• Journal of Information Technology Applications and Management
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• v.17 no.2
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• pp.91-109
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• 2010

The success of new product is a key factor for getting competitive advantages. Marketing research has been investigating marketing capability, manufacturing technical capability, cross-functional integration, market knowledge competence, and competitive environment as the key success factors of new product advantage. Recently, the role of IT capability in enhancing new product advantage is assumed in the literature. However, the empirical studies on the role of IT capability are lacking. The purpose of this study is to identify the effects of IT capability on new product competitive advantages and the relationship between IT capability and the key success factors for new product advantage. Conducting an empirical field study, the IT capability is shown to have a direct effect on new product competitive advantages, and to be also the key success factor to make up for marketing capability and manufacturing technical capability. This study develops a conceptual model on the role of IT capability in enhancing new product competitive advantages by integrating marketing research and IS research.

• Asia pacific journal of information systems
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• v.23 no.3
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• pp.25-53
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• 2013

This study analyzes the effect of Computer Aided Innovation (CAI) to improve R&D Capabilities empirically. Survey was distributed by e-mail and Google Docs, targeting CTO of 235 SMEs. 142 surveys were returned back (rate of return 60.4%) from companies. Survey results from 119 companies (83.8%) which are effective samples except no-response, insincere response, estimated value, etc. were used for statistics analysis. Companies with less than 50billion KRW sales of entire researched companies occupy 76.5% in terms of sample traits. Companies with less than 300 employees occupy 83.2%. In terms of the type of company business Partners (called 'partners with big companies' hereunder) who work with big companies for business occupy 68.1%. SMEs based on their own business (called 'independent small companies') appear to occupy 31.9%. The present status of holding IT system according to traits of company business was classified into partners with big companies versus independent SMEs. The present status of ERP is 18.5% to 34.5%. QMS is 11.8% to 9.2%. And PLM (Product Life-cycle Management) is 6.7% to 2.5%. The holding of 3D CAD is 47.1% to 21%. IT system-holding and its application of independent SMEs seemed very vulnerable, compared with partner companies of big companies. This study is comprised of IT infra and IT Utilization as CAI capacity factors which are independent variables. factors of R&D capabilities which are independent variables are organization capability, process capability, HR capability, technology-accumulating capability, and internal/external collaboration capability. The highest average value of variables was 4.24 in organization capability 2. The lowest average value was 3.01 in IT infra which makes users access to data and information in other areas and use them with ease when required during new product development. It seems that the inferior environment of IT infra of general SMEs is reflected in CAI itself. In order to review the validity used to measure variables, Factors have been analyzed. 7 factors which have over 1.0 pure value of their dependent and independent variables were extracted. These factors appear to explain 71.167% in total of total variances. From the result of factor analysis about measurable variables in this study, reliability of each item was checked by Cronbach's Alpha coefficient. All measurable factors at least over 0.611 seemed to acquire reliability. Next, correlation has been done to explain certain phenomenon by correlation analysis between variables. As R&D capabilities factors which are arranged as dependent variables, organization capability, process capability, HR capability, technology-accumulating capability, and internal/external collaboration capability turned out that they acquire significant correlation at 99% reliability level in all variables of IT infra and IT Utilization which are independent variables. In addition, correlation coefficient between each factor is less than 0.8, which proves that the validity of this study judgement has been acquired. The pair with the highest coefficient had 0.628 for IT utilization and technology-accumulating capability. Regression model which can estimate independent variables was used in this study under the hypothesis that there is linear relation between independent variables and dependent variables so as to identify CAI capability's impact factors on R&D. The total explanations of IT infra among CAI capability for independent variables such as organization capability, process capability, human resources capability, technology-accumulating capability, and collaboration capability are 10.3%, 7%, 11.9%, 30.9%, and 10.5% respectively. IT Utilization exposes comprehensively low explanatory capability with 12.4%, 5.9%, 11.1%, 38.9%, and 13.4% for organization capability, process capability, human resources capability, technology-accumulating capability, and collaboration capability respectively. However, both factors of independent variables expose very high explanatory capability relatively for technology-accumulating capability among independent variable. Regression formula which is comprised of independent variables and dependent variables are all significant (P<0.005). The suitability of regression model seems high. When the results of test for dependent variables and independent variables are estimated, the hypothesis of 10 different factors appeared all significant in regression analysis model coefficient (P<0.01) which is estimated to affect in the hypothesis. As a result of liner regression analysis between two independent variables drawn by influence factor analysis for R&D capability and R&D capability. IT infra and IT Utilization which are CAI capability factors has positive correlation to organization capability, process capability, human resources capability, technology-accumulating capability, and collaboration capability with inside and outside which are dependent variables, R&D capability factors. It was identified as a significant factor which affects R&D capability. However, considering adjustable variables, a big gap is found, compared to entire company. First of all, in case of partner companies with big companies, in IT infra as CAI capability, organization capability, process capability, human resources capability, and technology capability out of R&D capacities seems to have positive correlation. However, collaboration capability appeared insignificance. IT utilization which is a CAI capability factor seemed to have positive relation to organization capability, process capability, human resources capability, and internal/external collaboration capability just as those of entire companies. Next, by analyzing independent types of SMEs as an adjustable variable, very different results were found from those of entire companies or partner companies with big companies. First of all, all factors in IT infra except technology-accumulating capability were rejected. IT utilization was rejected except technology-accumulating capability and collaboration capability. Comprehending the above adjustable variables, the following results were drawn in this study. First, in case of big companies or partner companies with big companies, IT infra and IT utilization affect improving R&D Capabilities positively. It was because most of big companies encourage innovation by using IT utilization and IT infra building over certain level to their partner companies. Second, in all companies, IT infra and IT utilization as CAI capability affect improving technology-accumulating capability positively at least as R&D capability factor. The most of factor explanation is low at around 10%. However, technology-accumulating capability is rather high around 25.6% to 38.4%. It was found that CAI capability contributes to technology-accumulating capability highly. Companies shouldn't consider IT infra and IT utilization as a simple product developing tool in R&D section. However, they have to consider to use them as a management innovating strategy tool which proceeds entire-company management innovation centered in new product development. Not only the improvement of technology-accumulating capability in department of R&D. Centered in new product development, it has to be used as original management innovative strategy which proceeds entire company management innovation. It suggests that it can be a method to improve technology-accumulating capability in R&D section and Dynamic capability to acquire sustainable competitive advantage.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.11 no.18
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• pp.71-80
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• 1988

