• Korean Management Science Review
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• v.32 no.2
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• pp.15-35
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• 2015

Large business groups typically have central firms through which their controlling families establish (or acquire) new firms and maintain control over other member firms. Research on corporate governance has developed metrics to identify those central firms and investigated an impact of the centrality on ownership structure and firm's financial performance. This paper introduces centrality metrics used in social network analysis (SNA) to measure how crucial a role each firm plays in the ownership structure of its business group. Then, the SNA centrality metrics are compared with the metrics developed in corporate governance field. Also, we test the relationship between the SNA centrality metrics and firm's value. Experimental results show that the SNA centrality metrics are closely correlated with the centrality metrics used in corporate governance and are significantly correlated with firm's value.

• Journal of Korean Institute of Industrial Engineers
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• v.33 no.1
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• pp.11-25
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• 2007

Despite surging interests in analyzing business processes, there are few scientific approaches to analysis and redesign of organizational structures which can greatly affect the performance of business processes. This paper presents a method for deriving and analyzing organizational relations from process models using social network analysis techniques. Process models contain information on who performs which processes and activities, along with the assignment of organizational units such as departments and roles to related activities. To derive social relations between organizational units from process models, three types of metrics are formally defined: transfer of work metrics, subcontracting metrics, and cooperation metrics. By applying these metrics, various relations among organizational units can be derived and analyzed. To verify the proposed method and metrics, they are applied to standard process models of the semiconductor and electronic, and automotive industry in Korea. This paper presents a taxonomy for diagnosing organization structure based on the presented approach. The paper also discusses how to combine analyses in the taxonomy for redesign of organizational structures.

• Journal of information and communication convergence engineering
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• v.15 no.3
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• pp.165-169
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• 2017

Altmetrics is an emergent research area whereby social media is applied as a source of metrics to evaluate scientific impact. Recently, the interest in altmetrics has been growing. Traditional scientific impact evaluation indictors are based on the number of publications, citation counts and peer reviews of a researcher. As research publications were increasingly placed online, usage metrics as well as webometrics appeared. This paper explores the potential benefits of altmetrics and the deep relationship between each metrics. Firstly, we found a weak-to-medium correlation among the 11 altmetrics and visualized such correlation. Secondly, we conducted principal component analysis and exploratory factor analysis on altmetrics of social media, divided the 11 altmetrics into four feature sets, confirming the dispersion and relative concentration of altmetrics groups and developed the altmetrics evaluation model. We can use this model to evaluate the scientific impact of articles on social media.

• Journal of Information Science Theory and Practice
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• v.3 no.2
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• pp.16-30
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• 2015

The term megajournal is used to describe publication platforms, like PLOS ONE, that claim to incorporate peer review processes and web technologies that allow fast review and publishing. These platforms also publish without the constraints of periodic issues and instead publish daily. We conducted a yearlong bibliometric profile of a sample of articles published in the first several months after the launch of PeerJ, a peer reviewed, open access publishing platform in the medical and biological sciences. The profile included a study of author characteristics, peer review characteristics, usage and social metrics, and a citation analysis. We found that about 43% of the articles are collaborated on by authors from different nations. Publication delay averaged 68 days, based on the median. Almost 74% of the articles were coauthored by males and females, but less than a third were first authored by females. Usage and social metrics tended to be high after publication but declined sharply over the course of a year. Citations increased as social metrics declined. Google Scholar and Scopus citation counts were highly correlated after the first year of data collection (Spearman rho = 0.86). An analysis of reference lists indicated that articles tended to include unique journal titles. The purpose of the study is not to generalize to other journals but to chart the origin of PeerJ in order to compare to future analyses of other megajournals, which may play increasingly substantial roles in science communication.

