Artificial intelligence and organizational resilience: the mediating role of agility, innovation, and digital leadership

Purpose This study investigates the complex interplay between technological and ethical factors influencing artificial intelligence (AI) adoption in entrepreneurship and startup ecosystems, with a particular focus on how these dynamics impact innovation outcomes and organizational performance. Design/methodology/approach Employing a comprehensive analytical framework, the research examines quantitative data to assess the relationships among technology related factors (such as interactivity, relative advantage, and perceived intelligence), ethical principles (including fairness, accountability, transparency, accuracy, and autonomy), ethical dilemma, and their collective influence on AI adoption and exploitative innovation within entrepreneurial contexts. Data was collected using a self-administrated questionnaire to 207 respondents, in the Iran entrepreneurship and startup ecosystem. The Partial Least Square-Structural Equation Modeling (PLS-SEM) technique was used to examine the proposed hypotheses of the study. Findings The findings reveal that technology related factors specifically interactivity, relative advantage, perceived intelligence, transparency, and accuracy significantly drive AI adoption among entrepreneurs. In contrast, ethical considerations such as fairness, accountability, and autonomy do not exhibit a direct influence on adoption decisions. Also, the moderating relationship of ethical dilemma between exploitative innovation and organizational performance by AI adaptation was rejected. Notably, the study highlights the pivotal mediating role of exploitative innovation, AI adoption enhances exploitative innovation, which in turn improves organizational performance; however, there is no direct relationship between AI adoption and organizational performance. Practical implications Entrepreneurs and startup leaders should prioritize AI technologies that offer clear interactive capabilities, relative advantages, and transparent, accurate operations to maximize adoption and performance benefits. While ethical principles remain important, their influence may be more pronounced at later stages of implementation or in highly regulated sectors. Policymakers and ecosystem builders are encouraged to focus on fostering environments that support the practical integration of AI, particularly in ways that enhance exploitative innovation and organizational scalability. Originality/value This research provides novel insights by disentangling the relative importance of technological versus ethical factors in AI adoption within entrepreneurial settings. It advances the literature by empirically demonstrating the limited direct impact of certain ethical considerations on adoption decisions and by highlighting the central role of exploitative innovation as a mediator between AI implementation and organizational outcomes.

This study investigates the impact of Artificial Intelligence (AI) adoption on the sustainable performance of small and medium-sized enterprises (SMEs). Employing a hybrid quantitative approach, this research combines Partial Least Squares Structural Equation Modeling (PLS-SEM) and Artificial Neural Networks (ANN) to examine the influence of various organizational, technological, and external factors on AI adoption. Key factors considered include top management support, employee capability, customer pressure, complexity, vendor support, and relative advantage. Data collected from 305 SMEs across multiple sectors were analyzed. The results reveal that all the proposed factors significantly and positively affect AI adoption, with top management support, employee capability, and relative advantage being the most influential predictors. Additionally, the adoption of AI technologies substantially enhances the economic, social, and environmental performance of SMEs, reflecting improvements in operational efficiency, cost reduction, and social value creation. The ANN results confirm the robustness of the SEM findings, highlighting the critical role of AI in driving sustainability outcomes. Furthermore, the study emphasizes the positive mediation effects of AI adoption on organizational performance, indicating that AI adoption serves as a key enabler in achieving both short-term operational gains and long-term sustainability objectives. This research contributes to the understanding of AI’s transformative role in enhancing the sustainable performance of SMEs in developing economies, offering strategic insights for both policymakers and business leaders.

This study examines the determinants of Artificial Intelligence (AI) adoption and its influence on employee engagement in Egyptian organizations, utilizing the Technology–Organization–Environment (TOE) framework. Based on survey data from 210 professionals across various industries and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM), the findings reveal that relative advantage, compatibility, and top management support significantly drive AI adoption. In contrast, complexity, security/privacy concerns, organizational readiness, competitive pressure, and external and government support do not show significant effects. Furthermore, AI adoption demonstrates a positive but modest impact on employee engagement, suggesting the presence of other contributing factors. These results reflect the unique socio-economic conditions in Egypt, where internal organizational factors outweigh external influences in shaping technology adoption. The findings have practical implications for organizations and policymakers, emphasizing the need to prioritize internal drivers—such as leadership commitment, perceived benefits, and system compatibility—when promoting AI adoption. Additionally, organizations should align AI initiatives with employee needs and workplace culture to enhance engagement outcomes.

