• International Commerce and Information Review
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• v.16 no.4
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• pp.247-261
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• 2014

In this study, it investigates the highly controversial issue "low carbon car subsidy". Through the policy's intent, purpose, and necessity, it aims to present alternatives for automotive industry development. Introducing the low carbon car subsidy will bring a huge change to the vehicle purchase practices by changing vehicle purchase cost. It expects that this change will reduce greenhouse gas emission from vehicles. For successful settlement of the system, it shall set up the target sections for subsidy and levy appropriately in order to get the nation's consensus. Additionally, it has to conduct sufficient reviews the measures such as adjustment to the existing auto tax, divided payments of burden charge, etc before enforcing the system. In terms of the automobile industry, it must do their level best in technical development in order to meet the carbon dioxide emission level of imported cars until the enforcement. Also, the government has to strengthen its support to the industry.

• Journal of Global Scholars of Marketing Science
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• v.14
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• pp.39-58
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• 2004

After SERVQUAL was proposed as a measure of perceived service quality, several arguments have been made against its validity and some competing measures have been developed. This paper reviews those controversies. Particularly, it compares the concept of expectation in the research of service quality with that in the research of satisfaction, re-purchase intention and suggests the authors' opinion. In addition, the paper suggests sequential casual relations among expectation, perceived performance and perceived service quality. With the help of previous researches concerning "SERVQUAL" and the related topics, six variables concerning travel services were identified. The six variables are tangibility, reliability, responsiveness, assurances, empathy and costs. Seven hypotheses were developed using these variables. The review of literature covers service's concept, model and method of service quality evaluation, and relationship among the concepts which is related to the service quality. The results of our study suggest further research is necessary to clarify which one is a better tool because we have a conflicting research outcome in terms of model fitting. The study also shows that assurances and costs have positive effect on customer satisfaction. It also turned out that customer satisfaction has positive effect on repurchase intention.

• Journal of the Korea Fashion and Costume Design Association
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• v.20 no.3
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• pp.27-32
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• 2018

Hanbok is a our own history and tradition and is an icon of Korean history and culture. Today, the costumes are moving around the world fashion trend due to the development of mass media and internet at the same time. This is an important clue to marketing activities and can be used for predictive analysis. Although Hanbok is changing little by little every year, research on the trend of Hanbok is rare. This study analyzed the results of searching for 'Hanbok' as a keyword in Portal Site Naver and posting a customized purchase of Hanbok for marriage between 2016 and 2017. The analysis was based on analyzing the photos uploaded, and analyzing purchase reason in the On-line review. Most buyer of Hanbok purchased for prepare marriage. The choice of a customized hanbok is mostly to search online or to use the fair. The most important factor in choosing a custom Hanbok that appeared in online reviews is color and then price. The color of the jacket is mostly light color and the off-white color is the most used and the long skirt such as the pink system, the chorale system and the red system, and it can be seen that the pink skirt is overwhelmingly large. In the design of Hanbok, The sleeves were straight and narrow, and the length was the chest line. The collar were enlarged and widened. 고름 used the sole color instead of the jacket and skirt color, and it was narrow not long. skirt's pleats was wide, and designed to overlap with double color of the fabric.

• The Journal of Information Systems
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• v.26 no.4
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• pp.107-135
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• 2017

Purpose Along with the growth of smart phones and social networking service (SNS), social commerce continues to expand. Although online reviews have become an important source of the information that consumers use to make purchasing decisions, theoretical development and empirical testing in this area are still limited. Thus, there is a need to develop further understanding about the influence of electronic word-of-mouth (eWOM). Drawing upon the heuristic - systematic model (HSM) which is one of the dual-process theories, this study develops a research model that explains key factors influencing consumers' eWOM credibility. Furthermore, this study verifies that consumer's eWOM credibility is a key determinant of eWOM and purchase intentions. Design/methodology/approach The proposed model is empirically tested with 493 users who have experience in WeChat-based social commerce. The structural equation model (SEM) analysis is used to evaluate the research model and hypotheses. Findings The major findings are as follows. First, argument quality of eWOM (a systematic factor) has a positive effect on eWOM credibility. Second, source credibility and recommendation consistency of eWOM (heuristic factors) are positively associated with eWOM credibility. Finally, purchase and eWOM intentions greatly depend on eWOM credibility. These results confirm the effectiveness of HSM in explaining eWOM mechanisms in SNS-based social commerce. The details of findings and implications are presented.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.239-244
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• 2015

