• Asia Marketing Journal
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• v.13 no.3
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• pp.25-53
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• 2011

This study probes the effect of loyalty program on the customer retention based on the real transaction data(n=2,892) acquired from education service industry. We try to figure out the outcomes of reward program through more than 1 year-long data gathered and analyzed according to quasi-experimental design(i.e., before and after design). We adopt this kinds of research scheme in regard that previous studies measured the effect of loyalty program by dividing the customers into two group(i.e., members vs. non-members) after the firms or stores had started the program. We believe that it might not avoid the self-selection bias. The research questions of this study could be explained such as: First, most research said that the loyalty programs could increase the customer loyalty and contribute to the sustainable growth of company. But there are little confirmation that this promotional tool could be justified in terms of financial perspective. Thus, we are interested in both the retention rate and financial outcomes caused by the introduction of loyalty programs. Second, reward programs target mainly current customer. Especially CRM(customer relationship management) said that it is more profitable for company to build positive relationship with current customer instead of pursuing new customer. And it claims that reward program is excellent means to achieve this goal. For this purpose, we check in this study whether there is a interaction effect between loyalty program and customer type in retaining customer. Third, it is said that dis-satisfied customers are more likely to leave the company than satisfied customers. While, Bolton, Kannan and Bramlett(2000) claimed that reward program could contribute to minimize the effect of negative service by building emotional link with customer, it is not empirically confirmed. This point of view explained that the loyalty programs might work as exit barrier to current customer. Thus, this study tries to identify whether there is a interaction effect between loyalty program and service experience in keeping customer. To achieve this purpose, this study adopt both Kaplan-Meier survival analysis and Cox proportional hazard model. The research outcomes show that the average retention period is 179 days before introducing loyalty program but it is increased to 227 days after reward is given to the customers. Since this difference is statistically significant, it could be said that H1 is supported. In addition, the contribution margin coming from increased transaction period is bigger than the cost for administering loyalty programs. To address other research questions, we probe the interaction effect between loyalty program and other factors(i.e., customer type and service experience) affecting it. The analysis of Cox proportional hazard model said that the current customer is more likely to engage in building relationship with company compared to new customer. In addition, retention rate of satisfied customer is significantly increased in relation to dis-satisfied customer. Interestingly, the transaction period of dis-satisfied customer is notably increased after introducing loyalty programs. Thus, it could be said that H2, H3, and H4 are also supported. In summary, we found that the loyalty programs have values as a promotional tool in forming positive relationship with customer and building exit barrier.

• Journal of Environmental Impact Assessment
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• v.32 no.6
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• pp.450-462
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• 2023

This study suggested methodology of Citizen Science as a tool of ecosystem conservation and management to achieve Wetland Protected Area (WPA) Conservation Plan and examined whose applicability in 3 WPAs (Jangrok of Gwangju metropolitan city, Madongho of Goseong in South Gyeongsang Province, and Incheongang estuary of Gochang in North Jeolla Province). It consists of a) figuring out main interests and stakeholder or beneficiaries of WPA and their information demand based on conservation, utilization, and management target in the WPA Conservation Plan, b) conducting research activities to gain outcome to address stakeholder's demand, and c) returning the research outcome to citizen scientists and making diffusion to the society. Based on the suggested method and process, citizen scientists conducted ecosystem monitoring (plants including Invasive Alien Plants, terrestrial insects, traces of mammals, discovering unknown wetland). As a result, citizen scientists contributed to collecting species information of 16 plans, 43 species of terrestrial insects, 5 mammals including Lutra lutra (Endangered Species I) and Prionailurus bengalensis (Endangered Species II). The authors constructed and provided distribution map of Invasive Alien Plants, which included information of location and density which citizen scientists registered, for Environment Agencies and local governments who manage 3 WPAs to aid data-based ecosystem policy, In further studies, not only accumulating research data and outcomes acquired from citizen science to suffice the policy demands but also deliberate reviewing policy applicability and social·economic ripple effect should be processed for the suggested Citizen Science in WPA to be settled down as a tool of ecosystem conservation and management.

