• The Journal of the Convergence on Culture Technology
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• v.7 no.3
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• pp.497-508
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• 2021

After the spread of COVID-19, the OTT market has grown rapidly. Accordingly, competition of OTT platforms has intensified. Among them, TVing provides users distinct characteristics of service, such as media and commerce, to differentiate it from other platforms. It is important to offer appropriate usability when proving combined service in one mobile application. Otherwise, it can adversely affect the user experience. In this regard, this research conducted usability testing of TVing and found out what user experience should be considered when integrating different characteristics of service in one mobile application. Through the usability testing, we measured and analyzed effectiveness, efficiency, and satisfaction based on the ISO usability testing guidelines. As a result, three important considerations of UX design for mobile application of combined services were found; 1) connectivity between services, 2) easy navigation, 3) consistent UI design, and 4) relations between subjects and provided information. This design consideration is expected to be applicable not only to TVing mobile application, but also to the situation where composed services are provided in one application.

• Journal of Service Research and Studies
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• v.13 no.1
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• pp.127-143
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• 2023

Currently, the domestic traditional market has not escaped the swamp of stagnation that began in the early 2000s despite various projects promoted by many related players such as the central government and local governments. In order to overcome the crisis faced by the traditional market, various R&Ds have recently been conducted on how to build a smart traditional market that combines information and communication technologies such as big data analysis, artificial intelligence, and the Internet of Things. This study analyzes various previous studies, users of traditional markets, and application cases of ICT technology in foreign traditional markets since 2012 and proposes a model to build a smart traditional market using ICT technology based on the analysis. The model proposed in this study includes building a traditional market metaverse that can interact with visitors, certifying visits to traditional markets through digital signage with NFC technology, improving accuracy of fire detection functions using IoT and AI technology, developing smartphone apps for market launch information and event notification, and an e-commerce system. If a smart traditional market platform is implemented and operated based on the smart traditional market platform model presented in this study, it will not only draw interest in the traditional market to MZ generation and foreigners, but also contribute to revitalizing the traditional market in the future.

• The Journal of Bigdata
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• v.8 no.1
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• pp.131-156
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• 2023

The traditional retail industry significantly changed over the past decade due to the mobile and online technologies. This change has been accompanied by a shift in consumer behavior regarding purchasing patterns. Despite the rise of online shopping, there are still specific categories of products, such as "Processed food" in Mongolia, for which traditional shopping remains the preferred purchase method. To prepare for the inevitable future of retail businesses, firms need to closely analyze the performance of their offline stores to plan their further actions in a new multi-channel environment. Retailers must integrate diverse channels into their operations to stay relevant and adjust to the shifting market. In this research, we have analyzed the performance data such as sales, profit, and amount of sales of offline stores by using clustering approach. From the clustering, we have found the several distinct insights by comparing the circumstances and performance of retail stores. For the certain retail stores, we have proposed three different strategies: a fulfillment hub store between online and offline channels, an experience store to elongate customers' time on the premises, and a merge between two non-related channels that could complement each other to increase traffic based on the store characteristics. With the proposed strategies, it may enhance the user experience and profit at the same time.

• The Journal of Information Systems
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• v.25 no.3
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• pp.31-60
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• 2016

Purpose In 2016, the market size of mobile(m-) shopping goes beyeond more than half of a total of online shopping. People use smartphones as the main device for m-commerce. Under the circmustances, this study attempts to address why people prefer to use smartphone-based m-commerce. In other words, it is necessary to understand the main value that smartphone-based m-commerce creates. Drawing on the studies of consumption value, this study focuses on utilitarian value in predicting customers' continuance intention in the context of smartphone-based m-commerce, recognizing that utilitarian value is a key extrinsic motivation in the goal-oriented, performance-oriented shopping contexts. Furthermore, this study identifies factors affecting customers' utilitarian value from the perspective of benefits and costs, following the notion that it represents the result of evaluating a trade-off of benefits and costs caused by smartphone-based m commerce. More specifically, in this study, ubiquitous service, location-based service (LBS), transaction speed, and price utility belong to the benefit dimension, whereas technology anxiety and cognitive effort belong to the cost dimension. Design/methodology/approach To test the proposed hypotheses, the study conducted partial least squares (PLS) analysis with a total of 294 data collected on users with experience in smartphone-based m-commerce. Findings The results show that first, utilitarian value is increased by the benefits, such as ubiquitous service, transaction speed, and price utility. However, LBS has no direct effect on utilitarian value. Second, the noteworthy finding is that ubiquitous service and LBS greatly increase transaction speed. Third, technology anxiety and cognitive effort considered as the cost dimension are negatively associated with utilitarian value but their impacts on it are non-significant. Finally, the results support the argument that utilitarian value is a determinant of continuance intention. Overall, the findings imply that utilitarian value greatly depends on the peception on benefits rather than the aspect of cost in smartphone-based m-commerce. Overall, the findings offer new insight into the studies of m-commerce by considering and verifying the impacts of its benefits and costs simultaneously.

