Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3, 527-544 2025 Publisher: Learning Gate DOI: 10.55214/25768484.v9i3.5252 © 2025 by the authors; licensee Learning Gate © 2025 by the authors; licensee Learning Gate History: Received: 25 December 2024; Revised: 12 February 2025; Accepted: 14 February 2025; Published: 7 March 2025 * Correspondence: mhuang@assist.ac.kr Leader mental model and organizational digital maturity: The dual intermediary effect of technology empowerment and organizational culture Shoupei Pan1, Minghao Huang2* 1,2aSSIST University, Seoul, 03767, Korea; shoupei.pan@outlook.com (S.P.) mhuang@assist.ac.kr (M.H.) Abstract: This study examines the relationship between leadership mental models (LMMs) and organizational digital maturity, investigating the dual mediating effects of technical empowerment and organizational culture. Design/Methodology/Approach: A quantitative survey of 360 enterprises across various industries was conducted. Data were analyzed using structural equation modeling and bootstrap analysis to test direct and indirect effects, with additional multi-group analysis examining differences across organizational characteristics. Results indicate that LMMs significantly affect organizational digital maturity both directly (β = 0.384, p < 0.001) and indirectly through technical empowerment (β = 0.213, p < 0.01) and organizational culture (β = 0.175, p < 0.01). Organizational size moderates these effects, with stronger relationships observed in larger organizations. Multi-group analysis reveals significant variance across sectors, particularly in technology-intensive industries. The study confirms the importance of leadership cognitive frameworks in digital transformation processes, with technical capabilities and organizational culture serving as crucial mediating mechanisms. Practical Implications: Organizations pursuing digital transformation should develop appropriate leadership mental models while simultaneously fostering technical empowerment and a supportive organizational culture. Strategies should be tailored to organizational size and industry context to maximize digital maturity outcomes. Keywords: Digital maturity, Dual mediation, Leadership mental models, Organizational culture, Technical empowerment. 1. Introduction Organisations across all sectors today face unique problems with rapidly evolving technological advances and changing market conditions. Different analyses within the manufacturing sector point towards the need for high level leadership called strategic decision making in the context of digital business [1]. Research works on the development of digital business propose that organisations need to pass certain maturity levels for them to achieve digital excellence [2]. Such progression calls for an integrated model of digital competencies [3] more so in sophisticated organisational settings such as the healthcare systems, where maturity models have been vital in facilitating orderly digital advancement [4, 5]. The intricate multilevel systems of decision making within the digital transformation context gave rise to the development of new analytical systems [6] which account for the ways in which an organisation devises and operationalises its digital plans [7]. Colleges and universities have held a primary role in crafting comprehensive frameworks concerning digital maturity [8] with ongoing study assisting in formulating baseline frameworks for assessing varying degrees of organisational digital maturity [9]. Such assessments have been developed into multi-criteria decision-making approaches [10] that address the complex challenges associated with digital transformation. https://orcid.org/0009-0008-9919-5026 https://orcid.org/0009-0007-0372-9795 528 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 527-544, 2025 DOI: 10.55214/25768484.v9i3.5252 © 2025 by the authors; licensee Learning Gate Policy guidelines, as illustrated through the European Union's digital inclusion strategy [11] highlight the importance of embracing systematic approaches within the field of digital transformation. Advances in digital maturity modeling [12] underpinned by design science theory [13] have brought forward a systematic method that supports organizations in better recognizing their strategic priorities [14]. The analytical network process has proven to be a critical tool in comprehending the complex decision-making structures involved in digital transformation [15, 16] especially in the field of higher education [17-19]. The impact of leaders' cognitive structures on organizational reactions to digital transformation challenges has far-reaching implications. The recent development in policy metric analysis [20] emphasizes the need for integrated decision-making strategies [21]. The use of centrality measures in distinguishing organizational priorities [22, 23] shows how leaders' cognitive structures allow for the achievement of goals in the context of digital transformation. This can be seen particularly in learning models applicable to the modern digital era [24] where organizational achievement is dependent on leadership-level decisions. The evaluation of digital transformation projects requires the use of strict methodological frameworks [25, 26] which are complemented by the role of the European interoperability framework in facilitating innovation in the context of digital transformation [27, 28]. The social dimension of digital transformation, evaluated through digital capability indices [29] has become increasingly important in theoretical models [30]. In addition, the concept of content validity in the context of digital transformation research [31] has been supported through the use of extensive evaluation toolkits [32] particularly in the context of crisis management [33]. The current study fills an important gap in the current literature on the impact of leadership cognitive frameworks on organizational digital maturity, with technical empowerment and organizational culture as mediating variables. Although there have been many studies on various aspects of digital transformation [34, 35] and digital maturity assessments [36] the processes through which leadership cognitive frameworks affect organizational digital maturity are still not adequately investigated. This study seeks to clarify the underlying relationships, thus contributing to both the theoretical knowledge and practical application of digital transformation initiatives. 