• Asia pacific journal of information systems
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• v.21 no.1
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• pp.81-101
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• 2011

In today's rapidly changing and increasingly competitive business environment, new product development in tune with market trends in a timely manner has been a matter of the utmost concern for all enterprises. Indeed, developing a sustainable new business has been a top priority for not only business enterprises, but also for the government policy makers accountable for the health of Its national economy as well as for decision makers in what type of organizations. Further, for a soft landing of new businesses, building a government-initiated industry base has been claimed to be necessary as a way to effectively boost corporate activities. However, the existing methodology in new service and new product development is not suitable for nurturing industry, because it is mainly focused on the research and development of corporate business activities instead of new product development. The approach for developing new business is based on 'innovation' and 'convergence.' Yet, the convergence among technologies, supplies, businesses and industries is believed to be more effective than innovation alone as a way to gain momentum. Therefore, it has become more important than ever to study a new methodology based on convergence in industrial quality new product development (NPD) and new service development (NDS). In this research, therefore, we reviewed any restrictions in the existing new product and new service development methodology and the existing business model development methodology. In doing so, we conducted industry standard collaboration analysis on a new service model development methodology in the private sector and the public sector. This approach is fundamentally different from the existing one in that ours focuses on new business development under private management. The suggested framework can be categorized into industry level and service level. First, in the industry level, we define new business opportunities In occurrence of convergence between businesses. For this, we analyze the existing industry at the industry level to identify the opportunities in a market and its business attractiveness, based on which the convergence industry is formulated. Also, through the analysis of environment and market opportunity at the industry level. we can trace how different industries are lined to one another so as to extend the result of the study to develop better insights into industry expansion and new industry emergence. After then, in the service level, we elicit the service for the defined new business, which is composed of private service and supporting service for nurturing industry. Private service includes 3steps: plan-design-do; supporting service for nurturing industry has 4 steps: selection-make environment- business preparation-do and see. The existing methodology focuses on mainly securing business competitiveness, building a business model for success, and offering new services based on the core competence of companies. This suggested methodology, on other hand, suggests the necessity of service development, when new business opportunities arise, in relation to the opportunity analysis of supporting service based on the clear understanding of new business supporting infrastructure optimization. Meanwhile, we have performed case studies on the printing and publishing field with the restrict procedure and development system to assure the feasibility and practical application. Even though the printing and publishing industry is considered a typical knowledge convergence industry, it is also known as a low-demand and low-value industry in Korea. For this reason, we apply the new methodology and suggest the direction and the possibility of how the printing and publishing industry can be transformed as a core dynamic force for new growth. Then, we suggest the base composition service for industry promotion(public) and business opportunities for private's profitability(private).

• Korean Security Journal
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• no.21
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• pp.75-94
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• 2009

Since mechanical security systems are mostly composed of electronic, information and communication devices, they have effects in the aspects of overall social environment and crime-oriented environment. Also, the importance is increasing for wireless recognition of RFID and tracing function, which will be usefully utilized in controlling the incomings and outgoings of people/vehicles or allowance, surveillance and control. This is resulting from the increase in the care for the elderly according to the overall social environment, namely, the aging society, and the number of women entering, as well as the increase in the number of heinous crimes. The purpose of this study is to examine the theoretical considerations on ubiquitous sensor network and present a direction for securities companies for their development by focusing on the technological and application areas. To present strategies of response to a new environment for security companies, First, a diversification strategy is needed for security companies. The survival of only high level of security companies in accordance with the principle of liberal market competition will bring forth qualitative growth and competitiveness of security market. Second, active promotion by security companies is needed. It is no exaggeration to say that we are living in the modern society in the sea of advertisements and propaganda. The promotional activities that emphasize the areas of activity or importance of security need to be actively carried out using the mass media to change the aware of people regarding security companies, and they need to come up with a plan to simultaneously carry out the promotional activities that emphasize the public aspect of security by well utilizing the recent trend that the activities of security agents are being used as a topic in movies or TV dramas. Third, technically complementary establishment of ubiquitous sensor network and electronic tag is needed. Since they are used in mobile electronic tag services such as U-Home and U-Health Care, they are used throughout our lives by forming electronic tag environment within safe ubiquitous sensor network based on the existing privacy guideline for the support of mobile electronic tag terminal commercialization, reduction in communication and information usage costs, continuous technical development and strengthening of privacy protection, and the system of cooperation of academic-industrial-research needs to be established among the academic world and private research institutes for these parts.

