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

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.5
• /
• pp.2484-2498
• /
• 2017

Various types of social media make the people share their personal experience in different ways. In some social networking sites. Some users post their reviews, some users can support these reviews with comments, and some users just rate the reviews as kind of support or not. Unfortunately, there is rare explicit negative comments towards other reviews. This means if there is a link between two users, it must be positive link. Apparently, the negative link is invisible in these social network. Or in other word, the negative links are redundant to positive links. In this work, we first discuss the feature extraction from social media data and propose new method to compute the distance between each pair of comments or reviews on social media. Then we investigate whether we can predict negative links via regression analysis when only positive links are manifested from social media data. In particular, we provide a principled way to mathematically incorporate multi-facet data in a novel framework, Constructing Negative Links, CsNL to predict negative links for discovering the hidden information. Additionally, we investigate the ways of solution to general negative link predication problems with CsNL and its extension. Experiments are performed on real-world data and results show that negative links is predictable with multi-facet of social media data by the proposed framework CsNL. Essentially, high prediction accuracy suggests that negative links are redundant to positive links. Further experiments are performed to evaluate coefficients on different kernels. The results show that user generated content dominates the prediction performance of CsNL.

• Science of Emotion and Sensibility
• /
• v.24 no.4
• /
• pp.79-90
• /
• 2021

Consumers have an increasingly active role in the marketing cycle, using social media channels to create, distribute, and consume digital content. In this context, this paper investigates the impact of user- and marketer-generated content on consumer purchase intentions and the approach to designing an effective social media marketing platform. Referencing a literature review of social media marketing and consumer purchase intentions, a case study of the social media-marketing platform, 0.8L, was undertaken using both qualitative and quantitative results through content analysis and a participatory survey. First, about 450 consumer reviews for ten sunscreen products posted on the 0.8L platform were compared with products' marketer-generated content. Next, 55 subjects participated in a survey regarding purchase intentions toward moisturizing creams on the 0.8L platform. The results indicated that user-generated content (i.e., texts and photos) provided more personal experiences of the product usage process, whereas marketers focused on distinctive product photos and features. Moreover, customer reviews (particularly high volume and narrative format) had more impact on purchase decisions than marketer information in the online cosmetics market. Real users' honest reviews (both positive and negative) were found to aid companies' prompt and straightforward assessment of newly released products. In addition to the importance of customer-driven marketing practices, distinctive user experience design features of a competitive social media-marketing platform are identified to facilitate the creation and sharing of sincere customer reviews that resonate with potential buyers.

• Journal of the Korea Society of Computer and Information
• /
• v.26 no.8
• /
• pp.39-46
• /
• 2021

When conducting social big data analysis for franchise stores, reviews of multiple branches of a franchise can be collected together, from which analysis results can be distorted significantly. To improve its accuracy, it should be possible to filter reviews of other branches properly which are not subject to the analysis. This paper presents a method for social big data analysis which reflects characteristics of franchise stores. The proposed method consists of search key configuration and review filtering. For the former, the open data provided by Small Business Promotion Agency is used to extract region names for collecting reviews more accurately. For the latter, open search APIs provided by Naver or Kakao are used to obtain franchise branch information for filtering reviews of other branches that are not subject to analysis. To verify performance of the proposed method, experiments were conducted based on real social reviews collected from online, where the results showed that the accuracy of the proposed review filtering was 93.6% on the average.

• Asia Marketing Journal
• /
• v.26 no.1
• /
• pp.23-30
• /
• 2024

Firms often engage in manipulating online reviews as a promotional activity to influence consumers' evaluation on their products. With the prevalence of the promotional activities, consumers may notice and discount the reviews generated by the promotional activities. Discounting the firm-generating reviews may cause systematic measurement errors in the valence variable and lead to a negative bias when estimating the effect of consumers' organic reviews on demand. To correct the bias, this study proposes including product-specific bias-correction terms representing the proportion of extreme reviews in analysis. For illustration, the proposed method is applied to a demand model for data of movies released in South Korea. The results confirm a negative bias in the estimate of the valence sensitivity of demand. The negative bias potentially leads to an underestimation of the magnitude of the contagion effect through social interactions, a key component of evaluating the value of a satisfied consumer.

• Journal of Intelligence and Information Systems
• /
• v.21 no.4
• /
• pp.53-77
• /
• 2015

