• Journal of Korean Society of Transportation
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• v.24 no.2 s.88
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• pp.91-100
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• 2006

This paper examines the situation and tasks of UK rail privatization, especially focusing on after the Hatfield rail accident. Earlier research which focused on the UK's Privatization had little knowledge of the explanations for recent changes. Moreover they had difficulty making a direct comparison between national rail and the privatized rail. Therefore we aye left without a good explanation which has a comprehensive perspective. I attempt to show the change in the rail privatization Process and its outcome, focusing on after the Hatfield rail accident. This Paper argues that the UK's vail privatization process has a regulatory framework which is too complicated with overlapping responsibilities that brought about inefficiency, increasing costs and a superficial safety regime. Especially the planning of rail and infrastructure maintenance did not come to play an appropriate role. However after 2000, the government took charge of setting the strategy for railways, and the Office of Rail Regulation covered safety performance and cost. explain that these changes present a good opportunity to solve the problem of passing the buck for poor performance. Through the analysis, I find that the passenger rail network is well-suited to deliver long distance business and commuters and that the subsidy from the government is decreasing. However, performance, for example punctuality and reliability. should be improved. Especially the Hatfield rail accident caused a reduction in the satisfaction of passengers. In future. the problems of rising costs and monopoly franchise system should be addressed.

• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.1
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• pp.1-18
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• 2021

This study was intended to present measures for the improvement of the apartment house landscaping supervision system by examining the adequacy of landscaping supervision, which is aimed at improving the quality of landscape plants and facilities in apartment house landscaping sites. Additionally, this study aims to identify the problems occurring in the process of the performance of landscaping supervision and to provide the evidence for legislative activities and revision of the laws currently being pushed forward for the mandatory deployment of apartment house landscaping supervision personnel. The results of the analysis showed that no landscaping supervision personnel was deployed to apartment complexes with less than 1,500 households and that the landscaping comprised 19% to 46% of the entire construction process. The civil engineering firm performed the landscaping supervision, which made it impracticable to fully focus on the construction quality in the field of landscaping. The quality control in terms of landscape plants revealed differences in quality control, depending on the competence and experience of the civil engineer supervising the personnel, where the landscaping supervision personnel was not deployed. The apartment houses landscaping supervision activity index was analyzed, and the results showed that the supervision activity index for apartment house A was 72.0, B was 70.4, and apartment houses C to G ranged from 38.7 to 46.9, which suggested that the difference in quality control, process control, and technical support affected the construction quality and occurrence of defects.The improvement of landscaping process quality control and process management will be carried out more smoothly and the rate of defects will be drastically reduced if the landscaping supervision personnel placement threshold is lowered from 1,500 households to 300 households in complexes. The results of this study are expected to be useful in promoting and re-establishing the landscaping industry based on the improvement of construction quality in the field of landscaping in connection with the construction of apartment houses.

• Asia pacific journal of information systems
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• v.22 no.1
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• pp.29-52
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• 2012

