• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.55-79
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• 2013

In this paper, we propose a hybrid genetic algorithm (HGA) approach to effectively solve the reverse logistics network with centralized centers (RLNCC). For the proposed HGA approach, genetic algorithm (GA) is used as a main algorithm. For implementing GA, a new bit-string representation scheme using 0 and 1 values is suggested, which can easily make initial population of GA. As genetic operators, the elitist strategy in enlarged sampling space developed by Gen and Chang (1997), a new two-point crossover operator, and a new random mutation operator are used for selection, crossover and mutation, respectively. For hybrid concept of GA, an iterative hill climbing method (IHCM) developed by Michalewicz (1994) is inserted into HGA search loop. The IHCM is one of local search techniques and precisely explores the space converged by GA search. The RLNCC is composed of collection centers, remanufacturing centers, redistribution centers, and secondary markets in reverse logistics networks. Of the centers and secondary markets, only one collection center, remanufacturing center, redistribution center, and secondary market should be opened in reverse logistics networks. Some assumptions are considered for effectively implementing the RLNCC The RLNCC is represented by a mixed integer programming (MIP) model using indexes, parameters and decision variables. The objective function of the MIP model is to minimize the total cost which is consisted of transportation cost, fixed cost, and handling cost. The transportation cost is obtained by transporting the returned products between each centers and secondary markets. The fixed cost is calculated by opening or closing decision at each center and secondary markets. That is, if there are three collection centers (the opening costs of collection center 1 2, and 3 are 10.5, 12.1, 8.9, respectively), and the collection center 1 is opened and the remainders are all closed, then the fixed cost is 10.5. The handling cost means the cost of treating the products returned from customers at each center and secondary markets which are opened at each RLNCC stage. The RLNCC is solved by the proposed HGA approach. In numerical experiment, the proposed HGA and a conventional competing approach is compared with each other using various measures of performance. For the conventional competing approach, the GA approach by Yun (2013) is used. The GA approach has not any local search technique such as the IHCM proposed the HGA approach. As measures of performance, CPU time, optimal solution, and optimal setting are used. Two types of the RLNCC with different numbers of customers, collection centers, remanufacturing centers, redistribution centers and secondary markets are presented for comparing the performances of the HGA and GA approaches. The MIP models using the two types of the RLNCC are programmed by Visual Basic Version 6.0, and the computer implementing environment is the IBM compatible PC with 3.06Ghz CPU speed and 1GB RAM on Windows XP. The parameters used in the HGA and GA approaches are that the total number of generations is 10,000, population size 20, crossover rate 0.5, mutation rate 0.1, and the search range for the IHCM is 2.0. Total 20 iterations are made for eliminating the randomness of the searches of the HGA and GA approaches. With performance comparisons, network representations by opening/closing decision, and convergence processes using two types of the RLNCCs, the experimental result shows that the HGA has significantly better performance in terms of the optimal solution than the GA, though the GA is slightly quicker than the HGA in terms of the CPU time. Finally, it has been proved that the proposed HGA approach is more efficient than conventional GA approach in two types of the RLNCC since the former has a GA search process as well as a local search process for additional search scheme, while the latter has a GA search process alone. For a future study, much more large-sized RLNCCs will be tested for robustness of our approach.

• Journal of Venture Innovation
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• v.7 no.2
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• pp.77-99
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• 2024

