Classification Shifting Research Articles

Discrepancy model to RtI: Gauging teacher preparedness for this shift in specific learning disability classification

AbstractThe Individuals with Disabilities Education Improvement Act (2004) allows alternate pathways for school districts to identify and classify students with a specific learning disability (SLD). Response to Intervention (RtI) is one of the frameworks schools can use when eliminating the use of the discrepancy model. The premise of RtI posits that tiered instruction can assist all students who are struggling through utilizing research‐based interventions. When these tiers of intervention services are exhausted with limited to no response to the intervention, a child can be classified under the SLD category. There are documented concerns with effective implementation of RtI frameworks. This qualitative exploratory study examined the perceptions of teachers in a graduate education program regarding the knowledge, usage and implementation of RtI to classify students with a SLD. Through thematic analysis, findings indicate a need for more training in districts, better preparation at the university level and more support in applying RtI in the classroom. Implications for practice and future research are discussed.

Does integrated reporting offer firms more legitimacy?

PurposeThe study delves into the impact of integrating reporting (IR) on three earnings management tools, namely classification shifting (CS), real-based earnings management (REM) and accrual-based earnings management (AEM) under the Indian institutional settings.Design/methodology/approachThe data analysis involved the application of panel data regression models. Our dataset comprises 2,244 firm-years listed on the Bombay Stock Exchange spanning over financial years from March 2015 to 2021. To address endogeneity and self-selection bias concerns, a propensity score matching technique has been employed.FindingsOur empirical results exhibit that IR-adopting firms are engaged in earnings management. Further, we find that IR-adopting firms have reduced their engagement in AEM and REM, however, their CS practices have been increased, indicating the substitution relationship between earnings management tools after the adoption of IR. It implies that firms shift their preference from more to less observable earnings management tools after the adoption of the IR, which aligns with the idea that firms adopt IR to gain legitimacy, however, their intention to deceive stakeholders through earnings management remains unchanged. The inclination of firms toward CS can be ascribed to its cost-effectiveness, as it leaves net profit unchanged, hence less likelihood of being detected by auditors. Overall, our results align with the principle of legitimacy theory.Research limitations/implicationsThe study focuses exclusively on three primary forms of accounting manipulation and assesses IR holistically, rather than investigating the influence of each capital individually within IR.Practical implicationsWith a shift towards less detectable methods like CS, auditors must adapt their scrutiny and be mindful of their clients' IR adoption. Investors should scrutinize IR-adopting firms' financial disclosures, especially line items, as CS does not impact the net profits.Originality/valueIt is the pioneering research to thoroughly explore the impact of IR on different earnings management tools and strengthen the conceptual frameworks of legitimacy theory by documenting that firms adopt IR to gain legitimacy, however their intention to engage in earnings management remains intact.

The Impact of Classification Shifting on Firm Value (An Empirical Study)

The Impact of Classification Shifting on Firm Value (An Empirical Study)

Hereditary Patterns and Genetic Associations in Obsessive-Compulsive Disorder (OCD): Neuropsychiatric Insights, Genetic Influences, and Treatment Perspectives.

Obsessive-Compulsive Disorder (OCD), a prevalent neuropsychiatric condition, affects approximately 2%-3% of the global population. This paper provides an extensive overview of OCD, detailing its clinical manifestations, neurobiological underpinnings, and therapeutic approaches. It examines OCD's classification shift in the DSM-5, the role of the cortico-striatothalamo- cortical pathway in its development, and the various factors contributing to its etiology, such as genes, environmental factors, and genetic predispositions. The challenges in diagnosing OCD and the effectiveness of both psychological and pharmacotherapeutic treatments are discussed. The paper also highlights the significant overlap between OCD and other mental health disorders, emphasizing its impact on global disability. Moreover, the role of genetic factors in OCD, including twin studies and gene association studies, is elaborated, underscoring the complex interplay of hereditary and environmental influences in its manifestation. The review further delves into the polygenic nature of OCD, illustrating how multiple genes contribute to its development, and explores the implications of genetic studies in understanding the disorder's complexity. Additionally, this research study delves into the concept of polygenic inheritance in complex diseases, highlighting the role of multiple genes in increasing OCD risk. A Genome-wide Association Study (GWAS) is employed to assess Single Nucleotide Polymorphisms (SNPs) to unearth genetic associations with OCD. This comprehensive analysis provides valuable insights into OCD's genetic landscape, paving the way for enhanced diagnostic approaches and treatment modalities.

