Building Dynamic AI Capabilities for Sustainable Employee Performance: The Roles of Trust, Knowledge Management, and Competitive Advantage

Building Dynamic AI Capabilities for Sustainable Employee Performance: The Roles of Trust, Knowledge Management, and Competitive Advantage | 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 Article Building Dynamic AI Capabilities for Sustainable Employee Performance: The Roles of Trust, Knowledge Management, and Competitive Advantage Samuel Tarigan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8078220/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract The accelerated adoption of Artificial Intelligence (AI) is reshaping organizational landscapes, yet many firms struggle to translate AI investments into meaningful employee performance gains due to insufficient integration between AI systems and organizational capabilities. This study proposes a comprehensive model examining how AI system implementation and trust in AI influence sustainable employee performance through dynamic capabilities, company competitiveness, and knowledge management capacity. Using Partial Least Squares Structural Equation Modeling (PLS-SEM) with data from Indonesian firms, the research validates an integrated framework connecting technological, psychological, and organizational factors. Findings reveal that both AI implementation and trust significantly enhance organizational capabilities, with AI implementation showing stronger effects. Company competitiveness and dynamic capabilities serve as critical mediators between AI factors and employee performance, while knowledge management capacity shows no direct performance impact. The model explains 78.3% of variance in employee performance, demonstrating substantial predictive power. Results emphasize that sustainable performance requires simultaneous development of strategic capabilities supported by trust mechanisms, not merely technological deployment. This study contributes by integrating previously isolated constructs and provides practical guidance for managers aligning AI initiatives with capability development and competitive positioning for organizational resilience. Business and commerce/Business and management Social science/Business and management Business and commerce/Information systems and information technology Physical sciences/Mathematics and computing Artificial Intelligence Trust in AI Dynamic Capabilities Knowledge Management Employee Performance Organizational Competitiveness Digital Transformation Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction The accelerated adoption of Artificial Intelligence (AI) technologies across industries is reshaping organizational landscapes, not only by automating tasks but also by transforming decision-making, learning processes, and employee performance outcomes. In practice, many firms still struggle to translate AI investments into meaningful employee-level performance gains due to insufficient integration between AI systems and organizational culture (Gomez et al., 2024; Liu et al. 2023 ). This misalignment reflects a broader challenge: AI implementation must be supported by dynamic internal capabilities that allow organizations to adapt, learn, and sustain competitive advantage in volatile environments (Murcia, et al., 2022 ; Duong, C. D., 2025). In this context, employee performance is no longer driven by individual competencies alone, but also by how effectively organizations integrate AI technologies with trust-building mechanisms and knowledge-sharing infrastructures (Jaruwanakul, 2024). A growing body of research emphasizes the critical role of trust in AI systems as a precursor to successful technology assimilation and workforce engagement. Employees are more likely to adopt and utilize AI tools when they perceive them as transparent, reliable, and aligned with their work goals (Wei & Fonti, 2023 ). Trust also plays a vital role in enabling knowledge sharing, collaboration, and organizational learning, all of which are essential for dynamic capability development (Zhou, Y., 2025; Kmieciak, R, 2021 ). Without cognitive trust in AI, organizations risk encountering resistance, reduced knowledge absorption, and ultimately, suboptimal performance outcomes (Cokcroft, S. 2013). Thus, building trust is not merely a technical issue, but a strategic necessity that connects technology with human-centric innovation processes. Knowledge Management Capacity (KMC) adds another crucial layer to this relationship. KMC refers to an organization’s ability to acquire, distribute, and apply knowledge effectively, especially under dynamic conditions. Recent studies have demonstrated that when aligned with AI systems, robust knowledge management fosters adaptive learning, innovation, and competitive advantage (Ojika & Owobu, 2025; Murcia, et al., 2022 ; Liu et al., 2023 ). However, having knowledge infrastructure alone is insufficient if it is not embedded within a culture of trust and supported by agile organizational routines. Trust in AI acts as a catalyst for knowledge processes to evolve into dynamic capabilities that drive sustainable employee performance and long-term strategic relevance (Duong, C. D., 2025; Cokcroft, S. 2013; Rezaei, 2025). Despite these insights, current research tends to examine trust, AI capability, and knowledge management in isolation rather than within an integrated framework. Most studies fail to explain how these elements interact to shape dynamic organizational capabilities and employee-level outcomes such as sustainable performance (Lau, 2021 ; Saxena & Mirsha., 2025). Furthermore, the mechanisms by which trust in AI and knowledge capacity contribute to building firm competitiveness in AI-enabled environments remain underexplored (Wei & Fonti, 2023 ; Trachuk & Linder, 2024 ). This fragmentation limits our understanding of how organizations can move from deploying AI tools toward leveraging them as strategic enablers of human productivity and agility. To bridge this gap, the present study proposes a comprehensive model examining the role of AI system implementation, trust in AI, and knowledge management capacity in building dynamic organizational capabilities and achieving sustainable employee performance. Specifically, the research addresses the following questions, 1) How do AI system implementation and trust in AI influence organizational capabilities such as competitiveness, dynamic capabilities, and knowledge management capacity? 2) How do these organizational capabilities mediate the relationship between AI-related factors and sustainable employee performance? This study offers a novel perspective by integrating AI implementation, trust in AI, and knowledge management capacity within the framework of dynamic organizational capabilities. While previous studies have often examined these constructs separately, this research emphasizes their interrelated roles in influencing sustainable employee performance. By positioning trust in AI as both a psychological factor and an organizational resource, the study provides a broader understanding of how human–technology alignment supports adaptability and competitiveness. Overall, this approach contributes to the literature by connecting AI adoption, organizational learning, and performance sustainability within a unified conceptual model. 2. Literature Review Prior studies increasingly recognize that AI implementation alone is not sufficient to ensure performance gains unless it is integrated within a broader set of organizational capabilities (Hoang & Hien, 2024 ). AI systems, when strategically implemented, enable firms to automate decision processes, improve data utilization, and increase responsiveness to market changes (Murcia, et al., 2022 ). However, the real challenge lies in how organizations absorb and translate the potential of AI into operational advantage. The literature emphasizes the importance of alignment between technology, people, and processes in shaping the effectiveness of AI adoption (Gomez et al., 2024). Without complementary enablers, such as trust and knowledge systems, even sophisticated AI systems may be underutilized or misapplied (Duong, C. D., 2025). Trust in AI emerges as a critical factor influencing how employees interact with intelligent systems. Trust determines the extent to which users perceive AI as reliable, transparent, and aligned with their roles (Wei & Fonti, 2023 ; Li & Zhou, 2025 ). High levels of trust are positively associated with greater intention to use AI, increased collaboration between humans and machines, and enhanced learning outcomes (Jaruwanakul, 2024). Moreover, trust is not developed in isolation—it is reinforced by organizational culture, past experiences, and knowledge-sharing practices (Zhou, Y., 2025). Knowledge Management Capacity (KMC) also plays a strategic role, allowing firms to absorb AI-generated insights and circulate them across departments. Effective KMC supports innovation, accelerates learning, and contributes to dynamic adaptability, especially when paired with a culture of trust (Lau, 2021 ; Ojika & Owobu, 2025). These elements collectively influence the development of dynamic capabilities—organizational routines that allow firms to reconfigure resources and remain competitive in fast-changing environments (Hoang & Hien, 2024 ;Teece et al., 1997 ; Supriyanto et al., 2024 ). The literature shows that dynamic capabilities and firm competitiveness are shaped not only by AI investment, but by the firm's capacity to combine technology with knowledge and human-centric practices (Murcia, et al., 2022 ). Ultimately, these capabilities are instrumental in sustaining employee performance over time. Employees embedded in agile, knowledge-driven, and trust-rich environments are more likely to perform consistently, adapt to change, and contribute to innovation (Saxena & Mirsha., 2025). While previous studies have examined these constructs in various contexts, there appears to be limited research integrating them into a unified model that examines how AI-related enablers influence employee work performance through organizational mechanisms. Table 1 presents a comparative overview of key prior research and positions the current study within the existing literature. Table 1 Comparison with Previous Studies Study Focus Key Variables Mediating Mechanisms Context Scope Papagiannidis et al. (2022) AI adoption and organizational performance AI implementation, dynamic capabilities Not examined Cross-industry firms Examines direct AI-performance link Wei & Fonti ( 2023 ) Trust in AI systems Trust, AI adoption Not examined Technology users Focuses on adoption intent Jaruwanakul (2024) Trust and AI collaboration Trust, human-AI collaboration Not examined Knowledge workers Examines collaboration behavior Kioskli et al. (2024) Organizational culture and AI trust Trust, organizational culture Not examined European firms Explores trust antecedents Lau ( 2021 ) Knowledge management and innovation KMC, innovation performance Not examined SMEs Focuses on KMC-innovation link Ojika & Owobu (2025) KMC and organizational agility KMC, agility Not examined Nigerian firms Examines KMC in agility context Supriyanto et al. ( 2024 ) Dynamic capabilities and competitiveness Dynamic capabilities, firm competitiveness Not examined Indonesian manufacturing Explores DC-competitiveness relationship Samadhiya et al. (2023) AI and employee performance AI tools, employee productivity Not examined Service sector Direct AI-performance examination Current Study AI enablers and employee performance AI implementation, trust in AI, KMC, dynamic capabilities, firm competitiveness, employee performance Dynamic capabilities and firm competitiveness as serial mediators Indonesian firms across sectors Examines integrated model with organizational mediators [INSERT Table 1 HERE] As shown in Table 1 , existing research has predominantly examined specific relationships between pairs of constructs rather than integrated pathways. Studies such as Murcia, et al., 2022 ) and Supriyanto et al. ( 2024 ) have explored dynamic capabilities and competitiveness separately from AI-specific enablers. Research on trust in AI (Wei & Fonti, 2023 ; Jaruwanakul, 2024) has primarily focused on adoption intentions or collaboration behaviors rather than performance outcomes mediated by organizational capabilities. Knowledge management studies (Lau, 2021 ; Ojika & Owobu, 2025) have examined KMC's role in fostering innovation and agility, though not in conjunction with AI or trust within the same analytical framework. The current study seeks to contribute to this literature by examining a model that positions AI implementation, trust in AI, and knowledge management capacity as interconnected enablers that may jointly influence dynamic capabilities and firm competitiveness, which in turn may shape employee work performance. This approach—illustrated in the conceptual framework (Fig. 1 )—offers an integrated perspective on how technology-oriented and human-centric factors may interact to produce organizational and individual-level outcomes in the Indonesian context, where AI adoption patterns and organizational readiness vary considerably across sectors. [INSERT FIGURE 1 HERE] 3. Methodology This research adopts a quantitative approach using Structural Equation Modeling with Partial Least Squares (PLS-SEM) to investigate the relationships between trust in AI, AI system implementation, knowledge management capacity, dynamic capabilities, company competitiveness, and sustainable employee performance. PLS-SEM is well suited for predictive models with complex structures, especially when theory is still emerging or when constructs are measured reflectively (Hair et al., 2021 ). Data were collected through an online questionnaire using previously validated items, adapted to reflect the context of AI implementation in organizational settings. The study was conducted among postgraduate management students enrolled at an accredited higher education institution in Indonesia, with participants originating from various cities across the country. As illustrated in Fig. 2 , the methodological process involves sequential stages: data collection, measurement model assessment, structural model assessment, and hypothesis testing. SmartPLS 4.0 software was used for model estimation, given its ability to handle small to medium sample sizes and complex inter-variable relationships. To assess the measurement model, the study first examined convergent validity, discriminant validity, and collinearity diagnostics. Convergent validity was evaluated using factor loadings, Composite Reliability (CR), and Average Variance Extracted (AVE). Indicators were considered valid if outer loadings exceeded 0.70, CR values exceeded 0.70, and AVE exceeded the threshold of 0.50, as recommended by Fornell and Larcker ( 1981 ). Discriminant validity was tested using the Heterotrait-Monotrait (HTMT) ratio, with acceptable values below 0.90 (Henseler et al., 2015 ). Multicollinearity was assessed using the Variance Inflation Factor (VIF), and items with VIF values below 5.00 were retained, indicating no critical issues of redundancy (Hair et al., 2021 ). [INSERT FIGURE 2 HERE] After validating the measurement model, the structural model was evaluated through path coefficients, explanatory power (R²), and predictive relevance (Q²). R² values indicate the amount of variance explained in the endogenous constructs, with thresholds of 0.25 (weak), 0.50 (moderate), and 0.75 (substantial) suggested by Hair et al. ( 2021 ). Q² values were derived using the blindfolding technique to test out-of-sample predictive relevance, where values above 0 indicate acceptable predictive capability (Chin, 1998 ). Additionally, model significance was tested using bootstrapping procedures with 5,000 subsamples, generating confidence intervals and t-values to assess the statistical significance of each path. The threshold for significance was set at p < 0.05 for confirming or rejecting each hypothesis. Table 2 lists the measurement items used to operationalize each construct. Items for Trust in AI (TIAI), AI System Implementation (AISTI), and Knowledge Management Capacity (KMC) were adapted from recent studies on AI readiness and digital capability (Wei & Fonti, 2023 ; Ojika & Owobu, 2025). Meanwhile, constructs such as Dynamic Capabilities (DC) and Company Competitiveness (CC) reflect organizational agility and strategic positioning in response to AI (Murcia, et al., 2022 ). Sustainable Employee Work Performance (SEWP) was measured as an outcome variable reflecting how AI supports accuracy, speed, and effectiveness at the individual level (Saxena & Mirsha., 2025). All items were measured using a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). This robust operationalization ensured the construct validity and reliability of the instrument prior to testing the structural hypotheses. Table 2 Measurement Item Construct Indicator Item Statement Trust in AI (TIAI) TIAI_01 I believe AI systems can make reliable decisions in our work processes TIAI_02 I trust that AI technology will perform consistently and accurately TIAI_05 I believe that AI technology will deliver the