variables were included to account for individual demographic factors that might influence technology adoption and decision-making. These included age and level of management (middle management and senior management). These demographic characteristics were controlled because prior research has indicated that they significantly impact technology adoption and decision-making practices (Addai, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Cruz-C\u0026aacute;rdenas et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). By including these variables, the study aimed to control for their potential influence, ensuring that the results are not biased by external factors.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePilot testing\u003c/h3\u003e\n\u003cp\u003eTo ensure the reliability and validity of the measurement scales, a pilot study was conducted with 30 managerial staff members from various financial institutions not included in the main study. The purpose of the pilot test was to assess the clarity, relevance, and applicability of the survey items in the context of Ghanaian financial institutions. Feedback gathered from the pilot study was used to refine the questionnaire, with minor adjustments made to the wording of certain items for improved clarity and cultural relevance, ensuring alignment with local practices. After these revisions, the final questionnaire exhibited strong reliability, with a Cronbach's alpha of 0.86 for all major constructs, indicating high internal consistency (Cheung et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of the measurement model\u003c/h2\u003e \u003cp\u003eAn initial exploratory factor analysis (EFA) was performed to assess the factor structure of the constructs: technology adoption, effective decision-making, risk management capabilities, managerial competence, and digital culture. The factor loadings of all items exceeded the critical threshold of 0.70, indicating strong alignment with their respective constructs (Hair et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The results confirmed that the model exhibited single-dimensionality, meaning the items collectively capture their intended purpose (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Furthermore, goodness-of-fit indices demonstrated that the model fit the data well, supporting the sturdiness of the measurement model in capturing the relationships between the underlying variables.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eFactor loadings and psychometric properties of the measures\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasures\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLoadings\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVariance Exp. (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eα\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechnology adoption (TA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.660\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.918**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTA8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.904**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.885**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTA6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.872**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTA3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.855**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTA5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.837**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTA7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.830**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTA4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.811**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk management capabilities (RMC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.627\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.877**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.860**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMC7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.853**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMC6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.851**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMC5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.838**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMC4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.822**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.820**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eManagerial competence (MC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.608\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.906**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMC5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.891**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.868**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMC4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.850**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.827**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital culture (DC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.883**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDC4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.870**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.855**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDC6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.838**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDC5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.824**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.817**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEffective decision-making (EDM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.610\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEDM4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.911**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEDM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.883**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEDM6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.870**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEDM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.866**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEDM5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.854**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEDM3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.837**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNote(s)\u003c/b\u003e: \u003cem\u003eKMO\u0026thinsp;=\u0026thinsp;0.815; Bartlett\u0026rsquo;s test of sphericity: χ2\u0026thinsp;=\u0026thinsp;1907.359, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eSource(s)\u003c/b\u003e: \u003cem\u003eField survey (2025)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSuitability for factor analysis and factor structure\u003c/h2\u003e \u003cp\u003eThe appropriateness of the data for factor analysis was assessed using Bartlett\u0026rsquo;s Test of Sphericity and the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy. A KMO value of 0.815 was obtained, indicating high sampling adequacy and confirming that the sample size was sufficient for the analysis (Kaiser, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1974\u003c/span\u003e). Additionally, Bartlett\u0026rsquo;s Test was significant (χ\u0026sup2; = 1907.359, p \u0026lt; .001), supporting the appropriateness of the data for factor extraction. Principal Component