Jul 2026· Journal of Economic, Finance Research and Review· 0 citations
TL;DR
A framework in which the microfoundations of dynamic capabilities operate through organizational readiness to shape AI-driven industrial management capability and, in turn, operational and managerial performance outcomes is developed.
Abstract
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.
This article proposes a competency-based framework for diagnosing organizational change trajectories driven by artificial intelligence (AI) integration. It extends a four-stage typology of AI-enabled organizational models by showing that each stage can be identified through the configuration of employee competencies, independently of technological indicators.
Drawing on structural-configurational, dynamic capabilities and sociomaterial perspectives, the article develops a five-stage competency assessment procedure and a classification matrix across twelve competency areas, and uses vertical coherence gaps and directional asymmetry of renewal to explain transitions between models.
Each organizational model type is associated with a distinct employee competency profile that serves as both a theoretical boundary marker and a diagnostic indicator. Transitions between models are not uniformly linear: their pace and stability depend on governance maturity, regulatory context and the clarity of an organization's strategic purpose regarding AI.
The framework is conceptual and requires empirical validation across sectors.
The framework gives change managers and human resource (HR) practitioners a structured basis for assessing an organization's AI integration stage and mapping competency gaps against a target model.
By foregrounding human competencies, the framework supports a human-centered path of AI adoption that preserves meaningful human agency in increasingly automated work.
This is the first study to link employee competency profiles systematically to discrete AI-enabled organizational model types, connecting stage models of AI adoption with competency management research.
Marek Jabłoński· Journal of Organizational Ch...· 0 citations
The study contributes to information systems research by reframing AI readiness from a static resource inventory to an evolving organisational capability and offers managers a diagnostic logic for sequencing AI investments and avoiding premature scaling.
K. Jonak, Andrzej Wodecki· Discover Artificial Intellig...· 0 citations
Artificial Intelligence (AI) technology has transitioned from an advanced corporate application into an accessible operational utility for contemporary business management. For Small and Medium Enterprises (SMEs), integrating AI capabilities offers substantial potential to optimize internal workflows, enhance decision-making precision, and strengthen market positioning. However, actual AI adoption among smaller enterprises in developing economies remains fragmented due to resource limitations and technical risks. Moving beyond a conventional application of the Technology-Organization-Environment (TOE) framework, this paper proposes an extended theoretical framework that integrates AI-specific trustworthiness and organizational digital maturity as contingency mechanisms. The conceptual model evaluates primary drivers across technological (relative advantage, complexity), organizational (top management support, financial readiness), and environmental contexts (competitive pressure, regulatory support), while incorporating Perceived AI Trustworthiness as a mediating mechanism and Organizational Digital Maturity as a moderating condition. The paper formulates eight testable propositions and details a quantitative methodology based on Partial Least Squares Structural Equation Modeling (PLS-SEM) for a target sample of N=300 SMEs. This framework advances theoretical understandings of technology adoption in emerging economies and offers actionable directives for policymakers and business managers seeking to accelerate digital maturity.
Nor Fazalina Salleh, N. H. Asnawi, Norfazlina Ghazali et al.· International journal of res...· 0 citations
AI capability is a new strategic capability in the organization that goes beyond operational efficiency and can support the quality strategic decision-making, sustainable performance of an organization, and high decision quality. Though AI capability is evolving, current research remains disparate in how to transform an AI capability to a organizational value with the role of governance, leadership, and organizations capability. To solve this, in this study, a integrated conceptual framework grounded in the theory of resource-based view(RBV), dynamic capabilities theory(DCT) and the AI Governance literature is developed and empirically tested. In the model, the sequential relation between AI capability, AI governance, strategic decision quality, organizational agility, and organizational performance was proposed and the moderating role of digital leadership was examined. An explanatory sequential mixed-methods research design was used. The empirical analysis includes two phases. In the first phase, a cross-sectional survey of 446 senior executives and strategic decision makers of public and private organizations was conducted to empirically test the proposed integrated model using Partial Least Squares Structural Equation Modeling (PLS-SEM). In the second phase, qualitative data from 30 semi-structured interviews with senior executives was collected to gain a deep understanding of AI governance, digital leadership and organizational agility practices. Multi-group analysis further revealed differences in the proposed relationships for public and private organizations. Findings revealed that AI capability not only significantly strengthens the AI governance, and consequently the strategic decision quality, but it also improve the organizational agility, resulting in improved performance. Furthermore, digital leadership has a positive effect on reinforcing the association between AI governance and the strategic decision quality. Overall, this study integrates the technology capability, the organizational capability and the leadership capability to establish an AI-enabled strategic decision-making and performance management framework, and provides strategic insights for organizations that aim to realize greater value from their AI investments.