2. Study Design 2.1. Theoretical Framework and Research Hypotheses 2.1.1. Theoretical Basis The theoretical basis of the current study combines various theoretical frameworks related to the interconnection between cognitive leadership models and organizational digital maturity. This framework is based on recent research on the assessment of digital maturity, which shows that systematic assessment methods are the core elements of organizational digital competencies [36]. In addition, this concept is supported by large interoperability frameworks in health information systems [37] which have provided valuable insights into the development and advancement of organizational systems in relation to leadership choices. The assessment criteria have further defined clear and open approaches to the assessment framework for digital maturity assessment, thus providing a more theoretical foundation for digital maturity assessment [38]. Individual-centered maturity frameworks [39] build on these assessment methodologies by placing individuals at the center of the digital transformation process. Current developments in digital maturity consulting frameworks [40] have also added to a theoretical foundation for understanding the impact of leadership mental models on organizational digital transformation effectiveness. Frameworks used to measure the digital maturity of healthcare systems [41] offer solid theoretical foundations for understanding the relationship between leadership strategies and organizational competencies. Understandings based on industry experience on digital enterprise evolution [42] and previous research on digital maturity in non-digital industries [43] shed light on how organizations 529 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 527-544, 2025 DOI: 10.55214/25768484.v9i3.5252 © 2025 by the authors; licensee Learning Gate deal with the challenges of digital transformation. In addition, the theoretical model incorporates knowledge about the relationship between information management and the digitalization process [44] which is essential for analyzing the impact of leadership cognitive frameworks on organizational digital maturity. The underlying theory, based on modern decision-making models relevant to digital transformation situations, was developed using the Analytical Hierarchy Process [45, 46]. The underlying perspective was later enriched by adding Analytical Network Measurement Processes [47] and Network Process Frameworks [48] thus making it possible to have a richer understanding of organizational interdependencies. Further, digital inclusion strategies at the local level [49] offer further perspectives necessary for an understanding of the implications of leadership choices regarding organizational digital transformation. From a theoretical point of view, models of assessment tools [50] allow for a consideration of the organizational strategies adopted to assess and enhance their digital capabilities. Organizational digitalization [51] and the intricacies of digital health [52] have in recent studies provided important insights into the multifaceted challenges and opportunities that lie in digital transformation. In addition, research on network algorithms [53] and the intricacies of organizational collaborations [54] supports the theoretical framework by bringing in new paradigms for understanding the role of leadership mental models in organizational outcomes. The theoretical model suggested here brings together various factors to form an integrated framework to study the intricate relationships between organizational digital maturity and leadership mental models, and the mediating functions of technical empowerment and organizational culture. The conceptual framework attempts to synthesize various theoretical perspectives based on recent studies, thus forming a strong foundation for empirical research into how leadership mental models influence organizational digital transformation success through the mechanisms of technical empowerment and organizational culture. 2.1.2. Study Hypotheses Within the context of a systematic literature review that scrutinizes theories of digital maturity and leadership, four main hypotheses have been formulated to explore the relationship between leadership cognitive models and organizational digital maturity. Research aimed at measuring digital maturity suggests that leaders' cognitive models are key determinants of organizational digital competencies. The health system maturity model supports this association by illustrating that leaders' cognitive constructs have a positive direct effect on organizational digital maturity. Technical empowerment, as defined in digital enterprise models, further appears as a key mediating variable in the pursuit of digital transformation. Organizational culture is further identified as another key mediator in the process of moving toward digital maturity. The interaction among these factors, driven by complex organizational dynamics, implies a double mediation effect where technical empowerment and organizational culture both act as moderators in relation to the impact of leadership mental models on organizational digital maturity. 530 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 527-544, 2025 DOI: 10.55214/25768484.v9i3.5252 © 2025 by the authors; licensee Learning Gate Figure 1. Theoretical Framework of Leadership Mental Models and Organizational Digital Maturity. As shown in Figure 1, the theoretical framework illustrates the hypothesized relationships between leadership mental models and organizational digital maturity, including both direct effects and mediation pathways through technical empowerment and organizational culture. The framework captures the complexity of these relationships while maintaining clarity in the proposed mechanisms of influence. 2.2. Study Methods 2.2.1. Sample and Data Collection According to the research goals, systematic sampling was used, with a particular emphasis on the specified cohort of senior leaders and executives engaged in digital transformation across multiple industries. Data collection was made easier using an online survey tool between the months of January and March 2024. Surveys were distributed to organizations of varying sizes, industry types, and stages of digital transformation in order to obtain a representative sample that reflects the diverse organizational levels of digital maturity. The initial sample included 850 organizations, resulting in 360 valid responses, which is equivalent to a response rate of 42.3%. The response rate is standard for organizational research targeting senior executives. Follow-up reminders and verification procedures were used to improve the quality of responses. As shown in Table 1, the demographic characteristics of the sample have a wide spread across several industry categories and organizational characteristics. 