Analysis (PCA) revealed that the factors accounted for 75.065% of the total variance, indicating that the selected variables were highly explanatory and well-represented within the data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eReliability and validity assessment\u003c/h2\u003e \u003cp\u003eReliability was examined using Composite Reliability (CR), with values for all constructs exceeding the threshold of 0.70. Specifically, the constructs demonstrated the following composite reliability coefficients: technology adoption (CR\u0026thinsp;=\u0026thinsp;0.912), risk management capabilities (CR\u0026thinsp;=\u0026thinsp;0.849), managerial competence (CR\u0026thinsp;=\u0026thinsp;0.895), digital culture (CR\u0026thinsp;=\u0026thinsp;0.883), and effective decision-making (CR\u0026thinsp;=\u0026thinsp;0.913). The Average Variance Extracted (AVE) was computed to assess convergent validity for each construct. All AVE values were greater than the 0.50 threshold. This confirms that the items within each construct effectively measured the intended concept (Cheung et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Discriminant validity was established through the square root of AVE, which was found to be higher than the inter-construct correlations. This confirms that the constructs were distinct and not overly correlated. Moreover, to assess multicollinearity, the Variance Inflation Factor (VIF) values were examined, with all scores well below the threshold of 5, signifying that multicollinearity was not a concern (Hair et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCommon method bias (CMB) and the normality of data\u003c/h2\u003e \u003cp\u003eGiven the cross-sectional nature of the data, the potential for CMB was carefully considered. To mitigate this risk, the Harman\u0026rsquo;s Single Factor Test was employed. The results showed that the single-factor model did not adequately fit the data (CFI\u0026thinsp;=\u0026thinsp;0.582, SRMR\u0026thinsp;=\u0026thinsp;0.194, RMSEA\u0026thinsp;=\u0026thinsp;0.471) compared to the five-factor model (CFI\u0026thinsp;=\u0026thinsp;0.829, SRMR\u0026thinsp;=\u0026thinsp;0.081, RMSEA\u0026thinsp;=\u0026thinsp;0.073). This indicates that CMB was not a significant issue. Additionally, the study employed multiple data collection methods with varied response channels to reduce the likelihood of bias in the responses. The use of well-designed questionnaires further minimised self-report biases, ensuring that the relationships identified in the model accurately reflect the constructs of interest. The normality of the data was assessed using both Kolmogorov-Smirnov and Shapiro-Wilk tests, both of which indicated a normal distribution (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Additionally, correlation coefficients were examined to verify the linkages between the constructs. The values were found to be below the 0.70 threshold, indicating no concern with multicollinearity (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eDescriptives and intercorrelation between the variables (n\u0026thinsp;=\u0026thinsp;291)\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eMeasures\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003eIntercorrelations\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTechnology adoption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRisk management capabilities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.30\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eManagerial competence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.39\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.40\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDigital culture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.51\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.27\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.26\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEffective decision-making\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.47\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.38\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.44\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.38\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMSV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStand. Deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e**p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eHypotheses Testing\u003c/h2\u003e \u003cp\u003eTo investigate the relationships proposed in H1 and H2, which explore the connections between technology adoption and effective decision-making, and the mediating role of risk management capabilities, data analysis was conducted using Model 4 of the PROCESS macro. The results confirmed a statistically significant positive association between technology adoption and effective decision-making (β\u0026thinsp;=\u0026thinsp;0.207, t\u0026thinsp;=\u0026thinsp;12.176, LLCI\u0026thinsp;=\u0026thinsp;0.174, ULCI\u0026thinsp;=\u0026thinsp;0.240, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), supporting H1. Additionally, the analysis confirmed that risk management capabilities partially mediate the association between technology adoption and effective decision-making (β\u0026thinsp;=\u0026thinsp;0.319, t\u0026thinsp;=\u0026thinsp;3.396, LLCI\u0026thinsp;=\u0026thinsp;0.134, ULCI\u0026thinsp;=\u0026thinsp;0.503, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) since the direct relationship was significant, thus validating H2.\u003c/p\u003e \u003cp\u003eIn assessing the moderated mediation effects postulated in H3 and H4, Model 7 of the PROCESS macro was utilised. The results revealed that managerial competence significantly moderates this relationship between technology adoption and effective decision-making through risk management capabilities (β\u0026thinsp;=\u0026thinsp;0.291, t\u0026thinsp;=\u0026thinsp;9.387, LLCI\u0026thinsp;=\u0026thinsp;0.230, ULCI\u0026thinsp;=\u0026thinsp;0.352, ΔR\u0026sup2; = 0.171, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Specifically, the interaction term indicated that higher managerial competence strengthens the positive impact of technology adoption on decision-making by improving the effectiveness of risk management capabilities, supporting H3.\u003c/p\u003e \u003cp\u003eSimilarly, the analysis for H4 indicated that digital culture also moderates the nexus between technology adoption and effective decision-making through risk management (β\u0026thinsp;=\u0026thinsp;0.185, t\u0026thinsp;=\u0026thinsp;7.115, LLCI\u0026thinsp;=\u0026thinsp;0.134, ULCI\u0026thinsp;=\u0026thinsp;0.236, ΔR\u0026sup2; = 0.106, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This shows that financial institutions with a strong digital culture experience greater benefits from technology adoption, as employees within such a culture are more proficient at integrating technology into their decision-making and risk management processes. This finding affirms H4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examined the direct and indirect effects of technology adoption on effective decision-making in financial institutions via risk management capabilities, with a focus on managerial competence and digital culture as moderators. The first hypothesis, which proposed a significant association between technology adoption and effective decision-making, was supported. The results suggest that financial institutions adopting advanced technologies are better equipped to make informed and timely decisions. This finding aligns with the TAM, which posits that technology adoption is influenced by its perceived usefulness and ease of use (Davis, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). In decision-making, technological tools such as predictive modelling provide valuable insights and enhance decision quality. This is consistent with the work of Chen (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and Ahmed et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), who argue that technology improves decision-making by providing real-time data and advanced risk assessments, thereby empowering leaders to make strategic, data-driven decisions. By adopting technology, institutions can reduce uncertainty, speed up their decision-making processes, and better align decisions with organisational goals.