• Journal of Distribution Research
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• v.17 no.3
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• pp.1-24
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• 2012

Franchises have recently emerged as the most rapidly expanding industry positioned to create a large impact in the domestic economic. The Chinese franchise industry developed rapidly in the period prior and subsequent to WTO accession with more than 50% of new franchises brands emerging since 2000. M&A transactions in the Chinese franchise industry have progressed actively. In the period from 2005-2007, due to the wholesale and retail market opening in accordance with the guidelines laid forth within the MOU by the WTO the Chinese franchise market is now the largest market in the world all despite a short history of only 20 years. The amount of franchise market research on China is disproportional to its current size and development potential. Beginning in the 1990s, market research conducted by the International Franchise Association focused on emerging markets in Eastern Europe and China. While the research dealt with the Chinese investment environment, it insufficiently explained the market region and cultural environment. The purpose of this research is (i) to investigate the determinants of the performance of franchise systems in China and (ii) new contract renewals based on performance factors. This study will complement existing research in terms of the franchisee perspective. This study may also prove of the benefit to the franchise companies entering the Chinese franchise market enabling them to develop an effective strategy. This study shows that support, incentives, and system standardization by franchisor yielded a positive effect on management performance. This is consistent with previous studies by Shin (2000) and Kim (2008) targeting Korean franchises. Therefore, in the Chinese market, the franchisor must focus on support, incentives, and system standardization rather than concentrate only on the recruitment of franchisees in order to improve revenue. Hypotheses regarding franchisor control have been dismissed in existing research, in the opinion of this study, due to their complexity and inability to control the merchant as a one-kind-assessment-standard. Our findings show that the franchisees' financial condition, management ability and entrepreneurial spirit, among franchisee's characteristics, have a positive effect on franchisor's business performance and satisfaction for the franchisee. This is consistent with previous studies on headquarters' management performance of Lussier (1996), Heo and Jang (2008), and franchisees' financial condition, management ability and entrepreneurial spirit effect on franchisor's satisfaction of Weaven and Franzer (2007), Kim (2009), Han (2009), and Yoon etc. (2008). Therefore, when permitting a franchisee, financial condition, management ability, entrepreneurship of the franchisee should be carefully considered. Among relational factors between franchisor and franchisee, trust has the positive influence on the management performance of the franchisor while conflict has a negative effect. However, trust, commitment and conflict factors have been shown not to have any impact on the satisfaction of the franchise headquarters. This result is consistent with the previous studies of Pavlou and Ba (2000), Morrison (1999), Weaven and Frazer (2007), Kim and Park (1994), Sohn (2007) which show that trust between franchisor and the franchisees have a positive effect and that conflict has a negative impact on franchisor's management performance. Other factors causing a negative effective on the franchisor's management performance are a rapid environmental changes and uncertainty in the business. This is consistent with Campbell et al (2007), Kim and Kim (2009), Han and Baek (2008). Finally, the high management performance and satisfaction of the franchise headquarters has a positive effect on the intention of franchise renewal. In the case of large markets such as China, the franchisor's strategy and the role is very important. In this study, we also investigated the characteristics of franchisor and franchisee, relationship, and environmental uncertainty affecting on the management performance and satisfaction of franchisor. Recently, Korean franchises are attempting to enter foreign markets through the rise in popularity of Korean culture and entertainment commonly referred to as the Korean wave. This study provides recommendations for Korean franchises intending on entering the Chinese market. First, in order to achieve stable profits, the franchise corporation needs to support the operation of the individual franchisee through incentives and standardization of services. Second, because trust between the franchisor and franchisee has a positive effect on management performance, on-going discussion and cooperation is necessary to reduce the level of conflict.

• Journal of radiological science and technology
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• v.33 no.1
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• pp.37-44
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• 2010