Consumers differ in the way they make a purchase. An audio mania would willingly make a bold, yet serious, decision to buy a top-of-the-line home theater system, while he is not interested in replacing his two-decade-old shabby car. On the contrary, an automobile enthusiast wouldn't mind spending forty thousand dollars to buy a new Jaguar convertible, yet cares little about his junky component system. It is product involvement that helps us explain such differences among individuals in the purchase style. Product involvement refers to the extent to which a product is perceived to be important to a consumer (Zaichkowsky, 2001). Product involvement is an important factor that strongly influences consumer's purchase decision-making process, and thus has been of prime interest to consumer behavior researchers. Furthermore, researchers found that involvement is closely related to perceived risk (Dholakia, 2001). While abundant research exists addressing how product involvement relates to overall perceived risk, little attention has been paid to the relationship between involvement and different types of perceived risk in an electronic commerce setting. Given that perceived risk can be a substantial barrier to the online purchase (Jarvenpaa, 2000), research addressing such an issue will offer useful implications on what specific types of perceived risk an online firm should focus on mitigating if it is to increase sales to a fullest potential. Meanwhile, past research has focused on such consumer responses as information search and dissemination as a consequence of involvement, neglecting other behavioral responses like online merchant selection. For one example, will a consumer seriously considering the purchase of a pricey Guzzi bag perceive a great degree of risk associated with online buying and therefore choose to buy it from a digital storefront rather than from an online marketplace to mitigate risk? Will a consumer require greater trust on the part of the online merchant when the perceived risk of online buying is rather high? We intend to find answers to these research questions through an empirical study. This paper explores the impact of enduring product involvement and perceived risks on required trust level, and further on online merchant choice. For the purpose of the research, five types or components of perceived risk are taken into consideration, including financial, performance, delivery, psychological, and social risks. A research model has been built around the constructs under consideration, and 12 hypotheses have been developed based on the research model to examine the relationships between enduring involvement and five components of perceived risk, between five components of perceived risk and required trust level, between enduring involvement and required trust level, and finally between required trust level and preference toward an e-tailer. To attain our research objectives, we conducted an empirical analysis consisting of two phases of data collection: a pilot test and main survey. The pilot test was conducted using 25 college students to ensure that the questionnaire items are clear and straightforward. Then the main survey was conducted using 295 college students at a major university for nine days between December 13, 2010 and December 21, 2010. The measures employed to test the model included eight constructs: (1) enduring involvement, (2) financial risk, (3) performance risk, (4) delivery risk, (5) psychological risk, (6) social risk, (7) required trust level, (8) preference toward an e-tailer. The statistical package, SPSS 17.0, was used to test the internal consistency among the items within the individual measures. Based on the Cronbach's ${\alpha}$ coefficients of the individual measure, the reliability of all the variables is supported. Meanwhile, the Amos 18.0 package was employed to perform a confirmatory factor analysis designed to assess the unidimensionality of the measures. The goodness of fit for the measurement model was satisfied. Unidimensionality was tested using convergent, discriminant, and nomological validity. The statistical evidences proved that the three types of validity were all satisfied. Now the structured equation modeling technique was used to analyze the individual paths along the relationships among the research constructs. The results indicated that enduring involvement has significant positive relationships with all the five components of perceived risk, while only performance risk is significantly related to trust level required by consumers for purchase. It can be inferred from the findings that product performance problems are mostly likely to occur when a merchant behaves in an opportunistic manner. Positive relationships were also found between involvement and required trust level and between required trust level and online merchant choice. Enduring involvement is concerned with the pleasure a consumer derives from a product class and/or with the desire for knowledge for the product class, and thus is likely to motivate the consumer to look for ways of mitigating perceived risk by requiring a higher level of trust on the part of the online merchant. Likewise, a consumer requiring a high level of trust on the merchant will choose a digital storefront rather than an e-marketplace, since a digital storefront is believed to be trustworthier than an e-marketplace, as it fulfills orders by itself rather than acting as an intermediary. The findings of the present research provide both academic and practical implications. The first academic implication is that enduring product involvement is a strong motivator of consumer responses, especially the selection of a merchant, in the context of electronic shopping. Secondly, academicians are advised to pay attention to the finding that an individual component or type of perceived risk can be used as an important research construct, since it would allow one to pinpoint the specific types of risk that are influenced by antecedents or that influence consequents. Meanwhile, our research provides implications useful for online merchants (both online storefronts and e-marketplaces). Merchants may develop strategies to attract consumers by managing perceived performance risk involved in purchase decisions, since it was found to have significant positive relationship with the level of trust required by a consumer on the part of the merchant. One way to manage performance risk would be to thoroughly examine the product before shipping to ensure that it has no deficiencies or flaws. Secondly, digital storefronts are advised to focus on symbolic goods (e.g., cars, cell phones, fashion outfits, and handbags) in which consumers are relatively more involved than others, whereas e- marketplaces should put their emphasis on non-symbolic goods (e.g., drinks, books, MP3 players, and bike accessories).

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.71-84
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• 2013

