Artificial intelligence adoption and audit quality in an emerging country: Does auditor experience matter?

This study aims to examine the effect of auditor specialization, auditor characteristics, and board independence on audit quality through intellectual capital in service companies in West Java. The research was conducted using a quantitative approach with a sample of 384 service companies in West Java. Data were analyzed using Partial Least Square-Structural Equation Modeling (PLS-SEM) with SmartPLS 4.0 software. The results of this study showed that auditor specialization, auditor experience, and board independence had a positive and significant effect on audit quality. Meanwhile, intellectual capital has a significant mediating effect on the relationship between auditor specialization, auditor experience, and board independence on audit quality. The findings of this study provide important insights into the factors that contribute to the enhancement of audit quality in service companies. This study suggests that service companies in West Java should consider hiring specialized auditors, experienced auditors, and independent boards to improve audit quality. Additionally, companies should also focus on the development of intellectual capital, which plays an important role in enhancing audit quality.

Audit quality remains a central concern in contemporary financial ecosystems, where public trust hinges upon the credibility of financial ‎disclosures and the ethical integrity of auditors. This study critically examines the indirect effects of auditor independence, experience, time ‎pressure, and workload on financial reporting quality through the mediating roles of professional scepticism and risk assessment. Drawing ‎on the critical-positivist paradigm, this research integrates behavioral, structural, and ethical dimensions to provide a holistic understanding ‎of how human and contextual factors interact to shape audit outcomes. Using a quantitative survey method with 178 professional auditors ‎and data analyzed via Partial Least Squares Structural Equation Modeling (PLS-SEM), the findings reveal that professional scepticism and ‎risk assessment quality serve as significant mediators linking auditor attributes to financial reporting quality. Specifically, auditor experience, ‎time pressure, and workload exhibit strong indirect effects on reporting quality. At the same time, independence demonstrates a weaker, ‎statistically insignificant path—suggesting that institutional and client-related pressures may undermine formal autonomy. The results ‎highlight a paradox wherein professional scepticism, shaped by experience and workload, enhances audit quality yet remains vulnerable to ‎procedural fatigue and cognitive overload under excessive pressure. The findings underscore that audit quality transcends technical compliance; it embodies a form of ethical consciousness in which auditors must navigate the competing demands of independence, judgment, and ‎responsibility. The study contributes to the audit quality discourse by revealing that fostering reflective professional scepticism and managing a balanced workload are essential to sustaining the credibility of financial reporting in an increasingly complex audit environment.

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.

Purpose: This study analyses the degree to which audit experience and expertise affect audit quality through the moderating effect of Professional Qualification. 
 Design/Methodology/Approach: The study used a survey research design from a sample of about 400 audit staff of the Ghana Audit Service. 
 A conceptual framework was developed by adopting the framework for Audit Quality (2014) instituted by the International Auditing and Assurance Board (IAASB) and tested using PLS-SEM method of analysis.
 Findings: The study found that in the Public Sector, both audit expertise and experience influence audit quality.
 Furthermore, it was also established that professional qualification did not moderate the relationships between work experience, audit expertise and audit quality. 
 Professional qualification in audit quality was also found to be significant in the study.
 Implications/Research Limitation: The results of the study extend and add to the growing empirical research studies on audit expertise, work experience, and audit quality, as well as in the area of public sector auditing. However, the study was limited to only public sector auditors working with Ghana's Supreme Audit Institution (SAI), the Audit Service.
 Practical Implication: The study results have the prospect of encouraging top management to intensify the need to consciously institute and implement strategies that incorporate people with the requisite audit expertise and experience into the public sector to maximise and enhance audit quality.
 Originality/Value: This study is the first from a public sector perspective that directly examines how audit quality is influenced by audit expertise and experience and how professional qualification moderates this effect.
 Nonetheless, this research is distinct from other empirical studies on the basis of the method used and the context of the study.

