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COVID-19 has forced audit firms to change the way they operate. One change has been to rely more on information technology (IT) and IT human capital to overcome COVID-19-related challenges. We refer to audit firms’ use of these two resources as audit firm informatization (AFI). It is important to understand whether AFI helps audit firms address challenges created by the pandemic. Thus, this study examines the impact of AFI on audit quality during the COVID-19 pandemic in China with a focus on IT human capital.

We use a mixed-methods approach. First, we perform multivariate regression analyses on archival data. Specifically, we investigate the relationship between IT human capital and audit quality and the two mechanisms (i.e. improved efficiency and reduced audit risk) underlying the relationship. We also investigate how this relationship is moderated by features of clients, audit firms and individual auditors. Then we use interviews to corroborate the results of our regression analyses.

Our analyses of archival data show that IT human capital positively affects audit quality through improved efficiency and reduced audit risk and that this positive impact is more pronounced for clients in non-manufacturing industries, those with a more opaque information environment, audit firms with greater industry coverage and individual auditors with less experience. Our interview data indicate that audit firms with more advanced AFI and a higher level of IT human capital in particular are less disrupted by the pandemic and are better able to use IT to address challenges associated with COVID-19. Furthermore, the results confirm that improved efficiency and reduced audit risk are the mechanisms through which AFI enhances audit quality. Finally, we identify issues associated with the use of IT.

This study is the first to investigate how IT human capital (and by extension AFI) influences audit quality in the context of the COVID-19 pandemic. Our findings should be of interest to practitioners and setters of auditing standards.

The purpose of this study is to determine the relationship between accounting fraud and corporate sustainability.

Companies listed on the Shenzhen Stock Exchange in 2019 are used to estimate a pooled ordinary least square regression model using panel data. Accounting fraud is represented by accounting disclosure, which is measured by its quality and timeliness, while corporate sustainability is measured by earnings management and corporate social responsibility.

Empirical findings support the hypothesis that the quality and timeliness of accounting disclosure have a statistically favorable impact on the management of company earnings and corporate social responsibility, respectively. Accounting fraud also has an impact on the sustainable development of the company.

Although the inferences of this study are limited to Chinese listed companies, this study may interest other scholars to explore similar topics.

The purpose of this study is to investigate the effectiveness of a composite lubrication system combining polytetrafluoroethylene (PTFE) film and oil lubrication in steel–steel friction pairs.

A PTFE layer was sintered on the surface of a steel disk, and a lubricant with additives was applied to the surface of the steel disk. A friction and wear tester was used to evaluate the tribological properties and insulation capacity. Fourier transform infrared spectrometer was used to analyze the changes in the composition of the lubricant, and X-ray photoelectron spectroscopy was used to analyze the chemical composition of the worn surface.

It was found that incorporating the PTFE film with PSAIL 2280 significantly enhanced both the friction reduction and insulation capabilities at the electrical contact interface during sliding. The system consistently achieved ultra-low friction coefficients (COF < 0.01) under loads of 2–4 N and elucidated the underlying lubrication mechanisms.

This work not only confirm the potential of PTFE films in insulating electrical contact lubrication but also offer a viable approach for maintaining efficient and stable low-friction wear conditions.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-06-2024-0222/

This study aims to investigate whether auditors compromise their independence for economically important clients in family business settings.

The authors empirically examine the research question based on China for the years 2011 to 2020. The dependent variable is the auditors’ propensity to issue modified audit opinions, which is a proxy for auditor independence. The authors use relative client audit fees as a proxy for client importance. To address endogeneity issues in the selection of family firms, the authors use the two-stage least squares regression model and, subsequently, the propensity score matching and Hausman firm fixed effect modeling.

This study reveals that the propensity to issue modified audit opinions is positively correlated with client importance. Big-N auditors are more likely to issue modified audit opinions for their economically important family firm clients, whereas such evidence is not found for non-Big-N auditors. Results are consistent and robust to endogeneity test and sensitivity analysis.