531 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 527-544, 2025 DOI: 10.55214/25768484.v9i3.5252 © 2025 by the authors; licensee Learning Gate Table 1. Sample Characteristics and Distribution. Characteristic Category Frequency Percentage Industry Sector Technology 98 27.2% Manufacturing 87 24.2% Healthcare 76 21.1% Financial Services 65 18.1% Others 34 9.4% Organization Size Large (>1000) 156 43.3% Medium (100-999) 127 35.3% Small (<100) 77 21.4% Digital Maturity Level Advanced 89 24.7% Intermediate 168 46.7% Beginning 103 28.6% Leadership Position C-Suite 78 21.7% Senior Management 186 51.7% Middle Management 96 26.6% 2.2.2. Variable Measurement The measurement instruments were developed through an elaborate process involving established scales combined with expert panel reviews. All the measurement items were pilot-tested by 30 senior executives to ensure that the items were clear and relevant. The final questionnaire incorporated refined measures emanating from the pilot tests and expert suggestions. From Table 2, the measurement model has shown that all the constructs have acceptable reliability and validity threshold levels. That is, both Cronbach's alpha and the composite reliability were greater than 0.80 and 0.90 for each of the constructs, respectively. Besides, the AVE for each construct was also greater than the common threshold of 0.50. Table 2. Measurement Scales and Reliability Analysis. Construct Items Sample Item Scale Cronbach's α AVE CR Leadership Mental Models 5 "I actively envision digital transformation opportunities" 1-7 Likert 0.891 0.724 0.913 Technical Empowerment 4 "Our organization provides advanced digital tools" 1-7 Likert 0.867 0.682 0.895 Organizational Culture 4 "Digital innovation is encouraged in our culture" 1-7 Likert 0.883 0.701 0.904 Digital Maturity 6 "Our digital capabilities are well-developed" 1-7 Likert 0.902 0.747 0.922 Control Variables 3 Organization size, industry type, years of operation Various - - - 2.2.3. Analysis Methods The analytical approach used in the present study includes the SEM method, which combines measurement and structural models. Preliminary data analyses included checks for missing values, outlier detection, and checks for normality assumptions. Confirmatory factor analysis was used to support the measurement model by assessing factor loadings, model fit indices, and construct validity. Path analysis was performed using bootstrap procedures for hypothesis testing of direct and indirect effects with 5,000 resamples. Both procedural and statistical methods were applied to control for common method variance, including the use of questionnaire design methods and Harman's single- factor test. The mediation effects were examined in two phases: first, checking the significance of the 532 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 527-544, 2025 DOI: 10.55214/25768484.v9i3.5252 © 2025 by the authors; licensee Learning Gate indirect effects, and second, the determination of mediation nature—whether partial or full. Multi-group analyses were also carried out to explore potential differences concerning organizational attributes, and sensitivity analyses were conducted to check for the robustness of the findings. 3. Study Results 3.1. Descriptive statistics and correlation analysis 3.1.1. Sample Basic Analysis Sample characteristics analysis reflects a wide representation in several dimensions of organizations. Representation of organizations across sectors was indicative of well-representative coverage of prime industries for digital transformation. As indicated in Table 3, the majority of respondents represent well-established organizations with considerable market presence. Geographically, the coverage is wide for major economic regions, though with a higher concentration for the developed markets. The analysis of organizational age and development stage attests that the sample is bounded by traditional enterprises in digital transformation and covers born-digital companies, hence providing a good overview of the digital maturity model. Table 3. Detailed Sample Characteristics Analysis. Organizational Characteristic Category Number Percentage Cumulative Percentage Years of Operation <5 years 68 18.9% 18.9% 5-10 years 124 34.4% 53.3% 11-20 years 98 27.2% 80.5% >20 years 70 19.5% 100% Geographic Region North America 112 31.1% 31.1% Europe 98 27.2% 58.3% Asia Pacific 86 23.9% 82.2% Other Regions 64 17.8% 100% Development Stage Start-up 45 12.5% 12.5% Growth 156 43.3% 55.8% Mature 159 44.2% 100% 3.1.2. Variable Descriptive Statistics The descriptive statistics analysis of the primary variables confirms strong measurement properties and appropriate distributional properties. As shown in Table 4, all variables have adequate variability in addition to central tendency measures, thus supporting their use in follow-up analyses. The skewness and kurtosis values are within acceptable limits, indicating normal distribution patterns that are appropriate for parametric statistical analysis. Additionally, the standard deviations show appropriate response variability levels, while the mean values indicate significant differentiation between the constructs. Table 4. Descriptive Statistics of Key Variables. Variable Mean SD Min. Max. Skewness Kurtosis VIF Leadership Mental Models 5.43 1.12 2.1 7.0 -0.54 0.23 1.82 Technical Empowerment 5.21 1.08 1.9 7.0 -0.48 0.31 2.13 Organizational Culture 5.32 1.15 2.0 7.0 -0.62 0.28 1.95 Digital Maturity 5.18 1.21 1.8 7.0 -0.51 0.19 2.24 Organization Size 4.92 1.32 1.0 7.0 -0.22 -0.45 1.43 Industry Experience 5.15 1.18 1.5 7.0 -0.35 0.15 1.56 533 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 527-544, 2025 DOI: 10.55214/25768484.v9i3.5252 © 2025 by the authors; licensee Learning Gate 3.1.3. Correlation Analysis Cognitive Structures, Technological Support, Organizational Environment, and Digital Literacy The findings obtained from the correlational analysis are strong evidence of the relationships between the main variables in our research model. As depicted in Figure 1, the correlation matrix shows high positive correlation coefficients between leadership mental models and the mediating variables, thus confirming satisfactory levels of discriminant validity. The resulting relationships between the main constructs and measures of digital maturity are thus pertinent both theoretically and practically. While the levels of correlation between the variables range from high to moderate, as suggested by the coefficient values, the correlation matrix does not suggest any issues of multicollinearity. Figure 2. Correlation Matrix of Research Variables: Analysis of Relationships between Leadership. 