• Asia pacific journal of information systems
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• v.16 no.3
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• pp.67-93
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• 2006

Some consumers prefer online and others prefer offline. What makes them prefer online or offline? There has been a lack of theoretical development to adequately explain consumers' channel switching behavior between traditional physical stores and new virtual stores. Through consumers' purchase decision processes, this study examined the reasons why consumers changed channels depending on purchase process stages. Consumer's purchase decision process could be divided into three stages: pre-purchase stage, purchase stage, and post-purchase stage. We used the intention of channel selection as a surrogate dependent variable of channel selection. And some constructs, that is, channel function, channel benefits, customer relationship benefits, and perceived behavioral control, were selected as independent variables. In buying look-and-feel products, it was identified that consumers preferred virtual stores to physical stores at pre-purchase stage. To put it concretely, all constructs except channel benefits were more influenced to consumers at virtual stores. This result implied that information searching function, which is a main function at pre-purchase stage, was better supported by virtual stores than physical stores. In purchase stage, consumers preferred physical stores to virtual stores. Specially, all constructs influenced much more to consumers at physical stores. This result implied that although escrow service and trusted third parties were introduced, consumers felt that financial risk, performance risk, social risk, etc. still remained highly online. Finally, consumers did not prefer any channel at post-purchase stage. But three independent variables, i.e. channel function, channel benefits, and customer relationship benefits, were significantly preferred at physical stores rather than virtual stores at post-purchase stage. So we concluded that physical stores were a little more preferred to virtual stores at post-purchase stage. Through this study, it was identified that most consumers might switch channels according to purchase process stages. So, first of all, sales representatives should decide that what benefits should be given them through virtual stores at the pre-purchase stage and through physical stores at the purchase and post-purchase stages, and then devise collaborative channel strategies.

• Journal of the Korea Institute of Military Science and Technology
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• v.11 no.6
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• pp.127-134
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• 2008

An autonomous reconnaissance ground robot performs its duty in various different environments such as mountain-scape, desert and under-water through changing its shape and form according to the environment it is working in. Making a prototype robot for each environment requires extra cost and time. It is also difficult to modify the problem after production. In this paper, we propose the adoption of M&S(Modeling & Simulation) environment for the production and design of the autonomous reconnaissance ground robot. The proposed method on the M&S environment contributed to the more effective and less time consuming production of the robot through the Pre-Modeling and Pre-Simulation process. For example, we showed the design and implementation of the autonomous reconnaissance ground robot under the proposed environment and tools.

• Journal of Distribution Science
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• v.16 no.10
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• pp.65-76
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• 2018

Purpose - This paper investigates how the market value of the firms are impacted by distribution information technology investment in Korea over time and across markets, industries and project characteristics. This is the first empirical study on the market payoffs from the RFID investment in Korea. The purpose of this study is to provide a appropriate guideline for investors and practitioners with respect to the announcement representing RFID adoption in Korea. This reaction guideline will stimulate the practitioners to monitor and evaluate the benefits and costs of the innovative RFID technology. Research design, data, and methodology - This paper employs event study methodology to analyze the payoffs from distribution information technology investment announcements over a fifteen-year period from 2003 to 2017. Event study method is based on the assumptions such as market efficiency, unanticipated RFID invest announcements and no confounding effects in the data. This study collected the information on RFID investment announcements by using a full text search engine Bigkinds provided by Korea Press Foundation over a fifteen-year period from January 2003 through December 2017. This paper selected 88 announcements representing RFID adoption by 46 firms. This paper estimated the payoffs from RFID investment announcement through events windows by using the market model of Mcwilliams and Siegel (1997) and calculated the Z-values. Using this test statistics we could infer if RFID adoption make large differences in abnormal returns across various classifications of the firms. Results - There is significant positive market returns from the announcement representing distribution information technology investment in the pre-2009 time period, the significances of payoffs disappear in the post-2009 time period. For this reason investors or practitioners can understand the importance of market entry time and the fact that the greater rewards may belong to early innovators while late imitators cannot reap such a rewards. This paper also find that there is a large differences in the payoffs from the announcement across markets, industries and project characteristics. Conclusions - Analysing the selected sample of 88 announcements representing RFID Adoption over fifteen-year period from 2003 to 2017, this study find that there is not only significant abnormal excess returns from RFID investment announcements but also there is great differences in the abnormal returns over time and across firm sizes or affiliated markets, industries, and project characteristics. This means that there are considerable values for the investors across various firm classifications. The findings of this paper provide useful implications for the practitioners to make judicious decisions whether to adopt the innovative technologies in general or not considering the various concrete circumstances in Korea.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.3
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• pp.927-932
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• 2015

