• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.85-97
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• 2021

Online consumer activities have increased considerably since the COVID-19 outbreak. For the products and services which have an impact on everyday life, online reviews and recommendations can play a significant role in consumer decision-making processes. Thus, to better serve their customers, online firms are required to build online-centric marketing strategies. Especially, it is essential to define core value of customers based on the online customer reviews and to propose these values to their customers. This study discovers specific perceived values of customers in regard to a certain product and service, using online customer reviews and proposes a customer value proposition methodology which enables online firms to develop more effective marketing strategies. In order to discover customers value, the methodology employs a text-mining technology, which combines a sentiment analysis and topic modeling. By the methodology, customer emotions and value factors can be more clearly defined. It is expected that online firms can better identify value elements of their respective customers, provide appropriate value propositions, and thus gain sustainable competitive advantage.

• Journal of Information Technology Services
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• v.17 no.2
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• pp.165-177
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• 2018

Recently, online commerces are becoming more common due to factors such as mobile technology development and smart device dissemination, and online review has a big influence on potential buyer's purchase decision. This study presents a set of analytical methodologies for understanding the meaning of customer reviews of products in online transaction. Using techniques currently developed in deep learning are implemented Hierarchical Attention Network for analyze meaning in online reviews. By using these techniques, we could solve time consuming pre-data analysis time problem and multiple topic problems. To this end, this study analyzes customer reviews of laptops sold in domestic online shopping malls. Our result successfully demonstrates over 90% classification accuracy. Therefore, this study classified the unstructured text data in the semantic analysis and confirmed the practical application possibility of the review analysis process.

• East Asian Journal of Business Economics (EAJBE)
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• v.8 no.1
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• pp.1-19
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• 2020

Purpose - This study is to classify quality characteristics of online environment-friendly agricultural products shopping malls into 6 categories and to empirically test their relationship with customer trust, perceived manageability, perceived utility and purchase intention. Research design, data, and methodology - This study targeted adults who have purchased ecofriendly agricultural production online malls for 4 weeks from September 3 to September 30, 2019. The survey type used was a structuralized self-report survey questionnaire made to meet the research purpose in 2019 as the time range. Out of 800 questionnaires, 500 copies are used after excluding surveys with insincere responses. Results - First, results to hypothesis 1, which was about independent variables and customer trust. Analysis showed that health, familiarity, platform reputation, reviews and product quality were found to have significant effect on customer trust; the hypothesis was adopted. On the other hand, system security did not affect customer trust significantly; it was rejected. Second, customer trust was shown to have significant effect on perceived manageability and perceived utility, so the hypothesis was adopted. Third, the hypothesis that perceived manageability moves onto perceived utility was adopted. Moreover, the hypothesis that perceived manageability moves onto purchase intention and the hypothesis that perceived utility moves onto purchase intention were adopted as well. Conclusions - Furthermore, the results of the study imply that it's imperative for online environment-friendly agricultural products shopping malls to consider their characteristics as the means to increase purchase intention of customers.

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

• Korean Journal of Human Ecology
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• v.18 no.5
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• pp.1063-1075
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• 2009

This study intends to provide foundational data for enhancing the welfare of customers purchasing digital products through analyzing the notes from written on-line reviews. The data used for the analysis are 6,342 on-line reviews for cell phones and digital cameras released from November, 2007 until April, 2008, which was posted on Naver Knowledge Shopping from November, 2007 until June, 2008. Through the on-line reviews, this article analyzed the evaluations on the digital products' hardware, software, design, service, price, and other criteria and the customers' emotional experience in the process of purchase, use, and possession. According to the results of the analysis, negative evaluation and emotional experience were originated from the company's information provision methods and purchase process. In addition, insufficient information searches in the process of online purchases, consumers' low right consciousness, and impolite on-line reviews were also problematic. Customers' evaluations and emotional experiences on digital products were conducted in a complex way. Based on that, this research makes suggestions in the company's marketing, customer education, and theoretical aspect.

• Asia pacific journal of information systems
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• v.33 no.2
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• pp.261-281
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• 2023

