• Management & Information Systems Review
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• v.36 no.3
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• pp.131-150
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• 2017

The purpose of this study is to observe the spread pattern of open source software (OSS) while establishing relations with surrounding actors during its operation period. In order to investigate the change pattern of participants in the OSS, we use a netnography on the basis of online data, which can trace the change patterns of the OSS depending on the passage of time. For this, the cases of three OSSs (e.g. jQuery, MooTools, and YUI), which are JavaScript frameworks, were compared, and the corresponding data were collected from the open application programming interface (API) of GitHub as well as blog and web searches. This research utilizes the translation process of the actor-network theory to categorize the stages of the change patterns on the OSS translation process. In the project commencement stage, we identified the type of three different OSS-related actors and defined associated relationships among them. The period, when a master commences a project at first, is refined through the course for the maintenance of source codes with persons concerned (i.e. project growth stage). Thereafter, the period when the users have gone through the observation and learning period by being exposed to promotion activities and codes usage respectively, and becoming to active participants, is regarded as the 'leap of participants' stage. Our results emphasize the importance of promotion processes in participants' selection of the OSS for participation and confirm the crowding-out effect that the rapid speed of OSS development retarded the emergence of participants.

• Management & Information Systems Review
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• v.35 no.1
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• pp.85-116
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• 2016

The present study aims to explore the causes of employment difficulty on the basis of job applicant's interest from P-E (person-environment) fit perspective. Our approach relied on a textual analytic method to reveal insights from their situational interests in a job search during the change of labor market. Thus, to investigate the type of major interests and psychological responses, user-generated texts in a social community were collected for analysis between January 1, 2013 through December 31, 2015 by crawling the online-community in regard to job seeking and sharing information and opinions. The results of topic analysis indicated user's primary interests were divided into four types: perception of vocation expectation, employment pre-preparation behaviors, perception of labor market, and job-seeking stress. Specially, job applicants put mainly concerns of monetary reward and a form of employment, rather than their work values or career exploration, thus youth job applicants expressed their psychological responses using contextualized language (e.g., slang, vulgarisms) for projecting their unstable state under uncertainty in response to environmental changes. Additionally, they have perceived activities in the restricted preparation (e.g., certification, English exam) as determinant factors for success in employment and suffered form job-seeking stress. On the basis of these findings, current unemployment matters are totally attributed to the absence of pursing the value of vocation and job in individuals, organizations, and society. Concretely, job seekers are preoccupied with occupational prestige in social aspect and have undecided vocational value. On the other hand, most companies have no perception of the importance of human resources and have overlooked the needs for proper work environment development in respect of stimulating individual motivation. The attempt in this study to reinterpret the effect of environment as for classifying job applicant's interests in reference to linguistic and psychological theories not only helps conduct a more comprehensive meaning for understanding social matters, but guides new directions for future research on job applicant's psychological factors (e.g., attitudes, motivation) using topic analysis.

• Management & Information Systems Review
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• v.32 no.5
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• pp.193-231
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• 2013

As developed IT, Word-of-Mouth(WOM) used varied terms as buzz marketing and viral marketing, and impressed that importance. Despite introduced new marketing tool on managers and professionals, online word-of-mouth including SNS lack of study on social network what based viral in marketing. In social network, patterns of relationship between individuals influence each other individual behaviors. Therefore this research grouped friendship-network and advice-network by characteristics, studied on trust of information source that antecedents of word-of-mouth in network. This study examined that affect- and cognition based trust affect WOM acceptance as WOM behaviors and examined effect of type of product as moderating variable. Additional this literature studied that WOM acceptance affect WOM recommend. To find the Influence of Trust on Word-of-Mouth Behaviors, a survey has done 206 samples(undergraduate students). The results of this study are as following : First, type of trust different friendship network and advice network. Affect-based trust is outstanding in friendship network than in advice network, while cognition-based trust stands out in advice network than another. Second, affect- and cognition based trust positive affect WOM acceptance. Contrary to expectations, what is preconceived trust in network have a similar effect for WOM acceptance regardless of type of trust. Third, WOM acceptance positive affect WOM recommend. Fourth, affect based trust affect WOM acceptance of hedonic product rather than utilitarian product. Upon especially in friendship network terms, affect-based trust has a more effect on WOM acceptance than cognition-based trust. This study has many implications. First, it is important that trust what have an influence WOM acceptance grouped affect- and cognition based trust. Second, it confirmed that trust is antecedents of positive WOM. Third, it is important that network grouped friendship network and advice-network by trust. Fourth, it gave managerial implications that they have to supply WOM through which network by type of product. We This study classified network and trust based on previous study. Then it examined relations between WOM behaviors. Further research could do enrich various things for example various age group, valence of message, quality of information.

