• Journal of Digital Convergence
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• v.15 no.1
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• pp.453-459
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• 2017

This study was conducted to review the usage behavior of mobile social network games that are attracting attention as a new growth engine in the game market. To that end, a survey was conducted on 250 male and female university students. The result of the study showed that first, the relationship formation motivation and seeking fun during leisure times in association with mobile social network games had a positive effect on game attitudes. Second, the relationship formation motivation had a positive effect on the continuous use intention. Third, the relationship formation motivation and the fun-seeking motivation had a positive effect on word-of-mouth recommendation, while the relationship formation motivation and advertisement recommendation motivation had a positive effect on the intention to recommendation online formats. Fourth, the attitude towards mobile social network games had a positive effect on the continuous use intention. Lastly, the attitude towards mobile social network games had a positive effect on only the intention to recommendation through word-of-mouth. This study is expected to provide useful and basic data for the development of quality game content that will cater to users' needs.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.2
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• pp.583-600
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• 2014

Social media is considered a valuable platform for gathering and analyzing the collective and subconscious opinions of people in Internet and mobile environments, where they express, explicitly and implicitly, their daily preferences for brands and products. Extracting and tracking the various attitudes and concerns that people express through social media could enable us to categorize brands and decipher individuals' cognitive decision-making structure in their choice of brands. We investigate the cognitive network structure of consumers by building an inter-category map through the mining of big data. In so doing, we create an improved online recommendation model. Building on economic sociology theory, we suggest a framework for revealing collective preference by analyzing the patterns of brand names that users frequently mention in the online public sphere. We expect that our study will be useful for those conducting theoretical research on digital marketing strategies and doing practical work on branding strategies.

• Information Systems Review
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• v.18 no.2
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• pp.59-77
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• 2016

Although various recommendation techniques have been applied to the e-commerce market, few studies compare the intent to use these techniques from the customer's perspective. In this paper, we conduct a comparative analysis of customers' intention to use five recommendation techniques widely adapted by online shopping malls and focus on the differences in purchasing electronic goods and apparel products. The recommendation techniques are as follows: best-seller recommendation, merchandiser recommendation, content-based recommendation, collaborative filtering recommendation, and social recommendation. Additionally, we examine which factors influence customer intent to use the recommendation services. Data were collected through a survey administered to 220 e-commerce users with prior experience with recommendation services. Collected data were examined using analysis of variance and regression analysis. Results indicate statistically significant differences in customers' intention to use recommendation services according to the recommendation technique. In particular, the best-seller recommendation technique is preferred when purchasing electronic goods, whereas the content-based recommendation technique is preferred for apparel purchases. Factors such as personal characteristics and personality, purchasing tendency, as well as perception of the product or recommendation service affect a customer's intention to use a recommendation service. However, the influence of these factors varies depending on the recommendation technique. This study provides guidelines for companies to adopt appropriate recommendation techniques according to product categories and personal characteristics of customers.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.115-130
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• 2011

Many online stores bring features that can build trust in their customers. More so, the number of products or content services on online stores has been increasing rapidly. Hence, personalization on online stores is considered to be an important technology to companies and customers. Recommender systems that provide favorable products and customer product reviews to users are the most commonly used features in this purpose. There are many studies to that investigated the relationship between social presence as an antecedent of trust and provision of recommender systems or customer product reviews. Many online stores have made efforts to increase perceived social presence of their customers through customer reviews, recommender systems, and analyzing associations among products. Primarily because social presence can increase customer trust or reuse intention for online stores. However, there were few studies that investigated the interactions between recommendation type, product type and provision of customer product reviews on social presence. Therefore, one of the purposes of this study is to identify the effects of personalized recommender systems and compare the role of customer reviews with product types. This study performed an experiment to see these interactions. Experimental web pages were developed with $2{\times}2$ factorial setting based on how to provide social presence to users with customer reviews and two product types such as hedonic and utilitarian. The hedonic type was a ringtone chosen from Nate.com while the utilitarian was a TOEIC study aid book selected from Yes24.com. To conduct the experiment, web based experiments were conducted for the participants who have been shopping on the online stores. Participants were a total of 240 and 30% of the participants had the chance of getting the presents. We found out that social presence increased for hedonic products when personalized recommendations were given compared to non.personalized recommendations. Although providing customer reviews for two product types did not significantly increase social presence, provision of customer product reviews for hedonic (ringtone) increased perceived social presence. Otherwise, provision of customer product reviews could not increase social presence when the systems recommend utilitarian products (TOEIC study.aid books). Therefore, it appears that the effects of increasing perceived social presence with customer reviews have a difference for product types. In short, the role of customer reviews could be different based on which product types were considered by customers when they are making a decision related to purchasing on the online stores. Additionally, there were no differences for increasing perceived social presence when providing customer reviews. Our participants might have focused on how recommendations had been provided and what products were recommended because our developed systems were providing recommendations after participants rating their preferences. Thus, the effects of customer reviews could appear more clearly if our participants had actual purchase opportunity for the recommendations. Personalized recommender systems can increase social presence of customers more than nonpersonalized recommender systems by using user preference. Online stores could find out how they can increase perceived social presence and satisfaction of their customers when customers want to find the proper products with recommender systems and customer reviews. In addition, the role of customer reviews of the personalized recommendations can be different based on types of the recommended products. Even if this study conducted two product types such as hedonic and utilitarian, the results revealed that customer reviews for hedonic increased social presence of customers more than customer reviews for utilitarian. Thus, online stores need to consider the role of providing customer reviews with highly personalized information based on their product types when they develop the personalized recommender systems.

