Reconfiguring Strategic Capabilities in the Digital Era: How AI-Enabled Dynamic Capability, Data-Driven Culture, and Organizational Learning Shape Firm Performance

Abstract

In the era of digital transformation, organizations increasingly invest in Artificial Intelligence (AI) to enhance competitiveness, yet persistent evidence shows that AI investment does not automatically translate into superior firm performance. Drawing on the Resource-Based View (RBV) and Dynamic Capabilities Theory (DCT), this study aims to explain this paradox by examining how AI-enabled dynamic capability (AIDC) is converted into performance outcomes through organizational mechanisms. Specifically, the study investigates the mediating roles of organizational data-driven culture (DDC) and organizational learning (OL). Data were collected from 254 senior managers and executives in U.S. firms actively employing AI technologies and analyzed using partial least squares structural equation modeling (PLS-SEM). The results indicate that AIDC exerts a significant direct effect on firm performance as well as indirect effects through both DDC and OL. Serial mediation analysis reveals that AIDC enhances performance by first fostering a data-driven mindset and subsequently institutionalizing learning processes that translate AI-generated insights into actionable organizational routines. Moreover, DDC plays a contingent moderating role in the AIDC–performance relationship, revealing a nonlinear effect whereby excessive reliance on data weakens the marginal performance benefits of AIDC. Taken together, these findings demonstrate the dual role of data-driven culture: while DDC functions as an enabling mediator that facilitates AI value creation, beyond a threshold it constrains dynamic reconfiguration by limiting managerial discretion and strategic flexibility. This insight exposes the “dark side” of data-driven culture and extends the RBV and DCT by introducing a boundary condition to the performance effects of AI-enabled capabilities. From a managerial perspective, the study highlights the importance of balancing analytical discipline with adaptive learning to sustain digital efficiency and strategic agility.

1. Introduction

In the contemporary digital economy, Artificial Intelligence (AI) has become a transformative force shaping organizational strategies, processes, and performance. The global AI market, valued at USD 136.55 billion in 2022, is projected to grow to USD 309.6 billion by 2026, reflecting the substantial investments organizations are making in AI technologies [1]. AI applications such as machine learning, natural language processing, and predictive analytics are increasingly embedded into business processes to improve operational efficiency, responsiveness, and innovation [2]. However, the adoption of AI alone does not guarantee performance gains. Realizing its potential requires the integration of AI into strategic frameworks and the development of complementary organizational capabilities [3,4]. Without such integration, firms risk underutilizing technological investments—an issue commonly referred to as the “AI paradox,” where substantial digital investments fail to translate into commensurate performance returns [5].

Building on this premise, scholars highlight that technological resources alone are insufficient to drive superior outcomes; they must be combined with dynamic capabilities that enable organizations to sense opportunities, seize them, and transform operations in response to environmental turbulence [6,7]. Although prior research has recognized the potential of AI to enhance firm performance, the mechanisms linking AI-enabled dynamic capability (AIDC) to organizational outcomes remain underexplored [8]. In this study, firm performance is conceptualized explicitly as operational and financial outcomes—such as efficiency, cost reduction, quality improvement, profitability, and return on investment—rather than environmental or social performance [4,9,10]. Specifically, the roles of data-driven culture (DDC) and organizational learning (OL) in mediating this relationship have received limited empirical attention. Most existing studies examine isolated constructs, overlooking the synergistic effects of AIDC, DDC, and OL on firm performance in digitally turbulent contexts [11,12,13]. Addressing this gap is essential for advancing theory in digital transformation and capability reconfiguration.

This study examines how AIDC drives firm performance through the mediating effects of DDC and OL. Drawing on the Resource-Based View (RBV) [14] and Dynamic Capabilities Theory (DCT) [15], the research proposes a comprehensive framework to explain these relationships. The RBV emphasizes the strategic value of rare and inimitable resources, while DCT extends this logic by stressing the ability to reconfigure capabilities in rapidly changing environments. AIDC embodies these dynamic capabilities, allowing organizations to leverage AI for continuous process improvement and innovation [16]. At the same time, DDC fosters evidence-based decision-making, and OL enables firms to assimilate and apply knowledge—both of which are critical for realizing the performance benefits of AI-driven digital transformation [17,18].

Importantly, while improvements in operational efficiency and resource utilization may indirectly support broader sustainability objectives, this study does not directly measure environmental or social performance outcomes. Accordingly, the term performance is used in this study to denote sustained competitive performance in operational and financial terms, consistently with prior digital transformation and strategic management research [19,20,21].

This research makes three contributions. First, it advances the digital transformation literature by offering a holistic model that links AIDC to firm performance through cultural and learning mechanisms, thus addressing the fragmented treatment of these constructs in prior studies. Second, it enriches the RBV and DCT perspectives by showing how AI-enabled dynamic capabilities must be complemented by organizational culture and learning processes to achieve sustained competitive advantage in digitally turbulent environments, rather than environmentally defined sustainability [22,23]. Third, it provides actionable insights for managers on how fostering a strong DDC and promoting OL can maximize returns from AI investments [24].

To guide this investigation, the following research questions are posed:

• How does AIDC directly influence firm performance?
• To what extent does a DDC mediate the relationship between AIDC and firm performance?
• How does OL mediate the relationship between AIDC and firm performance?

Practical examples illustrate the importance of this inquiry. A multinational corporation may invest heavily in AI to optimize supply chain operations but fail to achieve performance gains due to weak data-driven decision-making and limited organizational learning [25]. Conversely, a competitor that embeds AI within a culture of data-driven practices and continuous learning realizes significant improvements in operational efficiency and financial outcomes [26]. These contrasting cases underscore the need to integrate AIDC with DDC and OL. Accordingly, this study develops a model that clarifies the pathways through which AI-enabled dynamic capabilities enhance firm performance, offering contributions for both scholarly debate and managerial practice in digital transformation.

2. Theoretical Background, Literature Review and Hypothesis

2.1. Underpinning Theories

This study is anchored in the RBV and DCT, which together provide a robust foundation for understanding how Artificial Intelligence-Enabled Dynamic Capability (AIDC) enhances FP through DDC and OL. The RBV emphasizes that firms achieve sustainable competitive advantage when they possess resources that are valuable, rare, inimitable, and non-substitutable (VRIN) [14,27]. In the context of digital transformation, intangible assets such as AI capabilities, data culture, and learning routines meet these VRIN conditions and are increasingly central to competitive differentiation [28,29]. AIDC fits this perspective as a strategic resource that enables organizations to embed AI into processes and routines, while DDC and OL function as organizational capacities that transform the technological potential of AI into measurable outcomes. Importantly, the RBV suggests that technology alone is insufficient; its value is realized through firm-specific routines, absorptive capacities, and supportive cultures that facilitate performance impact [3,30]. Empirical studies confirm that technological investments achieve superior results only when embedded in enabling organizational contexts [6,31]. Thus, the RBV provides a theoretical rationale for examining how AIDC, mediated by DDC and OL, becomes a performance-enhancing capability.