• Management & Information Systems Review
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• v.35 no.4
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• pp.99-122
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• 2016

The social networking services (SNSs) have become popular among smartphone users, and one of the most popular services. In order to explain users' motivations toward SNS, this study considers uses and gratification theory which can explain individuals' motivations to select certain media channels. The purposes of this study is to investigate the relationships between motivations and addiction of SNS, and between addiction of SNS and decline in the subjective wellbeing. We examine moderating effects of social tie based on the social capital theory in the relationships between SNS addiction and decline in the subjective wellbeing. The motivations of SNS are subdivided into emotional motive (entertainment and fantasy) and cognitive motive (information share burden and challenge burden) based on the use and gratifications theory. The addiction of SNS is subdivided into time tolerance, withdrawal symptoms, interruption, and barrier of living. The data used in this study were collected from 286 SNS users through surveys. The data analysis in this study was performed using AMOS 17.0, and we used SEM(Structural Equation Modeling) methods in order to test the research model. The result shows that the emotional motive(entertainment and fantasy) and cognitive motive(information share burden and challenge burden) have an effect on the addiction of SNS. Especially emotional motive such as entertainment and users' fantasy toward SNS is an important factor that can cause SNS addiction. The addiction of SNS such as time tolerance, withdrawal symptoms, interruption, and barrier of living has an effect on the decline in the subjective wellbeing. Our result show that social tie partially moderates the relationship SNS addiction and decline in the subjective wellbeing. In addition, social tie between interruption of SNS and decline in the subjective wellbeing is an important moderating factor. The results focuses on the understanding toward relationship between SNS addiction based on the online and decline in the subjective wellbeing in the real world. The findings of this study also provides theoretical as well as practical implications which reflect the major features of SNS, and moderating effects of social tie based on the social capital.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.161-177
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• 2015

With the rapid evolution of technology, the size, number, and the type of databases has increased concomitantly, so data mining approaches face many challenging applications from databases. One such application is discovery of fraud patterns from agricultural product wholesale transaction instances. The agricultural product wholesale market in Korea is huge, and vast numbers of transactions have been made every day. The demand for agricultural products continues to grow, and the use of electronic auction systems raises the efficiency of operations of wholesale market. Certainly, the number of unusual transactions is also assumed to be increased in proportion to the trading amount, where an unusual transaction is often the first sign of fraud. However, it is very difficult to identify and detect these transactions and the corresponding fraud occurred in agricultural product wholesale market because the types of fraud are more intelligent than ever before. The fraud can be detected by verifying the overall transaction records manually, but it requires significant amount of human resources, and ultimately is not a practical approach. Frauds also can be revealed by victim's report or complaint. But there are usually no victims in the agricultural product wholesale frauds because they are committed by collusion of an auction company and an intermediary wholesaler. Nevertheless, it is required to monitor transaction records continuously and to make an effort to prevent any fraud, because the fraud not only disturbs the fair trade order of the market but also reduces the credibility of the market rapidly. Applying data mining to such an environment is very useful since it can discover unknown fraud patterns or features from a large volume of transaction data properly. The objective of this research is to empirically investigate the factors necessary to detect fraud transactions in an agricultural product wholesale market by developing a data mining based fraud detection model. One of major frauds is the phantom transaction, which is a colluding transaction by the seller(auction company or forwarder) and buyer(intermediary wholesaler) to commit the fraud transaction. They pretend to fulfill the transaction by recording false data in the online transaction processing system without actually selling products, and the seller receives money from the buyer. This leads to the overstatement of sales performance and illegal money transfers, which reduces the credibility of market. This paper reviews the environment of wholesale market such as types of transactions, roles of participants of the market, and various types and characteristics of frauds, and introduces the whole process of developing the phantom transaction detection model. The process consists of the following 4 modules: (1) Data cleaning and standardization (2) Statistical data analysis such as distribution and correlation analysis, (3) Construction of classification model using decision-tree induction approach, (4) Verification of the model in terms of hit ratio. We collected real data from 6 associations of agricultural producers in metropolitan markets. Final model with a decision-tree induction approach revealed that monthly average trading price of item offered by forwarders is a key variable in detecting the phantom transaction. The verification procedure also confirmed the suitability of the results. However, even though the performance of the results of this research is satisfactory, sensitive issues are still remained for improving classification accuracy and conciseness of rules. One such issue is the robustness of data mining model. Data mining is very much data-oriented, so data mining models tend to be very sensitive to changes of data or situations. Thus, it is evident that this non-robustness of data mining model requires continuous remodeling as data or situation changes. We hope that this paper suggest valuable guideline to organizations and companies that consider introducing or constructing a fraud detection model in the future.

