Search Results (5)

This study examined the influence of cognitive biases on financial literacy test outcomes across four generational groups: Gen Z, Millennials, Gen X, and Baby Boomers. Using the National Financial Capability Test and an online in silico experiment, we analyzed how cognitive biases influence the likely responses of each generation. The results indicate that the current test format aligns more closely with Baby Boomers, who are less affected by certain biases but tend to exhibit resistance to new financial strategies. A key contribution of this research is the identification of generational bias profiles and actionable recommendations for tailoring financial literacy assessments to reflect these differences. Our approach not only advances behavioral finance literature but also introduces innovative methodology through AI-driven simulations, providing a replicable framework for exploring cognitive influences in decision-making. The findings underscore the need for tailored financial education programs that consider these cognitive biases, aiming to foster unbiased financial decision-making across age groups. Full article

Taiwan’s bed and breakfast (B&B) industry has experienced significant development in its nearly 25-year history, transforming from B&B run by retirees to mature service providers that adopt modern business and Internet technology skills in line with sustainable lodging development. This study explores the basic professional capabilities required for the sustainable development of B&B management, including social-emotional intelligence (EQ) capabilities and their impact on the quality of sustainable tourism services. The study used the K–S Z test to assess the importance of various abilities, including emotional intelligence abilities, financial management abilities, technical skills abilities and marketing abilities. The findings, validated with p-values less than 0.05, confirm the multi-disciplinary nature of sustainable tourism management skills in B&Bs and highlight their importance in sustainable service attitudes and strategic marketing. The identified capabilities are not only crucial for the sustainable development of the B&B industry, but are also crucial for contributing to the B&B’s national diplomacy and sustainable development status in global tourism. This study provides both novice and experienced B&B operators with actionable insights to improve their operational efficiency and achieve sustainable tourism development goals. Full article

The purpose of this study is to explore how machine learning technologies can improve healthcare operations management. A machine learning-based model to solve a specific medical problem is developed to achieve this research purpose. Specifically, this study presents an AI solution for malaria infection diagnosis by applying the CNN (convolutional neural network) algorithm. Based on malaria microscopy image data from the NIH National Library of Medicine, a total of 24,958 images were used for deep learning training, and 2600 images were selected for final testing of the proposed diagnostic architecture. The empirical results indicate that the CNN diagnostic model correctly classified most malaria-infected and non-infected cases with minimal misclassification, with performance metrics of precision (0.97), recall (0.99), and f1-score (0.98) for uninfected cells, and precision (0.99), recall (0.97), and f1-score (0.98) for parasite cells. The CNN diagnostic solution rapidly processed a large number of cases with a high reliable accuracy of 97.81%. The performance of this CNN model was further validated through the k-fold cross-validation test. These results suggest the advantage of machine learning-based diagnostic methods over conventional manual diagnostic methods in improving healthcare operational capabilities in terms of diagnostic quality, processing costs, lead time, and productivity. In addition, a machine learning diagnosis system is more likely to enhance the financial profitability of healthcare operations by reducing the risk of unnecessary medical disputes related to diagnostic errors. As an extension for future research, propositions with a research framework are presented to examine the impacts of machine learning on healthcare operations management for safety and quality of life in global communities. Full article

While emerging economies face the challenge of competing with developed nations, they are capable of catching up to the developed world. In this context, financial development and the degree of economic openness may provide better living conditions for the current generation without giving up future generations’ prosperity. Therefore, this research’s prime intention is to investigate the impact of economic openness and financial development on economic progress, employing Pakistan’s time-series data from 1975–2018. To examine the long-term association between economic openness, financial development, and economic progress, Autoregressive Distributed Lag (ARDL) cointegration tests were performed and the results present a long-term association between these variables. Findings from ARDL estimates indicate that the relationship between financial development and economic progress is significantly positive in the long term. Contrastingly, the relationship between economic openness and economic progress is significantly positive in the short term. A fully modified ordinary least square technique was applied to check the robustness of the long-term links. The Granger causality test revealed that economic progress is motivated by both economic openness and financial development in an emerging economy such as Pakistan. Thus, policies boosting financial development and economic openness are proposed to put the emerging economies on a path of sustainable economic development. Full article

The sector of SME is the major force for the national economic and social development. Financial risk is one of the key threats to the activity of small and medium enterprises. The most common manifestation of the financial risk of SMEs is difficulty in financing the business and lack of funds for development. Banks are unwilling to grant loans to such companies. Moreover, it is the rising operating costs that cause shrinking profits, which may result in corporate debt, difficulty in debt repayment, and consequently, high financial risk of these entities. Numerous differences in conducting the activity of small and large enterprises intensify this risk and mean that the model of credit financing for companies is not adjusted to the capabilities and principles of the operation of small enterprises. Therefore, risk management is one of the most important internal processes in small and medium enterprises. The identification of factors that affect the level of financial risk in these entities is therefore crucial. The main objective of this research was to analyze the impact of selected parametric characteristics of the SME sector on the intensity of financial risk they take. This objective was accomplished on the basis of the survey with the participation of Polish SMEs. In order to test the adopted research assumptions, the linear regression model was used with four continuous variables for each type of the identified financial risk. Based on the final research results, the logit model was obtained for the risk of insufficient profits. It was indicated that both the internationalization of the company and the ability to manage risk are the only factors that affect a high level of risk of low income. The article ends with the discussion and the comparison with some previous research in this area. Full article