• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.31-49
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• 2023

With the growth of the food-catering industry, consumer preferences and the number of dine-in restaurants are gradually increasing. Thus, personalized recommendation services are required to select a restaurant suitable for consumer preferences. Previous studies have used questionnaires and star-rating approaches, which do not effectively depict consumer preferences. Online reviews are the most essential sources of information in this regard. However, previous studies have aggregated online reviews into long documents, and traditional machine-learning methods have been applied to these to extract semantic representations; however, such approaches fail to consider the surrounding word or context. Therefore, this study proposes a novel review textual-based restaurant recommendation model (RT-RRM) that uses deep learning to effectively extract consumer preferences from online reviews. The proposed model concatenates consumer-restaurant interactions with the extracted high-level semantic representations and predicts consumer preferences accurately and effectively. Experiments on real-world datasets show that the proposed model exhibits excellent recommendation performance compared with several baseline models.

• Journal of Korea Society of Industrial Information Systems
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• v.20 no.2
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• pp.85-96
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• 2015

Despite the growth of body-related industry has generated considerable interest in this topic among both academics and practitioners, there has been lack of studies that explore body esteem toward online fashion products leads to purchase intentions, which self-monitoring moderates the relationship between body esteem toward online fashion products and purchase intentions. To fill this gap, this study aim to propose a research model where body esteem influences on the purchase intentions, with self-monitoring as a moderator. In order to test hypotheses, response from 172 consumers were achieved, and the proposed model was estimated by using hierarchical moderated regression analysis. The empirical results showed that body esteem negatively related to purchase intentions toward online fashion products. Additionally, this study indicated that the moderating effects of self-monitoring exist between body esteem and purchase intentions toward online fashion products, thereby implying the importance of contingent role of self-monitoring in managing online channels.

• Information Systems Review
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• v.23 no.4
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• pp.165-182
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• 2021

In nowadays, Covid-19 has transformed patterns of consumers' behavior into a non-face-to-face mode. As the patterns of consumption have been digitalized, it has become a daily routine for consumers who perform so-called shadow work, which involves unpaid jobs that they have to do by themselves. In mobile grocery service context, consumers' shadow work could lead to shopping avoidance as well as switching toward other shopping channels. Thus, this study is to examine how consumers' perception of shadow work affect mobile shopping avoidance and switching intention toward other shopping channels. This study collected 283 survey data from online respondents who have experience on subscription services for ordering groceries in online. We also tested our research model by using partial least squares. Based on our results, this study has found that the perception of shadow work had a positive effect on mobile shopping avoidance as well as switching intention. We expect that our findings could contribute to relevant research on shadow work and suggest practical implications for digital platforms dealing with subscription business models

• Journal of Internet Computing and Services
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• v.18 no.4
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• pp.121-131
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• 2017

With emergence of Internet, social media, and mobile service, the consumers have actively presented their opinions and sentiment, and then it is spreading out real time as well. The user-generated text data on the Internet and social media is not only the communication text among the users but also the valuable resource to be analyzed for knowing the users' intent and sentiment. In special, economic participants have strongly asked that the social big data and its' analytics supports to recognize and forecast the economic trend in future. In this regard, the governments and the businesses are trying to apply the social big data into making the social and economic solutions. Therefore, this study aims to reveal the capability of social big data analysis for the economic use. The research proposed a social big data analysis model and an online consumer sentiment index. To test the model and index, the researchers developed an economic survey ontology, defined a sentiment dictionary for sentiment analysis, conducted classification and sentiment analysis, and calculated the online consumer sentiment index. In addition, the online consumer sentiment index was compared and validated with the composite consumer survey index of the Bank of Korea.

• Journal of Digital Convergence
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• v.11 no.2
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• pp.115-125
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• 2013

Unexpected consumer dissatisfaction emerges through rapid growth and expansion of on-line shopping channel. This research focused on the fashion online retail channels' negative aspect caused by service failure which possibly disappointed consumers. We also tried to seek for appropriate service recovery types based on frequently offered recovery types on-line. Data from college students were analyzed. Results indicate that fitting problem, insufficient information, product defect, inventory problem and slow delivery were the main service failure types in apparel e-shopping. Regression analysis identified that among these types, insufficient information, product defect, and slow delivery had significant influence on channel satisfaction after post recovery effort. Results also confirmed significant relationships between channel satisfaction and channel switching. Consumers perceived benefit level causes overall channel satisfaction level to rise while perceived risk leads to lower level of channel satisfaction. Choosing desirable service recovery activities in each service failure situations is necessary in order to raise consumer's channel satisfaction in online apparel shopping.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.171-189
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• 2022

