• Journal of Korean Institute of Industrial Engineers
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• v.39 no.4
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• pp.260-270
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• 2013

This paper proposes a strategic portfolio model for managing performance of online games. The portfolio matrix is composed of two dimensions: financial performance and non-financial performance. Financial performance is measured by the conventional measure, average revenue per user (ARPU). In terms of non-financial performance, five non-financial key performance indicators (KPIs) that have been widely used in the online game industry are utilized: RU (Register User), VU (Visiting User), TS (Time Spent), ACU (Average Current User), MCU (Maximum Current User). Data envelopment analysis (DEA) is then employed to produce a single performance measure aggregating the five KPIs. DEA is a linear programming model for measuring the relative efficiency of decision making unit (DMUs) with multiple inputs and outputs. This study employs DEA as a tool for multiple criteria decision making (MCDM), in particular, the pure output model without inputs. Combining the two types of performance produces the online game portfolio matrix with four quadrants: Dark Horse, Stop Loss, Jack Pot, Luxury Goods. A case study of 39 online games provided by company 'N' is provided. The proposed portfolio model is expected to be fruitfully used for strategic decision making of online game companies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.9
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• pp.3075-3093
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• 2014

In this paper, we consider an advanced wireless user, called primary-secondary user (PSU) who is capable of harvesting renewable energy and connecting to both the primary network and cognitive radio networks simultaneously. Recently, energy harvesting has received a great deal of attention from the research community and is a promising approach for maintaining long lifetime of users. On the other hand, the cognitive radio function allows the wireless user to access other primary networks in an opportunistic manner as secondary users in order to receive more throughput in the current time slot. Subsequently, in the paper we propose the channel access policy for a PSU with consideration of the energy harvesting, based on a Partially Observable Markov decision process (POMDP) in which the optimal action from the action set will be selected to maximize expected long-term throughput. The simulation results show that the proposed POMDP-based channel access scheme improves the throughput of PSU, but it requires more computations to make an action decision regarding channel access.

• Asian Journal of Business Environment
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• v.13 no.1
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• pp.9-19
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• 2023

Purpose: The purpose of this research paper is to explore what factors that affect customer purchase decisions in the online environment, particularly after the COVID-19 pandemic in the case of Vietnamese customers. Research Design, Data and Methodology: To clarify which factor has the most significant impacts on online purchasing decision-making process, this study proposed a research model including factors such as customer trust, proposensity to trust, system assurance, the quality of website design, attitude, and customer satisfaction. This study collected the data via online survey. Data analysis was conducted by AMOS 25.0 using the Structural Equation Modeling (SEM) method. Result: The results of this study shows that the purchase decisions were positively affected by customers' attitude, satisfaction, trust, and the quality of websites design. Additionally, factors such as perceived size and reputation and system assurance, have impacts on buyers' trust, while the propensity to trust has no significant impact. Conclusion: This study provides managerial implications. The results provide which factors should be improved to foster trust, attitude, customer satisfaction, and purchase decision in the online environment. The results also provide managerial implication on marketing strategies how to enhance better relationships with customers and to consider environmental issues in the era of post COVID-19.

• The Journal of the Korea Contents Association
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• v.12 no.2
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• pp.472-482
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• 2012

There are many investors in the stock market, and more and more people get interested in the stock investment. In order to avoid risks and make profit in the stock investment, we have to determine several aspects using various information. That is, we have to select profitable stocks and determine appropriate buying/selling prices and holding period. This paper proposes a data mining tool for the investors' decision support. The data mining tool makes stock investors apply machine learning techniques and generate stock price prediction model. Also it helps determine buying/selling prices and holding period. It supports individual investor's own decision making using past data. Using the proposed tool, users can manage stock data, generate their own stock price prediction models, and establish trading policy via investment simulation. Users can select technical indicators which they think affect future stock price. Then they can generate stock price prediction models using the indicators and test the models. They also perform investment simulation using proper models to find appropriate trading policy consisting of buying/selling prices and holding period. Using the proposed data mining tool, stock investors can expect more profit with the help of stock price prediction model and trading policy validated on past data, instead of with an emotional decision.

• Transactions of the KSME C: Technology and Education
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• v.4 no.1
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• pp.11-18
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• 2016

Under the Renewable Heat Obligation (RHO) public buildings in the Republic of Korea larger than $10,000m^2$ must achieve an 11% overall reduction to thermal energy consumption. Well intended solutions have been proposed. However, not all option is evaluated on the same basis, potentially resulting in incomplete or sub-optimal solutions. What's more once projects are implemented, there are inconsistencies in the methods used to measure and evaluate operating performance of the post-retrofit case. The RETScreen decision tools and methodology can be used by decision makers, policy developers, architects, engineers and community leaders to evaluate and select the most effective solutions for Korea's RHO needs.