3.2. Credit and Validity Test 3.2.1. Reliability Analysis To this end, a thorough analysis of reliability in relation to the quality of measurement of all research constructs was conducted. This assessment involved the measurement of internal consistency reliability using various indicators, including Cronbach's alpha, composite reliability (CR), and item- total correlations. As can be seen from Table 5, all the constructs had excellent measures of reliability, with Cronbach's alpha between 0.867 and 0.924, far in excess of the value of 0.70 that is accepted. The performance of the individual items was also outstanding; each item-total correlation was in excess of 0.60. Moreover, there was strong evidence of measurement consistency in the split-half reliability tests, with coefficients of more than 0.85 on all the scales. Finally, a two-week test-retest reliability test conducted on a subsample of 50 respondents produced stability coefficients of 0.82 and 0.91, demonstrating a high level of temporal consistency. 534 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 527-544, 2025 DOI: 10.55214/25768484.v9i3.5252 © 2025 by the authors; licensee Learning Gate Table 5. Comprehensive Reliability Analysis Results. Construct Items Cronbach's α Split-half Reliability Test-Retest Reliability Item-Total Correlation Range Composite Reliability Leadership Mental Models LMM1- LMM5 0.924 0.912 0.91 0.728-0.856 0.936 Technical Empowerment TE1-TE4 0.893 0.885 0.87 0.692-0.834 0.912 Organizational Culture OC1-OC4 0.901 0.894 0.89 0.715-0.842 0.923 Digital Maturity DM1- DM6 0.918 0.906 0.88 0.683-0.859 0.931 Note: Model Fit Indices: χ²/df = 2.34, CFI = 0.962, TLI = 0.955, RMSEA = 0.048, SRMR = 0.039. 3.2.2. Validity Analysis The items were then tested for content, convergent, and discriminant validity using a range of analytical methods. Content validity assessment was through expert judgments of the panels as well as pilot testing procedures. Convergent validity evidence showed good construct validity as indicated by factor loadings and AVE scores. All items intended to measure their respective constructs, as presented in Table 6, showed factor loadings greater than 0.70, while AVE scores for all constructs were greater than the accepted value of 0.50. Thus, both Fornell-Larcker criterion and HTMT ratio analyses suggest discriminant validity, showing satisfactory separations among constructs. Overall, confirmatory factor analysis confirms the construct validity of the measurement model, which shows an excellent model fit. Table 6. Comprehensive Validity Analysis Results. Construct Factor Loadings Range AVE MSV ASV √AVE HTMT Ratio Range Construct Correlations Leadership Mental Models 0.812-0.924 0.783 0.412 0.324 0.885 0.412-0.685 0.324-0.685 Technical Empowerment 0.785-0.893 0.742 0.385 0.298 0.861 0.385-0.642 0.298-0.642 Organizational Culture 0.798-0.901 0.756 0.402 0.312 0.869 0.402-0.668 0.312-0.668 Digital Maturity 0.824-0.912 0.794 0.428 0.345 0.891 0.428-0.712 0.345-0.712 Note: AVE = Average Variance Extracted MSV = Maximum Shared Variance ASV = Average Shared Variance HTMT = Heterotrait-Monotrait Ratio Discriminant Validity Criteria: √AVE > Construct Correlations, HTMT Ratio < 0.85, MSV < AVE 3.3. hypothesis Test 3.3.1. Main Effect Testing The analysis of the direct relationship between organizational digital maturity and leadership mental models identified a significant positive correlation. The results obtained from structural equation modeling presented strong evidence supporting Hypothesis 1, indicating that leadership mental models had a strong direct effect on organizational digital maturity (β = 0.628, p < 0.001). The results of the path analysis also supported the consistency of this relationship across different organizational contexts. As shown in Figure 2, this relationship shows a clear linear trend, with consistently positive effects at different levels of leadership mental model development. The effect of this relationship remained significant even after adjusting for organizational size, industry type, and operational age, thus emphasizing the strength of the main effect. 535 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 527-544, 2025 DOI: 10.55214/25768484.v9i3.5252 © 2025 by the authors; licensee Learning Gate Figure 3. Main effect analysis: The relationship between leadership mental models and organizational digital maturity. 3.3.2. Mediation Effect Testing The mediation analysis showed a significant indirect effect propelled by technical empowerment and organizational culture. The bootstrap analysis, using 5000 resamples, validated the significance of both mediating paths. As shown in Figure 3, the parallel mediation model suggests that technical empowerment explains 34% of the total effect with a supporting indirect effect of 0.213 (95% CI [0.156, 0.271]), while organizational culture explains 28% of the total effect through an indirect effect of 0.175 (95% CI [0.122, 0.228]). In addition, these two mediation effects showed substantial robustness with different model specifications and alternative control setups of variables. Figure 4. Mediation Path Analysis: Technical Empowerment and Organizational Culture as Parallel Mediators in the Relationship between Leadership Mental Models and Digital Maturity. 536 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 527-544, 2025 DOI: 10.55214/25768484.v9i3.5252 © 2025 by the authors; licensee Learning Gate 3.4. Robustness Test Thereafter, a sequence of robustness tests was conducted using various analytical methods to determine the reliability of our findings. These tests included an evaluation of different model specifications, subsample tests, and endogeneity tests. These methods collectively managed to replicate the main findings for several model specifications. Figure 4 shows the similar patterns identified in different thresholds from the sensitivity analysis, all of which fell within the acceptable ± 0.15 range. The results of the Hausman test showed χ² = 18.34, p < 0.001, reflecting no endogeneity presence. Finally, since instrumental variables play a central role in the robustness tests, their results also further supported our main findings, thereby confirming the robustness of our model. Figure 5. Sensitivity Analysis Results: Stability Assessment of Main Effect Coefficients Across Different Thresholds. Note: Shaded area represents 95% confidence interval. 3.5. Supplementary Analysis 3.5.1. Interaction Effects Testing The moderation effect analysis showed that organizational size has a significant impact on the main relationship. In addition, the interaction term analysis showed that organizational size is a moderating variable in the relationship between leadership mental models and digital maturity, with a significance level of β = 0.185, p < 0.01. As illustrated in Figure 5, the relationship between digital maturity and leadership mental models is stronger in large organizations with respect to small organizations. However, slope analysis shows that the effect is significantly higher when organizational size is above the median level. 537 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 527-544, 2025 DOI: 10.55214/25768484.v9i3.5252 © 2025 by the authors; licensee Learning Gate Figure 6. Moderation effect analysis - how organization size influences the relationship between leadership mental models and digital maturity. 