• Journal of the Society of Disaster Information
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• v.18 no.1
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• pp.202-217
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• 2022

Purpose: The recent increased use of Personal Mobility (PM) has been accompanied by a rise in the annual number of accidents. Accordingly, the safety requirements for PM use are being strengthened, but the laws/systems, infrastructure, and management systems remain insufficient for fostering a safe environment. Therefore, this study comprehensively searches the main problems and improvement methods through a review of previous studies that are related to PM. Then the priorities according to the importance of the improvement methods are presented through the Delphi survey. Method: The research method is mainly composed of a literature study and an expert survey (Delphi survey). Prior research and improvement cases (local governments, government departments, companies, etc.) are reviewed to derive problems and improvements, and a problem/improvement classification table is created based on keywords. Based on the classification contents, an expert survey is conducted to derive a priority improvement plan. Result: The PM-related problems were in 'non-compliance with traffic laws, lack of knowledge, inexperienced operation, and lack of safety awareness' in relation to human factors, and 'device characteristics, road-drivable space, road facilities, parking facilities' in relation to physical factors. 'Management/supervision, product management, user management, education/training' as administrative factors and legal factors are divided into 'absence/sufficiency of law, confusion/duplication, reduced effectiveness'. Improvement tasks related to this include 'PM education/public relations, parking/return, road improvement, PM registration/management, insurance, safety standards, traffic standards, PM device safety, PM supplementary facilities, enforcement/management, dedicated organization, service providers, management system, and related laws/institutional improvement', and 42 detailed tasks are derived for these 14 core tasks. The results for the importance evaluation of detailed tasks show that the tasks with a high overall average for the evaluation items of cost, time, effect, urgency, and feasibility were 'strengthening crackdown/instruction activities, education publicity/campaign, truancy PM management, and clarification of traffic rules'. Conclusion: The PM market is experiencing gradual growth based on shared services and a safe environment for PM use must be ensured along with industrial revitalization. In this respect, this study seeks out the major problems and improvement plans related to PM from a comprehensive point of view and prioritizes the necessary improvement measures. Therefore, it can serve as a basis of data for future policy establishment. In the future, in-depth data supplementation will be required for each key improvement area for practical policy application.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.31-39
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• 2023

The purpose of this study is to propose a scientific performance evaluation framework for measuring and managing the overall outcome of complex types of projects that are linked to public demand-based commercialization, such as information system projects and public procurement, in integrated national R&D projects. In the case of integrated national R&D projects that involve multiple research institutes to form a single final product, and in the case of demand-based demonstration and commercialization of the project results, the existing evaluation system that evaluates performance based on the short-term outputs of the detailed tasks comprising the R&D project has limitations in evaluating the mid- and long-term effects and practicality of the integrated research products. (Moreover, as the paradigm of national R&D projects is changing to a mission-oriented one that emphasizes efficiency, there is a need to change the performance evaluation of national R&D projects to focus on the effectiveness and practicality of the results.) In this study, we propose a performance evaluation framework from a structural perspective to evaluate the completeness of each national R&D project from a practical perspective, such as its effectiveness, beyond simple short-term output, by utilizing the Hourglass model. In particular, it presents an integrated performance evaluation framework that links the top-down and bottom-up approaches leading to Tool-System-Service-Effect according to the structure of R&D projects. By applying the proposed detailed evaluation indicators and performance evaluation frame to actual national R&D projects, the validity of the indicators and the effectiveness of the proposed performance evaluation frame were verified, and these results are expected to provide academic, policy, and industrial implications for the performance evaluation system of national R&D projects that emphasize efficiency in the future.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.3
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• pp.53-68
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• 2024