promised benefits to our organization AI Systems/ Technology Implementation (AISTI) AISTI_01 AI technology is successfully integrated into our core business processes AISTI_02 Our organization has effectively implemented AI systems in relevant operational areas AISTI_03 AI technology functions as intended in our organizational workflows Company Competitiveness (CC) CC_03 AI technology has enhanced our organization's ability to respond to market changes CC_04 AI implementation has strengthened our organization's market leadership position Dynamic Capabilities (DC) DC_01 Our organization is effective in identifying AI opportunities and threats from the external environment DC_02 Our leaders actively monitor AI technology trends and market changes DC_06 Our leaders are effective in transforming the organization's resource base for AI implementation Knowledge Management Capacity (KMC) KMC_01 AI systems have enhanced our organization's ability to capture and store knowledge KMC_03 Our AI implementation has improved the organization's ability to create new knowledge KMC_06 Our AI implementation has improved decision-making through better knowledge utilization Sustainable Employee-Work Performance (SEWP) SEWP_01 AI implementation reduces the risk of errors in work performance SEWP_02 AI accelerates and improves decision-making to achieve sustainable employee work performance SEWP_03 AI enhances the efficiency of our organization's work performance [INSERT Table 2 HERE] 4. Result 4.1 Analytical Model (First-Order Construct) Figure 3 illustrates the structural relationships among six core constructs using a Partial Least Squares (PLS) approach. The model reveals that Trust in AI (TIAI) and AI Systems/Technology Implementation (AISTI) are foundational drivers that significantly influence Company Competitiveness (CC), Dynamic Capabilities (DC), and Knowledge Management Capacity (KMC). These three mediating constructs subsequently enhance Sustainable Employee-Work Performance (SEWP), indicating a layered and interconnected pathway from technological trust and adoption to individual-level performance outcomes. This highlights the importance of both human trust and systemic implementation in unlocking the strategic value of AI in organizational contexts. [INSERT FIGURE 3 HERE] To validate the strength of these relationships, Table 3 presents the results of the measurement model, which confirm strong convergent validity and reliability. The outer loadings for all items exceed the recommended threshold of 0.7, while Cronbach’s Alpha and Composite Reliability values are consistently high across constructs, reflecting excellent internal consistency. Notably, constructs like SEWP and KMC exhibit particularly strong measurement quality, suggesting that they serve as robust outcome and mediating variables, respectively. These findings reinforce the model’s theoretical proposition: that enhancing AI trust and implementation can build critical organizational capabilities, which in turn drive sustained employee performance. Table 3 Results Indicate Convergent Validity Constructs Item Outer loadings Cronbach’s Alpha Composite Reliability AI Systems/ Technology Implementation (AISTI) AISTI_01 0.941 0.942 0.963 AISTI_02 0.945 AISTI_03 0.954 Company Competitiveness (CC) CC_03 0.973 0.944 0.973 CC_04 0.973 Dynamic Capabilities (DC) DC_01 0.931 0.921 0.950 DC_02 0.942 DC_06 0.915 Knowledge Management Capacity (KMC) KMC_01 0.927 0.926 0.953 KMC_03 0.946 KMC_06 0.928 Sustainable Employee-Work Performance (SEWP) SEWP_01 0.944 0.944 0.964 SEWP_02 0.954 SEWP_03 0.947 Trust in AI (TIAI) TIAI_01 0.892 0.884 0.928 TIAI_02 0.932 TIAI_05 0.878 [INSERT Table 3 HERE] Table 4 presents the Heterotrait-Monotrait Ratio (HTMT) values, which are used to assess discriminant validity between constructs in the model. All HTMT values fall below the conservative threshold of 0.90, indicating that each construct is empirically distinct from the others. The strongest discriminant separation is observed between constructs such as TIAI and KMC (0.634) and TIAI and SEWP (0.664), suggesting that trust in AI is conceptually well differentiated from both knowledge management and employee performance. Meanwhile, higher—but still acceptable—HTMT values such as those between DC and KMC (0.852) and DC and SEWP (0.849) reflect the natural conceptual closeness between dynamic capabilities and organizational performance outcomes. Overall, the results confirm that the constructs possess adequate discriminant validity, supporting the model’s structural integrity. Table 4 HTMT Values Factor AISTI CC DC KMC SEWP TIAI AISTI 0.947 CC 0.785 0.973 DC 0.738 0.825 0.929 KMC 0.752 0.777 0.852 0.934 SEWP 0.713 0.837 0.849 0.789 0.949 TIAI 0.662 0.726 0.683 0.634 0.664 0.901 [INSERT Table 4 HERE] 4.2 Measurement Model (Second-Order Construct) The second-order model results highlight the pivotal role of AI Systems/Technology Implementation (AISTI) and Trust in AI (TIAI) in shaping key organizational capabilities. AISTI has a strong and statistically significant influence on Company Competitiveness (CC), Dynamic Capabilities (DC), and Knowledge Management Capacity (KMC), with path coefficients all above 0.50 and p-values below 0.001. This indicates that effective and structured implementation of AI systems substantially enhances a firm's ability to remain competitive, agile, and knowledge-driven. In parallel, TIAI also contributes significantly to these three constructs, though with slightly lower coefficients, suggesting that psychological acceptance of AI complements—but does not substitute—actual system deployment. [INSERT Table 5 HERE] Table 5 Second-order Model Results Path Path Coef T statistics P values AISTI -> CC 0.542 5.529 0 AISTI -> DC 0.508 6.003 0 AISTI -> KMC 0.591 5.618 0 CC -> SEWP 0.398 3.323 0.001 DC -> SEWP 0.407 3.988 0 KMC -> SEWP 0.133 1.067 0.286 TIAI -> CC 0.367 3.787 0 TIAI -> DC 0.346 4.699 0 TIAI -> KMC 0.243 2.387 0.017 When examining their impact on Sustainable Employee-Work Performance (SEWP), only CC and DC emerge as significant predictors, while KMC does not show a meaningful direct effect. This implies that competitive positioning and adaptability are more directly tied to employee performance outcomes compared to knowledge capacity, which may operate more indirectly. These findings suggest that for organizations seeking to optimize employee outcomes through AI adoption, focusing on building competitiveness and dynamic capabilities is essential. Moreover, while trust in AI enhances internal capacities, its greatest value may lie in reinforcing the success of tangible AI implementations across the enterprise. 4.3 Structural Model (Path Analysis) Structural Equation Modeling (SEM) was employed to examine the hypothesized relationships among constructs using a second-order path analysis approach, with a focus on assessing both the significance and strength of direct effects within the proposed framework. The application of PLS-SEM allowed for the simultaneous estimation of measurement reliability and structural paths, providing a robust method to explore the impact of AI Systems/Technology Implementation (AISTI) and Trust in AI (TIAI) on organizational capabilities—namely Company Competitiveness (CC), Dynamic Capabilities (DC), and Knowledge Management Capacity (KMC)—as well as their subsequent influence on Sustainable Employee-Work Performance (SEWP). Bootstrapping with 5,000 resamples was conducted to test the significance of the path coefficients, revealing that 8 out of 9 proposed paths were statistically significant, supporting the majority of the hypothesized relationships. Specifically, AISTI demonstrated strong and significant effects on CC (β = 0.542, p < 0.001), DC (β = 0.508, p < 0.001), and KMC (β = 0.591, p < 0.001), while TIAI also significantly influenced CC (β = 0.367, p < 0.001), DC (β = 0.346, p < 0.001), and KMC (β = 0.243, p = 0.017), albeit with slightly lower magnitudes. In terms of performance outcomes, both CC (β = 0.398, p = 0.001) and DC (β = 0.407, p < 0.001) were found to significantly predict SEWP, whereas KMC showed a non-significant relationship (β = 0.133, p = 0.286), suggesting that while knowledge capacity is a critical resource, its impact on individual-level work performance may be more indirect or mediated. Additionally, R² values for endogenous constructs were found to be substantial, with SEWP achieving the highest explained variance (R² = 0.783), followed by CC (R² = 0.691), DC (R² = 0.612), and KMC (R² = 0.599), indicating a strong model fit and explanatory power. These findings collectively support the model's structural integrity and highlight the interplay between AI adoption, organizational capability building, and sustainable employee performance in the context of digital transformation. 4.4 Superiority of the Structural Model The results obtained from the structural model provide solid empirical evidence regarding the reliability of individual indicators, as well as the convergent and discriminant validity of the overall analytical framework. To assess the explanatory power of the model, the coefficient of determination (R²) was used as a key metric, as supported by the PLS-SEM methodology. This approach, akin to traditional regression analysis, evaluates the proportion of variance in each dependent construct that is explained by its associated independent variables. Higher R² values are generally indicative of a stronger predictive capability of the structural model. In this study, the R² values were generated using SmartPLS and revealed substantial predictive power across all key constructs. Specifically, the model explained 78.3% of the variance in Sustainable Employee-Work Performance (SEWP), highlighting it as a highly influenced outcome variable within the framework. Additionally, the variance explained for Company Competitiveness (CC) was 69.1%, for Dynamic Capabilities (DC) was 61.2%, and for Knowledge Management Capacity (KMC) was 59.9%, all of which reflect strong model fit based on PLS-SEM benchmarks. These findings suggest that the exogenous constructs—AI Systems/Technology Implementation (AISTI) and Trust in AI (TIAI)—serve as powerful predictors of organizational capabilities and employee-level outcomes. According to Chin’s guidelines, R² values above 0.67 are considered substantial, which reinforces the superiority and robustness of the proposed structural model in capturing the dynamics of AI integration, organizational competence, and sustainable performance. 4.5 The Structural Model Predictive Weight Figure 4 presents the results of the bootstrapping analysis, which was used to evaluate the predictive weight and statistical significance of the structural model’s path coefficients. The analysis confirmed that the majority of the hypothesized relationships are statistically significant at the 0.05 level or below. Both AI Systems/Technology Implementation (AISTI) and Trust in AI (TIAI) exert significant positive effects on Company Competitiveness (CC), Dynamic Capabilities (DC), and Knowledge Management Capacity (KMC), with all related p-values below 0.05. Furthermore, CC and DC significantly predict Sustainable Employee-Work Performance (SEWP), with p-values of 0.001 and 0.000 respectively, indicating their critical role as mediators between AI-related constructs and performance outcomes. [INSERT FIGURE 4 HERE] However, one notable finding is that the relationship between Knowledge Management Capacity (KMC) and SEWP is not statistically significant, with a p-value of 0.286. This path is illustrated in the model using a dashed red line, indicating that despite its conceptual relevance, KMC does not demonstrate a direct predictive weight on employee performance within this framework. This suggests that the influence of knowledge management may be more indirect or mediated through other organizational capabilities like CC or DC. Overall, the bootstrapping results reinforce the model’s predictive strength while also revealing areas—like KMC to SEWP—where the theoretical linkage may require further investigation or contextual interpretation. 5. Discussion 5.1 RQ1: How do AI system implementation and trust in AI influence organizational capabilities such as competitiveness, dynamic capabilities, and knowledge management capacity? The findings indicate that both AI Systems/Technology Implementation (AISTI) and Trust in AI (TIAI) significantly enhance organizational capabilities, with AISTI exerting stronger effects across all three constructs (Murcia, et al., 2022 ). This suggests that organizations investing in AI must ensure not only technical deployment but also consistent, system-wide integration to cultivate strategic responsiveness (Duong, C. D., 2025). Trust, while slightly less impactful in magnitude, remains a vital enabler that facilitates employees' willingness to interact with and accept AI systems (Wei & Fonti, 2023 ; Jaruwanakul, 2024). These insights reinforce the notion that both structural (technology implementation) and relational (trust-building) mechanisms are necessary for capability development in digitally transforming firms (Zhou, Y., 2025). Recent studies emphasize that employees' cognitive trust in AI moderates the effectiveness of technology adoption, particularly in contexts where perceived risks and transparency affect user engagement and system utilization (Übellacker, 2025 ; Wasesa et al., 2025 ). Without adequate trust mechanisms, even well-integrated AI systems may fail to generate adaptive or dynamic capabilities, as employees' reluctance to rely on AI tools can limit organizational learning and responsiveness (Jaruwanakul, 2024). Trust building requires transparency in AI decision-making processes, clear communication about system capabilities and limitations, and consistent demonstration of AI reliability over time (Wei & Fonti, 2023 ). Organizations that prioritize both technological robustness and relational trust are better positioned to unlock the full potential of AI investments in building organizational capabilities (Zhou, Y., 2025). 5.2 RQ2: How do these organizational capabilities mediate the relationship between AI-related factors and sustainable employee performance? The results show that Company Competitiveness (CC) and Dynamic Capabilities (DC) serve as significant mediators between AI-related factors and Sustainable Employee-Work Performance (SEWP), while Knowledge Management Capacity (KMC) does not have a direct effect on SEWP. This implies that employee-level outcomes are more strongly driven by organizational agility and market adaptability rather than by the presence of knowledge systems alone (Teece et al., 1997 ; Supriyanto et al., 2024 ). The strong explanatory power of the model (R² = 0.783 for SEWP) confirms that these constructs are meaningfully connected and relevant for performance outcomes (Murcia, et al., 2022 ). These results help clarify the pathways through which AI integration contributes to sustainable employee performance through strategic capability building and adaptive organizational routines (Saxena & Mirsha., 2025). The non-significant relationship between KMC and SEWP invites further scrutiny and may suggest that knowledge infrastructures, while necessary, are not sufficient unless they are embedded within actionable routines that transform stored knowledge into applied insight (Rezaei, 2025; Cui, 2025 ). Contemporary research on AI-driven knowledge management highlights that the effectiveness of knowledge systems depends on learning loops, data contextualization, and continuous knowledge coupling processes (Rezaei, 2025). Although KMC remains an important internal resource, its performance-related impact may require additional organizational enablers such as absorptive capacity or innovation leadership to be fully realized (Ojika & Owobu, 2025; Cokcroft, S. 2013). Thus, building performance-centric capabilities involves not just knowledge accumulation but the ability to act on that knowledge in fast-changing environments (Teece et al., 1997 ; Supriyanto et al., 2024 ). Organizations must therefore focus on converting "available knowledge" into "usable knowledge" through adaptive structures and trust-based collaboration (Cui, 2025 ). 