Mobile commerce provides a convenient shopping experience in which users can buy products without the constraints of time and space. Mobile commerce has already set off a mega trend in Korea. The market size is estimated at approximately 15 trillion won (KRW) for 2015, thus far. In the Korean market, social commerce and open market are key components. Social commerce has an overwhelming open market in terms of the number of users in the Korean mobile commerce market. From the point of view of the industry, quick market entry, and content curation are considered to be the major success factors, reflecting the rapid growth of social commerce in the market. However, academics' empirical research and analysis to prove the success rate of social commerce is still insufficient. Henceforward, it is to be expected that social commerce and the open market in the Korean mobile commerce will compete intensively. So it is important to conduct an empirical analysis to prove the differences in user experience between social commerce and open market. This paper is an exploratory study that shows a comparative analysis of social commerce and the open market regarding user experience, which is based on the mobile users' reviews. Firstly, this study includes a collection of approximately 10,000 user reviews of social commerce and open market listed Google play. A collection of mobile user reviews were classified into topics, such as perceived usefulness and perceived ease of use through LDA topic modeling. Then, a sentimental analysis and co-occurrence analysis on the topics of perceived usefulness and perceived ease of use was conducted. The study's results demonstrated that social commerce users have a more positive experience in terms of service usefulness and convenience versus open market in the mobile commerce market. Social commerce has provided positive user experiences to mobile users in terms of service areas, like 'delivery,' 'coupon,' and 'discount,' while open market has been faced with user complaints in terms of technical problems and inconveniences like 'login error,' 'view details,' and 'stoppage.' This result has shown that social commerce has a good performance in terms of user service experience, since the aggressive marketing campaign conducted and there have been investments in building logistics infrastructure. However, the open market still has mobile optimization problems, since the open market in mobile commerce still has not resolved user complaints and inconveniences from technical problems. This study presents an exploratory research method used to analyze user experience by utilizing an empirical approach to user reviews. In contrast to previous studies, which conducted surveys to analyze user experience, this study was conducted by using empirical analysis that incorporates user reviews for reflecting users' vivid and actual experiences. Specifically, by using an LDA topic model and TAM this study presents its methodology, which shows an analysis of user reviews that are effective due to the method of dividing user reviews into service areas and technical areas from a new perspective. The methodology of this study has not only proven the differences in user experience between social commerce and open market, but also has provided a deep understanding of user experience in Korean mobile commerce. In addition, the results of this study have important implications on social commerce and open market by proving that user insights can be utilized in establishing competitive and groundbreaking strategies in the market. The limitations and research direction for follow-up studies are as follows. In a follow-up study, it will be required to design a more elaborate technique of the text analysis. This study could not clearly refine the user reviews, even though the ones online have inherent typos and mistakes. This study has proven that the user reviews are an invaluable source to analyze user experience. The methodology of this study can be expected to further expand comparative research of services using user reviews. Even at this moment, users around the world are posting their reviews about service experiences after using the mobile game, commerce, and messenger applications.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.10
• /
• pp.5120-5142
• /
• 2018

Social media enables customers to share their views, opinions and experiences as product reviews. These product reviews facilitate customers in buying quality products. Due to the significance of online reviews, fake reviews, commonly known as spam reviews are generated to mislead the potential customers in decision-making. To cater this issue, review spam detection has become an active research area. Existing studies carried out for review spam detection have exploited feature engineering approach; however limited number of features are considered. This paper proposes a Feature-Centric Model for Review Spam Detection (FMRSD) to detect spam reviews. The proposed model examines a wide range of feature sets including ratings, sentiments, content, and users. The experimentation reveals that the proposed technique outperforms the baseline and provides better results.

• The Journal of Asian Finance, Economics and Business
• /
• v.7 no.11
• /
• pp.381-391
• /
• 2020

This research article investigates the way eWOM in social media influences the formation of destination image through development of trust and satisfaction for the potential tourist. The research involved administering an 18-point questionnaire taking online reviews, tourist involvement, and eWOM, destination image components of trust and satisfaction as variables. Data was collected from 554 individuals forming a cross-section of social media users and analyzed using multi-variate techniques (Reliability, CFA, and SEM). Results indicate a positive and significant relationship between all except online review and destination trust and satisfaction. Indirect and direct effects indicate that eWOM fully mediates the relationship between destination satisfaction and involvement and partially mediates the relationship between destination trust and involvement. In the case of online reviews, eWOM acts as a full mediator between destination trust and destination satisfaction for the future traveler using social media. The study proposes that components of image vary depending upon the degree of involvement, volume online reviews and eWOM generated also termed as 'virality' and these in turn influence the intention to revisit or recommend a destination. The study highlights its utility for National Tourist Organizations (NTOs) and online travel intermediaries to enhance destination marketing efforts.

• Journal of the Korea Society of Computer and Information
• /
• v.25 no.10
• /
• pp.87-94
• /
• 2020

Social reviews such as SNS feeds and blog articles have been widely used to extract keywords reflecting opinions and complaints from users' perspective, and often include proper nouns or new words reflecting recent trends. In general, these words are not included in a dictionary, so conventional morphological analyzers may not detect and extract those words from the reviews properly. In addition, due to their high processing time, it is inadequate to provide analysis results in a timely manner. This paper presents a method for efficient keyword extraction from social reviews based on the notion of cohesion scoring. Cohesion scores can be calculated based on word frequencies, so keyword extraction can be performed without a dictionary when using it. On the other hand, their accuracy can be degraded when input data with poor spacing is given. Regarding this, an algorithm is presented which improves the existing cohesion scoring mechanism using the structure of a word tree. Our experiment results show that it took only 0.008 seconds to extract keywords from 1,000 reviews in the proposed method while resulting in 15.5% error ratio which is better than the existing morphological analyzers.

• Journal of Preventive Medicine and Public Health
• /
• v.54 no.4
• /
• pp.265-274
• /
• 2021

Objectives: Given the importance of social determinants of health in promoting the health of slum residents, this study was conducted with the aim of identifying the main dimensions and components of these determinants. Methods: This scoping review study was conducted according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). A comprehensive search was performed of PubMed, ProQuest, Scopus, and Web of Science for articles conducted from 2010 to the end of 2019. Studies were selected based on inclusion criteria, with a special focus on studies dealing with the social determinants of physical and mental health or illness. Results: Thirty-three articles were selected to extract information on the social determinants of health. After reviewing the articles, 7 main dimensions (housing, socioeconomic status of the family, nutrition, neighborhood characteristics, social support and social capital, occupational factors, and health behaviors) and 87 components were extracted as social determinants of health among slum dwellers. Conclusions: This framework could be used by planners, managers, and policy-makers when making decisions affecting the health of these settlements' residents due to the common characteristics of slums around the world, especially in developing countries.