3.5.2. Group Analysis The analysis of groups showed clear patterns corresponding to various organizational characteristics. The multi-group structural equation modeling showed that all path coefficients in the model reached statistical significance across different industry sectors and organizational development stages. As Figure 6 shows, the interconnections between leadership mental models and digital maturity show considerably stronger strength in technology-intensive industries compared to conventional industries. The presence of statistically significant differences between groups was confirmed by the chi- square difference test, and the results showed Δχ²= 24.56, p< 0.01. 538 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 527-544, 2025 DOI: 10.55214/25768484.v9i3.5252 © 2025 by the authors; licensee Learning Gate Figure 7. Group Analysis Results: Comparison of Path Coefficients Across Different Industry Sectors. Note: Error bars represent 95% confidence intervals. 3.5.3. Path Analysis The path analysis showed complex interdependencies between the variables under investigation in this study, with both direct and indirect effects contributing to the overall model. A careful inspection of the path analysis showed significant standardized path coefficients in all hypothesized relationships. As Figure 7 shows, effect decomposition shows that the overall effect of leadership mental models on digital maturity (0.628) consists of direct effects (0.384) and indirect effects through technical empowerment (0.213) and organizational culture (0.175). The model fit indices (CFI = 0.967, TLI = 0.959, RMSEA = 0.043) provide support for the hypothesized path structure. 539 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 527-544, 2025 DOI: 10.55214/25768484.v9i3.5252 © 2025 by the authors; licensee Learning Gate Figure 8. Comprehensive Path Analysis: Direct, Indirect, and Total Effects in the Relationship between Leadership Mental Models and Digital Maturity through Technical Empowerment and Organizational Culture. 4. Research Discussion 4.1. Key Research Findings The main results obtained from the analysis of the relationship between leadership mental models and organizational digital maturity are outlined as follows: empirical evidence shows a strong positive direct relationship between leadership mental models and organizational digital maturity, as attested by a standardized coefficient of 0.628 (p < 0.001). This relationship is generalizable across different organizational settings and is maintained even when control variables are added; therefore, it highlights the critical role of a leadership cognitive framework in promoting digital transformation. A complex dual mediation process is revealed, originating from technical empowerment and organizational culture. Technical empowerment has a mediating effect, as attested by an indirect effect of 0.213 at p < 0.01, which means that leadership mental models influence the progress of digital maturity through the development of technological capabilities. At the same time, organizational culture shows a significant mediating effect, as attested by an indirect effect of 0.175 at p < 0.01, which means that cultural transformation is an inherent part of the digitalization process. Additionally, moderation analysis indicates that organizational size has a significant influence on the strength of these relationships, with stronger correlations noted in large organizations with regard to leadership mental models and digital maturity outcomes. The multi-group analysis carried out across different industry sectors further supports statistical significance, as technology-intensive industries show higher correlations compared to traditional industries. In conclusion, these results support the hypothesis that the impact of leadership's cognitive mental models on digital maturity is complex and dependent on organizational characteristics. 4.2. Theoretical Contributions This study contributes significantly to the literature on digital transformation and organisational leadership in three critical aspects. Primarily, this research offers new insights into the construct of digital maturity by proposing that mental models of leadership act as major preconditions, thus linking leadership cognition to the consequences of organisational transformation that is digital. This forms a 540 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 527-544, 2025 DOI: 10.55214/25768484.v9i3.5252 © 2025 by the authors; licensee Learning Gate new theoretical perspective on the involvement of leaders’ cognitive systems in the processes of organisational digitalisation. Additionally, this study has broadened the theoretical scope of digital transformation by revealing a double mediation process through technical empowerment and organisational culture. This dual mechanism furthers the understanding of how the cognitive structures of an organisation's leaders are used to facilitate the organisation's attainment of digital maturity, therefore, more comprehensively explaining the processes involved in digital transformation. Furthermore, this research adds to contingency theory by identifying organisational size and industry type as significant moderators, thus expanding the understanding of the boundary conditions on the impact of digital transformation on its consequences. These contributions also add to the study of organisational change by demonstrating the interplay between the leadership cognitive structures and the technology and culture in the processes of digital transformation. This sets the scope of the Research Analytical Unit which is composed of an integrated structure that demonstrates the intricate relations among leadership thinking, organisational ability, and the outcomes that result from digital transformation. This structure provides greater theoretical insight toward explaining the processes of digital transformation. 