Management Inducements for Cash Flows Classification Shifting in Egyptian Companies: Analysis of Core Operating Cash Flows Inflation Strategies—An applied study

Management Inducements for Cash Flows Classification Shifting in Egyptian Companies: Analysis of Core Operating Cash Flows Inflation Strategies—An applied study

Ventilator and the Vaccine

During the Covid-19 pandemic, the fat body was caught up in a complicated logics of life and death in the North American context, where “obesity” was regarded as an “underlying condition” for greater risk of severe disease and death from Covid. As such, bodies with high BMIs were refused ICU ventilator care in certain jurisdictions, which fat activists classified as eugenics. At the same time, some vaccination campaigns prioritized people with higher BMIs for scarcely available Covid vaccinations, also on the basis of fat bodies’ higher risk status for Covid death. This paper explores the seeming tension between two articulations of fat during the Covid pandemic, whereby fat bodies were simultaneously worthy of life and of death in the same moment. Using MBembe’s conceptualization of necropolitics, which draws out and expands upon Foucault’s notion of biopolitics, I argue that the two perspectives on fatness operated in tandem, within an overall temporal shift in classification of obesity; from that of a risk factor for eventual death to that of an emergent threat. Such a temporal shift, I argue, relied on well-worn eugenic patterns in Canada, through which “normative” white bodies were prioritized for life through a complex necropolitical practice by which fat bodies were both made live and let die.

Relative Norm Alignment for Tackling Domain Shift in Deep Multi-modal Classification

Multi-modal learning has gained significant attention due to its ability to enhance machine learning algorithms. However, it brings challenges related to modality heterogeneity and domain shift. In this work, we address these challenges by proposing a new approach called Relative Norm Alignment (RNA) loss. RNA loss exploits the observation that variations in marginal distributions between modalities manifest as discrepancies in their mean feature norms, and rebalances feature norms across domains, modalities, and classes. This rebalancing improves the accuracy of models on test data from unseen (“target”) distributions. In the context of Unsupervised Domain Adaptation (UDA), we use unlabeled target data to enhance feature transferability. We achieve this by combining RNA loss with an adversarial domain loss and an Information Maximization term that regularizes predictions on target data. We present a comprehensive analysis and ablation of our method for both Domain Generalization and UDA settings, testing our approach on different modalities for tasks such as first and third person action recognition, object recognition, and fatigue detection. Experimental results show that our approach achieves competitive or state-of-the-art performance on the proposed benchmarks, showing the versatility and effectiveness of our method in a wide range of applications.

Core Earnings Incentive Contracts and Classification Shifting

Core Earnings Incentive Contracts and Classification Shifting

Navigating Japan's COVID-19 Vaccination Challenges: A Shift in Legal Classification and the Opt-out System.

On January 20, 2023, the Japanese government announced easing the legal handling of the novel coronavirus disease 2019 (COVID-19) under its Infectious Diseases Control Act, effective May 8, 2023. While free vaccines will continue in fiscal year 2023, the future of mass vaccinations remains uncertain. The opt-out system, wherein local governments schedule vaccinations on behalf of the residents, may potentially alleviate issues associated with the currently adopted opt-in approach, such as procedural intricacies and scheduling difficulties, thereby facilitating the recovery of vaccination rates and simultaneously addressing vaccine wastage concerns. Given that COVID-19 still presents a substantial risk to specific groups, such as the elderly, recognizing the benefits of the opt-out system and the ethical and geographical challenges it poses is essential. With the collaboration of local governments and healthcare institutions, ongoing surveillance and scientific assessment are indispensable.

Classification Shifting and Earnings Predictability

The literature measures classification shifting as the relation between unexpected core earnings and income-decreasing special items. The general view in this literature is that managers shift core expenses to special items to inflate core earnings to achieve self-motivated reporting objectives. However, an additional possibility is that classification shifting helps investors better predict future performance. We find evidence of this positive consequence of classification shifting. Our study raises the possibility that measures of classification shifting in certain settings do not reflect managers’ opportunistic reporting. Given the relatively limited evidence in the literature on the consequences of classification shifting on investors, we believe these findings need to be considered as the literature moves forward.