We sought to confirm an independent factor about in-stent restenosis (ISR) in the patients who underwent drug-eluting stent (DES) and know a possibility as a predictor of measured coronary artery calcium score by MDCT. A total of 178 patients (159 men, $61.7{\pm}10.0$ years of age) with 190 coronary artery lesions were included in this study out of 1,131 patients who underwent percutaneous coronary intervention (PCI) with DES implantation for significant stenosis on MDCT at Chonnam National University Hospital between May 2006 and May 2009. All lesions were divided into two groups with the presence of ISR : group I (re ISR, N = 57) and group II (no ISR, N = 133). Compared to group II, group I was more likely to be older ($65.8{\pm}9.0$ vs. $60.2{\pm}9.9$ years, p = 0.0001), diabetic (21.8% vs. 52.6%, p = 0.0001), have old myocardial infarction (8.8% vs. 2.3%, p = 0.040), left main stem disease (5.3% vs. 0.8%, p = 0.047), and smaller stent size ($3.1{\pm}0.3\;mm$ vs. $3.3{\pm}0.4\;mm$, p = 0.004). Group II was more likely to be smokers (19.3% vs. 42.1%, p = 0.003), have dyslipidemia (8.8% vs. 23.3%, p = 0.019). Left ventricular ejection fraction, lesion complexity, and stent length were not different between the two groups. Total CAC score was $389.3{\pm}458.3$ in group I and $371.2{\pm}500.8$ in group II (p = 0.185). No statistical difference was observed between the groups in CAC score in the culprit vessel, left main stem, left anterior descending artery, left circumflex artery, and right coronary artery. On multivariate logistic regression analysis, left main stem disease (OR = 168.0, 95% CI = 7.83-3,604.3, p = 0.001), male sex (OR = 36.5, 95% CI = 5.89-2,226.9, p = 0.0001), and the presence of diabetes (OR = 2.62, 95% CI = 1.071-6.450, p = 0.035) were independent predictors of ISR after DES implantation. In patients who underwent DES implantation for significant coronary stenosis on MDCT, ISR was associated with left main stem disease, male sex, and the presence of diabetes. However, CAC score by MDCT was not a predictor of ISR in this study population.

• 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.

• Journal of Haehwa Medicine
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• v.9 no.1
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• pp.595-646
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• 2000