The proliferation of online customer reviews has completely changed how consumers purchase. Consumers now heavily depend on authentic experiences shared by previous customers. However, deceptive reviews that aim to manipulate customer decision-making to promote or defame a product or service pose a risk to businesses and buyers. The studies investigating consumer perception of deceptive reviews found that one of the important cues is based on review content. This study aims to investigate the impact of the information amount of review on the review truthfulness. This study adopted the Information Manipulation Theory (IMT) as an overarching theory, which asserts that the violations of one or more of the Gricean maxim are deceptive behaviors. It is regarded as a quantity violation if the required information amount is not delivered or more information is delivered; that is an attempt at deception. A topic modeling algorithm is implemented to reveal the distribution of each topic embedded in a text. This study measures information amount as topic diversity based on the results of topic modeling, and topic diversity shows how heterogeneous a text review is. Two datasets of restaurant reviews on Yelp.com, which have Filtered (deceptive) and Unfiltered (genuine) reviews, were used to test the hypotheses. Reviews that contain more diverse topics tend to be truthful. However, excessive topic diversity produces an inverted U-shaped relationship with truthfulness. Moreover, we find an interaction effect between topic diversity and reviews' ratings. This result suggests that the impact of topic diversity is strengthened when deceptive reviews have lower ratings. This study contributes to the existing literature on IMT by building the connection between topic diversity in a review and its truthfulness. In addition, the empirical results show that topic diversity is a reliable measure for gauging information amount of reviews.

• Journal of Distribution Research
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• v.17 no.3
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• pp.25-57
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• 2012

This study identified motives for writing apparel product reviews in online stores, and determined what motives increase the behavior of writing reviews. It also classified store customers based on the type of writing motives, and clarified the characteristics of internet purchase behavior and of a demographic profile. Data were collected from 252 females aged 20s' and 30s' who have experience of reading and writing reviews on online shopping. The five types of writing motives were altruistic information sharing, remedying of a grievance and vengeance, economic incentives, helping new product development, and the expression of satisfaction feelings. Among five motives, altruistic information sharing, economic incentives, and helping new product development stimulate writing reviews. Store customers who write reviews were classified into three groups based on their writing motive types: Other consumer advocates(29.8%), self-interested shoppers(40.5%) and shoppers with moderate motives(29.8%). There were significant differences among three groups in writing behavior (the frequency of writing reviews, writing intent of reviews, duration of writing reviews, and frequency of online shopping) and age. Based on results, managerial implications were suggested. Long Abstract : The purpose of present study is to identify the types of writing motives on online shopping, and to clarify the motives affecting the behavior of writing reviews. This study also classifies online shoppers based on the motive types, and identifies the characteristics of the classified groups in terms of writing behavior, frequency of online shopping, and demographics. Use and Gratification Theory was adopted in this study. Qualitative research (focus group interview) and quantitative research were used. Korean women(20 to 39 years old) who reported experience with purchasing clothing online, and reading and writing reviews were selected as samples(n=252). Most of the respondents were relatively young (20-34yrs., 86.1%,), single (61.1%), employed(61.1%) and residents living in big cities(50.9%). About 69.8% of respondents read and 40.5% write apparel reviews frequently or very frequently. 24.6% of the respondents indicated an "average" in their writing frequency. Based on the qualitative result of focus group interviews and previous studies on motives for online community activities, measurement items of motives for writing after-purchase reviews were developed. All items were used a five-point Likert scale with endpoints 1 (strongly disagree) and 5 (strongly agree). The degree of writing behavior was measured by items concerning experience of writing reviews, frequency of writing reviews, amount of writing reviews, and intention of writing reviews. A five-point scale(strongly disagree-strongly agree) was employed. SPSS 18.0 was used for exploratory factor analysis, K-means cluster analysis, one-way ANOVA(Scheffe test) and ${\chi}^2$-test. Confirmatory factor analysis and path model analysis were conducted by AMOS 18.0. By conducting principal components factor analysis (varimax rotation, extracting factors with eigenvalues above 1.0) on the measurement items, five factors were identified: Altruistic information sharing, remedying of a grievance and vengeance, economic incentives, helping new product development, and expression of satisfaction feelings(see Table 1). The measurement model including these final items was analyzed by confirmatory factor analysis. The measurement model had good fit indices(GFI=.918, AGFI=.884, RMR=.070, RMSEA=.054, TLI=.941) except for the probability value associated with the ${\chi}^2$ test(${\chi}^2$=189.078, df=109, p=.00). Convergent validities of all variables were confirmed using composite reliability. All SMC values were found to be lower than AVEs confirming discriminant validity. The path model's goodness-of-fit was greater than the recommended limits based on several indices(GFI=.905, AGFI=.872, RMR=.070, RMSEA=.052, TLI=.935; ${\chi}^2$=260.433, df=155, p=.00). Table 2 shows that motives of altruistic information sharing, economic incentives and helping new product development significantly increased the degree of writing product reviews of online shopping. In particular, the effect of altruistic information sharing and pursuit of economic incentives on the behavior of writing reviews were larger than the effect of helping new product development. As shown in table 3, online store shoppers were classified into three groups: Other consumer advocates (29.8%), self-interested shoppers (40.5%), and moderate shoppers (29.8%). There were significant differences among the three groups in the degree of writing reviews (experience of writing reviews, frequency of writing reviews, amount of writing reviews, intention of writing reviews, and duration of writing reviews, frequency of online shopping) and age. For five aspects of writing behavior, the group of other consumer advocates who is mainly comprised of 20s had higher scores than the other two groups. There were not any significant differences between self-interested group and moderate group regarding writing behavior and demographics.

