Jul 2026· Journal of Enterprise Information Management· pp. 1-28· 0 citations· 42 references
TL;DR
This study develops and validates an artificial intelligence (AI) maturity construct grounded in dynamic capabilities theory and resolves a theoretical misspecification in prior maturity models by treating dynamic capabilities theory as the primary mechanism and employing a separately measured reflective AI maturity construct.
Abstract
This study develops and validates an artificial intelligence (AI) maturity construct grounded in dynamic capabilities theory, conceptualizing AI maturity as a reflective construct shaped by three antecedents: organizational readiness (OR), technological deployment (TD) and business model innovation (BMI). Rather than treating AI maturity as a static composite of its dimensions, this study conceptualizes it as an emergent capability that – viewed through a resource orchestration lens (Sirmon et al., 2011) – coordinates sensing, seizing and transforming activities and links them to multidimensional firm performance.
Survey data from 132 small- and medium-sized enterprises (SMEs) across multiple industries in South Korea are analyzed using partial least squares structural equation modeling (SmartPLS 4). AI maturity is operationalized as a reflective construct measured by dedicated items, with OR, TD and BMI specified as antecedent predictors. Digital transformation performance, firm age and firm size are included as control variables. Bootstrapping with 5,000 subsamples is employed for significance testing, and specific indirect effects are estimated via bias-corrected confidence intervals.
Business model innovation emerged as the strongest antecedent of AI maturity, followed by organizational readiness and technological deployment. AI maturity significantly enhances perceived AI-enabled performance outcomes – operational efficiency, market performance and financial stability. Specific indirect effects indicate full mediation through AI maturity for the organizational readiness and business model innovation pathways, whereas the technological deployment pathway is only marginally significant and is therefore classified as inconclusive.
The cross-sectional, single-country design limits causal inference and generalizability. Future research should employ longitudinal, multi-country designs with larger samples to validate the strategy-first antecedent pattern and test boundary conditions across diverse institutional contexts.
The BMI > OR > TD pattern of relative predictive strength is consistent with a strategy-first interpretation under dynamic capabilities theory; however, longitudinal designs are required to validate any temporal sequencing of investments. AI maturity serves as a diagnostic tool enabling managers to identify capability imbalances across the organizational, technological and business model dimensions.
This study advances AI maturity research by grounding AI maturity in dynamic capabilities microfoundations and providing empirical evidence of a strategy-first antecedent pattern in AI maturity formation. It resolves a theoretical misspecification in prior maturity models by treating dynamic capabilities theory as the primary mechanism and employing a separately measured reflective AI maturity construct.
Despite substantial AI technology investment, many firms fail to translate isolated AI applications into integrated capabilities that deliver strategic returns and drive business model changes. Grounded in service-dominant logic (SDL), this study proposes and empirically tests a theoretical framework that positions AI capability (AIC) as a key antecedent in the nomological network of business model innovation (BMI). Drawing on a three-wave, two-week-interval longitudinal survey of 193 Chinese digital-intensive firms across IT, technical services, and digital leasing industries, and employing PLS-SEM, we examine associations among focal constructs, specifically, the mediating role of customer responsiveness (CR) and the moderating effect of digital organizational culture (DOC). This design mitigates common method bias and establishes temporal causal ordering. Empirical results indicate that AIC positively relates to BMI both directly and indirectly through CR, and that DOC significantly enhances the indirect effect of AIC on BMI via CR, particularly under high levels of AI-enabled sensing and interpretation. However, causal inference is limited by the cross-sectional nature of the data and self-reported measures. This study makes three key theoretical contributions. First, we identify CR as a market-oriented mechanism linking AIC to BMI, shifting focus from prior internal efficiency-focused mechanisms to customer-centric value co-creation. Second, we extend SDL to the AI context by clarifying how DOC shapes the strategic transformation of ambiguous probabilistic AI outputs into market-oriented actions. Third, we introduce DOC as an internal boundary condition for AIC, complementing prior research on external environmental moderators. These findings provide actionable guidance for managers seeking to unlock the strategic value of AI investments. Findings reflect statistical associations rather than confirmed causal effects, and results are based on perceptual survey data from Chinese digital firms.
The relationship between artificial intelligence (AI) adoption and firm performance in small and medium-sized enterprises(SMEs) has attracted growing scholarly attention, yet the literature remains theoretically fragmented and dominated by techno-optimistic assumptions that portray AI as a direct pathway to superior performance. This study addresses two central research questions: why do AI adoption outcomes remain heterogeneous across SMEs, and through what organizational mechanisms does AI create business value? A systematic search of the Scopus database, followed by PRISMA-guided screening, yielded 29peer-reviewed articles analyzed through VOS viewer bibliometric mapping and qualitative content analysis. Four thematic clusters were identified: AI Adoption and SME Sustainable Performance; AI Investment and Firm-Level Heterogeneity; Green Intellectual Capital and Innovation; and AI Application and Service Productivity in China. Drawing on these clusters, an integrative Antecedents-Mediators-Moderators-Outcomes (AMMO) framework is proposed. Findings demonstrate that AI adoption outcomes are contingent upon complementary organizational capabilities, knowledge management infrastructure, human capital quality, and institutional context rather than technology deployment alone. Five value-creating mechanism categories are identified: innovation and transformation, human capital and knowledge, operational and productivity, strategic and behavioral, and financial and investment mechanisms. The study contributes by challenging techno-deterministic assumptions, advancing the AMMO framework as a conceptual architecture for future inquiry, and positioning green intellectual capital as an emerging theoretical frontier. Implications for managers, policymakers, and future researchers are discussed.
