AI Adoption, Audit Outcomes, and Professional Reputation

This study examines the impact of technological integration on sustainable performance in Pakistani manufacturing SMEs from the export sector, focusing on the mediating role of employee capacity building and the moderating role of organizational learning, drawing on dynamic capabilities theory. Data were collected from manufacturing SMEs in Pakistan via a structured questionnaire, and structural equation modeling (SEM) was employed using Smart-PLS (4.1) for analysis. The results reveal that both AI adoption and technological readiness significantly enhance employee capacity building, which, in turn, positively impacts sustainable performance. Employee capacity building significantly mediates the relationships between both AI adoption and sustainable performance as well as between technological readiness and sustainable performance. Notably, organizational learning shows a significant moderating effect between technological readiness and employee capacity building but an insignificant moderating effect between AI adoption and employee capacity building. These findings offer actionable insights for managers in optimizing AI and readiness strategies to enhance capacity building and sustainable outcomes. This study contributes to the literature by being one of the first to integrate AI adoption and technological readiness with employee capacity building as a mediator and organizational learning as a moderator in the context of SMEs. Unlike prior research, which typically examines these factors independently, our framework offers a holistic approach to understanding sustainable performance.

This empirical quantitative study aimed to investigate the influence of employee integration, maintenance and motivation, and technology and AI adoption on employee retention in the UAE Abu Dhabi police force, and the moderation effects of technology and AI adoption on the relationships between employee integration and employee retention, and between maintenance and motivation and employee retention. The resource-based view theory was used to provide theoretical support for the proposed conceptual framework, which included four variables: employee integration, maintenance and motivation, technology and AI adoption, and employee retention. The data was collected using printed questionnaires from 371 employees, and analysed using structural equation modelling. The results showed that employee integration, maintenance and motivation, and technology and AI adoption were all significant predictors of employee retention in the UAE Abu Dhabi police force. The moderation effects of technology and AI adoption on the relationships between employee integration and employee retention, and between maintenance and motivation and employee retention were not significant. The study recommends that the UAE Abu Dhabi police force invest in employee integration, maintenance and motivation, and technology and AI adoption to improve employee retention. Further research is needed to investigate the relationship between technology and AI adoption and employee retention in more detail.

The adoption of AI is an ongoing phenomenon in today’s economy in all the industries. The purpose of this paper is to examine the economic impact of AI adoption in the region of ASEAN. To achieve this objective, structural questionnaire was developed for the various industry experts in targeted region. A sample of 240 experts was finally obtained over a time span of 6 weeks through online structural questionnaire approach. For measuring AI adoption, twelve items, initial economic impact (seven items), and subsequent economic impact (six items) were finally added in the questionnaire. For analyses purpose, descriptive statistics, structural equation modelling, and regression analyseswereapplied, examining the both initial and subsequent economic impact of AI adoption. Findings through structural model indicates that overall both initial and subsequent impact are significantly determined by AI adoption in related industries. Additionally, in depth analyses for the individual AI items as their initial and subsequent economic impact indicate that Usage of the data for AI adoption, clear strategy for AI adoption, successful mapping for AI adoption and overall positive attitude towards AI adoption have their significant and positive influence on initial economic indicators. Whereas, as per subsequent economic impact, factors like effective usage of data for AI adoption, assessing the right skills of individuals for AI adoption and positive attitude towards AI adoption are significantly impacting on material investment, capital investment, increasing unemployment, higher economic output, higher return on capital and higher wages for the existing labor. These findings have provided an outstanding evidence in the field of AI and its economic impact in the region of ASEAN and can be considered as initial contribution in related fields. Both industry exports and macroeconomic decision makers can significantly utilize the findings to develop their conceptual framework and understanding for the integration between AI adoption and economy. Additionally, this study can work as reasonable justification for implementing the more adoption of AI in various industries as it has positive economic outcome (both initial and subsequent). However, one of the key limitations of this study is limited sample size and only 240 industry exports were targeted from selected industries in ASEAN. Future study could be reimplemented on similar topic with expanding the sample size for better findings and more generalization.