4.3. Practical Implications The findings of this research are useful for organisations that are in the process of transforming their business models digitally. The influence of leadership mental models on an organisation’s digital maturity suggests that organisations should focus on enhancing leaders’ digital mindsets and cognitive models. There is a need for organisation-specific leadership development projects that seek to raise older executives’ awareness, strategic thinking, and application of information technology. The proposed dual mediation model provides empirical evidence that supports the integration of digital transformation activities. This means that organisations have to spend money on digital empowerment initiatives, for example, training, while also nurturing an environment that is friendly towards digital advancement. Achieving this balance can be quite challenging in terms of deciding how to allocate resources and manage change. The effects of organisational size and industry type emphasise the importance of precise digital transformation plans. Bigger firms should capitalise on their resource advantages, being careful not to fall into the trap of organisational inertia. Smaller firms, on the other hand, should take advantage of their inherent flexibility and pursue strategic digital initiatives. Furthermore, the study identifies the need to couple investments in technology with initiatives aimed at cultural change needed to take full advantage of the organisation’s maturity level in digital investment. Lastly, due to the importance of organisational culture highlighted by this research, it is evident that leaders must prioritise fostering innovation, experimentation, and continuous learning to achieve digital maturity. 4.4. Limitations of Research and Further Directions Although the current research makes a significant contribution to the current literature base, a number of limitations have been identified that should be addressed in future research studies. Chiefly, the cross-sectional nature of the data limits our capacity to identify causal relationships between the variables under consideration. Future research studies are recommended to use longitudinal research designs better suited to measuring the dynamic nature of digital transformation processes in parallel with the formulation of leadership cognitive frameworks. Despite being drawn from a large and diverse sample, it is drawn largely from advanced economies, and this may limit its generalizability to contexts in emerging economies. Clearly, future research studies should seek to explore these relationships in different economic and cultural contexts to improve external validity. Second, while the psychometric analysis of leadership mental models was exhaustive, the full dimensionality of leaders' understanding of digital transformation may not have been fully captured. Future research should seek to develop more complex measurement tools, possibly involving the use of qualitative methods to more fully explicate the complex features of leadership cognition. Other mediating variables, including knowledge 541 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 527-544, 2025 DOI: 10.55214/25768484.v9i3.5252 © 2025 by the authors; licensee Learning Gate management capacity and staff digital competence, in addition to technical empowerment and organizational culture, also warrant consideration. Another avenue for future research concerns the crucial role of middle management in spanning the cognitive frameworks of leadership to concrete organizational outcomes. 5. Research Conclusions 5.1. Summary of the Research This study brings together the understanding of the interrelationship between leadership mental models and organizational digital maturity, hence considerably augmenting understanding of its underlying mechanisms and contextual boundaries. Employing rigorous empirical examinations on a broad range of industries and organizational settings, this study corroborates that leadership mental models have a significant impact on organizational digital maturity through direct and indirect mechanisms. Notably, the identification of technical empowerment and organizational culture as simultaneous mediators adds to the current pool of knowledge on how the cognitive frameworks of leadership shape outcomes in organizational digital transformation. This research assumes that such a relationship is subject to organizational variables such as operational scale and industry sector, hence providing a more complex understanding of the dynamics involved in digital transformation. The strength of the analytical framework, in which various statistical methods and sensitivity analyses are employed, enhances the validity and reliability of these findings. This thereby sets a robust and integrated theoretical basis that allows the research to integrate ideas from leadership theory, organizational transformation, and digital transformation literature, thus providing a foundation for future studies in this area. The study indicates that successful digital transformation requires a sophisticated understanding of the relationships between leadership cognitive models and organizational processes and systems. 5.2. Recommendations and Strategies Based on these findings, a wide range of recommendations is put forward to enable digital transformation in organizations: the creation of leadership mental models through organization-wide, tailored programs that focus on both theoretical knowledge and practical experience in digital transformation. This goal can be achieved through executive education programs, experiential digital learning experiences, and cross-industry knowledge-sharing programs. Therefore, it is suggested that the creation of technical empowerment systems focus on both infrastructural improvements and capability building, as organizations need to develop the required competencies to maximize their investments in digital technologies. A systematic approach to cultural transformation is needed, accompanied by appropriate change management approaches that tackle both the structural and behavioral aspects of organizational culture. Additionally, organizations need to create clear metrics for measuring digital maturity and establish monitoring frameworks to measure progress and adjust strategies as needed. The implications of organizational size suggest that larger organizations should focus on building agile structures and decision-making processes, whereas smaller organizations should leverage their inherent agility to undertake focused digital initiatives. Therefore, industry-specific factors should determine the pace and scope of digital transformation initiatives. For instance, industries with high-tech features may require the adoption of more aggressive transformation strategies, while traditional industries may adopt more incremental approaches. 