• Clean Technology
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• v.29 no.4
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• pp.288-296
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• 2023

Coal is abundantly available compared to other energy sources and is used as a versatile energy resource worldwide. To address the environmental issues stemming from conventional coal utilization, efforts are underway to develop clean coal utilization technologies, with IGCC technology being a notable example. In IGCC plants, coal is subjected to a CMD process where both drying and pulverization are achieved by supplying hot air. However, if the temperature of the supplied hot air is excessively high, it can lead to devolatilization and spontaneous combustion, thereby compromising the stable operation of the CMD process. This study aimed to measure the devolatilization and spontaneous combustion temperatures of different types of bituminous coal, and to explore their correlations with the characteristics of the coals. Six coal types exhibited devolatilization between 350 and 400 ℃, while three coal types showed devolatilization at temperatures exceeding 400 ℃. Spontaneous combustion ℃curred in one coal type below 100 ℃, six coal types between 100 and 150 ℃, and two coal types above 150 ℃. The measured initiation temperatures were compared with the coal characteristics including the oxygen, moisture, Fe₂O₃, and CaO content, the H/C ratio, and the O/C ratio to establish correlations. Regression analysis was used to calculate the regression coefficients and determination coefficients for each ignition temperature. It was found that 52.44% of the FC/VM data significantly influenced the volatile matter ignition temperature, and 59.10% of the Fe₂O₃ data significantly affected the spontaneous combustionignition temperature.

• Journal of Science Education
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• v.47 no.3
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• pp.225-237
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• 2023

The purpose of this study is to explore the perceptions of elementary pre-service teachers regarding their interest in science. A survey was conducted among 187 elementary pre-service teachers enrolled at Non-Metropolitan Area A University of Education. Data collection was carried out concurrently with three elementary pre-service teachers who agreed to participate in online interviews. The survey responses provided by the elementary pre-service teachers were analyzed using a qualitative text analysis method. Interest in science was observed to decrease during middle school, followed by the upper grades of elementary school and then the lower grades. The reasons for the decline in interest in science were interpreted as stemming from negative experiences with science education within the context of individual circumstances in the school setting. Strategies to address the decline and enhance interest in science were discussed across individual, family, school, teacher, local community, and national levels, considering both short-term and long-term perspectives. These strategies encompassed various inquiry activities and experiences related to the field of science, engagement in science-related activities, student-centered instruction, teacher professional development, support for elementary students and teachers, and policy measures. The multifaceted approach and efforts aimed to open avenues for positive feedback regarding science on an individual level and foster experiences related to science were interpreted as part of an effort to counteract the decline in interest in science. Lastly, given the current situation of declining interest in science and the need to enhance students' interest, it was implicitly and explicitly discussed that pre-service teachers should focus on improving their expertise in curriculum instruction. This research, by exploring the conceptual characteristics of interest in science, perceptions of changes, and educational needs related to interest in science among elementary pre-service teachers, is expected to have academic significance as foundational research data for the current status of declining interest in science.

• Journal of Korea Water Resources Association
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• v.57 no.3
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• pp.151-164
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• 2024

In water treatment plants supplying potable water, the management of chlorine concentration in water treatment processes involving pre-chlorination or intermediate chlorination requires process control. To address this, research has been conducted on water quality prediction techniques utilizing AI technology. This study developed an AI-based predictive model for automating the process control of chlorine disinfection, targeting the prediction of residual chlorine concentration downstream of sedimentation basins in water treatment processes. The AI-based model, which learns from past water quality observation data to predict future water quality, offers a simpler and more efficient approach compared to complex physicochemical and biological water quality models. The model was tested by predicting the residual chlorine concentration downstream of the sedimentation basins at Plant, using multiple regression models and AI-based models like Random Forest and LSTM, and the results were compared. For optimal prediction of residual chlorine concentration, the input-output structure of the AI model included the residual chlorine concentration upstream of the sedimentation basin, turbidity, pH, water temperature, electrical conductivity, inflow of raw water, alkalinity, NH₃, etc. as independent variables, and the desired residual chlorine concentration of the effluent from the sedimentation basin as the dependent variable. The independent variables were selected from observable data at the water treatment plant, which are influential on the residual chlorine concentration downstream of the sedimentation basin. The analysis showed that, for Plant, the model based on Random Forest had the lowest error compared to multiple regression models, neural network models, model trees, and other Random Forest models. The optimal predicted residual chlorine concentration downstream of the sedimentation basin presented in this study is expected to enable real-time control of chlorine dosing in previous treatment stages, thereby enhancing water treatment efficiency and reducing chemical costs.

