• Proceedings of the Korea Contents Association Conference
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• 2019.05a
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• pp.153-154
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• 2019

4차산업혁명시대는 AI로 대표된다. 인간의 노동과 기술이 로봇과 AI로 대체될 것이다. 교육분야에도 AI기술이 도입되고 있고 이로 인해 오프라인교육이 대부분 온라인교육으로 대체될 것이라는 생각이 지배적이다. 온 오프라인 교육의 수용자 선택의 변화를 분석하여 4차산업혁명시대의 교육 흐름과 변화를 예측하여 보았다. 공급의 측면에서 온라인교육의 매출은 계속 증가하고 있고 수요의 측면에서도 온라인교육의 지출은 증가하고 있다. 하지만 주목할 부분은 온라인교육이 아니라 오프라인교육의 변화이다. 온라인교육이 도입되며 오프라인교육의 총지출은 매년 감소하였다. 하지만 오프라인교육 총지출액은 전년 대비 매년 감소하였지만 그 감소율이 점차 줄어들다가 최근에는 증가세로 돌아섰다. 온라인교육서비스가 점차 발전하는 상황에서 오프라인교육의 지출 증가는 매우 주목할 만한 변화이다. 이는 수용자의 선택이 교육에 있어서만큼은 다름을 알 수 있고 4차산업혁명시대가 도래하여도 교육은 달라야 함을 인식할 수 있는 중요한 변화이다.

• Journal of the Korean Data and Information Science Society
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• v.21 no.3
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• pp.535-546
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• 2010

There are two methods in grouping customers for micro marketing promotion. The one is based on how much they paid and the other is based on how many times they purchased. In this study we are interested in the repurchase probability of customers. By analysing the customer's transaction data and demographic data, we develop a forecasting model of repurchase and make epurchase indexes of them. As a modeling tool we use the logistic regression model. Finally we categorize the customers into five groups in according to their repurchase indexes so that we can control customers effectively and get higher profit.

• The Journal of Bigdata
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• v.5 no.2
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• pp.1-15
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• 2020

The duty-free industry before COVID-19 has continuously grown since 2000, along with the increase of demand in tourism industry. To cope with the increased demand, the duty free companies have kept the strategies which focused on the sales volume. Therefore, they have developed the ways to increase the volume and capacity, not the efficient operations. In the most of previous research, however, authors have proposed the better strategies for marketing and supporting policies. It is very hard to find the previous research which dealt with the operations like logistics and inventory management. Therefore, in this study, it has been predicted the future demand of frequently purchased items in airport duty free shops based on the estimated number of departing passengers by the linear regression, which concluded with the appropriate inventory level. In addition, it has been analyzed the expected effects by introducing the inventory management policy considering the cost and efficiency of operations. Based on the results of this study, it may be possible to reduce total cost and improve productivity by predicting the excessive inventory problems at duty-free shops and improving cycles of supplying items.

• Journal of Digital Convergence
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• v.15 no.2
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• pp.121-133
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• 2017

The propose of study is to examine the characteristics of companies with high possibility to form an internal control weakness using forecasting model. This study use the actual listed/unlisted companies' data from K_financial institution. The first conclusion is that discriminant model is more valid than logit model to predict internal control weak companies. A discriminant model for predicting the vulnerability of internal control has high classification accuracy and has low the Type II error that is incorrectly classifying vulnerable companies to normal companies. The second conclusion is that the characteristic of weak internal control companies have a low credit rating, low asset soundness assessment, high delinquency rates, lower operating cash flow, high debt ratios, and minus operating profit to the net sales ratio. As not only a case of listed companies but unlisted companies which did not occur in previous studies are extended in this study, research results including the forecasting model can be used as a predictive tool of financial institutions predicting companies with high potential internal control weakness to prevent asset losses.

• The Korean Journal of Applied Statistics
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• v.30 no.1
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• pp.25-39
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• 2017

Over 5 million people participate in cycle racing betting and its revenue is more than 2 trillion won. This study predicts the ranking of cycle racing using various statistical analyses and identifies important variables which have influence on ranking. We propose competitive ranking prediction models using various classification and regression methods. Our model can predict rankings with low misclassification rates most of the time. We found that the ranking increases as the grade of a racer decreases and as overall scores increase. Inversely, we can observe that the ranking decreases when the grade of a racer increases, race number four is given, and the ranking of the last race of a racer decreases. We also found that prediction accuracy can be improved when we use centered data per race instead of raw data. However, the real profit from the future data was not high when we applied our prediction model because our model can predict only low-return events well.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.49-65
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• 2014