Blood Cell Revolution: Unveiling 11 Distinct Types with ‘Naturalize’ Augmentation

Artificial intelligence (AI) has emerged as a cutting-edge tool, simultaneously accelerating, securing, and enhancing the diagnosis and treatment of patients. An exemplification of this capability is evident in the analysis of peripheral blood smears (PBS). In university medical centers, hematologists routinely examine hundreds of PBS slides daily to validate or correct outcomes produced by advanced hematology analyzers assessing samples from potentially problematic patients. This process may logically lead to erroneous PBC readings, posing risks to patient health. AI functions as a transformative tool, significantly improving the accuracy and precision of readings and diagnoses. This study reshapes the parameters of blood cell classification, harnessing the capabilities of AI and broadening the scope from 5 to 11 specific blood cell categories with the challenging 11-class PBC dataset. This transformation facilitates a more profound exploration of blood cell diversity, surpassing prior constraints in medical image analysis. Our approach combines state-of-the-art deep learning techniques, including pre-trained ConvNets, ViTb16 models, and custom CNN architectures. We employ transfer learning, fine-tuning, and ensemble strategies, such as CBAM and Averaging ensembles, to achieve unprecedented accuracy and interpretability. Our fully fine-tuned EfficientNetV2 B0 model sets a new standard, with a macro-average precision, recall, and F1-score of 91%, 90%, and 90%, respectively, and an average accuracy of 93%. This breakthrough underscores the transformative potential of 11-class blood cell classification for more precise medical diagnoses. Moreover, our groundbreaking “Naturalize” augmentation technique produces remarkable results. The 2K-PBC dataset generated with “Naturalize” boasts a macro-average precision, recall, and F1-score of 97%, along with an average accuracy of 96% when leveraging the fully fine-tuned EfficientNetV2 B0 model. This innovation not only elevates classification performance but also addresses data scarcity and bias in medical deep learning. Our research marks a paradigm shift in blood cell classification, enabling more nuanced and insightful medical analyses. The “Naturalize” technique’s impact extends beyond blood cell classification, emphasizing the vital role of diverse and comprehensive datasets in advancing healthcare applications through deep learning.

Mitigating the influence of domain shift in skin lesion classification: A benchmark study of unsupervised domain adaptation methods

The potential of deep neural networks in skin lesion classification has already been demonstrated to be on-par if not superior to the dermatologists’ diagnosis in experimental settings. However, the performance of these models usually deteriorates in real-world scenarios, where the test data differs significantly from the training data (i.e. domain shift). This concerning limitation for models intended to be used in real-world skin lesion classification tasks poses a risk to patients. For example, different image acquisition systems or previously unseen anatomical sites on the patient can suffice to cause such domain shifts. Mitigating the negative effect of such shifts is therefore crucial, but developing effective methods to address domain shift has proven to be challenging. In this study, we carry out a comparative analysis of eight different unsupervised domain adaptation methods to analyze their effectiveness in improving generalization for dermoscopic datasets. To ensure robustness of our findings, we test each method on a total of ten derived datasets, thereby covering a variety of possible domain shifts. In addition, we investigated which factors in the domain shifted datasets have an impact on the effectiveness of domain adaptation methods. Our findings show that all of the eight domain adaptation methods result in improved AUPRC for the majority of analyzed datasets. Altogether, these results indicate that unsupervised domain adaptations generally lead to performance improvements for the binary melanoma-nevus classification task regardless of the nature of the domain shift. However, small or heavily imbalanced datasets lead to a reduced conformity of the results due to the influence of these factors on the methods’ performance.

Comparison of the Revised 4 Th (2016) and 5 Th (2022) Editions of the World Health Organization (WHO) Classification in a Cohort of Patients with Lower-Risk Myelodysplastic Syndromes/Neoplasms (MDS) - a Glam Registry (REGLAM) Analysis