While the RBV explains why AI-related capabilities are strategically valuable, it does not fully address how they evolve in turbulent environments. DCT extends the RBV by focusing on a firm’s ability to integrate, build, and reconfigure resources in response to environmental change [15]. Unlike the RBV’s more static view, DCT emphasizes agility, learning, and innovation as central to sustaining competitiveness. From this perspective, AIDC reflects not merely the adoption of AI technologies but the capacity to sense opportunities, seize them, and transform organizational processes through AI [32,33]. Within this framework, DDC and OL operate as micro-foundations of dynamic capabilities, strengthening adaptability and resilience in volatile markets. DCT is therefore highly relevant to AI-driven transformation, as it explains how intangible organizational capabilities interact with technological resources to create value over time [34]. Empirical evidence further shows that firms with strong dynamic capabilities achieve higher levels of innovation, responsiveness, and performance outcomes [2,35].

Importantly, AIDC goes beyond conventional AI capabilities by capturing the firm’s higher-order managerial capacity to orchestrate AI resources across sensing, seizing, and reconfiguring mechanisms. Whereas AI capabilities typically refer to the availability of AI infrastructure, data quality, and technical expertise, AIDC reflects how managers purposefully deploy these resources to identify emerging opportunities (sensing), translate insights into strategic and operational decisions (seizing), and continuously reconfigure routines, structures, and workflows in response to environmental change (reconfiguring) [13,36,37,38]. In this sense, AIDC represents a dynamic, path-dependent capability rather than a static technological asset. This distinction is critical, as firms may possess advanced AI technologies yet fail to generate performance gains if they lack the organizational capacity to interpret AI-generated insights, act upon them in a timely manner, and realign internal processes accordingly [39,40]. By conceptualizing AIDC through the sensing–seizing–reconfiguring lens, this study positions AI as an enabler of adaptive organizational action rather than a standalone source of competitive advantage.

By combining the RBV and DCT, this study captures both the strategic value of AI-enabled resources and the processual dynamics that allow firms to reconfigure these resources in rapidly changing environments. The RBV highlights why AIDC, DDC, and OL serve as critical organizational assets, while DCT explains how these capabilities evolve and interact to sustain competitive advantage. Together, these theories provide a comprehensive lens for analyzing the pathways through which AIDC—mediated by DDC and OL—translates into superior firm performance.

2.2. AI-Enabled Dynamic Capability and Firm Performance

AIDC captures a firm’s ability to integrate, build, and reconfigure AI-based competencies to remain competitive in rapidly changing environments [3]. It reflects the organizational capacity to sense AI opportunities, seize them through timely adoption, and transform processes with advanced tools such as machine learning, natural language processing, and predictive analytics [41]. Conceptually, AIDC is a multidimensional construct encompassing technological infrastructure, skilled human capital, and strategic orientation toward digital transformation [42]. FP, in contrast, is broadly defined as the degree to which organizations achieve both financial and non-financial goals. Beyond traditional financial outcomes such as ROI and ROA, performance increasingly incorporates innovation, agility, and competitiveness—dimensions that are particularly critical in digital environments [2,43,44].

The link between AIDC and FP can be understood through the RBV and DCT. The RBV emphasizes that resources yield competitive advantage when they are valuable, rare, inimitable, and non-substitutable [4,14]. From this lens, AI capabilities become strategic assets when embedded within firm-specific routines that cannot be easily replicated. However, the RBV alone provides a relatively static perspective. DCT extends this reasoning by stressing the ability of firms to continuously integrate, adapt, and reconfigure resources in response to environmental change [15,45]. Accordingly, AIDC operates not only as a technological asset but as a dynamic capability that enables firms to innovate, adapt, and sustain superior performance in volatile markets.

Empirical evidence underscores the importance of this relationship. Mikalef et al. [16] show that firms with strong AIDC outperform competitors in both operational and financial performance. Dwivedi et al. [46] highlight that AI-driven dynamic capabilities enhance decision-making accuracy, resource optimization, and service innovation. In the manufacturing sector, Tariq et al. [47] demonstrate that AI-supported predictive maintenance improves throughput and product quality, while Wamba-Taguimdje et al. [48] argue that firms with advanced AIDC exhibit greater agility, innovativeness, and customer orientation, which translate into superior outcomes. These findings align with broader research suggesting that AIDC empowers firms to reduce costs, boost productivity, and deliver greater customer value, thereby driving both short-term efficiency and long-term competitiveness [48,49].

Overall, AIDC represents a critical enabler of firm performance in the digital era. By integrating AI into strategic and operational routines, firms not only achieve efficiency but also build the agility and innovation capacity necessary for long-term success. Moreover, when complemented by organizational mechanisms such as DDC and learning, the influence of AIDC on performance becomes even more pronounced [50]. Based on these insights, the study hypothesizes that:

H1. 

AI-enabled dynamic capability is positively associated with firm performance.

2.3. The Mediating Role of Organizational Data-Driven Culture

Organizational DDC has emerged as a foundational enabler for maximizing the value of digital technologies, particularly AI-enabled dynamic capability (AIDC), in driving firm performance. DDC reflects an organizational mindset that embeds data-informed decision-making into everyday practices and strategic choices [50]. As Ghafoori et al. [51] note, DDC entails cultivating an environment where employees are encouraged, trained, and empowered to rely on data when shaping operational and strategic outcomes. Beyond technical infrastructure, DDC incorporates leadership commitment, shared norms regarding data use, and employee data literacy [16]. Key elements include data accessibility, governance structures, decision-making autonomy, and a learning orientation, which collectively ensure that data circulates seamlessly across functions to inform both immediate actions and long-term strategic directions [52]. Although scholars widely acknowledge the strategic importance of DDC, there remains debate over its measurement, with some emphasizing technical enablers while others stress cultural and behavioral attributes [50].

Theoretically, DDC aligns closely with the RBV and DCT. From an RBV perspective, DDC constitutes a valuable and hard-to-imitate resource that strengthens the ability of firms to achieve sustained competitive advantage [14]. DCT extends this reasoning by highlighting that embedding data into routines and decision-making processes enhances a firm’s capacity to sense opportunities, adapt strategies, and reconfigure resources in response to dynamic environments [53]. Empirical studies further support this mediating role, showing that DDC strengthens the link between analytics or AI capabilities and firm performance by enabling knowledge sharing, accelerating decision-making, and fostering cross-functional collaboration [2,54]. Moreover, DDC reinforces OL by creating feedback loops and institutionalizing practices derived from data insights, thereby ensuring continuous improvement. Collectively, these mechanisms explain how DDC operates as a critical process mechanism through which AIDC is internalized and translated into tangible performance outcomes.