Background: Breast and cervical cancers are the most common causes of cancer mortality among women in India, but actually they are largely preventable diseases. Although early detection is the only way to reduce morbidity and mortality, there are limited data on breast and cervical cancer knowledge, safe practices and attitudes of teachers in India. The purpose of this study is to assess the level of awareness and impact of awareness programs in adoption of safe practices in prevention and early detection. Materials and Methods: This assessment was part of a pink chain campaign on cancer awareness. During cancer awareness events in 2011 at various women colleges in different parts in India, a pre-test related to cervical cancer and breast cancer was followed by an awareness program. Post-tests using the same questionnaire were conducted at the end of the interactive session, at 6 months and 1 year. Results: A total of 156 out of 182 teachers participated in the study (overall response rate was 85.7 %). Mean age of the study population was 42.4 years (range- 28-59 yrs). There was a significant increase in level of knowledge regarding cervical and breast cancer at 6 months and this was sustained at 1 year. Adoption of breast self examination (BSE) was significantly more frequent in comparison to CBE, mammography and the Pap test. Magazines and newspapers were sources for knowledge regarding screening tests for breast cancer in more than 60% of teachers where as more than 75% were educated by doctors regarding the Pap test. Post awareness at 6 months and 1 year, there was a significant change in alcohol and smoking habits. Major reasons for not doing screening test were found to be ignorance (50%), lethargic attitude (44.8%) and lack of time (34.6%). Conclusions: Level of knowledge of breast cancer risk factors, symptoms and screening methods was high as compared to cervical cancer. There was a significant increase in level of knowledge regarding cervical and breast cancer at 6 months and this was sustained at 1 year. Adoption of BSE was significantly greater in comparison to CBE, mammography and the Pap test. To inculcate safe practices in lifestyle of people, awareness programmes such as pink chain campaign should be conducted more widely and frequently.

• 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 Digital Convergence
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• v.16 no.12
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• pp.163-173
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• 2018

This study examined the difference between the information asymmetry in pre- and post K-IFRS adoption used each samples. Efficient market assumption suggests that capital markets already have recognized real value of PP&E and applied those values for estimating the item, in which case PP&E revaluation is not additional information in the capital market but simply an activity to makes costs. This study examined whether the information asymmetry had reduced significantly after adopting K-IFRS or not, verified each period samples those are pre- and post-adopting the asset revaluation since it could have been adopted in advance from 2008. As study results, I confirmed PP&E revaluation affected to reduce the information asymmetry in pre- adopting K-IFRS, but not in post- adopting K-IFRS. These results could be one of proofs which are supported that capital market have been judging PP&E revaluation as the window dressing.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2019.05a
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• pp.222-223
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• 2019

Many attention have been paid to the innovative technologies aiming at the paradigm shift such as the modular housing, pre-fabrication method, or long-life housing. Despite the government's efforts to diffuse the long-life housing, few case gained the high grades by applying various technologies required in the Long-life Housing Certification System. The concerns of cost increase and low profit are the main reason why most of the construction firms avoid the adoption of long-life housing. In this study, the variations of construction costs were analyzed according to the grades in the Long-life Housing Certification System. The results of this study will help the decision makers find the optimal solution in employing the long-life housing technologies during planning.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.2904-2926
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• 2022

Currently, diabetes is the most common chronic disease in the world, affecting 23.7% of the population in the Kingdom of Saudi Arabia. Diabetes may be the cause of lower-limb amputations, kidney failure and blindness among adults. Therefore, diagnosing the disease in its early stages is essential in order to save human lives. With the revolution in technology, Artificial Intelligence (AI) could play a central role in the early prediction of diabetes by employing Machine Learning (ML) technology. In this paper, we developed a diagnosis system using machine learning models for the detection of type 2 diabetes among adults, through the adoption of two different diabetes datasets: one for training and the other for the testing, to analyze and enhance the prediction accuracy. This work offers an enhanced classification accuracy as a result of employing several pre-processing methods before applying the ML models. According to the obtained results, the implemented Random Forest (RF) classifier offers the best classification accuracy with a classification score of 98.95%.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.304-310
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• 2011

The populations of major cities in Australia are increasing rapidly and facing an acute housing shortage. Traditional apartment procurement techniques involve lengthy lead-times and factory-based, or offsite manufactured (OSM) multi-storey apartment buildings may offer the opportunity to help fulfill the need by significantly reducing build times. Other advantages of OSM may include superior quality, low weight ratios, economies of scale achieved through repetition of prefabricated units, use on infill sites, sustainable design standards and better occupational health and safety. There are also positive labour and training implications, which may help to alleviate an industry-wide shortage of skills through use of semi-skilled labour. Previous uncertainties about the adoption of offsite due to the high capital costs and perception issues were generally based on pre-cast concrete structures, which are quite a different building type in terms of flexibility, construction, delivery and finishes. Identification of drivers and constraints assists in the determination of current industry status, allows for a benchmark to be established and future opportunities and directions for OSM to be determined.

• Acta Via Serica
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• v.8 no.2
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• pp.53-78
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• 2023

The paper presents some new results illustrating some developments related to the concept of the Silk Road and subsequent methodological reflections. New laboratory results of scientific analyses of plants, minerals, and human remains in combination with more conventional methods of research contribute to a better understanding of the multidirectionality of exchanges in Pre- and Protohistory. Unsuspected long-distance transfers of items, especially of metals (tin) and biological materials (plants, pathogens, etc.) are discovered. Adding ancient DNA and petroglyphs to the vexed question of the Indo-European migrations across Eurasia complexifies the familiar linguistic, historical, and archaeological research landscape. Recent excavations show the impact of the adoption of artistic elements adapted from the Achaemenid arts, far in the steppe world, and up to China. Multidirectional (including North-South lanes) and multidisciplinary approaches leave space and hope for more rigorous scientific modelizations for the archaeology of Eurasia and the Silk Road.