Online reviews play an essential role in the consumer's purchasing decision-making process, and thus, providing helpful and reliable reviews is essential to consumers. Previous online review helpfulness prediction studies mainly predicted review helpfulness based on the consistency of text and rating information of online reviews. However, there is a limitation in that representation capacity or review text and rating interaction. We propose a CNN-RHP model that effectively learns the interaction between review text and rating information to improve the limitations of previous studies. Multi-channel CNNs were applied to extract the semantic representation of the review text. We also converted rating into independent high-dimensional embedding vectors representing the same dimension as the text vector. The consistency between the review text and the rating information is learned based on element-wise operations between the review text and the star rating vector. To evaluate the performance of the proposed CNN-RHP model in this study, we used online reviews collected from Amazom.com. Experimental results show that the CNN-RHP model indicates excellent performance compared to several benchmark models. The results of this study can provide practical implications when providing services related to review helpfulness on online e-commerce platforms.

• Proceedings of the KAIS Fall Conference
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• 2009.12a
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• pp.57-60
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• 2009

국내 소비시장의 일부분인 재래시장은 현대화와 정보화로 인해 소비자가 감소함에 따라 시설개선, 홍보이벤트 등 외형적인 부분에 중점적으로 변화가 일어나고 있다. 하지만, 소비자들의 편리성을 고려한 서비스에 대해서는 그 대책이 미흡한 실정이다. 이에 따라 본 논문에서는 재래시장의 활성화를 위하여 이기종 센서 및 RFID 태그를 통해 재래시장의 실시간 정보를 얻고, 이를 소비자 및 판매자의 정보와 함께 규칙/사례기반추론 방식을 이용하여 다양한 맞춤형 서비스를 제공할 수 있는 상황인식 기반의 u-CMIS(ubiquitous-Conventional Market Information System) 모델을 제안한다. 본 논문에서 제안하는 u-CMIS 모델은 소비자가 재래시장을 효율적으로 이용할 수 있도록 기존의 온라인 마켓과 모바일 온라인마켓을 통해 언제 어디서나 실시간으로 재래시장의 정보 및 맞춤형 서비스를 제공받을 수 있으며, 이를 통해 소비시간을 단축하거나 물품을 저렴하게 구입할 수 있을 것이다.

• The Journal of the Korea Contents Association
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• v.2 no.3
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• pp.59-69
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• 2002

This article purpose are on the variables of consumer's Doe sensitivity. Our result from sets of data indicate that the web site trust, the web site interactivity and the perceived risk have an effect on price search. Our result is as follows. First, the more trust the web site, the lower the price search. Second, the more interactivity of the web site, the lower the price search. Third, the greater the depth of information at the web site, the higher the price search. forth, the higher the perceived risk, the higher the price search. Fifth, the higher the knowledge of product, the higher the price search.

• The Journal of the Korea Contents Association
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• v.19 no.3
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• pp.171-182
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• 2019

As online shopping malls continue to grow in popularity, various chances of consumption are provided to customers. Customers decide the purchase by exploiting information provided by shopping malls such as the reviews of actual purchasing users, the detailed information of items, and so on. It is required to provide objective and reliable information because customers have to decide on their own whether the massive information is credible. In this paper, we propose a personalized recommendation method considering an item confidence to recommend reliable items. The proposed method determines user preferences based on various behaviors for personalized recommendation. We also propose an user preference measurement that considers time weights to apply the latest propensity to consume. Finally, we predict the preference score of items that have not been used or purchased before, and we recommend items that have highest scores in terms of both the predicted preference score and the item confidence score.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2006.06a
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• pp.396-401
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• 2006

인터넷쇼핑몰의 신뢰도는 정확한 사업자신원정보로부터 출발한다. 우리나라의 경우, 인터넷쇼핑몰은 홈페이지에 사업자신원정보를 표시하도록 법으로 규정하고 있으며, 대부분의 인터넷쇼핑몰들이 규정을 잘 준수하고있다. 그러나 일부 인터넷쇼핑몰들이 고의로 사업자신원정보를 누락하거나 틀린 정보를 표시하기도 하며, 때로는 수시로 변하는 사업자신원정보를 일관성 있게 표시하지 않아 소비자들이 피해를 보거나 불편을 겪고있다. 또한 사업자의 신원을 정확히 밝히지 않는 악성 스팸 메일로 금융회사를 사칭하여 개인정보를 채가는 피싱(Phishing) 사기사건이 증가하고 있다. 이는 전자상거래에 대한 신뢰도를 저하시키는 큰 원안이 되고 있다. 이러한 문제를 해결하는 첫번째 노력이 온라인 사업자의 신원정보를 정확히 표시하고 소비자들이 진위여부를 파악 할 수 있도록 하는 것이다. 따라서 본 연구는 온라인 사업자의 신뢰도 제고를 위한 방안으로 사업자신원정보 표시에 대한 제 3자 인증서비스의 기본원칙을 제시하고 이에 따른 인증서비스 체계를 제시한다.