• Journal of Internet Computing and Services
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• v.21 no.1
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• pp.111-117
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• 2020

Dessert Ateliers (DA) are small shops that sell high-end homemade desserts such as macaroons, cakes, and cookies, and their popularity is increasing according to the emergence of small luxury trends. Even though each DA sells the same kinds of desserts, they are differentiated by the personality of their pastry chef; thus, there is a need to purchase desserts online that customers cannot see and purchase offline, and thus dessert e-commerce has emerged. However, it is impossible for customers to identify all the information of each DA and clearly understand customers' preferences when buying desserts through the dessert e-commerce. When a dessert e-commerce service provides a DA recommendation service, customers can reduce the time they hesitate before making a decision. Therefore, this paper proposes two kinds of DA recommendation method: a clustering-based recommendation method that calculates the similarity between customers' content and DAs and a dynamic weighting-based recommendation method that trains the importance of decision factors considering customer preferences. Various experiments were conducted using a real-world dataset to evaluate the performance of the proposed methods and it showed satisfactory results.

• The Research Journal of the Costume Culture
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• v.24 no.6
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• pp.803-816
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• 2016

The purposes of this study were to investigate the influences of channel assessments on the usage of multi-channels by product types, and the differences in the usage of multi-channels among product types in buying decision making process for fashion products. Data were collected from 510 consumers in their 20s to 50s with purchasing experiences through multi-channel distribution system and living in Seoul and Kyunggi province; 491 were analyzed after deleting incomplete questionnaires. Factor analysis, multiple regression analysis and one-way ANOVA were used for statistical analysis by using SPSS 18.0. The results were as follows: 5 factors were extracted for channel assessment: utility, accuracy, risk, price benefit and sharing information. Price benefits, utility and sharing information for online channel tended to influence positively on the usage of online channel and online+offline channels. Accuracy and low perceived risk of offline influenced positively on offline and on+offline channel usages. The usage levels of on-line and off-line channels for cosmetics were significantly lower than the usage levels for clothes and accessories on information search, evaluation of alternatives, and purchase stages. Significant differences were also found in the usage levels of multi-channels (on+off-line) on information search and evaluation of alternatives stages. The usage levels of the multi-channels for clothes were the highest followed by those of accessories and cosmetics in order.

• Journal of the Korean Society of Clothing and Textiles
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• v.35 no.7
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• pp.761-774
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• 2011

This study investigated the consumer review information considered important by consumers when making a purchase decision to buy apparel products online. Data were collected through focus group interviews. Eleven females in their 20s and 30s, who have extensive experience in reading consumer reviews posted on online apparel stores, participated in the study. The consumer review information considered important by participants is the information related to seven product attributes (size, fabric, design, color, sewing, price, and country of origin), seven benefits (functional, financial, esthetic, emotional, social, utilitarian benefits, and product value compared to price) of the apparel product and four store attributes (return/refund, delivery, reputation/credibility, and customer service). The findings from the study can serve as an important tool in developing survey questions in order to evaluate the quality of consumer review information and help online retailers plan methods to improve the quality of reviews.

• Journal of Information Technology Applications and Management
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• v.20 no.3
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• pp.79-92
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• 2013

Word-of-mouths (WOMs) are now an important information source for purchase decision. Due to the advance in internet technology, people now can share online their consumption experience, both positive and negative, to others. The WOMs may diffuse to not only their friends but also enormous online users. When consumers dissatisfy the product or service, they may share the dissatisfactory experience to others as WOM, which may bring out discussions for the product or service. The discussions on the negative WOM may help to communicate the negative information to enormous others, which may damage the sale of the product or service. The diffusion and life cycle of negative WOM is an important issue for managers. However, few studies focus on it. Thus, the current study focuses on the discussion pattern and life cycle of negative WOMs by observing the 782 discussion articles in a large online community.

• Journal of Information Technology Services
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• v.14 no.4
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• pp.159-169
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• 2015

Customer reviews are one of the important sources for purchase decision makings in online stores. Online stores have tried to provide useful reviews in product pages to customers. To assess the usefulness of customer reviews before other users have voted enough on the reviews, diverse aspects of reviews were utilized in prevous studies. Style and semantic information were utilized in many studies. This study aims to test diverse alogrithms and datasets for identifying a proper classification method and threshold to classify useful reviews. In particular, most researches utilized ratio type helpfulness index as Amazon.com used. However, there is another type of usefulness index utilized in TripAdviser.com or Yelp.com, count type helpfulness index. There was no proper threshold to classify useful reviews yet for count type helpfulness index. This study used reivews and their usefulness votes on restaurnats from Yelp.com to devise diverse datasets and applied text mining approaches to classify useful reviews. Random Forest, SVM, and GLMNET showed the greater values of accuracy than other approaches.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.214-222
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• 2021

