• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.2
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• pp.159-179
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• 2024

Based on the case of collaboration between large companies and startups, this study suggests the importance of establishing mutual cooperation and trust relationships for the success of open innovation strategy from the perspective of cooperative game theory. It also provides implications for how this can be implemented. Due to information asymmetry and differences in organizational culture and decision-making structures between large companies and startups, collaboration is likely to proceed in the form of non-cooperative games among players in general open innovation, leading to the paradox of open innovation, which lowers the degree of innovation. Accordingly, this study conducted a case study on collaboration between large company 'G' and startup 'S' based on the research question "How did we successfully promote open innovation through cooperative game-type collaboration?" The study found that successful open innovation requires (1) setting clear collaboration goals to solve the organizational problem between large companies and startups, (2) supporting human resources for qualitative growth of startups to solve reliability problems, (3) leading to strategic investment and joint promotion of new projects to solve the profit distribution problem. This study is significant in that it contributes to expanding the discussion of the success factors of open innovation to the importance of interaction and strategic judgment considering the organizational culture and decision-making structure among players, and empirically confirming the success conditions of open innovation from the perspective of cooperative game theory.

• The Journal of Society for e-Business Studies
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• v.26 no.3
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• pp.55-66
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• 2021

The mobile application (APP) market is growing at an unprecedented speed. Amid such growth, the global platform providers are mandating exclusive in-app payments and charging 30% for platform commission fees. A serious tension has arisen between mobile global platform providers and local content providers. The present study attempts to analyze the domestic mobile game market and in-app payment commission fees. This study estimates the size of the domestic mobile game market and platform commission fees by directly using publicly available financial statements and footnote information of some representative listed mobile game firms. Also, the study analyzes the cost structures of the same sample firms and attempts to draw some implications on sustainable growths of the mobile game ecosystem. We estimated that, in 2019, the domestic mobile game market is around 4.9 trillion Won and the ensuing in-app payment commission fees market was 1.5 trillion Won. High market share firms display a proportional increase in in-app payment commission fees in relation to sales growth. This, in turn, makes the in-app payment commission fees a primary cost item far exceeding employee salaries and R&D expenses. During the same period, low market share firms generated a mere profit or experienced net loss. Analysis of the cost structure reveals that these firms are even more liable to higher in-app payment commission fee cost structure than high market share. Most constituents of the mobile game ecosystem are small business entrepreneurs. By employing a micro-level analysis, the study estimates that, in 2019, a representative median firm generates 530 million Won in sales. At the same time, it spends 190 million Won in employee salaries, 50 Won million in R&D and 190 million Won in in-app payment commission fees, respectively. In the absence of other cost items, these three cost items alone account for 73.8% of sales revenue. The results imply that a sustainable growth of the local mobile game market heavily depends upon the cost structure of such representative median firm, the in-app payment commission fees being the primary cost item of such firm.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.75-100
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• 2018

Mobile devices have become an important channel for news content usage in our daily life. However, online news content readers' resistance to online news monetization is more serious than other digital content businesses, such as webtoons, music sources, videos, and games. Since major portal sites distribute online news content free of charge to increase their traffics, customers have been accustomed to free news content; hence this makes online news providers more difficult to switch their policies on business models (i.e., monetization policy). As a result, most online news providers are highly dependent on the advertising business model, which can lead to increasing number of false, exaggerated, or sensational advertisements inside the news website to maximize their advertising revenue. To reduce this advertising dependencies, many online news providers had attempted to switch their 'free' readers to 'paid' users, but most of them failed. However, recently, some online news media have been successfully applying the Pay-What-You-Want (PWYW) payment model, which allows readers to voluntarily pay fees for their favorite news content. These successful cases shed some lights to the managers of online news content provider regarding that the PWYW model can serve as an alternative business model. In this study, therefore, we collected 379 online news articles from Ohmynews.com that has been successfully employing the PWYW model, and analyzed the comparative importance of systematic attributes of online news content on readers' voluntary payment. More specifically, we derived the six systematic attributes (i.e., Type of Article Title, Image Stimulation, Article Readability, Article Type, Dominant Emotion, and Article-Image Similarity) and three or four levels within each attribute based on previous studies. Then, we conducted content analysis to measure five attributes except Article Readability attribute, measured by Flesch readability score. Before conducting main content analysis, the face reliabilities of chosen attributes were measured by three doctoral level researchers with 37 sample articles, and inter-coder reliabilities of the three coders were verified. Then, the main content analysis was conducted for two months from March 2017 with 379 online news articles. All 379 articles were reviewed by the same three coders, and 65 articles that showed inconsistency among coders were excluded before employing conjoint analysis. Finally, we examined the comparative importance of those six systematic attributes (Study 1), and levels within each of the six attributes (Study 2) through conjoint analysis with 314 online news articles. From the results of conjoint analysis, we found that Article Readability, Article-Image Similarity, and Type of Article Title are the most significant factors affecting online news readers' voluntary payment. First, it can be interpreted that if the level of readability of an online news article is in line with the readers' level of readership, the readers will voluntarily pay more. Second, the similarity between the content of the article and the image within it enables the readers to increase the information acceptance and to transmit the message of the article more effectively. Third, readers expect that the article title would reveal the content of the article, and the expectation influences the understanding and satisfaction of the article. Therefore, it is necessary to write an article with an appropriate readability level, and use images and title well matched with the content to make readers voluntarily pay more. We also examined the comparative importance of levels within each attribute in more details. Based on findings of two studies, two major and nine minor propositions are suggested for future empirical research. This study has academic implications in that it is one of the first studies applying both content analysis and conjoint analysis together to examine readers' voluntary payment behavior, rather than their intention to pay. In addition, online news content creators, providers, and managers could find some practical insights from this research in terms of how they should produce news content to make readers voluntarily pay more for their online news content.

