• Journal of Distribution Science
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• v.13 no.1
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• pp.57-70
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• 2015

Purpose - Open source software has high utilization in most of the server market. The utilization of open source software is a global trend. Particularly, Internet infrastructure and platform software open source software development has increased rapidly. Since 2003, the Korean government has published open source software promotion policies and a supply promotion policy. The dynamism of the open source software market, the lack of relevant expertise, and the market transformation due to reasons such as changes in the relevant technology occur slowly in relation to adoption. Therefore, this study proposes an assessment model of services provided in an open source software service company. In this study, the service level of open source software companies is classified into an enterprise-level assessment area, the service level assessment area, and service area. The assessment model is developed from an on-site driven evaluation index and proposed evaluation framework; the evaluation procedures and evaluation methods are used to achieve the research objective, involving an impartial evaluation model implemented after pilot testing and validation. Research Design, data, and methodology - This study adopted an iteration development model to accommodate various requirements, and presented and validated the assessment model to address the situation of the open source software service company. Phase 1 - Theoretical background and literature review Phase 2 - Research on an evaluation index based on the open source software service company Phase 3 - Index improvement through expert validation Phase 4 - Finalizing an evaluation model reflecting additional requirements Based on the open source software adoption case study and latest technology trends, we developed an open source software service concept definition and classification of public service activities for open source software service companies. We also presented open source software service company service level measures by developing a service level factor analysis assessment. The Behavior-Structure-Evolution Evaluation Model (BSEM) proposed in this study consisted of a rating methodology for calculating the level that can be granted through the assessment and evaluation of an enterprise-level data model. An open source software service company's service comprises the service area and service domain, while the technology acceptance model comprises the service area, technical domain, technical sub-domain, and open source software name. Finally, the evaluation index comprises the evaluation group, category, and items. Results - Utilization of an open source software service level evaluation model For the development of an open source software service level evaluation model, common service providers need to standardize the quality of the service, so that surveys and expert workshops performed in open source software service companies can establish the evaluation criteria according to their qualitative differences. Conclusion - Based on this evaluation model's systematic evaluation process and monitoring, an open source software service adoption company can acquire reliable information for open source software adoption. Inducing the growth of open source software service companies will facilitate the development of the open source software industry.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.10
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• pp.357-364
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• 2014

The first mission for the IoT based hyper-connectivity communication is developing a device-to-device communication technique in infra-less low-power and lossy networks. In a low-power and lossy wireless network, IoT devices and routers cannot keep the original path toward the destination since they have the limited memory. Different from the previous light-weight routing protocols focusing on the reduction of the control messages, the proposed scheme provides the light-weight IPv6 address auto-configuration, IPv6 neighbor discovery and routing protocol in a IoT capable infra-less wireless networks with the bloom filer and enhanced rank concepts. And for the first time we evaluate our proposed scheme based on the modeling of various probability distributions in the IoT environments with the lossy wireless link. Specifically, the proposed enhanced RPL based light-weight routing protocol improves the robustness with the multi-paths locally established based on the enhanced rank concepts even though lossy wireless links are existed. We showed the improvements of the proposed scheme up to 40% than the RPL based protocol.

• Journal of the Korea Convergence Society
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• v.12 no.5
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• pp.373-383
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• 2021

Our society is aging rapidly. In this super-aged society, the increase in healthcare costs are considered a national problem that undermines the sustainability of social security. Various services for healthcare for the elderly have been promoted to address this. However, most of them have focused on healthcare after the outbreak of chronic diseases and lack preventive healthcare. Most of the preventive healthcare projects are only pilots. In this paper, the current status of health care services for senior citizens at home and abroad was analyzed and based on this, the limitations and improvements were analyzed to propose the establishment of IoT-based Total Silver Care Center. IoT-based Total Silver Care Center may be conveniently monitored the health status of the elderly through various sensors, medical devices, and smart bands. And based on this, it can improve the quality of nursing services through time-saving and work efficiency of nursing providers. In addition, health care interventions may be provided in a timely manner if there is a change in the health status of users. And real-time imaging systems can help overcome mental difficulties.