The global business landscape is currently experiencing a transformative shift propelled by technological advancements, particularly Artificial Intelligence, which is reshaping operations and enhancing organizational capabilities. This paper investigates the adoption intention of AI within the Small and Medium Enterprises in the United Arab Emirates, a country recognized for its embrace of technological innovation. Addressing a key research gap, the study emphasizes the limited exploration of AI adoption determinants within SMEs in emerging economies. Built on the application of the technology–organization–environment framework and integrating the Technology Acceptance Model, the research identifies factors influencing AI adoption intention in UAE hospitality SMEs. A quantitative survey of 315 respondents was conducted, and data were analyzed using structural equation modeling (SEM) in SmartPLS. The findings reveal significant relationships between perceived competitive advantage, perceived usefulness, perceived top management support, perceived employee capability, perceived competitive pressure, perceived government regulations and AI adoption intention, affirming the validity of the proposed conceptual model. The results further show that organizational (top management support) and environmental factors (competitive pressure, government regulation) exert stronger influence than technological perceptions, highlighting the role of leadership and institutional pressures in shaping adoption. The study contributes theoretically by contextualizing TOE–TAM integration and practically by offering actionable insights for SME leaders, policymakers, and stakeholders to drive AI adoption.

PurposeArtificial intelligence (AI) is a powerful and promising technology that can foster the performance, and competitiveness of micro, small and medium enterprises (MSMEs). However, the adoption of AI among MSMEs is still low and slow, especially in developing countries like Jordan. This study aims to explore the elements that influence the intention to adopt AI among MSMEs in Jordan and examines the roles of firm innovativeness and government support within the context.Design/methodology/approachThe study develops a conceptual framework based on the integration of the technology acceptance model, the resource-based view, the uncertainty reduction theory and the communication privacy management. Using partial least squares structural equation modeling – through AMOS and R studio – and the importance–performance map analysis techniques, the responses of 471 MSME founders were analyzed.FindingsThe findings reveal that perceived usefulness, perceived ease of use and facilitating conditions are significant drivers of AI adoption, while perceived risks act as a barrier. AI autonomy positively influences both firm innovativeness and AI adoption intention. Firm innovativeness mediates the relationship between AI autonomy and AI adoption intention, and government support moderates the relationship between facilitating conditions and AI adoption intention.Practical implicationsThe findings provide valuable insights for policy formulation and strategy development aimed at promoting AI adoption among MSMEs. They highlight the need to address perceived risks and enhance facilitating conditions and underscore the potential of AI autonomy and firm innovativeness as drivers of AI adoption. The study also emphasizes the role of government support in fostering a conducive environment for AI adoption.Originality/valueAs in many emerging nations, the AI adoption research for MSMEs in Jordan (which constitute 99.5% of businesses), is under-researched. In addition, the study adds value to the entrepreneurship literature and integrates four theories to explore other significant factors such as firm innovativeness and AI autonomy.

This study investigates the impact of institutional pressures, organizational culture, and individual-level factors on the adoption of AI for strategic planning and decision-making activities among marketing employees, with a focus on the moderating roles of perceived accountability and personal innovativeness on the relationship between AI adoption intention and actual use. We employed an explanatory sequential mixed-method design for the research, combining both qualitative and quantitative methods. Initially, quantitative data was collected via online questionnaire with a sample of 699 Vietnamese marketing employees, analyzed by using the PLS-SEM model. Subsequently, qualitative data from 21 in-depth interviews was used to verify and explain the quantitative findings with more multidimensional perspectives. Quantitative results reveal that coercive pressures significantly drive AI adoption intention, while normative and mimetic pressures have limited effects. Organizational flexibility, top management commitment and support strongly influence AI adoption. About individual-level factors, AI literacy emerged as a critical enabler, whereas AI anxiety posed barriers. Moderating effects demonstrate that high personal innovativeness strengthens the relationship between AI adoption intention and actual use, while high perceived accountability weakens it. Qualitative findings were also discussed. This is the first mixed-method study to address a critical gap in the literature by examining the multifaceted influences on AI adoption in the workplace, particularly within the marketing sector. Through a mixed-method approach, the study offers a comprehensive view of how organizational and individual factors interact to drive AI adoption, providing valuable insights for businesses, policymakers, and researchers aiming to foster successful AI integration.