• Journal of Korea Entertainment Industry Association
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• v.15 no.7
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• pp.259-269
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• 2021

This study examines the legend of 'Grandmother Seolmundae and Five Hundred Generals', the cultural archetypes of Jeju, and analyzes the characteristics of its contents. After analyzing the feasibility of using the analyzed cultural prototype as cultural contents such as games and animations, based on this analysis, we tried to suggest a cultural planning approach based on the cultural prototype to the cultural agency. Therefore, the implications of this study are as follows. First, among the cultural prototypes in Korea, 'Grandmother Seolmundae and Five Hundred Generals', which represent the legends of Jeju Island, are being organized centered on many historical researchers and Chaerokga, but there is no precise establishment of the exact timing of the legend and how it arose. Therefore, when planning and developing content based on the cultural prototype, it is most important for cultural agencies to develop a story after researching historical evidence and opinions of local residents to identify a consistent point of view. Second, although the contents of the cultural archetype are arranged slightly differently by historians and recorders, the main contents and characteristics of the story are found to have a similar form. Therefore, cultural agencies should focus on finding the point of view and characteristics of a story, even if a story is written differently by different people when doing a cultural prototype. Third, when planning a game based on the cultural prototype, the main elements such as the elements to be expressed in the game and the fun elements should be found and presented. In particular, because fun and rules are the most important parts of games, if this part cannot be derived from the story of the cultural archetype or cannot be made, it is difficult to transform the cultural archetype into a game. Therefore, it can be seen that it is important for cultural agencies to set their game plan intentions in consideration of story expression and fun, even if it is the core or non-core of the entire story of the cultural archetype. Lastly, although the cultural prototype 'Grandmother Seolmundae and Five Hundred Generals' was presented as animation content, it is important to develop it considering the story, characters, media, and audience. Therefore, cultural agencies should be able to derive the elements such as stories, representative and auxiliary characters, and viewers that can be adapted from the cultural prototype as much as possible. It will be an important part of raising.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.33-49
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• 2018

