• Asia pacific journal of information systems
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• v.18 no.2
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• pp.41-72
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• 2008

The purpose of this study is the investigation of the effects of user participation, network effect, social influence, and usefulness on stickiness and continued use on Internet communities. In this research, stickiness refers to repeat visit and visit duration to an Internet community. Continued use means the willingness to continue to use an Internet community in the future. Internet community-based companies can earn money through selling the digital contents such as game, music, and avatar, advertizing on internet site, or offering an affiliate marketing. For such money making, stickiness and continued use of Internet users is much more important than the number of Internet users. We tried to answer following three questions. Fist, what is the effects of user participation on stickiness and continued use on Internet communities? Second, by what is user participation formed? Third, are network effect, social influence, and usefulness that was significant at prior research about technology acceptance model(TAM) still significant on internet communities? In this study, user participation, network effect, social influence, and usefulness are independent variables, stickiness is mediating variable, and continued use is dependent variable. Among independent variables, we are focused on user participation. User participation means that Internet user participates in the development of Internet community site (called mini-hompy or blog in Korea). User participation was studied from 1970 to 1997 at the research area of information system. But since 1997 when Internet started to spread to the public, user participation has hardly been studied. Given the importance of user participation at the success of Internet-based companies, it is very meaningful to study the research topic of user participation. To test the proposed model, we used a data set generated from the survey. The survey instrument was designed on the basis of a comprehensive literature review and interviews of experts, and was refined through several rounds of pretests, revisions, and pilot tests. The respondents of survey were the undergraduates and the graduate students who mainly used Internet communities. Data analysis was conducted using 217 respondents(response rate, 97.7 percent). We used structural equation modeling(SEM) implemented in partial least square(PLS). We chose PLS for two reason. First, our model has formative constructs. PLS uses components-based algorithm and can estimated formative constructs. Second, PLS is more appropriate when the research model is in an early stage of development. A review of the literature suggests that empirical tests of user participation is still sparse. The test of model was executed in the order of three research questions. First user participation had the direct effects on stickiness(${\beta}$=0.150, p<0.01) and continued use (${\beta}$=0.119, p<0.05). And user participation, as a partial mediation model, had a indirect effect on continued use mediated through stickiness (${\beta}$=0.007, p<0.05). Second, optional participation and prosuming participation significantly formed user participation. Optional participation, with a path magnitude as high as 0.986 (p<0.001), is a key determinant for the strength of user participation. Third, Network effect (${\beta}$=0.236, p<0.001). social influence (${\beta}$=0.135, p<0.05), and usefulness (${\beta}$=0.343, p<0.001) had directly significant impacts on stickiness. But network effect and social influence, as a full mediation model, had both indirectly significant impacts on continued use mediated through stickiness (${\beta}$=0.11, p<0.001, and ${\beta}$=0.063, p<0.05, respectively). Compared with this result, usefulness, as a partial mediation model, had a direct impact on continued use and a indirect impact on continued use mediated through stickiness. This study has three contributions. First this is the first empirical study showing that user participation is the significant driver of continued use. The researchers of information system have hardly studies user participation since late 1990s. And the researchers of marketing have studied a few lately. Second, this study enhanced the understanding of user participation. Up to recently, user participation has been studied from the bipolar viewpoint of participation v.s non-participation. Also, even the study on participation has been studied from the point of limited optional participation. But, this study proved the existence of prosuming participation to design and produce products or services, besides optional participation. And this study empirically proved that optional participation and prosuming participation were the key determinant for user participation. Third, our study compliments traditional studies of TAM. According prior literature about of TAM, the constructs of network effect, social influence, and usefulness had effects on the technology adoption. This study proved that these constructs still are significant on Internet communities.

• Progress in Medical Physics
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• v.18 no.2
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• pp.98-106
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• 2007