• Asia pacific journal of information systems
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• v.23 no.2
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• pp.1-20
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• 2013

Social commerce has been gaining explosive popularity, with typical examples of the model such as Groupon and Level Up. Both local business owners and consumers can benefit from this new e-commerce model. Local business owners have a chance to access potential customers and promote their products in a way that could not have otherwise been easily possible, and consumers can enjoy discounted offerings. However, questions have been increasingly raised about the value and future of the social commerce model. A recent survey shows that about a third of 324 business owners who ran a daily-deal promotion in Groupon went behind. Furthermore, more than half of the surveyed merchants did not express enthusiasm about running the promotion again. The same goes for the case in Korea, where more than half of the surveyed clients reported no significant change or even decrease in profits compared to before the use of social commerce model. Why do local business owners fail to exploit the benefits from the promotions and advertisements through the social commerce model and to make profits? Without answering this question, the model would fall under suspicion and even its sustainability might be challenged. This study aims to look into problems in the current social commerce transactions and provide implications for the social commerce model, so that the model would get a foothold for next growth. Drawing on justice theory, this study develops theoretical arguments for the effects of transaction characteristics on consumers' distributive justice and purchase intention in the social commerce. Specifically, this study focuses on two characteristics of social commerce transactions-the discount rate and the purchase rate of products-and investigates their effects on consumers' perception of distributive justice for discounted transactions in the social commerce and their perception of distributive justice for regular-priced transactions. This study also examines the relationship between distributive justice and purchase intention. We conducted an online experiment and gathered data from 115 participants to test the hypotheses. Each participant was randomly assigned to one of nine manipulated scenarios of social commerce transactions, which were generated based on the combination of three levels of purchase rate (high, medium, and low) and three levels of discount rate (high, medium, and low). We conducted MANOVA and post-hoc ANOVA to test hypotheses about the relationships between the transaction characteristics (purchase rate and discount rate) and distributive justice for each of the discounted transaction and the regular-priced transaction. We also employed a PLS analysis to test relations between distributive justice and purchase intentions. Analysis results show that a higher discount rate increases distributive justice for the discounted transaction but decreases distributive justice for the regular-priced transaction. This, coupled with the result that distributive justice for each type of transaction has a positive effect on the corresponding purchase intention, implies that a large discount in the social commerce may be helpful for attracting consumers, but harmful to the business after the promotion. However, further examination reveals curvilinear effects of the discount rate on both types of distributive justice. Specifically, we find distributive justice for the discounted transaction increases concavely as the discount rate increases while distributive justice for the regular-priced transaction decreases concavely with the dscount rate. This implies that there exists an appropriate discount rate which could promote the discounted transaction while not hurting future business of regular-priced transactions. Next, the purchase rate is found to be a critical factor that facilitates the regular-priced transaction. It has a convexly positive influence on distributive justice for the transaction. Therefore, an increase of the rate beyond some threshold would lead to a substantial level of distributive justice for the regular-priced transaction, threrby boosting future transactions. This implies that social commerce firms and sellers should employ various non-price stimuli to promote the purchase rate. Finally, we find no significant relationship between the purchase rate and distributive justice for the discounted transaction. Based on the above results, we provide several implications with future research directions.