Importantly, this study conceptualizes DDC as playing a dual role in the AIDC–firm performance relationship. As a mediating mechanism, DDC explains how AI-enabled dynamic capabilities are operationalized within organizational routines by embedding AI-generated insights into day-to-day decision-making and learning processes [13,36,55]. At the same time, DDC also functions as a contextual boundary condition that can shape the strength and direction of the AIDC–performance relationship by influencing managerial discretion, responsiveness, and strategic flexibility [56,57]. This dual-role conceptualization is theoretically consistent with organizational research distinguishing between mechanisms that transmit value and contextual conditions that constrain or amplify value creation [50]. Introducing this distinction at the model-development stage provides the theoretical foundation for examining DDC as both a mediator and a moderator in subsequent hypotheses. Accordingly, the following hypothesis is proposed:

H2. 

Organizational data-driven culture mediates the relationship between AI-enabled dynamic capability and firm performance.

2.4. The Mediating Role of Organizational Learning

OL is widely recognized as a strategic capability that enables firms to adapt, innovate, and remain competitive in turbulent business environments. Awad and Martín-Rojas [58] define OL as a process through which organizations develop, enhance, and manage knowledge and routines to improve performance. In a similar vein, Chiva and Alegre [59] highlight that OL encompasses both individual and collective learning, leading to behavioral and strategic transformation. The concept has since evolved into multidimensional perspectives that include knowledge acquisition, knowledge dissemination, shared interpretation, and organizational memory [60]. Together, these dimensions ensure that firms not only generate and capture knowledge but also embed it into processes that support continuous renewal and long-term effectiveness.

The theoretical underpinnings of OL are firmly rooted in the RBV and DCT. The RBV regards learning as an intangible, valuable, and inimitable capability that can generate sustained competitive advantage [14], while DCT views OL as a higher-order capability that enables the reconfiguration of resources in response to dynamic market shifts [61]. In this context, OL extends beyond the acquisition of information to the integration of insights into organizational routines and the transformation of collective practices [62]. Empirical studies consistently demonstrate that OL enhances firm innovation, agility, and overall performance, particularly in technology-intensive settings [63]. Within AI-enabled contexts, OL serves as a conduit through which data-driven insights are absorbed and converted into strategic actions [33]. Moreover, when supported by a strong DDC, OL is reinforced as employees are encouraged to experiment with data, question assumptions, and collaborate in applying insights [16]. Accordingly, OL functions as a crucial mediator that ensures AIDC is not only adopted but effectively assimilated and operationalized into performance gains.

H3. 

AI-enabled dynamic capability positively influences organizational learning through organizational data-driven culture.

H4. 

Organizational learning mediates the relationship between AI-enabled dynamic capability and firm performance.

2.5. Serial Mediation of Data-Driven Culture and Organizational Learning

AIDC represents a firm’s ability to sense, seize, and transform AI technologies to adapt to environmental changes and drive innovation [3,64]. It encompasses critical competencies such as data infrastructure, AI expertise, decision-making agility, and strategic alignment [6]. Unlike static AI resources, AIDC emphasizes adaptability, reconfiguration, and continuous learning, making it particularly valuable in volatile markets. To translate these capabilities into superior FP, organizations require complementary mechanisms, notably DDC and OL. DDC is understood as an organizational mindset that embeds data into decision-making, promotes transparency, and supports evidence-based performance monitoring [50,54]. It involves leadership commitment, employee empowerment to interpret data, and robust governance systems that ensure accessibility and reliability. When such a culture is present, AI and analytics are more likely to generate tangible business value [48]. OL, in turn, enables firms to acquire, disseminate, interpret, and institutionalize knowledge, thereby transforming insights into strategic actions [18]. In AI-driven contexts, OL includes adapting workflows to AI feedback, fostering human–algorithm collaboration, and embedding AI-derived knowledge into organizational routines.

The theoretical grounding for this relationship lies in the RBV and DCT. The RBV argues that AI capabilities and data-oriented practices qualify as strategic resources when they are valuable, rare, inimitable, and well-organized [14]. DCT extends this logic by emphasizing that organizations must continuously integrate and reconfigure these resources to address shifting market demands [64]. Within this framework, DDC and OL serve as enabling conditions that bridge AIDC and FP. Empirical studies provide strong support for this view: firms with higher AIDC demonstrate superior innovation and competitiveness [16]; DDC has been shown to enhance the performance impact of AI by shaping data-informed behaviors [54]; and OL has been empirically linked to performance improvement by embedding data-based knowledge into operations [36,65]. The interplay between these constructs is sequential: AIDC generates advanced insights, DDC ensures their integration into decision-making, and OL transforms them into strategic and operational improvements [50].

The integration of DDC and OL therefore offers a powerful pathway through which AIDC translates into improved FP. DDC institutionalizes evidence-based thinking, creating an environment in which employees engage with data and adopt a culture of continuous improvement [66,67]. OL then extends this process by ensuring that knowledge derived from data is processed, internalized, and applied to reshape organizational strategies and practices [18,60]. This sequential mediation aligns not only with DCT, which highlights adaptability and reconfiguration [61], but also with Sociotechnical Systems Theory, which stresses that technological resources such as AI must be complemented by cultural and learning mechanisms to achieve performance gains. Without these mediating elements, the potential of AIDC often remains underutilized [16]. Accordingly, this study proposes the following hypothesis:

H5. 

Organizational data-driven culture and organizational learning serially mediate the relationship between AI-enabled dynamic capability and firm performance.

2.6. Moderating Role of Organizational Data-Driven Culture

Organizational data-driven culture (DDC) is increasingly recognized as a critical contextual factor shaping how firms extract value from AI and other digital technologies. DDC refers to a shared set of values, norms, and practices in which data is systematically integrated into decision-making, innovation, and performance evaluation processes [32,68]. Its core dimensions include leadership commitment to data-based decision-making, widespread access to reliable data, analytical skill development, and a collective belief in evidence-based management [69]. From both the RBV and DCT, organizational culture represents a deeply embedded, intangible resource that conditions the effectiveness with which technological and dynamic capabilities are deployed [61,70].

Building on the mediating role of DDC, this study focuses on its function as a boundary condition that shapes when and how AI-enabled dynamic capabilities translate into firm performance. While a strong DDC generally enhances the effectiveness of AIDC by fostering analytical discipline and evidence-based action [71], emerging research on the “dark side of big data” suggests that excessive reliance on data can produce unintended negative consequences [72]. In highly data-intensive cultures, managers may experience information overload, leading to cognitive fatigue and delayed decision-making. Moreover, algorithmic rigidity may emerge when managers become overly dependent on analytical outputs and are reluctant to act in the absence of complete or confirmatory data, thereby missing time-sensitive opportunities in turbulent environments [73,74]. Such over-analytics can also crowd out managerial intuition, experiential judgment, and entrepreneurial experimentation, which are essential components of dynamic reconfiguration under uncertainty [75].

