• Journal of Intelligence and Information Systems
• /
• v.18 no.4
• /
• pp.19-42
• /
• 2012

Consumer consumption patterns are shifting rapidly as buyers migrate from offline markets to e-commerce routes, such as shopping channels on TV and internet shopping malls. In the offline markets consumers go shopping, see the shopping items, and choose from them. Recently consumers tend towards buying at shopping sites free from time and place. However, as e-commerce markets continue to expand, customers are complaining that it is becoming a bigger hassle to shop online. In the online shopping, shoppers have very limited information on the products. The delivered products can be different from what they have wanted. This case results to purchase cancellation. Because these things happen frequently, they are likely to refer to the consumer reviews and companies should be concerned about consumer's voice. E-commerce is a very important marketing tool for suppliers. It can recommend products to customers and connect them directly with suppliers with just a click of a button. The recommender system is being studied in various ways. Some of the more prominent ones include recommendation based on best-seller and demographics, contents filtering, and collaborative filtering. However, these systems all share two weaknesses : they cannot recommend products to consumers on a personal level, and they cannot recommend products to new consumers with no buying history. To fix these problems, we can use the information which has been collected from the questionnaires about their demographics and preference ratings. But, consumers feel these questionnaires are a burden and are unlikely to provide correct information. This study investigates combining collaborative filtering with the centrality of social network analysis. This centrality measure provides the information to infer the preference of new consumers from the shopping history of existing and previous ones. While the past researches had focused on the existing consumers with similar shopping patterns, this study tried to improve the accuracy of recommendation with all shopping information, which included not only similar shopping patterns but also dissimilar ones. Data used in this study, Movie Lens' data, was made by Group Lens research Project Team at University of Minnesota to recommend movies with a collaborative filtering technique. This data was built from the questionnaires of 943 respondents which gave the information on the preference ratings on 1,684 movies. Total data of 100,000 was organized by time, with initial data of 50,000 being existing customers and the latter 50,000 being new customers. The proposed recommender system consists of three systems : [+] group recommender system, [-] group recommender system, and integrated recommender system. [+] group recommender system looks at customers with similar buying patterns as 'neighbors', whereas [-] group recommender system looks at customers with opposite buying patterns as 'contraries'. Integrated recommender system uses both of the aforementioned recommender systems to recommend movies that both recommender systems pick. The study of three systems allows us to find the most suitable recommender system that will optimize accuracy and customer satisfaction. Our analysis showed that integrated recommender system is the best solution among the three systems studied, followed by [-] group recommended system and [+] group recommender system. This result conforms to the intuition that the accuracy of recommendation can be improved using all the relevant information. We provided contour maps and graphs to easily compare the accuracy of each recommender system. Although we saw improvement on accuracy with the integrated recommender system, we must remember that this research is based on static data with no live customers. In other words, consumers did not see the movies actually recommended from the system. Also, this recommendation system may not work well with products other than movies. Thus, it is important to note that recommendation systems need particular calibration for specific product/customer types.

• Asia pacific journal of information systems
• /
• v.17 no.3
• /
• pp.1-24
• /
• 2007

With the development of information technology (IT), various information systems (IS) such as Web-based systems and mobile systems have appeared utilizing different technologies. However, recent studies on IS use and user satisfaction rarely account for technological differences among IS and environmental characteristics where IS are intended to be used. The purpose of this research is to investigate the determinants of the use of Web-based ticketing systems for cultural activities and to empirically validate their relationships. Environmental psychology suggests that human beings respond to external stimuli from environments with their emotions, and their emotional states influence human actions, e.g., IS use in this research. Applying environmental psychology to the use of Web-based systems in the culture and entertainment industry, we propose that web site characteristics first influence a user's internal state of mind (i.e., flow) and then the flow state influences the IS use. Studies related to the state of flow collectively affirm the key role played by the flow construct in shaping individual attitudes and behaviors toward IS. Users' flow states are captured by their shopping enjoyment, perceived behavioral control, and the level of concentration on the IS use. Referring to social presence theory, we have included such web site characteristics as content quality, context of web site, and community quality. In our research model, a second order construct is utilized to represent web site quality, because flow theory suggests that holistic experiences with web-based systems (rather than individual characteristics of the web site) are important in explaining the IS use. Further, we have included trust as another important factor influencing the IS use since business transactions on the web encompass higher uncertainty comparing to offline transactions. In order to test our hypotheses, we have conducted an online survey which results in 1,141 valid responses in the final sample. The data were collected from respondents who have experiences in Internet ticketing systems. Although it was a convenient sample, the sample represents a wide variety of user demographics. Validity and reliability of the research instrument were tested and research hypotheses were examined using PLS Graph 3.0. The results indicate that web site characteristics significantly influence the level of user concentration, user's enjoyment in shopping, and perceived behavioral control. Further, the use of Internet ticketing systems is influenced by users' flow states and trust in the web channel. User satisfaction is turned out to be affected by the use of Internet ticketing systems. Unlike extant research on the relationship between web site characteristics and its use, our study has found that, in the culture and entertainment industry, the impact of web site characteristics on IS use is mediated by a user's flow state. This finding has a practical implication that web site design should include as many features that enhance shopping enjoyment and concentration. Other practical implications of these findings and future research implications are also discussed.