5.3 Theoretical Contributions This study contributes to closing the existing research gap by offering an integrated model that connects AI implementation, trust, and organizational capabilities to sustainable employee performance—an area that has been underexplored in past literature (Wei & Fonti, 2023 ; Lau, 2021 ). While prior research often isolates technological or human factors, this study confirms the value of a capability-based view of digital transformation, where outcomes emerge from interdependencies among technology, people, and strategic routines (Teece et al., 1997 ; Supriyanto et al., 2024 ;Li & Zhou, 2025 ). The findings reinforce calls in recent literature for more systemic, multi-level approaches to studying AI in management contexts (Lau, 2021 ; Saxena & Mirsha., 2025). By demonstrating how AI-related enablers work through organizational capabilities to shape employee performance, this research provides empirical support for integrating technological, relational, and strategic perspectives in AI adoption research (Murcia, et al., 2022 ). From a strategic management perspective, these findings reaffirm that AI implementation must be viewed as a cross-functional transformation rather than a purely technological investment (Duong, C. D., 2025; Gomez et al., 2024). The development of dynamic capabilities—sensing, seizing, and transforming—remains critical for translating AI potential into sustainable employee outcomes (Teece et al., 1997 ; Ettinger, 2025). Organizations that fail to align AI initiatives with capability development risk achieving technological sophistication without corresponding gains in agility or competitiveness (Murcia, et al., 2022 ). Thus, strategic responsiveness to AI should involve both technological readiness and the continuous renewal of organizational routines and structures (Supriyanto et al., 2024 ). Beyond the internal dynamics of AI implementation and trust, the findings suggest a broader insight into how digital maturity affects human-centric outcomes (Duong, C. D., 2025; Gomez et al., 2024). Organizations that effectively convert AI investments into competitive positioning and adaptive capacity are more likely to foster environments where employees can perform sustainably, especially in knowledge-intensive and rapidly evolving sectors (Papagiannidis et al., 2022). This supports the view that digital transformation should not only be evaluated in terms of system efficiency but also in its capacity to generate organizational learning and resilience (Saxena & Mirsha., 2025). Notably, trust in AI contributes to psychological safety, reducing resistance to technological change and enabling higher engagement with digital workflows (Zhou, Y., 2025). Thus, building digital trust may be a prerequisite for fully unlocking the performance benefits of AI (Wei & Fonti, 2023 ; Jaruwanakul, 2024). 5.4 Managerial Implications Translating these research findings into actionable strategies, organizations can adopt several practical approaches to maximize the performance benefits of AI investments. From a managerial perspective, these findings highlight the need for integrated strategies that address technology, trust, capabilities, and competitiveness simultaneously rather than sequentially. The results demonstrate that AI implementation effectiveness depends not only on technological deployment but also on how organizations build trust, develop dynamic capabilities, and enhance competitiveness (Duong, C. D., 2025; Gomez et al., 2024). Leaders must recognize that AI investments yield employee performance benefits primarily through organizational capability development rather than direct technology-to-performance pathways (Murcia, et al., 2022 ; Supriyanto et al., 2024 ). This understanding should guide resource allocation decisions and implementation strategies to ensure AI initiatives generate sustainable returns (Saxena & Mirsha., 2025). The following subsections outline specific action areas for practitioners. Building Trust in AI Systems. As the first critical step in effective AI adoption, organizations should establish transparent AI governance frameworks that clearly communicate how AI systems make decisions, what data they use, and how they complement rather than replace human judgment (Wei & Fonti, 2023 ; Übellacker, 2025 ). Practical steps include creating AI ethics committees, implementing explainable AI (XAI) tools that allow employees to understand system recommendations, and conducting regular training sessions where employees can interact with AI systems in low-stakes environments (Jaruwanakul, 2024). Managers should also establish feedback mechanisms where employees can report concerns about AI reliability or bias, and demonstrate responsiveness by making visible system improvements based on user input (Zhou, Y., 2025). Building trust requires consistent communication about both AI capabilities and limitations to set realistic expectations and prevent disillusionment (Wasesa et al., 2025 ). Once trust foundations are established, organizations can focus on leveraging AI to build dynamic capabilities. Developing Dynamic Capabilities Through AI. Given the significant mediating role of dynamic capabilities, organizations should design AI implementation roadmaps that specifically target the three dimensions of dynamic capabilities: sensing, seizing, and transforming (Teece et al., 1997 ; Ettinger, 2025). For sensing capabilities, deploy AI-powered market intelligence systems, customer analytics platforms, and competitive monitoring tools that provide real-time insights into environmental changes (Murcia, et al., 2022 ). For seizing capabilities, establish cross-functional AI task forces that can rapidly prototype solutions, test AI applications in pilot projects, and scale successful initiatives across departments (Supriyanto et al., 2024 ). For transforming capabilities, use AI to identify process bottlenecks, automate routine tasks, and free up employee time for higher-value strategic work (Duong, C. D., 2025). Managers should create organizational routines that regularly review AI-generated insights and translate them into actionable strategic decisions (Gomez et al., 2024). These dynamic capabilities must be directed toward enhancing competitive positioning to maximize performance impact. Enhancing Competitiveness Through Strategic AI Alignment. Recognizing that competitiveness serves as a key mediator to employee performance, leaders should conduct competitiveness audits that map how AI initiatives contribute to specific competitive advantages such as cost leadership, differentiation, or speed to market (Murcia, et al., 2022 ). This involves identifying core business processes where AI can create the most significant competitive impact, such as customer service automation, predictive maintenance, or personalized product recommendations (Saxena & Mirsha., 2025). Organizations should benchmark their AI capabilities against industry competitors and identify gaps that require investment or partnership (Supriyanto et al., 2024 ). Managers must also align AI projects with strategic priorities by establishing clear performance metrics that link AI implementation to competitive outcomes such as market share growth, customer satisfaction improvements, or operational cost reductions (Duong, C. D., 2025). Alongside competitiveness enhancement, attention must be given to knowledge management systems, though with a more nuanced approach. Activating Knowledge Management for Performance Impact. Addressing the finding that KMC does not directly affect employee performance, organizations should focus on converting knowledge repositories into decision-support systems that actively guide employee actions rather than merely storing information (Rezaei, 2025; Cui, 2025 ). Practical approaches include integrating AI-powered knowledge assistants into daily workflows, creating communities of practice where employees share AI use cases and lessons learned, and establishing knowledge brokers who help translate stored information into context-specific guidance (Ojika & Owobu, 2025). Organizations should move from passive knowledge databases to active learning systems that recommend relevant information based on employee tasks, provide just-in-time training, and facilitate peer-to-peer knowledge exchange (Cokcroft, S. 2013). Managers should also measure knowledge system effectiveness not by storage capacity but by utilization rates and impact on decision quality (Lau, 2021 ). These individual initiatives must be integrated into a coherent organizational strategy to maximize their combined impact. Implementing Integrated AI Strategies. Bringing together all previous recommendations, organizations should adopt a holistic implementation approach that simultaneously addresses technology, trust, capabilities, and competitiveness rather than treating these as sequential or independent initiatives (Murcia, et al., 2022 ; Saxena & Mirsha., 2025). This involves creating AI steering committees with representation from IT, HR, strategy, and operations to ensure cross-functional alignment (Duong, C. D., 2025). Leaders should establish stage-gate processes for AI projects that evaluate not only technical feasibility but also trust implications, capability requirements, and competitive positioning (Gomez et al., 2024). Regular AI maturity assessments should track progress across all dimensions—technology deployment, employee trust levels, dynamic capability development, and competitive advantage realization—to identify areas requiring additional investment or intervention (Supriyanto et al., 2024 ). By adopting this integrated approach, managers can maximize the employee performance benefits of AI investments and build sustainable organizational resilience in the face of technological disruption (Saxena & Mirsha., 2025). While these recommendations provide actionable guidance, it is important to acknowledge the limitations of this study and identify directions for future research. 6. Conclusion This study highlights that achieving sustainable employee work performance in the age of artificial intelligence (AI) cannot rely solely on technological deployment. Instead, it requires the development of strategic organizational capabilities, particularly company competitiveness and dynamic capabilities, supported by trust in AI systems and structured technology implementation. The findings demonstrate that both AI System Implementation (AISTI) and Trust in AI (TIAI) significantly influence key organizational capabilities, which in turn serve as critical mediators for employee performance outcomes. While Knowledge Management Capacity (KMC) remains a valuable internal resource, its direct effect on performance was found to be insignificant, suggesting that knowledge must be activated through agile and strategically aligned processes to yield measurable results. These insights underscore the importance of integrating technological enablers with human-centered strategies to fully leverage the value of AI-driven transformation. Despite its contributions, this study has several limitations that warrant consideration. The cross-sectional design restricts the ability to infer causality and limits understanding of how trust and organizational capabilities evolve over time. Additionally, the data were collected within a specific cultural and organizational context, which may affect the generalizability of the findings across industries or regions. The model also does not account for moderating variables such as leadership style, organizational learning culture, or digital maturity, which may amplify or dampen the observed relationships. Recognizing these limitations invites a cautious interpretation of the findings and highlights the need for further empirical refinement. Future research should explore these dynamics using longitudinal or mixed-method approaches to better capture the long-term effects of AI trust and implementation on organizational development. Researchers are also encouraged to investigate potential contextual moderators, including industry regulations, technological turbulence, and workforce digital literacy, to deepen understanding of how AI adoption shapes performance in diverse environments. Comparative studies across sectors or countries could offer additional insights into how external factors influence the role of AI in shaping human-centric outcomes. Expanding this research agenda will not only strengthen theoretical frameworks in digital transformation and human–AI collaboration but also provide practical guidance for organizations seeking to align digital strategies with sustainable performance and resilience. Declarations Acknowledgement The authors would like to express their sincere gratitude to all the employees and organizations in Indonesia who participated in this study by completing the online questionnaire. Their valuable insights and willingness to share their experiences with AI systems made this research possible. We also extend our appreciation to the reviewers and editors for their constructive feedback that helped improve the quality of this manuscript. Declaration Statements: Funding Statement This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Author Contribution S.T. was solely responsible for all aspects of this research, including conceptualization, literature review, research design and methodology, data collection, statistical analysis using PLS-SEM, interpretation of results, and manuscript writing. The author has read and approved the final manuscript. Competing Interests The authors declare no competing interests. Data availability The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. To protect participant confidentiality, only anonymized data will be shared in compliance with data protection regulations and ethical research standards. Ethical Approval This study received ethical approval from the Research Ethics Committee ( Komite Etik Penelitian ) of an accredited higher education institution in Indonesia. Ethical approval was granted by an officially recognized institutional ethics committee located in Indonesia, the country where the research was conducted. The research protocol was reviewed and classified as Low Risk (Approval No. 056/AKD/PR-1/STIEHB/X/2025, issued on 10 March 2025). All research procedures were conducted in accordance with the ethical principles outlined in the Declaration of Helsinki and applicable Indonesian national regulations and institutional guidelines for research involving human participants. The study ensured voluntary participation, informed consent, confidentiality, anonymity of responses, and data protection throughout the research process. Informed Consent Informed consent was obtained from all participants through an online survey platform during the second quarter of 2025 (April–June 2025), following the issuance of ethical approval. The study was conducted among postgraduate management students from various cities across Indonesia. Prior to completing the questionnaire, participants were presented with a study information sheet explaining the research objectives, the nature of their participation, and how their data would be used. Participation was entirely voluntary, and respondents provided their implied consent by proceeding with the survey after reading the information sheet. Participants were assured of the confidentiality and anonymity of their responses, and that their data would be used solely for academic research purposes. No personally identifiable information was collected. The right to withdraw from the study at any point without consequence was clearly communicated. By voluntarily completing and submitting the questionnaire, participants indicated their consent to participate. References Chin, W.W., 1998. The partial least squares approach to structural equation modeling. In Modern methods for business research (pp. 295-336). Psychology Press. Cockcroft, S. (2013). National health IT infrastructure through the media lens. Health Policy and Technology , 2 (4), 203-215. https://doi.org/10.1016/j.hlpt.2013.07.003. Cui, J. (2025). The influence of Automation Software Platforms and Knowledge Sharing Frameworks on Organizational Effectiveness in Chinese Technology Enterprises. Available at SSRN 5093047 . https://dx.doi.org/10.2139/ssrn.5093047. Duong, C. D. (2025). Artificial intelligence innovation, innovation niches and entrepreneurial performance: the curvilinear role of open innovation. Business Process Management Journal , 1-28. https://doi.org/10.1108/BPMJ-04-2025-0500. Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research , 18 (1), 39–50. https://doi.org/10.2307/3151312. Gómez-Bengoechea, G., & Jung, J. (2024). The Matthew effect: Evidence on firms’ digitalization distributional effects. Technology in Society , 76 , 102423. https://doi.org/10.1016/j.techsoc.2023.102423. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). SAGE Publications. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8. Hoang, D. V., & Hien, N. T. (2024). Digital capabilities, firm performance, and innovation capabilities: A combined approach of PLS-SEM and ANN. International Journal of Innovation Management , 28 (01n02), 2450007. https://doi.org/10.1142/S1363919624500075. Kmieciak, R. (2021). Trust, knowledge sharing, and innovative work behavior: empirical evidence from Poland. European Journal of Innovation Management , 24 (5), 1832-1859. https://doi.org/10.1108/EJIM-04-2020-0134. Lau, E. K. W. (2021, July). Relationship Between a Company’s Knowledge Management Strategy and Its Business Sustainability. In International Conference on Knowledge Management in Organizations (pp. 187-196). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-81635-3_16. Liu, S., Keil, M., Wang, L., & Lu, Y. (2023). Understanding critical risks of business process outsourcing from the vendor perspective: A dyadic comparison Delphi study. Information & Management , 60 (6), 103837. https://doi.org/10.1016/j.im.2023.103837. Li Z, Zhou Y (2025), "Starting with trust: unraveling the impact of AI trust on employee digital performance". Baltic Journal of Management , Vol. 20 No. 5 pp. 637–655, doi: https://doi.org/10.1108/BJM-03-2025-0177. Murcia, N. N., Ferreira, F. A., & Ferreira, J. J. (2022). Enhancing strategic management using a “quantified VRIO”: Adding value with the MCDA approach. Technological Forecasting and Social Change , 174 , 121251. https://doi.org/10.1016/j.techfore.2021.121251. Papagiannidis, E., Enholm, I. M., Dremel, C., Mikalef, P., & Krogstie, J. (2023). Toward AI governance: Identifying best practices and potential barriers and outcomes. Information Systems Frontiers , 25 (1), 123-141. https://doi.org/10.1007/s10796-022-10251-y. Rezaei Haji Abad, F. (2025). AI-Enhanced Administrative system: Automating Diagnosis Coding-in Healthcare-At Linköping University Hospital (Östergötland Region). https://urn.kb.se/resolve?urn=urn%3Anbn%3Ase%3Alnu%3Adiva-141393. Saxena M, Mishra DK (2025), "Artificial intelligence: the way ahead for employee engagement in corporate India". Global Knowledge, Memory and Communication , Vol. 74 No. 1-2 pp. 111–127, https://doi.org/10.1108/GKMC-09-2022-0215. Supriyanto, A., Said, D. L., & Anshori, A. G. (2024). A systematic literature review on dynamic capabilities in marketing. International Journal of Business and Society, 25(1), 333–356. https://journal.access-bg.org/. Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533. https://doi.org/10.1002/(SICI)1097-0266(199708)18:7%3C509::AID-SMJ882%3E3.0.CO;2-Z. Trachuk, A., & Linder, N. V. (2024). Decision-making factors for adopting artificial intelligence technologies in firms. Sustainability, 16(1), 375. https://doi.org/10.17747/2618-947X-2024-2-134-151. Übellacker, T. (2025). Making sense of AI limitations: how individual perceptions shape organizational readiness for AI adoption. arXiv preprint arXiv:2502.15870 . https://doi.org/10.48550/arXiv.2502.15870. Wasesa, I. R. H., Permatasari, D., Angkat, F. A., & Al-Khowarizmi, A. K. (2025). Integration of Artificial Intelligence in Management Information Systems to Improve the Effectiveness of Strategic Decision-Making in the Digital Era. Journal of Computer Science, Information Technology and Telecommunication Engineering , 6 (2), 923-929. https://doi.org/10.30596/jcositte.v6i2.26051. Wei, Y., & Fonti, F. (2023). Conformity: How mental processes affect cognitive trust in AI. Academy of Management Proceedings, 2023(1), 13299. https://doi.org/10.5465/AMPROC.2023.14246. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 10 Jan, 2026 Reviews received at journal 01 Jan, 2026 Reviews received at journal 28 Dec, 2025 Reviews received at journal 25 Dec, 2025 Reviewers agreed at journal 23 Dec, 2025 Reviewers agreed at journal 23 Dec, 2025 Reviewers agreed at journal 22 Dec, 2025 Reviewers agreed at journal 18 Dec, 2025 Reviewers invited by journal 18 Dec, 2025 Editor assigned by journal 18 Dec, 2025 Editor invited by journal 18 Dec, 2025 Submission checks completed at journal 10 Dec, 2025 First submitted to journal 10 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8078220","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":562624266,"identity":"a3401bc3-9d80-46d6-8fe1-d30b8ef459b4","order_by":0,"name":"Samuel Tarigan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABD0lEQVRIiWNgGAWjYNCCAhDB2AAkbHgYJHjAYjwMB/BpMWCQgGpJ4+EhQQsYHGaAaWHApUW+/XSaxAcDhjr+2YebX/zccV7GXrr3AMOPGgYZPhxaDM7kbpOcAbRF4lxim2Xvmds8PDLnEhh7jjHwSOLSwpC7TZoH5LAzjG0GvG1ALRI5Bgy8DQw8Brgc1v92m/QfoBZ5oBbDv23nwFoY/+LRwnADaAvI+wZnGJsf87YdAGthxmeLwY23my17DBgkNwJtYZZtS+bhuZGXcFjmmAROv8j352688aOCgV/uDPvjj2/b7OzZZ+QefPimxsYeV4hBwX8QwQaLHFCMSOBUiwyYPxClbBSMglEwCkYcAAAl5VTundS4XAAAAABJRU5ErkJggg==","orcid":"","institution":"Sekolah Tinggi Ilmu Ekonomi Harapan Bangsa","correspondingAuthor":true,"prefix":"","firstName":"Samuel","middleName":"","lastName":"Tarigan","suffix":""}],"badges":[],"createdAt":"2025-11-10 14:38:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8078220/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8078220/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":98795835,"identity":"b1767d65-763a-48a6-98ad-810e9dd9dacf","added_by":"auto","created_at":"2025-12-22 12:54:17","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":43538,"visible":true,"origin":"","legend":"","description":"","filename":"03112025AmendmentsFormattingManuscriptHSSC.docx","url":"https://assets-eu.researchsquare.com/files/rs-8078220/v1/d6367e2e2bf092380c676c63.docx"},{"id":98795383,"identity":"f1e7858f-fcf1-47e8-99c2-99741f529276","added_by":"auto","created_at":"2025-12-22 12:53:27","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":18069,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.ComparisonwithPreviousStudies.docx","url":"https://assets-eu.researchsquare.com/files/rs-8078220/v1/735634d3c29fa6e79f864a43.docx"},{"id":98795402,"identity":"9ad32e26-cd94-469b-8974-e24e43a221fe","added_by":"auto","created_at":"2025-12-22 12:53:33","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":18032,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.MeasurementItem.docx","url":"https://assets-eu.researchsquare.com/files/rs-8078220/v1/87fd67e86627a914f150276b.docx"},{"id":98795430,"identity":"12ddde70-35c8-4417-aa3f-d6bbc907ae66","added_by":"auto","created_at":"2025-12-22 12:53:33","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":18272,"visible":true,"origin":"","legend":"","description":"","filename":"Table3.ResultsIndicateConvergentValidity.docx","url":"https://assets-eu.researchsquare.com/files/rs-8078220/v1/df8cb2677e841c107d2aa1aa.docx"},{"id":98795350,"identity":"a2539312-d951-43d1-85a6-77bce63a972f","added_by":"auto","created_at":"2025-12-22 12:53:22","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":16993,"visible":true,"origin":"","legend":"","description":"","filename":"Table4.HTMTValues.docx","url":"https://assets-eu.researchsquare.com/files/rs-8078220/v1/81e7b0cef99cbc1c99a079ab.docx"},{"id":98797865,"identity":"36cd1fc5-746d-4dc2-8d00-33a8d5715771","added_by":"auto","created_at":"2025-12-22 13:58:59","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":17133,"visible":true,"origin":"","legend":"","description":"","filename":"Table5.SecondorderModelResults.docx","url":"https://assets-eu.researchsquare.com/files/rs-8078220/v1/dc9cd9d2996becac2fb29ae0.docx"},{"id":98795636,"identity":"8a2238eb-c245-4c8c-bf29-92a694e457a1","added_by":"auto","created_at":"2025-12-22 12:53:45","extension":"json","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3924,"visible":true,"origin":"","legend":"","description":"","filename":"f6f83f3732e64c51940367b4c42ab708.json","url":"https://assets-eu.researchsquare.com/files/rs-8078220/v1/29d1be6b1883dd50a4f48bef.json"},{"id":98795311,"identity":"0916411c-40a5-4fcb-ab82-4a2f9eb1dc65","added_by":"auto","created_at":"2025-12-22 12:53:17","extension":"xml","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":113671,"visible":true,"origin":"","legend":"","description":"","filename":"f6f83f3732e64c51940367b4c42ab7081enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8078220/v1/ca579cb34eace0f2eb0793f7.xml"},{"id":98795435,"identity":"236d985d-026f-437b-ab49-e04a28051530","added_by":"auto","created_at":"2025-12-22 12:53:34","extension":"jpg","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":50945,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1.ConceptualFramework.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8078220/v1/376a5d6f097b1c17f27d3c1d.jpg"},{"id":98795282,"identity":"bd79ac5a-4131-4ce1-a737-20bc2116e010","added_by":"auto","created_at":"2025-12-22 12:53:14","extension":"jpg","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":97875,"visible":true,"origin":"","legend":"","description":"","filename":"Figure2.MethodologyFlowchart.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8078220/v1/29eb5ba63bcf8ddf3ac745d7.jpg"},{"id":98797908,"identity":"305705ea-0655-4f57-971a-dc77cc247d0e","added_by":"auto","created_at":"2025-12-22 14:03:15","extension":"jpg","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":84923,"visible":true,"origin":"","legend":"","description":"","filename":"Figure3.ThePLSModel.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8078220/v1/570c7877a09270a881c199fb.jpg"},{"id":98795372,"identity":"461e8e23-fff8-4041-9322-495825f6e714","added_by":"auto","created_at":"2025-12-22 12:53:23","extension":"jpg","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":80698,"visible":true,"origin":"","legend":"","description":"","filename":"Figure4.BootstrappingAnalysis.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8078220/v1/1bf82865b5f608ce3bdb2a27.jpg"},{"id":98795649,"identity":"7909a603-daf9-4060-ad55-b7e1f5add7e8","added_by":"auto","created_at":"2025-12-22 12:53:56","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":55293,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure1.ConceptualFramework.png","url":"https://assets-eu.researchsquare.com/files/rs-8078220/v1/05be529198c9fb709fbd53d3.png"},{"id":98795399,"identity":"9f30150f-ab00-4a10-9e43-f4457164d388","added_by":"auto","created_at":"2025-12-22 12:53:33","extension":"png","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":88751,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure2.MethodologyFlowchart.png","url":"https://assets-eu.researchsquare.com/files/rs-8078220/v1/97f5a93301a09d2037161b47.png"},{"id":98795423,"identity":"d51cc76f-ed31-4033-a72b-e2fb7c34d43b","added_by":"auto","created_at":"2025-12-22 12:53:33","extension":"png","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":89921,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure3.ThePLSModel.png","url":"https://assets-eu.researchsquare.com/files/rs-8078220/v1/2ecc7a9c1a1eb064bf255cb8.png"},{"id":98795650,"identity":"f8ee7f7f-80b4-4547-9b37-6ac0a2c364c2","added_by":"auto","created_at":"2025-12-22 12:53:56","extension":"png","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":28218,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure4.BootstrappingAnalysis.png","url":"https://assets-eu.researchsquare.com/files/rs-8078220/v1/15085d2b0e217a156b9c5d6c.png"},{"id":98795433,"identity":"a2c0ad55-0835-4ef1-98e3-e5d39c4e997c","added_by":"auto","created_at":"2025-12-22 12:53:34","extension":"xml","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":110854,"visible":true,"origin":"","legend":"","description":"","filename":"f6f83f3732e64c51940367b4c42ab7081structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8078220/v1/98049f5202d6b6394edc5864.xml"},{"id":98795352,"identity":"cdfa4765-b8bc-4a7f-b92b-45d887cb37c9","added_by":"auto","created_at":"2025-12-22 12:53:22","extension":"html","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":121052,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8078220/v1/048d1fafbc62cdb7d47a8d2b.html"},{"id":98795387,"identity":"cfdb7e0f-ef3f-4521-97ee-a709bb2951e4","added_by":"auto","created_at":"2025-12-22 12:53:28","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":50945,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual Framework\u003c/p\u003e","description":"","filename":"Figure1.ConceptualFramework.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8078220/v1/ba93494362432b9ba32aa5e5.jpg"},{"id":98795816,"identity":"65acc92e-d038-49b5-891d-d7a44921d0dd","added_by":"auto","created_at":"2025-12-22 12:54:08","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":97875,"visible":true,"origin":"","legend":"\u003cp\u003eMethodology Flowchart\u003c/p\u003e","description":"","filename":"Figure2.MethodologyFlowchart.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8078220/v1/2224b97e0f0da0c9f72a215b.jpg"},{"id":98795648,"identity":"cecd33ff-3eff-473f-921a-9514d02521f5","added_by":"auto","created_at":"2025-12-22 12:53:55","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":84923,"visible":true,"origin":"","legend":"\u003cp\u003eThe PLS Model\u003c/p\u003e","description":"","filename":"Figure3.ThePLSModel.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8078220/v1/d540e2be7bced4930fe4b453.jpg"},{"id":98795834,"identity":"a17c1276-a4e4-49a9-99ad-55134c3dc315","added_by":"auto","created_at":"2025-12-22 12:54:17","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":80698,"visible":true,"origin":"","legend":"\u003cp\u003eBootstrapping Analysis\u003c/p\u003e","description":"","filename":"Figure4.BootstrappingAnalysis.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8078220/v1/b844ef9c1708454fee4228ff.jpg"},{"id":98800171,"identity":"04146251-dede-4f78-86ec-cdd2635e3221","added_by":"auto","created_at":"2025-12-22 14:14:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1174048,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8078220/v1/201703a8-3b93-46ac-8f94-45d9271ada58.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Building Dynamic AI Capabilities for Sustainable Employee Performance: The Roles of Trust, Knowledge Management, and Competitive Advantage","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe accelerated adoption of Artificial Intelligence (AI) technologies across industries is reshaping organizational landscapes, not only by automating tasks but also by transforming decision-making, learning processes, and employee performance outcomes. In practice, many firms still struggle to translate AI investments into meaningful employee-level performance gains due to insufficient integration between AI systems and organizational culture (Gomez et al., 2024; Liu et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This misalignment reflects a broader challenge: AI implementation must be supported by dynamic internal capabilities that allow organizations to adapt, learn, and sustain competitive advantage in volatile environments (Murcia, et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Duong, C. D., 2025). In this context, employee performance is no longer driven by individual competencies alone, but also by how effectively organizations integrate AI technologies with trust-building mechanisms and knowledge-sharing infrastructures (Jaruwanakul, 2024).\u003c/p\u003e \u003cp\u003eA growing body of research emphasizes the critical role of trust in AI systems as a precursor to successful technology assimilation and workforce engagement. Employees are more likely to adopt and utilize AI tools when they perceive them as transparent, reliable, and aligned with their work goals (Wei \u0026amp; Fonti, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Trust also plays a vital role in enabling knowledge sharing, collaboration, and organizational learning, all of which are essential for dynamic capability development (Zhou, Y., 2025; Kmieciak, R, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Without cognitive trust in AI, organizations risk encountering resistance, reduced knowledge absorption, and ultimately, suboptimal performance outcomes (Cokcroft, S. 2013). Thus, building trust is not merely a technical issue, but a strategic necessity that connects technology with human-centric innovation processes.\u003c/p\u003e \u003cp\u003eKnowledge Management Capacity (KMC) adds another crucial layer to this relationship. KMC refers to an organization\u0026rsquo;s ability to acquire, distribute, and apply knowledge effectively, especially under dynamic conditions. Recent studies have demonstrated that when aligned with AI systems, robust knowledge management fosters adaptive learning, innovation, and competitive advantage (Ojika \u0026amp; Owobu, 2025; Murcia, et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, having knowledge infrastructure alone is insufficient if it is not embedded within a culture of trust and supported by agile organizational routines. Trust in AI acts as a catalyst for knowledge processes to evolve into dynamic capabilities that drive sustainable employee performance and long-term strategic relevance (Duong, C. D., 2025; Cokcroft, S. 2013; Rezaei, 2025).\u003c/p\u003e \u003cp\u003eDespite these insights, current research tends to examine trust, AI capability, and knowledge management in isolation rather than within an integrated framework. Most studies fail to explain how these elements interact to shape dynamic organizational capabilities and employee-level outcomes such as sustainable performance (Lau, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Saxena \u0026amp; Mirsha., 2025). Furthermore, the mechanisms by which trust in AI and knowledge capacity contribute to building firm competitiveness in AI-enabled environments remain underexplored (Wei \u0026amp; Fonti, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Trachuk \u0026amp; Linder, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This fragmentation limits our understanding of how organizations can move from deploying AI tools toward leveraging them as strategic enablers of human productivity and agility.\u003c/p\u003e \u003cp\u003eTo bridge this gap, the present study proposes a comprehensive model examining the role of AI system implementation, trust in AI, and knowledge management capacity in building dynamic organizational capabilities and achieving sustainable employee performance. Specifically, the research addresses the following questions, 1) How do AI system implementation and trust in AI influence organizational capabilities such as competitiveness, dynamic capabilities, and knowledge management capacity? 