5.3. Final Comments The present research represents a turning point in the understanding of how leadership mental models impact organizational digital maturity, hence solving both theoretical and practical lacunas related to digital transformation projects. Therefore, there is an interactive relationship between organizational capabilities, leadership perceptions, and the resultant consequences of digital transformation. Indeed, the hypothesized dual mediation model that involves technical empowerment 542 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 527-544, 2025 DOI: 10.55214/25768484.v9i3.5252 © 2025 by the authors; licensee Learning Gate and organizational culture is a significant addition to the literature on digital transformation; hence, it deepens our understanding of the mechanisms through which leadership elicits certain organizational reactions, having implications for how organizations may navigate the inevitable hurdles presented by an increasingly digital world. Given the decisive significance of contextual factors, it is critical that any digital transformation project is carefully tailored, considering organizational characteristics while emphasizing important leadership and cultural dimensions. This research hopes to create a sound foundation for future research studies that could investigate the dynamic factors of digital transformation and the ever-changing role of leadership in shaping an organization's digital maturity profile. The findings of this research—its framework and implications—are expected to be a reliable guide for both scholars and practitioners undertaking digital transformations, both now and in the foreseeable future. Transparency: The authors confirm that the manuscript is an honest, accurate, and transparent account of the study; that no vital features of the study have been omitted; and that any discrepancies from the study as planned have been explained. This study followed all ethical practices during writing. Copyright: © 2025 by the authors. This open-access article is distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). References [1] G. Ali, "Comparing AHP and ANP: An application of strategic decisions making in a manufacturing company," International Journal of Business and Social Science, vol. 3, no. 11, pp. 194–208, 2012. [2] S. Berghaus and A. Back, "Stages in digital business transformation: Results of an empirical maturity study," in Proceedings of the 10th Mediterranean Conference on Information Systems (MCIS 2016), Paphos, Cyprus, 2016. [3] S. Carretero Gomez, R. Vuorikari, and Y. Punie, Dig comp 2.1: The digital competence framework for citizens with eight proficiency levels and examples of use. Luxembourg: Publications Office of the European Union, 2017. [4] J. V. Carvalho, Á. Rocha, R. van de Wetering, and A. Abreu, "A maturity model for hospital information systems," Journal of Business Research, vol. 94, pp. 388-399, 2019. https://doi.org/10.1016/j.jbusres.2017.12.012 [5] J. V. Carvalho, Á. Rocha, J. Vasconcelos, and A. Abreu, "A health data analytics maturity model for hospitals information systems," International Journal of Information Management, vol. 46, pp. 278-285, 2019. [6] B. Chang, C.-W. Chang, and C.-H. Wu, "Fuzzy DEMATEL method for developing supplier selection criteria," Expert Systems with Applications, vol. 38, no. 3, pp. 1850-1858, 2011. https://doi.org/10.1016/j.eswa.2010.07.114 [7] S. Chanias and T. Hess, "Understanding digital transformation strategy formation: Insights from Europe's automotive industry," Proceedings of the 24th European Conference on Information Systems (ECIS 2016), Istanbul, Turkey, 2016. [8] V. Đurek, N. Begičević Ređep, and B. Divjak, "Digital maturity framework for higher education institutions," in Proceedings of the Central European Conference on Information and Intelligent Systems 2017, 2017, p. 213. [9] V. Đurek, N. Kadoić, and N. Begičević Ređep, "Assessing the digital maturity level of higher education institutions," in Proceedings of the 41st Jubilee International Convention MIPRO 2018, 2018, pp. 747–752. [10] M. Dzeko, N. Kadoić, and Z. Dobrović, "Metamodeling SNAP, a multi-criteria method for effective strategic decision making on e-learning issues," in Proceedings of the 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), IEEE, 2019, pp. 849–853. [11] European Commission, Union of equality: Strategy for the rights of persons with disabilities 2021–2030. Luxembourg: Publications Office of the European Union, 2020. [12] T. Haryanti, N. A. Rakhmawati, and A. P. Subriadi, "The extended digital maturity model," Big Data and Cognitive Computing, vol. 7, no. 1, p. 17, 2023. https://doi.org/10.3390/bdcc7010017 [13] A. R. Hevner, S. T. March, J. Park, and S. Ram, "Design science in information systems research," MIS Quarterly, vol. 28, no. 1, pp. 75-105, 2004. https://doi.org/10.2307/25148625 [14] A. Janeš, N. Kadoić, and N. Begičević Ređep, "Differences in prioritisation of the BSC's strategic goals using AHP and ANP methods," Journal of Information and Organizational Sciences, vol. 42, no. 1, pp. 1–24, 2018. https://doi.org/10.31341/jios.42.1.1 [15] N. Kadoić, "Characteristics of the analytic network process, a multi-criteria decision-making method," Croatian Operational Research Review, vol. 9, no. 2, pp. 235-244, 2018. https://doi.org/10.17535/crorr.2018.0018 https://creativecommons.org/licenses/by/4.0/ https://doi.org/10.1016/j.jbusres.2017.12.012 https://doi.org/10.1016/j.eswa.2010.07.114 https://doi.org/10.3390/bdcc7010017 https://doi.org/10.2307/25148625 https://doi.org/10.31341/jios.42.1.1 https://doi.org/10.17535/crorr.2018.0018 543 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 527-544, 2025 DOI: 10.55214/25768484.v9i3.5252 © 2025 by the authors; licensee Learning Gate [16] N. Kadoić, "A new method for analyzing complex decision-making problems based on analytical network process and social network analysis," Doctoral Dissertation, University of Zagreb, 2018. [17] N. Kadoić, N. B. Ređep, and B. Divjak, "A new method for strategic decision-making in higher education," Central European Journal of Operations Research, vol. 26, pp. 611-628, 2018. https://doi.org/10.1007/s10100-017-0482-5 [18] N. Kadoić, N. Begičević Ređep, and B. Divjak, "Decision making with the analytic network process," in Proceedings of the 14th International Symposium on Operational Research, Bled, Slovenia, 2017, pp. 180–186. [19] N. Kadoić, N. Begičević Ređep, and B. Divjak, "Structuring e-learning multi-criteria decision making problems," in Proceedings of the 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO 2017), Opatija, Croatia, 2017, pp. 811–817. [20] N. Kadoić, N. Begičević Ređep, and D. Kupres, "Using SNAP to analyze Policy measures in e-learning roadmaps," Axioms, vol. 12, no. 12, p. 1110, 2023. https://doi.org/10.3390/axioms12121110 [21] N. Kadoić, B. Divjak, and N. Begičević Ređep, "Integrating the DEMATEL with the analytic network process for effective decision-making," Central European Journal of Operations Research, vol. 27, no. 3, pp. 653-678, 2019. https://doi.org/10.1007/s10100-018-0601-4 [22] N. Kadoić and N. Begičević Ređep, "Ranking the balanced scorecard goals of higher education institutions using the centrality measures," in Proceedings of the 11th International Conference on Education and New Learning Technologies (EDULEARN19), Palma, Spain, 2019, pp. 7366–7373. [23] N. Kadoić, N. Begičević Ređep, and B. Divjak, "Application of page rank centrality in multi-criteria decision making," in Proceedings of the 15th International Symposium on Operational Research (SOR '19), Bled, Slovenia, 2019, pp. 54–59. [24] P. Kampylis, Y. Punie, and