Generative AI is receiving a lot of attention around the world, and ways to effectively utilize it in the business environment are being explored. In particular, since the public release of the ChatGPT service, which applies the GPT-3.5 model, a large language model developed by OpenAI, it has attracted more attention and has had a significant impact on the entire industry. This study focuses on the emergence of Generative AI, especially ChatGPT, which applies OpenAI's GPT-3.5 model, to investigate its impact on the startup industry and compare the changes that occurred before and after its emergence. This study aims to shed light on the actual application and impact of generative AI in the business environment by examining in detail how generative AI is being used in the startup industry and analyzing the impact of ChatGPT's emergence on the industry. To this end, we collected company information of generative AI-related startups that appeared before and after the ChatGPT announcement and analyzed changes in industry, business content, and investment information. Through keyword analysis, topic modeling, and network analysis, we identified trends in the startup industry and how the introduction of generative AI has revolutionized the startup industry. As a result of the study, we found that the number of startups related to Generative AI has increased since the emergence of ChatGPT, and in particular, the total and average amount of funding for Generative AI-related startups has increased significantly. We also found that various industries are attempting to apply Generative AI technology, and the development of services and products such as enterprise applications and SaaS using Generative AI has been actively promoted, influencing the emergence of new business models. The findings of this study confirm the impact of Generative AI on the startup industry and contribute to our understanding of how the emergence of this innovative new technology can change the business ecosystem.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.27-65
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• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.1-19
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• 2019

Artificial intelligence (AI) is one of the main driving forces leading the Fourth Industrial Revolution. The technologies associated with AI have already shown superior abilities that are equal to or better than people in many fields including image and speech recognition. Particularly, many efforts have been actively given to identify the current technology trends and analyze development directions of it, because AI technologies can be utilized in a wide range of fields including medical, financial, manufacturing, service, and education fields. Major platforms that can develop complex AI algorithms for learning, reasoning, and recognition have been open to the public as open source projects. As a result, technologies and services that utilize them have increased rapidly. It has been confirmed as one of the major reasons for the fast development of AI technologies. Additionally, the spread of the technology is greatly in debt to open source software, developed by major global companies, supporting natural language recognition, speech recognition, and image recognition. Therefore, this study aimed to identify the practical trend of AI technology development by analyzing OSS projects associated with AI, which have been developed by the online collaboration of many parties. This study searched and collected a list of major projects related to AI, which were generated from 2000 to July 2018 on Github. This study confirmed the development trends of major technologies in detail by applying text mining technique targeting topic information, which indicates the characteristics of the collected projects and technical fields. The results of the analysis showed that the number of software development projects by year was less than 100 projects per year until 2013. However, it increased to 229 projects in 2014 and 597 projects in 2015. Particularly, the number of open source projects related to AI increased rapidly in 2016 (2,559 OSS projects). It was confirmed that the number of projects initiated in 2017 was 14,213, which is almost four-folds of the number of total projects generated from 2009 to 2016 (3,555 projects). The number of projects initiated from Jan to Jul 2018 was 8,737. The development trend of AI-related technologies was evaluated by dividing the study period into three phases. The appearance frequency of topics indicate the technology trends of AI-related OSS projects. The results showed that the natural language processing technology has continued to be at the top in all years. It implied that OSS had been developed continuously. Until 2015, Python, C ++, and Java, programming languages, were listed as the top ten frequently appeared topics. However, after 2016, programming languages other than Python disappeared from the top ten topics. Instead of them, platforms supporting the development of AI algorithms, such as TensorFlow and Keras, are showing high appearance frequency. Additionally, reinforcement learning algorithms and convolutional neural networks, which have been used in various fields, were frequently appeared topics. The results of topic network analysis showed that the most important topics of degree centrality were similar to those of appearance frequency. The main difference was that visualization and medical imaging topics were found at the top of the list, although they were not in the top of the list from 2009 to 2012. The results indicated that OSS was developed in the medical field in order to utilize the AI technology. Moreover, although the computer vision was in the top 10 of the appearance frequency list from 2013 to 2015, they were not in the top 10 of the degree centrality. The topics at the top of the degree centrality list were similar to those at the top of the appearance frequency list. It was found that the ranks of the composite neural network and reinforcement learning were changed slightly. The trend of technology development was examined using the appearance frequency of topics and degree centrality. The results showed that machine learning revealed the highest frequency and the highest degree centrality in all years. Moreover, it is noteworthy that, although the deep learning topic showed a low frequency and a low degree centrality between 2009 and 2012, their ranks abruptly increased between 2013 and 2015. It was confirmed that in recent years both technologies had high appearance frequency and degree centrality. TensorFlow first appeared during the phase of 2013-2015, and the appearance frequency and degree centrality of it soared between 2016 and 2018 to be at the top of the lists after deep learning, python. Computer vision and reinforcement learning did not show an abrupt increase or decrease, and they had relatively low appearance frequency and degree centrality compared with the above-mentioned topics. Based on these analysis results, it is possible to identify the fields in which AI technologies are actively developed. The results of this study can be used as a baseline dataset for more empirical analysis on future technology trends that can be converged.

