How does AI accelerate firm innovation? The mediating role of resource reconfiguration

Artificial intelligence, dynamic capabilities, and innovation resilience: The contingent roles of market competition intensity and technological turbulence

Supported by government policies, the innovation of China’s solar photovoltaic industry has been triggered greatly. As an important part of public policy, various talent policies have been issued to increase support for innovation. Therefore, it is essential to study the relationship between talent policy and firm innovation of Chinese solar photovoltaic industry. However, there is less empirical research on this topic. In this context, this study examined whether talent policy influences firm innovation. To examine the underlying causal mechanism, this study explored the role of human capital quality structure and human capital function structure in the relationship between talent policy and firm innovation. Based on the buffering and bridging mechanism of resource dependence theory, an empirical model of talent policy, human capital structure and firm innovation were conceptualized. Using an unbalanced panel data of 101 listed firms of the solar photovoltaic industry in China from 2008 to 2021, the random effect GLS regression was employed to empirically test the impact of talent policy on firm innovation, the mediating effects of human capital quality structure and human capital function structure, and examined the heterogeneous impact of nature of property rights and R&D intensity on the above relationships. The results show that talent policy can significantly promote firm innovation. Human capital structure plays mediating effects on the relationship between talent policy and firm innovation. Moreover, the talent policy and human capital structure’s effects on firm innovation are more significant in non-state-owned firms and firms with low R&D intensity. This study provides an important reference for promoting firm innovation through human capital structure in the construction of the talent policy.

While prior research has examined the role of smart technologies (e.g., IoT and AI) in sustainability, the combined influence of IoT, AI, and organizational capabilities on hotel sustainable performance, particularly through the mediating roles of data-driven decision-making and innovation capability, remains underexplored. This study investigates how the integration of smart technologies, specifically the Internet of Things (IoT) and artificial intelligence (AI), as well as dynamic managerial capabilities focusing on data-driven decision-making (DDM) and innovation capability (IC), enhances hotel sustainable performance (HSP) within the context of Saudi Arabia’s hospitality sector. Grounded in the Resource-Based View (RBV) and Dynamic Capabilities Theory (DCT), the research develops and tests a conceptual model that explores both the mediating roles of DDM and IC in the link between IoT and HSP and the moderating role of AI application in the relationships between IoT and DDM, IC, and HSP. Using data collected from 312 managers of four- and five-star hotels across Saudi Arabia, the study employed Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine the hypothesized relationships. The results reveal that IoT has a significant positive effect on HSP, DDM, and IC. Further, the IoT-HSP relationship is partially mediated by both DDM and IC. Furthermore, AI significantly strengthens the relationships between IoT and DDM, IoT and IC, and IoT and HSP, highlighting AI’s crucial role as an enabler of digital transformation and sustainability. The findings extend the RBV and DCT by demonstrating how technological resources, when combined with dynamic managerial capabilities, lead to superior sustainability outcomes. Practically, the study emphasizes that hotels must pair digital adoption with employee training, innovation culture, and AI-powered analytics to enhance HSP.

Artificial Intelligence (AI) has emerged as a strategic resource with the potential to enhance organizational performance in the financial services sector; however, empirical evidence on its performance outcomes remains inconclusive, particularly in emerging economies. This study examined the influence of artificial intelligence as a strategic resource on organizational performance in Kenya’s financial services sector, with specific focus on the mediating role of dynamic capabilities. The study was guided by six objectives: to assess the effects of AI-driven customer engagement, AI-powered risk management and fraud detection, AI-enabled process automation, AI-supported strategic decision-making, the joint effect of AI dimensions, and the mediating role of dynamic capabilities on organizational performance. The study was anchored on the Resource-Based View (RBV) and Dynamic Capabilities Perspective (DCP), supported by the Technology–Organization–Environment (TOE) framework and the Knowledge-Based View (KBV). A positivist research philosophy was adopted, employing a descriptive and explanatory cross-sectional survey design. Primary data were collected using structured questionnaires from 263 managers and technical staff drawn from commercial banks and FinTech firms operating in Kenya, achieving a response rate of 94.3%. Data were analyzed using SPSS through descriptive statistics, reliability and validity tests, correlation analysis, multiple regression analysis, and mediation analysis. Reliability was confirmed with Cronbach’s alpha coefficients exceeding the 0.70 threshold, while diagnostic tests confirmed compliance with regression assumptions. The findings revealed that AI-driven customer engagement, AI-powered risk management and fraud detection, AI-enabled process automation, and AI-supported strategic decision-making each had a positive and statistically significant effect on organizational performance (p < 0.05). These effects manifested through improved customer satisfaction and retention, reduced fraud losses, enhanced regulatory compliance, lower operational costs, improved efficiency, and stronger data-driven decision-making. Further analysis showed that the joint effect of AI dimensions was statistically significant and stronger than individual effects, confirming the presence of synergistic performance gains from integrated AI deployment. Mediation analysis established that dynamic capabilities, sensing, seizing, reconfiguring, and learning, significantly mediated the relationship between joint AI parameters and organizational performance. The study concludes that while AI adoption enhances organizational performance in Kenya’s financial services sector, sustainable performance gains depend on the development of strong dynamic capabilities. The findings validate the applicability of RBV and Dynamic Capabilities Theory in explaining AI-driven performance in emerging economies. The study recommends integrated AI strategies, continuous workforce reskilling, strengthened organizational learning systems, and robust AI governance frameworks. At the policy level, supportive AI regulations, investment in digital infrastructure, and regulatory sandboxes are recommended to promote responsible AI adoption.