• The Journal of Industrial Distribution & Business
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• v.5 no.1
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• pp.27-37
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• 2014

Purpose - This study analyzed the characteristics of social networking sites (SNSs) using related literatures, and researched the models discussed in precedent studies, to investigate the effects of SNS characteristics upon consumers'awareness, purchase intention, and recommendation. The purpose of the study was to investigate the use of SNSs as a marketing tool. Design, methodology, and approach - For an empirical analysis, the author distributed questionnaires online and offline, to verify the models and hypotheses. Respondents were persons aged 17 or older, who were frequent users of SNSs. The questionnaire survey was conducted for 11 days from September 30, 2013 to October 10, 2013. The author distributed 450 copies and received 430 responses. Finally, 412 copies were used for the analysis after excluding 18 copies having poor answers. Results - The findings about SNS users' behavior could be used as material in the future use of SNS as a marketing tool. Further, the study provided not only theories about SNS characteristics, but also variables and items that were verified during the empirical study. Conclusions - Further studies are needed to overcome the limitations and to establish various kinds of SNS marketing strategies in detail.

• Journal of Digital Contents Society
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• v.19 no.4
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• pp.731-737
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• 2018

The recommendation system is actively researched for the purpose of suggesting information that users may be interested in in many fields such as contents, online commerce, social network, advertisement system, and the like. However, there are many recommendation systems that propose based on past preference data, and it is difficult to provide users with little or no data in the past. Therefore, interest in higher-order data analysis is increasing and Matrix Factorization is attracting attention. In this paper, we study and propose a comparison and replay of the Factorization Machines Leaning(FM) model which is attracting attention in the recommendation system and High-Order Factorization Machines Learning(HOFM) which is a high - dimensional data analysis.

• Human Ecology Research
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• v.61 no.3
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• pp.475-492
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• 2023

This study investigated player responses to microtransactions in freemium Massively multiplayer online roleplaying games (MMORPG), specifically focusing on the game LostArk using English language review data. To this end, structural topic modeling was employed and the following six microtransaction-relevant topics were identified: microtransactions, developer issues, real money trade (RMT), random number generator (RNG) upgrade system, game content, and collectibles & adventure. The first four topics were classified as being "not recommended". However, the proportions of microtransaction-related topics were relatively lower than the other topics. Additionally, this study did not extract keywords related to unfairness and unethical issues in previous microtransaction research. The last two topics, game content, and collectibles & adventure were "recommended" topics, indicating positive functions of microtransactions such as enhancing the game experience by purchasing virtual items. Moreover, it was found that players who do not engage in microtransactions can still be satisfied through continuous game content updates. Additionally, an examination of the interaction effect between time and recommendation status revealed that while the frequency with which the six microtransaction-related topics were mentioned increased over time in the reviews, the ratio of recommendations to non-recommendations varied differently. This study contributes to game-related research by revealing players' authentic opinions on microtransactions in freemium MMORPGs, thereby providing practical implications for game companies.

• Information Systems Review
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• v.17 no.2
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• pp.219-238
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• 2015

As Internet and IT have been developed, people have been exposed to large amounts of information. So, many online information providers recommend relevant information to users to relieve an information overload. However, information recommendation which is based on the taste and preference of a user can lead to a problem of unbalanced information consumption. Prior research about online information has not investigated the side-effect of a recommendation function. This research suggests IT solutions for alleviating unbalanced information consumption behavior. Based on adaptation level theory and expectancy theory, we proposed self-reference point diagnosticity interface and social-reference point diagnosticity interface to help people to consume information following their own information consuming goal. We hypothesized positive impacts of these two interfaces on the self-awareness about information consuming pattern. And we predicted that self-awareness has a positive impact on the motivation and actual behavior to conform the ideal information consuming pattern which the user sets. Laboratory experiment was executed as a research method. As a result, the self-reference diagnosticity interface leaded to higher self-awareness and mitigated the unbalanced information consumption. But, the social-reference diagnosticity interface and the motivation to improve the information consuming behavior had no significant results. Academic and practical implications are discussed.

• Journal of the Korea Society of Computer and Information
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• v.21 no.5
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• pp.19-29
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• 2016

This paper suggests a method to refine a massive collective intelligence data, and visualize with multilevel sentiment network, in order to understand information in an intuitive and semantic way. For this study, we first calculated a frequency of sentiment words from each movie review. Second, we designed a Heatmap visualization to effectively discover the main emotions on each online movie review. Third, we formed a Sentiment-Movie Network combining the MDS Map and Social Network in order to fix the movie network topology, while creating a network graph to enable the clustering of similar nodes. Finally, we evaluated our progress to verify if it is actually helpful to improve user cognition for multilevel analysis experience compared to the existing network system, thus concluded that our method provides improved user experience in terms of cognition, being appropriate as an alternative method for semantic understanding.

• Korean Association of Arts Management
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• no.52
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• pp.75-105
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• 2019

In studying culture consumption today, it is essential to understand and analyze the actual visitors' experiences in detail. This is deeply related to the fact that we can utilize subjective experience records that were previously inaccessible as data since plenty of people actually record many performance experiences in the media space such as social media. This study attempts to examine what elements actually consists of people's performance experience based on actual expression of the performance experience that exists online. For this, we collected two types of data. First, we collected posts which required performance recommendation on online platforms such as Jisik-In and Cafes to see how people describe what they want and analyzed data focusing on the modifiers. Results show that people mainly use modifiers that reflect the specific situation of the individual such as companion or age. In addition we analyzed how the experience was described after the show through the review posts of ticket booking site. Results show how expressions are centered around companions, revisit intentions, and viewing experiences besides elements such as story and music, which have been known as main satisfaction elements of performance experience in previous studies. In addition, we discussed the practical implications and limitations of the study as well as the theoretical discussion.