Small and medium-sized venture companies and small business owners play an important role in the Korean economy. According to the Ministry of SMEs and Startups' statistics survey in 2019, the proportion of all employees of small and medium-sized venture companies in Korea was 89.8%, which is overwhelmingly higher than that of Japan's 77.8% and Taiwan's 76.6%, so job creation for small and medium-sized venture companies in Korea is absolute. Compared to large companies, it is known that it is difficult for small and medium-sized venture companies in Korea to secure a competitive advantage due to low cost competitiveness, difficulties in R&D/marketing due to lack of capital, difficulties in securing networks, and lack of management know-how. Many scholars, including Kerr and Schumpeter, have studied entrepreneurship since the mid-1950s. Recently, research has been conducted in various fields such as business administration, psychology, and economics. However, research on the personal characteristics of CEOs of small and medium-sized venture companies such as entrepreneurial passion and corporate reputation, which is the overall image of companies, is still insufficient, and comparisons of effects between variables between groups (by industry, by pressure, etc.) have not yet been presented. Entrepreneurial passion plays an important role in promoting new business creation, job creation, and economic growth. Therefore, entrepreneurial Cultivating and promoting passion will also contribute to socioeconomic development. These are government policies, educational programs, and social awareness It can be achieved through improvement, etc., and is especially important in the current market conditions where innovation and growth are important. In conclusion Entrepreneurial passion is an important factor that has a positive effect on all individuals, businesses, and society. Therefore, research on entrepreneurial passion is It is an essential task to maximize these positive effects. In this study, in order to overcome the limitations of existing studies, entrepreneurs' Whether entrepreneurial passion, which is a personal characteristic, affects competitive advantage, absorption capacity and product and process innovation capabilities are Whether there is a mediating effect between entrepreneurial passion and competitive advantage. Finally, in the relationship between entrepreneurial passion and absorption capacity. The corporate reputation verifies whether it has a moderating effect, etc., and compares the influencing factors between variables by industry and pressure. It is intended to be verified empirically. In this study, 2023 conducted by the Small and Medium Business Distribution Center under the Ministry of SMEs and Startups for 312 CEOs of small and medium-sized venture companies and small business owners who participated in the online market support project for small and medium-sized venture companies and small business owners, December 2023 Survey and analysis were conducted from 1st to 20th, and the analysis used Smart PLS 4.0 to determine the impact on the relationship between variables. It was empirically verified. As a result of the study, it was found that entrepreneurial passion had a positive effect on absorption capacity, and the product was found And it was found to have a positive effect on process innovation capability and competitive advantage. In addition, the higher the corporate reputation, the more entrepreneurial it is. It was confirmed that passion had a greater effect on absorption capacity.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.71-88
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• 2017

Language models were originally developed for speech recognition and language processing. Using a set of example sentences, a language model predicts the next word or character based on sequential input data. N-gram models have been widely used but this model cannot model the correlation between the input units efficiently since it is a probabilistic model which are based on the frequency of each unit in the training set. Recently, as the deep learning algorithm has been developed, a recurrent neural network (RNN) model and a long short-term memory (LSTM) model have been widely used for the neural language model (Ahn, 2016; Kim et al., 2016; Lee et al., 2016). These models can reflect dependency between the objects that are entered sequentially into the model (Gers and Schmidhuber, 2001; Mikolov et al., 2010; Sundermeyer et al., 2012). In order to learning the neural language model, texts need to be decomposed into words or morphemes. Since, however, a training set of sentences includes a huge number of words or morphemes in general, the size of dictionary is very large and so it increases model complexity. In addition, word-level or morpheme-level models are able to generate vocabularies only which are contained in the training set. Furthermore, with highly morphological languages such as Turkish, Hungarian, Russian, Finnish or Korean, morpheme analyzers have more chance to cause errors in decomposition process (Lankinen et al., 2016). Therefore, this paper proposes a phoneme-level language model for Korean language based on LSTM models. A phoneme such as a vowel or a consonant is the smallest unit that comprises Korean texts. We construct the language model using three or four LSTM layers. Each model was trained using Stochastic Gradient Algorithm and more advanced optimization algorithms such as Adagrad, RMSprop, Adadelta, Adam, Adamax, and Nadam. Simulation study was done with Old Testament texts using a deep learning package Keras based the Theano. After pre-processing the texts, the dataset included 74 of unique characters including vowels, consonants, and punctuation marks. Then we constructed an input vector with 20 consecutive characters and an output with a following 21st character. Finally, total 1,023,411 sets of input-output vectors were included in the dataset and we divided them into training, validation, testsets with proportion 70:15:15. All the simulation were conducted on a system equipped with an Intel Xeon CPU (16 cores) and a NVIDIA GeForce GTX 1080 GPU. We compared the loss function evaluated for the validation set, the perplexity evaluated for the test set, and the time to be taken for training each model. As a result, all the optimization algorithms but the stochastic gradient algorithm showed similar validation loss and perplexity, which are clearly superior to those of the stochastic gradient algorithm. The stochastic gradient algorithm took the longest time to be trained for both 3- and 4-LSTM models. On average, the 4-LSTM layer model took 69% longer training time than the 3-LSTM layer model. However, the validation loss and perplexity were not improved significantly or became even worse for specific conditions. On the other hand, when comparing the automatically generated sentences, the 4-LSTM layer model tended to generate the sentences which are closer to the natural language than the 3-LSTM model. Although there were slight differences in the completeness of the generated sentences between the models, the sentence generation performance was quite satisfactory in any simulation conditions: they generated only legitimate Korean letters and the use of postposition and the conjugation of verbs were almost perfect in the sense of grammar. The results of this study are expected to be widely used for the processing of Korean language in the field of language processing and speech recognition, which are the basis of artificial intelligence systems.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.3 no.1
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• pp.28-30
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• 1978