• Journal of Preventive Medicine and Public Health
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• v.57 no.3
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• pp.298-303
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• 2024

Objectives: The aim of this study was to estimate drug prescription indicators in outpatient services provided at Iran Social Security Organization (SSO) healthcare facilities. Methods: Data on all prescribed drugs for outpatient visits from 2017 to 2018 were extracted from the SSO database. The data were categorized into 4 main subgroups: patient characteristics, provider characteristics, service characteristics, and type of healthcare facility. Logistic regression models were used to detect risk factors for inappropriate drug prescriptions. SPSS and IBM Modeler software were utilized for data analysis. Results: In 2017, approximately 150 981 752 drug items were issued to outpatients referred to SSO healthcare facilities in Iran. The average number of drug items per outpatient prescription was estimated at 3.33. The proportion of prescriptions that included an injection was 17.5%, and the rate of prescriptions that included an antibiotic was 37.5%. Factors such as patient sex and age, provider specialty, type of facility, and time of outpatient visit were associated with the risk of inappropriate prescriptions. Conclusions: In this study, all drug prescription criteria exceeded the recommended limits set by the World Health Organization. To improve the current prescription patterns throughout the country, it would be beneficial to provide providers with monthly and annual reports and to consider implementing some prescription policies for physicians.

• Journal of Preventive Medicine and Public Health
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• v.46 no.6
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• pp.319-328
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• 2013

Objectives: This study estimated the association of cardiovascular health behaviors with the risk of all-cause and cardiovascular disease (CVD) mortality in middle-aged men in Korea. Methods: In total, 12 538 men aged 40 to 59 years were enrolled in 1993 and followed up through 2011. Cardiovascular health metrics defined the following lifestyle behaviors proposed by the American Heart Association: smoking, physical activity, body mass index, diet habit score, total cholesterol, blood pressure, and fasting blood glucose. The cardiovascular health metrics score was calculated as a single categorical variable, by assigning 1 point to each ideal healthy behavior. A Cox proportional hazards regression model was used to estimate the hazard ratio of cardiovascular health behavior. Population attributable risks (PARs) were calculated from the significant cardiovascular health metrics. Results: There were 1054 total and 171 CVD deaths over 230 690 person-years of follow-up. The prevalence of meeting all 7 cardiovascular health metrics was 0.67%. Current smoking, elevated blood pressure, and high fasting blood glucose were significantly associated with all-cause and CVD mortality. The adjusted PARs for the 3 significant metrics combined were 35.2% (95% confidence interval [CI], 21.7 to 47.4) and 52.8% (95% CI, 22.0 to 74.0) for all-cause and CVD mortality, respectively. The adjusted hazard ratios of the groups with a 6-7 vs. 0-2 cardiovascular health metrics score were 0.42 (95% CI, 0.31 to 0.59) for all-cause mortality and 0.10 (95% CI, 0.03 to 0.29) for CVD mortality. Conclusions: Among cardiovascular health behaviors, not smoking, normal blood pressure, and recommended fasting blood glucose levels were associated with reduced risks of all-cause and CVD mortality. Meeting a greater number of cardiovascular health metrics was associated with a lower risk of all-cause and CVD mortality.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.302-309
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• 2009

The effectiveness of Building Information Modeling (BIM) tools and processes has been recognized by the industry and owners are beginning to adopt Triple Bottom Line accounting practices, to enhance economic performance and environmental and social performance. However, the widespread and practical application of Green BIM remains largely unrealized. The authors identify that lack of understanding of the applicability of sustainability metrics to BIM design process is a significant barrier to this adoption. Through literature review this paper outlines the various sustainability metrics available to construction and elaborates on the potential of BIM for sustainable design. The paper maps and correlates applicable concepts of sustainability evaluation systems to BIM and describes the constraints in current BIM tools.

• The Journal of the Korea Contents Association
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• v.12 no.5
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• pp.470-477
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• 2012

Climate related policies have been considered due to increase of energy and fossil fuel consumption. Therefore, research has been done to reduce $CO_2$ and to implement green construction. This paper proposes green construction performance metrics for transportation facilities based on previous literature review and survey. The metrics are composed with economic, social, and environment parts and number of metrics are 14, 10, and 12 respectively. The survey was performed to evaluate relevance, clarity, timeliness, comparable, and obtainable of the proposed metrics. Then implementation strategy is also proposed for effective use.