Background: The5 th (2022) edition of the WHO Classification for MDS recognizes MDS patients into two groups: MDS with defining genetic abnormalities and MDS morphologically defined. Further, the revised International Prognostic Scoring system (IPSS-R) assigns MDS patients into one of prognostic groups with distinct survival probabilities. However, both the IPSS-R and the 2022 WHO classification were developed based on data largely generated from patients in the high-income countries. There are limited data of how these systems perform in patients from developing and middle-income countries (LMIC). The primary objective of this study was to compare the performance of the two WHO classification: the revised 4 th (2016) and 5 th (2022) editions in IPSS-R defined Lower-Risk MDS Cohort of patients from LMIC in the GLAM registry. Methods: TheGlam Registry enrolls patients from 16 Latin-American countries, For this analysis, we selected Lower risk MDS (defined as IPSS <3.5) patients from Argentina, Brazil, Chile, Colombia, Mexico, Paraguay, and Uruguay. Patients with CMML and higher-Risk MDS were excluded. The study was conducted in compliance with local regulations, and all subjects signed inform consents. Descriptive statistics, Sankey Diagram, Kaplan Meier methods and the Confidential Interval for the 5-year survival probability were used to report the results. Overall survival (OS) was measured from time of diagnosis to last contact or death, and progression-free survival (PFS) was measured from time of diagnosis to disease progression, progression to acute myeloid leukemia (AML), or death. Results: A total of 223 LR-MDS patients were included in this analysis. Baseline characteristics and demographics are described in Table 1. Median age was 69 years, 47% were males, and 71% were non-Hispanic whites. The median blast count was 1% (range, 0-8%), and only 7% had therapy-related MDS. According to WHO-2016 edition, patients were classified as MDS-RS-SLD (n = 15 [6.72 %]); MDS-RS-MLD (n = 23 [10.3 %]); MDS-RS-T (n = 6 [2.7 %]), MDS-del(5q) (n = 15 [6.7 %]), MDS-SLD (n = 34 [15.2 %]), MDS-MLD (n = 116 [52 %]), MDS-EB1 (n = 11 [4.9 %]), MDS-EB2 (n = 0 [0 %]), and MDS-U (n = 3 [0.66 %]). According to WHO-2022 classification, subjects were classified as: MDS-del(5q) (n = 15 [6.7 %]), MDS-LB-SF3B1-RS (n = 40 [17.9% %]), MDS-biTP53 (n = 0 [0 %]), MDS-LB (n = 131 [58.7 %]), MDS-h (n = 27 [12.1 %]), MDS-IB1 (n = 10 [4.4 %]), MDS-IB2 (n = 0 [0 %]) and MDS-f (n = 0). Figure 1 represents the shifts in classification of patients between the 2016 and 2022 version. The 5-year survival probabilities (%) of MDS-SLD vs MDS-MLD (WHO-2016) was 62.5% (95CI 37.8-79.7) vs. 54.6% (95CI 39.9-65.6), and the 5-year PFS probabilities (%) for MDS-SLD vs MDS-MLD were 62.5% (95CI 37.8-79.7) vs 53.6% (95CI 39.9-65.6) respectively. There were no cases of biTP53-mutation among the 12.1% of patients who had testing for TP53. Three patients were re-classified from MDS-RS-MLD (WHO-2016) to MDS-LB (WHO-2022) because SF3B1 mutation was associated with complex karyotype, del(5q), del(7q) and/or TP53 monoallelic. Four patients with MDS-U (WHO-2016) were re-classified to MDS-LB (WHO-2022). MDS-LB category (WHO-2022) was a large and a very heterogeneous group with an OS and LFS of 5.2 years. Conclusions: Our study, to our knowledge, provides one of the first, if not the first, datasets from LMIC to describe characteristics of IPSS-R lower-risk MDS pts and their re-classification and corresponding survival according to the WHO 2016 and 2022 classifications. Limited availability of molecular analysis in real-life settings (e.g., TP53 mutations) highlights some of the challenges of using the 2022 WHO classification (as well as IPSS-M) LMIC. Understanding the epidemiology of MDS pts in LMIC is important especially as some of the newly approved agents for lower risk MDS are starting to be used in these countries.

A Unified Efficient Deep Learning Architecture for Rapid Safety Objects Classification Using Normalized Quantization-Aware Learning

The efficient recognition and classification of personal protective equipment are essential for ensuring the safety of personnel in complex industrial settings. Using the existing methods, manually performing macro-level classification and identification of personnel in intricate spheres is tedious, time-consuming, and inefficient. The availability of several artificial intelligence models in recent times presents a new paradigm shift in object classification and tracking in complex settings. In this study, several compact and efficient deep learning model architectures are explored, and a new efficient model is constructed by fusing the learning capabilities of the individual, efficient models for better object feature learning and optimal inferencing. The proposed model ensures rapid identification of personnel in complex working environments for appropriate safety measures. The new model construct follows the contributory learning theory whereby each fussed model brings its learned features that are then combined to obtain a more accurate and rapid model using normalized quantization-aware learning. The major contribution of the work is the introduction of a normalized quantization-aware learning strategy to fuse the features learned by each of the contributing models. During the investigation, a separable convolutional driven model was constructed as a base model, and then the various efficient architectures were combined for the rapid identification and classification of the various hardhat classes used in complex industrial settings. A remarkable rapid classification and accuracy were recorded with the new resultant model.