Purpose This study investigates the individual-level determinants influencing the adoption of Artificial Intelligence (AI) in the accounting profession within an emerging economy context. It explores how perceptions such as usefulness, ease of use, trust, threat, and susceptibility shape AI adoption, and examines the mediating role of accounting profit. The research is grounded in an extended Technology Acceptance Model and contextualized for Bangladesh to address the gap in AI adoption literature in developing countries. Design/methodology/approach A structured online survey was administered to 478 accounting professionals across various sectors in Bangladesh. The questionnaire included established measures adapted from prior studies and was analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The model assessed both direct and mediated paths from individual perceptions to AI adoption, with accounting profit as the mediating variable. Bias testing, robustness checks, and validity assessments were also conducted to ensure reliability of the findings. Findings Perceived usefulness, trust in AI, and perceived threat significantly influenced AI adoption. Perceived ease of use and perceived susceptibility did not show direct significance. Accounting profit was found to mediate the relationships between perceived usefulness, perceived threat, and trust with AI adoption. These results indicate that while individual perceptions matter, financial feasibility plays a critical role in actual adoption decisions within accounting contexts in emerging economies. Practical implications Accounting firms and policymakers in emerging markets must prioritize financial readiness alongside technology training. To enhance AI adoption, strategies should focus on building trust, communicating the tangible value of AI, and ensuring that AI investments align with profit goals. The findings also suggest firms should promote perceived usefulness and address risk concerns when introducing AI into accounting systems. Social implications The study highlights the socio-economic barriers that affect technology diffusion in emerging economies. As accounting professionals face trust and threat perceptions, along with resource limitations, the integration of AI requires institutional support, inclusive digital training, and clear cost-benefit communication. Broader AI adoption could enhance transparency, efficiency, and job transformation in financial services if these conditions are addressed. Originality/value This study offers a novel integration of individual-level behavioural constructs and financial performance (accounting profit) to explain AI adoption in accounting. By focussing on Bangladesh, it extends Technology Acceptance Model literature into a highly relevant yet underexplored emerging market context. The research also contributes to accounting technology literature by showing how strategic perceptions and profitability jointly shape innovation decisions in the profession.

