한국판 전두엽 기능 평가 검사(Frontal Assessment Battery;K-FAB)의 신뢰도와 타당도 연구

정여진(Yeo-Jin Chung),이계성(Kye-Seong Lee),신상은(Sang-Eun Shin),김현주(Hyun-Ju Kim),성경화(Kyung-Hwa Sung) 한국중독정신의학회 2009 중독정신의학 Vol.13 No.2

The Frontal Assessment Battery (FAB) is a short cognitive and behavioral battery, comprising six subtests, for the bedside screening of global executive dysfunction. This study determined the reliability and validity of the Korean version of the FAB (K-FAB) among psychiatric patients. We enrolled 69 subjects with various psychiatric diagnosies, including alcohol dependence, and concurrently administered. Kim’s Frontal-Executive Function Neuropsychological Test (K-FENT). Forty-four subjects completed both the K-FENT and Wisconsin Card Sorting Test (WCST). Two physicians independently conducted the K-FAB. Eeach rater was blind to score of other rater. We compared the total K-FAB score to the K-FENT and WCST results to determine its concurrent validity. Finally, we obtained receiver operating characteristic (ROC) curves to determine the optimum cut-off score. The internal consistency was sufficient (Cronbach’s alpha=0.797), and the K-FAB scores were highly correlated bet-ween the two raters. The total K-FAB score correlated significantly with EIQ (r=0.665, p<0.01), the summary score of the Executive Intelligence Test (EXIT). It also correlated with total number of correct responses, perseverative errors, and total number of catergories completed in WCST. At the cut-off value 13, the K-FAB showed good sensitivity (80%) and specificity (69%). The K-FAB is a useful and easy bedside test for screen-ing frontal-executive functional impairments in a variety of psychiatric patients.

A study on the applicability of a growth curve model for the Korean theater industry: A case of “Why? π!”

정재권 ( Jae Kwon Chung ),박도형 ( Do Hyung Park ),이동원 ( Dong Won Lee ),정여진 ( Yeo Jin Chung ) 한국경영공학회 2014 한국경영공학회지 Vol.19 No.4

This study investigates the applicability of a growth curve model for the Korean theater industry. In terms of market size, the theater industry in South Korea has grown with the efforts of production companies for example “The Best Plays” festival. However, most theater production companies especially play producers, struggle to obtain sufficient investments and pay a sufficient wage to their staff. Furthermore, it has been found that for play productions, important decisions, such as estimating demand and deciding when to discontinue a production, are made based on gut feeling. In fact, the play industry has received little attention in the business management related literature; thus, to support the theater industry, specifically management of play productions, this study investigates whether a growth curve model could be an effective management tool. Using data on the number of audiencesaudience of the play “Why? π!” obtained from Serendipper Company, the applicability of a growth curve model using the Bass model is analyzed. The results of this study can be used as a basis to develop a more accurate demand forecasting model applicable to the theater industry.

자기조직화 지도를 활용한 성장모형 기반의 시장 성장패턴 지도 구축

박도형(Do-Hyung Park),정재권(Jaekwon Chung),정여진(Yeo Jin Chung),이동원(Dongwon Lee) 한국지능정보시스템학회 2014 지능정보연구 Vol.20 No.4

Market forecasting aims to estimate the sales volume of a product or service that is sold to consumers for a specific selling period. From the perspective of the enterprise, accurate market forecasting assists in determining the timing of new product introduction, product design, and establishing production plans and marketing strategies that enable a more efficient decision-making process. Moreover, accurate market forecasting enables governments to efficiently establish a national budget organization. This study aims to generate a market growth curve for ICT (information and communication technology) goods using past time series data; categorize products showing similar growth patterns; understand markets in the industry; and forecast the future outlook of such products. This study suggests the useful and meaningful process (or methodology) to identify the market growth pattern with quantitative growth model and data mining algorithm. The study employs the following methodology. At the first stage, past time series data are collected based on the target products or services of categorized industry. The data, such as the volume of sales and domestic consumption for a specific product or service, are collected from the relevant government ministry, the National Statistical Office, and other relevant government organizations. For collected data that may not be analyzed due to the lack of past data and the alteration of code names, data pre-processing work should be performed. At the second stage of this process, an optimal model for market forecasting should be selected. This model can be varied on the basis of the characteristics of each categorized industry. As this study is focused on the ICT industry, which has more frequent new technology appearances resulting in changes of the market structure, Logistic model, Gompertz model, and Bass model are selected. A hybrid model that combines different models can also be considered. The hybrid model considered for use in this study analyzes the size of the market potential through the Logistic and Gompertz models, and then the figures are used for the Bass model. The third stage of this process is to evaluate which model most accurately explains the data. In order to do this, the parameter should be estimated on the basis of the collected past time series data to generate the models’ predictive value and calculate the root-mean squared error (RMSE). The model that shows the lowest average RMSE value for every product type is considered as the best model. At the fourth stage of this process, based on the estimated parameter value generated by the best model, a market growth pattern map is constructed with self-organizing map algorithm. A self-organizing map is learning with market pattern parameters for all products or services as input data, and the products or services are organized into an N X N map. The number of clusters increase from 2 to M, depending on the characteristics of the nodes on the map. The clusters are divided into zones, and the clusters with the ability to provide the most meaningful explanation are selected. Based on the final selection of clusters, the boundaries between the nodes are selected and, ultimately, the market growth pattern map is completed. The last step is to determine the final characteristics of the clusters as well as the market growth curve. The average of the market growth pattern parameters in the clusters is taken to be a representative figure. Using this figure, a growth curve is drawn for each cluster, and their characteristics are analyzed. Also, taking into consideration the product types in each cluster, their characteristics can be qualitatively generated. We expect that the process and system that this paper suggests can be used as a tool for forecasting demand in the ICT and other industries.

COVID-19 팬데믹에서 Airbnb 호스트의 마케팅 전략의 변화가 공유성과에 미치는 영향

김소영(Kim So Yeong),심지환(Sim Ji Hwan),정여진(Chung Yeo Jin) 한국지능정보시스템학회 2021 지능정보연구 Vol.27 No.3

The entire tourism industry is being hit hard by the COVID-19 as a global pandemic. Accommodation sharing services such as Airbnb, which have recently expanded due to the spread of the sharing economy, are particularly affected by the pandemic because transactions are made based on trust and communication between consumer and supplier. As the pandemic situation changes individuals perceptions and behavior of travel, strategies for the recovery of the tourism industry have been discussed. However, since most studies present macro strategies in terms of traditional lodging providers and the government, there is a significant lack of discussion on differentiated pandemic response strategies considering the peculiarity of the sharing economy centered on peer-to-peer transactions. This study discusses the marketing strategy for individual hosts of Airbnb during COVID-19. We empirically analyze the effect of changes in listing descriptions posted by the Airbnb hosts on listing performance after COVID-19 was outbroken. We extract nine aspects described in the listing descriptions using the Attention-Based Aspect Extraction model, which is a deep learning-based aspect extraction method. We model the effect of aspect changes on listing performance after the COVID-19 by observing the frequency of each aspect appeared in the text. In addition, we compare those effects across the types of Airbnb listing. Through this, this study presents an idea for a pandemic crisis response strategy that individual service providers of accommodation sharing services can take depending on the listing type.