Search results

This study aims to examine whether the newly available auditor tenure information is associated with non-GAAP earnings, as the recent requirement to disclose the initial year of auditor-client relationship in audit reports may give the impression that longer auditor tenure may be related to lower audit quality.

Using a sample of firm-quarters from 2017 to 2020, the authors conduct both univariate and regression analyses. We use hand-collected data for auditor tenure, SEC comment letters, and non-GAAP variables.

First, the authors find that the likelihood of disclosing non-GAAP earnings monotonically increases with auditor tenure on a univariate basis. Second, auditor tenure is negatively associated with aggressive non-GAAP reporting. Third, the authors document evidence of aggressive reporting in general; that is, items excluded in calculating non-GAAP earnings are associated with future performance. However, the association declines with longer auditor tenure. Finally, the authors report evidence that the likelihood of receiving an SEC comment letter that contains non-GAAP comments decreases with longer auditor tenure.

The results show that regulators need to consider both GAAP and non-GAAP disclosures’ costs and benefits when enacting auditor tenure regulation. Investors can benefit from the findings in evaluating the quality of non-GAAP earnings. The findings are also relevant to the SEC when allocating limited resources in monitoring non-GAAP reporting.

To the best of the authors’ knowledge, this is the first study showing that auditor tenure is associated with the quality of non-GAAP earnings. Given that financial reporting quality should be understood as a comprehensive system comprising both mandatory and voluntary disclosures, this study complements the literature that examines the effect of auditor tenure on financial reporting quality using GAAP reporting.

The shortage of organ donors is an under-researched global issue that demands immediate attention. This attention should begin at the government level and related organizations. In Malaysia, the shortage of organ donations has been a pressing issue faced by the Ministry of Health Malaysia (MOH) for a considerable length of time. In reaction to this issue, the MOH deployed the Organ Donation Awareness Strategic Campaign Plan by using the platform of social media to disseminate information regarding organ donation to the public. However, the number of registrations is still low among Malaysians. Moreover, the observation from the literature shows that there are limited studies which have been initiated to focus on social media in the context of organ donation campaigns.

The quantitative research design has been used to understand the issue. Three hundred and eighty-four completed questionnaires were collected from the target sample, which comprised university students in Malaysia. For this study, partial least squares structural equation modelling was used for data analysis.

The result shows that information usefulness is vital because it will lead individuals to adopt organ donation information on social media. More specifically, predictors that positively influence youth or university students to accept information as useful are visual information, information sharing, accessibility of information, needs of information and attitude towards information. Subsequently, information usefulness positively influences information adoption. In the meantime, information quality and credibility do not significantly affect information usefulness.

The findings of this study may assist MOH or interested parties in designing a sound marketing strategy in the context of organ donation promotion by providing empirical evidence.

The study provides empirical evidence about information characteristics in the context of organ donation promotion.

Although risk culture is a key determinant for an effective risk management, identifying the risk culture of a firm can be challenging due to the abstract concept of culture. This paper proposes a novel approach that uses unsupervised machine learning techniques to identify significant features needed to assess and differentiate between different forms of risk culture.

To convert the unstructured text in our sample of banks' 10K reports into structured data, a two-dimensional dictionary for text mining is built to capture risk culture characteristics and the bank's attitude towards the risk culture characteristics. A principal component analysis (PCA) reduction technique is applied to extract the significant features that define risk culture, before using a K-means unsupervised learning to cluster the reports into distinct risk culture groups.

The PCA identifies uncertainty, litigious and constraining sentiments among risk culture features to be significant in defining the risk culture of banks. Cluster analysis on the PCA factors proposes three distinct risk culture clusters: good, fair and poor. Consistent with regulatory expectations, a good or fair risk culture in banks is characterized by high profitability ratios, bank stability, lower default risk and good governance.

The relationship between culture and risk management can be difficult to study given that it is hard to measure culture from traditional data sources that are messy and diverse. This study offers a better understanding of risk culture using an unsupervised machine learning approach.