Aim/Purpose: This paper aims to investigate and understand the intentions of management undergraduate students in Hangzhou, China, regarding the adoption of Artificial Intelligence (AI) technologies in their education. It addresses the need to explore the factors influencing AI adoption in the educational context and contribute to the ongoing discourse on technology integration in higher education. Background: The paper addresses the problem by conducting a comprehensive investigation into the perceptions of management undergraduate students in Hangzhou, China, regarding the adoption of AI in education. The study explores various factors, including Perceived Relative Advantage and Trialability, to shed light on the nuanced dynamics influencing AI technology adoption in the context of higher education. Methodology: The study employs a quantitative research approach, utilizing the Confirmatory Tetrad Analysis (CTA) and Partial Least Squares Structural Equation Modeling (PLS-SEM) methodologies. The research sample consists of management undergraduate students in Hangzhou, China, and the methods include data screening, principal component analysis, confirmatory tetrad analysis, and evaluation of the measurement and structural models. We used a random sampling method to distribute 420 online, self-administered questionnaires among management students aged 18 to 21 at universities in Hangzhou. Contribution: This paper explores how management students in Hangzhou, China, perceive the adoption of AI in education. It identifies factors that influence AI adoption intention. Furthermore, the study emphasizes the complex nature of technology adoption in the changing educational technology landscape. It offers a thorough comprehension of this process while challenging and expanding the existing literature by revealing the insignificant impacts of certain factors. This highlights the need for an approach to AI integration in education that is context-specific and culturally sensitive. Findings: The study highlights students’ positive attitudes toward integrating AI in educational settings. Perceived relative advantage and trialability were found to impact AI adoption intention significantly. AI adoption is influenced by social and cultural contexts rather than factors like compatibility, complexity, and observability. Peer influence, instructor guidance, and the university environment were identified as pivotal in shaping students’ attitudes toward AI technologies. Recommendations for Practitioners: To promote the use of AI among management students in Hangzhou, practitioners should highlight the benefits and the ease of testing these technologies. It is essential to create communication strategies tailored to the student’s needs, consider cultural differences, and utilize the influence of peers and instructors. Establishing a supportive environment within the university that encourages innovation through policies and regulations is vital. Additionally, it is recommended that students’ attitudes towards AI be monitored constantly, and strategies adjusted accordingly to keep up with the changing technological landscape. Recommendation for Researchers: Researchers should conduct cross-disciplinary and cross-cultural studies with qualitative and longitudinal research designs to understand factors affecting AI adoption in education. It is essential to investigate compatibility, complexity, observability, individual attitudes, prior experience, and the evolving role of peers and instructors. Impact on Society: The study’s insights into the positive attitudes of management students in Hangzhou, China, toward AI adoption in education have broader societal implications. It reflects a readiness for transformative educational experiences in a region known for technological advancements. However, the study also underscores the importance of cautious integration, considering associated risks like data privacy and biases to ensure equitable benefits and uphold educational values. Future Research: Future research should delve into AI adoption in various academic disciplines and regions, employing longitudinal designs and qualitative methods to understand cultural influences and the roles of peers and instructors. Investigating moderating factors influencing specific factors’ relationship with AI adoption intention is essential for a comprehensive understanding.

This study investigates the relationship between artificial intelligence (AI) adoption, knowledge sharing, and employee performance in Malaysian small and medium-sized enterprises (SMEs), with knowledge sharing examined as a mediating mechanism. SMEs represent a vital component of Malaysia’s economy, yet many face resource limitations that affect their readiness to fully harness AI technologies. While AI is recognized for its potential to enhance efficiency and innovation, its impact on employee performance is not always straightforward. This research therefore, explores whether knowledge sharing acts as the bridge through which AI adoption translates into performance outcomes. Data were collected through a survey of SME employees across service and manufacturing sectors, and the responses were analysed using partial least squares structural equation modelling (PLS-SEM). Measurement model results confirmed strong reliability and validity for the constructs of AI adoption, knowledge sharing, and employee performance. Structural model assessment revealed that AI adoption significantly and positively influences knowledge sharing but does not directly affect employee performance. Meanwhile, knowledge sharing revealed a strong and significant relationship with employee performance and was also found to partially mediate the relationship between AI adoption and performance. The study findings highlight that AI’s value in SMEs lies not in the technology itself but in its ability to foster knowledge exchange, learning, and collaboration. In addition, employee performance improves when AI is embedded into organizational practices that encourage knowledge sharing, thereby complementing human creativity and expertise. Theoretically, this study integrated the Knowledge-Based View (KBV) and the Technology–Organization–Environment (TOE) framework to explain how AI adoption and knowledge sharing practices together influence employee performance. Practically, the results underscore the need for SME leaders to move beyond technology acquisition and focus on building collaborative cultures that enable knowledge sharing. Overall, this research contributes both theoretical and practical insights into how SMEs can strategically leverage AI adoption to enhance employee performance through the mediating mechanism of knowledge sharing.