Communication actions as a social band or Human community stick (fast) to human being ceaselessly w without stopping everywhere. All of comm. actions can be kept up and developed by the education of its own. Comm. actions have to include a character of social process, and so for it the social science should t to be some essential part of it. Therefore, Comm. education have to be schemed for achieving with a point of view of synthetical s science including technical and social factor. However, recentry Comm. education be suffered to lose of itowns essential attribute and individual i independence becausing to reduce social weight recklessly in their education It is a prindiple that Comm. science is an integrate science being composed of human, social and t technical subdepartments and so comm. education have to obey for Comm. constuctional theory, i international and social claim. Originally in Korea a educational idea and genealogy forming by the comm. scientific theory has I inherited on orthodoxy. But in 1961, communication college that is only the orthodox model of Comm. education, was f forced to close by some reckless policy and then the national administration for the Comm. education h have been weakened, and so recently it’s education became to degenerate as out of genealogy or n nonsystem alike some scattering Family. On the other side, today comm. science make to it’s modern scientific factor and to keep its l integrate level, therefore, all of educational provisions and administration for the telecomm. should t to be supplement to be fit for their plural chatacters. Comm. education have to occupy an individual educational system through the comm. theory, and t then it can be coexisted with neighbour scientific field equally and can include, connect coordinate o or effect its inference in each subfactor organically. Finally, educational system for telecommunication should to be requested as preeedence that i independent field including pluralism must be formed and sufficient autonomy be guarenteed, and s so Comm. education must be to restored its orthodox genealogy and be recovered individual system a and seIfrestraint field, and then it can be accomplished its own duty for nation and society.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.231-252
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• 2021

Artificial intelligence (AI) is a key technology that will change the future the most. It affects the industry as a whole and daily life in various ways. As data availability increases, artificial intelligence finds an optimal solution and infers/predicts through self-learning. Research and investment related to automation that discovers and solves problems on its own are ongoing continuously. Automation of artificial intelligence has benefits such as cost reduction, minimization of human intervention and the difference of human capability. However, there are side effects, such as limiting the artificial intelligence's autonomy and erroneous results due to algorithmic bias. In the labor market, it raises the fear of job replacement. Prior studies on the utilization of artificial intelligence have shown that individuals do not necessarily use the information (or advice) it provides. Algorithm error is more sensitive than human error; so, people avoid algorithms after seeing errors, which is called "algorithm aversion." Recently, artificial intelligence has begun to be understood from the perspective of the augmentation of human intelligence. We have started to be interested in Human-AI collaboration rather than AI alone without human. A study of 1500 companies in various industries found that human-AI collaboration outperformed AI alone. In the medicine area, pathologist-deep learning collaboration dropped the pathologist cancer diagnosis error rate by 85%. Leading AI companies, such as IBM and Microsoft, are starting to adopt the direction of AI as augmented intelligence. Human-AI collaboration is emphasized in the decision-making process, because artificial intelligence is superior in analysis ability based on information. Intuition is a unique human capability so that human-AI collaboration can make optimal decisions. In an environment where change is getting faster and uncertainty increases, the need for artificial intelligence in decision-making will increase. In addition, active discussions are expected on approaches that utilize artificial intelligence for rational decision-making. This study investigates the impact of artificial intelligence on decision-making focuses on human-AI collaboration and the interaction between the decision maker personal traits and advisor type. The advisors were classified into three types: human, artificial intelligence, and human-AI collaboration. We investigated perceived usefulness of advice and the utilization of advice in decision making and whether the decision-maker's personal traits are influencing factors. Three hundred and eleven adult male and female experimenters conducted a task that predicts the age of faces in photos and the results showed that the advisor type does not directly affect the utilization of advice. The decision-maker utilizes it only when they believed advice can improve prediction performance. In the case of human-AI collaboration, decision-makers higher evaluated the perceived usefulness of advice, regardless of the decision maker's personal traits and the advice was more actively utilized. If the type of advisor was artificial intelligence alone, decision-makers who scored high in conscientiousness, high in extroversion, or low in neuroticism, high evaluated the perceived usefulness of the advice so they utilized advice actively. This study has academic significance in that it focuses on human-AI collaboration that the recent growing interest in artificial intelligence roles. It has expanded the relevant research area by considering the role of artificial intelligence as an advisor of decision-making and judgment research, and in aspects of practical significance, suggested views that companies should consider in order to enhance AI capability. To improve the effectiveness of AI-based systems, companies not only must introduce high-performance systems, but also need employees who properly understand digital information presented by AI, and can add non-digital information to make decisions. Moreover, to increase utilization in AI-based systems, task-oriented competencies, such as analytical skills and information technology capabilities, are important. in addition, it is expected that greater performance will be achieved if employee's personal traits are considered.