From a dynamic capabilities perspective, these effects imply that beyond a certain threshold, a strong DDC may constrain rather than enable the sensing, seizing, and reconfiguring processes that AIDC is intended to support [13,36]. When organizations prioritize measurement precision over strategic agility, the reconfiguration of routines may slow, reducing the marginal performance benefits of AI-enabled capabilities [37,39,40,76]. This logic aligns with decision-paralysis and resource-crowding-out arguments, suggesting that data-driven practices, if not balanced with human judgment, may inadvertently weaken performance outcomes.

Empirical studies provide indirect support for this tension. While prior research shows that DDC strengthens the performance impact of analytics and AI capabilities by promoting data-informed decision-making and organizational agility [16,48], recent studies caution that excessive metric fixation can stifle innovation and adaptability in volatile contexts. Accordingly, this study advances a contingency perspective by proposing that DDC does not uniformly enhance the AIDC–performance relationship but instead moderates it in a way that reflects both its enabling and constraining effects.

H6. 

Organizational data-driven culture moderates the relationship between AI-enabled dynamic capability and firm performance, such that excessively strong data-driven cultures weaken the positive effect of AIDC on firm performance.

2.7. Conceptual Model

Drawing on the RBV and DCT, this study develops an integrated conceptual model explaining how AIDC is transformed into firm performance through complementary cultural and learning mechanisms. As illustrated in Figure 1, the framework specifies both process pathways and boundary conditions through which AI-enabled capabilities generate operational and financial outcomes. At its core, AIDC is conceptualized as a higher-order dynamic capability that enables firms to sense opportunities through data analytics, seize them via informed decision-making, and reconfigure organizational processes accordingly. Consistently with DCT, AIDC is therefore expected to exert a direct influence on firm performance (H1), while acknowledging that its performance impact is contingent on organizational mechanisms that translate AI insights into actionable routines rather than occurring automatically.

Accordingly, organizational DDC and OL are positioned as key mediating mechanisms in the model. DDC operates as a first-stage mediator, explaining how AIDC becomes embedded within everyday decision-making practices by fostering data accessibility, analytical discipline, and evidence-based norms (H2). OL functions as a subsequent mediating mechanism, capturing the extent to which AI- and data-driven insights are assimilated, shared, and applied to improve organizational processes and outcomes (H4). Together, these mechanisms form a sequential mediation pathway, whereby AIDC strengthens DDC, which in turn enables OL, ultimately enhancing firm performance (H5). This structure reflects a process logic in which cultural alignment precedes and facilitates learning-based value creation.

Beyond these transmission mechanisms, the model also specifies DDC as a moderating boundary condition. As depicted in Figure 1, DDC is hypothesized to shape the strength and direction of the AIDC–firm performance relationship (H6). This moderation adopts a contingency perspective, suggesting that while data-driven norms generally support the effective use of AI-enabled capabilities, excessively strong data orientation may constrain managerial discretion and strategic flexibility, thereby weakening the marginal performance benefits of AIDC. By integrating both mediation and moderation, the conceptual model captures the dual role of DDC as both a value-transmission mechanism and a contextual constraint, offering a nuanced explanation of how, why, and under what conditions AI-enabled dynamic capabilities contribute to firm performance.

3. Methods

3.1. Research Design and Sample

This study employed a quantitative, cross-sectional survey design to examine how AIDC influences FP through the mediating mechanisms of organizational DDC and OL. A survey-based approach was selected because it enables the systematic collection of perceptual data from senior decision-makers who are directly involved in strategic and technological initiatives, including AI adoption [13].

Data were collected using the Zoho Survey platform, which was selected for its secure data handling, anonymity controls, and suitability for executive-level research. Participants were recruited from verified U.S. business and industry directories, and eligibility was restricted to CEOs and senior management team (TMT) members. Senior managerial status was verified through self-reported job titles, organizational roles, and years of managerial experience, ensuring that respondents possessed sufficient strategic oversight and decision-making authority related to AI implementation. Prior to full deployment, the questionnaire was pre-tested with a panel of CEOs and senior executives to ensure clarity, relevance, and content validity.

The sampling frame focused on firms classified under the International Standard Industrial Classification (ISIC) codes 10–95, encompassing manufacturing and service industries. This broad sectoral coverage was intentionally selected because AI-enabled capabilities are increasingly relevant across both manufacturing and service contexts, where data analytics, automation, and digital decision-making play a critical strategic role [4]. Restricting the sample to ISIC 10–95 ensured sectoral diversity while excluding primary industries where AI adoption remains limited, thereby enhancing the relevance and comparability of responses.

A total of 800 survey invitations were distributed via personalized email invitations, followed by two reminder waves at two-week intervals, consistently with the tailored design method [77]. From the initial distribution, 286 valid responses were received, yielding a response rate of 33.5%, which is comparable to prior executive-level survey studies in management and information systems research [4,78]. After excluding incomplete questionnaires and firms employing fewer than 50 employees—as smaller firms are less likely to implement AI in a systematic and organization-wide manner [9]—the final dataset consisted of 254 usable responses (31.75%).

To assess non-response bias, an early–late respondent comparison was conducted following Armstrong and Overton [79]. Respondents were divided into early and late waves based on response timing, and independent-sample t-tests revealed no significant differences across key study variables, indicating that non-response bias was unlikely to be a concern.

In addition, an a priori statistical power analysis was conducted using G*Power 3.1. Assuming a medium effect size (f² = 0.10), a 5% significance level, and 95% statistical power, the minimum required sample size was 176 observations. The final sample of 254 responses exceeds this threshold, providing adequate statistical power for PLS-SEM analysis and supporting the robustness and generalizability of the findings.

3.2. Measures

The measurement instruments used in this study were adapted from well-established and widely validated scales identified through a comprehensive review of prior literature on artificial intelligence-enabled dynamic capability, data-driven culture, organizational learning, and firm performance. To ensure content validity and contextual relevance, the questionnaire was reviewed by three academic experts and four senior practitioners prior to data collection. The academic experts were selected based on the following criteria: (1) holding a doctoral degree in management, information systems, or a closely related field; (2) active publication records in top-tier journals addressing digital transformation, dynamic capabilities, or AI-enabled organizational change; and (3) a minimum of five years of post-PhD academic experience.

The senior practitioners were selected based on: (1) holding executive or senior managerial positions (e.g., CEO, CIO, CTO, or Director level); (2) direct involvement in AI or data-driven initiatives within their organizations; and (3) a minimum of five years of managerial experience. Their feedback contributed to refining item wording, sequencing, and clarity to ensure both academic rigor and managerial relevance. A pilot test was subsequently conducted with a small group of CEOs and senior managers experienced in AI adoption, resulting in minor adjustments to enhance face validity and reliability. All items were measured using a five-point Likert scale ranging from 1 (“strongly disagree”) to 5 (“strongly agree”).