Recently, the growth of e-commerce in Saudi Arabia has been exponential, bringing new remarkable challenges. A naive approach for product matching and categorization is needed to help consumers choose the right store to purchase a product. This paper presents a machine learning approach for product matching that combines deep learning techniques with standard artificial neural networks (ANNs). Existing methods focused on product matching, whereas our model compares products based on unstructured descriptions. We evaluated our electronics dataset model from three business-to-consumer (B2C) online stores by putting the match products collectively in one dataset. The performance evaluation based on k-mean classifier prediction from three real-world online stores demonstrates that the proposed algorithm outperforms the benchmarked approach by 80% on average F1-measure.

• Journal of Multimedia Information System
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• v.9 no.1
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• pp.61-68
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• 2022

The importance of online reviews is prevalent as more people access goods or places online and make decisions to visit or purchase. However, such reviews are generally provided by short sentences or mere star ratings; failing to provide a general overview of customer preferences and decision factors. This study explored and broke down restaurant reviews found on Google Maps. After collecting and analyzing 5,427 reviews, we vectorized the importance of words using the TF-IDF. We used a random forest machine learning algorithm to calculate the coefficient of positivity and negativity of words used in reviews. As the result, we were able to build a dictionary of words for positive and negative sentiment using each word's coefficient. We classified words into four major evaluation categories and derived insights into sentiment in each criterion. We believe the dictionary of review words and analyzing the major evaluation categories can help prospective restaurant visitors to read between the lines on restaurant reviews found on the Web.

• Asia pacific journal of information systems
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• v.29 no.2
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• pp.248-267
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• 2019

As merging online and offline channels into one single platform, individuals could easily and frequently switch between online and offline channels. In order for understanding such unique behaviors, this study attempts to explore why and how consumers choose their channels to search and purchase a product. We have drawn on multiple theories that have been used to explain individuals' judgment and decision making (i.e., construal level theory and regula-tory focus theory) in order to develop and tested two-way ANOVA based models of how both regulatory focus (e.g., promotion vs. prevention) and product types (e.g., experience goods vs. searching goods) including the psychological distance cue separately and jointly affect individuals' channel selection behavior (e.g., intention to use single channel vs. intention to use cross-channels). Our results have indicated that consumers with promotion-focus are more likely to use a single channel in experience goods rather than in searching goods when there exists the psychological cue. Based on our findings, the implication for both research and practice are discussed.

• The Research Journal of the Costume Culture
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• v.26 no.1
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• pp.109-124
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• 2018

As fashion and distribution companies have increasingly turned to implementing marketing activities that use omni-channel strategies, it is imperative to explore consumer-oriented evaluations of omni-channel shopping for fashion products. Through contributing to the growing research flow of consumer behavior within omni-channel contexts, the current study explores consumer motivations for omni-channel fashion shopping and their impacts on the decision-making stages of fashion products. The authors first performed in-depth interviews with six Korean consumers and confirmed the four types of consumer motivation for omni-channel shopping, and how decision-making processes react to fashion companies' omni- channel marketing strategies. These findings were used to set survey items for the main study. Based on the results and findings of previous literature, an online survey was conducted with 300 participants who had actual experience with omni-channel shopping for fashion products. The statistic results from the survey revealed the following: First, the in-depth interviews allowed the authors to confirm four factors of omni-channel shopping motivation (ubiquity, efficiency, convenience, and impulsiveness). Second, the survey showed the authors that among the four factors of omni-channel shopping orientation, impulsiveness had the greatest effect on consumer behaviors at the preand on-purchase stages, while the ubiquity factor had the greatest effect at the post-purchase stage. As such, the study empirically tested the omni-channel-specific factors of shopping orientation and motivation. In addition, it showed the effect of omni-channel marketing on various stages of the decision- making process and the study's limitations and implications were discussed.

• The Journal of the Korea Contents Association
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• v.5 no.3
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• pp.19-28
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• 2005

It is true that internet provides consumers with an efficient way to search information with minimal effort and cost, which facilitates better decision making. Especially, previous studies revealed that the online word-of-mouth marketing is widely used as a source of consumers' information seeking and purchase decision making. Even with this importance of the online word-of-mouth communication on internet few researches have systematically addressed the issue. This study investigates the effect of consumers' motives on perceived usefulness of word-of-mouth marketing in online shopping mall contents. The results are as follows: First, choice uncertainty, perceived sacrifice, and social pressure play an important role for perceived usefulness of word-of-mouth marketing. Second, perceived usefulness has directly affected consumers' quality perception. Thus, it is essential for internet companies to find ways to encourage their customers to engage in word-of-mouth communication on their websites.