• Information Systems Review
• /
• v.20 no.4
• /
• pp.1-23
• /
• 2018

A smart phone has been widely spread around world and makes people enjoy online shopping in any time and any place. Recently it also changes the distribution environment. O2O (Online-to-Offline) service becomes new normal due to its convenience of ease shopping of product and services. O2O service market shows steady and steep growth, It is reported that, however, 80% of the businesses has been discontinued within the first year because of unstable business models, customer dissatisfaction and distrust of service. Therefore, it is very important research issue to find out influential factors promoting continuous usage intention of O2O service. Previous study shows that it only considers online characteristics and lack of analysis about offline characteristics and social impact factors. The purpose of this paper is to find out continuous usage intention factors of O2O services by literature review, case analysis, and empirical test. A comprehensive research model and related hypothesis are developed and tested by using a structural equation, Survey was carried out among users who have used O2O service including payment service for at least once. Finally 611 samples are selected out of total 813 surveys. The result shows that the model is theoretically proved and 12 out of 17 hypotheses are accepted. The contribution of this paper is that it provides a new theoretical research model about continuous usage intention factors as well as practical guidelines about promoting continuous usage and growth strategies of O2O service.

• The Journal of the Korea Contents Association
• /
• v.22 no.9
• /
• pp.126-146
• /
• 2022

With the development of IT technology, consumers' shopping behaviors have diversified, and O2O services that remove the boundary between online and offline are increasing. With the development of O2O services, it is bringing about new changes in offline retailers that are facing limitations against the continuous growth of online. Drawing upon Technology Acceptance Model, this study investigates the effect of service quality perceived by O2O service users on consumers' intention to use O2O services. The result confirmed that the perceived quality, an external variable, affects the perceived usefulness and perceived ease of use, which are the main variables of the Technology Acceptance Model, and the perceived usefulness and perceived ease of use in turn have a significant effect on attitude and behavioral intention. In particular, it was found that the higher the perceived ease of use of the user, the higher the perceived usefulness and positive influence on the attitude. The results of this study suggest that in order to increase the utilization of O2O service by users, it is necessary to highlight the perceived quality of service and at the same time manage convenience and usefulness. This is expected to provide meaningful directions for companies attempting to advance O2O services.

• Journal of Venture Innovation
• /
• v.3 no.2
• /
• pp.23-39
• /
• 2020

The 4th Industrial Revolution and Covid 19 are moving the fashion industry from offline to online. Fashion shows that took place offline are being replaced by online. Online is greatly increasing consumers' digital customer experience based on digital technologies. In this study, we studied the effect of digital experience factors on digital customer satisfaction based on the Schmitt(1999)'s experience marketing. The effect of digital customer satisfaction on purchase, continuous use intention, and recommendation intention were also studied. In addition, the moderating effect of experience frequency was studied. We randomly sampled 180 individuals among fashion mall users.. SPSS 24, AMOS 23 and Process Macro 3.5 were used for statistical analysis. In the study in which digital experience factors influence digital customer satisfaction, all except the digital act showed positive influence. The impact of influence was digital sense (β = .366) > digital think (β = .225)> digital feel (β = .191) > digital relate(β = .163). Digital customer satisfaction have been positive impact on purchasing, continuance use and recommendation intention. In the moderating effect of digital experience frequency, between digital feel, digital act and digital customer experience showed a statistically effective relationship. Based on the this study, We suggested theoretical and practical implications.

• Journal of Intelligence and Information Systems
• /
• v.26 no.2
• /
• pp.27-42
• /
• 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 Distribution Research
• /
• v.16 no.2
• /
• pp.1-24
• /
• 2011

1. Introduction: Contrast to the offline purchasing environment, online store cannot offer the sense of touch or direct visual information of its product to the consumers. So the builder of the online shopping mall should provide more concrete and detailed product information(Kim 2008), and Alba (1997) also predicted that the quality of the offered information is determined by the post-purchase consumer satisfaction. In practice, many fashion and apparel online shopping malls offer the picture information with the product on the real person model to enhance the usefulness of product information. On the other virtual product experience has been suggested to the ways of overcoming the online consumers' limited perceptual capability (Jiang & Benbasat 2005). However, the adoption and the facilitation of the virtual reality tools requires high investment and technical specialty compared to the text/picture product information offerings (Shaffer 2006). This could make the entry barrier to the online shopping to the small retailers and sometimes it could be demanding high level of consumers' perceptual efforts. So the expensive technological solution could affects negatively to the consumer decision making processes. Nevertheless, most of the previous research on the online product information provision suggests the VR be the more effective tools. 2. Research Model and Hypothesis: Presented in