• Nuclear Engineering and Technology
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• v.56 no.4
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• pp.1501-1512
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• 2024

Plasma disruption in tokamak experiments is a challenging issue that causes damage to the device. Reliable prediction methods are needed, but the lack of full understanding of plasma disruption limits the effectiveness of physics-driven methods. Data-driven methods based on supervised learning are commonly used, and they rely on labelled training data. However, manual labelling of disruption precursors is a time-consuming and challenging task, as some precursors are difficult to accurately identify. The mainstream labelling methods assume that the precursor onset occurs at a fixed time before disruption, which leads to mislabeled samples and suboptimal prediction performance. In this paper, we present disruption prediction methods based on anomaly detection to address these issues, demonstrating good prediction performance on J-TEXT and EAST. By evaluating precursor onset times using different anomaly detection algorithms, it is found that labelling methods can be improved since the onset times of different shots are not necessarily the same. The study optimizes precursor labelling using the onset times inferred by the anomaly detection predictor and test the optimized labels on supervised learning disruption predictors. The results on J-TEXT and EAST show that the models trained on the optimized labels outperform those trained on fixed onset time labels.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.1-8
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• 2013

Data mining techniques are used to find important and meaningful information from huge databases, and pattern mining is one of the significant data mining techniques. Pattern mining is a method of discovering useful patterns from the huge databases. Frequent pattern mining which is one of the pattern mining extracts patterns having higher frequencies than a minimum support threshold from databases, and the patterns are called frequent patterns. Traditional frequent pattern mining is based on a single minimum support threshold for the whole database to perform mining frequent patterns. This single support model implicitly supposes that all of the items in the database have the same nature. In real world applications, however, each item in databases can have relative characteristics, and thus an appropriate pattern mining technique which reflects the characteristics is required. In the framework of frequent pattern mining, where the natures of items are not considered, it needs to set the single minimum support threshold to a too low value for mining patterns containing rare items. It leads to too many patterns including meaningless items though. In contrast, we cannot mine any pattern if a too high threshold is used. This dilemma is called the rare item problem. To solve this problem, the initial researches proposed approximate approaches which split data into several groups according to item frequencies or group related rare items. However, these methods cannot find all of the frequent patterns including rare frequent patterns due to being based on approximate techniques. Hence, pattern mining model with multiple minimum supports is proposed in order to solve the rare item problem. In the model, each item has a corresponding minimum support threshold, called MIS (Minimum Item Support), and it is calculated based on item frequencies in databases. The multiple minimum supports model finds all of the rare frequent patterns without generating meaningless patterns and losing significant patterns by applying the MIS. Meanwhile, candidate patterns are extracted during a process of mining frequent patterns, and the only single minimum support is compared with frequencies of the candidate patterns in the single minimum support model. Therefore, the characteristics of items consist of the candidate patterns are not reflected. In addition, the rare item problem occurs in the model. In order to address this issue in the multiple minimum supports model, the minimum MIS value among all of the values of items in a candidate pattern is used as a minimum support threshold with respect to the candidate pattern for considering its characteristics. For efficiently mining frequent patterns including rare frequent patterns by adopting the above concept, tree based algorithms of the multiple minimum supports model sort items in a tree according to MIS descending order in contrast to those of the single minimum support model, where the items are ordered in frequency descending order. In this paper, we study the characteristics of the frequent pattern mining based on multiple minimum supports and conduct performance evaluation with a general frequent pattern mining algorithm in terms of runtime, memory usage, and scalability. Experimental results show that the multiple minimum supports based algorithm outperforms the single minimum support based one and demands more memory usage for MIS information. Moreover, the compared algorithms have a good scalability in the results.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.27-42
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• 2020

Traditional companies with offline stores were unable to secure large display space due to the problems of cost. This limitation inevitably allowed limited kinds of products to be displayed on the shelves, which resulted in consumers being deprived of the opportunity to experience various items. Taking advantage of the virtual space called the Internet, online shopping goes beyond the limits of limitations in physical space of offline shopping and is now able to display numerous products on web pages that can satisfy consumers with a variety of needs. Paradoxically, however, this can also cause consumers to experience the difficulty of comparing and evaluating too many alternatives in their purchase decision-making process. As an effort to address this side effect, various kinds of consumer's purchase decision support systems have been studied, such as keyword-based item search service and recommender systems. These systems can reduce search time for items, prevent consumer from leaving while browsing, and contribute to the seller's increased sales. Among those systems, recommender systems based on association rule mining techniques can effectively detect interrelated products from transaction data such as orders. The association between products obtained by statistical analysis provides clues to predicting how interested consumers will be in another product. However, since its algorithm is based on the number of transactions, products not sold enough so far in the early days of launch may not be included in the list of recommendations even though they are highly likely to be sold. Such missing items may not have sufficient opportunities to be exposed to consumers to record sufficient sales, and then fall into a vicious cycle of a vicious cycle of declining sales and omission in the recommendation list. This situation is an inevitable outcome in situations in which recommendations are made based on past transaction histories, rather than on determining potential future sales possibilities. This study started with the idea that reflecting the means by which this potential possibility can be identified indirectly would help to select highly recommended products. In the light of the fact that the attributes of a product affect the consumer's purchasing decisions, this study was conducted to reflect them in the recommender systems. In other words, consumers who visit a product page have shown interest in the attributes of the product and would be also interested in other products with the same attributes. On such assumption, based on these attributes, the recommender system can select recommended products that can show a higher acceptance rate. Given that a category is one of the main attributes of a product, it can be a good indicator of not only direct associations between two items but also potential associations that have yet to be revealed. Based on this idea, the study devised a recommender system that reflects not only associations between products but also categories. Through regression analysis, two kinds of associations were combined to form a model that could predict the hit rate of recommendation. To evaluate the performance of the proposed model, another regression model was also developed based only on associations between products. Comparative experiments were designed to be similar to the environment in which products are actually recommended in online shopping malls. First, the association rules for all possible combinations of antecedent and consequent items were generated from the order data. Then, hit rates for each of the associated rules were predicted from the support and confidence that are calculated by each of the models. The comparative experiments using order data collected from an online shopping mall show that the recommendation accuracy can be improved by further reflecting not only the association between products but also categories in the recommendation of related products. The proposed model showed a 2 to 3 percent improvement in hit rates compared to the existing model. From a practical point of view, it is expected to have a positive effect on improving consumers' purchasing satisfaction and increasing sellers' sales.

