• 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 Distribution Science
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• v.10 no.4
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• pp.47-53
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• 2012

Businesses make use of advertising strategy using models to give consumers efficient product information. Modern advertisements often make use of models for greater reminiscence to create messages and remind viewers of the product. The purpose of this study was to examine the characteristics of each type of model. The subjects were 230 college students in their twenties or older, and the material was collected from October 20, 2011 to November 5, 2011 to examine the effects of model characteristics on buying intention as well as attitude toward a brand. A questionnaire survey was used; investigators gave one copy to each interviewee. The study investigated the characteristics of each model using a questionnaire of each 40 copies with five kinds of photographs. The characteristics of models had great influence on buying intention and attitude toward the brand: First, factor 2 (being honest and virtuous and having good credit and a good press assessment) and factor 3 (being interesting and a good communicator and creating good memories) had great influence on buying intention. Factor 2 was explained by reliability, and factor 3 by the efficiency of the model in creating a feeling. Second, factors 1 (being attractive, smart, unique, friendly, loved by others, and popular), 2, and 3 influenced attitude toward brand. Factor 1 encapsulated the outgoing characteristics of a model, factor 2 was based on reliability, and factor 3 was based on the efficiency of the model in creating a feeling. The model's positive effects on buying intention and attitudes toward brand shall be examined. For their positive influence on buying intention, reliability and efficiency shall be given attention. For their positive influence on attitude toward brand, creating a good impression, having outgoing characteristics, being reliable, and efficiency shall be given attention. The findings were as follows: Model characteristics influencing buying intention were similar to those influencing attitude toward brand. The differences were as follows. First, reliability and efficiency influenced buying intention. When customers were asked to consider the influence on buying intention of an advertisement, regardless of the strength of the buying intention, they considered these two characteristics. Customers decided to buy based not only on the credibility of the product as presented in the advertisement but also the transmission of the contents of the advertisement. Second, outgoing characteristics, reliability, and efficiency influenced attitude toward a brand. The attitude toward a brand was said to be the attitude toward the business. The attitude is produced even after buying, so businesses view it as very important. The attitude might vary depending upon the model used rather than the brand. Therefore, a model with outgoing characteristics was thought to be important. Therefore, attitude toward a brand whose model influenced buying intention as well as attitude toward brand had outgoing characteristics. The result is that an image the model was related to attitude toward the brand. As such, customers would buy the goods advertised. However, an outgoing image of a model was also important to create a positive attitude toward a business brand. For instance, talent Park Gyeong-Rim's photo was used to promote cosmetics about 10 years ago. When she worked as a model of cosmetics products, she had to make compensation for losses and damages because she made a mistake on a talk show program. At that time, customers who had bought the cosmetics product asked for refunds of several billion won. As such, models who are said to be the face of the businesses they represent can play an important role. To advertise in the most attractive and effective way, the current image of a model should be investigated by examining current activities and news articles after selecting the model, and the model's efficiency and attitude toward the brand should be examined. Factors that stimulate customers' buying decisions can be used to plan advertisement that have positive influence on a brand. This study had the limitation of investigating mainly college students and there were insufficient copies of the questionnaire. The investigation was not done widely but in detail so that a concrete investigation could not be done. Further studies shall supplement these shortcomings and discuss new directions.

• Journal of Global Scholars of Marketing Science
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• v.20 no.1
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• pp.59-69
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• 2010