Log data, which record the multitude of information created when operating computer systems, are utilized in many processes, from carrying out computer system inspection and process optimization to providing customized user optimization. In this paper, we propose a MongoDB-based unstructured log processing system in a cloud environment for processing the massive amount of log data of banks. Most of the log data generated during banking operations come from handling a client's business. Therefore, in order to gather, store, categorize, and analyze the log data generated while processing the client's business, a separate log data processing system needs to be established. However, the realization of flexible storage expansion functions for processing a massive amount of unstructured log data and executing a considerable number of functions to categorize and analyze the stored unstructured log data is difficult in existing computer environments. Thus, in this study, we use cloud computing technology to realize a cloud-based log data processing system for processing unstructured log data that are difficult to process using the existing computing infrastructure's analysis tools and management system. The proposed system uses the IaaS (Infrastructure as a Service) cloud environment to provide a flexible expansion of computing resources and includes the ability to flexibly expand resources such as storage space and memory under conditions such as extended storage or rapid increase in log data. Moreover, to overcome the processing limits of the existing analysis tool when a real-time analysis of the aggregated unstructured log data is required, the proposed system includes a Hadoop-based analysis module for quick and reliable parallel-distributed processing of the massive amount of log data. Furthermore, because the HDFS (Hadoop Distributed File System) stores data by generating copies of the block units of the aggregated log data, the proposed system offers automatic restore functions for the system to continually operate after it recovers from a malfunction. Finally, by establishing a distributed database using the NoSQL-based Mongo DB, the proposed system provides methods of effectively processing unstructured log data. Relational databases such as the MySQL databases have complex schemas that are inappropriate for processing unstructured log data. Further, strict schemas like those of relational databases cannot expand nodes in the case wherein the stored data are distributed to various nodes when the amount of data rapidly increases. NoSQL does not provide the complex computations that relational databases may provide but can easily expand the database through node dispersion when the amount of data increases rapidly; it is a non-relational database with an appropriate structure for processing unstructured data. The data models of the NoSQL are usually classified as Key-Value, column-oriented, and document-oriented types. Of these, the representative document-oriented data model, MongoDB, which has a free schema structure, is used in the proposed system. MongoDB is introduced to the proposed system because it makes it easy to process unstructured log data through a flexible schema structure, facilitates flexible node expansion when the amount of data is rapidly increasing, and provides an Auto-Sharding function that automatically expands storage. The proposed system is composed of a log collector module, a log graph generator module, a MongoDB module, a Hadoop-based analysis module, and a MySQL module. When the log data generated over the entire client business process of each bank are sent to the cloud server, the log collector module collects and classifies data according to the type of log data and distributes it to the MongoDB module and the MySQL module. The log graph generator module generates the results of the log analysis of the MongoDB module, Hadoop-based analysis module, and the MySQL module per analysis time and type of the aggregated log data, and provides them to the user through a web interface. Log data that require a real-time log data analysis are stored in the MySQL module and provided real-time by the log graph generator module. The aggregated log data per unit time are stored in the MongoDB module and plotted in a graph according to the user's various analysis conditions. The aggregated log data in the MongoDB module are parallel-distributed and processed by the Hadoop-based analysis module. A comparative evaluation is carried out against a log data processing system that uses only MySQL for inserting log data and estimating query performance; this evaluation proves the proposed system's superiority. Moreover, an optimal chunk size is confirmed through the log data insert performance evaluation of MongoDB for various chunk sizes.

• 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 Intelligence and Information Systems
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• v.26 no.2
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• pp.57-78
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• 2020

With the development of the Internet, consumers have had an opportunity to check product information easily through E-Commerce. Product reviews used in the process of purchasing goods are based on user experience, allowing consumers to engage as producers of information as well as refer to information. This can be a way to increase the efficiency of purchasing decisions from the perspective of consumers, and from the seller's point of view, it can help develop products and strengthen their competitiveness. However, it takes a lot of time and effort to understand the overall assessment and assessment dimensions of the products that I think are important in reading the vast amount of product reviews offered by E-Commerce for the products consumers want to compare. This is because product reviews are unstructured information and it is difficult to read sentiment of reviews and assessment dimension immediately. For example, consumers who want to purchase a laptop would like to check the assessment of comparative products at each dimension, such as performance, weight, delivery, speed, and design. Therefore, in this paper, we would like to propose a method to automatically generate multi-dimensional product assessment scores in product reviews that we would like to compare. The methods presented in this study consist largely of two phases. One is the pre-preparation phase and the second is the individual product scoring phase. In the pre-preparation phase, a dimensioned classification model and a sentiment analysis model are created based on a review of the large category product group review. By combining word embedding and association analysis, the dimensioned classification model complements the limitation that word embedding methods for finding relevance between dimensions and words in existing studies see only the distance of words in sentences. Sentiment analysis models generate CNN models by organizing learning data tagged with positives and negatives on a phrase unit for accurate polarity detection. Through this, the individual product scoring phase applies the models pre-prepared for the phrase unit review. Multi-dimensional assessment scores can be obtained by aggregating them by assessment dimension according to the proportion of reviews organized like this, which are grouped among those that are judged to describe a specific dimension for each phrase. In the experiment of this paper, approximately 260,000 reviews of the large category product group are collected to form a dimensioned classification model and a sentiment analysis model. In addition, reviews of the laptops of S and L companies selling at E-Commerce are collected and used as experimental data, respectively. The dimensioned classification model classified individual product reviews broken down into phrases into six assessment dimensions and combined the existing word embedding method with an association analysis indicating frequency between words and dimensions. As a result of combining word embedding and association analysis, the accuracy of the model increased by 13.7%. The sentiment analysis models could be seen to closely analyze the assessment when they were taught in a phrase unit rather than in sentences. As a result, it was confirmed that the accuracy was 29.4% higher than the sentence-based model. Through this study, both sellers and consumers can expect efficient decision making in purchasing and product development, given that they can make multi-dimensional comparisons of products. In addition, text reviews, which are unstructured data, were transformed into objective values such as frequency and morpheme, and they were analysed together using word embedding and association analysis to improve the objectivity aspects of more precise multi-dimensional analysis and research. This will be an attractive analysis model in terms of not only enabling more effective service deployment during the evolving E-Commerce market and fierce competition, but also satisfying both customers.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.159-172
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• 2010

