In the 21st century, the increasing complexity and uncertainty of the global economy have heightened the need for accurate economic forecasting. Per capita GDP, a critical indicator of living standards, economic growth, and productivity, plays a key role in government policy-making, corporate strategy, and investor decisions. However, predicting per capita GDP poses significant challenges due to its sensitivity to various economic and social factors. Traditional methods such as statistical analysis, regression, and time-series models have shown limitations in capturing nonlinear interactions and volatility of economic data. To address these limitations, this study develops a per capita GDP forecasting model based on deep learning, incorporating key macroeconomic variables—the Consumer Price Index (CPI) and unemployment rate (UR)—to enhance predictive accuracy. This study employs five deep-learning regression models (RNN, LSTM, GRU, TCN, and Transformer) applied to real and placebo datasets, each incorporating combinations of CPI and UR. The results demonstrate that deep learning models can effectively capture complex, nonlinear relationships in economic data, significantly improving predictive accuracy compared to traditional models. Among the models, the Transformer consistently achieves the highest R-squared and lowest error values across various metrics (MSE, RMSE, and MSLE), indicating its superior ability to model intricate economic patterns. In addition, including CPI and UR as additional predictors enhances model robustness, with the TCN and Transformer models showing particularly strong performance in capturing short-term economic fluctuations. The findings suggest that the deep learning models, especially the Transformer, offer valuable tools for policymakers and business leaders, providing reliable GDP forecasts that support economic decision-making, resource allocation, and strategic planning. Academically, this study advances the understanding of deep learning applications in economic forecasting, particularly in integrating significant macroeconomic variables for enhanced predictive performance. The developed model is a foundation for informed economic policy and strategic decisions, offering a robust and actionable framework for managing economic uncertainties. This research contributes to theoretical and applied economics, providing insights that bridge academic innovation with practical utility in economic forecasting.

This study aims to develop a success prediction model for crowdfunding by integrating ESG (Environmental, Social, and Governance) factors using machine learning techniques. Crowdfunding, a modern financing method conducted through online platforms, has become a popular avenue for raising funds, particularly for creative projects, startups, and social enterprises. Incorporating ESG factors into crowdfunding success prediction models is crucial, as these factors represent environmental responsibility, social accountability, and ethical governance, which are increasingly important to investors and sponsors. To achieve the research objectives, this study employed advanced machine learning algorithms, including XGBoost, LightGBM, AdaBoost, CatBoost, and NGBoost, to analyze data from a prominent reward-based crowdfunding platform in Korea, 'Wadiz.' Text mining techniques were used to quantify unstructured ESG-related data, and the predictive performance of models including ESG factors was compared with models excluding them. The results demonstrated that prediction accuracy significantly improved when ESG factors were incorporated. Among the ESG dimensions, environmental information contributed the highest prediction performance, followed by social activity information and governance information. The findings of this study contribute to academia by expanding the understanding of non-financial factors in crowdfunding success prediction and integrating ESG elements into machine learning models. This research also provides practical implications by offering insights for platform operators, project creators, and investors to improve decision-making processes through the strategic use of ESG factors. The proposed model enhances crowdfunding platforms' reliability and success rates, supports project makers in designing impactful campaigns, and aids investors in making sustainable and data-driven investment decisions. This study advances theoretical understanding and provides actionable strategies for enhancing the effectiveness of crowdfunding in practice.

This study examines the relationship between cosmetic packaging color and consumer attention by analyzing changes in pupil size using eye-tracking technology. A controlled experiment with 25 participants (mean age: 24.7 ± 3 years, 14 males and 11 females) was conducted to investigate the impact of eight packaging colors (black, white, blue, yellow, orange, turquoise, pink, and sky blue) on pupil dilation during gaze fixation and movement. Pupil size data were analyzed using SAS 9.4, with T-tests used to determine significant differences across colors. The results revealed that pink packaging elicited significantly larger pupil sizes during fixation, indicating heightened attention, while black, white, blue, and orange led to smaller pupil sizes when fixated, suggesting greater focus on the surrounding environment rather than the packaging. In contrast, yellow and turquoise exhibited no significant differences in pupil size during fixation and movement. Additionally, the study highlights that gaze fixation is a more meaningful indicator of attention than gaze movement, as fixation reflects focused interest in specific stimuli. The findings suggest that pink packaging is most effective in attracting consumer attention, while black, white, blue, and orange are better suited for enhancing focus on the surrounding environment. These insights emphasize the growing importance of packaging design in influencing consumer behavior, particularly through color selection. This study contributes to marketing practices by providing empirical evidence for the visual impact of packaging colors, offering valuable guidance for cosmetic industry practitioners. Future research should expand sample sizes and explore additional packaging attributes, such as shape and material, to derive more comprehensive insights.