2) How do these organizational capabilities mediate the relationship between AI-related factors and sustainable employee performance?\u003c/p\u003e \u003cp\u003eThis study offers a novel perspective by integrating AI implementation, trust in AI, and knowledge management capacity within the framework of dynamic organizational capabilities. While previous studies have often examined these constructs separately, this research emphasizes their interrelated roles in influencing sustainable employee performance. By positioning trust in AI as both a psychological factor and an organizational resource, the study provides a broader understanding of how human\u0026ndash;technology alignment supports adaptability and competitiveness. Overall, this approach contributes to the literature by connecting AI adoption, organizational learning, and performance sustainability within a unified conceptual model.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003ePrior studies increasingly recognize that AI implementation alone is not sufficient to ensure performance gains unless it is integrated within a broader set of organizational capabilities (Hoang \u0026amp; Hien, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). AI systems, when strategically implemented, enable firms to automate decision processes, improve data utilization, and increase responsiveness to market changes (Murcia, et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, the real challenge lies in how organizations absorb and translate the potential of AI into operational advantage. The literature emphasizes the importance of alignment between technology, people, and processes in shaping the effectiveness of AI adoption (Gomez et al., 2024). Without complementary enablers, such as trust and knowledge systems, even sophisticated AI systems may be underutilized or misapplied (Duong, C. D., 2025).\u003c/p\u003e \u003cp\u003eTrust in AI emerges as a critical factor influencing how employees interact with intelligent systems. Trust determines the extent to which users perceive AI as reliable, transparent, and aligned with their roles (Wei \u0026amp; Fonti, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Li \u0026amp; Zhou, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). High levels of trust are positively associated with greater intention to use AI, increased collaboration between humans and machines, and enhanced learning outcomes (Jaruwanakul, 2024). Moreover, trust is not developed in isolation\u0026mdash;it is reinforced by organizational culture, past experiences, and knowledge-sharing practices (Zhou, Y., 2025). Knowledge Management Capacity (KMC) also plays a strategic role, allowing firms to absorb AI-generated insights and circulate them across departments. Effective KMC supports innovation, accelerates learning, and contributes to dynamic adaptability, especially when paired with a culture of trust (Lau, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ojika \u0026amp; Owobu, 2025).\u003c/p\u003e \u003cp\u003eThese elements collectively influence the development of dynamic capabilities\u0026mdash;organizational routines that allow firms to reconfigure resources and remain competitive in fast-changing environments (Hoang \u0026amp; Hien, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e;Teece et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Supriyanto et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The literature shows that dynamic capabilities and firm competitiveness are shaped not only by AI investment, but by the firm's capacity to combine technology with knowledge and human-centric practices (Murcia, et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Ultimately, these capabilities are instrumental in sustaining employee performance over time. Employees embedded in agile, knowledge-driven, and trust-rich environments are more likely to perform consistently, adapt to change, and contribute to innovation (Saxena \u0026amp; Mirsha., 2025).\u003c/p\u003e \u003cp\u003eWhile previous studies have examined these constructs in various contexts, there appears to be limited research integrating them into a unified model that examines how AI-related enablers influence employee work performance through organizational mechanisms. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents a comparative overview of key prior research and positions the current study within the existing literature.\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\u003eComparison with Previous Studies\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\u003eStudy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFocus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKey Variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMediating Mechanisms\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eContext\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eScope\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePapagiannidis et al. (2022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI adoption and organizational performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAI implementation, dynamic capabilities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot examined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCross-industry firms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eExamines direct AI-performance link\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWei \u0026amp; Fonti (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrust in AI systems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTrust, AI adoption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot examined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTechnology users\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFocuses on adoption intent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJaruwanakul (2024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrust and AI collaboration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTrust, human-AI collaboration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot examined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKnowledge workers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eExamines collaboration behavior\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKioskli et al. (2024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrganizational culture and AI trust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTrust, organizational culture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot examined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean firms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eExplores trust antecedents\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLau (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKnowledge management and innovation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKMC, innovation performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot examined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSMEs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFocuses on KMC-innovation link\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOjika \u0026amp; Owobu (2025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKMC and organizational agility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKMC, agility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot examined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNigerian firms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eExamines KMC in agility context\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupriyanto et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDynamic capabilities and competitiveness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDynamic capabilities, firm competitiveness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot examined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIndonesian manufacturing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eExplores DC-competitiveness relationship\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSamadhiya et al. (2023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI and employee performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAI tools, employee productivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot examined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eService sector\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDirect AI-performance examination\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent Study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI enablers and employee performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAI implementation, trust in AI, KMC, dynamic capabilities, firm competitiveness, employee performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDynamic capabilities and firm competitiveness as serial mediators\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIndonesian firms across sectors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eExamines integrated model with organizational mediators\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e[INSERT\u003c/b\u003e Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cb\u003eHERE]\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, existing research has predominantly examined specific relationships between pairs of constructs rather than integrated pathways. Studies such as Murcia, et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Supriyanto et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) have explored dynamic capabilities and competitiveness separately from AI-specific enablers. Research on trust in AI (Wei \u0026amp; Fonti, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Jaruwanakul, 2024) has primarily focused on adoption intentions or collaboration behaviors rather than performance outcomes mediated by organizational capabilities. Knowledge management studies (Lau, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ojika \u0026amp; Owobu, 2025) have examined KMC's role in fostering innovation and agility, though not in conjunction with AI or trust within the same analytical framework.\u003c/p\u003e \u003cp\u003eThe current study seeks to contribute to this literature by examining a model that positions AI implementation, trust in AI, and knowledge management capacity as interconnected enablers that may jointly influence dynamic capabilities and firm competitiveness, which in turn may shape employee work performance. This approach\u0026mdash;illustrated in the conceptual framework (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u0026mdash;offers an integrated perspective on how technology-oriented and human-centric factors may interact to produce organizational and individual-level outcomes in the Indonesian context, where AI adoption patterns and organizational readiness vary considerably across sectors.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e[INSERT\u003c/b\u003e FIGURE \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cb\u003eHERE]\u003c/b\u003e\u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cp\u003eThis research adopts a quantitative approach using Structural Equation Modeling with Partial Least Squares (PLS-SEM) to investigate the relationships between trust in AI, AI system implementation, knowledge management capacity, dynamic capabilities, company competitiveness, and sustainable employee performance. PLS-SEM is well suited for predictive models with complex structures, especially when theory is still emerging or when constructs are measured reflectively (Hair et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Data were collected through an online questionnaire using previously validated items, adapted to reflect the context of AI implementation in organizational settings. The study was conducted among postgraduate management students enrolled at an accredited higher education institution in Indonesia, with participants originating from various cities across the country. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the methodological process involves sequential stages: data collection, measurement model assessment, structural model assessment, and hypothesis testing. SmartPLS 4.0 software was used for model estimation, given its ability to handle small to medium sample sizes and complex inter-variable relationships.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo assess the measurement model, the study first examined convergent validity, discriminant validity, and collinearity diagnostics. Convergent validity was evaluated using factor loadings, Composite Reliability (CR), and Average Variance Extracted (AVE). Indicators were considered valid if outer loadings exceeded 0.70, CR values exceeded 0.70, and AVE exceeded the threshold of 0.50, as recommended by Fornell and Larcker (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1981\u003c/span\u003e). Discriminant validity was tested using the Heterotrait-Monotrait (HTMT) ratio, with acceptable values below 0.90 (Henseler et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Multicollinearity was assessed using the Variance Inflation Factor (VIF), and items with VIF values below 5.00 were retained, indicating no critical issues of redundancy (Hair et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003e[INSERT\u003c/b\u003e FIGURE \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cb\u003eHERE]\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAfter validating the measurement model, the structural model was evaluated through path coefficients, explanatory power (R\u0026sup2;), and predictive relevance (Q\u0026sup2;). R\u0026sup2; values indicate the amount of variance explained in the endogenous constructs, with thresholds of 0.25 (weak), 0.50 (moderate), and 0.75 (substantial) suggested by Hair et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Q\u0026sup2; values were derived using the blindfolding technique to test out-of-sample predictive relevance, where values above 0 indicate acceptable predictive capability (Chin, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Additionally, model significance was tested using bootstrapping procedures with 5,000 subsamples, generating confidence intervals and t-values to assess the statistical significance of each path. The threshold for significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for confirming or rejecting each hypothesis.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e lists the measurement items used to operationalize each construct. Items for Trust in AI (TIAI), AI System Implementation (AISTI), and Knowledge Management Capacity (KMC) were adapted from recent studies on AI readiness and digital capability (Wei \u0026amp; Fonti, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ojika \u0026amp; Owobu, 2025). Meanwhile, constructs such as Dynamic Capabilities (DC) and Company Competitiveness (CC) reflect organizational agility and strategic positioning in response to AI (Murcia, et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Sustainable Employee Work Performance (SEWP) was measured as an outcome variable reflecting how AI supports accuracy, speed, and effectiveness at the individual level (Saxena \u0026amp; Mirsha., 2025). All items were measured using a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). This robust operationalization ensured the construct validity and reliability of the instrument prior to testing the structural hypotheses.