• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.4
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• pp.45-55
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• 2022

Accelerator is a private investment institution that provides startups with comprehensive solutions to solve various difficulties such as startup facilities, funds, commercialization, securing a market etc. In addition to the role of an investor as a new startup support model, accelerators have contributed much to improvement of business ability of startups through intensive mentoring. Considering that previous studies gave weight to the determinants of investment from the perspective of investors, this study made a comparative analysis on the relative importance of determinants of investment in startups among accelerators, investors and entrepreneurs through the method of AHP. Results show that accelerators and investors regard "managerial characteristics" of startups as of the highest importance, whereas entrepreneurs think that "market characteristics" of startups are the most important. The result stems from an empirical judgment from the perspective of investors that success of startups depends on the ability of entrepreneur, and it is considered that investors evaluated marketability of startups as the most important factor in consideration of investment payback period. The result is similar to the result of previous studies on the determinants of investment determinants of angel investors and venture capitals. This paper is expected to make a contribution to the advancement of investment decision-making model for accelerators to discover startups with high possibility to grow and achieve more in incubation and investment.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.2
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• pp.436-446
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• 2018

The purpose of this study is to examine the priorities of innovation activities, business performance, and maximization factors of SMEs. Support programs for each government department are operated by various industries. Various supports, including subsidies, grants, marketing, planning, and education, are provided to each company. Therefore, this study aims to analyze and identify the priorities of innovation activities that have a positive effect on business performance. The efficacy of the proposed model and the psychometric properties of structure were analyzed using the analytic hierarchy process (AHP). The hierarchical structure of corporate innovation activities are composed of 'R&D' and 'government support', and 'Inside R&D, Outsourcing R &D, Consortium R&D'. As a result of analyzing companies that received more than one type of R&D government support, it can be seen that 'government support' (72.1%) is more important than 'research and development' (27.8%). In addition, this study found key sub-factors loadings including Assistant Support (30.1%), Tax Support (22.7%), Funding Support (18.8%), Inside R&D (10.8%), Outsourcing R&D (10.3%), and Consortium R&D (7.2%). Analysis results suggest that the priorities of detailed innovation activities of R&D and government support affect product innovation and process innovation, which in turn, influence business performance and maximization of SMEs. This implies that SMEs who want to participate in the government support project will be helpful in setting the direction of innovation activities. This study also suggests the importance of strategic priorities among the decision elements for CEOs.

• Journal of the Society of Disaster Information
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• v.17 no.4
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• pp.864-881
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• 2021

Purpose: This study aims is to provide a total care solution preventing disaster based on Big Data and AI technology and to service safety considered by individual situations and various risk characteristics. The purpose is to suggest a method that customized comprehensive index services to prevent and respond to safety accidents for calculating the living safety index that quantitatively represent individual safety levels in relation to daily life safety. Method: In this study, we use method of mixing AHP(Analysis Hierarchy Process) and Likert Scale that extracted from consensus formation model of the expert group. We organize evaluation items that can evaluate life safety prevention services into risk indicators, vulnerability indicators, and prevention indicators. And We made up AHP hierarchical structure according to the AHP decision methodology and proposed a method to calculate relative weights between evaluation criteria through pairwise comparison of each level item. In addition, in consideration of the expansion of life safety prevention services in the future, the Likert scale is used instead of the AHP pair comparison and the weights between individual services are calculated. Result: We obtain result that is weights for life safety prevention services and reflected them in the individual risk index calculated through the artificial intelligence prediction model of life safety prevention services, so the comprehensive index was calculated. Conclusion: In order to apply the implemented model, a test environment consisting of a life safety prevention service app and platform was built, and the efficacy of the function was evaluated based on the user scenario. Through this, the life safety index presented in this study was confirmed to support the golden time for diagnosis, response and prevention of safety risks by comprehensively indication the user's current safety level.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 1998.11a
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• pp.20-25
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• 1998

In order to support effective decision-making related to inter-sewage planning, this study proposes the spatial distribution method of inter-sewage treatement area using spatial analysis of GIS, Communication system of database, spatial interaction of Gravity model. Evalution Indexs are consist of economic, social/political and environmental condition value which are explained by the analysis of AHP algorithm ,based on opinion of related experts. Network module in Arc/Info is applied in order to find out minimum pipeline root in Miho river watershed, one of the sub-basin of Geum river basin. This value also is utilized for the construction of cost decay function in gravity model.

