• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.157-175
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• 2012

It is difficult to evaluate the performance of process innovation of e-procurement which has large scale and complex processes. The existing evaluation methods for measuring the effects of process innovation have been mainly done with statistically quantitative methods by analyzing operational data or with qualitative methods by conducting surveys and interviews. However, these methods have some limitations to evaluate the effects because the performance evaluation of e-procurement process innovation should consider the interactions among participants who are active either directly or indirectly through the processes. This study considers the e-procurement process as a complex system and develops a simulation model based on MAS(Multi-Agent System) to evaluate the effects of e-procurement process innovation. Multi-agent based simulation allows observing interaction patterns of objects in virtual world through relationship among objects and their behavioral mechanism. Agent-based simulation is suitable especially for complex business problems. In this study, we used Netlogo Version 4.1.3 as a MAS simulation tool which was developed in Northwestern University. To do this, we developed a interaction model of agents in MAS environment. We defined process agents and task agents, and assigned their behavioral characteristics. The developed simulation model was applied to G2B system (KONEPS: Korea ON-line E-Procurement System) of Public Procurement Service (PPS) in Korea and used to evaluate the innovation effects of the G2B system. KONEPS is a successfully established e-procurement system started in the year 2002. KONEPS is a representative e-Procurement system which integrates characteristics of e-commerce into government for business procurement activities. KONEPS deserves the international recognition considering the annual transaction volume of 56 billion dollars, daily exchanges of electronic documents, users consisted of 121,000 suppliers and 37,000 public organizations, and the 4.5 billion dollars of cost saving. For the simulation, we analyzed the e-procurement of process of KONEPS into eight sub processes such as 'process 1: search products and acquisition of proposal', 'process 2 : review the methods of contracts and item features', 'process 3 : a notice of bid', 'process 4 : registration and confirmation of qualification', 'process 5 : bidding', 'process 6 : a screening test', 'process 7 : contracts', and 'process 8 : invoice and payment'. For the parameter settings of the agents behavior, we collected some data from the transactional database of PPS and some information by conducting a survey. The used data for the simulation are 'participants (government organizations, local government organizations and public institutions)', 'the number of bidding per year', 'the number of total contracts', 'the number of shopping mall transactions', 'the rate of contracts between bidding and shopping mall', 'the successful bidding ratio', and the estimated time for each process. The comparison was done for the difference of time consumption between 'before the innovation (As-was)' and 'after the innovation (As-is).' The results showed that there were productivity improvements in every eight sub processes. The decrease ratio of 'average number of task processing' was 92.7% and the decrease ratio of 'average time of task processing' was 95.4% in entire processes when we use G2B system comparing to the conventional method. Also, this study found that the process innovation effect will be enhanced if the task process related to the 'contract' can be improved. This study shows the usability and possibility of using MAS in process innovation evaluation and its modeling.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.311-328
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• 2022

Because of the spread of smartphones due to the development of information and communication technology, online shopping mall services can be used on computers and mobile devices. As a result, the number of users using the online shopping mall service increases rapidly, and the types of products traded are also growing. Therefore, to maximize profits, companies need to provide information that may interest users. To this end, the recommendation system presents necessary information or products to the user based on the user's past behavioral data or behavioral purchase records. Representative overseas companies that currently provide recommendation services include Netflix, Amazon, and YouTube. These companies support users' purchase decisions by recommending products to users using ratings, purchase records, and clickstream data that users give to the items. In addition, users refer to the ratings left by other users about the product before buying a product. Most users tend to provide ratings only to products they are satisfied with, and the higher the rating, the higher the purchase intention. And recently, e-commerce sites have provided users with the ability to vote on whether product reviews are helpful. Through this, the user makes a purchase decision by referring to reviews and ratings of products judged to be beneficial. Therefore, in this study, the correlation between the product rating and the helpful information of the review is identified. The valuable data of the evaluation is reflected in the recommendation system to check the recommendation performance. In addition, we want to compare the results of skipping all the ratings in the traditional collaborative filtering technique with the recommended performance results that reflect only the 4 and 5 ratings. For this purpose, electronic product data collected from Amazon was used in this study, and the experimental results confirmed a correlation between ratings and review usefulness information. In addition, as a result of comparing the recommendation performance by reflecting all the ratings and only the 4 and 5 points in the recommendation system, the recommendation performance of remembering only the 4 and 5 points in the recommendation system was higher. In addition, as a result of reflecting review usefulness information in the recommendation system, it was confirmed that the more valuable the review, the higher the recommendation performance. Therefore, these experimental results are expected to improve the performance of personalized recommendation services in the future and provide implications for e-commerce sites.