\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\u003eMeasurement Item\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eItem Statement\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTrust in AI (TIAI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTIAI_01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI believe AI systems can make reliable decisions in our work processes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTIAI_02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI trust that AI technology will perform consistently and accurately\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTIAI_05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI believe that AI technology will deliver the promised benefits to our organization\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAI Systems/ Technology Implementation (AISTI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAISTI_01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAI technology is successfully integrated into our core business processes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAISTI_02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOur organization has effectively implemented AI systems in relevant operational areas\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAISTI_03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAI technology functions as intended in our organizational workflows\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCompany Competitiveness (CC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCC_03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAI technology has enhanced our organization's ability to respond to market changes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCC_04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAI implementation has strengthened our organization's market leadership position\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDynamic Capabilities (DC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDC_01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOur organization is effective in identifying AI opportunities and threats from the external environment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDC_02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOur leaders actively monitor AI technology trends and market changes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDC_06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOur leaders are effective in transforming the organization's resource base for AI implementation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eKnowledge Management Capacity (KMC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKMC_01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAI systems have enhanced our organization's ability to capture and store knowledge\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKMC_03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOur AI implementation has improved the organization's ability to create new knowledge\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKMC_06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOur AI implementation has improved decision-making through better knowledge utilization\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSustainable Employee-Work Performance (SEWP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSEWP_01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAI implementation reduces the risk of errors in work performance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSEWP_02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAI accelerates and improves decision-making to achieve sustainable employee work performance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSEWP_03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAI enhances the efficiency of our organization's work performance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e[INSERT\u003c/b\u003e Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cb\u003eHERE]\u003c/b\u003e\u003c/p\u003e"},{"header":"4. Result","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Analytical Model (First-Order Construct)\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the structural relationships among six core constructs using a Partial Least Squares (PLS) approach. The model reveals that Trust in AI (TIAI) and AI Systems/Technology Implementation (AISTI) are foundational drivers that significantly influence Company Competitiveness (CC), Dynamic Capabilities (DC), and Knowledge Management Capacity (KMC). These three mediating constructs subsequently enhance Sustainable Employee-Work Performance (SEWP), indicating a layered and interconnected pathway from technological trust and adoption to individual-level performance outcomes. This highlights the importance of both human trust and systemic implementation in unlocking the strategic value of AI in organizational contexts.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e[INSERT\u003c/b\u003e FIGURE \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cb\u003eHERE]\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo validate the strength of these relationships, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the results of the measurement model, which confirm strong convergent validity and reliability. The outer loadings for all items exceed the recommended threshold of 0.7, while Cronbach\u0026rsquo;s Alpha and Composite Reliability values are consistently high across constructs, reflecting excellent internal consistency. Notably, constructs like SEWP and KMC exhibit particularly strong measurement quality, suggesting that they serve as robust outcome and mediating variables, respectively. These findings reinforce the model\u0026rsquo;s theoretical proposition: that enhancing AI trust and implementation can build critical organizational capabilities, which in turn drive sustained employee performance.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults Indicate Convergent Validity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstructs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOuter loadings\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCronbach\u0026rsquo;s Alpha\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eComposite Reliability\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAI Systems/ Technology Implementation (AISTI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAISTI_01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.963\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAISTI_02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAISTI_03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.954\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCompany Competitiveness (CC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCC_03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.973\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCC_04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.973\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDynamic Capabilities (DC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDC_01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.950\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDC_02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDC_06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.915\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eKnowledge Management Capacity (KMC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKMC_01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKMC_03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.946\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKMC_06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.928\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSustainable Employee-Work Performance (SEWP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSEWP_01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.964\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSEWP_02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.954\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSEWP_03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.947\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTrust in AI (TIAI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTIAI_01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.928\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTIAI_02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.932\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTIAI_05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.878\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e[INSERT\u003c/b\u003e Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cb\u003eHERE]\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the Heterotrait-Monotrait Ratio (HTMT) values, which are used to assess discriminant validity between constructs in the model. All HTMT values fall below the conservative threshold of 0.90, indicating that each construct is empirically distinct from the others. The strongest discriminant separation is observed between constructs such as TIAI and KMC (0.634) and TIAI and SEWP (0.664), suggesting that trust in AI is conceptually well differentiated from both knowledge management and employee performance. Meanwhile, higher\u0026mdash;but still acceptable\u0026mdash;HTMT values such as those between DC and KMC (0.852) and DC and SEWP (0.849) reflect the natural conceptual closeness between dynamic capabilities and organizational performance outcomes. Overall, the results confirm that the constructs possess adequate discriminant validity, supporting the model\u0026rsquo;s structural integrity.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHTMT Values\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAISTI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKMC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSEWP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTIAI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAISTI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.947\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.973\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\u003eDC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.929\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\u003eKMC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.934\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\u003eSEWP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.949\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\u003eTIAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.901\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e[INSERT\u003c/b\u003e Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e \u003cb\u003eHERE]\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Measurement Model (Second-Order Construct)\u003c/h2\u003e \u003cp\u003eThe second-order model results highlight the pivotal role of AI Systems/Technology Implementation (AISTI) and Trust in AI (TIAI) in shaping key organizational capabilities. AISTI has a strong and statistically significant influence on Company Competitiveness (CC), Dynamic Capabilities (DC), and Knowledge Management Capacity (KMC), with path coefficients all above 0.50 and p-values below 0.001. This indicates that effective and structured implementation of AI systems substantially enhances a firm's ability to remain competitive, agile, and knowledge-driven. In parallel, TIAI also contributes significantly to these three constructs, though with slightly lower coefficients, suggesting that psychological acceptance of AI complements\u0026mdash;but does not substitute\u0026mdash;actual system deployment.\u003c/p\u003e \u003cp\u003e \u003cb\u003e[INSERT\u003c/b\u003e Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e \u003cb\u003eHERE]\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSecond-order Model Results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePath\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePath Coef\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT statistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP values\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAISTI -\u0026gt; CC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAISTI -\u0026gt; DC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAISTI -\u0026gt; KMC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC -\u0026gt; SEWP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDC -\u0026gt; SEWP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKMC -\u0026gt; SEWP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.286\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIAI -\u0026gt; CC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIAI -\u0026gt; DC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIAI -\u0026gt; KMC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhen examining their impact on Sustainable Employee-Work Performance (SEWP), only CC and DC emerge as significant predictors, while KMC does not show a meaningful direct effect. This implies that competitive positioning and adaptability are more directly tied to employee performance outcomes compared to knowledge capacity, which may operate more indirectly. These findings suggest that for organizations seeking to optimize employee outcomes through AI adoption, focusing on building competitiveness and dynamic capabilities is essential. Moreover, while trust in AI enhances internal capacities, its greatest value may lie in reinforcing the success of tangible AI implementations across the enterprise.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Structural Model (Path Analysis)\u003c/h2\u003e \u003cp\u003eStructural Equation Modeling (SEM) was employed to examine the hypothesized relationships among constructs using a second-order path analysis approach, with a focus on assessing both the significance and strength of direct effects within the proposed framework. The application of PLS-SEM allowed for the simultaneous estimation of measurement reliability and structural paths, providing a robust method to explore the impact of AI Systems/Technology Implementation (AISTI) and Trust in AI (TIAI) on organizational capabilities\u0026mdash;namely Company Competitiveness (CC), Dynamic Capabilities (DC), and Knowledge Management Capacity (KMC)\u0026mdash;as well as their subsequent influence on Sustainable Employee-Work Performance (SEWP). Bootstrapping with 5,000 resamples was conducted to test the significance of the path coefficients, revealing that 8 out of 9 proposed paths were statistically significant, supporting the majority of the hypothesized relationships. Specifically, AISTI demonstrated strong and significant effects on CC (β\u0026thinsp;=\u0026thinsp;0.542, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), DC (β\u0026thinsp;=\u0026thinsp;0.508, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and KMC (β\u0026thinsp;=\u0026thinsp;0.591, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while TIAI also significantly influenced CC (β\u0026thinsp;=\u0026thinsp;0.367, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), DC (β\u0026thinsp;=\u0026thinsp;0.346, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and KMC (β\u0026thinsp;=\u0026thinsp;0.243, p\u0026thinsp;=\u0026thinsp;0.017), albeit with slightly lower magnitudes. In terms of performance outcomes, both CC (β\u0026thinsp;=\u0026thinsp;0.398, p\u0026thinsp;=\u0026thinsp;0.001) and DC (β\u0026thinsp;=\u0026thinsp;0.407, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were found to significantly predict SEWP, whereas KMC showed a non-significant relationship (β\u0026thinsp;=\u0026thinsp;0.133, p\u0026thinsp;=\u0026thinsp;0.286), suggesting that while knowledge capacity is a critical resource, its impact on individual-level work performance may be more indirect or mediated. Additionally, R\u0026sup2; values for endogenous constructs were found to be substantial, with SEWP achieving the highest explained variance (R\u0026sup2; = 0.783), followed by CC (R\u0026sup2; = 0.691), DC (R\u0026sup2; = 0.612), and KMC (R\u0026sup2; = 0.599), indicating a strong model fit and explanatory power. These findings collectively support the model's structural integrity and highlight the interplay between AI adoption, organizational capability building, and sustainable employee performance in the context of digital transformation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Superiority of the Structural Model\u003c/h2\u003e \u003cp\u003eThe results obtained from the structural model provide solid empirical evidence regarding the reliability of individual indicators, as well as the convergent and discriminant validity of the overall analytical framework. To assess the explanatory power of the model, the coefficient of determination (R\u0026sup2;) was used as a key metric, as supported by the PLS-SEM methodology. This approach, akin to traditional regression analysis, evaluates the proportion of variance in each dependent construct that is explained by its associated independent variables. Higher R\u0026sup2; values are generally indicative of a stronger predictive capability of the structural model. In this study, the R\u0026sup2; values were generated using SmartPLS and revealed substantial predictive power across all key constructs.\u003c/p\u003e \u003cp\u003eSpecifically, the model explained 78.3% of the variance in Sustainable Employee-Work Performance (SEWP), highlighting it as a highly influenced outcome variable within the framework. Additionally, the variance explained for Company Competitiveness (CC) was 69.1%, for Dynamic Capabilities (DC) was 61.2%, and for Knowledge Management Capacity (KMC) was 59.9%, all of which reflect strong model fit based on PLS-SEM benchmarks. These findings suggest that the exogenous constructs\u0026mdash;AI Systems/Technology Implementation (AISTI) and Trust in AI (TIAI)\u0026mdash;serve as powerful predictors of organizational capabilities and employee-level outcomes. According to Chin\u0026rsquo;s guidelines, R\u0026sup2; values above 0.67 are considered substantial, which reinforces the superiority and robustness of the proposed structural model in capturing the dynamics of AI integration, organizational competence, and sustainable performance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.5 The Structural Model Predictive Weight\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the results of the bootstrapping analysis, which was used to evaluate the predictive weight and statistical significance of the structural model\u0026rsquo;s path coefficients. The analysis confirmed that the majority of the hypothesized relationships are statistically significant at the 0.05 level or below. Both AI Systems/Technology Implementation (AISTI) and Trust in AI (TIAI) exert significant positive effects on Company Competitiveness (CC), Dynamic Capabilities (DC), and Knowledge Management Capacity (KMC), with all related p-values below 0.05. Furthermore, CC and DC significantly predict Sustainable Employee-Work Performance (SEWP), with p-values of 0.001 and 0.000 respectively, indicating their critical role as mediators between AI-related constructs and performance outcomes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e[INSERT\u003c/b\u003e FIGURE \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e \u003cb\u003eHERE]\u003c/b\u003e\u003c/p\u003e \u003cp\u003eHowever, one notable finding is that the relationship between Knowledge Management Capacity (KMC) and SEWP is not statistically significant, with a p-value of 0.286. This path is illustrated in the model using a dashed red line, indicating that despite its conceptual relevance, KMC does not demonstrate a direct predictive weight on employee performance within this framework. This suggests that the influence of knowledge management may be more indirect or mediated through other organizational capabilities like CC or DC. Overall, the bootstrapping results reinforce the model\u0026rsquo;s predictive strength while also revealing areas\u0026mdash;like KMC to SEWP\u0026mdash;where the theoretical linkage may require further investigation or contextual interpretation.