Despite the general agreement that a firm embodies its own culture, there is still a lack of empirical research on how a firm’s culture affects its value. Another caveat on previous studies is that they implicitly assume that a firm’s culture does not vary over time. In this paper, we examine the following two questions to address this lack: (1) Does a firm’s culture affect the firm’s value? (2) If a firm’s culture varies at different life cycle stages, do these changes have an impact on firm value? By using a competing values framework, we identify four types of corporate culture—adhocracy, market, clan, and hierarchy—and use life cycle stages to proxy for changes in a firm’s environment. The results reveal that adhocracy culture has a positive effect on a firm’s value. In contrast, we find a negative association between hierarchy culture and a firm’s value. This can be interpreted as the features of adhocracy culture, which gives autonomy to its members (flexible and discretion) and keeps challenging a firm to grow (external focus and differentiation), positively impacting firm value more than the other cultures. Furthermore, at a growth stage in which a firm faces dynamic environmental changes, both adhocracy and clan cultures have an incrementally positive effect on firm value. This implies that firms in mature or decline stages lose dynamic changes in their operational environment, therefore, the effect of culture on firm value is restricted in those stages.

(1) Background: The Chief Executive Officer’s (CEO’s) message on a hospital’s homepage on the Internet contains various components, such as the hospital’s future vision, promises to customers, availability of upgraded services and public activities. This statement usually includes non-financial information as well as financial information about the corporate entity owning/operating the hospital. In addition, it provides useful information about not only the company’s goals and vision, but also firm performance targets and strategies for the future. This study aims to investigate associations between the CEO’s message and the financial status of the institution. We used the balanced scorecard framework to analyze what content on the hospital’s homepage is related to the hospital’s various financial ratios. (2) Methods: We adopted a text-mining method to extract significantly repeated keywords from the CEO’s message on the hospital’s website. Then, we classified these keywords using a balanced scorecard approach. To examine the relationship between keywords in the CEO’s message and the hospital’s financial ratios, a t-test was conducted for the difference in the term frequency divided by inverse document frequency (TF-IDF) mean of the home page contents and its relationship with the views of the balanced scorecard framework. (3) Results: According to our empirical results on 65 samples collected from local hospitals, there are some significant relationships between the qualitative content of the hospital’s homepage and the quantitative financial ratios that indicate profitability, activity, leverage, liquidity, and accumulating reserves for proper business purposes. (4) Conclusions: The introduction section of a homepage is the part most accessible to customers, containing the aims and ideals of the hospital and reflecting the institution’s values and visions. In addition, in the coverage of financial status, the organization can either emphasize financial strength or focus on other areas to divert attention from any weakness shown in the financial information. This study reminds us of the importance of the hospital website’s disclosure, and what can be inferred from the financial status of the hospital. It also highlights the need for reconciliation and harmony between the quantitative data, financial statements, and qualitative data in the CEO’s message. (5) Implications: To the best of our knowledge, this paper is the first research attempting to investigate the relationship between text on the hospital’s homepage and the hospital’s financial ratios using text-mining techniques and the balanced scorecard framework. Hospitals play a crucial role in a country’s welfare and healthcare industry. Nevertheless, in many countries, hospital organizations tend to remain a source of critical fiscal deficits due to ineffective and sloppy management. We expect that the result of this paper can provide hospital managers with useful information to address that situation.

This paper investigates how tax benefits for companies affect future firm value and current corporate performance. In addition, this paper also examines the relationship between tax benefits and future firm value for each major industry. The findings of this paper are as follows. First, tax benefits granted to companies improve current corporate performance. The effect of tax benefits that reduce corporate tax costs increases net income, which directly increases current corporate performance, such as ROA (returns on assets) and ROE (returns on equity). Second, tax benefits granted to firms reduce future firm value. Industries that receive tax benefits may have inherent taxation, which can lead to fiercer competition and ultimately lower pre-tax profit margins due to the entry of new companies or the increase in production facilities. In addition, tax benefits that cause temporary differences among the types of tax benefits for a company through deferred tax payments may be factors that hinder future improvements in corporate value. These causes result in the fact that tax benefits for a company can negatively affect its value in the long term. This paper has the following contributions. First, the findings of this paper imply that there is a limit to the positive impact of tax benefits on firms on improving corporate value in the long run. Second, through empirical analysis, this study provides objective information that the impact of tax incentives on corporate value may differ by industry.