Prior investments in advanced digital technologies encourage AI adoption. Yet AI integrates differently across technologies depending on existing human–technology relations. When combined with robotics, it tends to reinforce labor substitution; when matched with advanced information technologies (AIT) – such as big data – it supports human–machine complementarity. Firm-level evidence from a large sample of Italian firms supports this distinction. Results show that while prior investments in robotics and AIT are positively associated with AI adoption, only AIT facilitate investments in AI-related training, suggesting that workforce upskilling plays a minor role when AI operates in machine–machine configuration. Additional evidence shows that organizational practices, sectoral conditions, and pre-existing capabilities all play a mediating role. Indeed, AI adoption is more common among firms that invest in worker engagement and operate in industrial sectors, while fiscal incentives support adoption only among already digitalized firms, underscoring the importance of firms’ technological readiness.

This research article focuses on investigating the impact of technology readiness (TR) on the adoption of artificial intelligence (AD) by accountants and auditors, utilizing intermediary factors, such as perceived usefulness (PU) and perceived ease-of-use (PEOU), within companies in Vietnam. Based on 143 survey responses, the results demonstrate a positive relationship between TR and AI adoption among professionals in the accounting and auditing industry. Additionally, the analysis reveals that the intermediary factors PU and PEOU positively influence AI adoption. TR consistently relates with PU and PEOU in applying artificial intelligence in accounting and auditing. The result of the experiment study is that technology readiness positively impacts the AI adoption of accountants and auditors from companies in Vietnam. Hence, perceived usefulness and ease of use mediate the relationship between technology readiness and the adoption of AI technologies by workers in the accounting and auditing industry. This study contributes not only academically by enriching scientific knowledge on AI adoption but also holds practical significance by suggesting training and development policies from a business perspective in the future.

What challenges are presented by AI adoption in healthcare? What role should AI governance play in regard to the opportunities and risks for generative AI (large language models like ChatGPT-4, etc.) in health? How do we responsibly evaluate new technology and ensure guardrails for its deployment, but without stifling innovation? How do we address societal fears and biases? What level of reliability and robustness should algorithms achieve before being adopted with confidence across healthcare? How can AI support health equity and expand access? We will explore these questions and more with some of the nation’s leading experts on these topics. Learning Objectives Understand key opportunities and challenges for AI governance in healthcare, particularly in regard to rapidly emerging generative AI applications Discuss various responsible AI and AI governance frameworks that seek to ensure safety and efficacy in AI adoption Explore the role of government, legislation, and regulators in supporting safe and equitable adoption of AI in healthcare Explore opportunities for government, industry, and academic partnerships to accelerate AI validation, development, and adoption of safety standards Consider the potential impact of AI on the healthcare workforce and provider and patient experiences Identify the risks we should be most concerned with and what approaches can we take to mitigate those risks? By the end of the presentation, participants should have a sense of the opportunities and challenges of adopting AI in healthcare, key risks and frameworks for mitigating them, and the potential role of government, academia, and industry in addressing these issues Generative AI application examples Patient Communication and Engagement Intelligent chatbots interacting with patients, answering their queries, providing health advice, and helping them manage their conditions Mental Health Support Provide psychological support to patients, helping them manage stress, anxiety, depression, and other mental health issues. Provide interventions based on CBT and other therapeutic techniques Automated Medical Documentation Note-taking can allowing doctors to focus more on patient care Education and Training Create educational content and personalized training for medical students and healthcare professionals, potentially simulating things like patient cases and providing guidance on diagnosis, etc. Synthesis and Summarization of Medical Research Make it easier for people to keep up with the latest developments Public Health Communication In a public health emergency, help craft health messages, FAQs, and timely and accurate updates for the public Generative AI challenges and limitations Ensuring the accuracy of AI-generated information Maintaining patient privacy Handling the ethical implications of AI and human-machine interactions in healthcare “Hallucinations” (false or inaccurate information) can occur when model has limited knowledge of a particular domain “Distraction” (losing track of earlier inputs) can occur when inputs go beyond allowable token count for model “context window” and models start to produce responses not relevant to queries and context.