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003e \u003cem\u003e5.1 RQ1: How do AI system implementation and trust in AI influence organizational capabilities such as competitiveness, dynamic capabilities, and knowledge management capacity?\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe findings indicate that both AI Systems/Technology Implementation (AISTI) and Trust in AI (TIAI) significantly enhance organizational capabilities, with AISTI exerting stronger effects across all three constructs (Murcia, et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This suggests that organizations investing in AI must ensure not only technical deployment but also consistent, system-wide integration to cultivate strategic responsiveness (Duong, C. D., 2025). Trust, while slightly less impactful in magnitude, remains a vital enabler that facilitates employees' willingness to interact with and accept AI systems (Wei \u0026amp; Fonti, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Jaruwanakul, 2024). These insights reinforce the notion that both structural (technology implementation) and relational (trust-building) mechanisms are necessary for capability development in digitally transforming firms (Zhou, Y., 2025).\u003c/p\u003e \u003cp\u003eRecent studies emphasize that employees' cognitive trust in AI moderates the effectiveness of technology adoption, particularly in contexts where perceived risks and transparency affect user engagement and system utilization (\u0026Uuml;bellacker, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Wasesa et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Without adequate trust mechanisms, even well-integrated AI systems may fail to generate adaptive or dynamic capabilities, as employees' reluctance to rely on AI tools can limit organizational learning and responsiveness (Jaruwanakul, 2024). Trust building requires transparency in AI decision-making processes, clear communication about system capabilities and limitations, and consistent demonstration of AI reliability over time (Wei \u0026amp; Fonti, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Organizations that prioritize both technological robustness and relational trust are better positioned to unlock the full potential of AI investments in building organizational capabilities (Zhou, Y., 2025).\u003c/p\u003e \u003cp\u003e \u003cem\u003e5.2 RQ2: How do these organizational capabilities mediate the relationship between AI-related factors and sustainable employee performance?\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe results show that Company Competitiveness (CC) and Dynamic Capabilities (DC) serve as significant mediators between AI-related factors and Sustainable Employee-Work Performance (SEWP), while Knowledge Management Capacity (KMC) does not have a direct effect on SEWP. This implies that employee-level outcomes are more strongly driven by organizational agility and market adaptability rather than by the presence of knowledge systems alone (Teece et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Supriyanto et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The strong explanatory power of the model (R\u0026sup2; = 0.783 for SEWP) confirms that these constructs are meaningfully connected and relevant for performance outcomes (Murcia, et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These results help clarify the pathways through which AI integration contributes to sustainable employee performance through strategic capability building and adaptive organizational routines (Saxena \u0026amp; Mirsha., 2025).\u003c/p\u003e \u003cp\u003eThe non-significant relationship between KMC and SEWP invites further scrutiny and may suggest that knowledge infrastructures, while necessary, are not sufficient unless they are embedded within actionable routines that transform stored knowledge into applied insight (Rezaei, 2025; Cui, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Contemporary research on AI-driven knowledge management highlights that the effectiveness of knowledge systems depends on learning loops, data contextualization, and continuous knowledge coupling processes (Rezaei, 2025). Although KMC remains an important internal resource, its performance-related impact may require additional organizational enablers such as absorptive capacity or innovation leadership to be fully realized (Ojika \u0026amp; Owobu, 2025; Cokcroft, S. 2013). Thus, building performance-centric capabilities involves not just knowledge accumulation but the ability to act on that knowledge in fast-changing environments (Teece et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Supriyanto et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Organizations must therefore focus on converting \"available knowledge\" into \"usable knowledge\" through adaptive structures and trust-based collaboration (Cui, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Theoretical Contributions\u003c/h2\u003e \u003cp\u003eThis study contributes to closing the existing research gap by offering an integrated model that connects AI implementation, trust, and organizational capabilities to sustainable employee performance\u0026mdash;an area that has been underexplored in past literature (Wei \u0026amp; Fonti, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lau, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). While prior research often isolates technological or human factors, this study confirms the value of a capability-based view of digital transformation, where outcomes emerge from interdependencies among technology, people, and strategic routines (Teece et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Supriyanto et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e;Li \u0026amp; Zhou, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The findings reinforce calls in recent literature for more systemic, multi-level approaches to studying AI in management contexts (Lau, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Saxena \u0026amp; Mirsha., 2025). By demonstrating how AI-related enablers work through organizational capabilities to shape employee performance, this research provides empirical support for integrating technological, relational, and strategic perspectives in AI adoption research (Murcia, et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFrom a strategic management perspective, these findings reaffirm that AI implementation must be viewed as a cross-functional transformation rather than a purely technological investment (Duong, C. D., 2025; Gomez et al., 2024). The development of dynamic capabilities\u0026mdash;sensing, seizing, and transforming\u0026mdash;remains critical for translating AI potential into sustainable employee outcomes (Teece et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Ettinger, 2025). Organizations that fail to align AI initiatives with capability development risk achieving technological sophistication without corresponding gains in agility or competitiveness (Murcia, et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Thus, strategic responsiveness to AI should involve both technological readiness and the continuous renewal of organizational routines and structures (Supriyanto et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBeyond the internal dynamics of AI implementation and trust, the findings suggest a broader insight into how digital maturity affects human-centric outcomes (Duong, C. D., 2025; Gomez et al., 2024). Organizations that effectively convert AI investments into competitive positioning and adaptive capacity are more likely to foster environments where employees can perform sustainably, especially in knowledge-intensive and rapidly evolving sectors (Papagiannidis et al., 2022). This supports the view that digital transformation should not only be evaluated in terms of system efficiency but also in its capacity to generate organizational learning and resilience (Saxena \u0026amp; Mirsha., 2025). Notably, trust in AI contributes to psychological safety, reducing resistance to technological change and enabling higher engagement with digital workflows (Zhou, Y., 2025). Thus, building digital trust may be a prerequisite for fully unlocking the performance benefits of AI (Wei \u0026amp; Fonti, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Jaruwanakul, 2024).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Managerial Implications\u003c/h2\u003e \u003cp\u003eTranslating these research findings into actionable strategies, organizations can adopt several practical approaches to maximize the performance benefits of AI investments. From a managerial perspective, these findings highlight the need for integrated strategies that address technology, trust, capabilities, and competitiveness simultaneously rather than sequentially. The results demonstrate that AI implementation effectiveness depends not only on technological deployment but also on how organizations build trust, develop dynamic capabilities, and enhance competitiveness (Duong, C. D., 2025; Gomez et al., 2024). Leaders must recognize that AI investments yield employee performance benefits primarily through organizational capability development rather than direct technology-to-performance pathways (Murcia, et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Supriyanto et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This understanding should guide resource allocation decisions and implementation strategies to ensure AI initiatives generate sustainable returns (Saxena \u0026amp; Mirsha., 2025). The following subsections outline specific action areas for practitioners.\u003c/p\u003e \u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eBuilding Trust in AI Systems. As the first critical step in effective AI adoption, organizations should establish transparent AI governance frameworks that clearly communicate how AI systems make decisions, what data they use, and how they complement rather than replace human judgment (Wei \u0026amp; Fonti, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; \u0026Uuml;bellacker, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Practical steps include creating AI ethics committees, implementing explainable AI (XAI) tools that allow employees to understand system recommendations, and conducting regular training sessions where employees can interact with AI systems in low-stakes environments (Jaruwanakul, 2024). Managers should also establish feedback mechanisms where employees can report concerns about AI reliability or bias, and demonstrate responsiveness by making visible system improvements based on user input (Zhou, Y., 2025). Building trust requires consistent communication about both AI capabilities and limitations to set realistic expectations and prevent disillusionment (Wasesa et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Once trust foundations are established, organizations can focus on leveraging AI to build dynamic capabilities.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eDeveloping Dynamic Capabilities Through AI. Given the significant mediating role of dynamic capabilities, organizations should design AI implementation roadmaps that specifically target the three dimensions of dynamic capabilities: sensing, seizing, and transforming (Teece et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Ettinger, 2025). For sensing capabilities, deploy AI-powered market intelligence systems, customer analytics platforms, and competitive monitoring tools that provide real-time insights into environmental changes (Murcia, et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For seizing capabilities, establish cross-functional AI task forces that can rapidly prototype solutions, test AI applications in pilot projects, and scale successful initiatives across departments (Supriyanto et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). For transforming capabilities, use AI to identify process bottlenecks, automate routine tasks, and free up employee time for higher-value strategic work (Duong, C. D., 2025). Managers should create organizational routines that regularly review AI-generated insights and translate them into actionable strategic decisions (Gomez et al., 2024). These dynamic capabilities must be directed toward enhancing competitive positioning to maximize performance impact.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eEnhancing Competitiveness Through Strategic AI Alignment. Recognizing that competitiveness serves as a key mediator to employee performance, leaders should conduct competitiveness audits that map how AI initiatives contribute to specific competitive advantages such as cost leadership, differentiation, or speed to market (Murcia, et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This involves identifying core business processes where AI can create the most significant competitive impact, such as customer service automation, predictive maintenance, or personalized product recommendations (Saxena \u0026amp; Mirsha., 2025). Organizations should benchmark their AI capabilities against industry competitors and identify gaps that require investment or partnership (Supriyanto et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Managers must also align AI projects with strategic priorities by establishing clear performance metrics that link AI implementation to competitive outcomes such as market share growth, customer satisfaction improvements, or operational cost reductions (Duong, C. D., 2025). Alongside competitiveness enhancement, attention must be given to knowledge management systems, though with a more nuanced approach.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eActivating Knowledge Management for Performance Impact. Addressing the finding that KMC does not directly affect employee performance, organizations should focus on converting knowledge repositories into decision-support systems that actively guide employee actions rather than merely storing information (Rezaei, 2025; Cui, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Practical approaches include integrating AI-powered knowledge assistants into daily workflows, creating communities of practice where employees share AI use cases and lessons learned, and establishing knowledge brokers who help translate stored information into context-specific guidance (Ojika \u0026amp; Owobu, 2025). Organizations should move from passive knowledge databases to active learning systems that recommend relevant information based on employee tasks, provide just-in-time training, and facilitate peer-to-peer knowledge exchange (Cokcroft, S. 2013). Managers should also measure knowledge system effectiveness not by storage capacity but by utilization rates and impact on decision quality (Lau, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These individual initiatives must be integrated into a coherent organizational strategy to maximize their combined impact.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eImplementing Integrated AI Strategies. Bringing together all previous recommendations, organizations should adopt a holistic implementation approach that simultaneously addresses technology, trust, capabilities, and competitiveness rather than treating these as sequential or independent initiatives (Murcia, et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Saxena \u0026amp; Mirsha., 2025). This involves creating AI steering committees with representation from IT, HR, strategy, and operations to ensure cross-functional alignment (Duong, C. D., 2025). Leaders should establish stage-gate processes for AI projects that evaluate not only technical feasibility but also trust implications, capability requirements, and competitive positioning (Gomez et al., 2024). Regular AI maturity assessments should track progress across all dimensions\u0026mdash;technology deployment, employee trust levels, dynamic capability development, and competitive advantage realization\u0026mdash;to identify areas requiring additional investment or intervention (Supriyanto et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). By adopting this integrated approach, managers can maximize the employee performance benefits of AI investments and build sustainable organizational resilience in the face of technological disruption (Saxena \u0026amp; Mirsha., 2025). While these recommendations provide actionable guidance, it is important to acknowledge the limitations of this study and identify directions for future research.