Purpose This study aims to empirically investigate the impact of adopting big data and data analytics (BD&A) on audit quality (AQ). Design/methodology/approach A questionnaire was distributed among audit practitioners working at audit firms in Egypt and 205 responses were collected. Partial least square structural equation modeling (PLS-SEM) was used to analyze and test research hypotheses. Findings The results reveal that BD&A has a direct significant positive effect on the audit process (AP) and auditor competence (AC). However, an insignificant impact of BD&A is found on audit fees (AF). In addition, the results indicate that BD&A has significant positive direct and indirect impacts on AQ. Research limitations/implications The results of this study will benefit several auditing stakeholders, such as audit firms, audit regulators, novice financial auditors and academic scholars. Originality/value This research is one of the earliest to empirically address the role of BD&A in enhancing AQ. It incorporates AP, AC and AF as mediators into a single model to explain the impact of BD&A on AQ. Also, it attempts to provide empirical evidence from a developing country with a less-regulated audit environment.

This study aim to investigates the impact of Artificial Intelligence (AI) adoption on audit quality, specifically examining the mediating role of performance expectancy within Public Accounting Firms in East Java. Utilizing a quantitative approach, data from 140 auditors were analyzed using Structural Equation Modeling-Partial Least Squares (SEM-PLS). Results indicate that AI adoption positively affects both audit quality and performance expectancy. Furthermore, performance expectancy significantly impacts audit quality and mediates the relationship between AI adoption and audit quality. This research highlights the critical role of auditors' performance expectations in realizing AI's benefits, offering insights for fostering effective AI integration to enhance audit practices. This research offers empirical evidence from the underrepresented Indonesian context, providing a nuanced understanding of how performance expectancy mediates the AI-audit quality link. It contributes valuable insights for strategic AI implementation in public accounting firms and lays groundwork for future behavioral studies in auditing.

This research investigates the impact of auditor experience and ethical perspectives on audit quality and financial reporting in Sharia banks in Indonesia. Utilizing a quantitative approach and employing Partial Least Squares Structural Equation Modeling (PLS-SEM), the study reveals a positive and significant relationship between auditor experience and both audit quality and the quality of financial reports. Ethical perspectives of auditors are also found to positively influence these outcomes. The findings highlight the importance of investing in auditor expertise and ethical considerations to ensure the integrity and reliability of financial information in Sharia banks. The study contributes valuable insights for practitioners and policymakers in the Islamic finance sector, emphasizing the crucial role of auditor experience and ethical perspectives in enhancing audit quality and financial reporting in Sharia banks.

The impact of artificial intelligence on people’s lives is demonstrated today. Previous literature has shown that the use of a specific technology is directly linked to the individuals’ intention to use it. The aim of this paper is to study the factors that determine the adoption and use of artificial intelligence and big data in Spain, using a research model based on the Unified Theory of Acceptance and Use of Technology (UTAUT), proposed by Venkatesh et al. (2003). This work addresses the specific gap in the validation of the original theoretical model of UTAUT in two dimensions, with respect to the adoption of artificial intelligence by citizens and with respect to the factors that influence this adoption, evaluating the previous ones and proposing some new ones considering the current context. The methodology used is based on a national survey, and it analyzes the research model using the statistical technique of Partial Least Squares Structural Equation Modelling (PLS-SEM), which details the mediating and moderating relationships between constructs. The results show that Intention to Use has a direct positive influence on the Use of artificial Intelligence and big data, confirming previous literature. Performance Expectancy is the strongest predictor of Intention to Use, and indirectly of the adoption of artificial intelligence and big data applications. Effort Expectancy, in its application to the adoption of AI and big data by citizens, is an indirect determinant mediated by the Intention to Use, but its total effect (direct + indirect) is not significant.

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.

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 examines the impact of AI adoption orientation on innovation performance in multinational corporations (MNCs), emphasizing team innovativeness as an intervening mechanism and technology orientation as a moderating factor. Using data from 410 respondents collected via a snowball sampling strategy and analyzed through partial least squares structural equation modeling (PLS-SEM), the findings reveal that artificial intelligence (AI) adoption orientation positively influences team innovativeness and innovation performance. Team innovativeness partially mediates this relationship, while technology orientation moderates the link between AI adoption and team innovativeness, underscoring the role of technological preparedness in enhancing innovation. The study contributes to theoretical understanding by integrating team dynamics and technology preparedness in AI-driven innovation. It provides practical insights for managers, policymakers, and organizational leaders on fostering an innovative culture and investing in technology skills to drive MNC competitiveness.