We can build regular relationships with customers by integrating SNS (Social Networking Service) and internet shopping mall functions. For example of direct dealing of agricultural products, consumers can find news of regular sellers (seeding, farming, harvesting and new products) in the timeline at their SNS home. Then, they can purchase the necessary products by one click motion. The sellers provide news and discount information for building regular customers. Besides these SNS personal connection building, our system provides shopping mall functions to consumer's SNS home pages with auto classified catalog of products. Then, consumes easily find necessary products and these purchase may lead to regular relationships with sellers. Consumers may redistribute recommendations and reviews and it enables direct communications between consumers who are unknown to each other.

• Knowledge Management Research
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• v.22 no.1
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• pp.269-286
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• 2021

As the un-tact and on-tact consumption culture has proliferated due to the impact of COVID-19, 'live commerce', a form of shopping while communicating with customers through real-time streaming broadcasting, is emerging in the commerce and distribution industry. Live commerce provides an environment where customers can get the convenience of online shopping and enjoy un-tact shopping more realistically while communicating with the broadcaster in real time, as if purchasing directly from an offline store. Therefore, purchases using live commerce are expected to increase further. In this study, based on the characteristics of live commerce, the main factors influencing the increase in purchase intention through live commerce were derived and their influences were verified. In particular, this study examined these factors in multiple dimensions with focusing on strong interactivity, realistic presence, and providing detailed reviews with high credibility for products as the features of live commerce. This research collected sample data from actual users of live commerce and empirically analyzed the significance of the factors influencing the purchase increase of live commerce, thereby providing implications for knowledge management in a newly changed commerce environment in the un-tact era.

• The Journal of Information Systems
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• v.30 no.4
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• pp.1-20
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• 2021

Purpose Nowadays, Autonomous Sensory Meridian Response (ASMR) is rapidly growing in popularity and increasingly appearing in marketing. Not even in TV commercial advertisement, ASMR also fast growing in one-person media communication, many brands and social media influencers used ASMR for their marketing contents. The purpose of this study is to measure consumers' perceptions about the products in ASMR marketing content and compare the differences in communication effect of ASMR content creator between product review and how-to in the same Macro tier influencer - the YouTuber that has 10,000-100,000 subscribers. Design/methodology/approach The research methods selected ASMRtist that do product review content and how-to content, Text comments data was collected from 200 videos of tech-device review videos and beauty-fashion videos. A total of 52,833 text comments were analyzed by applying the LDA topic modeling algorithm and social network analysis. Findings Through the result, we can know that ASMR is good at taking attention of viewers with ASMR triggers. In the Tech device reviews field, ASMR viewers also focus on the product like product's performance and purchase. However, there are many topics related to reaction of ASMR sound, trigger, relaxation. In the Beauty-fashion field, viewers' topics mainly focus on the reaction of the ASMR trigger, response to ASMRtist and other topics are talking about makeup - fashion, product, purchase. From LDA result, many ASMR viewers comment that they feel more comfortable when watching the marketing content that uses ASMR. This result has shown that ASMR marketing contents have a good performance in terms of user watching experience, so applying ASMR can take more consumer intention. And the result of social network analysis showed that product review ASMRtist have a higher communication effectiveness than how-to ASMRtist in the same tier. As an influencer marketing strategy, this study provides information to establish an efficient advertising strategy by using influencers that create ASMR content.