• Journal of Korean Home Economics Education Association
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• v.22 no.3
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• pp.129-145
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• 2010

In this study, when buying fashion products through internet shopping malls, it is researched about the buying behavior, the awareness of customer review, the use and posting of customer review and the accompanying awareness. The difference of awareness on the customer review according to their involvement of clothes, are examined from high school students in Seoul. And it is examined if they experienced any dissatisfaction after their purchase and what their behavior were. The questionnaire survey was taken by 508 students from 6 high schools in Seoul. The average, the standard deviation, the frequency, the t-test, the One way ANOVA and Duncan's Multiple Test were conducted for data analysis using SPSS 17.0. In the fashion products purchase behavior of the students, The reasons of buying were mainly because of the diversity and the convenience. Some students don't shop online because screen product and actual product are not the same. The awareness of the customer review represented high in the reliability and usefulness. The awareness on the influence of the customer review represented high in the contents direction and the numbers of the customer reviews but represented low in the timeliness. As to the awareness of the customer review, the student using it represented higher in all elements such as the usefulness, the reliability, and the influence than students who not use customer review. The students posting customer review recognized higher on the usefulness and the reliability of the customer review than those who did not post it, and were highly influenced by the numbers of customer reviews. The awareness of the customer review according to the involvement of clothes was the difference only in the usefulness. As to coping actions of students experiencing dissatisfaction, the proportion of the students coping with the public action and those who do not perform any action represented high.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.139-161
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• 2019

The utilization of the e-commerce market has become a common life style in today. It has become important part to know where and how to make reasonable purchases of good quality products for customers. This change in purchase psychology tends to make it difficult for customers to make purchasing decisions in vast amounts of information. In this case, the recommendation system has the effect of reducing the cost of information retrieval and improving the satisfaction by analyzing the purchasing behavior of the customer. Amazon and Netflix are considered to be the well-known examples of sales marketing using the recommendation system. In the case of Amazon, 60% of the recommendation is made by purchasing goods, and 35% of the sales increase was achieved. Netflix, on the other hand, found that 75% of movie recommendations were made using services. This personalization technique is considered to be one of the key strategies for one-to-one marketing that can be useful in online markets where salespeople do not exist. Recommendation techniques that are mainly used in recommendation systems today include collaborative filtering and content-based filtering. Furthermore, hybrid techniques and association rules that use these techniques in combination are also being used in various fields. Of these, collaborative filtering recommendation techniques are the most popular today. Collaborative filtering is a method of recommending products preferred by neighbors who have similar preferences or purchasing behavior, based on the assumption that users who have exhibited similar tendencies in purchasing or evaluating products in the past will have a similar tendency to other products. However, most of the existed systems are recommended only within the same category of products such as books and movies. This is because the recommendation system estimates the purchase satisfaction about new item which have never been bought yet using customer's purchase rating points of a similar commodity based on the transaction data. In addition, there is a problem about the reliability of purchase ratings used in the recommendation system. Reliability of customer purchase ratings is causing serious problems. In particular, 'Compensatory Review' refers to the intentional manipulation of a customer purchase rating by a company intervention. In fact, Amazon has been hard-pressed for these "compassionate reviews" since 2016 and has worked hard to reduce false information and increase credibility. The survey showed that the average rating for products with 'Compensated Review' was higher than those without 'Compensation Review'. And it turns out that 'Compensatory Review' is about 12 times less likely to give the lowest rating, and about 4 times less likely to leave a critical opinion. As such, customer purchase ratings are full of various noises. This problem is directly related to the performance of recommendation systems aimed at maximizing profits by attracting highly satisfied customers in most e-commerce transactions. In this study, we propose the possibility of using new indicators that can objectively substitute existing customer 's purchase ratings by using RFM multi-dimensional analysis technique to solve a series of problems. RFM multi-dimensional analysis technique is the most widely used analytical method in customer relationship management marketing(CRM), and is a data analysis method for selecting customers who are likely to purchase goods. As a result of verifying the actual purchase history data using the relevant index, the accuracy was as high as about 55%. This is a result of recommending a total of 4,386 different types of products that have never been bought before, thus the verification result means relatively high accuracy and utilization value. And this study suggests the possibility of general recommendation system that can be applied to various offline product data. If additional data is acquired in the future, the accuracy of the proposed recommendation system can be improved.