• Journal of Information Technology Services
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• v.10 no.2
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• pp.203-221
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• 2011

The purpose of this study is to investigate the applications of Structural Equation Modeling(SEM) into MIS area in recent years. Two methodologies, Lisrel and PLS, are adopted for the method comparison. A research model, based upon TAM(Technology Acceptance Model) is used for the analysis of the data set of a previous study. The research model includes six research variables that are composed of twenty-eight question items. 272 data are used for data analyses through Lisrel v.8.72 and Visual PLS v.1.04. This study shows the statistical results of Lisrel are the same to those of PLS. The contribution of this study can be suggested as the followings; (1) A theoretical comparison of two methodologies is shown, (2) A statistical analysis is done at a real-situated data set, and (3) Several implications are suggested.

• Proceedings of the PSK Conference
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• 2003.04a
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• pp.247.2-248
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• 2003

The three-dimensional quantitative structure-activity relationship (3D-QSAR) approach using comparative molecular field analysis (CoMFA) and comparative molecular similarity analysis (CoMSIA) was applied to 62 derivatives known as COX-2 selective inhibitors. Partial least square (PLS) analyses produced good predicted models with q2 value of 0.803 (s=0.285, F=215.401, r2=0.951) and 0.769 (s=0.192, F=245.364, r2=0.980) for CoMFA and CoMSIA, respectively. (omitted)

• Journal of Information Technology Services
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• v.14 no.4
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• pp.199-220
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• 2015

Recently, many internet users are using mobile music service. In this paper, we aim to examine the effects of system quality factors and relation quality factors on continuance intention in the context of mobile music service. In order to analyze the antecedents of continuance intention, we introduced independent variables such as ease of use, stability, trust, involvement and unity. We also consider customer value and customer satisfaction as mediators. For empirical analysis, we surveyed 240 users of mobile music service. We conduct PLS (Partial Least Square) analysis for SEM (Structural Equation Modeling). As a result, 8 of proposed 12 hypotheses were accepted. Especially, system quality, trust and unity show positive influence on customer satisfaction significantly. Involvement and unity are positively related to customer value. Customer value and customer satisfaction show positive influence on continuance intention significantly. The results of this research can be useful guidelines for mobile music service users and providers.

• Knowledge Management Research
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• v.19 no.4
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• pp.19-37
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• 2018

Knowledge transfer among employees integrates individual knowledge scattered within a firm, thus increases organizational effectiveness. In particular, the role of knowledge broker, which enables knowledge sharing across multiple teams or subunits, is critical for the success of knowledge management. This study classified the types of knowledge broker that facilitates knowledge flows among team, and examined the influences of various intra- and inter-team social networks. Survey responses from 128 employees of four R&D teams were gathered and analyzed using partial least square structural equation modeling. The results of analysis showed that all types of inter-team networks(i.e., emotional closeness network, frequency of interaction network, and perceived expertise network) had significant influences on related knowledge brokerage behaviors. In case of intra-team networks, only the emotional closeness network showed significant influence. These results proved the necessity of managing various types of intra- and inter-team networks to encourage knowledge brokerage behaviors within a firm.

• Clean Technology
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• v.17 no.3
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• pp.215-224
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• 2011

It is an important issue to develop quality assessing system for biofuel for the purpose of accelerating the mass production of biofuel. It is particularly challenging to conduct testing method in the mass production of bioethanol while meeting quality specifications such as ASTM (American Society for Testing & Materials) D4806-10. In order to address this challenge, this paper proposes on-line spectroscopic quality assesment system based on Near Infrared spectrum and Partial Least Squares method in Chemometrics. As a result of testing a number of preprocessing methods and Partial Least Squares, it was found out that Savitzky-Golay method showed the best performance in terms of spectrum correction, noise reduction, and model maintenance. The proposed system allows us to assess multiple quality components continuously using spectroscopic facilities with the cheap cost. Since the value of R2 is more than 0.99, it is possible to replace the laboratory analysis.

• Journal of Plant Biotechnology
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• v.43 no.3
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• pp.383-390
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• 2016

To determine whether FT-IR spectral analysis based on multivariate analysis could be used to discriminate Chinese cabbage breeding line at metabolic level, whole cell extracts of nine different breeding lines (three paternal, three maternal and three $F_1$ lines) were subjected to Fourier transform infrared spectroscopy (FT-IR). FT-IR spectral data of Chinese cabbage plants were analyzed by principal component analysis (PCA), partial least square discriminant analysis (PLS-DA), and hierarchical clustering analysis (HCA). The hierarchical dendrograms based on PLS-DA from two of three cross combinations showed that paternal, maternal, and their progeny $F_1$ lines samples were perfectly separated into three branches in breeding line dependent manner. However, a cross combination failed to fully discriminate them into three branches. Thus, hierarchical dendrograms based on PLS-DA of FT-IR spectral data of Chinese cabbage breeding lines could be used to represent the most probable chemotaxonomical relationship among maternal, paternal, and $F_1$ plants. Furthermore, these metabolic discrimination systems could be applied for rapid selection and classification of useful Chinese cabbage cultivars.