• The Journal of Bigdata
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• v.8 no.1
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• pp.111-129
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• 2023

With the diffusion of digital innovation, the adoption of innovative medical technologies based on artificial intelligence is increasing in the medical field. This is driving the launch and adoption of AI-based SaMD(Software as a Medical Device), but there is a lack of research on the factors that influence the adoption of SaMD by medical institutions. The purpose of this study is to identify key factors that influence medical institutions' decisions to adopt AI-based SaMDs, and to analyze the weights and priorities of these factors. For this purpose, we conducted Delphi surveys based on the results of literature studies on technology acceptance models in healthcare industry, medical AI and SaMD, and developed a research model by combining HOTE(Human, Organization, Technology and Environment) framework and HABIO(Holistic Approach {Business, Information, Organizational}) framework. Based on the research model with 5 main criteria and 22 sub-criteria, we conducted an AHP(Analytical Hierarchy Process) analysis among the experts from domestic medical institutions and SaMD providers to empirically analyze SaMD adoption factors. The results of this study showed that the priority of the main criteria for determining the adoption of AI-based SaMD was in the order of technical factors, economic factors, human factors, organizational factors, and environmental factors. The priority of sub-criteria was in the order of reliability, cost reduction, medical staff's acceptance, safety, top management's support, security, and licensing & regulatory levels. Specifically, technical factors such as reliability, safety, and security were found to be the most important factors for SaMD adoption. In addition, the comparisons and analyses of the weights and priorities of each group showed that the weights and priorities of SaMD adoption factors varied by type of institution, type of medical institution, and type of job in the medical institution.

• The Journal of Industrial Distribution & Business
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• v.5 no.4
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• pp.31-42
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• 2014

Purpose - According to the applied studies knowledge, management implementation can improve organizational performance. The main objective of this study is to develop an understanding of critical success factors that enhance the successful implementation of knowledge management. Research design, data, and methodology - This study used Analytical Hierarchy Procedure (AHP), which is a multi-criteria decision making model that works on fuzzy logic. Using this method, researchers can find the proportion of success due to the contribution of the critical success factors (CSFs). Results - The results show that more than 70% of respondents indicate the possibility of success in knowledge management implementation. Further, the results show that top management support has the greatest relationship with the success of knowledge management implementation. This was followed by information technology, performance measurement, and culture, which had a high relation with knowledge management success. Process and activities have a moderate positive relation, while education and training has a low relation with success. Because of an inappropriate p-value, knowledge management strategies show no relation to the success of knowledge management in the Iranian health Industry. Conclusions - This study was conducted because of a critical issue in the Iranian health industry that indicated that a significant portion of the workforce would retire in 5 to 10 years. Most highly experienced and knowledge oriented employees would become eligible for retirement. Therefore, knowledge management is presented as a complete solution in the Iranian health sector.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.29-45
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• 2015

Response modeling is a well-known research issue for those who have tried to get more superior performance in the capability of predicting the customers' response for the marketing promotion. The response model for customers would reduce the marketing cost by identifying prospective customers from very large customer database and predicting the purchasing intention of the selected customers while the promotion which is derived from an undifferentiated marketing strategy results in unnecessary cost. In addition, the big data environment has accelerated developing the response model with data mining techniques such as CBR, neural networks and support vector machines. And CBR is one of the most major tools in business because it is known as simple and robust to apply to the response model. However, CBR is an attractive data mining technique for data mining applications in business even though it hasn't shown high performance compared to other machine learning techniques. Thus many studies have tried to improve CBR and utilized in business data mining with the enhanced algorithms or the support of other techniques such as genetic algorithm, decision tree and AHP (Analytic Process Hierarchy). Ahn and Kim(2008) utilized logit, neural networks, CBR to predict that which customers would purchase the items promoted by marketing department and tried to optimized the number of k for k-nearest neighbor with genetic algorithm for the purpose of improving the performance of the integrated model. Hong and Park(2009) noted that the integrated approach with CBR for logit, neural networks, and Support Vector Machine (SVM) showed more improved prediction ability for response of customers to marketing promotion than each data mining models such as logit, neural networks, and SVM. This paper presented an approach to predict customers' response of marketing promotion with Case Based Reasoning. The proposed model was developed by applying different weights to each feature. We deployed logit model with a database including the promotion and the purchasing data of bath soap. After that, the coefficients were used to give different weights of CBR. We analyzed the performance of proposed weighted CBR based model compared to neural networks and pure CBR based model empirically and found that the proposed weighted CBR based model showed more superior performance than pure CBR model. Imbalanced data is a common problem to build data mining model to classify a class with real data such as bankruptcy prediction, intrusion detection, fraud detection, churn management, and response modeling. Imbalanced data means that the number of instance in one class is remarkably small or large compared to the number of instance in other classes. The classification model such as response modeling has a lot of trouble to recognize the pattern from data through learning because the model tends to ignore a small number of classes while classifying a large number of classes correctly. To resolve the problem caused from imbalanced data distribution, sampling method is one of the most representative approach. The sampling method could be categorized to under sampling and over sampling. However, CBR is not sensitive to data distribution because it doesn't learn from data unlike machine learning algorithm. In this study, we investigated the robustness of our proposed model while changing the ratio of response customers and nonresponse customers to the promotion program because the response customers for the suggested promotion is always a small part of nonresponse customers in the real world. We simulated the proposed model 100 times to validate the robustness with different ratio of response customers to response customers under the imbalanced data distribution. Finally, we found that our proposed CBR based model showed superior performance than compared models under the imbalanced data sets. Our study is expected to improve the performance of response model for the promotion program with CBR under imbalanced data distribution in the real world.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.36 no.4
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• pp.113-123
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• 2018

