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.
Abstract
While enterprise artificial intelligence (AI) adoption is crucial for high-quality economic development, many companies have yet to adopt AI in practice. Enterprise quality management (QM), serving as an internalized foundation of standardized processes and data governance, may critically enable AI adoption, yet this relationship remains underexplored. Utilizing panel data from Chinese A-share listed companies (2007–2023), we employ a fixed-effects regression model, supplemented by a series of methods to address endogeneity, including the instrumental variables approach, difference-in-differences, and event studies. Results indicate that QM significantly promotes enterprise AI adoption, which further enhances enterprise performance. This 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. Theoretically, this study extends the literature on the antecedents of AI adoption by identifying enterprise QM as a crucial, yet overlooked, internal driver. Practically, aligning AI integration with established quality frameworks, cultivating leadership with IT expertise, and fostering a supportive environment provide a viable pathway to overcome AI adoption barriers.
It is demonstrated that AI adoption outcomes are contingent upon complementary organizational capabilities, knowledge management infrastructure, human capital quality, and institutional context rather than technology deployment alone.
Ridha Rayan Furqan, W. Adawiyah, Ali Şahin et al.· The International Conference...· 0 citations
As global regulatory pressure on corporate sustainability intensifies, manufacturing enterprises face growing challenges in building effective Environmental, Social, and Governance (ESG) internal control systems. Traditional approaches, reliant on manual data collection and fragmented reporting, are increasingly inadequate for the scale and complexity of modern manufacturing operations. This paper investigates how Artificial Intelligence (AI) technologies can systematically optimize ESG internal control in manufacturing enterprises. Employing a combination of longitudinal case study analysis, literature review, and panel regression, this study uses Prince Holdings (Oji Holdings Corporation) as the primary case and draws on a panel dataset of 15,623 firm-year observations from 3,358 A-share manufacturing companies over 2018–2023. The findings demonstrate that AI adoption is significantly and positively associated with ESG internal control quality (β = 1.051, p < 0.001), with data governance capability identified as a partial mediator and organizational readiness as a positive moderator. Based on these findings, a five-layer AI-ESG optimization model aligned with the Committee of Sponsoring Organizations of the Treadway Commission (COSO) framework is proposed as a replicable blueprint for the manufacturing sector.
Shiyang Chen· Journal of Fintech and Busin...· 0 citations
A This study examines the interplay between environmental, social, and governance (ESG) practices, artificial intelligence (AI) adoption, and financial performance within Saudi Arabia’s financial sector. It investigates whether AI adoption moderates the ESG–performance relationship, reflecting the sector’s ongoing digital transformation under Vision 2030. Drawing on 224 firm-year observations across banks, diversified financials, real estate investment trusts (REITs), and insurance companies, the study employs content analysis of annual reports to identify AI implementation. Panel regression models are used to test the effects of ESG practices on both accounting-based (ROE) and market-based (Tobin’s Q) performance measures, while examining AI’s moderating role. The results reveal that ESG practices significantly enhance accounting-based performance, particularly return on equity, while board size exerts a positive and board independence a negative influence. However, ESG does not significantly affect market-based valuation (Tobin’s Q). Notably, AI adoption negatively moderates the ESG–financial performance link, suggesting short-term challenges in integrating digital transformation with sustainability strategies. This study contributes to literature in three key ways. First, it provides new evidence from financial institutions in a developing economy—Saudi Arabia—where ESG and AI integration remains underexplored. Second, unlike previous research that proxies AI adoption through R&D expenditure, this study captures actual deployment of AI tools in operational activities. Third, it extends the ESG–performance debate by introducing AI adoption as a novel moderating factor. The findings offer actionable insights for managers and policymakers in emerging markets, underscoring the importance of developing organizational capabilities that harmonize AI-driven innovation with ESG principles to foster sustainable long-term value creation.
Fatma Zehri, Raghad A. Alsudays, L. Aladwey· Journal of Risk and Financia...· 0 citations
AI is depicted as a strategic enabler in managerial accounting, with important implications for organizations in emerging markets seeking to leverage AI use for sustainable competitive advantage.
Huthaifa Al-Hazaima, Mohammad Barakat, Mahmoud Mahmoud et al.· Corporate & Business Strateg...· 0 citations
Quantitative measures of management quality point to wide variation in how practices are allocated within firms. We address this question by examining whether a rise in one asset’s strategic importance triggers adoption of management practices that optimize that asset. Our analysis focuses on business websites and the adoption of A/B testing, a data-driven management practice that enables firms to systematically experiment with and improve their websites. We exploit an abrupt, externally driven surge in online consumer activity as a natural experiment to identify the causal effect of a sudden rise in a digital asset’s strategic importance on adoption of this practice. Using novel monthly data on A/B testing adoption for 654,915 firms across 16 countries, we show that this sudden increase in asset importance led to a substantial rise in adoption. Adoption responses are concentrated among firms with stronger pre-existing management, data and analytics capabilities, rather than among larger or more productive firms. This is consistent with complementarities between advanced management practices and existing organizational capabilities. Finally, we find evidence that A/B testing adoption is associated with increases in revenue and labor productivity.
Timothy DeStefano, Richard Kneller, Jonathan Timmis· CESifo working papers· 0 citations
This study examines how AI adoption enablers influence AI-driven innovation, competitiveness and multidimensional firm performance among micro, small and medium-sized enterprises (MSMEs).
Data were collected from 268 Malaysian MSMEs and analysed using partial least squares structural equation modelling. A disjoint two-stage approach was used to model firm performance as a higher-order construct comprising technological, economic and sustainability performance. Necessary condition analysis (NCA) and importance-performance map analysis were also applied to identify performance bottlenecks and managerial priorities.
The results show that environmental conditions and technological readiness are positively and significantly associated with AI adoption, whereas organisational support does not operate as a sufficient adoption driver. However, AI adoption is strongly associated with AI-driven innovation, which in turn strengthens competitiveness and improves firm performance. The NCA further shows that high firm performance requires minimum threshold levels of technological readiness, environmental conditions, organisational support, AI adoption, AI-driven innovation and competitiveness.
Managers should not treat AI adoption as a symbolic digital upgrade. Performance gains depend on converting AI into process, product/service and business model innovation, and then into competitiveness. Policymakers should move beyond isolated AI grants towards coordinated support systems that combine infrastructure, skills development, advisory services, innovation financing and ecosystem confidence.
The study develops the Technology–Environment–Capability for AI Performance (TEC-AIP) Framework, integrating sufficiency and necessity logics to explain AI-enabled MSME performance in an emerging economy.
Mohammad Falahat, R. Thurasamy, Pureheart Ogheneogaga Irikefe et al.· Benchmarking : An Internatio...· 0 citations