• Journal of Global Scholars of Marketing Science
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• v.17 no.2
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• pp.1-29
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• 2007

The flow theory becomes one of the most important frameworks in the internet research arena. Hoffman and Novak proposed a hierarchical flow model showing the antecedents and outcomes of flow and the relationship among these variables in the hyper-media computer circumstances (Hoffman and Novak 1996). This model was further tested after their initial research (Novak, Hoffman, and Yung 2000). At their paper, Hoffman and Novak explained that the balance of challenge and skill leads to flow which means the positive optimal state of mind (Hoffman and Novak 1996). An imbalance between challenge and skill, leads to negative states of mind like anxiety, boredom, apathy (Csikszentmihalyi and Csikszentmihalyi 1988). Almost all research on the flow 4-channel model have been focusingon flow, the positive state of mind (Ellis, Voelkl, and Morris 1994 Mathwick and Rigdon 2004). However, it also needs to examine the formation of the negative states of minds and their outcomes. Flow researchers explain play or playfulness as antecedents or the early state of flow. However, play has been regarded as a distinct concept from flow in the flow literatures (Hoffman and Novak 1996; Novak, Hoffman, and Yung 2000). Mathwick and Rigdon discovered the influences of challenge and skill on play; they also observed the influence of play on web-loyalty and brand loyalty (Mathwick and Rigdon 2004). Unfortunately, they did not go so far as to test the influences of play on state of mind. This study focuses on the relationships between state of mind in the flow 4-channel model and play. Early research has attempted to hypothetically explain state of mind in flow theory, but has not been tested except flow until now. Also the importance of play has been emphasized in the flow theory, but has not been tested in the flow 4-channel model context. This researcher attempts to analyze the relationships among state of mind, skill of play, challenge, state of mind and web loyalty. For this objective, I developed a measure for state of mind and defined the concept of play as a trait. Then, the influences of challenge and skill on the state of mind and play under on-line shopping conditions were tested. Also the influences of play on state of mind were tested and those of flow and play on web loyalty were highlighted. 294 undergraduate students participated in this research survey. They were asked to respond about their perceptions of challenge, skill, state of mind, play, and web-loyalty to on-line shopping mall. Respondents were restricted to students who bought products on-line in a month. In case of buying products at two or more on-line shopping malls, they asked to respond about the shopping mall where they bought the most important one. Construct validity, discriminant validity, and convergent validity were used to check the measurement validations. Also, Cronbach's alpha was used to check scale reliability. A series of exploratory factor analyses was conducted. This researcher conducted confirmatory factor analyses to assess the validity of measurements. All items loaded significantly on their respective constructs. Also, all reliabilities were greater than.70. Chi-square difference tests and goodness of fit tests supported discriminant and convergent validity. The results of clustering and ANOVA showed that high challenge and high skill leaded to flow, low challenge and high skill leaded to boredom, and low challenge and low skill leaded to apathy. But, it was different from my expectation that high challenge and low skill didnot lead to anxiety but leaded to apathy. The results also showed that high challenge and high skill, and high challenge and low skill leaded to the highest play. Low challenge leaded to low play. 4 Structural Equation Models were built by flow, anxiety, boredom, apathy for analyzing not only the impact of play on state of mind and web-loyalty, but also that of state of mind on web-loyalty. According the analyses results of these models, play impacted flow and web-loyalty positively, but impacted anxiety, boredom, and apathy negatively. Results also showed that flow impacted web-loyalty positively, but anxiety, boredom, and apathy impacted web-loyalty negatively. The interpretations and implications of the test results of the hypotheses are as follows. First, respondents belonging to different clusters based on challenge and skill level experienced different states of mind such as flow, anxiety, boredom, apathy. The low challenge and low skill group felt the highest anxiety and apathy. It could be interpreted that this group feeling high anxiety or fear, then avoided attempts to shop on-line. Second, it was found that higher challenge leads to higher levels of play. Test results show that the play level of the high challenge and low skill group (anxiety group) was higher than that of the high challenge and high skill group (flow group). However, this was not significant. Third, play positively impacted flow and negatively impacted boredom. The negative impacts on anxiety and apathy were not significant. This means that the combination of challenge and skill creates different results. Forth, play and flow positively impacted web-loyalty, but anxiety, boredom, apathy had negative impacts. The effect of play on web-loyalty was stronger in case of anxiety, boredom, apathy group than fl ow group. These results show that challenge and skill influences state of mind and play. Results also demonstrate how play and flow influence web-loyalty. It implies that state of mind and play should be the core marketing variables in internet marketing. The flow theory has been focusing on flow and on the positive outcomes of flow experiences. But, this research shows that lots of consumers experience the negative state of mind rather than flow state in the internet shopping circumstance. Results show that the negative state of mind leads to low or negative web-loyalty. Play can have an important role with the web-loyalty when consumers have the negative state of mind. Results of structural equation model analyses show that play influences web-loyalty positively, even though consumers may be in the negative state of mind. This research found the impacts of challenge and skill on state of mind in the flow 4-channel model, not only flow but also anxiety, boredom, apathy. Also, it highlighted the role of play in the flow 4-channel model context and impacts on web-loyalty. However, tests show a few different results from hypothetical expectations such as the highest anxiety level of apathy group and insignificant impacts of play on anxiety and apathy. Further research needs to replicate this research and/or to compare 3-channel model with 4-channel model.

