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

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

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.93-111
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• 2013

As Internet and information technology (IT) continues to develop and evolve, the issue of big data has emerged at the foreground of scholarly and industrial attention. Big data is generally defined as data that exceed the range that can be collected, stored, managed and analyzed by existing conventional information systems and it also refers to the new technologies designed to effectively extract values from such data. With the widespread dissemination of IT systems, continual efforts have been made in various fields of industry such as R&D, manufacturing, and finance to collect and analyze immense quantities of data in order to extract meaningful information and to use this information to solve various problems. Since IT has converged with various industries in many aspects, digital data are now being generated at a remarkably accelerating rate while developments in state-of-the-art technology have led to continual enhancements in system performance. The types of big data that are currently receiving the most attention include information available within companies, such as information on consumer characteristics, information on purchase records, logistics information and log information indicating the usage of products and services by consumers, as well as information accumulated outside companies, such as information on the web search traffic of online users, social network information, and patent information. Among these various types of big data, web searches performed by online users constitute one of the most effective and important sources of information for marketing purposes because consumers search for information on the internet in order to make efficient and rational choices. Recently, Google has provided public access to its information on the web search traffic of online users through a service named Google Trends. Research that uses this web search traffic information to analyze the information search behavior of online users is now receiving much attention in academia and in fields of industry. Studies using web search traffic information can be broadly classified into two fields. The first field consists of empirical demonstrations that show how web search information can be used to forecast social phenomena, the purchasing power of consumers, the outcomes of political elections, etc. The other field focuses on using web search traffic information to observe consumer behavior, identifying the attributes of a product that consumers regard as important or tracking changes on consumers' expectations, for example, but relatively less research has been completed in this field. In particular, to the extent of our knowledge, hardly any studies related to brands have yet attempted to use web search traffic information to analyze the factors that influence consumers' purchasing activities. This study aims to demonstrate that consumers' web search traffic information can be used to derive the relations among brands and the relations between an individual brand and product attributes. When consumers input their search words on the web, they may use a single keyword for the search, but they also often input multiple keywords to seek related information (this is referred to as simultaneous searching). A consumer performs a simultaneous search either to simultaneously compare two product brands to obtain information on their similarities and differences, or to acquire more in-depth information about a specific attribute in a specific brand. Web search traffic information shows that the quantity of simultaneous searches using certain keywords increases when the relation is closer in the consumer's mind and it will be possible to derive the relations between each of the keywords by collecting this relational data and subjecting it to network analysis. Accordingly, this study proposes a method of analyzing how brands are positioned by consumers and what relationships exist between product attributes and an individual brand, using simultaneous search traffic information. It also presents case studies demonstrating the actual application of this method, with a focus on tablets, belonging to innovative product groups.

• Asia pacific journal of information systems
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• v.19 no.3
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• pp.25-49
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• 2009