Achievement of Sustainable Development Goals (SDGs) has become one of the most important goals across the globe due to their significant role in the economic prosperity and human well-being. Recently, the business industry has become a dominant concentration for SDGs. However, the importance of small- and medium-sized industries has been rarely debated in this perspective. In particular, no attempt has yet been made to estimate the commitment of small and medium Enterprises (SMEs) towards SDGs in the emerging market in Pakistan. This study fills the gap by examining the influence of top management support on SDGs (e.g., environment and community practices) with a mediating role of green supply chain management and government support as a moderator. The model of this study is tested through structural equation modeling on the cross-sectional data collected through a structured questionnaire from 313 Pakistani SMEs. The results show that top management support significantly influences green supply chain management, environment, and community-based SDGs. Green supply chain management plays a partial mediating role between top management support and environment practices as well as between top management support and community practices. Government support significantly strengthens the relationship between top management support and green supply chain management. This research recommends SMEs to encourage participation in green supply chain practices and SDGs. Additionally, this research strongly suggests the government to facilitate SMEs in the adoption of green practices that can resultantly contribute to SDGs. Further implications have stated.

<span id="docs-internal-guid-43377b33-7fff-4342-8e71-b985af862527"><span>Amidst the ongoing competitive challenges in the business environment, AI plays a fundamental role. Despite the pace of AI adoption has shown slow advancement in emerging markets, particularly in Pakistan. Forthcoming years will become essential for organisations to integrate AI into different facets of task management like recruitment and selection (R&S). The core purpose of the research is to address how numerous factors influence the adoption of AI under technology-organisation-environment (TOE) model. The study introduces a basis to examine the importance and interrelationship of key success factors in the adoption of AI. Six key factors influencing AI adoption were derived from a comprehensive review of the literature. The structural model was empirically validated using data collected via a Google-based mail survey conducted in Pakistan. The data is analysed through Structural Equation Modelling, revealing that factors such as relative advantage under Technology factor, technological competency, and support from top-management for Organisational factor, while competitive pressure and vendor support for Environment factor have a significant association with AI adoption regarding R&S in selected firms in Pakistan. The study’s findings offer valuable insights for organisations in Pakistan to refine their AI adoption strategies, particularly in R&S, helping them gain a competitive edge. It also highlights key factors specific to the Pakistani context, enhancing understanding of how local dynamics shape AI adoption in HR. These insights can guide organisations in overcoming challenges and optimising AI’s potential for organisation success.</span></span>

Purpose This study aims to explore how AI adoption and trust shape customer satisfaction in digital banking, emphasizing the mediating roles of behavioral intention and AI adoption. It seeks to understand how service quality and technology perceptions influence customer experiences in a digital environment. Design/methodology/approach A quantitative approach was adopted using data from 385 digital banking users. The measurement and structural models were analyzed using partial least squares structural equation modeling (PLS-SEM) in SmartPLS 4, with 5,000 bootstrap resamples to test significance. Findings Findings reveal that perceived usefulness, behavioral intention and AI adoption strongly enhance customer satisfaction. Behavioral intention mediates ease of use, usefulness and trust, while AI adoption links reliability, responsiveness and usefulness, emphasizing that trust and AI engagement together elevate digital satisfaction. Originality/value By integrating SERVQUAL and technology acceptance model, this study advances understanding of AI-driven service experiences, offering practical guidance for customer-centric digital banking strategies.