AI-enabled dynamic capability was measured using the multidimensional scale developed by Mikalef and Gupta [3]. Consistently with prior research, AIDC was modeled as a third-order construct, encompassing tangible resources (data, technology, and infrastructure), human resources (technical and business skills), and intangible resources (interdepartmental coordination, change capacity, and risk proclivity). The construct included 46 items reflecting the organization’s AI infrastructure, expertise, and capacity for technological reconfiguration [4].

The mediating variables—organizational data-driven culture (DDC) and organizational learning (OL)—were measured using the validated scales developed by Gupta and George [17], consisting of five and four items, respectively. DDC captures the extent to which organizational decision-making is systematically guided by data and analytics, while OL reflects the firm’s ability to acquire, assimilate, and apply knowledge for continuous improvement.

Firm performance (FP) was operationalized exclusively as a combination of operational and financial performance, drawing on well-established scales from Aydiner et al. [9] and Tatoglu et al. [80]. Operational performance items capture efficiency-related outcomes such as cost reduction, process improvement, and forecasting accuracy, while financial performance items assess profitability, return on investment, and sales growth. Accordingly, firm performance in this study is defined strictly in terms of operational efficiency and financial outcomes, ensuring conceptual consistency between the theoretical framework and the empirical measurement. This operationalization provides clear construct alignment and strengthens the validity of the performance assessment used in the analysis.

3.3. Common Method Bias

Given that the data for this study were collected using a single survey instrument, common method bias (CMB) was treated as a potential methodological concern and addressed through both procedural and statistical remedies [81]. During survey design and administration, respondents were assured of anonymity and confidentiality to reduce evaluation apprehension and socially desirable responding. In addition, items were refined through expert review and pilot testing, the questionnaire was structured into distinct sections for independent and dependent variables, and item order was randomized where appropriate to reduce pattern responding and priming effects [82]. These procedural measures were intended to minimize systematic measurement error attributable to the data collection method.

To assess CMB statistically, the study relied primarily on the full collinearity variance inflation factor (VIF) approach, which has been recommended as a robust diagnostic for common method variance in PLS-SEM [83]. Full collinearity VIF values were computed for all latent constructs, and all values were below the conservative threshold of 3.3, indicating that common method variance is unlikely to pose a serious threat to the validity of the estimated relationships [84]. In addition, a marker variable representing a theoretically unrelated construct was included in the survey and used as a supplementary check; incorporating this marker did not materially alter the pattern or significance of the main structural relationships [85]. Taken together, these results suggest that CMB is unlikely to substantially bias the study’s findings, although the results are interpreted as theoretically grounded associations rather than definitive causal effects.

3.4. Data Analysis Technique

To examine the hypothesized relationships and test the research model, this study employed Partial Least Squares Structural Equation Modeling (PLS-SEM) using SmartPLS version 4. PLS-SEM is widely recognized as a robust analytical technique for predictive and theory-building research, particularly when exploring complex models involving mediation and moderation effects [84,86]. The technique was deemed appropriate for this study because the model integrates multiple higher-order constructs—AIDC, DDC, and OL—and aims to predict FP rather than confirm an established theory [87]. Additionally, PLS-SEM is suitable for handling non-normal data distributions, relatively smaller sample sizes, and exploratory models with reflective and formative measurement constructs [88]. This analytical approach has been extensively applied in management and information systems research, particularly in studies examining digital transformation, AI capabilities, and organizational performance [13,89,90].

The analysis followed a two-step approach: assessment of the measurement model and evaluation of the structural model. The measurement model was first validated by examining indicator reliability, internal consistency (Cronbach’s alpha and composite reliability), and convergent and discriminant validity through the Fornell–Larcker criterion and the heterotrait–monotrait ratio (HTMT). Subsequently, the structural model was tested to assess the significance and strength of hypothesized relationships using a bootstrapping procedure with 5000 resamples to ensure the robustness of path estimates [84]. The coefficient of determination (R²), effect size (f²), and predictive relevance (Q²) were also examined to evaluate the model’s explanatory and predictive power. Overall, the use of PLS-SEM provided a rigorous and comprehensive framework for testing the proposed conceptual model and hypotheses in alignment with current best practices in social science and management research.

4. Results

4.1. Measurement Model Assessment

The measurement model was evaluated in accordance with the established guidelines for higher-order constructs using PLS-SEM [86,88]. The study employed both reflective and formative measurement specifications, aligning with the multidimensional and hierarchical nature of the research constructs. Specifically, AIDC was modeled as a third-order formative–reflective construct, composed of three second-order dimensions: tangible resources, human resources, and intangible resources. Each second-order construct was operationalized through reflective first-order constructs capturing data, technology, and basic resources (tangible); technical and business skills (human); and inter-departmental coordination, organizational change capacity, and risk proclivity (intangible) [3,4]. The repeated indicator approach was adopted for estimation, which is suitable for formative–reflective higher-order constructs [91].

Stage 1: Reflective Measurement Model Assessment. The reflective measurement model was first examined to ensure indicator reliability, internal consistency, and convergent validity. As shown in

Table 1. Measurement model assessment for the first-order reflective constructs.

Constructs	Items	Outer Loadings	VIF	CA	CR	AVE
AI-Enabled Dynamic Capability (AIDC)
Technical Skills (TSK)				0.932	0.945	0.710
	TSK1	0.823	2.005
	TSK2	0.840	2.044
	TSK3	0.865	1.936
	TSK4	0.833	1.682
	TSK5	0.863	2.496
	TSK6	0.825	2.983
	TSK7	0.847	2.170
Business Skills (BSK)				0.940	0.951	0.735
	BSK1	0.856	1.871
	BSK2	0.842	1.981
	BSK3	0.848	2.616
	BSK4	0.850	2.299
	BSK5	0.858	1.913
	BSK6	0.877	2.104
	BSK7	0.872	2.750
Inter-Departmental Coordination (IDC)				0.891	0.915	0.605
	IDC1	0.787	2.659
	IDC2	0.768	1.706
	IDC3	0.765	1.931
	IDC4	0.767	2.039
	IDC5	0.801	2.266
	IDC6	0.771	2.029
	IDC7	0.783	1.605
Organizational Change Capacity (OCC)				0.868	0.901	0.604
	OCC1	0.804	2.929
	OCC2	0.710	2.888
	OCC3	0.751	2.722
	OCC4	0.799	1.883
	OCC5	0.805	2.491
	OCC6	0.790	2.132
Risk Proclivity (RP)				0.829	0.897	0.745
	RP1	0.859	1.889
	RP2	0.863	2.486
	RP3	0.867	2.240
Data-Driven Culture (DDC)				0.853	0.895	0.630
	DDC1	0.742	2.228
	DDC2	0.793	2.583
	DDC3	0.788	1.831
	DDC4	0.820	1.782
	DDC5	0.823	2.560
Organizational Learning (OL)				0.838	0.892	0.673
	OL1	0.826	2.840
	OL2	0.837	2.032
	OL3	0.795	1.786
	OL4	0.824	2.386
Operational Performance (OP)				0.902	0.923	0.630
	OP1	0.791	2.012
	OP2	0.802	2.639
	OP3	0.827	2.719
	OP4	0.810	2.114
	OP5	0.730	1.958
	OP6	0.776	2.522
	OP7	0.818	2.978
Financial Performance (FP)				0.935	0.944	0.607
	FP1	0.825	2.890
	FP2	0.774	2.283
	FP3	0.785	1.937
	FP4	0.787	2.124