Process capability implies the qualitative capability of a process, and it is necessary to specify the process capability by quantification and to evaluate the level with the specified standardizaion. But the process capability index is currently used to evaluate the performance of quality control activity. without considering the characteristics of process structure or the economy of management. Here the researcher would like to redefine process capability and emphasize its index may be used as the measure of managerial assessment and the objective of process quality control in full consideration of the economic aspects of process characteristics. Too little or too much process capability causes a loss of or excess. The proper economic level of process capability varies to each process. The procedure of taking the optimum process capability index is derived from the expected profit function, whereas each method is studied in cases of normal process with one-sided specification and two-sided specification. In addition, the process capability index is presented as a method of quality assurance. And an example is exhibited on wrapping process of 'A' company. The results of this study are summarized at follows. First, though the norminalization of the process capability index is possible by the existing methods, the optimum process capability index can vary to the situation of each process. So, the optimum process capability index which is suggested in this dissertation should be used as the standard to assess process capability. Second, the process capability index can take its effect not just in indicating quality control or managerial records but also in the management of high qualify assurance.

• International Journal of Quality Innovation
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• v.3 no.2
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• pp.74-83
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• 2002

Process capability indices as an important kind of indices are intended to provide single-number assessments of the inherent process capability to meet specification limits on quality characteristic(5) of interest. In this paper the condition for the application of process capability indices is analyzed. On the basis of process capability indices, dynamic process capability indices as a new kind of indices to show the current process capability are discussed and the condition for the application of dynamic process capability indices is exhibited. Comparison between process capability index and dynamic process capability index and comparison between $D_p$ and $D_pk$ are made and the conclusions provide the approach for process control. According to the requirement of process capability indices provided by customer, quality control based on process capability indices dynamic process capability indices is discussed.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.11
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• pp.533-539
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• 2005

According to NERC definition, Available Transfer Capability (ATC) is a measure of the transfer capability remaining in the physical transmission network for the future commercial activity. To calculate Available Transfer Capability, accurate and defensible Total Transfer Capability, Capacity Benefit Margin and Transmission Reliability Margin should be calculated in advance. This paper proposes a method to quantify time varying Available Transfer Capability based on probabilistic approach. The uncertainties of power system and market are considered as complex random variables. Total Transfer Capability is determined by optimization technique such as SQP(Sequential Quadratic Programming). Transmission Reliability Margin with the desired probabilistic margin is calculated based on Probabilistic Load Flow analysis, and Capacity Benefit Margin is evaluated using LOLE of the system. Suggested Available Transfer Capability quantification method is verified using IEEE RTS with 72 bus. The proposed method shows efficiency and flexibility for the quantification of Available Transfer Capability.

• The Journal of Information Systems
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• v.15 no.3
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• pp.87-106
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• 2006

The customer-focused enterprise is interested in integrating every record of an interaction with a customer. This study is to investigate the structural relationship of data integration customer analysis capability, marketing & sales capability, customer service capability, and CRM performance. 205 survey data were collected from the company which implemented the CRM package. SEM analysis shows that data integration has influence on the CRM performance through the improvement of customer analysis capability, marketing 8t sales capability, and customer service capability. The revised model for further goodness-fitting model shows that data integration has influence on the improvement of customer analysis capability, marketing & sales capability, and customer service capability. but customer analysis capability has indirect influence on CRM performance through the improvement of marketing & sales capability, customer service capability.