Objective: Artificial Intelligence (AI) is a radically new force that is changing organizational strategies in the rapidly changing digital world, especially in developing economies such as Iraq. The paper examines the connection between the capabilities of AI and digital innovation and organizational performance in the context of Iraqi organizations in the face of infrastructural, economic, and cultural limitations. Basing the study on the Resource-Based View and Dynamic Capabilities Theory, a conceptual model is constructed and operates that the digital innovation by AI is associated with strategic transformation and performance results. Method: The research utilizes Structural Equation Modeling (SEM) in testing its hypotheses through the use of a quantitative cross-sectional study with data provided by 200 organizations. Results: Findings indicate that AI potentials have a great impact on promoting digital innovation, which, consequently, mediates the connection between AI and organizational performance. The results show that digital innovation is a key tool to utilize AI technologies in enhancing strategic agility and performance in the face of a turbulent environment. Novelty: The study does not only fill the existing gaps in empirical research in the Middle East but also provides practical implications to policymakers and managers interested in promoting innovation driven by AI. It has been suggested to invest in AI, become digitally literate, implement smart systems, and have an innovation-oriented organizational culture. Finally, the paper ends with the future research directions, which would help to contextualize the AI integration in the countries with emerging economies even better.

Purpose This study aims to examine the relationship between employee creativity and employee-oriented human resource management (EOHRM) in artificial intelligence (AI)-enabled workplaces. It examines the mediating role of AI–employee collaboration (AEC), playful work design (PWD) and the moderating role of AI empathy, based on the resource-based view (RBV) and dynamic capability theory. Design/methodology/approach In total, 302 workers in Vietnam’s aviation sector participated in a quantitative, cross-sectional study. To evaluate a moderated mediation model, data were examined using partial least squares structural equation modeling (PLS-SEM). Findings The results demonstrate that EOHRM significantly enhances employee creativity. This relationship is mediated by AEC and PWD, suggesting that these mechanisms are important pathways by which EOHRM promotes creative outcomes. Furthermore, AI empathy moderates these mediating effects, amplifying the positive influence of EOHRM on creativity when AI systems exhibit empathetic traits. Practical implications Organizations that want to foster innovation in an AI environment should prioritize EOHRM practices that build trust and psychological safety. Managers should design AI systems that facilitate collaboration and incorporate empathy features to support positive human–AI interactions. Encouraging creative job design can further enhance employees’ intrinsic motivation to innovate. Originality/value This study integrates the human resource management and AI literatures by conceptualizing EOHRM as both a strategic resource and a dynamic capability in fostering innovation. This study provides new insights into how sociotechnical systems can be designed to sustain innovation in highly regulated and technology-intensive industries.

The fast development of artificial intelligence (AI) is shifting the environment in which the visual arts organizations are organized and operate, as more and more of them implement the digital technologies in order to become more creative, efficient, and appealing to their audience. Nevertheless, even with the increased topicality of AI, its implementation in the arts industry is still sporadic and mostly limited to separate functional areas. This paper fills this gap by introducing the Intelligent Canvas framework, a three-dimensional conceptual framework integrating AI into four main pillars of an organization, namely human resource management, marketing, legal governance, and financial management. Based on the established theoretical lenses, such as the Resource-Based View, the Dynamic Capabilities Theory and the models of technology adoption, the paper builds a comprehensive picture of how AI can be used as an enabling and integrative process. The framework also focuses on the use of AI to improve talent management, customized marketing, the protection of intellectual property as well as in making financial decisions alongside the significance of cross-functional integration and ethical considerations. The paper adds to the literature by applying AI studies to the under-researched domain of visual arts organizations and providing a multi-dimensional solution to the digital revolution. The suggested model offers important implications to the researchers and practitioners who would like to use AI to achieve sustainable expansion, innovation and organizational results in the creative industry.