The impact of TV Drama success on TV Rating and the channel promotion effectiveness is very high. The cultural and business impact has been also demonstrated through the Korean Wave. Therefore, the early prediction of the blockbuster success of TV Drama is very important from the strategic perspective of the media industry. Previous studies have tried to predict the audience ratings and success of drama based on various methods. However, most of the studies have made simple predictions using intuitive methods such as the main actor and time zone. These studies have limitations in predicting. In this study, we propose a model for predicting the popularity of drama by analyzing the customer's viewing pattern based on various theories. This is not only a theoretical contribution but also has a contribution from the practical point of view that can be used in actual broadcasting companies. In this study, we collected data of 280 TV mini-series dramas, broadcasted over the terrestrial channels for 10 years from 2003 to 2012. From the data, we selected the most highly ranked and the least highly ranked 45 TV drama and analyzed the viewing patterns of them by 11-step. The various assumptions and conditions for modeling are based on existing studies, or by the opinions of actual broadcasters and by data mining techniques. Then, we developed a prediction model by measuring the viewing-time distance (difference) using Euclidean and Correlation method, which is termed in our study similarity (the sum of distance). Through the similarity measure, we predicted the success of dramas from the viewer's initial viewing-time pattern distribution using 1~5 episodes. In order to confirm that the model is shaken according to the measurement method, various distance measurement methods were applied and the model was checked for its dryness. And when the model was established, we could make a more predictive model using a grid search. Furthermore, we classified the viewers who had watched TV drama more than 70% of the total airtime as the "passionate viewer" when a new drama is broadcasted. Then we compared the drama's passionate viewer percentage the most highly ranked and the least highly ranked dramas. So that we can determine the possibility of blockbuster TV mini-series. We find that the initial viewing-time pattern is the key factor for the prediction of blockbuster dramas. From our model, block-buster dramas were correctly classified with the 75.47% accuracy with the initial viewing-time pattern analysis. This paper shows high prediction rate while suggesting audience rating method different from existing ones. Currently, broadcasters rely heavily on some famous actors called so-called star systems, so they are in more severe competition than ever due to rising production costs of broadcasting programs, long-term recession, aggressive investment in comprehensive programming channels and large corporations. Everyone is in a financially difficult situation. The basic revenue model of these broadcasters is advertising, and the execution of advertising is based on audience rating as a basic index. In the drama, there is uncertainty in the drama market that it is difficult to forecast the demand due to the nature of the commodity, while the drama market has a high financial contribution in the success of various contents of the broadcasting company. Therefore, to minimize the risk of failure. Thus, by analyzing the distribution of the first-time viewing time, it can be a practical help to establish a response strategy (organization/ marketing/story change, etc.) of the related company. Also, in this paper, we found that the behavior of the audience is crucial to the success of the program. In this paper, we define TV viewing as a measure of how enthusiastically watching TV is watched. We can predict the success of the program successfully by calculating the loyalty of the customer with the hot blood. This way of calculating loyalty can also be used to calculate loyalty to various platforms. It can also be used for marketing programs such as highlights, script previews, making movies, characters, games, and other marketing projects.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.95-108
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• 2017

Recently, AlphaGo which is Bakuk (Go) artificial intelligence program by Google DeepMind, had a huge victory against Lee Sedol. Many people thought that machines would not be able to win a man in Go games because the number of paths to make a one move is more than the number of atoms in the universe unlike chess, but the result was the opposite to what people predicted. After the match, artificial intelligence technology was focused as a core technology of the fourth industrial revolution and attracted attentions from various application domains. Especially, deep learning technique have been attracted as a core artificial intelligence technology used in the AlphaGo algorithm. The deep learning technique is already being applied to many problems. Especially, it shows good performance in image recognition field. In addition, it shows good performance in high dimensional data area such as voice, image and natural language, which was difficult to get good performance using existing machine learning techniques. However, in contrast, it is difficult to find deep leaning researches on traditional business data and structured data analysis. In this study, we tried to find out whether the deep learning techniques have been studied so far can be used not only for the recognition of high dimensional data but also for the binary classification problem of traditional business data analysis such as customer churn analysis, marketing response prediction, and default prediction. And we compare the performance of the deep learning techniques with that of traditional artificial neural network models. The experimental data in the paper is the telemarketing response data of a bank in Portugal. It has input variables such as age, occupation, loan status, and the number of previous telemarketing and has a binary target variable that records whether the customer intends to open an account or not. In this study, to evaluate the possibility of utilization of deep learning algorithms and techniques in binary classification problem, we compared the performance of various models using CNN, LSTM algorithm and dropout, which are widely used algorithms and techniques in deep learning, with that of MLP models which is a traditional artificial neural network model. However, since all the network design alternatives can not be tested due to the nature of the artificial neural network, the experiment was conducted based on restricted settings on the number of hidden layers, the number of neurons in the hidden layer, the number of output data (filters), and the application conditions of the dropout technique. The F1 Score was used to evaluate the performance of models to show how well the models work to classify the interesting class instead of the overall accuracy. The detail methods for applying each deep learning technique in the experiment is as follows. The CNN algorithm is a method that reads adjacent values from a specific value and recognizes the features, but it does not matter how close the distance of each business data field is because each field is usually independent. In this experiment, we set the filter size of the CNN algorithm as the number of fields to learn the whole characteristics of the data at once, and added a hidden layer to make decision based on the additional features. For the model having two LSTM layers, the input direction of the second layer is put in reversed position with first layer in order to reduce the influence from the position of each field. In the case of the dropout technique, we set the neurons to disappear with a probability of 0.5 for each hidden layer. The experimental results show that the predicted model with the highest F1 score was the CNN model using the dropout technique, and the next best model was the MLP model with two hidden layers using the dropout technique. In this study, we were able to get some findings as the experiment had proceeded. First, models using dropout techniques have a slightly more conservative prediction than those without dropout techniques, and it generally shows better performance in classification. Second, CNN models show better classification performance than MLP models. This is interesting because it has shown good performance in binary classification problems which it rarely have been applied to, as well as in the fields where it's effectiveness has been proven. Third, the LSTM algorithm seems to be unsuitable for binary classification problems because the training time is too long compared to the performance improvement. From these results, we can confirm that some of the deep learning algorithms can be applied to solve business binary classification problems.