I. Introduction Bi(痺) means blocking. BiJeung is one kind of symptoms making muscles, bones and jonts feel pain, numbness or edema. For example it can be gout or SLE etc. says that Bi is combination of PungHanSeup. And many doctors said that BiJeung is caused by food, fatigue, sex, stress and change of weather. Therefore we must treat BiJeung by character of patients and characteristic of the disease. Many famous doctors studied medical science by their fathers or teachers. So the history of medical science is long. So I studied ${\ll}Bijeungjujip{\gg}$. II. Final Decision 1. JoGeumTak(趙金鐸) devided BiJeung into Pung, Han, Seup and EumHeo, HeulHeo, YangHeo, GanSinHeo by charcter or reaction of pain. And he use DaeJinGyoTang, GyegiGakYakJiMoTang, SamyoSan, etc. 2. JangPaeGyeu(張沛圭) focused on division of HanYeol(寒熱; coldness and heat) in spite of complexity of BiJeung. He also used insects for treatment. They are very useful for treatment of BiJeung because they can remove EoHyeol(瘀血). 3. SeolMaeng(薛盟) said that the actual cause of BiJeung is Seup. So he thought that BiJeung can be divided into PungSeup, SeupYeol, HanSeup. And he established 6 rules to treat BiJeung and he studied herbs. 4. JangGi(張琪) introduced 10 prescriptions and 10 rules to cure BiJeung. The 1st prescription is for OyeSa, 2nd for internal Yeol, 3rd for old BiJeung, 4th for Soothing muscles, 5th for HanSeup, 6th for regular BiJeung, 7th for functional disorder, 8th for YeolBi, 9th for joint pain and 10th for pain of lower limb. 5. GangSeYoung(江世英) used PungYeongTang(風靈湯) for the treatment of PungBi, OGyeHeukHoTang(烏桂黑虎湯) for HanBi, BangGiMokGwaTang(防己木瓜湯) for SeupBi, YeolBiTang(熱痺湯) for YeolBi, WoDaeRyeokTang(牛大力湯) for GiHei, HyeolPungGeunTang(血楓根湯) for HyeolHeo, ToJiRyongTang(土地龍湯) for the acute stage of SeupBi, OJoRyongTang(五爪龍湯) for the chronic stage of SeupBi, and so on. 6. ShiGeumMook(施今墨) devided BiJeung into four types. They are PungSeupYeol, PungHanSeup, GiHyeolSil(氣血實) and GiHyeolHeo(氣血虛). And he introduced the eight rules of the treatment(SanPun(散風), ChukHan(逐寒), GeoSeuP(, CheongYeol(淸熱), TongRak(通絡), HwalHyeol(活血), HaengGi(行氣), BoHeo(補虛)). 7. WangYiYou(王李儒) explained the acute athritis and said that it can be applicable to HaneBi(行痺). And he used GyeJiJakYakJiMoTang(桂枝芍蘂知母湯) for HanBi and YeolBiJinTongTang(熱痺鎭痛湯) for YeolBi. 8. JangJinYeo(章眞如) said that YeolBi is more common than HanBi. The sympthoms of YeolBi are severe pain, fever, dried tongue, insomnia, etc. And he devided YeolBi into SilYeol and HeoYeol. In case of SilYeol, he used GyeoJiTangHapBaekHoTang(桂枝湯合白虎湯) and in case of HeoYeol he used JaEumYangAekTang(滋陰養液湯). 9. SaHaeJu(謝海洲) introduced three important rules of treatment and four appropriate rules of treatment of BiJeung. 10. YouDoJu(劉渡舟) said that YeolBi is more common than HanBi. He used GaGamMokBanGiTang(加減木防已湯) for YeolBi, GyeJiJakYakJiMoTang or GyeJiBuJaTang(桂枝附子湯) for HanBi and WooHwangHwan(牛黃丸) for the joint pain. 11. GangYiSon(江爾遜) focused on the internal cause. The most important internal cause is JeongGiHeo(正氣虛). So he tried to treat BiJeung by means of balance of Gi and Hyeol. So he ususlly used ODuTang(烏頭湯) and SamHwangTang(三黃湯) for YeolBi, OJeokSan(五積散) for HanBi, SamBiTang(三痺湯) for the chronic BiJeung. 12. HoGeonHwa(胡建華) said that to distinguish YeolBi from Hanbi is very difficult. So he used GyeJiJakYakJiMoTang in case of mixture of HanBi and YeoBi. 13. PiBokGo(畢福高) said that the most common BiJeung is HanBi. He usually used acupuncture with medicine. He followed the theory of EumYongHwa(嚴用和)-he focused on SeonBoHuSa(先補後瀉). 14. ChoiMunBin(崔文彬) used GeoPungHwalHyeolTang(祛風活血湯) for HanBi, SanHanTongRakTang(散寒通絡湯) for TongBi(痛痺), LiSeupHwaRakTang(利濕和絡湯) for ChakBi(着痺), CheongYeolTongGyeolChukBiTang(淸熱通經逐痺湯) for YeolBi(熱痺) and GeoPungHwalHyeolTang(祛風活血湯) for PiBi(皮痺). 15. YouleokSeon(劉赤選) introduced the common principle for the treatment of BiJeung. He used HaePuneDeungTang(海風藤湯) for HaengBi(行痺), SinChakTang(腎着湯), DokHwalGiSaengTang(獨活寄生湯) for TongBi(痛痺), TongPungBang(痛風方) for ChakBi(着痺) and SangGiYiMiTangGaYeongYangGakTang(桑枝苡米湯加羚羊角骨) for YeolBi(熱痺). 16. LimHakHwa(林鶴和) said about TanTan(movement disorders or numbness) and devided TanTan into the acute stage and the chronic stage. He used acupuncture at the meridian spot like YeolGyeol(列缺), HapGok(合谷), etc. And he also used MaHwangBuJaSeSinTang(麻黃附子細辛湯) in case of the acute stage. In the chronic stage he used BangPungTang(防風湯). 17. JinBaekGeun(陳伯勤) liked to use three rules(HwaHyeol(活血), ChiDam(治痰), BoSin(補腎)) to treat BiJeung. He used JinTongSan(鎭痛散) for the purpose of HwalHyeol(活血), SoHwalRakDan(小活絡丹) for ChiDam(治痰) and DokHwalGiSaengTang(獨活寄生湯) for BoSin(補腎). 18. YimGyeHak(任繼學) focused on YangHyeolJoGi(養血調氣) if the stage of BiJeung is chronic. And in the chronic stage he insisted on not using GalHwal(羌活), DokHwal(獨活) and BangPung(防風).