, all item loadings exceeded the recommended threshold of 0.70, confirming that the observed indicators strongly represent their respective latent variables [88]. The Cronbach alpha and composite reliability (CR) values ranged from 0.80 to 0.95, surpassing the 0.70 benchmark, thus demonstrating internal consistency reliability [84]. Moreover, the average variance extracted (AVE) for all constructs exceeded 0.50, supporting convergent validity and indicating that the latent constructs explain a sufficient proportion of variance in their indicators [92].

Stage 2: Discriminant and Formative Measurement Assessment. Discriminant validity was then assessed to ensure that constructs were empirically distinct. As presented in

Table 2. Discriminant validity of first-order reflective constructs.

Constructs	1	2	3	4	5	6	7	8
1. Business skills	0
2. Data Driven Culture	0.688	0
3. Financial performance	0.524	0.653	0
4. Inter-departmental Coordination	0.607	0.480	0.580	0
5. Organizational Change Capacity	0.617	0.600	0.608	0.723	0
6. Organizational learning	0.489	0.774	0.637	0.578	0.626	0
7. Operational Performance	0.599	0.609	0.806	0.467	0.675	0.708	0
8. Risk Proclivity	0.601	0.431	0.614	0.546	0.777	0.609	0.599	0
9. Technical Skills	0.735	0.582	0.519	0.679	0.680	0.568	0.506	0.707

, the heterotrait–monotrait (HTMT) ratios were within acceptable limits, with HTMT values below 0.85 [93], confirming adequate discriminant validity across constructs. The formative assessment for the higher-order construct (AIDC) was then conducted to evaluate multicollinearity, indicator weights, and significance levels. Results summarized in

Table 3. Assessing first-order formative constructs.

First-Order	Items	Weight	p-Values	VIF
Data Resources	DR1	0.157	0.008	1.874
	DR2	0.192	0.000	1.872
	DR3	0.192	0.000	2.345
	DR4	0.168	0.000	1.920
	DR5	0.391	0.000	1.973
	DR6	0.148	0.003	2.329
Technology Resources	TR1	0.172	0.000	2.354
	TR2	0.249	0.000	2.619
	TR3	0.124	0.000	1.912
	TR4	0.232	0.000	2.368
	TR5	0.183	0.016	2.187
	TR6	0.115	0.000	2.345
	TR7	0.246	0.000	2.967
Basic Resources	BR1	0.355	0.000	1.968
	BR2	0.421	0.000	2.108
	BR3	0.399	0.000	2.544

indicate that all variance inflation factor (VIF) values were below 3.3, suggesting no multicollinearity issues [83]. Additionally, all outer weights were statistically significant, affirming that each second-order dimension (tangible, human, and intangible resources) contributed uniquely and meaningfully to the formation of the overall AIDC construct. Collectively, these results establish strong reliability, convergent validity, discriminant validity, and formative validity, confirming that the measurement model is robust and suitable for subsequent structural model analysis.

4.2. Structural Model Assessment

Following the confirmation of the measurement model’s reliability and validity, the structural model was assessed to evaluate the hypothesized relationships among constructs. Consistently with the recommendations of Hair et al. [88] and Sarstedt et al. [86], the analysis examined the model’s path coefficients, explanatory power (R²), effect size (f²), and predictive relevance (Q²) to ensure overall model robustness. As shown in

Table 4. Hypothesis testing results.

Relationships	Path Coefficient	t-Statistics	CIs		p-Values	Decision
			2.5%	97.5%
Direct Effect
AIDC → FP	0.310	4.821	[0.176, 0.427]		0.000	Accepted
AIDC → DDC	0.538	9.326	[0.419, 0.647]		0.000
AIDC → OL	0.411	7.629	[0.308, 0.519]		0.000
DDC → OL	0.506	9.087	[0.396, 0.611]		0.000
DDC → FP	0.252	4.310	[0.139, 0.370]		0.000
OL → FP	0.176	2.883	[0.061, 0.301]		0.004
Indirect effect
AIDC → DDC → FP	0.136	3.917	[0.072, 0.207]		0.000	Accepted
AIDC → DDC → OL	0.272	6.622	[0.200, 0.356]		0.000	Accepted
AIDC → OL → FP	0.072	2.547	[0.024, 0.134]		0.011	Accepted
Serial mediation effect
AIDC → DDC → OL → FP	0.048	2.799	[0.017, 0.083]		0.005	Accepted
Interaction effect
AIDC × DDC → FP	−0.041	2.231	[−0.078, −0.006]		0.026	Accepted

, all hypothesized paths were statistically significant, and Figure 2 visually presents the structural model with standardized estimates. The R² values for data-driven culture (DDC = 0.755), organizational learning (OL = 0.775), and firm performance (FP = 0.806) indicate strong explanatory power, exceeding the minimum threshold of 0.50 [87]. The f² values demonstrate that AIDC exerts a large effect on DDC (f² = 0.398), a moderate effect on OL (f² = 0.219), and a smaller yet meaningful effect on FP (f² = 0.105), confirming AIDC’s central influence across the model [94].

Although the R² value for firm performance (R² = 0.806) appears high relative to many behavioral studies, such magnitudes are not unprecedented in tightly specified AI- and analytics-enabled capability–performance models. Recent evidence from AI capability research reports similarly strong explanatory power when technological capabilities are modeled together with closely aligned organizational mechanisms. For example, Neiroukh et al. [4] report an R² of 0.797 for organizational performance when AI capability operates through decision-making processes, suggesting that high R² values may emerge when capability configurations and performance outcomes are tightly coupled. Moreover, prior analytics literature emphasizes that the expected magnitude of R² should be evaluated relative to the phenomenon under investigation rather than against generic benchmarks, particularly in PLS-SEM studies examining capability orchestration and performance outcomes [95]. Nevertheless, the high explanatory power may partially reflect construct proximity between data-driven culture and perceived firm performance, as well as residual common method variance or potential endogeneity; accordingly, the results are interpreted as theoretically grounded associations rather than definitive causal effects.