• 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.

• Spatial Information Research
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• v.23 no.6
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• pp.57-66
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• 2015

Recently, the utilization of geospatial open platforms has been constantly increased and the interest in 3D geospatial data such as terrain, building and shopping mall has been increased significantly. In particular, rather than simplified 3D geospatial data, interest in high-precision 3D geospatial data which similarly represents the real world objects has increased significantly. In order to satisfy the demand for such the high-precision 3D geospatial data, various kinds of 3D geospatial open platforms has been developed and has provided services on the web. However, most of the 3D geospatial open platforms have been used plug-in module in order to ensure a fast 3D rendering performance on the web, despite the many problems such as difficulty of the installation, no supporting of cross browser/operating system and security issues. In addition, recently, the existing 3D geospatial open platforms based on plug-in module are facing a serious problem, by declaring the NPAPI service interruption in Chrome and Firefox browsers. In this study, we presents the design and implementation of a new 3D geospatial open platform based on HTML5/WebGL technology without the use of plug-ins. Such the new 3D geospatial open platform based on HTML5/WebGL may support cross browsers such as IE, Chrome, Firefox, Safari and cross OS platforms such as Windows, Linux, Mac and mobile OS platforms.

• Journal of Distribution Science
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• v.12 no.4
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• pp.23-30
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• 2014

Purpose - The purpose of this study is to analyze the efficiency of retail businesses by dividing domestic retailers into discount stores, super supermarkets (SSMs), and department stores. It suggests retail-business investment strategies by using data environment analysis (DEA) to analyze how input elements such as store area, parking lot area, number of employees, and sales management expenses for the convenience of customers positively affect business performance measurements such as sales and visiting customers per day. Research Design, Data, and Methodology - The DEA model calculates a ratio of the weighted mean of various inputs to the weighted mean of various outputs and measures the efficiency of a specific decision making unit (DMU). The study included 19 companies (five discount store DMUs, ten SSM DMUs, and four department store DMUs). Because the business elements and sizes of retail store DMUs used in this analysis are different, average per-store input and output variables were used. Data were collected from "The Yearbook of Retail Industry in Korea (2012)." DEA analysis was used to determine differences in efficiency among discount stores, SSMs, and department stores in terms of the business elements of each retail business. It was also used to determine what business elements were excessively invested in by comparing and analyzing efficiency by business elements using SPSS software's ANOVA (Analysis of Variance). Results - The CCR and BCC efficiency analysis found that the efficiency of discount stores is low. We believe that the saturation state of discount stores is a major factor. The ANOVA analysis confirms the VRS hypothesis with a statistically significant difference among the three groups, based on an analysis confidence interval of 95%. CRS and SE were not found to be significantly different among the three groups. As for the post hoc test, which concretely shows differences by group, the Scheffe's multiple comparison analysis test found the average differences between group 1 (discount stores) and group 2 (SSM) to be statistically significant. Conclusions - The DEA efficiency analysis implies that investment in input elements, including store area, parking lot area, and sales management expenses, were excessive in the case of discount stores, while SSMs need to invest more in promotion activities such as gifts, events, and coupons for customer management. Department stores have found that small companies invest excessively in input elements. Department stores need to invest in differentiated shopping mall complexes. This study was limited in acquiring statistical data; various input variables which might have shown more secure customer management and promotional expenses could not be applied. As the study was limited in various aspects of the efficiency analyses because financial analyses of the companies and of causal relationships, including satisfaction and loyalty of visiting customers, were not done, these aspects will be examined in the next study.