The recommender system is one of the possible solutions to assist customers in finding the items they would like to purchase. To date, a variety of recommendation techniques have been developed. One of the most successful recommendation techniques is Collaborative Filtering (CF) that has been used in a number of different applications such as recommending Web pages, movies, music, articles and products. CF identifies customers whose tastes are similar to those of a given customer, and recommends items those customers have liked in the past. Numerous CF algorithms have been developed to increase the performance of recommender systems. Broadly, there are memory-based CF algorithms, model-based CF algorithms, and hybrid CF algorithms which combine CF with content-based techniques or other recommender systems. While many researchers have focused their efforts in improving CF performance, the theoretical justification of CF algorithms is lacking. That is, we do not know many things about how CF is done. Furthermore, the relative performances of CF algorithms are known to be domain and data dependent. It is very time-consuming and expensive to implement and launce a CF recommender system, and also the system unsuited for the given domain provides customers with poor quality recommendations that make them easily annoyed. Therefore, predicting the performances of CF algorithms in advance is practically important and needed. In this study, we propose an efficient approach to predict the performance of CF. Social Network Analysis (SNA) and Artificial Neural Network (ANN) are applied to develop our prediction model. CF can be modeled as a social network in which customers are nodes and purchase relationships between customers are links. SNA facilitates an exploration of the topological properties of the network structure that are implicit in data for CF recommendations. An ANN model is developed through an analysis of network topology, such as network density, inclusiveness, clustering coefficient, network centralization, and Krackhardt's efficiency. While network density, expressed as a proportion of the maximum possible number of links, captures the density of the whole network, the clustering coefficient captures the degree to which the overall network contains localized pockets of dense connectivity. Inclusiveness refers to the number of nodes which are included within the various connected parts of the social network. Centralization reflects the extent to which connections are concentrated in a small number of nodes rather than distributed equally among all nodes. Krackhardt's efficiency characterizes how dense the social network is beyond that barely needed to keep the social group even indirectly connected to one another. We use these social network measures as input variables of the ANN model. As an output variable, we use the recommendation accuracy measured by F1-measure. In order to evaluate the effectiveness of the ANN model, sales transaction data from H department store, one of the well-known department stores in Korea, was used. Total 396 experimental samples were gathered, and we used 40%, 40%, and 20% of them, for training, test, and validation, respectively. The 5-fold cross validation was also conducted to enhance the reliability of our experiments. The input variable measuring process consists of following three steps; analysis of customer similarities, construction of a social network, and analysis of social network patterns. We used Net Miner 3 and UCINET 6.0 for SNA, and Clementine 11.1 for ANN modeling. The experiments reported that the ANN model has 92.61% estimated accuracy and 0.0049 RMSE. Thus, we can know that our prediction model helps decide whether CF is useful for a given application with certain data characteristics.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.8 no.3
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• pp.125-146
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• 2013