• The Journal of Industrial Distribution & Business
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• v.3 no.1
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• pp.7-16
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• 2012

Despite the enforcement of SSM control laws such as 'the Law of Developing the Distribution Industry (LDDI)' and 'the Law of Promoting Mutual Cooperation between Large and Small/medium Enterprises (LPMC)' stipulating the business adjustment system, the number of super-supermarkets (SSMs) has ever been expanding in Korea. In France, however, Super Centers are being regulated most strongly and directly in the whole Europe viewing that there is not a single SSM in Paris, which is emphasized to be the outcome from French government's regulation exerted on the opening of large scale retail stores. In France, the authority to approve store opening is deeply centralized and the store opening regulation is a socio-economic regulation driven by economic laws whereas EU strongly regulates the distribution industry. To control the French distribution industry, such seven laws and regulations as Commission départementale d'urbanisme commercial guidelines (CDLIC) (1969), the Royer Law (1973), the Doubin Law (1990), the Sapin Law (1993), the Raffarin Law (1996), solidarite et renouvellement urbains (SRU) (2000), and Loi de modernisation de l'économie (LME) (2009) have been promulgated one by one since the amendment of the Fontanet guidelines, through which commercial adjustment laws and regulations have been complemented and reinforced while regulatory measures have been taken. Even in the course of forming such strong regulatory laws, InterMarche, the largest supermarket chain in France, has been in existence as a global enterprise specialized in retail distribution with over 4,000 stores in Europe. InterMarche's business can be divided largely into two segments of food and non-food. As a supermarket chain, InterMarche's food segment has 2,300 stores in Europe and as a hard-discounter store chain in France, Netto has 420 stores. Restaumarch is a chain of traditional family restaurants and the steak house restaurant chain of Poivre Rouge has 4 restaurants currently. In addition, there are others like Ecomarche which is a supermarket chain for small and medium cities. In the non-food segment, the DIY and gardening chain of Bricomarche has a total of 620 stores in Europe. And the car-related chain of Roady has a total of 158 stores in Europe. There is the clothing chain of Veti as well. In view of InterMarche's management strategies, since its distribution strategy is to sell goods at cheap prices, buying goods cheap only is not enough. In other words, in order to sell goods cheap, it is all important to buy goods cheap, manage them cheap, systemize them cheap, and transport them cheap. In quality assurance, InterMarche has guaranteed the purchase safety for consumers by providing its own private brand products. InterMarche has 90 private brands of its own, thus being the retailer with the largest number of distributor brands in France. In view of its IT service strategy, InterMarche is utilizing a high performance IT system so as to obtainas much of the market information as possible and also to find out the best locations for opening stores. In its global expansion strategy of international alliance, InterMarche has established the ALDIS group together with the distribution enterprises of both Spain and Germany in order to expand its food purchase, whereas in the non-food segment, it has established the ARENA group in alliance with 11 international distribution enterprises. Such strategies of InterMarche have been intended to find out the consumer needs for both price and quality of goods and to secure the purchase and supply networks which are closely localized. It is necessary to cope promptly with the constantly changing circumstances through being unified with relevant regions and by providing diversified customer services as well. In view of the InterMarche's positive policy for promoting local partnerships as well as the assistance for enhancing the local economic structure, implications are existing for those retail distributors of our country.

