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This study aims to analyze why listed Taiwanese firms uniquely rejected the early adoption of International Financial Reporting Standards (IFRS) in 2012. It investigates the underlying decision-making processes behind this policy reluctance to further understand the continuous phenomenon of rare voluntary IFRS adoption.

It reports on fieldwork evidence obtained in situ by in-depth interviewing in Mandarin. It uses qualitative methods, complemented by quantitative cost-benefit metrics of IFRS adoption. It presents five diverse illustrative case-study vignettes, using a judgment sample based on expert opinion.

While the net-benefits of implementing IFRS varied across firms, this study’s unanimous finding was that no firms (in the sample or population) adopted IFRS early, despite stated intentions to the contrary. The key reasons for shunning early IFRS adoption were found to be frequent changes in regulations, insufficient benefits from adopting IFRS and the undermining of comparability across companies, compounded with scarce preparation time. Further, this study found that the Taiwanese accounting regulator’s reluctance toward IFRS adoption, partly caused by a long-standing US influence, contributed to this anomalous outcome.

This study recommends two critical policy changes: more realistic timelines and less frequent regulatory changes.

To the best of the authors’ knowledge, this is the first study to investigate the reasons behind the anomaly of no early adoption of IFRS in Taiwan, using new primary data and illustrative case studies. Its novelty lies in extending understanding beyond the existing quantitative literature on accounting standards, using new “thick” qualitative evidence on motives for such choices and decision-making processes, which have been neglected in previous work.

This paper aims to investigate the impact of the implementation of the critical audit matters (CAMs) disclosure requirement and the subsequent relationship between CAM disclosures and audit report lag, as well as audit fees in the USA.

This study used difference-in-differences analyses to investigate the impact that the implementation of the requirement for auditors to report CAMs on their audit report has on the audit process. It also used levels regression models to examine the relationship that CAM disclosures have with audit report lag and audit fees.

This study found that the implementation of the CAM disclosure requirement in the USA reduced audit report lag while not significantly affecting audit fees. This suggests that the CAM disclosure requirement may increase the cooperation between auditors and managers and improve the efficiency of the audit process.

This study’s results are informative for assessing the economic impact of requiring CAM disclosures, which should be of importance to regulators, auditors and accounting researchers.

This study used different approaches to investigate two aspects of the CAM disclosure requirement – the effect of the implementation of the disclosure requirement and the subsequent effects related to CAM reporting outcomes. Unlike many previous studies investigating CAM disclosures, which relied on experiments and questionnaires, this study used actual CAM disclosure data in the USA to investigate the impact on audit report lag and audit fees.

This study aims to propose a refined dynamic network slacks-based measure (DNSBM) to evaluate the efficiency of China's regional green innovation system which consists of basic research, applied research and commercialization stages and explore the influencing factors of the stage efficiency.

A two-step procedure is employed. The first step proposes an improved DNSBM model with flexible settings of stages' input or output efficiency and uses second order cone programming (SOCP) to solve the non-linear problem. In the second step, least absolute shrinkage and selection operator (LASSO) and Tobit models are used to explore the influencing factors of the stage efficiency. Global Dynamic Malmquist Productivity Index (GDMPI) and Dagum Gini coefficient decomposition method are introduced for further discussion of the productivity change and regional differences.

On average, Chinese provincial green innovation efficiency should be improved by 24.11% to become efficient. The commercialization stage outperforms the stages of basic research and applied research. Comparisons between the proposed model and input-oriented, output-oriented and non-oriented DNSBM models show that the proposed model is more advanced because it allows some stages to have output-oriented model characteristics while the other stages have input-oriented model characteristics. The examination of the influencing factors reveals that the three stages of the green innovation system have quite diverse influencing factors. Further discussion reveals that Chinese green innovation productivity has increased by 39.85%, which is driven mainly by technology progress, and the increasing tendency of regional differences between northern and southern China should be paid attention to.

This study proposes an improved dynamic three-stage slacks-based measure (SBM) model that allows calculating output efficiency in some stages and input efficiency in the other stages with the application of SOCP approach. In order to capture productivity change, this study develops a GDMPI based on the DNSBM model. In practice, the efficiency of regional green innovation in China and the factors that influence each stage are examined.