• Asia pacific journal of information systems
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• v.19 no.4
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• pp.125-148
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• 2009

UGC(User Generated Contents) is emerging as the center of e-business in the web 2.0 era. The trend reflects changing roles of users in production and consumption of contents on websites and helps us to understand new strategies of websites such as web portals and social network websites. Nowadays, we consume contents created by other non-professional users for both utilitarian (e.g., knowledge) and hedonic values (e.g., fun). Also, contents produced by ourselves (e.g., photo, video) are posted on websites so that our friends, family, and even the public can consume those contents. This means that non-professionals, who used to be passive audience in the past, are now creating contents and share their UGCs with others in the Web. Accessible media, tools, and applications have also reduced difficulty and complexity in the process of creating contents. Realizing that users create plenty of materials which are very interesting to other people, media companies (i.e., web portals and social networking websites) are adjusting their strategies and business models accordingly. Increased demand of UGC may lead to website visits which are the source of benefits from advertising. Therefore, they put more efforts into making their websites open platforms where UGCs can be created and shared among users without technical and methodological difficulties. Many websites have increasingly adopted new technologies such as RSS and openAPI. Some have even changed the structure of web pages so that UGC can be seen several times to more visitors. This mainstream of UGCs on websites indicates that acquiring more UGCs and supporting participating users have become important things to media companies. Although those companies need to understand why general users have shown increasing interest in creating and posting contents and what is important to them in the process of productions, few research results exist in this area to address these issues. Also, behavioral process in creating video UGCs has not been explored enough for the public to fully understand it. With a solid theoretical background (i.e., theory of implementation intentions), parts of our proposed research model mirror the process of user behaviors in creating video contents, which consist of intention to upload, intention to edit, edit, and upload. In addition, in order to explain how those behavioral intentions are developed, we investigated influences of antecedents from three motivational perspectives (i.e., intrinsic, editing software-oriented, and website's network effect-oriented). First, from the intrinsic motivation perspective, we studied the roles of self-expression, enjoyment, and social attention in forming intention to edit with preferred editing software or in forming intention to upload video contents to preferred websites. Second, we explored the roles of editing software for non-professionals to edit video contents, in terms of how it makes production process easier and how it is useful in the process. Finally, from the website characteristic-oriented perspective, we investigated the role of a website's network externality as an antecedent of users' intention to upload to preferred websites. The rationale is that posting UGCs on websites are basically social-oriented behaviors; thus, users prefer a website with the high level of network externality for contents uploading. This study adopted a longitudinal research design; we emailed recipients twice with different questionnaires. Guided by invitation email including a link to web survey page, respondents answered most of questions except edit and upload at the first survey. They were asked to provide information about UGC editing software they mainly used and preferred website to upload edited contents, and then asked to answer related questions. For example, before answering questions regarding network externality, they individually had to declare the name of the website to which they would be willing to upload. At the end of the first survey, we asked if they agreed to participate in the corresponding survey in a month. During twenty days, 333 complete responses were gathered in the first survey. One month later, we emailed those recipients to ask for participation in the second survey. 185 of the 333 recipients (about 56 percentages) answered in the second survey. Personalized questionnaires were provided for them to remind the names of editing software and website that they reported in the first survey. They answered the degree of editing with the software and the degree of uploading video contents to the website for the past one month. To all recipients of the two surveys, exchange tickets for books (about 5,000~10,000 Korean Won) were provided according to the frequency of participations. PLS analysis shows that user behaviors in creating video contents are well explained by the theory of implementation intentions. In fact, intention to upload significantly influences intention to edit in the process of accomplishing the goal behavior, upload. These relationships show the behavioral process that has been unclear in users' creating video contents for uploading and also highlight important roles of editing in the process. Regarding the intrinsic motivations, the results illustrated that users are likely to edit their own video contents in order to express their own intrinsic traits such as thoughts and feelings. Also, their intention to upload contents in preferred website is formed because they want to attract much attention from others through contents reflecting themselves. This result well corresponds to the roles of the website characteristic, namely, network externality. Based on the PLS results, the network effect of a website has significant influence on users' intention to upload to the preferred website. This indicates that users with social attention motivations are likely to upload their video UGCs to a website whose network size is big enough to realize their motivations easily. Finally, regarding editing software characteristic-oriented motivations, making exclusively-provided editing software more user-friendly (i.e., easy of use, usefulness) plays an important role in leading to users' intention to edit. Our research contributes to both academic scholars and professionals. For researchers, our results show that the theory of implementation intentions is well applied to the video UGC context and very useful to explain the relationship between implementation intentions and goal behaviors. With the theory, this study theoretically and empirically confirmed that editing is a different and important behavior from uploading behavior, and we tested the behavioral process of ordinary users in creating video UGCs, focusing on significant motivational factors in each step. In addition, parts of our research model are also rooted in the solid theoretical background such as the technology acceptance model and the theory of network externality to explain the effects of UGC-related motivations. For practitioners, our results suggest that media companies need to restructure their websites so that users' needs for social interaction through UGC (e.g., self-expression, social attention) are well met. Also, we emphasize strategic importance of the network size of websites in leading non-professionals to upload video contents to the websites. Those websites need to find a way to utilize the network effects for acquiring more UGCs. Finally, we suggest that some ways to improve editing software be considered as a way to increase edit behavior which is a very important process leading to UGC uploading.