• International Commerce and Information Review
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• v.18 no.4
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• pp.81-112
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• 2016

About 60 percent of cosmetic products sold in the dominant Taobao internet shopping mall proved recently to be counterfeit products made in China. This paper is to investigate the anti-counterfeiting strategies to clean up fake goods for Korean cosmetics which have been exported to China. Using the scienter rule the paper classified the forgery action for Korean cosmetics into four broad groups - counterfeit, piracy, illegally distributed product, and brand prior register. This paper suggested interior company's anti-counterfeiting strategies for each group, focusing on the construction for an integrated anti-counterfeiting system including consumer's rationality through the government cooperation between Korea and China.

• The Journal of the Korea Contents Association
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• v.20 no.4
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• pp.124-137
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• 2020

The need for customized products has also been increasing as more active consumers consume according to their values in recent years. Accordingly, fashion customizing web is becoming popular, but because custom freedom is low, we want to increase custom freedom by applying knitwear. To this end, a theoretical review was conducted through prior research and literature research on customization, knit design, and GUI, and based on this, a case analysis was conducted focusing on knit-making programs and fashion customizing web. Knit designs have more considerations than other fashion design process, resulting in more UIs, so users should use visual elements that are easily recognizable. Therefore, a draft assessment item was derived based on the preceding survey and three Delphi surveys were conducted on experts based on the draft. Each item was modified and deleted during the Delphi research process to produce the Custom Knitware Web GUI Design Guide. Through this study, we were able to identify the need for intuitive understanding and application of knit custom functions in GUI design of custom knitwear web. Through this research, it is expected that this data will be used to improve the usability of custom knitwear websites and to refer to knit design fields that utilize knit machines.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.6
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• pp.609-616
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• 2016

This paper is proposed a customized market analysis method using SVM and collaborative filtering. The proposed customized market analysis scheme is consists of DC(Data Classification) module, ICF(Improved Collaborative Filtering) module, and CMA(Customized Market Analysis) module. DC module classifies the characteristics of on-line and off-line shopping mall and traditional markets into price, quality, and quantity using SVM. ICF module calculates the similarity by adding age weight and job weight, and generates network using the similarity of purchased item each users, and makes a recommendation list of neighbor nodes. And CMA module provides the result of customized market analysis using the data classification result of DC module and the recommendation list of ICF module. As a result of comparing the proposed customized recommendation list with the existing user based recommendation list, the case of recommendation list using the existing collaborative filtering scheme, precision is 0.53, recall is 0.56, and F-measure is 0.57. But the case of proposed customized recommendation list, precision is 0.78, recall is 0.85, and F-measure is 0.81. That is, the proposed customized recommendation list shows more precision.

• Journal of Physiology & Pathology in Korean Medicine
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• v.18 no.2
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• pp.344-348
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• 2004

This study regarded irregular life styles such as nigh-shifts as contrary to the norm advised in the rules of Yang-saeng to analyze the problems brought about in relation to the concept of 'Yin-deficiency syndrome'. Yin-deficiency survey was given to sales workers on a big shopping mall in Seoul to compare the measurements of daytime workers to those of nigh-shift workers. The measurement of complaining symptoms related to Yin-deficiency of daytime workers and night-shift workers were compared, In comparison of the daytime workers and the night-shift workers, night-shift workers showed higher measurements than the daytime workers in the item of irritable fever on the five Hearts, flushing of the zygomatic region in the afternoon, tidal fever, dizziness, insomnia, yellow and scanty urination, and constipation. Especially tidal fever, insomnia, and constipation showed statistically significant difference. The total of ten items consisting of Yin-deficiency-related symptoms showed statistically significant high score in night-shift workers than the daytime workers. 'Factors for deficiency-type Heat' consisting of irritable fever on the five Hearts, flushing of the zygomatic region in the afternoon, tidal fever, and dried mouth and throat showed statistically significant high score in night-shift workers than the daytime workers. 'Accompanying factors' consisting of night sweats, emaciation, dizziness, insomnia, yellowish and scanty urination, and constipation also showed statistically and significantly high score in night-shift workers than the daytime workers. From the above results that night-shift workers show high degree of Yin-deficiency than daytime workers, and those overworking irregularly also show high degree of Yin-deficiency than those who work for adequate amount of time regularly implies that sitting up at night for work and sleeping at daytime, excessive work, and irregular life styles all function as high-risk factor for Yin-deficiency.