• Journal of the Korea Convergence Society
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• v.10 no.3
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• pp.395-400
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• 2019

The Web environment of the frontend domain is increasingly competitive to preempt the new standard of presentation layer. N-Screen, a service that enables users to seamlessly use one content in various devices in Korea, is competing for market preemption by recognizing it as a core service of the future. In the cloud computing, N-screen is a typical service type. However, most of the frontend research required for groupware in enterprise environments has been limited to responsive web design for the web and native apps for mobile. Gradually, the need for MVC design patterns is increasingly widening in enterprise environments to overcome the cultural differences of companies and to support one source multi-use strategy supporting multiple devices and development productivity. Therefore, in this study, we will analyze and design JPetStore with hybrid application design based on Spring MVC, e-government standard framework environment of next generation web standard, and provide reference model of frontend hybrid apps design in future enterprise environment.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.127-141
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• 2017

Advances in Internet technologies and the proliferation of mobile devices enabled consumers to approach a wide range of goods and services, while causing an adverse effect that they have hard time reaching their congenial items even if they devote much time to searching for them. Accordingly, businesses are using the recommender systems to provide tools for consumers to find the desired items more easily. Association Rule Mining (ARM) technology is advantageous to recommender systems in that ARM provides intuitive form of a rule with interestingness measures (support, confidence, and lift) describing the relationship between items. Given an item, its relevant items can be distinguished with the help of the measures that show the strength of relationship between items. Based on the strength, the most pertinent items can be chosen among other items and exposed to a given item's web page. However, the diversity of the measures may confuse which items are more recommendable. Given two rules, for example, one rule's support and confidence may not be concurrently superior to the other rule's. Such discrepancy of the measures in distinguishing one rule's superiority from other rules may cause difficulty in selecting proper items for recommendation. In addition, in an online environment where a web page or mobile screen can provide a limited number of recommendations that attract consumer interest, the prudent selection of items to be included in the list of recommendations is very important. The exposure of items of little interest may lead consumers to ignore the recommendations. Then, such consumers will possibly not pay attention to other forms of marketing activities. Therefore, the measures should be aligned with the probability of consumer's acceptance of recommendations. For this reason, this study proposes a model-based approach to combine those measures into one unified measure that can consistently determine the ranking of recommended items. A regression model was designed to describe how well the measures (independent variables; i.e., support, confidence, and lift) explain consumer's acceptance of recommendations (dependent variables, hit rate of recommended items). The model is intuitive to understand and easy to use in that the equation consists of the commonly used measures for ARM and can be used in the estimation of hit rates. The experiment using transaction data from one of the Korea's largest online shopping malls was conducted to show that the proposed model can improve the hit rates of recommendations. From the top of the list to 13th place, recommended items in the higher rakings from the proposed model show the higher hit rates than those from the competitive model's. The result shows that the proposed model's performance is superior to the competitive model's in online recommendation environment. In a web page, consumers are provided around ten recommendations with which the proposed model outperforms. Moreover, a mobile device cannot expose many items simultaneously due to its limited screen size. Therefore, the result shows that the newly devised recommendation technique is suitable for the mobile recommender systems. While this study has been conducted to cover the cross-selling in online shopping malls that handle merchandise, the proposed method can be expected to be applied in various situations under which association rules apply. For example, this model can be applied to medical diagnostic systems that predict candidate diseases from a patient's symptoms. To increase the efficiency of the model, additional variables will need to be considered for the elaboration of the model in future studies. For example, price can be a good candidate for an explanatory variable because it has a major impact on consumer purchase decisions. If the prices of recommended items are much higher than the items in which a consumer is interested, the consumer may hesitate to accept the recommendations.

• 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.