LEO Consortium for Real World Evidence (CReWE): Outcomes after Second-Line Therapy in Large B-Cell Lymphoma By Treatment Era

Background: Recently, large B-cell lymphomas (LBCLs) have undergone major shifts in classification and treatment paradigm, particularly with the advent of CD19-targeted chimeric antigen receptor T cell therapy (CAR-T). Initially approved as third-line treatment in LBCL in 2017, as of early 2022 CAR-T is indicated for second-line therapy (2L) in patients with relapsed or refractory (R/R) disease within 12 months of frontline treatment or who are not candidates for autologous stem cell transplant (ASCT). Given this quickly changing landscape, we aimed to describe disease characteristics, patterns of care, and survival outcomes in patients with R/R LBCL receiving two or more lines of systemic therapy across modern treatment eras. Methods: We developed a database of patients with R/R LBCL from 8 U.S. academic centers in the Lymphoma Epidemiology of Outcomes (LEO) Cohort study (NCT02736357) and Consortium for Real World Evidence (CReWE). Unlike the SCHOLAR-1 dataset (M Crump et al, Blood 2017), our cohort included any patient receiving 2L, regardless of timing of progression/relapse. For this analysis, eligible patients were aged ≥18 years and received 2L between 2002-2022. Patients with histologic transformation, post-transplant lymphoproliferative disorder, primary CNS lymphoma, primary mediastinal B-cell lymphoma, Burkitt lymphoma, or plasmablastic lymphoma were excluded. Prognostic factors, therapy details (including intent for ASCT and/or CAR-T), and outcomes, including treatment response, event-free survival (EFS), and overall survival (OS), were abstracted for all lines of therapy. Treatment eras were defined as pre-CAR-T (2002-2010), CAR-T available via clinical trial (2011-2017), and post-FDA approval of CAR-T (2018-2022). Results: Of 1760 patients initiating 2L for R/R disease, 1523 were eligible for analysis. Median age at start of 2L was 62 (interquartile range [IQR] 53-70), and 65% were male; 11% were non-White, and 8% were Hispanic. High-grade subtypes comprised 16% of all cases. IPI was available for 1013 patients at time of 2L therapy, with 54% having IPI 3-5. Median time from diagnosis to 2L was 8.7 months (IQR 5.6-18.5), with 834 patients not having achieved complete response (CR) to 1L, 285 patients relapsing <12 months after 1L, and 404 patients relapsing ≥12 months after 1L. The median number of total lines of therapy received was 4 (range 2-18), with 586 and 347 receiving ASCT and CAR-T, respectively, at any line of treatment. At a median follow-up of 48 months from the start of 2L, 926 patients (61%) had died. Progressive lymphoma was the primary cause of death in 75% of deceased patients, with 8% of reported deaths due to therapy. 2L was received at an academic medical center in 83% cases, and 209 patients (14%) received 2L on a clinical trial. ASCT and/or CAR-T was planned at 2L for 989 patients (65%), of whom 463 ultimately received ASCT, 88 received CAR-T, and 21 received allogeneic transplant at 2L. 494 patients were not considered for ASCT or CAR-T at 2L, and 40 were unknown for ASCT/CAR-T intent. Breakdown by treatment era (n = 1518 with available treatment start date) is shown in the Table. Median EFS from start of 2L was 4.2 months (95% confidence interval [CI]: 3.8-4.8). Median OS from start of 2L was 18 months (95% CI: 17-22), and 2- and 5-year OS estimates were 46% (95% CI: 44-49%) and 35% (95% CI: 33-38%), respectively. EFS and OS improved significantly for patients initiating 2L between 2011-2017 compared to 2002-2010. However, EFS and OS in the 2018-2022 era remained similar to 2011-2017 (Figure). Conclusions: To our knowledge, this study is the first-ever large-scale attempt to describe patterns of care and outcomes in LBCL 2L in the modern era, inclusive of all treatment approaches. The rich and unique LEO CReWE dataset captures evolving practice approaches over time in terms of both intent for and receipt of cellular therapies. Likely reflecting the rapid development of many novel immune effector cell products and bispecific antibodies, we observed increased enrollment on 2L clinical trials in the 2018-2022 period, including a substantial proportion of patients receiving CAR-T in 2L. Importantly, survival increased in treatment eras during which CAR-T was available. Ongoing analyses of factors associated with improved survival will be presented at the meeting.