In June 2005, Yonsei University Medical Center, Severance Hospital upgraded a full-PACS system by adding twenty (5 mega pixels) Totoku ME511L flat panel LCD display devices for diagnostic interpretation purposes. Here we report upon the quantitative (or visual) acceptance testing of the twenty Totoku ME511L display devices for reflection, luminance response, luminance spatial dependency, resolution, noise, veiling glare, and display chromaticity based on AAPM TG 18 report. The tools used in the tests included a telescopic photometer, which was used as a colorimeter, illuminance meter, light sources for reflection assessment, light-blocking devices, and digital TG18 test patterns. For selected 8 flat panel displays, mean diffuse reflection coefficient ($R_d$) was $0.019{\pm}0.02sr^{-1}$. In the luminance response test, luminance ratio (LR), maximum luminance difference ($L_{max}$), and deviation of contrast response were $550{\pm}100,\;2.0{\pm}1.9%\;and\;5.8{\pm}1.8%$, respectively. In the luminance uniformity test, maximum luminance deviation was $14.3{\pm}5.5%$ for the 10% luminance of the TG18-UNL10 test pattern. In the resolution test with luminance measurement method, percent luminance (${\Dalta}L$) at the center was $0.94{\pm}0.64%$. In all cases of noise testing, rectangular target In every square in the three quadrants was visible and all 15 targets except the smallest one in the every corner pattern and the center pattern. The glare ratio (GR) was $12,346{\pm}1,995$. The color uniformity, (u',v'), was $0.0025{\pm}0.0008$. Also, the research results of qualify control guideline of primary disply devices suitable for domestic circumstance are presented All test results are in-line with the criteria recommended by AAPM TG18 report and are thus fully acceptable for diagnostic image interpretation. As a result, the acceptance testing schedule described provides not only an acceptance standard but also guidelines for quality control, optimized viewing conditions, and a means for determining the upgrading time of LCD display devices for diagnostic interpretation.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.79-96
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• 2012

As the pace of competition dramatically accelerates and the complexity of change grows, a variety of research have been conducted to improve firms' short-term performance and to enhance firms' long-term survival. In particular, researchers and practitioners have paid their attention to identify promising technologies that lead competitive advantage to a firm. Discovery of promising technology depends on how a firm evaluates the value of technologies, thus many evaluating methods have been proposed. Experts' opinion based approaches have been widely accepted to predict the value of technologies. Whereas this approach provides in-depth analysis and ensures validity of analysis results, it is usually cost-and time-ineffective and is limited to qualitative evaluation. Considerable studies attempt to forecast the value of technology by using patent information to overcome the limitation of experts' opinion based approach. Patent based technology evaluation has served as a valuable assessment approach of the technological forecasting because it contains a full and practical description of technology with uniform structure. Furthermore, it provides information that is not divulged in any other sources. Although patent information based approach has contributed to our understanding of prediction of promising technologies, it has some limitations because prediction has been made based on the past patent information, and the interpretations of patent analyses are not consistent. In order to fill this gap, this study proposes a technology forecasting methodology by integrating patent information approach and artificial intelligence method. The methodology consists of three modules : evaluation of technologies promising, implementation of technologies value prediction model, and recommendation of promising technologies. In the first module, technologies promising is evaluated from three different and complementary dimensions; impact, fusion, and diffusion perspectives. The impact of technologies refers to their influence on future technologies development and improvement, and is also clearly associated with their monetary value. The fusion of technologies denotes the extent to which a technology fuses different technologies, and represents the breadth of search underlying the technology. The fusion of technologies can be calculated based on technology or patent, thus this study measures two types of fusion index; fusion index per technology and fusion index per patent. Finally, the diffusion of technologies denotes their degree of applicability across scientific and technological fields. In the same vein, diffusion index per technology and diffusion index per patent are considered respectively. In the second module, technologies value prediction model is implemented using artificial intelligence method. This studies use the values of five indexes (i.e., impact index, fusion index per technology, fusion index per patent, diffusion index per technology and diffusion index per patent) at different time (e.g., t-n, t-n-1, t-n-2, ${\cdots}$) as input variables. The out variables are values of five indexes at time t, which is used for learning. The learning method adopted in this study is backpropagation algorithm. In the third module, this study recommends final promising technologies based on analytic hierarchy process. AHP provides relative importance of each index, leading to final promising index for technology. Applicability of the proposed methodology is tested by using U.S. patents in international patent class G06F (i.e., electronic digital data processing) from 2000 to 2008. The results show that mean absolute error value for prediction produced by the proposed methodology is lower than the value produced by multiple regression analysis in cases of fusion indexes. However, mean absolute error value of the proposed methodology is slightly higher than the value of multiple regression analysis. These unexpected results may be explained, in part, by small number of patents. Since this study only uses patent data in class G06F, number of sample patent data is relatively small, leading to incomplete learning to satisfy complex artificial intelligence structure. In addition, fusion index per technology and impact index are found to be important criteria to predict promising technology. This study attempts to extend the existing knowledge by proposing a new methodology for prediction technology value by integrating patent information analysis and artificial intelligence network. It helps managers who want to technology develop planning and policy maker who want to implement technology policy by providing quantitative prediction methodology. In addition, this study could help other researchers by proving a deeper understanding of the complex technological forecasting field.