• Industry Promotion Research
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• v.4 no.1
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• pp.79-86
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• 2019

The quality of service delivery by governments to their citizens is a subject of concern in the contemporary society. E-governance is a critical aspect that is transforming government operation and service delivery to citizens and other bodies through application of information technology. This research explores the state of e-governing focusing on nations that are developing with special attention to Pakistan. The difficulties and benefits encountered are identified. The results are vital for implementers of e-governing systems in these countries. The methodology applied entails a secondary research that involves analysis and synthesis of literature relating the research topic. The results reveal that Pakistan has made incredible steps in setting up e-governance systems with growth in internet use and access of data from a digital platform. The findings reveal that e-government is associated with multiple benefits including enhanced quality of services, cost efficiency in service provision, enhance transparency and elimination of corruption, provide the basis for eradication of poverty, boost economic stability of a country, and provide room for direct democracy. The research also found that developing countries experience challenges in form of financial constraints, poor ICT infrastructure, illiteracy on e-government, political consensus constraints, legal obstacles, social and cultural constraints. E-government has the capability to transform the quality of governance provided by governments, and policymakers and implementers should address the constraints that act as a hindrance to its implementation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.336-344
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• 2021

The purpose of this study was to identify the self-awareness of smartphone dependence among undergraduate students and their response to the same. The data was drawn from a survey on smartphone overdependence conducted by the Ministry of Science and information and communications technology (ICT) and the National Information Society Agency in 2017. The responses of 1,735 undergraduate students were analyzed by frequency, percentage, mean, standard deviation, minimum-maximum value, ��2-test, independent t-test, Pearson's correlation coefficient, and stepwise multiple regression analysis. The results indicated that 22.3% of participants were at risk of smartphone dependence, and 63.6% of them were unaware of their dependence on smartphones. The perception of smartphone dependence was significantly associated with a higher risk of smartphone dependence (��=.35, p=.000) and the increasing use of applications such as games (��=.19, p=.000), television/video (��=.11, p=.000), and learning (��=.11, p=.000). Of the participants with dependence awareness, only a few knew about the existence of centers to prevent smartphone and internet dependence. Moreover, they rarely utilized these centers. However, the participants felt the need for more counseling agencies (26.8%), programs for dealing with oneself (23.2%), information about smartphone dependence (14.9%), and help to overcome dependence (10.9%). These findings show the need to establish public services so that students can easily access correct information on smartphone dependence and address this problem.

• International journal of advanced smart convergence
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• v.11 no.1
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• pp.19-27
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• 2022

Across the world, 'housing' comprises a significant portion of wealth and assets. For this reason, fluctuations in real estate prices are highly sensitive issues to individual households. In Korea, housing prices have steadily increased over the years, and thus many Koreans view the real estate market as an effective channel for their investments. However, if one purchases a real estate property for the purpose of investing, then there are several risks involved when prices begin to fluctuate. The purpose of this study is to design a real estate price 'return rate' prediction model to help mitigate the risks involved with real estate investments and promote reasonable real estate purchases. Various approaches are explored to develop a model capable of predicting real estate prices based on an understanding of the immovability of the real estate market. This study employs the LSTM method, which is based on artificial intelligence and deep learning, to predict real estate prices and validate the model. LSTM networks are based on recurrent neural networks (RNN) but add cell states (which act as a type of conveyer belt) to the hidden states. LSTM networks are able to obtain cell states and hidden states in a recursive manner. Data on the actual trading prices of apartments in autonomous districts between January 2006 and December 2019 are collected from the Actual Trading Price Disclosure System of the Ministry of Land, Infrastructure and Transport (MOLIT). Additionally, basic data on apartments and commercial buildings are collected from the Public Data Portal and Seoul Metropolitan Government's data portal. The collected actual trading price data are scaled to monthly average trading amounts, and each data entry is pre-processed according to address to produce 168 data entries. An LSTM model for return rate prediction is prepared based on a time series dataset where the training period is set as April 2015~August 2017 (29 months), the validation period is set as September 2017~September 2018 (13 months), and the test period is set as December 2018~December 2019 (13 months). The results of the return rate prediction study are as follows. First, the model achieved a prediction similarity level of almost 76%. After collecting time series data and preparing the final prediction model, it was confirmed that 76% of models could be achieved. All in all, the results demonstrate the reliability of the LSTM-based model for return rate prediction.