• Asia Marketing Journal
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• v.12 no.4
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• pp.137-164
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• 2011

In the past, retailers secured customer loyalty by offering convenient locations, unique assortments of goods, better services than competitors, and good credit policy. All this has changed. Goods assortments among stores have become more alike as national-brand manufacturers place their goods in more and more retail stores. Service differentiation also has eroded. Many department stores have trimmed services, and many discount stores have increased theirs. Customers have become smarter shoppers. They don't pay more for identical brands, especially when service differences have diminished. In the face of increased competition from discount storess and specialty stores, department stores are waging a comeback war. Growth of intertype competition, competition between store-based and non-store-based retailing and growing investment in technology are changing the way consumers shop and retailers sell. Different types of stores-discount stores, catalog showrooms, department stores-all compete for the same consumers by carrying the same type of merchandise. The biggest winners are retailers that have helped shoppers to be economically cautious, simplified their increasingly busy and complicated lives, and provided an emotional connection. The growth of e-retailers has forced traditional brick-and-mortar retailers to respond. Basically brick-and-mortar retailers utilize their natural advantages, such as products that shoppers can actually see, touch, and test, real-life customer service, and no delivery lag time for small-sized purchases. They also provide a shopping experience as a strong differentiator. They are adopting practices as calling each shopper a "guest". The store atmosphere should match the basic motivations of the shopper. If target consumers are more likely to be in a task-oriented and functional mindset, then a simpler, more restrained in-store environment may be better. Consistent with this reasoning, some retailers of experiential products are creating in-store entertainment to attract customers who want fun and excitement. The retail experience must deliver value to turn a one-time visitor into a loyal customer. Retailers need a tool that measures the full range of components that define experience-based value. This study uses an experiential value scale(EVS) developed by Mathwick, Malhotra and Rigdon(2001) which reflects the benefits derived from perceptions of playfulness, aesthetics, customer "return on investment" and service excellence. EVS is useful to predict differences in shopping preferences and patronage behavior of customers. EVS consists of items measuring efficiency, economic value, visual appeal, entertainment value, service excellence, escapism, and intrinsic enjoyment, which are subscales of experiencial value. Efficiency, economic value, service excellence are linked to the utilitarian shopping value. And visual appeal, entertainment value, escapism and intrinsic enjoyment are linked to hedonic shopping value. It has been found that consumers value hedonic experiences activated from escapism and attractiveness of shopping environment as much as the product quality, price, and the convenient location. As a result, many department stores, discount stores, and other retailers are introducing differential marketing strategy based on emotional/hedonic values. Many researches suggest that consumers go shopping not only for buying products but also for various shopping experiences. In other words, they seek the practical, rational value as well as social, recreational values in the shopping process(Babin et al, 1994; Bloch et al, 1994). Retailers may enhance buyer's loyalty to store by providing excellent emotional/hedonic value such as the excitement from shopping, not just the practical value of buying good products efficiently. We investigate the effect of perceived shopping values on the emotional experience and store loyalty based on the EVS(Experiential Value Scales) developed by Holbrook(1994), Mathwick, Malhotra and Rigdon(2001). This study assumes that the relative effect of shopping value dimensions on the responses of shoppers will differ according to types of stores and analyzes the moderating effect of store type(department store VS. discount store) on the causal relationship between shopping value dimensions and store loyalty. Emprical results show that utilitarian values of shopping experience and hedonic value of shipping experience give the positive effect on the emotional response of consumers and store loyalty. We also found the moderating effect of store types. The effect of utilitarian shopping values on the attitude toward discount store is higher than the effect of utilitarian shopping values on the attitude toword department store. And the effect of hedonic shopping value on the emotional response to discount store is higher than on the emotional response to department store. The empirical results reflect on the recent trend that discount stores try to fulfill the hedonic needs of consumers as well as utilitarian needs(i.e, low price) that discount stores traditionally have focused on

