The findings indicate that AI capability maturity is positively associated with Human-AI Integration, which in turn is positively associated with Marketing Agility and Competitive Performance, and Data Governance Quality positively moderates the relationship between Human-AI Integration and Marketing Agility, highlighting governance as an important enabling condition.
Abstract
Artificial intelligence (AI) is increasingly embedded in marketing decision-making, yet it remains unclear whether AI itself constitutes a source of sustained competitive advantage. Drawing on Resource-Based Theory and the dynamic capabilities perspective, this study examines whether AI capability maturity is associated with competitive performance through complementary organizational and socio-cognitive mechanisms. Using survey data from 312 marketing and digital leaders and analyzing the data through partial least squares structural equation modeling (PLS-SEM), the findings indicate that AI capability maturity is positively associated with Human-AI Integration, which in turn is positively associated with Marketing Agility and Competitive Performance. A significant serial mediation effect suggests that the relationship between AI capability maturity and competitive performance operates primarily through layered capability development rather than technology possession alone. Furthermore, Data Governance Quality positively moderates the relationship between Human-AI Integration and Marketing Agility, highlighting governance as an important enabling condition. The study extends Resource-Based Theory to intelligent systems by demonstrating that AI-enabled value creation is associated with complementary integration and adaptive capabilities rather than technological resources alone. From a psychological perspective, the findings highlight the importance of trust calibration, interpretability, reliance behavior, decision confidence, and Human-AI collaboration in AI-assisted decision-making. Managerially, the results suggest that organizations should invest not only in AI technologies but also in integration routines, governance mechanisms, and agile marketing processes to maximize the potential benefits associated with AI-enabled decision-making.
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
Digital marketing has evolved from a functional promotional activity to a strategic capability
that shapes competitive advantage in data-intensive markets. This conceptual paper examines
how digital marketing analytics functions as a core strategic tool, enabling firms to enhance
strategic decision-making, develop dynamic capabilities, and foster customer engagement.
Drawing on the resource-based view and dynamic capabilities theory, the study proposes an
integrative conceptual framework linking digital marketing analytics capability, strategic
decision-making quality, dynamic capabilities, customer engagement, and competitive
advantage. Customer engagement is identified as a key mediating mechanism through which
analytics-driven strategies translate into sustained value creation. The paper also discusses
managerial and policy implications, highlighting the importance of analytics capability
development, data-driven culture, ethical governance, and regulatory support. By providing a
theoretically grounded framework, the study offers a foundation for future empirical research on
data-driven marketing strategies and competitive performance in both developed and emerging
markets.
Alexandra Idaere Korubo-Fi· IIARD International Journal...· 0 citations
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.
Luoxi Pu, R. Radics, Muhammad Umar et al.· International Journal of Eme...· 0 citations
Positive effect of QM on AI adoption is amplified by high innovation sustainability and chief executive officers (CEOs) with IT backgrounds, and is particularly pronounced in large, non-state-owned firms within highly competitive industries and the eastern regions of China.
The role of artificial intelligence (AI) in project evaluation, capital allocation, and sustainability performance has gained attention, yet the firm-level channel through green investment remains underexplored. This study examines whether AI-driven decision capability increases green investment intensity and whether such investment improves environmental sustainability and financial performance under the moderating role of governance quality. The analysis uses panel data for 750 firms from 2015 to 2024 and applies descriptive statistics, panel regression, mediation and moderation tests, dynamic robustness checks, and machine learning models. The results show that AI-driven decision capability significantly increases green investment intensity. Green investment positively affects environmental sustainability and financial performance and mediates the link between AI capability and firm outcomes. Governance quality strengthens the effect of green investment on both outcomes. The findings indicate that AI creates sustainable value through strategic capital allocation, especially when firms maintain effective governance mechanisms.
M. Qamruzzaman, A. Almulhim, Syed Nazmus Sakib et al.· Journal of Intelligent Decis...· 0 citations
It is demonstrated that innovation efficiency emerges through a sequential organizational transformation process rather than directly from AI Leadership, and through a multistage transformation process involving capability development and workforce adaptation.
Vuk Mirčetić, Aleksandar Ignjatović Pertini, Stefan Milojević et al.· Administrative Sciences· 0 citations