• Asia Marketing Journal
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• v.14 no.2
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• pp.23-37
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• 2012

Individuals often make their judgments on the basis of the ease or difficulty with which information comes to mind (for reviews, see Greifeneder, Bless, and Pham 2010; Schwarz 1998, 2004). Recent research, however, has documented that variables known to determine the degree of cognitive resources invested in information processing such as personal relevance (Grayson and Schwarz 1999; Rothman and Schwarz 1998), accuracy motivation (Aarts and Dijksterhuis 1999), and processing capacity (Menon and Raghubir 2003) can affect the extent to which individuals draw on metacognitive difficulty in making their judgments. The primary aim of this research is thus to investigate whether individuals with substantial cognitive resources or those with lack of cognitive resources are more likely to draw on metacognitive difficulty when making their product evaluations. The findings from two laboratory experiments indicate that individuals who perceive a greater level of fit between their self-regulatory orientation and temporal construal (Experiment 1), and between their self-construal and the type of product benefit appeal (Experiment 2) are more likely than those who perceive the lack of such fit to evaluate a target product less positively after thinking of many rather than a few positive reasons. The findings provide supporting evidence for the two-stage backward inference process involved with the effect of metacognitive difficulty on consumer judgments in that consumer judgments based on metacognitive difficulty may require greater cognitive resources than those based on the content of information generated. Also, the current research documents further empirical evidence for the relationship between self-regulatory orientation-construal level fit and cognitive resources such that perceived regulatory-construal level fit can increase consumer willingness to invest cognitive resources into their judgment tasks. Last, the findings can help marketers differentiate purchase situations where asking consumers to think of many positive benefits from purchase situations where asking consumers to think of a few key benefits is relatively more beneficial.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.57-78
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• 2020

With the development of the Internet, consumers have had an opportunity to check product information easily through E-Commerce. Product reviews used in the process of purchasing goods are based on user experience, allowing consumers to engage as producers of information as well as refer to information. This can be a way to increase the efficiency of purchasing decisions from the perspective of consumers, and from the seller's point of view, it can help develop products and strengthen their competitiveness. However, it takes a lot of time and effort to understand the overall assessment and assessment dimensions of the products that I think are important in reading the vast amount of product reviews offered by E-Commerce for the products consumers want to compare. This is because product reviews are unstructured information and it is difficult to read sentiment of reviews and assessment dimension immediately. For example, consumers who want to purchase a laptop would like to check the assessment of comparative products at each dimension, such as performance, weight, delivery, speed, and design. Therefore, in this paper, we would like to propose a method to automatically generate multi-dimensional product assessment scores in product reviews that we would like to compare. The methods presented in this study consist largely of two phases. One is the pre-preparation phase and the second is the individual product scoring phase. In the pre-preparation phase, a dimensioned classification model and a sentiment analysis model are created based on a review of the large category product group review. By combining word embedding and association analysis, the dimensioned classification model complements the limitation that word embedding methods for finding relevance between dimensions and words in existing studies see only the distance of words in sentences. Sentiment analysis models generate CNN models by organizing learning data tagged with positives and negatives on a phrase unit for accurate polarity detection. Through this, the individual product scoring phase applies the models pre-prepared for the phrase unit review. Multi-dimensional assessment scores can be obtained by aggregating them by assessment dimension according to the proportion of reviews organized like this, which are grouped among those that are judged to describe a specific dimension for each phrase. In the experiment of this paper, approximately 260,000 reviews of the large category product group are collected to form a dimensioned classification model and a sentiment analysis model. In addition, reviews of the laptops of S and L companies selling at E-Commerce are collected and used as experimental data, respectively. The dimensioned classification model classified individual product reviews broken down into phrases into six assessment dimensions and combined the existing word embedding method with an association analysis indicating frequency between words and dimensions. As a result of combining word embedding and association analysis, the accuracy of the model increased by 13.7%. The sentiment analysis models could be seen to closely analyze the assessment when they were taught in a phrase unit rather than in sentences. As a result, it was confirmed that the accuracy was 29.4% higher than the sentence-based model. Through this study, both sellers and consumers can expect efficient decision making in purchasing and product development, given that they can make multi-dimensional comparisons of products. In addition, text reviews, which are unstructured data, were transformed into objective values such as frequency and morpheme, and they were analysed together using word embedding and association analysis to improve the objectivity aspects of more precise multi-dimensional analysis and research. This will be an attractive analysis model in terms of not only enabling more effective service deployment during the evolving E-Commerce market and fierce competition, but also satisfying both customers.