• Tuberculosis and Respiratory Diseases
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• v.50 no.2
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• pp.182-195
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• 2001

Background : Idiopathic pulmonary fibrosis(IPF) is a devastating illness for which there is little effective treatment. The key cytokines currently implicated in the fibrotic process are the transforming growth factor-${\beta}_1$(TGF-${\beta}_1$), tumor necrosis factor-$\alpha$(TNF-$\alpha$), endothelin-1(ET-1) and interferon-$\gamma$(IFN-$\gamma$). The rat model for paraquat-induced pulmonary fibrosis was chosen to investigate the role of ET-1 in this disease. Both ET-1 and TGF-${\beta}_1$ expression in lung lesions were examined using immunohistochemical staining. After $Bosentan^{(R)}$ administration, an orally active ET-$l_A$ and ET-$1_B$ receptor antagonist, the degree of pulmonary fibrosis and ET-1 and TGF-${\beta}_1$ expression were analyzed. Method : Sprague-Dawley rats were divided into three groups, the control group, the fibrosis group, and the fibrosis-$Bosentan^{(R)}$-treated group. The animals were sacrificed periodically at 1, 3, 5, 7, 10, 14 days after administering saline or paraquat. The effects between groups were compared with the results of light microscopy and immunohistochemical staining for ET-1 and TGF-${\beta}_1$. The degree of fibrosis was evaluated by H&E and Masson's trichrome staining, which were graded by a computerized image analyzer. The degree of immunohistochemical staining was categorized by a semi-quantitative analysis method. Results : The lung collagen content had increased in the paraquat instillated animals by day 3, and continued to increase up to day 14. A daily treatment by gavage with $Bosentan^{(R)}$ (100mg/kg) did not prevent the increase in collagen deposition on the lung that was induced by paraquat instillation. There were increased immunohistochemical stains of ET-1 on the exudate, macrophages, vascular endothelial cells and pneumocytes in the paraquat instillated group. Furthermore, TGF-${\beta}_1$ expression was higher on the exudate, macrophages, some inflammatory cells, pneumocytes( type I, and II), vascular endothelium and the respiratory epithelial cells around the fibrotic area. After Bosentan treatment, there were no definite changes in ET-1 and TGF-${\beta}_1$ expression. Conclusion : Fibrosis of the Paraquat instillated group was more advanced when compared with the control group. In addition, there was increased ET-1 and TGF-${\beta}_1$ expression around the fibrotic area. ET-1 is associated with lung fibrosis but there was little effect of the ET-1 receptor blocker($Bosentan^{(R)}$) on antifibrosis.Background : Idiopathic pulmonary fibrosis(IPF) is a devastating illness for which there is little effective treatment. The key cytokines currently implicated in the fibrotic process are the transforming growth factor-${\beta}_1$(TGF-${\beta}_1$), tumor necrosis factor-$\alpha$(TNF-$\alpha$), endothelin-1(ET-1) and interferon-$\gamma$(IFN-$\gamma$). The rat model for paraquat-induced pulmonary fibrosis was chosen to investigate the role of ET-1 in this disease. Both ET-1 and TGF-${\beta}_1$ expression in lung lesions were examined using immunohistochemical staining. After $Bosentan^{(R)}$ administration, an orally active ET-$1_A$ and ET-$1_B$ receptor antagonist, the degree of pulmonary fibrosis and ET-1 and TGF-${\beta}_1$ expression were analyzed. Method : Sprague-Dawley rats were divided into three groups, the control group, the fibrosis group, and the fibrosis-$Bosentan^{(R)}$-treated group. The animals were sacrificed periodically at 1, 3, 5, 7, 10, 14 days after administering saline or paraquat. The effects between groups were compared with the results of light microscopy and immunohistochemical staining for ET-1 and TGF-${\beta}_1$. The degree of fibrosis was evaluated by H&E and Masson's trichrome staining, which were graded by a computerized image analyzer. The degree of immunohistochemical staining was categorized by a semi-quantitative analysis method. Results : The lung collagen content had increased in the paraquat instillated animals by day 3, and continued to increase up to day 14. A daily treatment by gavage with $Bosentan^{(R)}$ (100mg/kg) did not prevent the increase in collagen deposition on the lung that was induced by paraquat instillation. There were increased immunohistochemical stains of ET-1 on the exudate, macrophages, vascular endothelial cells and pneumocytes in the paraquat instillated group. Furthermore, TGF-${\beta}_1$ expression was higher on the exudate, macrophages, some inflammatory cells, pneumocytes( type I, and II), vascular endothelium and the respiratory epithelial cells around the fibrotic area. After Bosentan treatment, there were no definite changes in ET-1 and TGF-${\beta}_1$ expression. Conclusion : Fibrosis of the Paraquat instillated group was more advanced when compared with the control group. In addition, there was increased ET-1 and TGF-${\beta}_1$ expression around the fibrotic area. ET-1 is associated with lung fibrosis but there was little effect of the ET-1 receptor blocker($Bosentan^{(R)}$) on antifibrosis.