• The Korean Journal of Archival Studies
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• no.72
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• pp.91-163
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• 2022

The focus of this paper lies on the fact that the 'management' and 'utilization' of audiovisual archives are still not specialized in both the public and the private sectors. The use of online video platforms including 'YouTube' has became common. Accordingly the production and collection of high-definition and high-capacity audiovisual archives has been rapidly increasing. However, it also emphasizes that there are no references or principles in the current Public Records Act and its enforcement rules, public standards, and guidelines. This paper ultimately examines the provisions that are related to audiovisual archives of the current Public Records Act, which needed to be revised and enacted due to the lack of an audiovisual archives management manual of national institutions, public broadcasters, and organizations can refer to. In addition, this study tries to find out what kind of systems and guidelines are used in audiovisual archives management. This paper examines the current state of standardization of audiovisual records of the National Archives. It also analyses the systems and the guidelines methodically for efficient audiovisual record management in the public records management sector. It suggests the new direction of relevant public standards and guidelines through this research. Futhermore, it measures to activate the audiovisual management policy-making functions of the National Archives. The necessity of establishing a Public Audiovisual Archives as an organization was also reviewed in this paper. The Public Audiovisual Archives will collect Public Audio and Videos systematically and comprehensively through the legal deposit system. And it will be operated by the management and the utilization system so that it can be used for public as a collective memory. Finally, it will takes a charge of a professional role in audiovisual record management field, such as technology standardization to safeguard and protect the copyrights through this process.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.1-17
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• 2023

With the recent rapid development of ICT(Information and Communication Technology) and the popularization of digital devices, the size of the online market continues to grow. As a result, we live in a flood of information. Thus, customers are facing information overload problems that require a lot of time and money to select products. Therefore, a personalized recommender system has become an essential methodology to address such issues. Collaborative Filtering(CF) is the most widely used recommender system. Traditional recommender systems mainly utilize quantitative data such as rating values, resulting in poor recommendation accuracy. Quantitative data cannot fully reflect the user's preference. To solve such a problem, studies that reflect qualitative data, such as review contents, are being actively conducted these days. To quantify user review contents, text mining was used in this study. The general CF consists of the following three steps: user-item matrix generation, Top-N neighborhood group search, and Top-K recommendation list generation. In this study, we propose a recommendation algorithm that applies an extended similarity measure, which utilize quantified review contents in addition to user rating values. After calculating review similarity by applying TF-IDF, Word2Vec, and Doc2Vec techniques to review content, extended similarity is created by combining user rating similarity and quantified review contents. To verify this, we used user ratings and review data from the e-commerce site Amazon's "Health and Personal Care". The proposed recommendation model using extended similarity measure showed superior performance to the traditional recommendation model using only user rating value-based similarity measure. In addition, among the various text mining techniques, the similarity obtained using the TF-IDF technique showed the best performance when used in the neighbor group search and recommendation list generation step.

• Knowledge Management Research
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• v.23 no.4
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• pp.315-338
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• 2022

In 1986, Korea established legal systems to support small and medium-sized start-ups, which becomes the main pillars of national development. The legal systems have stimulated start-up ecosystems to have more than 1 million new start-up companies founded every year during the past 30 years. To analyze the trend of Korea's start-up ecosystem, in this study, we collected 1.18 million news articles from 1991 to 2020. Then, we extracted news articles that have the keywords "start-up", "venture", and "start-up". We employed network analysis and topic modeling to analyze collected news articles. Our analysis can contribute to analyzing the government policy direction shown in the history of start-up support policy. Specifically, our analysis identifies the dynamic characteristics of government influenced by external environmental factors (e.g., society, economy, and culture). The results of our analysis suggest that the start-up ecosystems in Korea have changed and developed mainly by the government policies for corporation governance, industrial development planning, deregulation, and economic prosperity plan. Our frequency keyword analysis contributes to understanding entrepreneurial productivity attributed to activities among the networked components in industrial ecosystems. Our analyses and results provide practitioners and researchers with practical and academic implications that can help to establish dedicated support policies through forecast tasks of the economic environment surrounding the start-ups. Korean entrepreneurial productivity has been empowered by growing numbers of large companies in the mobile phone industry. The spectrum of large companies incorporates content startups, platform providers, online shopping malls, and youth-oriented start-ups. In addition, economic situational factors contribute to the growth of Korean entrepreneurial productivity the economic, which are related to the global expansions of the mobile industry, and government efforts to foster start-ups. Our research is methodologically implicative. We employ natural language processes for 30 years of media articles, which enables more rigorous analysis compared to the existing studies which only observe changes in government and policy based on a qualitative manner.