나형종 교수

The purpose of this study is to apply a combination of sentiment mining techniques and a sustainability balanced scorecard to CEO messages in sustainability management reports to predict corporate financial ratios. We classify the contents of CEO messages into the six perspectives suggested by the sustainability balanced scorecard (SBSC). From the sentiment mining results, we first document that positive words dominate CEO messages in sustainability management reports. Moreover, words related to

(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 f

This study aims to provide research results through empirical analysis on how customers’ reactions on social media affect the present and future value of a company. This research selected Korean KOSPI-listed companies that actually own and operate YouTube channels, and collected data through text mining the comments on YouTube videos with high views. In addition, the TF-IDF value was calculated, keywords were extracted, and keywords were classified into three groups through topic modeling. The c

This study proposes a hybrid machine learning framework that integrates structured financial indicators and unstructured textual strategy disclosures to improve firm-level management performance prediction. Using corporate business reports from South Korean listed firms, strategic text was extracted and categorized under the Balanced Scorecard (BSC) framework into financial, customer, internal process, and learning and growth dimensions. Various machine learning and deep learning models—includin

This research examines the association between CEO messages and current and future corporate value on the websites of fashion companies. The research methods of this paper are as follows: First, we extract the fashion firm samples among companies listed in Korea’s KOSPI and KOSDAQ in 2020. Second, CEO messages’ text data on the homepage of the fashion companies are obtained by hand-collecting. The repeated words with high TF-IDF values are selected as keywords using text-mining techniques. Third

(1) Background: The CEO message of hospital homepage contain various contents such as the hospital's future vision, promises with customers, upgraded services and public activities. The CEO’s message of the homepage includes non-financial information as well as financial information of corporates. Also, it provides useful information for not only company's goals and vision but also firm performance and strategies for the future. This study aims to investigate associations between CEO’s message o

The hotel industry has faced significant challenges in both the short and long term, particularly due to the impact of COVID-19, highlighting the need for strategic adjustments to ensure sustainability and growth. This study investigates the strategic elements emphasized in CEO messages published on hotel company websites and their relationship with current and future corporate performance. Utilizing text mining techniques and the Sustainability Balanced Scorecard (SBSC) framework, this research

This study concentrated on a business report that typically reveals a company’s non-financial information, aiming to uncover its strategic direction. Using text-mining techniques, the research extracted and analyzed the report’s overview sections, identifying key strategic themes categorized into the financial, customer, learning and growth, and internal process perspectives. The empirical analysis applied a two-stage model to assess how shifts in company strategies affect profitability, stabili

South Korea’s declining school-age population has intensified competition among universities, particularly in freshman recruitment, with non-metropolitan institutions facing disproportionate challenges. This study investigates regional disparities in recruitment rates by applying a range of statistical and deep learning models—including Generative Adversarial Networks (GAN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Autoencoders, and Transformer architectures—to predict fre