H1 posited that AIDC is positively associated with FP. The findings support this hypothesis (β = 0.310, p < 0.001), as presented in Table 4 and Figure 2, indicating that firms with stronger AI-enabled capabilities achieve superior performance outcomes. This suggests that AIDC enhances strategic agility, resource efficiency, and innovation capacity, enabling firms to outperform competitors in dynamic markets [3,4,46].

H2 predicted that organizational DDC mediates the relationship between AIDC and FP. The results confirmed a significant indirect effect (β = 0.136, p < 0.001), demonstrating that DDC acts as a conduit through which AI capabilities translate into data-informed decision-making and operational improvement. This finding reinforces the notion that data-oriented cultural norms are essential for realizing the business value of AI implementation [17,50].

H3 examined whether AIDC positively influences OL through DDC. The results (β = 0.272, p < 0.001) provide strong support for this relationship, suggesting that when AI initiatives are embedded within a data-driven culture, they enhance knowledge acquisition, dissemination, and application across the organization. This aligns with prior evidence that cultural mechanisms stimulate organizational learning in digitally transformed environments [58,60].

H4 proposed that OL mediates the relationship between AIDC and FP, and the results (β = 0.072, p < 0.05) confirm this hypothesis. Although the indirect effect is smaller in magnitude, it remains statistically significant, indicating that AI capabilities improve performance partially through enhanced learning routines and absorptive capacities. This finding supports prior studies suggesting that learning-based mechanisms enable firms to transform AI insights into strategic actions [16,33].

H5 suggested a serial mediation effect of DDC and OL in the relationship between AIDC and FP. The analysis revealed a significant serial mediation (β = 0.048, p < 0.05), confirming that AIDC fosters a data-driven culture that promotes organizational learning, ultimately improving firm performance. This sequential pathway demonstrates the cumulative and reinforcing nature of cultural and learning mechanisms in transforming technological capabilities into sustainable performance advantages [18,54].

H6 examined the moderating role of DDC on the relationship between AIDC and FP. The results indicated a negative but significant moderation effect (β = −0.041, p < 0.05), suggesting that although DDC enhances the value realization of AI capabilities, an overly rigid reliance on data-driven decision-making may constrain the flexibility necessary for performance optimization. As illustrated in Figure 3, the interaction effect reveals that the positive relationship between AIDC and FP weakens slightly when DDC is excessively high, indicating that data orientation must be balanced with managerial intuition and contextual judgment. This nuanced finding aligns with prior studies emphasizing that while data-centricity fosters consistency, excessive data dependence may reduce responsiveness in turbulent environments [32,68].

Finally, the predictive power analysis, showing that the model exhibits strong predictive relevance, with all Q² values exceeding zero, confirms substantial out-of-sample predictive accuracy [84]. Collectively, the high R² values, significant path coefficients, and strong predictive relevance confirm that the proposed model effectively captures how AIDC drives firm performance through the combined mechanisms of culture, learning, and adaptive organizational capability.

To further establish the model’s out-of-sample predictive power, this study employed the PLSpredict procedure using a 10-fold cross-validation approach, following the guidelines of Shmueli et al. [96]. The results, reported in

Table 5. PLSpredict Results for Firm Performance Indicators.

Indicator	Predicted Q2	RMSE (PLS-SEM)	MAE (PLS-SEM)	RMSE (LM)	MAE (LM)
FP1	0.438	0.627	0.498	0.650	0.504
FP2	0.438	0.693	0.537	0.731	0.550
FP3	0.458	0.652	0.519	0.707	0.550
FP4	0.432	0.702	0.527	0.771	0.560
OP1	0.440	0.719	0.543	0.767	0.554
OP2	0.441	0.679	0.531	0.716	0.532
OP3	0.465	0.666	0.521	0.696	0.530
OP4	0.393	0.683	0.528	0.698	0.534
OP5	0.297	0.831	0.647	0.869	0.680
OP6	0.354	0.745	0.590	0.794	0.610
OP7	0.423	0.698	0.544	0.732	0.555

Notes: PLSpredict was conducted using 10-fold cross-validation. Positive predicted Q² values indicate out-of-sample predictive relevance. Lower RMSE and MAE values for PLS-SEM compared with the Linear Model (LM) demonstrate superior predictive accuracy.

, show that all predicted Q² values for firm performance indicators were positive (ranging from 0.297 to 0.465), indicating that the model exhibits predictive accuracy beyond naïve benchmarks. Moreover, the prediction errors (RMSE and MAE) generated by the PLS-SEM model were consistently lower than those of the linear model (LM) benchmark across all firm performance items, demonstrating superior predictive performance at the indicator level. This pattern confirms strong out-of-sample predictive validity and supports the model’s suitability for prediction-oriented research in AI-enabled organizational capability and performance contexts, thereby addressing recent methodological expectations in IS and PLS-SEM research [97].

5. Discussion and Implications

5.1. Discussion of Findings

This study provides robust evidence on how AIDC enhances FP through distinct cultural and learning-based mechanisms. The positive and significant direct effect of AIDC on FP (β = 0.310, t = 4.821) confirms that firms capable of integrating, building, and reconfiguring AI-based competencies achieve superior operational and financial outcomes. From an RBV perspective, this finding underscores that AI-related assets become performance-enhancing only when embedded within firm-specific routines, while DCT explains this effect through firms’ ability to sense, seize, and reconfigure opportunities in turbulent environments [38,98].

Beyond the direct relationship, the findings demonstrate that organizational DDC serves as a critical mediating mechanism. AIDC significantly strengthens DDC (β = 0.538, t = 9.326), which in turn positively affects FP (β = 0.252, t = 4.310), resulting in a significant indirect effect (β = 0.136, t = 3.917). This indicates that AI capabilities generate value not automatically but through their institutionalization within data-informed decision-making norms. In line with DCT, DDC functions as a micro-foundation that enables firms to translate AI insights into coordinated action and strategic alignment rather than isolated analytical outputs [13,36,55].

The results further confirm the mediating role of OL in the AIDC–FP relationship. AIDC exerts a significant positive effect on OL (β = 0.411, t = 7.629), and OL subsequently enhances FP (β = 0.176, t = 2.883), yielding a significant indirect pathway (β = 0.072, t = 2.547). This finding reinforces DCT’s assertion that learning underpins firms’ capacity for continuous reconfiguration, enabling organizations to absorb, interpret, and apply AI-generated knowledge to improve performance outcomes [99].

Importantly, the study uncovers a sequential mediation effect, whereby AIDC first fosters DDC, which then strengthens OL, collectively enhancing FP (β = 0.048, t = 2.799). This serial mechanism highlights a process logic in which cultural alignment precedes and facilitates learning-based value creation, demonstrating that AI-driven performance gains emerge from the co-evolution of technology, culture, and learning rather than from isolated capability investments.