• Knowledge Management Research
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• v.21 no.3
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• pp.249-269
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• 2020

Recently, as the online market has matured, it is facing many problems to prevent the growth. The most common problem is the homogenization of online products, which fails to increase the number of customers any more. Moreover, although the portion of the online market has increased significantly, it now becomes essential to expand offline for further development. In response, many online firms have recently sought to expand their businesses and marketing channels by securing offline spaces that can complement the limitations of online platforms, on top of their existing advantages of online channels. Based on their competitive advantage in terms of analyzing large volumes of customer data utilizing information technologies (e.g., big data and artificial intelligence), they are reinforcing their offline influence as well through this online for offline (O4O) business model. On the other hand, most of the existing research has primarily focused on online to offline (O2O) business model, and there is still a lack of research on O4O business models, which have been actively attempted in various industrial fields in recent years. Since a few of O4O-related studies have been conducted only in an experience marketing setting following a case study method, it is critical to conduct an empirical study on O4O selection attributes and their impact on customer satisfaction and loyalty. Therefore, focusing on China's representative O4O business model, 'Fresh Hema,' this study attempts to identify some key selection attributes specialized for O4O services from the customers' viewpoint and examine the impact of these attributes on customer satisfaction and loyalty. The results of the structural equation modeling (SEM) with 300 O4O (Fresh Hema) experienced customers, reveal that, out of seven O4O selection attributes, four (mobile app quality, mobile payment, product quality, and store facilities) have an impact on customer satisfaction, which also leads to customer loyalty (reuse intention, recommendation intention, and brand attachment). This study would help managers in an O4O area well adapt to rapidly changing customer needs and provide them with some guidelines for enhancing both customer satisfaction and loyalty by allocating more resources to more significant selection attributes, rather than less significant ones.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.99-107
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• 2024

Along with economic growth and industrial development, there is an increasing demand for various electronic components and device production of semiconductor, SMT component, and electrical battery products. However, these products may contain foreign substances coming from manufacturing process such as iron, aluminum, plastic and so on, which could lead to serious problems or malfunctioning of the product, and fire on the electric vehicle. To solve these problems, it is necessary to determine whether there are foreign materials inside the product, and may tests have been done by means of non-destructive testing methodology such as ultrasound ot X-ray. Nevertheless, there are technical challenges and limitation in acquiring X-ray images and determining the presence of foreign materials. In particular Small-sized or low-density foreign materials may not be visible even when X-ray equipment is used, and noise can also make it difficult to detect foreign objects. Moreover, in order to meet the manufacturing speed requirement, the x-ray acquisition time should be reduced, which can result in the very low signal- to-noise ratio(SNR) lowering the foreign material detection accuracy. Therefore, in this paper, we propose a five-step approach to overcome the limitations of low resolution, which make it challenging to detect foreign substances. Firstly, global contrast of X-ray images are increased through histogram stretching methodology. Second, to strengthen the high frequency signal and local contrast, we applied local contrast enhancement technique. Third, to improve the edge clearness, Unsharp masking is applied to enhance edges, making objects more visible. Forth, the super-resolution method of the Residual Dense Block (RDB) is used for noise reduction and image enhancement. Last, the Yolov5 algorithm is employed to train and detect foreign objects after learning. Using the proposed method in this study, experimental results show an improvement of more than 10% in performance metrics such as precision compared to low-density images.