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This paper examines the organizational resilience of audit firms during the early stages of COVID-19. The unexpected restrictions placed on travel and on-site working created unanticipated barriers for auditors in Hong Kong. The authors expect that auditors with greater organizational resilience can respond to unexpected situations and restore expected performance levels relatively quickly.

The authors utilize a sample of 1,008 companies listed on Hong Kong Stock Exchange (HKEX) with a financial year-end of December 31. The authors identify five proxies contributing to organizational resilience: auditor size, industry specialization, diversity, geographic proximity to the client and auditing a new client. The authors use audit report timeliness as this study's main dependent variable.

This study's full-sample results suggest that larger auditors, industry specialists and auditors with closer relationships to clients issued more timely audit reports during the pandemic. The analysis of a subsample of companies that initially published unaudited financial statements reveals that industry expertise and longer auditor-client relationships significantly reduced the need for year-end audit adjustments. Finally, the authors find that larger auditors were more likely to offload clients, whereas industry specialists were more likely to retain clients.

The results of the paper suggests that audit firm characteristics associated cognitive abilities, behavioral characteristics and contextual conditions are associated with audit firm organizational resilience and, consequently, helps auditors respond unexpected changes in the audit environment.

The findings of the paper are informative for those involved in audit firm management or auditor hiring and retention decisions.

This study is the first to link organizational resilience to the performance of audit firms in a time of unexpected events. The authors connect three auditor and two auditor-client dimensions to the organizational resilience of the audit firms.

This paper focuses on bank audit committees and examines whether audit committee members who are former auditors are associated with the acquisition of audit and non-audit services from their former employers.

The study empirically examines a sample of large banks that are included in the S&P Composite 1500.

The paper reports significantly lower audit fees and a higher proportion of non-audit fees to total fees when the audit committee chair is an alumnus of the incumbent audit firm. Moreover, additional analysis reveals that these findings are stronger for banks with more earnings management.

Overall, the findings indicate that audit firms might consider banks using their alumni as audit committee chairs to be less risky or easier to audit, thus requiring relatively less effort from the auditors. The reduced effort required to audit clients with audit firm alumni on their audit committees then has the effect of reducing the audit fees charged. Alternatively, their auditing experience and cognitive proximity might influence the assessment of the need for auditing or the ability to negotiate lower audit fees on the part of audit firm alumni.

This paper provides empirical evidence of the association between audit firm alumni in influential positions on an audit committee and fees paid to those audit firms in the banking industry. The findings contribute to the literature by suggesting that banks with affiliated former auditors chairing their audit committees not only have significantly lower audit fees but also a higher proportion is spent on non-audit services.

This paper aims to identify ethical challenges of using artificial intelligence (AI)-based accounting systems for decision-making and discusses its findings based on Rest's four-component model of antecedents for ethical decision-making. This study derives implications for accounting and auditing scholars and practitioners.

This research is rooted in the hermeneutics tradition of interpretative accounting research, in which the reader and the texts engage in a form of dialogue. To substantiate this dialogue, the authors conduct a theoretically informed, narrative (semi-systematic) literature review spanning the years 2015–2020. This review's narrative is driven by the depicted contexts and the accounting/auditing practices found in selected articles are used as sample instead of the research or methods.

In the thematic coding of the selected papers the authors identify five major ethical challenges of AI-based decision-making in accounting: objectivity, privacy, transparency, accountability and trustworthiness. Using Rest's component model of antecedents for ethical decision-making as a stable framework for our structure, the authors critically discuss the challenges and their relevance for a future human–machine collaboration within varying agency between humans and AI.

This paper contributes to the literature on accounting as a subjectivising as well as mediating practice in a socio-material context. It does so by providing a solid base of arguments that AI alone, despite its enabling and mediating role in accounting, cannot make ethical accounting decisions because it lacks the necessary preconditions in terms of Rest's model of antecedents. What is more, as AI is bound to pre-set goals and subjected to human made conditions despite its autonomous learning and adaptive practices, it lacks true agency. As a consequence, accountability needs to be shared between humans and AI. The authors suggest that related governance as well as internal and external auditing processes need to be adapted in terms of skills and awareness to ensure an ethical AI-based decision-making.

This paper aims to examine the association between audit quality threatening behaviour (AQTB) and three team equality dimensions: deindividuation, social identity and gender equality. Discrimination among auditors has been experienced in accounting firms across the world, which can lead to behaviour that risks the quality of work. The negative influence of this behaviour can have consequences for clients, audit firms, regulators and the wider society due to the threat on audit quality.

A questionnaire was conducted at a Big 4 audit firm in Sweden. Members of audit teams that worked together on one specific engagement were asked to give their perceptions of their experience of equality and behaviours within the team. Hypotheses were tested using ordered logistic regression and partial least squares structural equation model.

Audit teams that experience deindividuation conduct more AQTBs and audit teams with higher social identity conduct less AQTBs. However, the audit team’s social identity can moderate the audit teams’ experience with deindividuation and reduce AQTB.

