• Journal of Information Technology Services
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• v.12 no.4
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• pp.267-287
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• 2013

As the online bookstore market has been saturated and the level of competition has become more intense, maintaining competitive advantage by mitigating consumers' perceived risks can be considered as one of good alternative strategies a company should use. Although studies that identify the types of consumers' perceived risks in the context of online bookstores as well as validate the relationships between perceived risk and its antecedent/consequent factors in an integrated manner are strongly required, there has been less attention paid to these matters. Therefore, based on previous literature, we identify five types of perceived risks (financial, performance, online payment, delivery, and seller's response risk) and validate the impacts of online bookstore specific characteristics and user specific characteristics on perceived risk. In addition, we also verify causal relationship between perceived risk and intention to use online bookstores. The results of PLS test using 108 samples collected from undergraduate and graduate students confirm that perceived risk has a negative impact on intention to use and four antecedents (reputation, service quality, self-efficacy, and user experience) are significantly related to perceived risk.

• East Asian Journal of Business Economics (EAJBE)
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• v.7 no.1
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• pp.1-16
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• 2019

Purpose - For the development of fresh food shopping malls, consumers should continue to experience loyalty and favorability for the company's products or brands, and this should lead directly to purchase so that active word-ofmouth and recommendation should be encouraged. Therefore, the purpose of this study is to investigate the effect of e-service quality and e-ERM on e-loyalty with customer satisfaction and commitment as mediators. Research design, data, and methodology - This study was conducted by sample survey method on 320 online customers who have experience in using major online fresh food shopping malls for more than one year. Data analysis methods were frequency analysis, confirmatory factor analysis, reliability analysis, correlation analysis, and structural equation model analysis. Result - Hypothesis 1 through Hypothesis 7 were all supported. The results of this study suggest that e-service quality and e-CRM of online fresh food shopping malls have a significant effect on satisfaction and commitment. Therefore, the conclusion has been derived that the focus of this study, that such satisfaction and commitment have a significant effect on e-customer loyalty. has been supported theoretically and empirically. Conclusion - This study suggests that studies on customer loyalty based on activation commerce factors related to fresh food in online shopping malls will be an index that can reflect on customer's needs corresponding with future trends of not only online shopping malls but also offline shopping malls.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.4
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• pp.260-270
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• 2013

This paper proposes a strategic portfolio model for managing performance of online games. The portfolio matrix is composed of two dimensions: financial performance and non-financial performance. Financial performance is measured by the conventional measure, average revenue per user (ARPU). In terms of non-financial performance, five non-financial key performance indicators (KPIs) that have been widely used in the online game industry are utilized: RU (Register User), VU (Visiting User), TS (Time Spent), ACU (Average Current User), MCU (Maximum Current User). Data envelopment analysis (DEA) is then employed to produce a single performance measure aggregating the five KPIs. DEA is a linear programming model for measuring the relative efficiency of decision making unit (DMUs) with multiple inputs and outputs. This study employs DEA as a tool for multiple criteria decision making (MCDM), in particular, the pure output model without inputs. Combining the two types of performance produces the online game portfolio matrix with four quadrants: Dark Horse, Stop Loss, Jack Pot, Luxury Goods. A case study of 39 online games provided by company 'N' is provided. The proposed portfolio model is expected to be fruitfully used for strategic decision making of online game companies.

• Science of Emotion and Sensibility
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• v.10 no.3
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• pp.321-330
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• 2007

The objective of this study is to increase the credibility of online board game and to improve the process of user management system to resolve the negative and time consuming complaints of 'intended game over' caused by other online users who arbitrary stop the game. The suggested system would create profits to company with less investment in time and expenses regarding the forced game over through the online realtime game management system.

• Asia pacific journal of information systems
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• v.34 no.1
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• pp.226-247
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• 2024

Most studies on recommender systems to evaluate recommendation performances focus on offline evaluation methods utilizing past customer transaction records. However, evaluating recommendation performance through real-world stimulation becomes challenging. Moreover, such methods cannot evaluate the duration of the recommendation effect. This study measures the personalized recommendation (stimulus) effect when the product recommendation to customers leads to actual purchases and evaluates the duration of the stimulus personalized recommendation effect leading to purchases. The results revealed a 4.58% improvement in recommendation performance in the online environment compared with that in the offline environment. Furthermore, there is little difference in recommendation performance in offline experiments by period, whereas the recommendation performance declines with time in online experiments.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.37-51
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• 2015