Word of Mouth (WOM) is a behavior used by consumers to transfer or communicate their product or service experience to other consumers. Due to the popularity of social media such as Facebook, Twitter, blogs, and online communities, electronic WOM (e-WOM) has become important to the success of products or services. As a result, most enterprises pay close attention to e-WOM for their products or services. This is especially important for movies, as these are experiential products. This paper aims to identify the network factors of an online movie community that impact box office revenue using social network analysis. In addition to traditional WOM factors (volume and valence of WOM), network centrality measures of the online community are included as influential factors in box office revenue. Based on previous research results, we develop five hypotheses on the relationships between potential influential factors (WOM volume, WOM valence, degree centrality, betweenness centrality, closeness centrality) and box office revenue. The first hypothesis is that the accumulated volume of WOM in online product communities is positively related to the total revenue of movies. The second hypothesis is that the accumulated valence of WOM in online product communities is positively related to the total revenue of movies. The third hypothesis is that the average of degree centralities of reviewers in online product communities is positively related to the total revenue of movies. The fourth hypothesis is that the average of betweenness centralities of reviewers in online product communities is positively related to the total revenue of movies. The fifth hypothesis is that the average of betweenness centralities of reviewers in online product communities is positively related to the total revenue of movies. To verify our research model, we collect movie review data from the Internet Movie Database (IMDb), which is a representative online movie community, and movie revenue data from the Box-Office-Mojo website. The movies in this analysis include weekly top-10 movies from September 1, 2012, to September 1, 2013, with in total. We collect movie metadata such as screening periods and user ratings; and community data in IMDb including reviewer identification, review content, review times, responder identification, reply content, reply times, and reply relationships. For the same period, the revenue data from Box-Office-Mojo is collected on a weekly basis. Movie community networks are constructed based on reply relationships between reviewers. Using a social network analysis tool, NodeXL, we calculate the averages of three centralities including degree, betweenness, and closeness centrality for each movie. Correlation analysis of focal variables and the dependent variable (final revenue) shows that three centrality measures are highly correlated, prompting us to perform multiple regressions separately with each centrality measure. Consistent with previous research results, our regression analysis results show that the volume and valence of WOM are positively related to the final box office revenue of movies. Moreover, the averages of betweenness centralities from initial community networks impact the final movie revenues. However, both of the averages of degree centralities and closeness centralities do not influence final movie performance. Based on the regression results, three hypotheses, 1, 2, and 4, are accepted, and two hypotheses, 3 and 5, are rejected. This study tries to link the network structure of e-WOM on online product communities with the product's performance. Based on the analysis of a real online movie community, the results show that online community network structures can work as a predictor of movie performance. The results show that the betweenness centralities of the reviewer community are critical for the prediction of movie performance. However, degree centralities and closeness centralities do not influence movie performance. As future research topics, similar analyses are required for other product categories such as electronic goods and online content to generalize the study results.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2000.11a
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• pp.231-231
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• 2000