J. Devine, Promoting effective digital-age learning: A European framework for digitally- competent educational organisations. Luxembourg: Publications Office of the European Union. https://doi.org/10.2791/54070, 2015. [25] J. King, K. McKegg, J. Oakden, and N. Wehipeihana, "Rubrics: A method for surfacing values and improving the credibility of evaluation," Journal of MultiDisciplinary Evaluation, vol. 9, no. 21, pp. 11-20, 2013. [26] L. A. Knight, "Using rubrics to assess information literacy," Reference Services Review, vol. 34, no. 1, pp. 43-55, 2006. https://doi.org/10.1108/00907320610640752 [27] A. Kouroubali and D. G. Katehakis, "The new European interoperability framework as a facilitator of digital transformation for citizen empowerment," Journal of Biomedical Informatics, vol. 94, p. 103166, 2019. https://doi.org/10.1016/j.jbi.2019.103166 [28] A. Kouroubali, A. Papastilianou, and D. G. Katehakis, "Preliminary assessment of the interoperability maturity of healthcare digital services vs public services of other sectors," Studies in Health Technology and Informatics, vol. 264, pp. 654–658, 2019. [29] T. Krištof, Z. Morić, and L. Maras, "Human digital index," International Journal of Digital Technology & Economy, vol. 3, no. 2, pp. 95–122, 2018. https://doi.org/10.22598/ij-dte.2018.3.2.95 [30] W. Kuechler and V. Vaishnavi, "A framework for theory development in design science research: multiple perspectives," Journal of the Association for Information systems, vol. 13, no. 6, pp. 395–423, 2012. [31] C. H. Lawshe, "A quantitative approach to content validity," A quantitative approach to content validity, vol. 28, no. 4, pp. 563–575, 1975. https://doi.org/10.1111/j.1744-6570.1975.tb01393.x [32] S.-T. Liaw, R. Zhou, S. Ansari, and J. Gao, "A digital health profile & maturity assessment toolkit: Cocreation and testing in the Pacific Islands," Journal of the American Medical Informatics Association, vol. 28, no. 3, pp. 494–503, 2021. https://doi.org/10.1093/jamia/ocaa294 [33] Z. Liu, Y. Shi, and B. Yang, "Open innovation in times of crisis: An overview of the healthcare sector in response to the COVID-19 Pandemic," Journal of Open Innovation: Technology, Market, and Complexity, vol. 8, no. 1, p. 21, 2022. https://doi.org/10.3390/joitmc8010021 [34] K. Mehta and R. Sharma, Prioritising the critical success factors of e-learning systems by using DEMATEL. In Redefining Virtual Teaching Learning Pedagogy. United Kingdom: Wiley, 2023, pp. 401-420. [35] S. Nambisan, M. Wright, and M. Feldman, "The digital transformation of innovation and entrepreneurship: Progress, challenges and key themes," Research policy, vol. 48, no. 8, p. 103773, 2019. [36] T. Neunaber and S. Meister, "Digital maturity and its measurement of general practitioners: A scoping review," International Journal of Environmental Research and Public Health, vol. 20, no. 5, p. 4377, 2023. https://doi.org/10.3390/ijerph20054377 [37] J. Nyangena et al., "Maturity assessment of Kenya’s health information system interoperability readiness," BMJ Health & Care Informatics, vol. 28, no. 1, p. e100241, 2021. https://doi.org/10.1136/bmjhci-2020-100241 [38] J. Oakden, how to ensure transparent and clear assessment that respects diverse lines of evidence. New Zealand: Kinnect Group, 2013. [39] J. Pak and Y. Song, Health capability maturity model: Person-centered approach in personal health records systems. USA: Association for Information Systems, 2016. [40] P. Phiri, H. Cavalini, S. Shetty, and G. Delanerolle, "Digital maturity consulting and strategizing to optimize services: Overview," Journal of Medical Internet Research, vol. 25, p. e37545, 2023. https://doi.org/10.2196/37545 https://doi.org/10.1007/s10100-017-0482-5 https://doi.org/10.3390/axioms12121110 https://doi.org/10.1007/s10100-018-0601-4 https://doi.org/10.2791/54070 https://doi.org/10.1108/00907320610640752 https://doi.org/10.1016/j.jbi.2019.103166 https://doi.org/10.22598/ij-dte.2018.3.2.95 https://doi.org/10.1111/j.1744-6570.1975.tb01393.x https://doi.org/10.1093/jamia/ocaa294 https://doi.org/10.3390/joitmc8010021 https://doi.org/10.3390/ijerph20054377 https://doi.org/10.1136/bmjhci-2020-100241 https://doi.org/10.2196/37545 544 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 527-544, 2025 DOI: 10.55214/25768484.v9i3.5252 © 2025 by the authors; licensee Learning Gate [41] L. Ö. Polatlı, E. Delice, H. Tozan, and A. Erturk, "Digital maturity assesment models for health systems," Journal of Health Systems and Policies, vol. 4, no. 2, pp. 63-77, 2021. [42] PWC, Industry 4.0: Building the digital enterprise (PwC). Germany, 2016. [43] G. Remané, A. Hanelt, F. Wiesboeck, and L. M. Kolbe, Digital maturity in traditional industries—an exploratory analysis. Portugal: Association for Information Systems, 2017. [44] R. Riedl, A. Benlian, T. Hess, D. Stelzer, and H. Sikora, "On the relationship between information management and digitalization," Business & Information Systems Engineering, vol. 59, pp. 475-482, 2017. [45] T. L. Saaty, Fundamentals of the analytic hierarchy process. Netherlands: Springer, 2001, pp. 15-35. [46] T. L. Saaty, "Decision making with the analytic hierarchy process," International Journal of Services Sciences, vol. 1, no. 1, pp. 83-98, 2008. [47] T. L. Saaty and M. Sodenkamp, The analytic hierarchy and analytic network measurement processes: The measurement of intangibles. In Handbook of Multicriteria Analysis. Germany: Springer, 2010, pp. 91-166. [48] T. L. Saaty and L. G. Vargas, "The analytic network process," Iranian Journal of Operations Research, vol. 1, pp. 1-28, 2013. [49] Scottish Council for Voluntary Organizations (SCVO), Taking a placebased approach to digital inclusion, The story so far. Scotland: Scottish Council for Voluntary Organizations, 2022. [50] D. D. Stevens and A. J. Levi, Introduction to rubrics: An Assessment tool to save grading time, convey effective feedback, and promote student learning. Stylus Publishing.: USA, 2005. [51] Table of Third Social Sector Entities of Catalonia, Study on the state of digitalization in the third sector. Spain: Table of Third Social Sector Entities of Catalonia, 2022. [52] R. Van Kessel et al., "Digital health paradox: international policy perspectives to address increased health inequalities for people living with disabilities," Journal of Medical Internet Research, vol. 24, no. 2, p. e33819, 2022. https://doi.org/10.2196/33819 [53] W. Xing and A. Ghorbani, "Weighted page rank algorithm," in Proceedings of the Second Annual Conference on Communication Networks and Services Research, 2004, pp. 305-314. [54] S.-H. Yin, C.-C. Wang, L.-Y. Teng, and Y. M. Hsing, "Application of DEMATEL, ISM, and ANP for key success factor (KSF) complexity analysis in R&D alliance," Scientific Research and Essays, vol. 7, no. 19, pp. 1872-1890, 2012. https://doi.org/10.2196/33819