Advances in technology have resulted in the emergence of new information systems. The convergence of IT and manufacturing sectors has blurred the boundaries among industries. Also, such convergence has become established as a paradigm to build a new area. Especially the convergence of broadcasting and telecommunication, notably in the case of IPTV (Internet Protocol Television), is among the most salient examples of its kind in recent years as a major case of disruptive technology innovation. Despite its much fanfare, such convergence, however, has not fulfilled the expectation; it has not produced positive economic effects while negatively affecting the growth of IPIV. Stakeholders in and around IPIV including telecommunication companies, broadcasting corporations, and government bodies wish to gain control of IPTV under their wings. IPTV has drifted in the midst of conflicts among the stakeholders in and around IPTV, particularly telecommunication and broadcasting organizations in a broad sense. Our empirical research intends to deal with how audiences accept IPTV and how firms provide IPTV services to utilize their resources. Three research questions in this paper include, first, whether Technology Acceptance Model (TAM) can sufficiently explain the acceptance of IPTV as an information system. The second question concerns with empirically testing the playful aspect of IPTV to increase its audience acceptance. Last, but not least, this paper deals with how firms can efficiently and effectively allocate their limited resources to increase IPTV viewers. To answer those three main questions of our study, we collect data from 197 current subscribers of high speed internet service and/or cable/satellite television. Empirical results show that 'perceived usefulness (PU) $\rightarrow$ Intention to use' and 'perceived ease of use (PEU) $\rightarrow$ Intention to use' are significant. Also, 'perceived ease of use' is significantly related to 'perceived usefulness.' Perceived ease of handling IPTV without much effort can positively influence the perceived value of IPTV. In this regard, engineers and designers of IPTV should pay more attention to the user-friendly interface of IPTV. In addition, 'perceived playfulness (PP)' of IPTV is positively related to 'intention to use'. Flow, fun and entertainment have recently gained greater attention in the research concerned with information systems. Such attention is due to the changing features of information systems in recent years that combine the functional and leisure attributes. These results give practical implications to the design of IPTV that reflects not just leisure but also functional elements. This paper also investigates the relationship between 'perceived ease of use (PEU)' and 'perceived playfulness (PP).' PEU is positively related to pp. Audiences without fear can be attracted more easily to the user-friendly IPTV, thereby perceiving the fun and entertainment with ease. Practical implications from this finding are that, to attract more interest and involvement from the audience, IPTV needs to be designed with similar or even more user friendly interface. Of the factors related to 'intention to use', 'perceived usefulness (PU)' and 'perceived ease of use (PEU)' have greater impacts than 'perceived playfulness (PP).' Between PU and PEU, their impacts on 'intention to use' are not significantly different statistically. Managerial implications of this finding are that firms in preparation for the launch of IPTV service should prioritize the functions and interface of IPTV. This empirical paper also provides further insight into the ways in which firms can strategically allocate their limited resources so as to appeal to viewers, both current and potential, of IPTV.

• Journal of the Korea Society of Computer and Information
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• v.29 no.4
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• pp.83-89
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• 2024

After the end of the service of Internet Explorer, the use of ActiveX ended, and the Non-ActiveX policy spread. HTML5 is used as a standard protocol for web pages established based on the Non-ActiveX policy. HTML5, developed in the W3C(World Wide Web Consortium), provides a better web application experience through API, with various elements and properties added to the browser without plug-in. However, new security vulnerabilities have been discovered from newly added technologies, and these vulnerabilities have widened the scope of attacks. There is a lack of research to find possible security vulnerabilities in HTML5-applied websites. This paper proposes a model for detecting tags and attributes with web vulnerabilities by detecting and analyzing security vulnerabilities in web pages of public institutions where plug-ins have been removed within the last five years. If the proposed model is applied to the web page, it can analyze the compliance and vulnerabilities of the web page to date even after the plug-in is removed, providing reliable web services. And it is expected to help prevent financial and physical problems caused by hacking damage.

• Korean journal of food and cookery science
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• v.19 no.4
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• pp.529-538
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• 2003

The objectives of this study were to measure customers' cognition and overall satisfaction, and to identify relatively important attributes for the overall satisfaction, of home-delivered meals. Questionnaires were distributed to 243 customers. The statistical data analyses were completed by x$^2$-tests, ANOV A, factor analysis, reliability analysis and regression analysis using SPSS version 10. 56.6% of customers get obtained information from the internet, with 31.3% of these using this method at least once a week, but 72.9% of customers used this method less than once per years. The major reasons for ordering home-delivered meals were tired of cooking, more economical and no time to cook. The results were significantly different in relation to age, occupation and monthly income. The major reasons for hesitation about ordering home-delivered meals were meals should be prepared in households, not sanitary and the use of too many artificial flavors. The results for this factor were significantly different in relation to gender, age and monthly income(p<0.01). The most preferred kinds of home-delivery meals were Korean soup (guk), stew, soup (tang), speciality dishes and party dishes. The customer's cognition of kindness of the delivery staff was highest, with food temperature being the lowest among the options. The food and service level factors were derived from a factor based analysis of customer's cognition towards home-delivered meals. The customer's cognition of food taste, food quantity, kindness of delivery staff and packaging container shape were significantly different according to the use frequency and use period. The packaging method, sanitation, kindness of delivery staff, price and taste were the most relatively important attributes for overall satisfaction with home-delivered meals.