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This study aims to test the effect of conforming tax avoidance on earnings persistence.

The authors use multiple linear regression incorporating firm and year-fixed effects to examine the association between conforming tax avoidance and earnings persistence. Additionally, the authors explore the moderating effect of conforming tax avoidance on the association between current earnings and future (one-year) operating cash flow (OCF). To guarantee the strength and reliability of the results, the authors perform several additional tests.

The empirical findings reveal a statistically significantly negative association between conforming tax avoidance and earnings persistence. Additionally, the authors observe that conforming tax avoidance significantly and negatively moderates the relationship between current earnings and future OCF. Furthermore, the principal regression outcomes exhibit resilience in a range of robustness checks.

This research contributes to the accounting literature by offering a novel perspective on understanding one of the potential reasons behind firms exhibiting low earnings quality or persistence. Specifically, the authors attribute this phenomenon to the downward impact of conforming tax management. Furthermore, the results propose that the tax authority should be vigilant toward companies exhibiting low earnings persistence, particularly quasi-private firms and those with low financial constraints, as they may have implemented or be motivated to engage in conforming tax strategies that preceded the observed reduction in earnings persistence.

The purpose of this paper is to explore how variations in management’s tone within management’s discussion and analysis (MD&A) sections of 10-K reports can serve as an indicator of tax avoidance and highlight the complex relationship between such linguistic shifts and the tax avoidance decisions within firms.

The paper uses a textual analysis approach to identify linguistic cues in MD&A sections of 10-K filings related to tax avoidance, going beyond traditional quantitative measures. The study uses differences in negative word occurrences in MD&A to measure management’s tone change and examines various measures of tax avoidance. The sample covers the period from 1993 to 2017 and comprises all firms with 10-K filings available on EDGAR, totaling over 30,000 firm-year observations.

The findings indicate a complementary relationship between tax avoidance and other drivers of firm performance. When firms have more negative management’s tone, they are less willing to engage in tax avoidance and vice versa. The study’s approach with management’s tone change provides a different and statistically significant improvement in model fit for detecting tax avoidance.

This paper provides actionable insights for detecting tax avoidance through the analysis of management’s tone in corporate disclosures, offering a new tool for researchers, investors and tax authorities. It highlights the importance of linguistic cues as indicators of tax avoidance behavior, complementing traditional financial metrics.

The paper contributes to the literature by using management’s tone change as a time-varying factor to explain tax avoidance behavior. It uncovers a larger set of linguistic cues in MD&A that can be used to detect tax avoidance. This research provides a complementary approach to traditional quantitative tax avoidance measures and offers insights into the overall relationship between tax avoidance and firm performance, going beyond one-dimensional measures typically used in prior literature.

This study aims to propose a new conforming tax measure based on the work of Badertscher et al. (2019).

This study divides total tax avoidance/management (TM) into nonconforming and conforming portions through a regression. The residual of the regression is treated as the conforming tax measure. In addition, the new conforming tax measure is validated via three approaches. Then, this study examines the moderating effect of nonconforming earnings management (EM) on the relationship between conforming TM and firm performance.

The empirical results show that the model has stronger explanatory power than the model proposed by Badertscher et al. (2019). Additionally, the validation results show that the mean value of the conforming tax measure is lower in quasi-private corporations (financially constrained companies) than in matched public corporations (nonfinancially constrained companies), and firms under high market capital pressure are less motivated to engage in conforming tax practices. Furthermore, nonconforming EM positively moderates the conforming tax–ROA association, implying that nonconforming EM can reduce financial reporting costs resulting from conforming tax practices.

This study contributes to conforming tax research in the following ways. First, this study proposes a new conforming tax measure by substituting the cash book tax difference (BTD) for the BTD in the model of Badertscher et al. (2019) (“BKRW”). Second, this study demonstrates theoretically why the cash BTD should outperform the BTD in computing the BKRW conforming tax measure and confirm this empirically. Third, this study presents a three-way conceptual schema that divides corporations into two groups along each of three tax-relevant dimensions. The group of firms that use both conforming and nonconforming tax strategies have different characteristics compared to the other group. This study also validates the conforming tax measure across the two-group dichotomies.

Prefabricated housing is a construction method that largely reduces construction waste and promotes sustainable development. However, the adoption of this method of construction is inhibited by the lack of demand by consumers. The purpose of this research is to explore consumer education strategies aimed at overcoming prefabricated housing challenges in China.

The study conducted a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive search was conducted using databases such as Scopus and Web of Science, without any restriction on publication date. A total of 33 relevant articles were retrieved and analyzed using the content analysis method. The findings were then summarized and presented using the tabulation technique.

This study identified several barriers to prefabricated housing, including negative perception, limited public understanding, risk-averse culture and lack of green value. To foster market demand, relevant authorities should consider implementing effective educational mechanisms, such as comparative advertising, social media marketing, school programs and critical adult education programs. These strategies can effectively overcome the existing negative perception, alleviate concerns and create a positive environment for the growth of prefabricated housing in China.

This research provides practical and theoretical implications by identifying key challenges to prefabricated housing and suggesting targeted educational strategies to address them. It offers valuable guidance for policymakers, industry stakeholders and researchers, contributing to the advancement of sustainable housing practices.

The study explores how Taiwan’s electronic health data systems can be used to build algorithms that reduce or eliminate medical errors and to advance precision medicine.

This study is a narrative review of the literature.

The body of medical knowledge has grown far too large for human clinicians to parse. In theory, electronic health records could augment clinical decision-making with electronic clinical decision support systems (CDSSs). However, computer scientists and clinicians have made remarkably little progress in building CDSSs, because health data tend to be siloed across many different systems that are not interoperable and cannot be linked using common identifiers. As a result, medicine in the USA is often practiced inconsistently with poor adherence to the best preventive and clinical practices. Poor information technology infrastructure contributes to medical errors and waste, resulting in suboptimal care and tens of thousands of premature deaths every year. Taiwan’s national health system, in contrast, is underpinned by a coordinated system of electronic data systems but remains underutilized. In this paper, the authors present a theoretical path toward developing artificial intelligence (AI)-driven CDSS systems using Taiwan’s National Health Insurance Research Database. Such a system could in theory not only optimize care and prevent clinical errors but also empower patients to track their progress in achieving their personal health goals.

While research teams have previously built AI systems with limited applications, this study provides a framework for building global AI-based CDSS systems using one of the world’s few unified electronic health data systems.