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Wyświetlanie 1-2 z 2
Tytuł:
Quo Vadis, earnings management? Analysis of manipulation determinants in Central European environment
Autorzy:
Valaskova, Katarina
Adamko, Peter
Frajtova Michalikova, Katarina
Macek, Jaroslav
Powiązania:
https://bibliotekanauki.pl/articles/19233715.pdf
Data publikacji:
2021
Wydawca:
Instytut Badań Gospodarczych
Tematy:
earnings management
discretionary accruals
aggressive accounting
conservative accounting
Opis:
Research background: The paper investigates the earnings management phenomenon in the context of Central European countries, attempting to identify the factors and incentives that can influence earnings management behavior on a sample of 8,156 enterprises from Slovakia, the Czech Republic, Hungary, and Poland. Purpose of the article: The main purpose of the manuscript is to prove that there are significant differences in earnings management practices (measured by discretionary accruals) across the countries and to find the firm-specific features that influence the way enterprises manage their earnings. Methods: The modified Jones model was used to calculate the discretionary accruals, which are further analyzed across the countries. The statistically significant differences were confirmed across the countries. Thus, the impact of the economic sector, firm size, firm age, legal form, and ownership structure on earnings management behavior is studied by the Kruskal-Wallis test. The Dunn-Bonferroni post hoc tests then revealed the significant differences across the categories of the investigated earnings management determinants. To find the association between the particular earnings management practice (income-increasing or income-decreasing manipulation), correspondence analysis was used to visualize the mutual relations. Findings & value added: The results of the realized investigation revealed that the economic sector is one of the most important earnings management determinants, as its statistical significance was confirmed in each analyzed country. The correspondence analysis determined specific sectors, where income-increasing manipulation with earnings is practiced (NACE codes F, J, K, M, N), and vice versa, income-decreasing earnings management is characteristic for enterprises in sectors A, C, D, G or L. In specific economic conditions, firm size is also a relevant indicator (Hungary), or firm age and legal form and ownership structure (Poland). The recognition of crucial earnings management incentives may be helpful for authorities, policymakers, analysts and auditors when identifying various techniques and practices of earnings manipulation which could vary across the sectors and taking necessary measures to mitigate potential financial risks.
Źródło:
Oeconomia Copernicana; 2021, 12, 3; 631-669
2083-1277
Pojawia się w:
Oeconomia Copernicana
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The economics of deep and machine learning-based algorithms for COVID-19 prediction, detection, and diagnosis shaping the organizational management of hospitals
Autorzy:
Lăzăroiu, George
Gedeon, Tom
Rogalska, Elżbieta
Andronie, Mihai
Frajtova Michalikova, Katarina
Musova, Zdenka
Iatagan, Mariana
Uță, Cristian
Michalkova, Lucia
Kovacova, Maria
Ștefănescu, Roxana
Hurloiu, Iulian
Zabojnik, Stanislav
Stefko, Robert
Dijmărescu, Adrian
Dijmărescu, Irina
Geamănu, Marinela
Powiązania:
https://bibliotekanauki.pl/articles/39832736.pdf
Data publikacji:
2024
Wydawca:
Instytut Badań Gospodarczych
Tematy:
deep and machine learning
COVID 19
prediction
detection
diagnosis
organizational management
hospital
Opis:
Research background: Deep and machine learning-based algorithms can assist in COVID-19 image-based medical diagnosis and symptom tracing, optimize intensive care unit admission, and use clinical data to determine patient prioritization and mortality risk, being pivotal in qualitative care provision, reducing medical errors, and increasing patient survival rates, thus diminishing the massive healthcare system burden in relation to severe COVID-19 inpatient stay duration, while increasing operational costs throughout the organizational management of hospitals. Data-driven financial and scenario-based contingency planning, predictive modelling tools, and risk pooling mechanisms should be deployed for additional medical equipment and unforeseen healthcare demand expenses. Purpose of the article: We show that deep and machine learning-based and clinical decision making systems can optimize patient survival likelihood and treatment outcomes with regard to susceptible, infected, and recovered individuals, performing accurate analyses by data modeling based on vital and clinical signs, surveillance data, and infection-related biomarkers, and furthering hospital facility optimization in terms of intensive care unit bed allocation. Methods: The review software systems employed for article screening and quality evaluation were: AMSTAR, AXIS, DistillerSR, Eppi-Reviewer, MMAT, PICO Portal, Rayyan, ROBIS, and SRDR. Findings & value added: Deep and machine learning-based clinical decision support tools can forecast COVID-19 spread, confirmed cases, and infection and mortality rates for data-driven appropriate treatment and resource allocations in effective therapeutic and diagnosis protocol development, by determining suitable measures and regulations and by using symptoms and comorbidities, vital signs, clinical and laboratory data and medical records across intensive care units, impacting the healthcare financing infrastructure. As a result of heightened use of personal protective equipment, hospital pharmacy and medication, outpatient treatment, and medical supplies, revenue loss and financial vulnerability occur, also due to expenses related to hiring additional staff and to critical resource expenditures. Hospital costs for COVID-19 medical care, screening, treatment capacity expansion, and personal protective equipment can lead to further financial losses while affecting COVID-19 frontline hospital workers and patients.
Źródło:
Oeconomia Copernicana; 2024, 15, 1; 27-58
2083-1277
Pojawia się w:
Oeconomia Copernicana
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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