• Health Policy and Management
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• v.13 no.3
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• pp.21-34
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• 2003

Patient revisit to used hospital is a key factor in determining a health care organization's competitive advantage and survival. This article examines the relationship between customer's satisfaction and his/her revisit associated with three different methods which are the Chi Square Automatic Interaction Detection(CHAID) for segmenting the outpatient group, logistic regression and neural networks for addressing the outpatient's revisit. The main findings indicate that the important factors on outpatient's revisit are physician's kindness, nurse's skill, overall level of satisfaction, hospital reputation, recommendation, level of diagnoses and outpatient's age. Among these ones, physician's kindness is the most important factor as guidelines for decision of their revisit. The decision maker of hospital should select the strategy containing the variable amount of the level of revisit and size of outpatient's group under the constraint on the hospital's time, budget and manpower given. Finally, this study shows that neural networks, as non-parametric technique, appear to more correctly predict revisit than does logistic regression as a parametric estimation technique.

• Journal of the Korean Operations Research and Management Science Society
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• v.31 no.3
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• pp.81-96
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• 2006

We consider a rental company that dynamically manages Its capacity level through capacity addition and return While serving customer with its own capacity, the company expands its capacity by renting items from an outside source so that it can avoid lost opportunities of rental which occur when stock is not sufficient. If stock becomes sufficiently large enough to cope with demands, the company returns expanded capacity to the outside source. Formulating the model into a Markov decision problem, we identify an optimal capacity management Policy which states when the company should expand its capacity and when it should return expanded capacity after capacity addition. Since it is intractable to analytically find the optimal capacity management policy and the optimal size of capacity expansion, we present a numerical procedure that finds these optimal values based on the value iteration method. Numerical analysis is implemented and we observe monotonic properties of the optimal performance measures by system parameters, which are meaningful in developing effective heuristic policies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.4
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• pp.1516-1528
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• 2015

We introduce attribute set based signature (ASBS), a new cryptographic primitive which organizes user attributes into a recursive set based structure such that dynamic constraints can be imposed on how those attributes may be combined to satisfy a signing policy. Compared with attribute based signature (ABS), ASBS is more flexible and efficient in managing user attributes and specifying signing policies. We present a practical construction of ASBS and prove its security in the standard model under three subgroup decision related assumptions. Its efficiency is comparable to that of the most efficient ABS scheme.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.10
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• pp.3394-3408
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• 2014

Conventional mobile state (MS) and base station (BS) association based on average signal strength often results in imbalance of cell load which may require more powerful processor at BSs and degrades the perceived transmission rate of MSs. To deal with this problem, a Markov decision process (MDP) for load balancing in a multi-cell system with multi-carriers is formulated. To solve the problem, exploiting Sarsa algorithm of on-line learning type [12], ${\alpha}$-controllable load balancing algorithm is proposed. It is designed to control tradeoff between the cell load deviation of BSs and the perceived transmission rates of MSs. We also propose an ${\varepsilon}$-differential soft greedy policy for on-line learning which is proven to be asymptotically convergent to the optimal greedy policy under some condition. Simulation results verify that the ${\alpha}$-controllable load balancing algorithm controls the behavior of the algorithm depending on the choice of ${\alpha}$. It is shown to be very efficient in balancing cell loads of BSs with low ${\alpha}$.

• Health Policy and Management
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• v.6 no.2
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• pp.1-13
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• 1996

To evalate the efficiency of public and private hospitals, the author used Data Envelopment Analysis(DEA), a mathematical linear programming method calculating the of ficiency of a unity(DMU: Decision Making Unit) in relation to the other units in analysis. DEA was applied to thirty three (10 public and 23 private) general hospitals wiwith 160 to 299 beds. In respect to productivity, public hospitals appeared to be a little more efficient than private ones, even though it's statisticansignificant. However, the efficiency score for profitability conversed that these contrary results were due to the caring of more medical protection patients in public hospitals, who brought less revenlue to te hospital than other patients. Public hospitals' superiority to private counterparts in productivity, which are aguged mainly based on cared patients, suggests that the former contributes so much positively to social utility. In particular, the fact that public hospitals are caring more medical protection patients, namely the poverty group whom the society should bear a burden of by all means, seems to be desirable in respect of role of publi hospitals.