• Journal of Korea Game Society
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• v.5 no.2
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• pp.19-27
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• 2005

The core price policy of on-line game marketing are FPP(Fixed Pre Paid model and PPU(Pay Per Use) model. These two models have been a on-line game company's billing system and a fundamental of MMORPG in Korea. However, they took root billing system only for first movers recently. In now, the market share of several first movers is exceeding 80%, late movers witch have same billing system cannot take part in pair competition. Even though in MMORPG, many games of late movers were favorably noticed by a lot of gamers during Evaluation. Test, a lot of companies are bankrupt before make business. Late Movers declare free game first thing, they maintain their existence and win over customers in on-line game market. And next, they guarantee item selling, give multiple experience value and game money, at last, induce their customers to pay service. As it makes trouble between pay user and free user, and it linked up with the collapse of game contents balance that designed for FPP billing system, And then meet unexpected result which reduction of game life cycle. In this Paper, we classified several contents services based on game contents, and suggested contents premium services which adopted low cost strategy lead to micro payment. we hope it will apply to late movers' new billing system in MMORPG.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.826-833
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• 2009

Currently, the rise of social tagging has changing taxonomy to folksonomy. Tag represents a new approach to organizing information. Nonhierarchical classification allows data to be freely gathered, allows easy access, and has the ability to move directly to other content topics. Tag is expected to play a key role in clustering various types of contents, it is expand to network in the common interests among users. First, this paper determine the relationships among user, tags and resources in social tagging system and examine the circumstances of what aspects to users when creating a tag related to features of websites. Therefore, this study uses tags from the social bookmarking service 'del.icio.us' to analyze the features of tag words when adding a new web page to a list. To do this, websites features classified into 7 items, it is known as tag classification related to resources. Experiments were conducted to test the proposed classify method in the area of music, photography and games. This paper attempts to investigate the perspective in which users apply a tag to a webpage and establish the capacity of expanding a social service that offers the opportunity to create a new business model.

• The Journal of the Korea Contents Association
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• v.7 no.4
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• pp.1-9
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• 2007

Cellular operators develop high speed mobile internet and multi-function cellular phones to activate new business model based on mobile internet services. Domestic cellular operators evolve their mobile networks from cdma2000-1x and EvDo to HSDPA to activate high speed mobile internet services. They also develop mobile internet platforms such as WIPI, BREW, and J2ME on multi-function cellular phones having multimedia solutions such as camera, MP3, MPEG, 3D game engine, DMB, PAN such as bluetooth, IrDA, W-LAN, and location information using GPS. But, content providers have problems of redevelopment of the same mobile internet application on different mobile internet platforms provided by cellular operators. In this paper, we develop conversion solutions for interoperability of mobile internet applications on WIPI and BREW using an one-pass compiler. We confirm the performance the proposed conversion solutions for the API conversion rate, the converted file size, and the full conversion time using the popular mobile games which are the killer applications on WiPI and BREW.

• Journal of Distribution Science
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• v.6 no.1
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• pp.47-62
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• 2008

Joining the WTO in 2001, China became a number of the global economic system. China succeeded in vying to host Beijing 2008 Olympic Games and World Expo 2010 Shanghai. It is China's honor and opportunity to have high economic growth in the coming future. In 2007, the total cost of public logistics decreased by 18.2% than 2006 to 4540.6 billion RMB, accounting for 18.4% of the GDP. So, China logistics is a huge industry and a growing market full of charm. The statistic ratios of China's logistics and growth trends show us it is an important issue to build and run an effective logistics system. However, research on China's logistics systems and supply chain is lacked. This study is focus on the logistic location strategy in China including the study of factories and warehouses geographic strategy concerned with SCM. The core of this study is to propose a New Multi-Facilities Location Decision Model. This study banded the revised gravity center, the standard single facility location decision model(Gravity Center Model) and the transportation model into a new Multi-Facilities Location Decision Model. In addition, this study suggested the gravity center of population, the gravity center of each industry, the location decision graded-list of each industry of china using the gravity center model and the revised gravity center model. The new Multi-Facilities Location Decision Model proposed in this study can be used to solve the location decision problem of more than two facilities. And it can be used in the fields such as the location decision of production facility and service facility, the location of distribution and logistics, the location of broadcast and satellite communications, the location of wireless communication tower and so on.