Jul 2026· International journal of computer information systems and industrial management applications· 0 citations
TL;DR
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.
Abstract
Artificial Intelligence (AI) has emerged as a transformative technology that is reshaping organizational processes, strategic decision-making, and competitive advantage across industries. In today's dynamic and highly competitive business environment, organizations must continuously enhance their agility to respond effectively to technological disruptions, changing customer expectations, and market uncertainties. This study examines the relationship between Artificial Intelligence Adoption (AIA) and Organizational Agility (OA) while assessing the influence of Organizational Context (OC) from a management perspective. The research aims to investigate how AI adoption contributes to organizational agility and whether organizational context significantly influences this relationship. The study adopts a quantitative research approach using a structured questionnaire administered to managerial and professional employees across various industries. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to evaluate both the measurement and structural models. The measurement model was assessed through Cronbach's Alpha, rho_A, Composite Reliability (CR), and Average Variance Extracted (AVE), while the structural model was examined using bootstrapping techniques to test the proposed hypotheses. The findings demonstrate that all constructs exhibit excellent reliability and convergent validity, with Cronbach's Alpha values exceeding 0.94, Composite Reliability values above 0.95, and AVE values greater than 0.70. Structural model analysis reveals that Artificial Intelligence Adoption has a significant positive effect on Organizational Agility (β = 0.708, p < 0.001) and Organizational Context (β = 0.962, p < 0.001). Furthermore, Organizational Context significantly influences Organizational Agility (β = −0.378, p = 0.013), indicating that certain contextual organizational characteristics may constrain agility despite increased AI adoption. These 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. The study contributes to the literature by integrating perspectives from the Technology–Organization–Environment (TOE) Framework, Resource-Based View (RBV), and Dynamic Capabilities Theory to explain the role of AI in improving organizational agility. From a practical perspective, the findings emphasize that organizations should complement AI investments with flexible organizational structures, transformational leadership, employee capability development, and innovation-oriented cultures to maximize the benefits of digital transformation. The study concludes that Artificial Intelligence is not merely a technological innovation but a strategic organizational capability that enables firms to achieve sustainable competitiveness and long-term organizational agility in an increasingly digital business environment.
This study investigates how AI adoption enhances organizational innovation capability and, in turn, improves economic, environmental, and social dimensions of business performance, and links digital transformation with sustainability outcomes.
S. P, Sriharan M, S. P et al.· International Journal for Re...· 0 citations
The study concludes that AI adoption serves as a strategic organizational capability that significantly enhances strategic planning effectiveness and suggests that organizations leveraging AI technologies are more likely to develop effective strategies, improve decision quality, enhance forecasting accuracy, and strengthen organizational adaptability.
Mark Ian C. Abrias, Nerissa M. Revilla· World Journal of Advanced Re...· 0 citations
Artificial Intelligence (AI) is increasingly being adopted as a strategic capability within the Indonesian telecommunications industry to support digital transformation, enhance operational efficiency, and achieve long-term sustainability performance amid intensifying competition. However, successful AI adoption is determined not only by technological readiness but also by organizational conditions, environmental pressures, and psychological assurances related to trust and data privacy. This study aims to examine the effects of the Technology–Organization–Environment (TOE) framework, Trust in AI, and privacy assurance on organizational Intention to Adopt AI, as well as their subsequent influence on actual AI use behavior and corporate sustainability performance within the Indonesian telecommunications sector. A quantitative research approach was employed using survey data collected from 231 managerial and technical professionals across telecommunications firms. The data were analyzed using Structural Equation Modeling (SEM) with SmartPLS 4 software. The findings indicate that technological, organizational, and environmental readiness, together with trust and privacy assurance, have significant positive effects on Intention to Adopt AI. Furthermore, Intention to Adopt AI is found to be a strong predictor of actual AI use behavior, indicating that adoption intention translates into practical implementation rather than merely reflecting favorable attitudes. Consistent and integrated AI utilization subsequently contributes significantly to corporate sustainability performance, encompassing economic, operational, and environmental dimensions. These results suggest that the sustainability benefits of AI emerge when contextual readiness and psychological assurance jointly enable organizations to move beyond adoption intention toward sustained AI utilization. This study provides valuable insights for industry practitioners and policymakers in designing AI adoption strategies that support sustainable business performance in emerging telecommunications markets.
This study examines the impact of artificial intelligence (AI) adoption on employee competence and organizational performance, with digital leadership as a moderating variable. Using a quantitative approach with hierarchical moderation regression method, data were collected through structured questionnaires distributed to 210 employees across technology and banking companies in Indonesia. The results indicate that AI adoption significantly and positively affects both employee competence (β = 0.423, p < 0.01) and organizational performance (β = 0.381, p < 0.01). Furthermore, digital leadership significantly moderates the relationship between AI adoption and organizational performance (β = 0.267, p < 0.05), demonstrating that strong digital leadership amplifies the positive effect of AI adoption on performance outcomes. The findings contribute to the human resource management literature by highlighting the strategic importance of cultivating digital leadership capabilities when implementing AI-driven transformations. Organizations are recommended to invest concurrently in AI infrastructure and digital leadership development programs.
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