This study enriches the literature on auditor independence and the effect of family firms’ ownership structure factors on audit reporting behavior for their economically important clients. Findings may prove useful for managers and practitioners interested in family business.

Traditional library book recommendation methods are mainly based on association rules and user profiles. They may help to learn about students' interest in different types of books, e.g., students majoring in science and engineering tend to pay more attention to computer books. Nevertheless, most of them still need to identify users' interests accurately. To solve the problem, the authors propose a novel embedding-driven model called InFo, which refers to users' intrinsic interests and academic preferences to provide personalized library book recommendations.

The authors analyze the characteristics and challenges in real library book recommendations and then propose a method considering feature interactions. Specifically, the authors leverage the attention unit to extract students' preferences for different categories of books from their borrowing history, after which we feed the unit into the Factorization Machine with other context-aware features to learn students' hybrid interests. The authors employ a convolution neural network to extract high-order correlations among feature maps which are obtained by the outer product between feature embeddings.

The authors evaluate the model by conducting experiments on a real-world dataset in one university. The results show that the model outperforms other state-of-the-art methods in terms of two metrics called Recall and NDCG.

It requires a specific data size to prevent overfitting during model training, and the proposed method may face the user/item cold-start challenge.

The embedding-driven book recommendation model could be applied in real libraries to provide valuable recommendations based on readers' preferences.

The proposed method is a practical embedding-driven model that accurately captures diverse user preferences.

This study aims to examine whether the adoption of artificial intelligence (AI) by audit firms and their clients affects audit efficiency and audit quality.

This 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.

The 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.

Findings 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.

To solve the problem that the underwater vehicles is difficult to turn and exit in a small range in the face of complex marine environment such as concave and ring under the limitation of its limitation of its shape and maximum steering angle, this paper aims to propose an improved ant colony algorithm based on trap filling strategy and energy consumption constraint strategy.

Firstly, on the basis of searching the global path, the disturbed terrain was pre-filled in the complex marine environments. Based on the energy constraint strategy, the ant colony algorithm was improved to make the search path of the underwater vehicle meet the requirements of the lowest energy consumption and the shortest path in the complex obstacle environment.

The simulation results showed that the modified grid environment diagram effectively reduced the redundancy search and improved the optimization efficiency. Aiming at the problem of “the shortest distance is not the lowest energy consumption” in the traditional path optimization algorithm, the energy consumption level was reduced by 26.41% after increasing the energy consumption constraint, although the path length and the number of inflection points were slightly higher than the shortest path constraint, which was more conducive to the navigation of underwater vehicles.

The method proposed in this paper is not only suitable for trajectory planning of underwater robots but also suitable for trajectory planning of land robots.

The construction industry has been investigating “where Henry Ford is in the industry system.” Given that listed construction enterprises are the backbone of the promotion of the high-quality development of the industry, their research and innovation are of considerable importance. This study aims to comprehensively assess the research and development (R&D) status quo and trends within various types of construction enterprises in order to identify effective strategies to enhance R&D efficiency in the construction industry.

Based on the data won from annual reports and the CSMAR database for the period 2016–2020, this study examines 104 listed construction enterprises in China. By applying both the data envelopment analysis (DEA) method and the Malmquist productivity index, this research compares and analyzes the static and dynamic differences in R&D efficiency across different types of construction enterprises.

Results suggest that the magnitude of change in the Malmquist decomposition index of 104 listed construction enterprises gradually narrowed, but the comprehensive technological level remained relatively low. Although state-owned enterprises had an advantage in scale efficiency, meaning they could maximize output with given inputs, their technological progress efficiency, also known as the degree of technological innovation, was significantly lower than that of private enterprises. As one finding, state-owned enterprises in comparison with private enterprises experience significant R&D inefficiency. It represents the main cause of their low degree of technological innovation and efficiency.

This study assesses the R&D efficiency of listed construction enterprises in China from the perspective of different market segments, state-owned and private enterprises and suggests approaches to improve strategies for various corporate types. Thus, the study’s new findings contribute to addressing the challenge of low R&D levels in the construction industry in the fields of engineering, construction and architectural management.