Automatic correction of performance drift under acquisition shift in medical image classification

Image-based prediction models for disease detection are sensitive to changes in data acquisition such as the replacement of scanner hardware or updates to the image processing software. The resulting differences in image characteristics may lead to drifts in clinically relevant performance metrics which could cause harm in clinical decision making, even for models that generalise in terms of area under the receiver-operating characteristic curve. We propose Unsupervised Prediction Alignment, a generic automatic recalibration method that requires no ground truth annotations and only limited amounts of unlabelled example images from the shifted data distribution. We illustrate the effectiveness of the proposed method to detect and correct performance drift in mammography-based breast cancer screening and on publicly available histopathology data. We show that the proposed method can preserve the expected performance in terms of sensitivity/specificity under various realistic scenarios of image acquisition shift, thus offering an important safeguard for clinical deployment.

Firm's life cycle and cash flow classification: evidence from Indian firms

PurposeThe study examines the influence of a firm's life cycle on the cash flow classification of Indian firms.Design/methodology/approachThe study employs Dickinson's (2011) cash flow patterns to classify firm years under various life-cycle stages. Cash flow classification is employed to measure a firm's classification shifting (CS) practices. The study includes Indian firms listed on the Bombay Stock Exchange during 2012–2020, an ordinary least squares regression model, a fixed-effect model and a panel corrected with standard error regression method.FindingsFirms face different opportunities and challenges at different stages of the firm's life cycle and therefore adopt cash flow CS. The results show that firms adopt cash flow CS during introduction, growth and decline stage of life cycle either to boost or to reduce operating cash flows.Originality/valueThis study is one of its kind to study the influence of a firm's life cycle on the cash flow classification of Indian firms.

The BP oil spill and income classification shifting of oil and gas companies

Literature has documented income classification shifting to increase core earnings. There has been little research on whether firms reduce income increasing classification shifting or classification shift to reduce core earnings - classify non-core items as core expenses. Also, research on classification shifting under non-market incentives is limited. We fill these gaps by examining the shifting behavior of oil and gas firms in response to the BP oil spill. The oil spill was an unexpected event that led to regulations and restrictions, as well as adverse publicity. This could have encouraged petroleum firms to suppress core earnings. We show that after the oil spill, (1) the likelihood of petroleum firms' classification shifting to inflate core earnings declines relative to other firms and (2) the likelihood of petroleum companies' shifting to report lower core earnings increases relative to other firms.

The Impact of Financial Statement Comparability on Income Classification Shifting: Evidence from Egypt

The Impact of Financial Statement Comparability on Income Classification Shifting: Evidence from Egypt

Audit quality and classification shifting: evidence from UK and Germany

PurposeThe authors examine the impact of audit quality (AQ) on classification shifting (CS) among non-financial firms operating in the UK and Germany.Design/methodology/approachThis paper used various audit committee variables (size, meetings, gender diversity and financial expertise) to measure AQ and its impact on CS. The authors used a total of 2,110 firm-year observations from 2010 to 2019.FindingsThe authors found that the presence of female members on the audit committee and audit committee financial expertise deter the UK and German managers from shifting core expenses and revenue items into special items to inflate core earnings. However, audit committee size is positively related to CS among German firms but has no impact on UK firms. The authors also document evidence that audit committee meetings restrain UK managers from engaging in CS. However, the authors found no impact on CS among German firms. The study results hold even after employing several tests.Research limitations/implicationsOverall, the study findings provide broad support in an international setting for the board to improve its auditing practices and offer essential information to investors to assess how AQ affects the financial reporting process.Originality/valueMost CS studies used market-oriented economies such as the USA and UK and ignored bank-based economies such as Germany, France and Japan. The authors provide a comparison among bank and market-oriented economies on whether the AQ has a similar impact on CS or not among them.