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.1-15
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• 2021

If it is difficult for us to make decisions, we ask for advice from friends or people around us. When we decide to buy products online, we read anonymous reviews and buy them. With the advent of the Data-driven era, IT technology's development is spilling out many data from individuals to objects. Companies or individuals have accumulated, processed, and analyzed such a large amount of data that they can now make decisions or execute directly using data that used to depend on experts. Nowadays, the recommender system plays a vital role in determining the user's preferences to purchase goods and uses a recommender system to induce clicks on web services (Facebook, Amazon, Netflix, Youtube). For example, Youtube's recommender system, which is used by 1 billion people worldwide every month, includes videos that users like, "like" and videos they watched. Recommended system research is deeply linked to practical business. Therefore, many researchers are interested in building better solutions. Recommender systems use the information obtained from their users to generate recommendations because the development of the provided recommender systems requires information on items that are likely to be preferred by the user. We began to trust patterns and rules derived from data rather than empirical intuition through the recommender systems. The capacity and development of data have led machine learning to develop deep learning. However, such recommender systems are not all solutions. Proceeding with the recommender systems, there should be no scarcity in all data and a sufficient amount. Also, it requires detailed information about the individual. The recommender systems work correctly when these conditions operate. The recommender systems become a complex problem for both consumers and sellers when the interaction log is insufficient. Because the seller's perspective needs to make recommendations at a personal level to the consumer and receive appropriate recommendations with reliable data from the consumer's perspective. In this paper, to improve the accuracy problem for "appropriate recommendation" to consumers, the recommender systems are proposed in combination with context-based deep learning. This research is to combine user-based data to create hybrid Recommender Systems. The hybrid approach developed is not a collaborative type of Recommender Systems, but a collaborative extension that integrates user data with deep learning. Customer review data were used for the data set. Consumers buy products in online shopping malls and then evaluate product reviews. Rating reviews are based on reviews from buyers who have already purchased, giving users confidence before purchasing the product. However, the recommendation system mainly uses scores or ratings rather than reviews to suggest items purchased by many users. In fact, consumer reviews include product opinions and user sentiment that will be spent on evaluation. By incorporating these parts into the study, this paper aims to improve the recommendation system. This study is an algorithm used when individuals have difficulty in selecting an item. Consumer reviews and record patterns made it possible to rely on recommendations appropriately. The algorithm implements a recommendation system through collaborative filtering. This study's predictive accuracy is measured by Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Netflix is strategically using the referral system in its programs through competitions that reduce RMSE every year, making fair use of predictive accuracy. Research on hybrid recommender systems combining the NLP approach for personalization recommender systems, deep learning base, etc. has been increasing. Among NLP studies, sentiment analysis began to take shape in the mid-2000s as user review data increased. Sentiment analysis is a text classification task based on machine learning. The machine learning-based sentiment analysis has a disadvantage in that it is difficult to identify the review's information expression because it is challenging to consider the text's characteristics. In this study, we propose a deep learning recommender system that utilizes BERT's sentiment analysis by minimizing the disadvantages of machine learning. This study offers a deep learning recommender system that uses BERT's sentiment analysis by reducing the disadvantages of machine learning. The comparison model was performed through a recommender system based on Naive-CF(collaborative filtering), SVD(singular value decomposition)-CF, MF(matrix factorization)-CF, BPR-MF(Bayesian personalized ranking matrix factorization)-CF, LSTM, CNN-LSTM, GRU(Gated Recurrent Units). As a result of the experiment, the recommender system based on BERT was the best.