• Journal of Plant Biotechnology
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• v.43 no.2
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• pp.213-222
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• 2016

This study aims to establish a system for the rapid discrimination of Zoysia species using metabolite fingerprinting of FT-IR spectroscopy combined with multivariate analysis. Whole cell extracts from leaves of 19 identified Zoysia japonica, 6 identified Zoysia sinica, and 38 different unidentified Zoysia species were subjected to Fourier transform infrared spectroscopy (FT-IR). PCA (principle component analysis) and PLS-DA (partial least square discriminant analysis) from FT-IR spectral data successfully divided the 25 identified turf grasses into two groups, representing good agreement with species identification using molecular markers. PC (principal component) loading values show that the $1,100{\sim}950cm^{-1}$ region of the FT-IR spectra are important for the discrimination of Zoysia species. A dendrogram based on hierarchical clustering analysis (HCA) from the PCA and PLS-DA data of turf grasses showed that turf grass samples were divided into Zoysia japonica and Zoysia sinica in a species-dependent manner. PCA and PLS-DA from FT-IR spectral data of Zoysia species identified and unidentified by molecular markers successfully divided the 49 turf grasses into Z. japonica and Z. sinica. In particular, PLS-DA and the HCA dendrogram could mostly discriminate the 47 Z. japonica grasses into two groups depending on their origins (mountainous areas and island area). Considering these results, we suggest that FT-IR fingerprinting combined with multivariate analysis could be applied to discriminate between Zoysia species as well as their geographical origins of various Zoysia species.

• Journal of Plant Biotechnology
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• v.39 no.3
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• pp.121-126
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• 2012

To determine whether FT-IR spectroscopy combined with multivariate analysis for whole cell extracts can be used to discriminate major leguminous plant at metabolic level, seed extracts of six leguminous plants were subjected to Fourier transform infrared spectroscopy (FT-IR). FT-IR spectral data from seed extracts were analyzed by principal component analysis (PCA), partial least square discriminant analysis (PLS-DA) and hierarchical clustering analysis (HCA). The PCA could not fully discriminate six leguminous plants, however PLS-DA could successfully discriminate six leguminous plants. The hierarchical dendrogram based on PLS-DA separated the six leguminous plants into four branches. The first branch was consisted of all three Vigna species including Vigna radiata var. radiate, Vigna angularis var. angularis and Vigna unguiculata subsp. Unguiculata. Whereas Pisum sativum var. sativum, Glycine max L and Phaseolus vulgaris var. vulgaris were clustered into a separate branch respectively. The overall results showed that metabolic discrimination system were in accordance with known phylogenic taxonomy. Thus we suggested that the hierarchical dendrogram based on PLS-DA of FT-IR spectral data from seed extracts represented the most probable chemotaxonomical relationship between six leguminous plants.

• The Journal of the Korea Contents Association
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• v.17 no.10
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• pp.179-189
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• 2017

The purpose of this paper is to develop a comprehensive research model that can explain potential customers' intentions to adopt and use VPA(Virtual Personal Assistants) devices. This study proposes and validates a new theoretical model that extends the perceived value theory. Partial least squares analysis is employed to test the research model and corresponding hypotheses on data collected from 313 survey samples. Perceived value, perceived usefulness, perceived enjoyment are important factors affecting the adoption of VPA devices. To increase potential users' intention to use, VPA device manufacturers should not only focus on developing utilitarian functions but also fun features based on open development echo-system. This study is the important empirical attempt to examine user acceptance of VPA devices, as most of the prior literature has concerned analysis of usage patterns or technical features.

• Journal of the Korea Convergence Society
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• v.10 no.3
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• pp.23-30
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• 2019

The purpose of this study is to find out the structure of the substance that affects antidiabetic using the candidate drug information for diabetes treatment. A quantitative structure activity relationship model based on machine learning method was constructed and major molecular descriptors were determined for each experimental data variables from coefficient values using a partial least squares algorithm. The results of the analysis of the molecular access system fingerprint data reflecting the candidate drug structure information were higher than those of the in vitro data analysis in terms of goodness-of-fit, and the major molecular expression factors affecting the antidiabetic effect were also variously derived. If the proposed method is applied to the new drug development environment, it is possible to reduce the cost for conducting candidate screening experiment and to shorten the search time for new drug development.