Nowadays, the performance of the mobile game sales is influencing the ranking of game companies listed on KOSDAQ. In the meantime, venture capital companies had focused on online game. Recently, however, they have great interest in mobile games and mobile game companies. In addition, angel investors and accelerators are increasing investment for the mobile game companies. The most important issues for mobile game investor is how to evaluate the mobile game companies and their contents. Therefore, this study derived the evaluation factors for the mobile game company. And research method converged of the opinions of both supply side and demand side of the game industry. Ten professionals who are responsible for the supply of the game industry and CEO group & development experts of game development company were selected for survey in this study. Also ten professionals who are responsible for the demand of the game industry and the investment company were selected for survey in this study. And Delphi technique was performed according to the survey. Management skills, development capabilities, game play, feasibility, operational capabilities has emerged as five evaluation factors to evaluate the mobile game company. And the 20 sub-factors including CEO's reliability were derived. AHP(Analytic Hierarchy Process) theory is applied to analyze the importance of the qualitative elements which were derived by Delphi technique. As a result, the analysis hierarchy of evaluation factors for the mobile game company was created. Pair-wise comparison for each element was performed to analyze the importance. As a result, 'Core fun of the game' (12,2%), 'Involvement of the game' (10.3%), 'Security Reliability' (8.9%), 'Core developers' ability' (7.6%) appeared in order of importance. The significance of this study is offering more objective methodology for realistic assessment and importance of elements to evaluate mobile game company.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.12 no.1
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• pp.1-13
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• 2017

The purpose of this study is to find out which factors determine the success of start-up in the initial market and what are the most important determinants. For the empirical analysis, the questionnaire related to the analysis of success factors for start-up success was designed according to the quantitative analysis (AHP technique). First, we selected 8 representative success factors for successful start-up in the initial market. In order to determine the degree of priority among these factors, we surveyed 12 entrepreneurs who are interested in entrepreneurship, universities, research institutes, and public officials. As a result of the empirical analysis, 51% of the funds in the tier 1 were ranked as the top priority to determine success factors. Followed by research and development (32.5%), management (8.7%) and marketing (7.8%). In particular, when each of the four items is calculated as 100 according to the result of the tier 1, and the tier 2 is converted, the foreign investment is analyzed as 43.7%. It was followed by 15.14% of R & D facilities, 14.07% of ideas, 8.7% of managerial ability, 7.29% of domestic investment, 5.85% of buyer feedback, 3.3% of development strategy and 1.95% of marketing strategy. Among the eight success factors, overseas investment items showed the closest preference to half, and it was the most important variable that determines the success or failure of market entry. The implication of this study is that many start-ups in Korea expect to receive investment and support from overseas accelerators. This means that overseas investment itself has been recognized as a start-up that makes services and products that can be used in the global market. A high preference for attracting foreign investment is due to the fact that the amount of investment is larger than that of Korea and that it can flexibly cope with the pressure on the performance compared to domestic investors. In this study, it was meaningful that we could confirm this fact through questionnaires of start-up experts. In future research, we need to find a viable alternative through studying how to provide start-up to foreign direct investment at the national level.

• Information Systems Review
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• v.14 no.1
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• pp.85-103
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• 2012

ISMP is a method of writing clearly the user requirements in the RFP(Request for Proposal) of the IS development projects. Unlike the conventional methods of RFP preparation that describe the user requirements of target systems in a rather superficial manner, ISMP systematically identifies the businesses needs and the status of information technology, analyzes in detail the user requirements, and defines in detail the specific functions of the target systems. By increasing the clarity of RFP, the scale and complexity of related businesses can be calculated accurately, many responding companies can prepare proposals clearly, and the level of fairness during the evaluation of many proposals can be improved, as well. Above all though, the problems that are posed as chronic challenges in this field, i.e., the misunderstanding and conflicts between the users and developers, excessive burden on developers, etc. can be resolved. This study is a case study that analyzes the execution process, execution accomplishment, problems, and the success factors of two pilot projects that introduced ISMP for the first time. ISMP performance procedures of actual site were verified, and how the user needs in the request for quote are described was examined. The satisfaction levels of ISMP RFP for quote were found to be high as compared to the conventional RFP. Although occurred were some problems such as RFP preparation difficulties, increased workload, etc. due to the lack of understanding and execution experience of ISMP, in overall, also occurred were some positive effects such as the establishment of the scope of target systems, improved information sharing and cooperation between the users and the developers, seamless communication between issuing customer corporations and IT service companies, reduction of changes in user requirements, etc. As a result of conducting action research type in-depth interviews on the persons in charge of actual work, factors were derived as ISMP success factors: prior consensus on the need for ISMP, the acquisition of execution resources resulting from the support of CEO and CIO, and the selection of specification level of the user requirements. The results of this study will provide useful site information to the corporations that are considering adopting ISMP and IT service firms, and present meaningful suggestions on the future study directions to researchers in the field of IT service competitive advantages.