The most theoretically distinctive finding is the negative moderation effect of DDC on the AIDC–FP relationship (β = −0.041, t = 2.231, p = 0.026). While DDC generally supports the effective deployment of AI-enabled capabilities, excessively strong data-driven norms appear to weaken the marginal performance benefits of AIDC [56,57]. This result suggests that over-reliance on analytics may induce information overload, algorithmic rigidity, and reduced managerial discretion, thereby constraining rapid reconfiguration in dynamic contexts [100]. Consistently with DCT, effective AI-driven performance therefore requires balance rather than maximization, where data-informed decision-making complements—rather than replaces—managerial judgment and experiential insight.

Taken together, these findings extend the RBV and DCT by demonstrating that AIDC’s performance impact is contingent upon both enabling mechanisms (DDC and OL) and boundary conditions (excessive data orientation). The results position balance between analytics and human judgment as a central condition for realizing AI-enabled performance gains, advancing a more nuanced understanding of how firms can effectively leverage AI in complex and volatile environments.

5.2. Theoretical Implications

This study advances theoretical understanding at the intersection of AIDC, DDC, and OL by integrating insights from the RBV and DCT. Consistently with the RBV [14,27], the findings reaffirm that AI-related capabilities constitute valuable, rare, and difficult-to-imitate organizational resources; however, their performance value materializes only when they are embedded within firm-specific cultural and learning infrastructures. By empirically demonstrating that AIDC alone is insufficient to generate performance gains, this study extends the RBV by highlighting the contingent role of organizational context in unlocking the value of digital resources, thereby moving beyond static assumptions about technology-driven advantage [3,4,98]. This aligns with recent evidence showing that AI value creation depends critically on complementary organizational mechanisms rather than technology deployment per se [36].

From a DCT perspective, this research makes a more substantial contribution by unpacking the micro-foundational mechanisms through which AIDC is transformed into performance outcomes [15,61]. Specifically, AIDC represents the technological foundation that enables sensing and seizing opportunities, DDC provides the cultural infrastructure that institutionalizes data-informed decision-making, and OL ensures the assimilation and reconfiguration of knowledge into actionable routines. The confirmed serial mediation of DDC and OL refines DCT by illustrating a process logic in which cultural alignment precedes and facilitates learning-based reconfiguration, emphasizing that dynamic capabilities evolve through the interaction of technological, cultural, and cognitive processes rather than through technology adoption alone [7,8,39]. Recent studies similarly argue that AI-enabled dynamic capabilities are enacted through learning-intensive and culture-dependent pathways that support continuous adaptation in turbulent environments [58,65].

Importantly, this study also extends both the RBV and DCT by introducing a boundary condition to the assumed positive role of data-driven culture. The negative moderating effect of DDC on the AIDC–performance relationship challenges dominant linear assumptions in the digital transformation literature, which often portrays data orientation as uniformly beneficial [50]. By demonstrating that excessive data-driven norms can constrain managerial discretion and strategic flexibility, the study introduces a balancing logic into dynamic capability theory, suggesting that optimal performance emerges not from maximal data reliance, but from the alignment between analytical discipline and human judgment. This perspective is consistent with emerging research emphasizing that rigid analytics-driven governance may suppress experimentation, intuition, and entrepreneurial action—capabilities that are essential under environmental uncertainty [56,100]. Accordingly, this contribution positions DDC simultaneously as an enabling mechanism and a potential constraint, offering a more nuanced and theoretically mature account of confirming how AI-enabled capabilities shape firm performance in dynamic and turbulent contexts [53,76].

5.3. Practical Implications

The findings of this study offer several actionable implications for managers and organizational leaders seeking to leverage AIDC to enhance firm performance. First, the results clearly demonstrate that investing in AI technologies alone is insufficient to generate performance gains. Managers should therefore focus not only on acquiring AI tools, but also on building the organizational conditions that allow AI insights to be translated into action, particularly through the development of a DDC and OL. Practical initiatives include investing in data literacy programs, aligning incentives with evidence-based decision-making, and ensuring that AI outputs are systematically integrated into strategic and operational routines rather than remaining isolated within technical units.

Second, the findings highlight that data-driven culture is a double-edged managerial instrument. While a strong DDC amplifies the performance benefits of AIDC by fostering analytical discipline and coordination, the negative moderation effect indicates that excessive or rigid data reliance can undermine agility and strategic responsiveness. Managers should therefore avoid dogmatic data dependence and instead promote a balanced decision-making approach in which analytical insights complement—rather than replace—managerial judgment, experience, and intuition. In practice, this means encouraging discretion in time-sensitive decisions, tolerating experimentation even when data is incomplete, and designing governance structures that allow managers to challenge algorithmic recommendations when contextual knowledge warrants it.

Third, the mediating role of OL underscores the importance of institutionalizing learning processes that convert AI-generated insights into organizational knowledge. Managers can operationalize this by establishing cross-functional learning forums, post-implementation reviews of AI-supported decisions, and knowledge-sharing mechanisms that disseminate lessons learned across departments. By embedding AI within continuous learning cycles, firms strengthen their ability to reconfigure routines and adapt to changing environments, consistently with the logic of dynamic capabilities. Overall, the study suggests that the most effective AI strategies are not those that maximize data usage per se, but those that align technological capability, cultural flexibility, and learning-oriented routines to sustain performance over time.

5.4. Limitations and Future Research Directions

Despite its contributions, this study has several limitations that should be acknowledged. First, the cross-sectional research design restricts causal inference, as the relationships among AIDC, organizational DDC, OL, and FP were examined at a single point in time. Accordingly, the reported findings should be interpreted as theoretically grounded associations rather than definitive causal effects. Future research could employ longitudinal designs to capture the temporal sequencing of capability development and performance outcomes, thereby providing deeper insights into how AIDC, DDC, and OL co-evolve over time. In addition, although multiple procedural and statistical remedies were applied, the possibility of residual common method variance cannot be entirely ruled out, reinforcing the need for cautious interpretation.

Second, potential endogeneity concerns may arise from factors such as reverse causality and omitted variables, particularly given that high-performing firms may be more inclined to invest in AI and data-driven practices. While the study’s strong theoretical grounding in the RBV and DCT helps mitigate these concerns conceptually, endogeneity cannot be fully eliminated in cross-sectional survey research. To address this issue, future studies are encouraged to adopt multi-source data collection strategies, instrumental variable techniques, or quasi-experimental approaches, which would allow for more rigorous control of endogeneity and strengthen causal claims. Beyond methodological extensions, future research could explore additional boundary conditions, such as digital leadership or governance mechanisms, to further refine understanding of how AI-enabled capabilities translate into sustained firm performance.