Purpose This study aims to analyze the impact of artificial intelligence (AI) on business innovation in Indian startups across multiple sectors, including Fintech, Healthcare, Retail, Manufacturing and EdTech. By employing a sector-specific approach, the research aims to evaluate how AI adoption promotes innovation, enhances competitive advantage and contributes to improved business performance. Design/methodology/approach The quantitative study uses Structural Equation Modeling for empirical validation. A cross-sectional survey was done among 279 Indian company founders, managers and AI leaders. Data was collected using stratified sampling to represent Fintech, Healthcare, Retail, Manufacturing and EdTech. We collected responses using online questionnaires and organized interviews. AI adoption, innovation and commercial success are quantified. Findings The study expects to show that AI adoption boosts business innovation, enhancing product, process and business model advancements. AI-driven startups likely gain competitive advantages through improved innovation capabilities and digital readiness. Findings may confirm AI’s varied impact across industries, with Fintech and Healthcare leading in adoption, while Manufacturing and EdTech exhibit emerging trends. Research limitations/implications The study focuses on Indian startups, limiting the generalizability to other regions. Data is collected at a single point in time, which may not capture long-term AI adoption effects. Future research should explore longitudinal studies and global comparisons to enhance applicability. Originality/value As the analysis is based on the Resource-Based View, Disruptive Innovation Theory and Dynamic Capabilities Theory, this research demonstrates the impact of AI on business innovation and the advantages of Indian startups, using real-world examples. The knowledge provided will enable policymakers and strategists to maximize their use of AI for ongoing growth and competitiveness.

The introduction of Artificial Intelligence (AI) has altered the strategic aspect of financial management with the use of digital economy. The paper talks about how analytical solutions induced by AI enhance the strategic financial decision-making and business model change process in digitally oriented systems. Based on the quantitative theoretical rationale, the paper makes explanations regarding the relationship between AI adoptions, decision making effectiveness, strategic agility, cognitive fit, and financial performance without utilizing the empirical data. The argument is a synthesis of the concepts of Technological Determinism, Resource-Based View (RBV), Dynamic Capabilities Theory (DCT), Cognitive Fit Theory (CFT) to propose an integrated perspective of the strategic impact of AI. These findings have demonstrated that AI is an emerging capability, which supports accuracy in decision-making, greater organizational receptiveness and information-driven business frameworks. Besides, an effective integration of strategies between human managers and AI systems is determined to be dependent on cognitive alignment. The results of the research come to the conclusion that the application of AI in financial decisions is not only the technological improvement but also the strategic requirement to remain competitive in the digital age.

Digital transformation and artificial intelligence (AI) are redefining how organizations attract, develop, and retain talent, challenging the traditional foundations of human resource management (HRM). In Malaysia’s Electrical and Electronics (E&E) manufacturing sector, a key engine of export and innovation, engineers are vital to competitiveness, yet their retention has become increasingly difficult amid automation, evolving skill demands, and growing concerns over AI’s ethical use. This conceptual study revisits talent management and engineer retention through an integrated multitheoretical lens combining the Resource-Based View (RBV), Dynamic Capabilities Theory (DCT), and Human-Centric Artificial Intelligence (HCAI). It advances a Human–AI Synergy Framework that links AI-driven Talent Management Practices (AITMP), such as recruitment analytics, adaptive learning, predictive retention, and data-informed performance management, to Employer Branding (EB) as a mediating mechanism that enhances engineers’ Intention to Stay (ITS). The relationship is moderated by HCAI principles of fairness, transparency, explainability, and privacy, and enabled by Dynamic HR Capabilities (DHC) that allow organizations to sense, seize, and reconfigure talent systems in response to digital change. Practically, the study offers actionable insights for Malaysia’s E&E companies, including skills personalization, ethical AI governance, augmented leadership, and the infusion of “technological empathy” into employer branding. Conceptually, it positions AI-enabled HRM as both a strategic asset and a moral commitment to human dignity and sustainable retention. The paper concludes with a structured agenda for empirical validation through PLS-SEM mediation and moderation analysis, longitudinal transformation studies, and cross-ASEAN comparative research on AI trust, ethics, and workforce engagement.

Based on dynamic capability theory, this study investigates the mechanism through which artificial intelligence (AI) adoption affects firm Using panel data from 634 Chinese listed manufacturing firms during 2018-2023, we find that: (1) AI adoption has a significant positive effect on firm innovation performance; (2) organizational learning capability partially mediates the relationship between AI adoption and firm innovation performance. positive effect on firm innovation performance; (2) organizational learning capability partially mediates the relationship between AI adoption and innovation performance; (3) environmental dynamism positively moderates the relationship between AI adoption and organizational learning capability, such that the positive effect of AI adoption and organizational learning capability is not only on firm innovation performance, but also on firm innovation performance. capability, such that the positive effect of AI adoption on organizational learning capability becomes stronger when environmental dynamism is higher. This study not only enriches the application of dynamic capability theory in the context of digital transformation but also provides important implications for firms to implement AI. This study not only enriches the application of dynamic capability theory in the context of digital transformation but also provides important implications for firms to implement AI strategy and enhance innovation capability.