With a unique data set of practising audit teams, this study is the first to investigate how audit team equality is related to AQTB. Contributions are made to practitioners about audit team dynamics since the AQTB occurs as part of the audit decision-making process that influences audit quality. Inequality also has recruitment and reputation consequences. Thus, contributions are made to the audit market that is interested in audit quality. The study also contributes empirical evidence from an audit team context about behavioural outcomes and the social identity and deindividuation model theory (Klein et al., 2007; Reicher et al., 1995).

This study examines whether and how a client's business strategy can affect the relationship between auditor characteristics and financial reporting quality.

In this study, auditor industry specialization and tenure were used as proxies for auditor characteristics. The client business strategy was measured using the resource allocation index method. Finally, discretionary accruals are used to assess financial reporting quality. This study includes 1,450 firm-year observations and 145 companies listed on the Tehran Stock Exchange (TSE) over a ten-year period from 2011 to 2020. The research hypotheses were analyzed using a multivariate regression model and panel data.

The results show that auditor industry specialization increases financial reporting quality. This relationship improves when the client's business strategy deviates from the industry–normal strategy. The research findings state that auditor tenure has a positive association with financial reporting quality, and this relationship is strengthened when the company's business strategy deviates from the normal industry strategy.

The findings of this study provide important evidence for investors, firm management, and auditing firms. Investors must consider the auditor characteristics when selecting companies listed on the TSE. Managers of Iranian companies are advised to consider the auditor's characteristics when choosing an audit firm to increase financial reporting quality. Audit firms should evaluate their business strategies in audit planning to increase the quality of financial reporting.

To the best of the authors’ knowledge, this is the first empirical study to examine the relationship between auditor characteristics and the financial reporting quality in the emerging capital market by considering the clients' business strategy.

Accounting information systems are mainly rule-based, and data are usually available and well-structured. However, many accounting systems are yet to catch up with current technological developments. Thus, artificial intelligence (AI) in financial accounting is often applied only in pilot projects. Using AI-based forecasts in accounting enables proactive management and detailed analysis. However, thus far, there is little knowledge about which prediction models have already been evaluated for accounting problems. Given this lack of research, our study aims to summarize existing findings on how AI is used for forecasting purposes in financial accounting. Therefore, the authors aim to provide a comprehensive overview and agenda for future researchers to gain more generalizable knowledge.

The authors identify existing research on AI-based forecasting in financial accounting by conducting a systematic literature review. For this purpose, the authors used Scopus and Web of Science as scientific databases. The data collection resulted in a final sample size of 47 studies. These studies were analyzed regarding their forecasting purpose, sample size, period and applied machine learning algorithms.

The authors identified three application areas and presented details regarding the accuracy and AI methods used. Our findings show that sociotechnical and generalizable knowledge is still missing. Therefore, the authors also develop an open research agenda that future researchers can address to enable the more frequent and efficient use of AI-based forecasts in financial accounting.

Owing to the rapid development of AI algorithms, our results can only provide an overview of the current state of research. Therefore, it is likely that new AI algorithms will be applied, which have not yet been covered in existing research. However, interested researchers can use our findings and future research agenda to develop this field further.

Given the high relevance of AI in financial accounting, our results have several implications and potential benefits for practitioners. First, the authors provide an overview of AI algorithms used in different accounting use cases. Based on this overview, companies can evaluate the AI algorithms that are most suitable for their practical needs. Second, practitioners can use our results as a benchmark of what prediction accuracy is achievable and should strive for. Finally, our study identified several blind spots in the research, such as ensuring employee acceptance of machine learning algorithms in companies. However, companies should consider this to implement AI in financial accounting successfully.

To the best of our knowledge, no study has yet been conducted that provided a comprehensive overview of AI-based forecasting in financial accounting. Given the high potential of AI in accounting, the authors aimed to bridge this research gap. Moreover, our cross-application view provides general insights into the superiority of specific algorithms.

The study aims to identify the practical borders of AI legal personality and accountability in human-centric services.

Using a framework tailored for AI studies, this research analyses structured interview data collected from auditors based in Poland.

The study identified new constructs to complement the taxonomy of arguments for AI legal personality: cognitive strain, consciousness, cyborg paradox, reasoning replicability, relativism, AI misuse, excessive human effort and substitution.

The insights presented herein are primarily derived from the perspectives of Polish auditors. There is a need for further exploration into the viewpoints of other key stakeholders, such as lawyers, judges and policymakers, across various global contexts.

The findings of this study hold significant potential to guide the formulation of regulatory frameworks tailored to AI applications in human-centric services. The proposed sui generis AI personality institution offers a dynamic and adaptable alternative to conventional legal personality models.

The outcomes of this research contribute to the ongoing public discourse on AI’s societal impact. It encourages a balanced assessment of the potential advantages and challenges associated with granting legal personality to AI systems.

This paper advocates for establishing a sui generis AI personality institution alongside a joint accountability model. This dual framework addresses the current uncertainties surrounding human, general AI and super AI characteristics and facilitates the joint accountability of responsible AI entities and their ultimate beneficiaries.