• Tuberculosis and Respiratory Diseases
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• v.44 no.2
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• pp.360-378
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• 1997

Background : Severe acute lung injury(ALI), also known as the adult respiratory distress syndrome(ARDS), is a heterogenous nature of dynamic and explosive clinical synrome that exacts a mortality of approximately 50%. Endotoxin(ETX) is an abundant component of the outer membrane of gram-negative bacteria capable of inducing severe lung injury in gram-negative sepsis and gram-negative bacterial pneumonia, which are among the most common predisposing causes of ARDS. The influx of PMNs into airway tissue is a pathological hallmark of LPS-induced lung injury. And there is a substantial evidence suggesting that cytokines are important mediators of lung injury in gram-negative sepsis. However, the kinetics of phagocytes and cytokines by an exact time sequence and their respective pathogenic importance remain to be elucidated. This study was performed to investigate the role of phagocytes and proinflammatory cytokines in ETX-induced ALI through a time course of changes in the concentration of protein, $TNF{\alpha}$ and IL-6, and counts of total and its differential cells in BALF. The consecutive histologic findings were also evaluated. Method : The experimental animals, healthy male Sprague-Dawley, weighted $200{\pm}50g$, were divided into control- and ALI- group. ALI was induced by an intravenous administration of ETX, 5mg/kg. Above mentioned all parameters were examined at 0(control), 3, 6, 24, 72 h after administration of ETX. $TNF{\alpha}$ and IL-6 cone. in BALF were measured by a bioassay. Results : The protein concentration and total leukocyte count(TC) in BALF was significantly increased at 3h compared to controls(p < 0.05). The protein conc. was significantly elavated during observation period, but TC was significantly decreased at 72h(p < 0.05 vs. 24h). There was a close relationship between TC and protein cone. in BALF(r = 0.65, p < 0.001). The PMN and monocyte count was well correlated with TC in BALF, and the correlation of PMN(r = 0.97, p < 0.001) appeared to be more meaningful than that of monocyte(r = 0.61, p < 0.001). There was also a significant correlation between protein cone. and PMN or monocyte count in BALF(PMN vs. monocyte : r = 0.55, p < 0.005 vs. r = 0.64, p < 0.001). The count of monocyte was significantly elavated during observation period though a meaningful reduction of PMN count in BALF at 72h, this observation suggested that monocyte may, at least, partipate in the process of lung injury steadly. In this study, there was no relationship between IL-6 and $TNF{\alpha}$ cone., and $TNF{\alpha}$ but not IL-6 was correlated with TC(r = 0.61, p < 0.05) and monocyte(r = 0.67, p < 0.05) in BALF only at 3, 6h after ETX introduced. In particular, the IL-6 cone. increased earlier and rapidly peaked than $TNF{\alpha}$ cone. in BALF. In histologic findings, the cell counts of lung slices were increased from 3 to 72h(p < 0.001 vs. NC). Alveolar wall-thickness was increased from 6 to 24h(p < 0.001 vs. NC). There was a significant correlation between the cell counts of lung slices and alveolar wall-thickness(r= 0.61, p < 0.001). This result suggested that the cellular infiltrations might be followed by the alterations of interstitium, and the edematous change of alveolar wall might be most rapidly recovered to its normal condition in the process of repair. Conclusion : We concluded that although the role of PMN is partly certain in ETX-induced ALI, it is somewhat inadequate to its known major impact on ALL Alveolar macrophage and/or non-immune cells such as pulmonary endothelial or epithelial cells, may be more importantly contributed to the initiation and perpetual progression of ETX-induced ALI. The IL-6 in ETX-induced ALI was independent to $TNF{\alpha}$, measured by a bioassay in BALF. The early rise in IL-6 in BALF implies multiple origins of the IL-6.