• Journal of the Korean Geographical Society
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• v.30 no.1
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• pp.16-34
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• 1995

This study aims to analyze the impact of cement industry on region change. For this study Maepo-Eub was selected as study area, where three cement factories are located. The data for analysis were obtained from interviews, questionaire surveys and the employee list of each cement factory. The analytic procedures for this study are as follows: 1) The change of regional employment was analyzed by development was industry in terms of the permanent address, education level, occupational status of the employee. 2) The degree of population growth are analyzed by developmental staae of the industry. Some conclusions from this study follows: 1) As these cement factories were built at Maepo in the 1960's, there were plenty of employment opportunities. Thus many technicians and workers flooded in Maepo-Eub. in the 1970's with the expansion of production facilities therewere much more immigrants to the industrial region, while there were outflow in the neighboring rural areas. In the 1980's the opportunity for the employment of cement factories have been decreased due to the introduction of the automation processes and larger, sized machines. Among the employee of three cement factories the native of Chungcheongbukdo (65%; in them Danyang 52%, Jecheon 32%) is dominant, the second is from Kangwon-do (13%), and the third is from Kyungsangbuk-do (11%) adjacent to Chungcheongbuk-do. It means that there are more employment opportunity in the near places of cement factories. 2) In the period of 1960's study area had experineed rapid social increase in population by the development of cement industry. That is, cement industries created new job opportunities and attracted large population concentration into this area. In the period of 1970's the population of the industrial region have increased continuously, while the population of neighboring rural areas have decreased. In the period of 1980's the population of Maepo decreased steadily because of decrease of labour forces through automation and commuting. Thus in the early stage of idustrial development large population concentrated in the neighboring villages of cement factories, and formed residential areas, commercial areas and service areas. As agricultural and was encroached, rural people left their regions to live in the more convenient suburbs. 3) People engaged in cement industry think that cement industry has a favorable influence on regional development, such as creating job opportunity, raising income level, developing business and service sector, and leading high economic growth. While farmers and some people think that cement industries as a pollution causing factories have a harmful influence on regional development, sucha as injuring the crops, causing environmental pollution, and being harmful to health. If pollution problems are solved, I think Maepo will play an important role as a regional center which can offer employment opportunity, business and service function to pheripheral rural areas, and raise a income level.

• The KIPS Transactions:PartC
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• v.10C no.5
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• pp.625-634
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• 2003

More than a decade after its initial proposal, deployment of IP Multicast has been limited due to the problem of traffic control in multicast routing, multicast address allocation in global internet, reliable multicast transport techniques etc. Lately, according to increase of multicast application service such as internet broadcast, real time security information service etc., overlay multicast is developed as a new internet multicast technology. In this paper, we describe an overlay multicast protocol and propose fast join mechanism that considers switching of the tree. To find a potential parent, an existing search algorithm descends the tree from the root by one level at a time, and it causes long joining latency. Also, it is try to select the nearest node as a potential parent. However, it can't select the nearest node by the degree limit of the node. As a result, the generated tree has low efficiency. To reduce long joining latency and improve the efficiency of the tree, we propose searching two levels of the tree at a time. This method forwards joining request message to own children node. So, at ordinary times, there is no overhead to keep the tree. But the joining request came, the increasing number of searching messages will reduce a long joining latency. Also searching more nodes will be helpful to construct more efficient trees. In order to evaluate the performance of our fast join mechanism, we measure the metrics such as the search latency and the number of searched node and the number of switching by the number of members and degree limit. The simulation results show that the performance of our mechanism is superior to that of the existing mechanism.