• Asia Marketing Journal
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• v.11 no.1
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• pp.39-72
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• 2009

With the growing importance of accountability, it is getting necessary to test the impact of marketing expenditure on marketing performance. Including recent ROM, we can find a few researches about marketing accountability. But there are a few problems about definitions and metric of marketing expenditure and marketing performance. Therefore, by defining and analyzing the impact of marketing expenditure on marketing performance, we are going to set the classification scheme of marketing expenditure and marketing performance. Based on research findings, new definitions and metrics are proposed as follows. First, we suggest the classification scheme of marketing expenditure. Marketing expenditure is defined as expense accounts in the balance sheet for doing marketing tasks. Marketing expenditures includes many accounts, for example, marketing research, advertising, sales promotion, foreign market development, physical distribution, after services. Among these marketing investment, advertising expenses have a positive effect on marketing performance. Second, we suggest the classification scheme of marketing performance. Already, marketing performance has been defined as financial metrics, customer metrics, market metrics, and corporate social responsibility. But, in this study, we find that the process model is not relevant for explaining association between the performance metrics. The process model is a virtuous cycle: "customer metrics→market metrics→financial metrics→firm valuation metrics." But, in this study, it is not supported or a little significant association between these metrics. Based on these results, we suggest the balance model or flower model as the classification scheme of marketing performance.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.23-46
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• 2021

Collaborative filtering, which is often used in personalization recommendations, is recognized as a very useful technique to find similar customers and recommend products to them based on their purchase history. However, the traditional collaborative filtering technique has raised the question of having difficulty calculating the similarity for new customers or products due to the method of calculating similaritiesbased on direct connections and common features among customers. For this reason, a hybrid technique was designed to use content-based filtering techniques together. On the one hand, efforts have been made to solve these problems by applying the structural characteristics of social networks. This applies a method of indirectly calculating similarities through their similar customers placed between them. This means creating a customer's network based on purchasing data and calculating the similarity between the two based on the features of the network that indirectly connects the two customers within this network. Such similarity can be used as a measure to predict whether the target customer accepts recommendations. The centrality metrics of networks can be utilized for the calculation of these similarities. Different centrality metrics have important implications in that they may have different effects on recommended performance. In this study, furthermore, the effect of these centrality metrics on the performance of recommendation may vary depending on recommender algorithms. In addition, recommendation techniques using network analysis can be expected to contribute to increasing recommendation performance even if they apply not only to new customers or products but also to entire customers or products. By considering a customer's purchase of an item as a link generated between the customer and the item on the network, the prediction of user acceptance of recommendation is solved as a prediction of whether a new link will be created between them. As the classification models fit the purpose of solving the binary problem of whether the link is engaged or not, decision tree, k-nearest neighbors (KNN), logistic regression, artificial neural network, and support vector machine (SVM) are selected in the research. The data for performance evaluation used order data collected from an online shopping mall over four years and two months. Among them, the previous three years and eight months constitute social networks composed of and the experiment was conducted by organizing the data collected into the social network. The next four months' records were used to train and evaluate recommender models. Experiments with the centrality metrics applied to each model show that the recommendation acceptance rates of the centrality metrics are different for each algorithm at a meaningful level. In this work, we analyzed only four commonly used centrality metrics: degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. Eigenvector centrality records the lowest performance in all models except support vector machines. Closeness centrality and betweenness centrality show similar performance across all models. Degree centrality ranking moderate across overall models while betweenness centrality always ranking higher than degree centrality. Finally, closeness centrality is characterized by distinct differences in performance according to the model. It ranks first in logistic regression, artificial neural network, and decision tree withnumerically high performance. However, it only records very low rankings in support vector machine and K-neighborhood with low-performance levels. As the experiment results reveal, in a classification model, network centrality metrics over a subnetwork that connects the two nodes can effectively predict the connectivity between two nodes in a social network. Furthermore, each metric has a different performance depending on the classification model type. This result implies that choosing appropriate metrics for each algorithm can lead to achieving higher recommendation performance. In general, betweenness centrality can guarantee a high level of performance in any model. It would be possible to consider the introduction of proximity centrality to obtain higher performance for certain models.