• Journal of Global Scholars of Marketing Science
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• v.18 no.4
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• pp.119-155
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• 2008

Customer Relationship Management(CRM) has been a sustainable competitive edge of many companies. CRM analyzes customer data for designing and executing targeted marketing analysing customer behavior in order to make decisions relating to products and services including management information system. It is critical for companies to get and maintain profitable customers. How to manage relationships with customers effectively has become an important issue for both academicians and practitioners in recent years. However, the existing academic literature and the practical applications of customer relationship management(CRM) strategies have been focused on the technical process and organizational structure about the implementation of CRM. These limited focus on CRM lead to the result of numerous reports of failed implementations of various types of CRM projects. Many of these failures are also related to the absence of marketing approach. Identifying successful factors and outcomes focused on marketing concept before introducing a CRM project are a pre-implementation requirements. Many researchers have attempted to find the factors that contribute to the success of CRM. However, these research have some limitations in terms of marketing approach without explaining how the marketing based factors contribute to the CRM success. An understanding of how to manage relationship with crucial customers effectively based marketing approach has become an important topic for both academicians and practitioners. However, the existing papers did not provide a clear antecedent and outcomes factors focused on marketing approach. This paper attempt to validate whether or not such various marketing factors would impact on relational quality and CRM performance in terms of marketing oriented perceptivity. More specifically, marketing oriented factors involving market orientation, customer orientation, customer information orientation, and core customer orientation can influence relationship quality(satisfaction and trust) and CRM outcome(customer retention and customer share). Another major goals of this research are to identify the effect of relationship quality on CRM outcomes consisted of customer retention and share to show the relationship strength between two factors. Based on meta analysis for conventional studies, I can construct the following research model. An empirical study was undertaken to test the hypotheses with data from various companies. Multiple regression analysis and t-test were employed to test the hypotheses. The reliability and validity of our measurements were tested by using Cronbach's alpha coefficient and principal factor analysis respectively, and seven hypotheses were tested through performing correlation test and multiple regression analysis. The first key outcome is a theoretically and empirically sound CRM factors(marketing orientation, customer orientation, customer information orientation, and core customer orientation.) in the perceptive of marketing. The intensification of ${\beta}$coefficient among antecedents factors in terms of marketing was not same. In particular, The effects on customer trust of marketing based CRM antecedents were significantly confirmed excluding core customer orientation. It was notable that the direct effects of core customer orientation on customer trust were not exist. This means that customer trust which is firmly formed by long term tasks will not be directly linked to the core customer orientation. the enduring management concerned with this interactions is probably more important for the successful implementation of CRM. The second key result is that the implementation and operation of successful CRM process in terms of marketing approach have a strong positive association with both relationship quality(customer trust/customer satisfaction) and CRM performance(customer retention and customer possession). The final key fact that relationship quality has a strong positive effect on customer retention and customer share confirms that improvements in customer satisfaction and trust improve accessibility to customers, provide more consistent service and ensure value-for-money within the front office which result in growth of customer retention and customer share. Particularly, customer satisfaction and trust which is main components of relationship quality are found to be positively related to the customer retention and customer share. Interactive managements of these main variables play key roles in connecting the successful antecedent of CRM with final outcome involving customer retention and share. Based on research results, This paper suggest managerial implications concerned with constructions and executions of CRM focusing on the marketing perceptivity. I can conclude in general the CRM can be achieved by the recognition of antecedents and outcomes based on marketing concept. The implementation of marketing concept oriented CRM will be connected with finding out about customers' purchasing habits, opinions and preferences profiling individuals and groups to market more effectively and increase sales changing the way you operate to improve customer service and marketing. Benefiting from CRM is not just a question of investing the right software, but adapt CRM users to the concept of marketing including marketing orientation, customer orientation, and customer information orientation. No one deny that CRM is a process or methodology used to develop stronger relationships being composed of many technological components, but thinking about CRM in primarily technological terms is a big mistake. We can infer from this paper that the more useful way to think and implement about CRM is as a process that will help bring together lots of pieces of marketing concept about customers, marketing effectiveness, and market trends. Finally, a real situation we conducted our research may enable academics and practitioners to understand the antecedents and outcomes in the perceptive of marketing more clearly.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.143-163
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• 2016