The purpose of this study is to explore the key factors influencing Artificial Intelligence (AI) adoption in education among Pakistani university students. Specifically, it examines how AI Readiness (AIRD), AI Confidence (AICF), and Social Influence (SI) affect students’ Perceived Ease of Use (PEOU) and Perceived Usefulness (PU), and how these perceptions shape their Attitudes toward AI (ATT). The study also investigates the mediating roles of PEOU and PU. A quantitative research design was adopted using survey data collected from Pakistani students. Partial Least Squares Structural Equation Modelling (PLS-SEM) was applied through Smart PLS 4 to assess both the measurement and structural models. The results reveal that AIRD, AICF, and SI significantly influence students’ perceptions of ease of use, while AIRD and SI also positively impact perceived usefulness. However, AI confidence does not appear to shape perceived usefulness. Notably, perceived ease of use plays a substantial role in forming positive attitudes toward AI, while perceived usefulness does not have a direct effect. Mediation analysis further confirms that PEOU mediates the relationship between AIRD, AICF, SI, and ATT, whereas PU does not. The findings underscore the critical importance of usability over perceived benefits in shaping students' acceptance of AI technologies. In contexts where AI adoption is still emerging, ease of use appears to be the dominant factor influencing attitudes. Educators and policymakers should focus on enhancing students’ readiness and confidence in using AI, promoting user-friendly tools, and leveraging social influence to drive adoption. These insights are crucial for designing inclusive strategies that support effective AI integration into educational environments.

Objectives: In this study, we investigate the intentions of research scholars to use ChatGPT in their academic research endeavors by employing an extended Unified Theory of Acceptance and Use of Technology model, the UTAUT2, which examines factors such as performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, habit, ethical concerns, and research excellence. By incorporating “research excellence” into the model, we aim to provide insights into artificial intelligence (AI) adoption in academic research settings and contribute to the broader discourse on the future of academic practices. Method: We conducted a survey among 400 research scholars in Indian higher education institutions to collect data. We then analyzed the data using Partial Least Squares-Structural Equation Modeling (PLS-SEM). Results: The analysis revealed that performance expectancy, facilitating conditions, hedonic motivation, habit, and research excellence significantly influence the intention to use ChatGPT, with research excellence emerging as the strongest predictor. Effort expectancy, social influence, and ethical concerns, however, did not significantly impact adoption intentions. Conclusions: The findings indicate that several key factors, particularly research excellence, play a critical role in influencing research scholars’ intentions to integrate ChatGPT into their academic activities. The study demonstrates the importance of these factors in fostering AI adoption within the context of academic research. Implications for Practice: The results of this study offer valuable insights for researchers, administrators, and policymakers who aim to create environments conducive to effective AI utilization in research academia. By understanding the factors that influence AI adoption, stakeholders can better support research scholars in integrating ChatGPT into their work, enhancing academic practices, and advancing the use of AI in research.

This study investigates the organizational factors influencing technological readiness for Artificial Intelligence (AI) adoption, using a theoretical lens grounded in Sociotechnical Systems Theory (STS). A survey of 210 participants from educational and industrial institutions in China was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results confirm that AI adoption significantly enhances organizational efficiency and employee engagement, which in turn mediate its impact on technological readiness. By incorporating mediation analysis, the study provides a nuanced understanding of the indirect mechanisms through which AI adoption influences readiness, offering both theoretical and managerial insights for AI-enabled transformation. The integration of STS and rigorous empirical modeling presents a methodologically sound and practically relevant contribution to the field.

Small and Medium Enterprises (SMEs) face significant challenges in a com- petitive market. Artificial Intelligence (AI) has emerged as a critical tool for enhancing business operations, yet the specific impact of AI on the competi- tive advantage of SMEs remains underexplored. This study aims to investigate how AI adoption influences the competitive advantage of SMEs, focusing on key performance indicators such as cost efficiency, market share, customer retention, and innovation. The study adopts a quantitative research approach using Par- tial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS software. Data was collected from 200 SMEs across various industries that have implemented AI technologies. A survey was conducted to assess AI adoption and its impact on competitive advantage. The results indicate that AI adoption positively affects all four dimensions of competitive advantage. The strongest impact was observed on cost efficiency, followed by customer retention, inno- vation, and market share. The relationships between AI adoption and competi- tive advantage were statistically significant. AI adoption provides SMEs with a powerful means to enhance competitiveness by improving operational effi- ciency, customer loyalty, and innovation. Policymakers should support SMEs in overcoming adoption barriers to fully realize these benefits.

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.