\u003c/p\u003e \u003cp\u003eThe second hypothesis, which suggested that risk management capabilities mediate the relationship between technology adoption and effective decision-making, was also supported. The results revealed that integrating technology into risk management processes significantly enhanced decision-making within financial institutions. This mediation effect suggests that while technology adoption provides the tools for risk assessment and mitigation, it is the strengthened risk management capabilities that act as the mechanism through which technology influences decision-making. This finding aligns with the growing body of literature emphasising the importance of risk management in shaping organisational decision-making (Anderson and Fraser, 2020). By improving risk management capabilities, technology adoption helps financial institutions minimise uncertainties and enhance their decision-making processes.\u003c/p\u003e \u003cp\u003eThe third and fourth hypotheses explored the moderating effects of managerial competence and digital culture on the relationship between technology adoption and effective decision-making through risk management capabilities. Both hypotheses were supported by the results, which highlighted the critical roles these organisational factors play in the successful integration of technology in decision-making processes. First, managerial competence was found to positively moderate the relationship between technology adoption and effective decision-making through risk management capabilities. This finding suggests that financial institutions with skilled and knowledgeable managers are better positioned to leverage technological tools for enhanced decision-making within risk management frameworks. From the perspective of Contingency Theory, which underscores the importance of context-specific factors, this result emphasises the vital role of managerial expertise in the successful integration of technology (Tushman and O'Reilly, 1996). Similarly, digital culture was found to moderate the relationship between technology adoption and effective decision-making through risk management capabilities. This suggests that institutions with a strong digital culture, characterised by openness to change, adaptability, and a proactive approach to technology, experience more significant benefits from technology adoption. A robust digital culture fosters an environment where employees embrace technological innovations, integrate them into their daily work, and use them effectively in decision-making processes.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eImplications\u003c/h2\u003e \u003cp\u003eThis study makes a significant contribution to the body of knowledge by exploring the direct effects of technology adoption on decision-making processes within financial institutions. It particularly advances the understanding of how risk management capabilities mediate this relationship and how managerial competence and digital culture serve as moderators in the context of technology adoption. By investigating these dynamics, this research fills a notable gap in the literature, particularly within the financial sector, where technology adoption is essential for enhancing decision-making. The findings suggest that technology adoption not only enhances risk management capabilities but also improves decision-making by providing managers with the tools necessary to analyse and mitigate risks effectively. Furthermore, the study reveals the critical role of organisational factors, such as managerial competence and digital culture, in ensuring that technology adoption translates into tangible improvements in decision-making. This study broadens the scope of knowledge on the intersection of technology, risk management, and decision-making in organisations.\u003c/p\u003e \u003cp\u003eTheoretically, this study contributes to the development of the TAM and Contingency Theory. By applying TAM to examine the association between technology adoption and effective decision-making, this research supports and extends existing literature by demonstrating how the perceived usefulness and ease of use of technology enhance organisational decision-making outcomes. This study further extends the original framework of TAM, showcasing its relevance in the financial sector, where decision-making is heavily reliant on accurate, timely data and advanced risk assessments. Moreover, the study\u0026rsquo;s use of Contingency Theory in analysing the moderated mediation effects adds depth to our understanding of how contextual factors, such as managerial competence and digital culture, influence the effectiveness of technology adoption. By linking these theoretical frameworks, the study provides a more holistic understanding of how technology interacts with organisational variables to shape decision-making and risk management outcomes.\u003c/p\u003e \u003cp\u003eFrom a managerial perspective, the findings offer practical, actionable recommendations for improving decision-making and risk management in financial institutions. First, the positive direct relationship between technology adoption and effective decision-making underscores the importance of investing in advanced technologies. Financial institutions should prioritise the integration of technological tools that enhance their decision-making processes by providing more accurate and real-time data. Managers should advocate for technological upgrades that improve data-driven decision-making, enabling quicker, more informed choices and reducing the uncertainty inherent in complex financial environments. Second, the mediation effect of risk management capabilities suggests that organisations need to ensure that their risk management frameworks are equipped to handle the influx of technological tools. Managers must focus on building robust risk management strategies that integrate new technologies, allowing them to optimise decision-making. Lastly, the moderating effects of managerial competence and digital culture highlight the essential role of leadership and organisational culture in the success of technology adoption. To maximise the positive influence of technology, financial institutions should invest in leadership development programs that focus on enhancing managerial skills and digital literacy. Additionally, fostering a digital culture within the organisation will further strengthen the positive association between technology and decision-making through risk management capabilities. By prioritising these actionable strategies, managers can enhance their institution's ability to leverage technology for better risk management and more effective decision-making.