PurposeThis study aims to examine whether the adoption of artificial intelligence (AI) by audit firms and their clients affects audit efficiency and audit quality.Design/methodology/approachThis study empirically examines the abovementioned research question based on data from China for the years 2011 to 2020. It uses audit report lag as a proxy for audit efficiency and the likelihood of annual report restatement as a proxy for audit quality. It adopts the propensity score matching and the two-stage OLS regression model to address the endogeneity issue led by firms’ innate complicated functions.FindingsThe findings show that when audit firms and their clients use AI separately, there's a positive link between AI use and audit report lag. However, when audit firms and clients use AI together, there's a negative link between AI use and audit report delays that enhance overall audit efficiency. Next, the authors observe a negative link between AI use and the likelihood of a restatement. Finally, the authors find that the association between AI adoption and audit quality is driven by increased audit effort lag. Results are consistent and robust to endogeneity tests and sensitivity analyses.Originality/valueFindings can complement the audit quality and corporate governance literature by clarifying that external audit must evolve through digitalization and the incorporation of newly developed digital tools, such as AI.

Artificial Intelligence and its impact on business sustainability in B2B firms: A hybrid PLS-ANN approach.

This study investigates the dynamic interplay among audit risk, auditor ethics, and audit opinion accuracy, emphasizing professional skepticism as a crucial mediating construct. The primary aim is to examine how audit risk and auditor ethics influence audit opinion accuracy by elucidating the role of professional skepticism as an intervening behavioral mechanism. Consequently, the study addresses a central research question regarding the behavioral pathways that shape audit judgment accuracy. Using a quantitative, questionnaire-based design, data were collected from 101 auditors at public accounting firms in DKI Jakarta, Indonesia, via convenience sampling and analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM). The empirical results indicate that both audit risk and auditor ethics significantly enhance audit opinion accuracy. Professional skepticism independently improves judgment quality and serves as a significant mediator between auditor ethics and audit opinion accuracy, whereas the effect of audit risk remains direct and unmediated. The study’s novelty, grounded in agency theory, reconceptualizes professional skepticism as a dynamic mechanism linking ethics to outcomes, advancing behavioral auditing beyond traditional direct-effects models. Practically, audit firms should reinforce ethical frameworks, training programs, and quality control systems that explicitly cultivate professional skepticism. Regulators and professional bodies are encouraged to integrate behavioral auditing and skeptical judgment into auditor education to further enhance audit quality. Keywords: Professional Skepticism; Auditor Ethics; Audit Risk; Audit Opinion Accuracy; Audit Quality.

In recent years, there has been increasing interest and demand for the application of good corporate governance mechanisms and compliance programs as a means of reducing corruption in institutional companies. Since corporate corruption is widespread in all societies, both developed and developing, and since corruption has a serious impact on economic, sustainable, and social development, there has been widespread agreement that the application of good corporate governance mechanisms and compliance programs are effective tools that can reduce the serious consequences of corruption in large companies. Therefore, this study aimed to study the impact of the application of different corporate governance mechanisms and compliance programs on reducing corporate corruption. The study also included the advantages and disadvantages of each corporate governance mechanism according to the latest findings of experimental studies. The study also assessed the extent to which Egyptian companies comply with the Egyptian legislation and laws issued for corporate governance, and identified the best mechanisms that can be applied in Egyptian companies to govern those companies and implement compliance programs, and the impact of the application of good governance in reducing Egyptian corporate corruption, increasing employee and customer satisfaction, and reducing the depletion of company resources. In addition, the study addressed the main challenges facing Egyptian companies in implementing good corporate governance and compliance. The results of the study indicated that corporate governance mechanisms proposed by most international agencies and organizations concerned with combating corruption have an effective impact in reducing corruption in companies. However, to achieve the desired results from each good corporate governance mechanism, recent experimental studies have proposed some requirements or precautions that companies or governments can commit to to maximize the benefits of these mechanisms. On the other hand, the results of the study showed that Egyptian companies, both in the private and public sectors, have taken serious steps in implementing good corporate governance and implementing compliance programs. However, companies do not fully comply with the instructions of good corporate governance, and there are still some shortcomings in the implementation of good corporate governance and compliance programs. These shortcomings are due to several different reasons, which are mainly related to the non-mandatory nature of corporate governance in Egypt, in addition to the lack of sufficient incentives for companies to implement the proposed programs. Keywords: Governance mechanisms – compliance programs – corruption