Artificial Intelligence (AI) is reshaping decision-making in the travel and tourism industry, enhancing efficiency, customer engagement, and strategic planning. However, AI adoption remains fragmented, with limited research on firm-level enablers. This study examines AI adoption in decision-making among Malaysian tourism firms using the Comprehensive Theory of Use and Adoption of Technology (CTUAT), Resource-Based View (RBV), and Dynamic Capabilities Theory. A survey of 343 mid-to-top-level managers was analyzed using SmartPLS-SEM to assess the influence of strategic orientation, organizational resources, employee readiness, and innovativeness on AI adoption, alongside the mediating role of performance effectiveness and the moderating effect of technological turbulence on competitive advantage. Findings reveal that strategic orientation, organizational resources, and employee readiness significantly drive AI adoption, while top management support has no direct impact, challenging conventional technology adoption models. AI adoption enhances competitive advantage, but technological turbulence weakens this relationship, highlighting the need for adaptive AI strategies. The study advances firm-centric AI adoption frameworks, emphasizing organizational agility and digital transformation. Practically, firms must align AI investments with strategic goals, enhance workforce AI readiness, and adopt flexible governance frameworks. Policymakers should foster AI-friendly regulations and financial incentives to support adoption, particularly among SMEs.

Purpose: The aim of the study was to evaluate the impact of artificial intelligence on supply chain efficiency in Turkey. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Artificial intelligence (AI) has significantly enhanced supply chain efficiency in Turkey. AI applications such as predictive analytics, demand forecasting, and optimization algorithms have streamlined operations, reduced costs, and improved decision-making. Automation of repetitive tasks through AI has increased productivity and accuracy in inventory management and logistics. Unique Contribution to Theory, Practice and Policy: Resource-based view (RBV) theory, dynamic capabilities theory & technology acceptance model (TAM) may be used to anchor future studies on the impact of artificial intelligence on supply chain efficiency in Turkey. Invest in continuous training and development programs for supply chain professionals to effectively utilize AI tools. Develop industry standards and regulatory frameworks for the ethical use of AI in supply chains. These policies should address data privacy, security, and the responsible use of AI technologies to protect stakeholders.

The aim of the study was to analyze the role of artificial intelligence and machine learning in optimizing supply chain processes Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources, preferably because of its low-cost advantage as compared to field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Artificial intelligence (AI) and machine learning (ML) play a pivotal role in optimizing supply chain processes by enhancing demand forecasting accuracy through sophisticated algorithms. They streamline inventory management by predicting demand patterns and automating replenishment tasks, leading to reduced stockouts and excess inventory. AI- powered analytics enable real-time insights into supply chain performance, identifying bottlenecks and inefficiencies for proactive decision-making. Unique Contribution to Theory, Practice and Policy: Theory of technology acceptance model (TAM), resource-based view (RBV) theory & dynamic capabilities theory may be used to anchor future studies on analyzing the role of artificial intelligence and machine learning in optimizing supply chain processes. Encourage supply chain stakeholders to adopt blockchain solutions for enhanced transparency, traceability, and efficiency. Advocate for regulatory frameworks that promote the adoption of blockchain technology in supply chains while addressing concerns related to data privacy, interoperability, and standardization. Keywords Demand Forecasting, Resource utilization, Reduce costs, Minimize errors, and Increase Productivity, Artificial Intelligence, Machine Learning, Inventory, Transportation, Warehousing

Nowadays, supply chains have evolved into highly complex networks that are becoming ever more interdependent, unpredictable, and vulnerable to disruptions. Their complexity stretches traditional SCM models, requiring the use of more brilliant and reactive systems. An enabling technology that transforms, Artificial Intelligence (AI) provides solutions in predictive intelligence, automation, real-time tracking, and intelligent decision-making. This paper consolidates recent research to investigate how AI technologies are reshaping global SCM. I then explore the digitalization of the supply chain, the main AI technologies, and the ethical considerations. Building on a foundation established from the Resource-Based View (RBV) and Dynamic Capability Theory (DCT), this paper contextualizes AI's strategic importance. It details AI's potential to increase the accuracy of demand forecasting, reduce operational costs, and improve resilience, but recognizes data quality, enormous upfront implementation costs, and algorithmic transparency as obstacles. This has to be concentrated in the (research and practice) in ethical frameworks, human-AI cooperation, and SME inclusivity as key contributing fields to ensure future digital transformation.