Purpose This study investigates the interplay between artificial intelligence (AI) adoption, ethical capitalistic orientation and the moderating role of human–AI synergy, a new construct developed via grounded theory. This study aims to understand how these variables influence organizational decision-making in the AI era, specifically promoting ethical practices and mitigating negative outcomes such as employee displacement. Design/methodology/approach Using both qualitative and quantitative regression analysis, this study examines the relationships among AI adoption, ethical capitalistic orientation and human–AI synergy. Statistical tools were used to test hypotheses, identify AI adoption’s impact on organizational ethics and assess human–AI synergy’s moderating role. This research also explores how combining AI and human collaboration can foster a more ethical, socially responsible business model. Findings Findings show AI adoption positively impacts ethical capitalistic orientation (β1 = 0.45, p = 0.0001). Human–AI synergy significantly enhances this effect (β2 = 0.30, p = 0.0005) and moderates the relationship between AI adoption and ethical practices (β4 = 0.15, p = 0.0030). The study emphasizes focusing on human–AI collaboration over workforce displacement to maintain ethical practices and achieve efficiency. These results highlight the importance of ethical AI adoption and social responsibility. Originality/value This study introduces human–AI synergy as a novel construct, demonstrating its critical moderating role in the relationship between AI adoption and ethical capitalistic orientation. It offers new insights into how businesses can leverage AI without workforce reduction or unethical practices. This research emphasizes building ethical frameworks for AI adoption, presenting a novel perspective integrating technology with human-centered decision-making.

Purpose This study aims to investigate the antecedents of artificial intelligence (AI) adoption and sustainable performance (SP) in the context of manufacturing small and medium-sized enterprises (SMEs) in Jordan. Design/methodology/approach To fill the gap in the literature and achieve the research work objectives, the technology–organization–environment (TOE) framework and the partial least squares structural equation modeling (PLS-SEM) approach were used to analyze the sample data collected (n = 190) from manufacturing SMEs. Findings The findings indicate that technological, organizational and environmental context significantly influence the manufacturing SMEs’ AI adoption. Furthermore, AI adoption positively associated with economic, environmental and social performance. Originality/value This paper explains the TOE theory in an innovative technological setting. The paper also provides knowledge about how AI adoption may influence sustainable performance. Overall, this study provides new investigation into the literature concerning AI technologies and sustainable performance through the TOE lens.

Artificial intelligence, dynamic capabilities, and corporate financial asset allocation

Purpose The purpose of this study is to examine how AI adoption enhances operational performance in manufacturing firms and to investigate how variations in firms’ strategic focus moderate this relationship. Specifically, this study explores how AI adoption functions as a dynamic capability that enhances operational performance in manufacturing firms and investigates how different strategic orientations—exploration, exploitation, or ambidexterity—moderate this effect from the perspective of the Attention-Based View (ABV) of the firm. Design/methodology/approach This study employs a quantitative research method, using a sample of 426 Chinese manufacturing firms to examine the impact of artificial intelligence (AI) adoption on operational performance and to analyze the moderating role of firms’ strategic focus (exploration, exploitation, and ambidexterity). Data were collected through a structured questionnaire survey. To ensure the robustness of the findings and address potential endogeneity issues, hierarchical regression analysis and the two-stage least squares (2SLS) method were employed. Findings This study reveals that AI adoption significantly enhances the operational performance of manufacturing enterprises, but this effect is moderated by firms’ strategic focus. A high exploration tendency weakens the performance-enhancing effect of AI adoption due to implementation instability caused by excessive experimentation. A high exploitation tendency also reduces the positive impact of AI adoption, as over-reliance on existing processes constrains AI’s transformative potential. Furthermore, an ambidextrous strategy (coexistence of high exploration and high exploitation) further diminishes the positive effect of AI adoption, indicating that resource dispersion and increased coordination costs may offset its benefits. Originality/value From the perspective of Dynamic Capabilities Theory, this study empirically examines the impact of AI adoption—as a dynamic capability—on operational performance. Additionally, drawing on the Attention-Based View (ABV) of the firm, it uncovers the moderating role of firms’ strategic focus, addressing an existing research gap concerning strategic-level decision-making in AI adoption. The findings offer theoretical insights that guide enterprise managers in optimizing AI adoption strategies, helping them strike a balance between innovation and efficiency to maximize the benefits of digital transformation.