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Is your home a health hazard? 15 surprisingly filthy everyday items, from taps to toothbrushes
No full textA numerical study of the relevance of the electrode-tissue contact area in the application of soft coagulation
Get PDFErgebnisse der Umfrage unter Studierenden im Sommersemester 2025 zu ihren Erfahrungen mit Künstlicher Intelligenz und zu Prüfungsformen
Get PDFAdditive manufacturing of hybrid bond grinding wheels via digital light processing : Performance enhancement through composition alteration and groove incorporation
Get PDFAre professional bankers affected by behavioral biases, even though they are trained to make rational decisions?
No full textThis study investigates the extent to which professional bankers are influenced by behavioral biases despite being trained to make rational decisions. It examines confirmation bias, overconfidence, availability bias and regret aversion. A structured questionnaire consisting of 21 items was developed. Responses were collected from 123 participants across three groups: bankers, students and working professionals. This allowed for a comparison between experts and laymen. Statistical analyses included chi-square tests of independence, independent t-tests and ANOVA to explore bias frequency and group differences. The findings demonstrate that bankers are not protected from cognitive distortions. No statistically significant group-level differences were found that would indicate that bankers have greater rational decision-making. The study contributes to behavioral finance by testing the persistence of cognitive biases among financial professionals. The findings suggest that debiasing interventions and training programs are necessary to improve decision quality in banking
Effects of combined protein and exercise interventions on bone health in middle-aged and older adults — A systematic literature review and meta-analysis of randomized controlled trials
Get PDFMental health implications of fracture-related infections : a longitudinal quality of life study
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A Comparative Profitability Analysis between Electric Vehicle Producers and the Traditional Automobiles Industry: BYD, Tesla, and Industry Trends
No full textThis paper examines the profitability of BYD and Tesla compared to traditional automobiles industry in two distinct time scopes to find out the most statistically significant profitability determinants, and the profitability indicators, whose variability is explained at the highest level by the profitability determinants. The study utilizes quantitative financial data and applies Descriptive Statistics, Correlation Analysis and Regression Analysis for the 20-year period with ten public companies in the traditional automobiles industry, and for the 10-year period with 25 public companies in the traditional automobiles industry, BYD and Tesla, separately.
The findings exhibit slight decrease with increased volatility in profitability in the traditional automobiles industry over the 10-year period compared to the 20-year period, which is possibly attributed to the intensified competition and the shift towards cleaner energy solutions like electric vehicles (EVs). Over the 10-year period, BYD and Tesla outperformed the industry across most profitability levels, with Tesla showing more substantial volatility, while BYD displayed greater stability. However, the traditional automobiles industry still managed to achieve more extreme profitability values.
Key profitability indicators and determinants differ across the samples with certain level of similarity: Gross Profit Margin (GPM) dominates the final regression models as the primary dependent variable for the traditional automobiles industry, while Return on Assets (ROA) demonstrates the greatest significance for BYD, resulting in the adoption of both dependent variables for Tesla to enable meaningful comparisons. In terms of independent variables, liquidity, working capital management, and asset management are identified as statistically significant determinants of profitability for both electric vehicle (EV) producers and the traditional automobiles industry, whereas macroeconomic factors such as inflation (CPI), short-term interest rates (IRS), the Great Recession in 2008, the COVID-19 Pandemic, and the Russia-Ukraine War have greater impacts on the traditional automobiles industry over both time scopes than on the EV producers