Due to the development of internet technology and the rapid increase of internet data, various studies are actively conducted on how to use and analyze internet data for various purposes. In particular, in recent years, a number of studies have been performed on the applications of text mining techniques in order to overcome the limitations of the current application of structured data. Especially, there are various studies on sentimental analysis to score opinions based on the distribution of polarity such as positivity or negativity of vocabularies or sentences of the texts in documents. As a part of such studies, this study tries to predict ups and downs of stock prices of companies by performing sentimental analysis on news contexts of the particular companies in the Internet. A variety of news on companies is produced online by different economic agents, and it is diffused quickly and accessed easily in the Internet. So, based on inefficient market hypothesis, we can expect that news information of an individual company can be used to predict the fluctuations of stock prices of the company if we apply proper data analysis techniques. However, as the areas of corporate management activity are different, an analysis considering characteristics of each company is required in the analysis of text data based on machine-learning. In addition, since the news including positive or negative information on certain companies have various impacts on other companies or industry fields, an analysis for the prediction of the stock price of each company is necessary. Therefore, this study attempted to predict changes in the stock prices of the individual companies that applied a sentimental analysis of the online news data. Accordingly, this study chose top company in KOSPI 200 as the subjects of the analysis, and collected and analyzed online news data by each company produced for two years on a representative domestic search portal service, Naver. In addition, considering the differences in the meanings of vocabularies for each of the certain economic subjects, it aims to improve performance by building up a lexicon for each individual company and applying that to an analysis. As a result of the analysis, the accuracy of the prediction by each company are different, and the prediction accurate rate turned out to be 56% on average. Comparing the accuracy of the prediction of stock prices on industry sectors, 'energy/chemical', 'consumer goods for living' and 'consumer discretionary' showed a relatively higher accuracy of the prediction of stock prices than other industries, while it was found that the sectors such as 'information technology' and 'shipbuilding/transportation' industry had lower accuracy of prediction. The number of the representative companies in each industry collected was five each, so it is somewhat difficult to generalize, but it could be confirmed that there was a difference in the accuracy of the prediction of stock prices depending on industry sectors. In addition, at the individual company level, the companies such as 'Kangwon Land', 'KT & G' and 'SK Innovation' showed a relatively higher prediction accuracy as compared to other companies, while it showed that the companies such as 'Young Poong', 'LG', 'Samsung Life Insurance', and 'Doosan' had a low prediction accuracy of less than 50%. In this paper, we performed an analysis of the share price performance relative to the prediction of individual companies through the vocabulary of pre-built company to take advantage of the online news information. In this paper, we aim to improve performance of the stock prices prediction, applying online news information, through the stock price prediction of individual companies. Based on this, in the future, it will be possible to find ways to increase the stock price prediction accuracy by complementing the problem of unnecessary words that are added to the sentiment dictionary.

• Journal of Fashion Business
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• v.18 no.6
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• pp.157-173
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• 2014

The purpose of this research is to investigate the effects of fashion companies' micro-blogging marketing activities on Chinese consumer attitudes and purchasing intentions. In this research, the technology acceptance model (TAM) was used as the research framework, and innovativeness, self-efficacy, and perceived enjoyment variables were included in the model. Through an online survey, 195 respondents participated in this study. The results were as follows: fashion innovation and self-efficacy have a significant positive effect on perceived usefulness, as well as ease of use. These two factors have a significant positive effect on perceived enjoyment. Furthermore, and most significantly, this perceived enjoyment has a significant effect on the consumer's attitude toward the company, and intention to purchase the fashion company's products.

• Journal of Pharmaceutical Investigation
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• v.38 no.6
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• pp.429-432
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• 2008

Drug delivery systems (DDS) have entered mainstream in the pharmaceutical industry in the recent years. Major pharmaceutical companies as well as small or medium-sized biotechnology companies are developing various DDS-based products. We have established Drug Delivery System Company Database, which is an online searchable database of companies that develop DDS-based products and technologies or supply formulations and/or materials. Company summary, products and key technologies are listed in the database. DDS technology fields also include administration routes and indications of drugs. DDS terminologies, Statistical analysis, Useful Links, Glossary and Comments pages are also provided.

• Asia pacific journal of information systems
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• v.29 no.4
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• pp.673-689
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• 2019

The Internet enables businesses to acquire a great deal of information, including prices in the open markets. In this study, we investigate what the value of reference price information is to a company in the market and how the company can make use of such information. Using business analytics, we were able to estimate prices of used cars for a rent-a-car company. The results show that a smart pricing information system is useful for collecting online reference price information and for estimating future prices of used cars and rental prices.

• Journal of the Korea Fashion and Costume Design Association
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• v.18 no.2
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• pp.93-100
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• 2016

This paper aimed to provide the basic data on consumers' purchasing tendency required to start and operate online shopping malls on internet. The survey selected the female college students from 19 to 24 years old majoring fabric and fashion design in colleges in Gyeonggi-do. Total 283 questionnaires were selected for statistical analysis. The analysis results are presented below. The first online shopping was during the middle school times showing the highest responses as 63.54%, followed by high school times, college times and elementary school times in that sequence. Most female college students(97.88%) purchased goods from online shopping malls. The purposes of search in online shopping malls were 'need to purchase goods(47.18%)', 'habit/hobbies(27.57%)', 'need to collect data on goods(20.27%)' and 'to relieve stresses(4.98%)'. About 50% of respondents selected 'I visit mainly several online shopping malls. If there is no goods that I try to find, I search other sites and purchase what I want to buy(46.57%).' For the goods purchased from online shopping malls, everyday wears showed the highest ratio, 85.92%. About the time to purchase goods related to trends, most respondents selected 'purchase whenever it is necessary without respect to trends(87%).' Main considerations when the respondents purchased the goods from online shopping malls were 'design(64.98%)', 'price(18.41%)', 'quality(11.20%)', 'company recognition(2.53%)', 'color(1.44%)', and 'materials (1.44%)' in that sequence. 64.62% of respondents had the experience of returning goods after purchasing from online shopping malls. The reason why the respondents returned goods after purchasing from online shopping malls was mainly 'because of size(52.17%)', the response with the highest ratio. 42.24% responded that they experienced damage by washing the goods purchased from online shopping malls. It was found that the respondents didn't think about the country of manufacturing when purchasing goods from online shopping malls.