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and Research Suggestions\u003c/h2\u003e \u003cp\u003eThis study comes with several limitations that need to be addressed in future research. First, the cross-sectional design limits the ability to make causal inferences between technology adoption, risk management capabilities, and effective decision-making, as it only captures a snapshot of these constructs at one point in time. A longitudinal approach could provide deeper insights into the temporal dynamics and causal pathways of these relationships. Second, the study's exclusive focus on the financial institutions may limit the generalisability of the findings to other industries with different technological needs and organisational contexts. Expanding the scope to include other sectors such as education, healthcare, or manufacturing would offer a broader understanding of how technology adoption impacts decision-making across diverse settings. Finally, the reliance on questionnaires as the sole data collection method may introduce self-report bias and limit the depth of insight into the complex phenomena studied. Future research could benefit from employing a mixed-methods approach, integrating qualitative interviews or case studies, and supplementing self-reported data with objective performance metrics to enhance the validity and comprehensiveness of the findings.\u003c/p\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cu\u003eDATA AVAILABILITY STATEMENT\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of the study are available upon reasonable request by contacting the corresponding author.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eETHICAL STATEMENT\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003cem\u003e:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e: The author declares that he has no conflicts of interest that influence this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e: The research adhered to the ethical standards outlined by the journal. Furthermore, prior to commencement, approval was granted by the Institutional Review Board, ensuring compliance with ethical protocols.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent\u003c/strong\u003e: Before engaging in the research, all participants received a comprehensive overview of the study\u0026apos;s objectives and provided their informed consent.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthors' Contributions: All the authors conceived the study and performed data analysis. I.O. brought the idea, wrote the methodology, and supervised the research. M.A. and E.Y.A. wrote the draft; P.A. performed the analysis and wrote the results section. All authors read and approved the final manuscript for submission.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbikoye BE, Umeorah SC, Adelaja AO, Ayodele OF, Ogunsuji YM (2024) Regulatory compliance and efficiency in financial technologies: Challenges and innovations. World J Adv Res Reviews 23(1):1830\u0026ndash;1844. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.30574/wjarr.2024.23.1.2174\u003c/span\u003e\u003cspan address=\"10.30574/wjarr.2024.23.1.2174\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAddai P (2025) Leading with intelligence: How AI leadership, innovation culture, AI acceptance, and digital maturity transform talent management in public service. 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J Innov Knowl 10(3):100711. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jik.2025.100711\u003c/span\u003e\u003cspan address=\"10.1016/j.jik.2025.100711\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":false,"email":"","identity":"sn-business-and-economics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"43546","submissionUrl":"https://submission.nature.com/new-submission/43546/3","title":"SN Business \u0026 Economics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"VoR Journals","inReviewEnabled":false,"inReviewRevisionsEnabled":false},"keywords":"Technology adoption, Risk management capabilities, Decision-making, Managerial competence, Digital culture, Financial institutions","lastPublishedDoi":"10.21203/rs.3.rs-9227540/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9227540/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose:\u003c/strong\u003eThis study examines the influence of technology adoption in enhancing decision-making effectiveness within financial institutions, with a specific focus on the mediating role of risk management capabilities. It also investigates how managerial competence and digital culture moderate this relationship.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod: \u003c/strong\u003eA cross-sectional survey design was used to collect data from 291 managerial staff in financial institutions, who were selected using a multi-stage sampling approach. The PROCESS macro was applied to analyse the hypothesised model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFindings\u003c/strong\u003e: The study demonstrates that technology adoption significantly enhances effective decision-making in financial institutions, primarily through its impact on risk management capabilities. Furthermore, the moderating roles of managerial competence and digital culture were confirmed, with both factors amplifying the positive effects of technology adoption on decision-making through enhanced risk management capabilities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOriginality/Value\u003c/strong\u003e: This research provides a novel contribution to the understanding of how technology adoption influences decision-making in the financial sector. By focusing on managerial competence and digital culture as moderators and risk management capabilities as a mediator, the study offers practical insights for financial leaders to optimise decision-making through strategic technology integration.\u003c/p\u003e","manuscriptTitle":"Technology adoption and effective decision-making in financial institutions: Moderated-mediation analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-27 13:33:13","doi":"10.21203/rs.3.rs-9227540/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"312728907145562243060306857892926005284","date":"2026-03-26T11:09:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"72436879711704327700450742545445808125","date":"2026-03-26T06:27:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-26T06:24:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-26T05:49:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-26T05:49:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"SN Business \u0026 Economics","date":"2026-03-25T23:32:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":false,"email":"","identity":"sn-business-and-economics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"43546","submissionUrl":"https://submission.nature.com/new-submission/43546/3","title":"SN Business \u0026 Economics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"VoR Journals","inReviewEnabled":false,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"c7c93b28-9eef-4cdb-aa20-079cc6a67939","owner":[],"postedDate":"March 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-27T13:33:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-27 13:33:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9227540","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9227540","identity":"rs-9227540","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}