• Information Systems Review
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• v.20 no.1
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• pp.1-16
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• 2018

The development and generalization of the Internet increased the popularity of food delivery service applications in Korea. The food delivery market based on online-to-offline service is growing rapidly. This study compares the usability of Korean food delivery service application between that of Chinese food delivery service application. This study suggests improvement points for Korean food delivery service applications. To conduct this study, we explore the status of various food delivery service applications and conduct interviews and surveys based on the honeycomb model developed by Peter Morville. This study obtained the following results. First, all restaurants participating in the Korean food delivery service must be able to accept order through the application. Second, the shopping cart function must be able to accept order of all restaurants simultaneously. Third, when users look for menu recommendation, their purchase history and shopping cart functions should appear at the first page of the website. Users should be able to perceive the improved usability of the website using those functions. Fourth, when the search window is fixed on the top of each page, users should be able to find the information they need. Fifth, the application must allow users to find the exact location of the delivery person and the estimated delivery time. Finally, the restaurants'address should be disclosed and fast delivery time should be confirmed to enhance users'trust on the application. This study contributes to academia and industry by suggesting useful insight into food delivery service applications and improving the point of food delivery service application in Korea.

• Asia pacific journal of information systems
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• v.23 no.3
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• pp.131-154
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• 2013

New internet technologies have created a revolutionary new platform which allows consumers to make decision about product price and quality quickly and provides information about themselves through the transcript of online reviews. By expressing their feelings toward products or services on virtual opinion platforms, users extend their influence into cyberspace as electronic word-of-mouth (e-WOM). Existing research indicates that an impact of eWOM on the consumer decision process is influential. For both academic researchers and practitioners, investigating this phenomenon of information sharing in online website is essential given the increasing number of consumers using them as sources of purchase decisions. It is worthwhile to examine the extent to which opinion seekers are willing to accept and adopt online reviews and which factors encourage adoption. Discerning the most motivating aspects of information adoption in particular, could help electronic marketers better promote their brand and presence on the internet. The objectives of this study are to investigate how online WOM influences a persons' purchase decision by discovering which factors encourage information adoption. Especially focused on the self-efficacy, this research investigates how self-efficacy affects on information usefulness and adoption of online information. Although people are exposed to same review or comment about product or service, some accept the reviews while others do not. We notice that accepting online reviews mainly depends on the person's preference or personal characteristics. This study empirically examines this issue by using cognitive dissonance theory. Specifically, in the movie industry, we address few questions-is always positive WOM generating positive effect? What if the movie isn't the person's favorite genre? What if the person who is very self-assertive so doesn't take other's opinion easily? In these cases of cognitive dissonance, is always WOM generating same result? While many studies have focused on one direct of WOM which indicates positive (or negative) informative reviews or comments generate positive (or negative) results and more (or less) profits, this study investigates not only directional properties of WOM but also how people change their opinion towards product or service positive to negative, negative to positive through the online WOM. An experiment was conducted quantitatively by using a sample of 168 users who have experience within the online movie review site, 'Naver Movie'. Users were required to complete a survey regarding reviews and comments taken from the real movie page. The data reflected user's perceptions of online WOM information that determined users' adoption level. Analysis results provide empirical support for the proposed theoretical perspective. When user can't agree with the opinion of online WOM information, in other words, when cognitive dissonance between online WOM information and users' preference occurs, perceived self-efficacy significantly decreases customers' perception of usefulness. And this perception of usefulness plays an important role in determining users' intention to adopt online WOM information. Most of researches have been concentrated on characteristics of online WOM itself such as quality or vividness of information, credibility of source and direction of online WOM, etc. for describing effect of online WOM, but our results suggest that users' personal character (e.g., self-efficacy) plays decisive role for acceptance of online WOM information. Higher self-efficacy means lower possibility to accept the information that represents counter opinion because of cognitive dissonance, whereas the people that have lower self-efficacy are willing to accept the online WOM information as true and refer to purchase decision. This study suggests a model for understanding role of direction of online WOM information. Also, our result implicates the importance of online review supervision and personalized information service by confirming switching opinion negative to positive is more difficult than positive to negative through the online WOM information. This implication would help marketers to manage online reviews of their products or services.