This study aims to investigate how a firm's management team's capacity to efficiently use its resources affects the firm's exposure to climate change. Specifically, the authors investigate the intriguing question – does managerial ability affect a firm's climate change exposure?

The authors use an unbalanced panel dataset of 4,230 US based firms listed on Compustat from 2002–2019 and test the hypothesis by panel regression analysis. To mitigate endogeneity concerns, difference-in-differences and instrumental variable approaches are used.

The baseline analysis shows a negative, statistically significant impact of managerial ability on climate change exposure. The findings hold after controlling for endogeneity using two-stage least squares regression and difference-in-differences tests. The authors find the negative effect is stronger for managers engaged in socially responsible activities, and after climate change issues receiving greater public awareness following the 2006 release of the Stern Review and the 2016 signing of the Paris Accord.

Motivated by the resource-based theory and the natural resource-based view of the firm model, the empirical results support the view that greater managerial ability protects the firm against environmental challenges through efficient use of firm resources. Compared with traditional climate change measures that are plagued by disclosure issues, the use of the Sautner, Van Lent, Vilkov and Zhang's machine learning based dataset utilizing earning conference calls provides stronger, robust findings that will be useful to management and investors in environmental performance assessments.

Motivated by the resource-based theory and the natural resource-based view of the firm model, the empirical results support the view that greater managerial ability protects the firm against environmental challenges through efficient use of firm resources. Compared with traditional climate change measures that are plagued by disclosure issues, the use of the machine learning based dataset utilizing earning conference calls provides stronger, robust findings that will be useful to management and investors in environmental performance assessments.

This study aims to explore the gendered nature of housing insecurity by investigating how gender affects women’s experience moving from transitional to market housing. By describing women’s pathways out of supportive or transitional housing support, the authors show how patriarchal forces in housing policies and practices affect women’s efforts to find secure housing. The authors argue that gender-neutral approaches to housing will fail to meet women’s needs.

This study explores the narratives from women accessing support services in Halifax, Canada. The first author conducted deep narrative interviews with women seeking to move from transition to market housing.

This research sheds light on the effects of gendered barriers to safe, suitable and affordable housing; how women’s experiences and expectations are shaped by these barriers; and, how housing-based supports must address the uniquely gendered experiences women face as they access market housing. In addition, this research reveals the importance of gender-responsive services that empower women facing a sexist housing market.

Little research has explored questions related to gender and housing among those seeking to move from transitional to marker housing, and existing research focuses on women’s housing insecurity as it relates to domestic violence. The sample of women included those having housing insecurity for a variety of reasons, including substance use and young motherhood.

In this study, the authors examine the relationships between market-based regulations and corporate carbon disclosure and carbon performance. The authors also investigate whether these relationships vary across emission-intensive and non-emission intensive industries.

The study sample consists of the world's 500 largest companies across most major industries over a recent five-year period. Country-specific random effect multiple regression analysis is used to test empirical models that predict relationships between market-based regulations and carbon disclosure and carbon performance.

Results indicate that market-based regulations significantly and positively affect corporate carbon performance. However, market-based regulations do not significantly affect corporate carbon disclosure. This study also finds that the association between regulatory pressures and carbon disclosure and carbon performance varies across emission-intensive and non-emission-intensive industries.

The findings of this study have key implications for policymakers, practitioners and future researchers in terms of understanding the factors that drive businesses to increase their carbon performance and disclosure. The study sample consists of only large firms, and future researchers can undertake similar studies with small and medium-sized firms.

The results of this study are expected to help business managers to identify the benefits of adopting market-based regulations. Regulators can use this study’s results to evaluate if market-based regulations effectively improve corporate carbon performance and disclosure. Furthermore, stakeholders may use this study to evaluate and improve their businesses' reporting of carbon disclosure and performance.

In contrast to current literature that has used command and control regulations as a proxy for regulation, this study uses market-based regulations as a proxy for climate change regulations. In addition, this study uses a more comprehensive measure of carbon disclosure and carbon performance compared to the previous studies. It also uses global multi-sector data from carbon disclosure project (CDP) in contrast to most current studies that use national data from annual reports of sample firms of specific sectors.

The study aims to analyze the synergy of artificial intelligence (AI), with scientometrics, webometrics and bibliometrics to unlock and to emphasize the potential of the applications and benefits of AI algorithms in these fields.