인터넷기업들은 불과 몇 달 전만 해도 수수께끼로 가득 찬 요지경이었다. 매출액은 늘어났지만 더 많은 손실이 발생했고, 엄청난 적자와는 정반대로 주가는 연일 상승곡선을 그리고 있었다. 오히려 손실을 줄이는 방안을 발표하면 주가가 떨어지는 기현상마저 보여 구경제의 질서에 익숙해 있던 투자자들이나 경영자들을 혼란스럽게 만들고 있다. 그런데 이처럼 높게 평가되던 인터넷 기업들의 주가가 최근에 들어 폭락하고 있다. eToys의 경우 주가가 최고치 였던 ＄86에서 94% 폭락한 ＄4.75에 거래되었고, CDNow는 83%, Buy.com은 81% 등 주요 온라인 업체들의 주가가 80% 이상 하락하였으며 그 외의 적지 않은 인터넷 기업들의 주가 역시 전성기에 비해 90-95%까지 폭락하였다. 이러한 이유로 최근 인터넷기업들의 정확한 가치평가를 하기 위한 연구들이 시도되고 있으며, 이러한 시도 중 비교적 객관적인 정보인 재무정보들을 이용하기 위한 연구들도 있다. 하지만 아직까지는 우리나라의 재무제표들이 제공하는 정보들이 부족하고 IMF이후 비정상적인 주가 등으로 인하여 실증하는데 어려움이 따르고 있다. 또한 인터넷 기업들은 전술한 바와 같이 기존 오프라인상의 제조업형태의 기업들처럼 일반적인 재무제표분석을 통한 가치평가에 어려움을 겪고 있다. 하지만 인터넷을 기반으로 한 디지털 경제에서도 오프라인기업에서와 똑같은 현상이 발생한다는 사실을 간과해서는 안 된다. 현금지출이 도달 가능한 현금유입의 수준을 넘어선다면 결국 도산하는 것은 인터넷기업들도 마찬가지이다. 현재 어떤 기업에 투자하는 것은 그 기업의 미래 현금흐름을 구매하고자 하는 것이다. 따라서 미래의 현금흐름이 커질수록 그 기업의 가치는 상승하게 된다. 현금흐름 분석이 특히 중요한 이유는 기업의 미래 현금흐름을 기업의 타인자본비용과 자기자본비용의 조합인 기회자본비용으로 할인함으로써 현재의 기업가치를 구할 수 있기 때문이다. 이처럼 기업이 영업활동이나 투자활동을 통해 현금을 창출하고 소비하는 경향은 해당 비즈니스 모델의 성격을 규정하는 자료도로 이용될 수 있다. 또한 최근 인터넷기업들의 부도가 발생하고 있는데, 기업의 부실원인이 어떤 것이든 사회전체의 생산력의 감소, 실업의 증가, 채권자 및 주주의 부의 감소, 심리적 불안으로 인한 경제활동의 위축, 기업 노하우의 소멸, 대외적 신용도의 하락 등과 같은 사회적·경제적 파급효과는 대단히 크다. 이상과 같은 기업부실의 효과를 고려할 때 부실기업을 미리 예측하는 일종의 조기경보장치를 갖는다는 것은 중요한 일이다. 현금흐름정보를 이용하여 기업의 부실을 예측하면 기업의 부실징후를 파악하는데 그치지 않고 부실의 원인을 파악하고 이에 대한 대응 전략을 수립하며 그 결과를 측정하는데 활용될 수도 있다. 따라서 본 연구에서는 기업의 부도예측 정보 중 현금흐름정보를 통하여 '인터넷기업의 미래 현금흐름측정, 부도예측신호효과, 부실원인파악, 비즈니스 모델의 성격규정 등을 할 수 있는가'를 검증하려고 한다.

• The Journal of the Korea Contents Association
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• v.22 no.4
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• pp.118-129
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• 2022

The purpose of this report is to study a strategic model of promotion activities through various analysis and sales forecasting by selecting wearable products for domestic online companies and collecting sales data. For data analysis, various algorithms are used for analysis and the results are selected as the optimal model. The gradation boosting model, which is selected as the best result, will allow nine independent variables to be entered, including promotion type, price, amount, gender, model, company, grade, sales date, and region, when predicting dependent variables through supervised learning. In this study, the review values set as dependent variables for each type of sales promotion were studied in more detail through the ensemble analysis technique, and the main purpose is to analyze and predict them. The purpose of this study is to study the grades. As a result of the analysis, the evaluation result is 95% of AUC, and F1 is about 93%. In the end, it was confirmed that among the types of sales promotion activities, value-added benefits affected the number of reviews and review grades, and that major variables affected the review and review grades.

• Journal of Service Research and Studies
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• v.12 no.4
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• pp.82-93
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• 2022

The food service industry is a major driver of global sustainable food consumption. By understanding food consumption behavior, restaurant managers can forecast demands and reduce pre-consumer food waste. This study investigates the relationship between influencing factors and the number of customers at restaurants and cafés. These factors are weather-related factors, including rain and temperature, and school-related factors, including exams and the day of the week. Based on these four factors, 24 possible combinations were created. Three representtive days were chosen for each weekday combination. Besides, one representative day was chosen for each weekend combination. In total, 48 days were sampled throughout the year. Customer data were collected from six restaurants and cafes on a Korean university campus. Conjoint analysis was used to determine the relative importance of each variable to customer numbers. Following that, utility scores were standardized and mapped to determine the best condition when the number of customers was at its peak. In addition, each store's sales were compared using Pearson's Correlation Coefficient. The findings support that temperature and rain influences are correlated with the number of customers. Furthermore, we discovered that temperature was far more significant than rain in determining the number of customers. The paper discusses the implications of weather to forecast food and beverage demand and predict meal choices.

• Proceedings of the Korean DIstribution Association Conference
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• 2007.11a
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• pp.155-178
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• 2007

The purpose of this study is to provide the strategic implication of the Korean open air market by examining the factors affecting their competitiveness. I have undertaken empirical research that uses the methodology of a mixture regression modeling, as a way to ascertain the determinants of competitiveness for the Korean open air market. I construct a mixture regression model which uses the proportions of merchandise categories as explanatory variables and the number of visitors as a dependent variable. The analysis of results show that competitive and non-competitive markets have different proportions of merchandise categories. The finding shows that stock farm products and home appliances are major influencers on the number of visitors in neighborhood markets. The finding also presents that stock farm products and processed foods are major influencers on the number of visitors in small & medium-sized city markets.