Dareen Alshamsi, Dr. Mohamed Manea Almansoori, Dalal S. Almansoori et al.· Journal of Intelligent Decis...· 0 citations
Artificial intelligence (AI) adoption is accelerating, yet enterprise value remains uneven because technical capability often outpaces organizational redesign, workforce adaptation, and governance maturity. This paper develops an original theoretical model, the AI productivity-governance frontier (PGF), to explain why the same agentic AI capability can generate measurable value in one organization but produce negligible or negative returns in another. Using integrative theoretical modelling, the study synthesizes recent empirical evidence on generative AI productivity, enterprise adoption, AI risk management, labor-market exposure, and prior conceptual work by Kwan Hong TAN on AI-form organizations, AI stakeholder recognition, and temporal displacement-adaptation equilibrium. The resulting PGF model formalizes AI value as the interaction between automation-augmentation gains, learning spillovers, decision velocity, scalability, and institutional absorptive capacity, offset by governance drag, risk externalities, and identity-coordination costs. The paper proposes six testable propositions and a practical maturity pathway moving from experimental AI use to validated autonomy. The central argument is that sustainable AI value does not increase monotonically with either automation intensity or governance intensity. Instead, organizations approach maximum value when they design human-AI work systems that combine use-case fit, accountable autonomy, adaptive reskilling, and proportionate assurance. The contribution is threefold: a formal value equation for enterprise AI, a governance-sensitive interpretation of AI productivity heterogeneity, and an implementation framework for managers, policymakers, and researchers studying AI-enabled business transformation.
K. Tan· International Journal of Sci...· 0 citations
Artificial Intelligence (AI) is reshaping how organizations handle knowledge from the moment they acquire it, to how they generate, validate, integrate, learn from, and put it to work. Research has touched on many of these pieces: AI capability, knowledge management, organizational learning, and innovation, but these areas often feel disconnected in theory. This paper pulls those threads together by building a structured framework for the literature and theory, showing how AI capability fuels innovation via interconnected knowledge processes across the organization.
The approach draws from the Knowledge-Based View (KBV), Nonaka and Takeuchi’s SECI model, Organizational Learning Theory, Dynamic Capability Theory, and recent work on AI capability. Here, AI capability is conceptualized as a higher-order, formative organizational capability. It’s an ensemble of technological infrastructure, data resources, skilled AI professionals, strong coordination and change management, and robust AI governance.
One notable theoretical advance in this work is the introduction of Knowledge Validation and Epistemic Governance. These act as a bridge between AI-powered knowledge acquisition or creation and its integration within the organization. The idea is simple: just because AI creates content doesn’t mean it’s automatically part of the organization’s knowledge. That content must first be checked for accuracy, origin, relevance to context, clarity, bias, and accountability before it’s fully integrated.
The framework presented sees knowledge acquisition and creation as parallel tracks that come together through validation and integration. From there, organizational learning, smarter decision making, and finally, innovation performance follow. It’s not a one-way street, either innovations feedback into new knowledge creation, and what the organization learns helps shape AI capability itself.
The paper offers eight propositions based in theory, and touches on what these mean for future research: how to test these ideas, ways to measure them, the importance of context, and tracking change over time. In sum, this contribution links AI capability to core theories in knowledge and organization, putting human judgment, epistemic governance, and knowledge validation at the center of how organizations learn and innovate with AI.
Vaivaw Kumar Singh· International Journal of Lat...· 0 citations