The purpose of this research is to develop evaluation indicators forperformance improvement of horticultural therapy garden. In order to achieve a therapeutic purpose, the gardening activity held by the trained horticultural therapist. Moreover, horticultural therapy is 'a medical model' for the treatment and basic premise of the research was set, as horticultural therapy garden is characterized area to support activities of patients and horticultural therapist functionally and efficiently. For this study, three times of Delphi and AHP techniques were proceeded to export panels who were recruited by purposive sampling. Through these techniques, it was possible to deduct the evaluation indicator which maximizes the performance of the horticultural therapy garden. The evaluation items were prioritized by typing and stratification of the indicator. The results and discussions were stated as followings. Firstly, a questionnaire of experts was conducted to horticultural therapists and civil servants who were in charge of horticultural therapy. As results(horticultural therapists: 87.8%, civil servants: 75.2%), It is possible to conclude that both positions have the high recognition and agreed on the necessity of horticultural therapy. Secondly, Delphi investigation was conducted three times in order to develop the evaluation indicator for performance evaluation. After Delphi analysis, total 34 of evaluation elements to improve the performance of the horticultural therapy garden by reliability and validity analysis results. Thirdly, AHP analysis of each evaluation indicator was conducted on the relative importance and weighting. Moreover, the results showed 'interaction between nature and human' as the most important element, and in order of 'plan of the program', 'social interaction', 'sustainable environmental', and 'universal design rule', respectively. On the other hand, the exports from the university and research institute evaluated the importance of 'interaction between nature and human', while horticultural therapists chose 'plan of the program' as the most important element. Fourthly, the total weight was used to develop weight applied evaluation indicator for the performance evaluation of the horticultural therapy garden. The weight applying to evaluation index is generally calculated multiply the evaluation scores and the total weight using AHP analysis. Finally, 'the evaluation indicator and evaluation score sheet for performance improvement of the horticultural therapy garden' was finally stated based on the relative order of priority between evaluation indicators and analyzing the weight. If it was deducted the improvement points for the efficiency of already established horticultural therapy garden using the 'weight applied evaluation sheet', it is possible to expand it by judging the importance with the decision of the priority because the item importance decided by experts was reflected. Moreover, in the condition of new garden establishment, it is expected to be helpful in suggesting ways for performance improvement and in setting the guidelines by understanding the major indicators of performance improvement in horticultural therapy activity.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.1
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• pp.98-108
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• 2013

The number of Hi-Pass lanes became 793 lanes at 316 expressway tollgates in 2011 due to the increase in the Hi-Pass use. In spite of the increase in the number of Hi-Pass lanes, there have been increased potential risks in tollgates where vehicles using a Hi-Pass lane must weave with other vehicles using a TCS lane. Therefore, there is a need for study on the safety in tollgates. To this end, this study aims at developing a methodology to evaluate the performance measures of diverse location countermeasures of Hi-Pass lanes in an efficient and systematic way. This study measured the mobility, safety and the convenience of installation and operation of Hi-Pass lanes using a microscopic traffic simulation tool, the surrogate safety assessment model and survey. In addition, this study aggregated the above three performance indexes using weight factors estimated using the AHP technique. For the test site, Dongsuwon interchange was selected. After building the microscopic traffic simulation model for the test site, the location countermeasures of Hi-Pass lanes applicable to the test site were compared with each other in terms of the mobility, safety and installing and operating convenience. As a result, there has been no apparent difference in mobility index based on delays. However, the countermeasures where Hi-Pass lanes are located in inside lanes generally showed better safety performance based on the number of conflicts. In addition, countermeasures with neighboring Hi-Pass lanes were favorable in terms of the safety and the convenience of installation and operation. The methodology proposed in this study was found to be useful to support decision makings by providing critical and quantitative information regarding the mobility, safety and the convenience of installation and operation.

• 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.