Artificial intelligence acceptance in managerial accounting and its strategic impact on financial performance: Evidence from industrial firms in emerging markets
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.
Abstract
The rapid integration of artificial intelligence (AI) into organizational processes has fundamentally altered the landscape of managerial accounting, yet empirical evidence on its behavioral adoption and financial consequences in emerging industrial markets remains limited (Vărzaru, 2022; Secinaro et al., 2024). This study examines AI acceptance in managerial accounting and assesses its strategic impact on the financial performance (FP) of industrial firms listed on the Amman Stock Exchange (ASE) in Jordan. The technology acceptance model (TAM) serves as the theoretical lens through which perceived usefulness (PU), perceived ease of use (PEU), behavioral intention (BI), and actual use (AU) are examined. A quantitative research design was adopted, with data collected from 228 managerial accountants across listed industrial firms. Partial least squares structural equation modeling (PLS-SEM) was employed to test the hypothesized relationships. The results confirm that PEU and PU positively influence BI, which in turn drives AU of AI systems. Furthermore, the AU of AI significantly enhances decision-making (DM) quality, which subsequently improves FP. These findings depict AI as a strategic enabler in managerial accounting, with important implications for organizations in emerging markets seeking to leverage AI use for sustainable competitive advantage.
: This study examines the empirical impact of Digital Business Strategy (DBS) and Artificial Intelligence Adoption Intensity (AIAI) on firm performance (measured via Return on Assets - ROA), together with the mediating role of dislosure-based organizational culture (OCDI). Grounded in the Resource-Based View (RBV) and Dynamic Capabilities Theory, the investigation addresses the "IT productivity paradox" in emerging tech markets by conceptualizing organizational culture as a pivotal internal transformation mechanism. Utilizing a balanced panel dataset of tech-sector firms listed on the Indonesia Stock Exchange (IDX) spanning 2021 – 2025 (25 firm-year observations), variable metrics were constructed using quantitative content analysis of annual reports, sustainability disclosures, and audited financial statements. Panel regression modeling (Fixed Effects and Common Effects) combined with firm-block bootstrap indirect effect testing revealed that both DBS (β = 0.412, p < 0.05) and AIAI (β = 0.385, p < 0.05) exert positive direct effects on organizational culture. Furthermore, organizational culture significantly enhances ROA (β = 0.298, p < 0.05) and partially mediates the relationship between digital strategies, AI adoption, and corporate financial performance. The findings confirm that technological and strategic investments require cultural alignment and organizational capabilities to deliver sustained economic value.
Muhammad Zaki Fauzi Rahman Rahim, M. Asdar, Abdul Rahman Kadir et al.· International Journal of Sci...· 0 citations
Purpose This study aims to examine the impact of integrating artificial intelligence techniques with management accounting methods on the financial reporting quality in Iraqi government banks. It also examines the impact of the Financial reporting quality on stakeholder confidence, and tests the mediating role of financial reporting quality in the relationship between the integration of artificial intelligence and management accounting and stakeholder confidence. Design/Methodology/Approach A structured questionnaire was used to survey 100 employees working in banks such Accountants, bankers, and specialized academics regarding AI and managerial accounting practices, and reporting quality. Quantitative models using structural equation modeling (SEM) were employed to analyze the direct and indirect relationships between the variables. Results The results indicate that integrating AI technologies with management accounting does not necessarily lead to a direct increase in trust in banks. Rather, this is achieved through high-quality financial reporting, which acts as an intermediary between technological advancement and trust building. While banks strive to generate strong internal key performance indicators (KPIs) and functional accuracy, these should be disseminated clearly to gain the trust of customers and investors. Practical Implications The researchers’ findings, which focus on integrating artificial intelligence with management accounting, do not directly increase trust in banks. Rather, this effect is mediated by high-quality financial reporting. Therefore, it is essential for the studied government banks to focus on producing accurate and clear performance indicators and publishing them transparently to enhance their credibility and gain the trust of customers and investors. Originality/Value This study makes a pivotal contribution to academic literature and adds to the limited body of research on the integration of artificial intelligence, management accounting, financial reporting, and trust in banks.
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
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
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.
Artificial intelligence (AI) is used in logistics, but the mechanisms linking AI utilization to firm performance remain insufficiently differentiated. Drawing on the information technology business value perspective and dynamic capabilities theory, this study examines whether managers’ perceptions of logistics-oriented AI utilization are associated with perceived firm performance through innovation capability and logistics efficiency, with managerial support treated as a secondary boundary condition. Cross-sectional survey data from 254 middle- and senior-level managers in Chinese logistics firms were analyzed using IBM SPSS Statistics 27 and IBM SPSS Amos 29 (IBM Corp., Armonk, NY, USA), and the PROCESS macro version 4.2 (Andrew F. Hayes, Calgary, AB, Canada), with Model 83 and 5000 bootstrap samples. Perceived AI utilization was positively associated with innovation capability, logistics efficiency, and perceived firm performance. Both mediators showed significant indirect effects, and their sequential indirect effect was also significant. The two individual indirect effects did not differ significantly, but both exceeded the sequential indirect effect. The proposed sequential, reverse-sequence, and parallel-mediation models produced identical fit indices, whereas the restricted direct-effects model showed weaker fit. Neither the AI utilization–managerial support interaction nor the moderated mediation indices was significant. Exploratory item-level analyses showed differentiated associations for demand forecasting and order allocation and for AI infrastructure; the pattern remained stable among 194 respondents involved in AI- or digital transformation-related activities. Innovation capability and logistics efficiency appear to function as complementary mechanisms, with a smaller capability-to-process pathway. Their relative ordering cannot be determined from the cross-sectional data. As the data are self-reported, the findings represent associations among managerial perceptions rather than objective causal effects. Sustainability implications are limited to operational efficiency because environmental outcomes were not directly measured.