• Korean Journal of Heritage: History & Science
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• v.52 no.2
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• pp.80-105
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• 2019

With the increasing complexity and irregularity of disaster types, the need for cultural asset preservation and management from a proactive perspective has increased as a number of cultural properties have been destroyed and damaged by various natural and humanistic factors. In consideration of these circumstances, the Cultural Heritage Administration enacted an Act in December 2005 to enforce the regular commission of surveys for the systematic preservation and management of cultural assets, and through a recent revision of this Act, the investigation cycle has been reduced from five to three years, and the object of regular inspections has been expanded to cover registered cultural properties. According to the ordinance, a periodic survey of city- or province-designated heritage is to be carried out mainly by metropolitan and provincial governments. The Seoul Metropolitan Government prepared a legal basis for commissioning regular surveys under the Seoul Special City Cultural Properties Protection Ordinance 2008 and, in recognition of the importance of preventive management due to the large number of cultural assets located in the city center and the high demand for visits, conducted regular surveys of the entire city-designated cultural assets from 2016 to 2018. Upon the first survey being completed, it was considered necessary to review the policy effectiveness of the system and to conduct a comprehensive review of the results of the regular surveys that had been carried out to enhance the management of cultural assets. Therefore, the present study examined the comprehensive management status of the cultural assets designated by the Seoul Metropolitan Government for three years (2016-2018), assessing the performance and identifying limitations. Additionally, ways to improve it were sought, and a DB establishment plan for the establishment of an integrated management system under the auspices of the Seoul Metropolitan Government was proposed. Specifically, survey forms were administered under the Guidelines for the Operation of Periodic Surveys of National Designated Cultural Assets; however, the types of survey forms were reclassified and further subdivided in consideration of the characteristics of the designated cultural assets, and manuals were developed for consistent and specific information technologies in respect of the scope and manner of the survey. Based on this analysis, it was confirmed that 401 cases (77.0%) out of 521 cases were generally well preserved; however, 102 cases (19.6%) were found to require special measures such as attention, precision diagnosis, and repair. Meanwhile, there were 18 cases (3.4%) of unsurveyed cultural assets. These were inaccessible to the investigation at this time due to reasons such as unknown location or closure to the public. Regarding the specific types of cultural assets, among a total of 171 cultural real estate properties, 63 cases (36.8%) of structural damage were caused by the failure and elimination of members, and 73 cases (42.7%) of surface area damage were the result of biological damage. Almost all plants and geological earth and scenic spots were well preserved. In the case of movable cultural assets, 25 cases (7.1%) among 350 cases were found to have changed location, and structural damage and surface area damage was found according to specific material properties, excluding ceramics. In particular, papers, textiles, and leather goods, with material properties that are vulnerable to damage, were found to have greater damage than those of other materials because they were owned and managed by individuals and temples. Thus, it has been confirmed that more proactive management is needed. Accordingly, an action plan for the comprehensive preservation and management status check shall be developed according to management status and urgency, and the project promotion plan and the focus management target should be selected and managed first. In particular, concerning movable cultural assets, there have been some cases in which new locations have gone unreported after changes in ownership (management); therefore, a new system is required to strengthen the obligation to report changes in ownership (management) or location. Based on the current status diagnosis and improvement measures, it is expected that the foundation of a proactive and efficient cultural asset management system can be realized through the establishment of an effective mid- to long-term database of the integrated management system pursued by the Seoul Metropolitan Government.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.85-107
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• 2019