• Asia pacific journal of information systems
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• v.25 no.3
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• pp.597-625
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• 2015

Prior research has emphasized the significant effect of service orientation on organizational performance. However, little research on service orientation has been conducted in the financial field, including the investment management service industry in which high quality service for clients is required. In this paper, we propose a research model that centers on the concept of service orientation as a type of dynamic capability affecting firm performance. The research variables include job competency, risk management capability, operational capability, service orientation, and service performance. We assume that service orientation partially mediates the effects of risk management capability and operational capability on service performance. To test the model, we collected data from 391 fund managers in 86 teams (37 investment management companies) and analyzed it with partial least squares (PLS) method. Each of the 391 fund managers was asked to answer team level measures, which is effective for team level analysis. We find that job competency positively affects both risk management capability and operational capability, which in turn affect service orientation. Risk management capability and operational capability are assumed to directly affect service performance. However, risk management capability does not influence perceived service performance, whereas operational capability does affect it. This result indicates that risk management capability does not directly affect service performance. However, via service orientation, considering that risk management inconveniences customers and is geared to enhance service orientation, service performance is positively affected. Operational capability does not influence service orientation, whereas it affects perceived service performance. This result reveals that operational capability directly affects firm performance. As expected, service orientation significantly affects the service performance perception of fund managers. This study contributes to the literature by introducing service orientation to the financial industry and measures and tests team-level service performance. Our findings also provide insights to practitioners because to enhance team performance, managers must focus on service orientation in addition to operational capability.

• Knowledge Management Research
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• v.20 no.3
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• pp.39-47
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• 2019

Response surface methodology (RSM) is a group of statistical modeling and optimization methods to improve the quality of design systematically in the quality engineering field. Its final goal is to identify the optimal setting of input variables optimizing a response. RSM is a kind of knowledge management tool since it studies a manufacturing or service process and extracts an important knowledge about it. In a real problem of RSM, it is a quite frequent situation that considers multiple responses simultaneously. To date, many approaches are proposed for solving (i.e., optimizing) a multi-response problem: process capability function approach, desirability function approach, loss function approach, and so on. The process capability function approach first estimates the mean and standard deviation models of each response. Then, it derives an individual process capability function for each response. The overall process capability function is obtained by aggregating the individual process capability function. The optimal setting is given by maximizing the overall process capability function. The existing process capability function methods usually use the arithmetic mean or geometric mean as an aggregation operator. However, these operators do not guarantee the Pareto optimality of their solution. Moreover, they may bring out an unacceptable result in terms of individual process capability function values. In this paper, we propose a maximin-based process capability function method which uses a maximin criterion as an aggregation operator. The proposed method is illustrated through a well-known multiresponse problem.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.5
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• pp.296-301
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• 2004

The Available Transfer Capability (ATC) is defined as the measure of the transfer capability remaining in the physical transmission network for further commercial activity above already committed uses. Available Transfer Capability (ATC) calculation is a complicated task, which involves the determination I of total transfer capability (TTC), transmission reliability margin (TRM) and capability benefit margin (CBM). As the electrical power industry is restructured and the electrical power exchange is updated per hour, it is important to accurately and rapidly quantify the available transfer capability (ATC) of the transmission system. In ATC calculation,. the existing CPF method is accurate but it has long calculation time. On the contrary, the method using PTDF is fast but it has relatively a considerable error. This paper proposed QFA method, which can reduce calculation time comparing with CPF method and has few errors in ATC calculation. It proved that the method can calculate ATC more fast and accurately in case study using IEEE 24 bus RTS.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.2
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• pp.78-85
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• 2019

High variance observed in the measurement system can cause high process variation that can affect process capability badly. Therefore, measurement system analysis is closely related to process capability analysis. Generally, the evaluation for measurement system and process variance is performed separately in the industry. That is, the measurement system analysis is implemented before process monitoring, process capability and process performance analysis even though these analyses are closely related. This paper presents the effective concurrent evaluation procedure for measurement system analysis and process capability analysis using the table that contains Process Performance (Pp), Gage Repeatability & Reproducibility (%R&R) and Number of Distinct Categories (NDC). Furthermore, the long-term process capability index (Pp), which takes into account both gage variance and process variance, is used instead of the short-term process capability (Cp) considering only process variance. The long-term capability index can reflect well the relationship between the measurement system and process capability. The quality measurement and improvement guidelines by region scale are also described in detail. In conclusion, this research proposes the procedure that can execute the measurement system analysis and process capability analysis at the same time. The proposed procedure can contribute to reduction of the measurement staff's effort and to improvement of accurate evaluation.