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis study highlights that achieving sustainable employee work performance in the age of artificial intelligence (AI) cannot rely solely on technological deployment. Instead, it requires the development of strategic organizational capabilities, particularly company competitiveness and dynamic capabilities, supported by trust in AI systems and structured technology implementation. The findings demonstrate that both AI System Implementation (AISTI) and Trust in AI (TIAI) significantly influence key organizational capabilities, which in turn serve as critical mediators for employee performance outcomes. While Knowledge Management Capacity (KMC) remains a valuable internal resource, its direct effect on performance was found to be insignificant, suggesting that knowledge must be activated through agile and strategically aligned processes to yield measurable results. These insights underscore the importance of integrating technological enablers with human-centered strategies to fully leverage the value of AI-driven transformation.\u003c/p\u003e \u003cp\u003eDespite its contributions, this study has several limitations that warrant consideration. The cross-sectional design restricts the ability to infer causality and limits understanding of how trust and organizational capabilities evolve over time. Additionally, the data were collected within a specific cultural and organizational context, which may affect the generalizability of the findings across industries or regions. The model also does not account for moderating variables such as leadership style, organizational learning culture, or digital maturity, which may amplify or dampen the observed relationships. Recognizing these limitations invites a cautious interpretation of the findings and highlights the need for further empirical refinement.\u003c/p\u003e \u003cp\u003eFuture research should explore these dynamics using longitudinal or mixed-method approaches to better capture the long-term effects of AI trust and implementation on organizational development. Researchers are also encouraged to investigate potential contextual moderators, including industry regulations, technological turbulence, and workforce digital literacy, to deepen understanding of how AI adoption shapes performance in diverse environments. Comparative studies across sectors or countries could offer additional insights into how external factors influence the role of AI in shaping human-centric outcomes. Expanding this research agenda will not only strengthen theoretical frameworks in digital transformation and human\u0026ndash;AI collaboration but also provide practical guidance for organizations seeking to align digital strategies with sustainable performance and resilience.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to express their sincere gratitude to all the employees and organizations in Indonesia who participated in this study by completing the online questionnaire. Their valuable insights and willingness to share their experiences with AI systems made this research possible. We also extend our appreciation to the reviewers and editors for their constructive feedback that helped improve the quality of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration Statements:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.T. was solely responsible for all aspects of this research, including conceptualization, literature review, research design and methodology, data collection, statistical analysis using PLS-SEM, interpretation of results, and manuscript writing. The author has read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. To protect participant confidentiality, only anonymized data will be shared in compliance with data protection regulations and ethical research standards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received ethical approval from the Research Ethics Committee (\u003cem\u003eKomite Etik Penelitian\u003c/em\u003e) of an accredited higher education institution in Indonesia. Ethical approval was granted by an officially recognized institutional ethics committee located in Indonesia, the country where the research was conducted. The research protocol was reviewed and classified as Low Risk (Approval No. 056/AKD/PR-1/STIEHB/X/2025, issued on 10 March 2025). All research procedures were conducted in accordance with the ethical principles outlined in the Declaration of Helsinki and applicable Indonesian national regulations and institutional guidelines for research involving human participants. The study ensured voluntary participation, informed consent, confidentiality, anonymity of responses, and data protection throughout the research process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all participants through an online survey platform during the second quarter of 2025 (April–June 2025), following the issuance of ethical approval. The study was conducted among postgraduate management students from various cities across Indonesia.\u003c/p\u003e\n\u003cp\u003ePrior to completing the questionnaire, participants were presented with a study information sheet explaining the research objectives, the nature of their participation, and how their data would be used. Participation was entirely voluntary, and respondents provided their implied consent by proceeding with the survey after reading the information sheet.\u003c/p\u003e\n\u003cp\u003eParticipants were assured of the confidentiality and anonymity of their responses, and that their data would be used solely for academic research purposes. No personally identifiable information was collected. The right to withdraw from the study at any point without consequence was clearly communicated. By voluntarily completing and submitting the questionnaire, participants indicated their consent to participate.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChin, W.W., 1998. The partial least squares approach to structural equation modeling. In \u003cem\u003eModern methods for business research\u003c/em\u003e (pp. 295-336). Psychology Press.\u003c/li\u003e\n\u003cli\u003eCockcroft, S. (2013). National health IT infrastructure through the media lens. \u003cem\u003eHealth Policy and Technology\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(4), 203-215. https://doi.org/10.1016/j.hlpt.2013.07.003. \u003c/li\u003e\n\u003cli\u003eCui, J. (2025). The influence of Automation Software Platforms and Knowledge Sharing Frameworks on Organizational Effectiveness in Chinese Technology Enterprises. \u003cem\u003eAvailable at SSRN 5093047\u003c/em\u003e. https://dx.doi.org/10.2139/ssrn.5093047. \u003c/li\u003e\n\u003cli\u003eDuong, C. D. (2025). Artificial intelligence innovation, innovation niches and entrepreneurial performance: the curvilinear role of open innovation. \u003cem\u003eBusiness Process Management Journal\u003c/em\u003e, 1-28. https://doi.org/10.1108/BPMJ-04-2025-0500. \u003c/li\u003e\n\u003cli\u003eFornell, C., \u0026amp; Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. \u003cem\u003eJournal of Marketing Research\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(1), 39\u0026ndash;50. https://doi.org/10.2307/3151312. \u003c/li\u003e\n\u003cli\u003eG\u0026oacute;mez-Bengoechea, G., \u0026amp; Jung, J. (2024). The Matthew effect: Evidence on firms\u0026rsquo; digitalization distributional effects. \u003cem\u003eTechnology in Society\u003c/em\u003e, \u003cem\u003e76\u003c/em\u003e, 102423. https://doi.org/10.1016/j.techsoc.2023.102423. \u003c/li\u003e\n\u003cli\u003eHair, J. F., Hult, G. T. M., Ringle, C. M., \u0026amp; Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). SAGE Publications.\u003c/li\u003e\n\u003cli\u003eHenseler, J., Ringle, C. M., \u0026amp; Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115\u0026ndash;135. https://doi.org/10.1007/s11747-014-0403-8. \u003c/li\u003e\n\u003cli\u003eHoang, D. V., \u0026amp; Hien, N. T. (2024). Digital capabilities, firm performance, and innovation capabilities: A combined approach of PLS-SEM and ANN. \u003cem\u003eInternational Journal of Innovation Management\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(01n02), 2450007. https://doi.org/10.1142/S1363919624500075. \u003c/li\u003e\n\u003cli\u003eKmieciak, R. (2021). Trust, knowledge sharing, and innovative work behavior: empirical evidence from Poland. \u003cem\u003eEuropean Journal of Innovation Management\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e(5), 1832-1859. https://doi.org/10.1108/EJIM-04-2020-0134. \u003c/li\u003e\n\u003cli\u003eLau, E. K. W. (2021, July). Relationship Between a Company\u0026rsquo;s Knowledge Management Strategy and Its Business Sustainability. In \u003cem\u003eInternational Conference on Knowledge Management in Organizations\u003c/em\u003e (pp. 187-196). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-81635-3_16. \u003c/li\u003e\n\u003cli\u003eLiu, S., Keil, M., Wang, L., \u0026amp; Lu, Y. (2023). Understanding critical risks of business process outsourcing from the vendor perspective: A dyadic comparison Delphi study. \u003cem\u003eInformation \u0026amp; Management\u003c/em\u003e, \u003cem\u003e60\u003c/em\u003e(6), 103837. https://doi.org/10.1016/j.im.2023.103837. \u003c/li\u003e\n\u003cli\u003eLi Z, Zhou Y (2025), \u0026quot;Starting with trust: unraveling the impact of AI trust on employee digital performance\u0026quot;. \u003cem\u003eBaltic Journal of Management\u003c/em\u003e, Vol. 20 No. 5 pp. 637\u0026ndash;655, doi: https://doi.org/10.1108/BJM-03-2025-0177. \u003c/li\u003e\n\u003cli\u003eMurcia, N. N., Ferreira, F. A., \u0026amp; Ferreira, J. J. (2022). Enhancing strategic management using a \u0026ldquo;quantified VRIO\u0026rdquo;: Adding value with the MCDA approach. \u003cem\u003eTechnological Forecasting and Social Change\u003c/em\u003e, \u003cem\u003e174\u003c/em\u003e, 121251. https://doi.org/10.1016/j.techfore.2021.121251. \u003c/li\u003e\n\u003cli\u003ePapagiannidis, E., Enholm, I. M., Dremel, C., Mikalef, P., \u0026amp; Krogstie, J. (2023). Toward AI governance: Identifying best practices and potential barriers and outcomes. \u003cem\u003eInformation Systems Frontiers\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(1), 123-141. https://doi.org/10.1007/s10796-022-10251-y. \u003c/li\u003e\n\u003cli\u003eRezaei Haji Abad, F. (2025). AI-Enhanced Administrative system: Automating Diagnosis Coding-in Healthcare-At Link\u0026ouml;ping University Hospital (\u0026Ouml;sterg\u0026ouml;tland Region). https://urn.kb.se/resolve?urn=urn%3Anbn%3Ase%3Alnu%3Adiva-141393. \u003c/li\u003e\n\u003cli\u003eSaxena M, Mishra DK (2025), \u0026quot;Artificial intelligence: the way ahead for employee engagement in corporate India\u0026quot;. \u003cem\u003eGlobal Knowledge, Memory and Communication\u003c/em\u003e, Vol. 74 No. 1-2 pp. 111\u0026ndash;127, https://doi.org/10.1108/GKMC-09-2022-0215.\u003c/li\u003e\n\u003cli\u003eSupriyanto, A., Said, D. L., \u0026amp; Anshori, A. G. (2024). A systematic literature review on dynamic capabilities in marketing. International Journal of Business and Society, 25(1), 333\u0026ndash;356. https://journal.access-bg.org/. \u003c/li\u003e\n\u003cli\u003eTeece, D. J., Pisano, G., \u0026amp; Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509\u0026ndash;533. https://doi.org/10.1002/(SICI)1097-0266(199708)18:7%3C509::AID-SMJ882%3E3.0.CO;2-Z. \u003c/li\u003e\n\u003cli\u003eTrachuk, A., \u0026amp; Linder, N. V. (2024). Decision-making factors for adopting artificial intelligence technologies in firms. Sustainability, 16(1), 375. https://doi.org/10.17747/2618-947X-2024-2-134-151.\u003c/li\u003e\n\u003cli\u003e\u0026Uuml;bellacker, T. (2025). Making sense of AI limitations: how individual perceptions shape organizational readiness for AI adoption. \u003cem\u003earXiv preprint arXiv:2502.15870\u003c/em\u003e. https://doi.org/10.48550/arXiv.2502.15870. \u003c/li\u003e\n\u003cli\u003eWasesa, I. R. H., Permatasari, D., Angkat, F. A., \u0026amp; Al-Khowarizmi, A. K. (2025). Integration of Artificial Intelligence in Management Information Systems to Improve the Effectiveness of Strategic Decision-Making in the Digital Era. \u003cem\u003eJournal of Computer Science, Information Technology and Telecommunication Engineering\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(2), 923-929. https://doi.org/10.30596/jcositte.v6i2.26051. \u003c/li\u003e\n\u003cli\u003eWei, Y., \u0026amp; Fonti, F. (2023). Conformity: How mental processes affect cognitive trust in AI. Academy of Management Proceedings, 2023(1), 13299. https://doi.org/10.5465/AMPROC.2023.14246.\u003c/li\u003e\n\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":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Artificial Intelligence, Trust in AI, Dynamic Capabilities, Knowledge Management, Employee Performance, Organizational Competitiveness, Digital Transformation","lastPublishedDoi":"10.21203/rs.3.rs-8078220/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8078220/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe accelerated adoption of Artificial Intelligence (AI) is reshaping organizational landscapes, yet many firms struggle to translate AI investments into meaningful employee performance gains due to insufficient integration between AI systems and organizational capabilities. This study proposes a comprehensive model examining how AI system implementation and trust in AI influence sustainable employee performance through dynamic capabilities, company competitiveness, and knowledge management capacity. Using Partial Least Squares Structural Equation Modeling (PLS-SEM) with data from Indonesian firms, the research validates an integrated framework connecting technological, psychological, and organizational factors. Findings reveal that both AI implementation and trust significantly enhance organizational capabilities, with AI implementation showing stronger effects. Company competitiveness and dynamic capabilities serve as critical mediators between AI factors and employee performance, while knowledge management capacity shows no direct performance impact. The model explains 78.3% of variance in employee performance, demonstrating substantial predictive power. Results emphasize that sustainable performance requires simultaneous development of strategic capabilities supported by trust mechanisms, not merely technological deployment. This study contributes by integrating previously isolated constructs and provides practical guidance for managers aligning AI initiatives with capability development and competitive positioning for organizational resilience.\u003c/p\u003e","manuscriptTitle":"Building Dynamic AI Capabilities for Sustainable Employee Performance: The Roles of Trust, Knowledge Management, and Competitive Advantage","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-22 12:37:09","doi":"10.21203/rs.3.rs-8078220/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-10T16:23:29+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-02T00:27:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-29T01:07:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-26T03:33:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"183976861155389286122926251262529327435","date":"2025-12-24T02:58:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"105109026158632757103718734549904911872","date":"2025-12-23T13:57:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"22654348279552048182386085143591969103","date":"2025-12-22T23:13:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"5020774025318197081507038943773213546","date":"2025-12-18T15:27:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-18T13:48:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-18T13:42:33+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-18T10:25:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-10T10:13:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2025-12-10T09:58:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b4e2dd70-6484-46dd-9904-1971da9ec15a","owner":[],"postedDate":"December 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":59915910,"name":"Business and commerce/Business and management"},{"id":59915911,"name":"Social science/Business and management"},{"id":59915912,"name":"Business and commerce/Information systems and information technology"},{"id":59915913,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-05-19T10:24:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-22 12:37:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8078220","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8078220","identity":"rs-8078220","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}