Online consumers browse products belonging to a particular product line or brand for purchase, or simply leave a wide range of navigation without making purchase. The research on the behavior and purchase of online consumers has been steadily progressed, and related services and applications based on behavior data of consumers have been developed in practice. In recent years, customization strategies and recommendation systems of consumers have been utilized due to the development of big data technology, and attempts are being made to optimize users' shopping experience. However, even in such an attempt, it is very unlikely that online consumers will actually be able to visit the website and switch to the purchase stage. This is because online consumers do not just visit the website to purchase products but use and browse the websites differently according to their shopping motives and purposes. Therefore, it is important to analyze various types of visits as well as visits to purchase, which is important for understanding the behaviors of online consumers. In this study, we explored the clustering analysis of session based on click stream data of e-commerce company in order to explain diversity and complexity of search behavior of online consumers and typified search behavior. For the analysis, we converted data points of more than 8 million pages units into visit units' sessions, resulting in a total of over 500,000 website visit sessions. For each visit session, 12 characteristics such as page view, duration, search diversity, and page type concentration were extracted for clustering analysis. Considering the size of the data set, we performed the analysis using the Mini-Batch K-means algorithm, which has advantages in terms of learning speed and efficiency while maintaining the clustering performance similar to that of the clustering algorithm K-means. The most optimized number of clusters was derived from four, and the differences in session unit characteristics and purchasing rates were identified for each cluster. The online consumer visits the website several times and learns about the product and decides the purchase. In order to analyze the purchasing process over several visits of the online consumer, we constructed the visiting sequence data of the consumer based on the navigation patterns in the web site derived clustering analysis. The visit sequence data includes a series of visiting sequences until one purchase is made, and the items constituting one sequence become cluster labels derived from the foregoing. We have separately established a sequence data for consumers who have made purchases and data on visits for consumers who have only explored products without making purchases during the same period of time. And then sequential pattern mining was applied to extract frequent patterns from each sequence data. The minimum support is set to 10%, and frequent patterns consist of a sequence of cluster labels. While there are common derived patterns in both sequence data, there are also frequent patterns derived only from one side of sequence data. We found that the consumers who made purchases through the comparative analysis of the extracted frequent patterns showed the visiting pattern to decide to purchase the product repeatedly while searching for the specific product. The implication of this study is that we analyze the search type of online consumers by using large - scale click stream data and analyze the patterns of them to explain the behavior of purchasing process with data-driven point. Most studies that typology of online consumers have focused on the characteristics of the type and what factors are key in distinguishing that type. In this study, we carried out an analysis to type the behavior of online consumers, and further analyzed what order the types could be organized into one another and become a series of search patterns. In addition, online retailers will be able to try to improve their purchasing conversion through marketing strategies and recommendations for various types of visit and will be able to evaluate the effect of the strategy through changes in consumers' visit patterns.

• 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.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.1
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• pp.75-91
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• 2024

Recently, global business environment is changing dramatically along with the acceleration of technological innovation amid the war, climatic change, and geopolitical instability. Accordingly, it is difficult to predict or plan for the future as the volatility, complexity, ambiguity, and uncertainty of the industrial ecosystem continue to increase. Therefore, organizations are undergoing inevitable restructuring in accordance with their survival strategy, for instance, removing marginal businesses or firing. Accordingly, office workers are seeking a startup as an alternative for their continuous economic activity amid rising anxiety factors that make them think they would lose their jobs unintentionally. Here, this study is aimed to verify through what paths office workers' self-determination influences the process of converting to a startup. For this study, an online survey was carried out, and 310 respondents' valid data were analyzed through SPSS and AMOS. To sum up the results, first, office workers' self-determination did not have significant effects on entrepreneurial intention. However, it was confirmed that self-determination had positive (+) effects on innovativeness and perception of the startup support system. This result shows that their psychology works to prepare step by step by accumulating innovative experiences and increasing perception of the startup support system from a long-term life path perspective rather than challenging startups right way. Second, innovativeness is found to have positive (+) effects on entrepreneurial intention. Also, perception of the startup support system had positive (+) effects on entrepreneurial intention. This implies that when considering startups, they are highly aware of the government's various startup support systems. Third, innovativeness is found to have positive (+) effects on perception of the startup support system. It is judged that perception of the startup support system is valid for prospective founders to exhibit their innovativeness and realize new ideas. Fourth, it was confirmed that innovativeness and perception of the startup support system mediated correlation between self-determination and entrepreneurial intention, and perception of the startup support system mediated correlation between innovativeness and entrepreneurial intention, which shows that it is a crucial factor in entrepreneurial intention. Although previous studies related to startups deal with students mostly, this study targets office workers who form a great part in economic activities, which makes it academically valuable in terms of being differentiated from others and extending the scope of research. Also, when we consider the fact that the motivation for self-determination alone fails to stimulate entrepreneurial intention and the complete mediation of innovativeness and the startup support system, it has great implications in practical aspects such as the government's human and material support systems. In the selection and analysis of samples, this study exhibits a limitation that the problem of common method bias is not completely resolved. Also, additional definitive research is needed on whether entrepreneurial intention is formed and converted into startup behavior. Academically and practically, this study deals with the relationship between humans' psychological motives and startups which has not been handled sufficiently in previous studies. The conversion of office workers to startups is expected to have effects on individuals' economic stability and the state's job creation; therefore, it needs to be investigated continuously for its great value.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.205-225
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• 2018