PurposeThis study explores the intricate relationship between trust dynamics and AI implementation in high-barrier environments within emerging economies. It investigates how government involvement, organisational leadership, managerial competence and organisational readiness collectively shape trust in AI implementation capability (TOIC). By examining these interactions, the research aims to provide a nuanced understanding of how government involvement influences trust within the firm.Design/methodology/approachThe study employs a conceptual framework grounded in the technology-organisation-environment (TOE) model, combined with a multilevel trust perspective. Data were collected through a survey of 412 information technology and information and communication technology (IT/ICT) firms and analysed using partial least squares structural equation modelling (PLS-SEM). This approach enabled the identification of key relationships and moderating effects between trust determinants and AI implementation.FindingsThree key insights emerged: (1) Government involvement indirectly influences trust by enhancing organisational readiness but can reduce trust when perceived as intrusive; (2) Top management support serves as a critical trust driver by shaping organisational readiness and aligning AI strategies with operational realities and (3) Managerial competence mediates trust development by translating strategic goals into operational practices, particularly in environments with high perceived barriers.Research limitations/implicationsThe study focuses on IT/ICT firms in Ghana, potentially limiting generalisability. Future research should explore these trust dynamics in diverse emerging markets to validate the findings and expand theoretical insights into AI adoption in high-barrier contexts.Practical implicationsThe findings underscore the need for governments to foster organisational readiness through training and infrastructure development rather than direct intervention. For business leaders, investing in top management support and managerial competence can enhance trust and facilitate successful AI adoption.Social implicationsBy clarifying the trust-building mechanisms for AI implementation, this research supports the development of policies that balance technological innovation with societal trust, ultimately contributing to more inclusive and sustainable technological progress.Originality/valueThis study extends the TOE framework by integrating trust as a core factor in AI implementation and highlighting the moderating effect of barriers. It offers practical insights into the paradoxical role of government involvement, demonstrating that trust in implementation capabilities is contingent on strategic alignment between policy initiatives and firm-level capabilities.

Halal Supply Chain Management (HSCM) is transforming the way halal products are certified, tracked, and distributed. However, challenges such as regulatory compliance, technological readiness, cost, and stakeholder trust continue to impact AI adoption in the halal industry. This study employs the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to analyze the causal relationships among key factors influencing AI adoption in halal supply chains. Seven key criteria were identified: Technological Infrastructure, Regulatory Compliance, Cost of Implementation, Data Security & Privacy, Scalability & Adaptability, Stakeholder Readiness, and Consumer Trust & Perception. The findings reveal that Technological Infrastructure is the most influential factor driving AI adoption, impacting Regulatory Compliance, Scalability, and Consumer Trust. Conversely, Consumer Trust & Perception emerged as the most dependent factor, highlighting the importance of transparency in AI-based halal certification. Regulatory Compliance plays a dual role as both a cause and an effect, emphasizing the need for standardized AI-driven halal certification frameworks. The results suggest that investment in AI-driven traceability, blockchain-based halal authentication, and automated compliance verification can significantly improve halal supply chain efficiency and consumer confidence. This study provides valuable insights for policymakers, halal certification bodies, and industry stakeholders by identifying key enablers and barriers to AI adoption. Future research should explore hybrid Multi-Criteria Decision-Making (MCDM) approaches, economic feasibility analyses, and real-world case studies to further validate AI-driven solutions in halal supply chains.

This research seeks to explore the influence of AI adoption on ESG performance and further assess the mediation effect of ESG performance in the relation between AI adoption and firm value. The research was carried out from 2020 to 2023 on companies in Indonesia, yielding 288 observational data points. A multivariate analysis was performed utilising partial least squares structural equation modelling (PLS-SEM) to assess the hypothesis. The findings from hypothesis testing demonstrate that AI adoption has a significant favourable impact on ESG performance. Similarly, ESG performance significantly enhances firm value. Additionally, the indirect effects analysis reveals that ESG performance effectively mediates the positive relationship between AI adoption and firm value. AI enhances ESG performance by serving as a strategic resource, improving efficiency, and advancing sustainability to meet stakeholder expectations, further enhancing corporate value. This research encourages government support, managerial integration, and standardised policies for AI-driven business sustainability.