The demographics of Internet users are the most basic and important sources for target marketing or personalized advertisements on the digital marketing channels which include email, mobile, and social media. However, it gradually has become difficult to collect the demographics of Internet users because their activities are anonymous in many cases. Although the marketing department is able to get the demographics using online or offline surveys, these approaches are very expensive, long processes, and likely to include false statements. Clickstream data is the recording an Internet user leaves behind while visiting websites. As the user clicks anywhere in the webpage, the activity is logged in semi-structured website log files. Such data allows us to see what pages users visited, how long they stayed there, how often they visited, when they usually visited, which site they prefer, what keywords they used to find the site, whether they purchased any, and so forth. For such a reason, some researchers tried to guess the demographics of Internet users by using their clickstream data. They derived various independent variables likely to be correlated to the demographics. The variables include search keyword, frequency and intensity for time, day and month, variety of websites visited, text information for web pages visited, etc. The demographic attributes to predict are also diverse according to the paper, and cover gender, age, job, location, income, education, marital status, presence of children. A variety of data mining methods, such as LSA, SVM, decision tree, neural network, logistic regression, and k-nearest neighbors, were used for prediction model building. However, this research has not yet identified which data mining method is appropriate to predict each demographic variable. Moreover, it is required to review independent variables studied so far and combine them as needed, and evaluate them for building the best prediction model. The objective of this study is to choose clickstream attributes mostly likely to be correlated to the demographics from the results of previous research, and then to identify which data mining method is fitting to predict each demographic attribute. Among the demographic attributes, this paper focus on predicting gender, age, marital status, residence, and job. And from the results of previous research, 64 clickstream attributes are applied to predict the demographic attributes. The overall process of predictive model building is compose of 4 steps. In the first step, we create user profiles which include 64 clickstream attributes and 5 demographic attributes. The second step performs the dimension reduction of clickstream variables to solve the curse of dimensionality and overfitting problem. We utilize three approaches which are based on decision tree, PCA, and cluster analysis. We build alternative predictive models for each demographic variable in the third step. SVM, neural network, and logistic regression are used for modeling. The last step evaluates the alternative models in view of model accuracy and selects the best model. For the experiments, we used clickstream data which represents 5 demographics and 16,962,705 online activities for 5,000 Internet users. IBM SPSS Modeler 17.0 was used for our prediction process, and the 5-fold cross validation was conducted to enhance the reliability of our experiments. As the experimental results, we can verify that there are a specific data mining method well-suited for each demographic variable. For example, age prediction is best performed when using the decision tree based dimension reduction and neural network whereas the prediction of gender and marital status is the most accurate by applying SVM without dimension reduction. We conclude that the online behaviors of the Internet users, captured from the clickstream data analysis, could be well used to predict their demographics, thereby being utilized to the digital marketing.