• Journal of Internet Computing and Services
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• v.16 no.2
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• pp.57-66
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• 2015

Considerable research efforts are being directed towards analyzing unstructured data such as text files and log files using commercial and noncommercial analytical tools. In particular, researchers are trying to extract meaningful knowledge through text mining in not only business but also many other areas such as politics, economics, and cultural studies. For instance, several studies have examined national pending issues by analyzing large volumes of text on various social issues. However, it is difficult to provide successful information services that can identify R&D documents on specific national pending issues. While users may specify certain keywords relating to national pending issues, they usually fail to retrieve appropriate R&D information primarily due to discrepancies between these terms and the corresponding terms actually used in the R&D documents. Thus, we need an intermediate logic to overcome these discrepancies, also to identify and package appropriate R&D information on specific national pending issues. To address this requirement, three methodologies are proposed in this study-a hybrid methodology for extracting and integrating keywords pertaining to national pending issues, a methodology for packaging R&D information that corresponds to national pending issues, and a methodology for constructing an associative issue network based on relevant R&D information. Data analysis techniques such as text mining, social network analysis, and association rules mining are utilized for establishing these methodologies. As the experiment result, the keyword enhancement rate by the proposed integration methodology reveals to be about 42.8%. For the second objective, three key analyses were conducted and a number of association rules between national pending issue keywords and R&D keywords were derived. The experiment regarding to the third objective, which is issue clustering based on R&D keywords is still in progress and expected to give tangible results in the future.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.1-8
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• 2013

Data mining techniques are used to find important and meaningful information from huge databases, and pattern mining is one of the significant data mining techniques. Pattern mining is a method of discovering useful patterns from the huge databases. Frequent pattern mining which is one of the pattern mining extracts patterns having higher frequencies than a minimum support threshold from databases, and the patterns are called frequent patterns. Traditional frequent pattern mining is based on a single minimum support threshold for the whole database to perform mining frequent patterns. This single support model implicitly supposes that all of the items in the database have the same nature. In real world applications, however, each item in databases can have relative characteristics, and thus an appropriate pattern mining technique which reflects the characteristics is required. In the framework of frequent pattern mining, where the natures of items are not considered, it needs to set the single minimum support threshold to a too low value for mining patterns containing rare items. It leads to too many patterns including meaningless items though. In contrast, we cannot mine any pattern if a too high threshold is used. This dilemma is called the rare item problem. To solve this problem, the initial researches proposed approximate approaches which split data into several groups according to item frequencies or group related rare items. However, these methods cannot find all of the frequent patterns including rare frequent patterns due to being based on approximate techniques. Hence, pattern mining model with multiple minimum supports is proposed in order to solve the rare item problem. In the model, each item has a corresponding minimum support threshold, called MIS (Minimum Item Support), and it is calculated based on item frequencies in databases. The multiple minimum supports model finds all of the rare frequent patterns without generating meaningless patterns and losing significant patterns by applying the MIS. Meanwhile, candidate patterns are extracted during a process of mining frequent patterns, and the only single minimum support is compared with frequencies of the candidate patterns in the single minimum support model. Therefore, the characteristics of items consist of the candidate patterns are not reflected. In addition, the rare item problem occurs in the model. In order to address this issue in the multiple minimum supports model, the minimum MIS value among all of the values of items in a candidate pattern is used as a minimum support threshold with respect to the candidate pattern for considering its characteristics. For efficiently mining frequent patterns including rare frequent patterns by adopting the above concept, tree based algorithms of the multiple minimum supports model sort items in a tree according to MIS descending order in contrast to those of the single minimum support model, where the items are ordered in frequency descending order. In this paper, we study the characteristics of the frequent pattern mining based on multiple minimum supports and conduct performance evaluation with a general frequent pattern mining algorithm in terms of runtime, memory usage, and scalability. Experimental results show that the multiple minimum supports based algorithm outperforms the single minimum support based one and demands more memory usage for MIS information. Moreover, the compared algorithms have a good scalability in the results.