Ridha Rayan Furqan, W. Adawiyah, Ali Şahin et al.· The International Conference...· 0 citations
Artificial intelligence (AI) is increasingly positioned as a central lever of industrial management, yet a large share of AI initiatives in manufacturing and operations settings fail to move beyond pilot stage. Existing explanations draw mainly on technology-adoption models, which treat AI implementation as a discrete adoption decision rather than as an ongoing organizational process. This conceptual paper addresses that limitation by integrating dynamic capabilities theory with the construct of organizational readiness for change to explain how industrial firms convert AI investment into sustained managerial and operational advantage. Following an integrative conceptual-article approach, the paper compares dynamic capabilities theory against competing lenses (the resource-based view, the technology-organization-environment framework, and technology-acceptance models), justifies an integrative choice, and develops a framework in which the microfoundations of dynamic capabilities (sensing, seizing, and transforming) operate through organizational readiness (change valence, change efficacy, and change commitment) to shape AI-driven industrial management capability and, in turn, operational and managerial performance outcomes. Six theory-grounded propositions are advanced, alongside a construct-definition table and a conceptual model. The framework contributes to the literature by repositioning organizational readiness as a dynamic, renewable capacity embedded within the seizing and transforming microfoundations rather than as a static precondition for adoption, and by offering industrial managers a process-based diagnostic for sequencing AI capability development. Limitations, boundary conditions, and an agenda for empirical validation, including multi-case and survey-based tests, are discussed.
Hoogendijk Ha· Journal of Economic, Finance...· 0 citations
The findings suggest that while AI serves as a strategic capability that enhances organizational responsiveness and innovation, its effectiveness depends on the presence of supportive organizational structures, leadership, and an adaptive culture.
Srinath T. K., Chandana H. S., Sagar Manjunath et al.· International journal of com...· 0 citations
To adopt artificial intelligence (AI) technology or not has become crucial for e-commerce SMEs seeking to enhance their marketing capabilities and competitive advantages in the increasingly digitalized business arena. This study aims to elucidate how organizational and individual determinants converge to influence AI adoption within e-commerce SMEs, and subsequently, how this adoption process orchestrates multi-dimensional marketing agility.
Integrating technology acceptance model (TAM) and dynamic capabilities theory (DCT), we developed a multi-level framework examining the relationships between adoption antecedents, usage behavior and strategic outcomes. Using data collected from 317 marketing managers in Chinese e-commerce SMEs, we leveraged a dual-stage analytical approach, combining PLS-SEM (partial least squares structural equation modeling) and artificial neural networks (ANN) analysis, to test our theoretical framework and reveal linear and non-linear relationships that may be obscured by traditional methods. Marketing agility was operationalized as a second-order construct comprising four dimensions and all measurement scales were adapted from established literature.
The results reveal that technology readiness and organizational support play dominant roles in shaping AI adoption intentions, while the impact of perceived usefulness and ease of use is relatively smaller. Furthermore, AI integration is demonstrated to be critical driver for our marketing agility construct. This study demonstrates the joint influencing mechanism of organizational and individual factors, providing empirical evidence to engage with the micro-foundations debates within the theoretical framework of dynamic capabilities. In practice, it provides an actionable roadmap for SMEs to transform AI potential into tangible marketing capabilities.
Upon the investigation, our contribution lies in empirically testing how individual psychological acceptance scales into organizational strategic agility, providing a non-linear and micro-foundational perspective that best reconciles TAM and DCT for the AI-driven marketing landscape of SMEs. Unlike traditional adoption models that focus solely on the “if” and “when” questions of technology use, this study reveals the “how” roadmap of strategic transformation.
Luoxi Pu, R. Radics, Muhammad Umar et al.· International Journal of Eme...· 0 citations
This study investigates the impact of Industry 4.0 technologies on sustainable performance within the Chinese industrial context. Specifically, it examines the mediating roles of adaptive capabilities, organizational innovativeness, and organizational resilience, alongside the moderating role of managerial cognition, within an integrated conceptual framework grounded in the resource‐based view (RBV) and dynamic capability theory (DCT). A quantitative cross‐sectional survey design was employed, collecting data from 356 managers and executives across Chinese manufacturing and technology‐intensive firms. Structured questionnaires utilizing validated scales from prior literature were administered, achieving a response rate of 29.7%. Data were analysed using partial least squares structural equation modelling (PLS‐SEM) via ADANCO 2.3. All seven hypothesized relationships were empirically supported. Industry 4.0 technologies positively influenced adaptive capabilities and organizational innovativeness, both of which strengthened organizational resilience, which subsequently enhanced sustainable performance. Two serial mediation pathways were confirmed, and managerial cognition significantly moderated the technology‐capability relationships. This study makes an original contribution by integrating serial mediation and moderation within a unified Industry 4.0 resilience framework, specifically contextualized within Chinese industrial firms. The findings offer actionable guidance for managers and policymakers seeking to leverage digital technologies to build organizational resilience and achieve long‐term sustainable performance.