By conducting a systematic literature review, our aim is to explore the potential of AI in revolutionizing the methods used to measure and analyze scholarly communication, identify emerging research trends and evaluate the impact of scientific publications. To achieve this, we implemented a comprehensive search strategy across reputable databases such as ProQuest, IEEE Explore, EBSCO, Web of Science and Scopus. Our search encompassed articles published from January 1, 2000, to September 2022, resulting in a thorough review of 61 relevant articles.

(1) Regarding scientometrics, the application of AI yields various distinct advantages, such as conducting analyses of publications, citations, research impact prediction, collaboration, research trend analysis and knowledge mapping, in a more objective and reliable framework. (2) In terms of webometrics, AI algorithms are able to enhance web crawling and data collection, web link analysis, web content analysis, social media analysis, web impact analysis and recommender systems. (3) Moreover, automation of data collection, analysis of citations, disambiguation of authors, analysis of co-authorship networks, assessment of research impact, text mining and recommender systems are considered as the potential of AI integration in the field of bibliometrics.

This study covers the particularly new benefits and potential of AI-enhanced scientometrics, webometrics and bibliometrics to highlight the significant prospects of the synergy of this integration through AI.

This study aims to examine how new regulation changes for the auditor’s report, so-called key audit matters (KAMs), influence tax avoidance.

This study uses data from firms listed on the Omani capital market over the period 2012–2019 and analyzes these data using pooled panel data regression with a robust standard error. It uses two common proxies for tax avoidance and two measures for the KAMs disclosure requirement.

This study finds a sharp decrease in the effective tax rate following the introduction of KAMs disclosure and the issuance of more KAMs in audit reports. This result is supported by several robustness checks. In an additional analysis, the authors observe interesting results, indicating that real earnings management mediates this association, while the audit committee plays a moderating role. The authors do not find a moderating effect of Big4 on this association, but find discrepancies within the Big4 firms in relation to this moderating effect.

The results of this study indicate that although the introduction of the KAMs disclosure requirement may have positive consequences, it may also lead to unintended negative consequences. This conclusion has not been comprehensively reported in literature.

This study aims to examine the relationship between the individual auditor’s industry specialization and the audit report lag (hereafter ARD). Further, it explores whether changing in the audit reporting requirement (i.e. the adoption of ISA701) influences the auditor’s industry specialization effect on the ARD.

A large data set of companies listed on the NASDAQ OMX Stockholm over the period 2010–2019 has been analyzed. Least squares regressions have been estimated to provide empirical evidence for the researched hypotheses.

The research findings indicate that the ARD is shorter for client firms audited by an industry specialist audit partner. Testing for the moderating role of changing in the auditing reporting regulation on the relation between the audit partner’s industry specialization and the ARD, the authors reveal that all client firms (except client firms with industry specialist audit partners) experienced an increase in the ARD. Overall, the baseline regression findings are found to be robust to the endogenous auditor choice and multiple measures of both the ARD and the auditor’s industry specialization.

This paper provides novel evidence on the relationship between the audit reporting lag and industry specialization from the individual auditor perspective, an issue that has hitherto been unexplored. The regression results further contribute to the upsurge debate about the consequences of changing in the audit reporting model by providing consistent support for the importance of industry specialization of the audit partner in minimizing costs derived from the former requirement.

This paper aims to explore the potential effect of key audit matters (KAM) on the audit report lag (ARL). In particular, it aims to discover whether the number of KAMs reported by an audit firm in Jordan is related to the length of its ARL.

The authors analysed data from the first three years of KAM reporting in Jordan (2017–2019) for 194 public listed Jordanian companies to examine the relation between the number of KAMs and the ARL, taking into account several control variables related to the Jordanian context.

This study found that there is no statistically significant relation between the number of KAMs reported by Jordanian audit firms and their ARLs, suggesting that the KAM reporting in Jordan is somewhat superficial, with the selection of what is actually reported as a KAM not directly related to the efforts needed to deal with its concerns. However, this study also found statistically significant positive relations between the ARL and each of audit fees, audit firm size, the issuance of a qualified audit opinion and company leverage and a statistically significant negative relation between the ARL and company profitability.

This is one of the very few studies to cover the potential relation between KAM reporting and the ARL. In a developing country context characterised by limited demand for an external audit of high quality, this study finds that auditors may decouple on their reporting of KAMs by not actually making significant efforts to deal with them.