Convolutional Neural Network (ConvNet) is one class of the powerful Deep Neural Network that can analyze and learn hierarchies of visual features. Originally, first neural network (Neocognitron) was introduced in the 80s. At that time, the neural network was not broadly used in both industry and academic field by cause of large-scale dataset shortage and low computational power. However, after a few decades later in 2012, Krizhevsky made a breakthrough on ILSVRC-12 visual recognition competition using Convolutional Neural Network. That breakthrough revived people interest in the neural network. The success of Convolutional Neural Network is achieved with two main factors. First of them is the emergence of advanced hardware (GPUs) for sufficient parallel computation. Second is the availability of large-scale datasets such as ImageNet (ILSVRC) dataset for training. Unfortunately, many new domains are bottlenecked by these factors. For most domains, it is difficult and requires lots of effort to gather large-scale dataset to train a ConvNet. Moreover, even if we have a large-scale dataset, training ConvNet from scratch is required expensive resource and time-consuming. These two obstacles can be solved by using transfer learning. Transfer learning is a method for transferring the knowledge from a source domain to new domain. There are two major Transfer learning cases. First one is ConvNet as fixed feature extractor, and the second one is Fine-tune the ConvNet on a new dataset. In the first case, using pre-trained ConvNet (such as on ImageNet) to compute feed-forward activations of the image into the ConvNet and extract activation features from specific layers. In the second case, replacing and retraining the ConvNet classifier on the new dataset, then fine-tune the weights of the pre-trained network with the backpropagation. In this paper, we focus on using multiple ConvNet layers as a fixed feature extractor only. However, applying features with high dimensional complexity that is directly extracted from multiple ConvNet layers is still a challenging problem. We observe that features extracted from multiple ConvNet layers address the different characteristics of the image which means better representation could be obtained by finding the optimal combination of multiple ConvNet layers. Based on that observation, we propose to employ multiple ConvNet layer representations for transfer learning instead of a single ConvNet layer representation. Overall, our primary pipeline has three steps. Firstly, images from target task are given as input to ConvNet, then that image will be feed-forwarded into pre-trained AlexNet, and the activation features from three fully connected convolutional layers are extracted. Secondly, activation features of three ConvNet layers are concatenated to obtain multiple ConvNet layers representation because it will gain more information about an image. When three fully connected layer features concatenated, the occurring image representation would have 9192 (4096+4096+1000) dimension features. However, features extracted from multiple ConvNet layers are redundant and noisy since they are extracted from the same ConvNet. Thus, a third step, we will use Principal Component Analysis (PCA) to select salient features before the training phase. When salient features are obtained, the classifier can classify image more accurately, and the performance of transfer learning can be improved. To evaluate proposed method, experiments are conducted in three standard datasets (Caltech-256, VOC07, and SUN397) to compare multiple ConvNet layer representations against single ConvNet layer representation by using PCA for feature selection and dimension reduction. Our experiments demonstrated the importance of feature selection for multiple ConvNet layer representation. Moreover, our proposed approach achieved 75.6% accuracy compared to 73.9% accuracy achieved by FC7 layer on the Caltech-256 dataset, 73.1% accuracy compared to 69.2% accuracy achieved by FC8 layer on the VOC07 dataset, 52.2% accuracy compared to 48.7% accuracy achieved by FC7 layer on the SUN397 dataset. We also showed that our proposed approach achieved superior performance, 2.8%, 2.1% and 3.1% accuracy improvement on Caltech-256, VOC07, and SUN397 dataset respectively compare to existing work.