Green or an environmental consciousness has been a major issue for businesses and government offices, as well as consumers, worldwide. In response to this movement, the Korean government announced, in the early 2000s, the era of "Green Growth" as a way to encourage green-related business activities. The Korean fashion industry, in various levels of involvement, presents diverse eco-friendly products as a part of the green movement. These apparel products include organic products and recycled clothing. For these companies to be successful, they need information about who are the consumers who consider green issues (e.g., environmental sustainability) as part of their personal values when making a decision for product purchase, use, and disposal. These consumers can be considered as eco-sumers. Previous studies have examined consumers' purchase intention for or with eco-friendly products. In addition, studies have examined influential factors used to identify the eco-sumers or green consumers. However, limited attention was paid to eco-sumers' disposal or recycling behavior of clothes in comparison with their green product purchases. Clothing disposal behaviors are ways that consumer can get rid of unused clothing and in clue temporarily lending the item or permanently eliminating the item by "handing down" (e.g., giving it to a younger sibling), donating, exchanging, selling, or simply throwing it away. Accordingly, examining purchasing behaviors of eco-friendly fashion items in conjunction with clothing disposal behaviors should improve understanding of a consumer's clothing consumption behavior from the environmental perspective. The purpose of this exploratory study is to provide descriptive information about Korean eco-sumers who have ecologically-favorable lifestyles and behaviors when buying and disposing of clothes. The objectives of this study are to (a) categorize Koreans on the basis of clothing disposal behaviors; (b) investigate the differences in demographics, lifestyles, and clothing consumption values among segments; and (c) compare the purchase intention of eco-friendly fashion items and influential factors among segments. A self-administered questionnaire was developed based on previous studies. The questionnaire included 10 items of clothing disposal behavior, 22 items of LOHAS (Lifestyles of Health and Sustainability) characteristics, and 19 items of consumption values, measured by five-point Likert-type scales. In addition, the purchase intention of two eco-friendly fashion items and 11 attributes of each item were measured by seven-point Likert type scales. Two polyester fleece pullovers, made from fabric created from recycled bottles with the PET identification code, were selected from one Korean brand and one US imported brand among outdoor sportswear brands. A brief description of each product with a color picture was provided in the survey. Demographic variables (i.e., gender, age, marital status, education level, income, occupation) were also included. The data were collected through a professional web survey agency during May 2009. A total of 600 final usable questionnaires were analyzed. The age of respondents ranged from 20 to 49 years old with a mean age of 34 years. Fifty percent of the respondents were males and about 58% were married, and 62% reported having earned university degrees. Principal components factor analysis with varimax rotation was used to identify the underlying dimensions of the clothing disposal behavior scale, and three factors were generated (i.e., reselling behavior, donating behavior, non-recycling behavior). To categorize the respondents on the basis of clothing disposal behaviors, k-mean cluster analysis was used, and three segments were obtained. These consumer segments were labeled as 'Resale Group', 'Donation Group', and 'Non-Recycling Group.' The classification results indicated approximately 98 percent of the original cases were correctly classified. With respect to demographic characteristics among the three segments, significant differences were found in gender, marital status, occupation, and age. LOHAS characteristics were reduced into the following five factors: self-satisfaction, family orientation, health concern, environmental concern, and voluntary service. Significant differences were found in the LOHAS factors among the three clusters. Resale Group and Donation Group showed a similar predisposition to LOHAS issues while the Non-Recycling Group presented the lowest mean scores on the LOHAS factors compared to the other segments. The Resale and Donation Groups described themselves as enjoying or being satisfied with their lives and spending spare-time with family. In addition, these two groups cared about health and organic foods, and tried to conserve energy and resources. Principal components factor analysis generated clothing consumption values into the following three factors: personal values, social value, and practical value. The ANOVA test with the factors showed differences primarily between the Resale Group and the other two groups. The Resale Group was more concerned about personal value and social value than the other segments. In contrast, the Non-Recycling Group presented the higher level of social value than did Donation Group. In a comparison of the intention to purchase eco-friendly products, the Resale Group showed the highest mean score on intent to purchase Product A. On the other hand, the Donation Group presented the highest intention to purchase for Product B among segments. In addition, the mean scores indicated that the Korean product (Product B) was more preferable for purchase than the U.S. product (Product A). Stepwise regression analysis was used to identify the influence of product attributes on the purchase intention of eco product. With respect to Product A, design, price and contribution to environmental preservation were significant to predict purchase intention for the Resale Group, while price and compatibility with my image factors were significant for the Donation Group. For the Non-Recycling Group, design, price compatibility with the factors of my image, participation to eco campaign, and contribution to environmental preservation were significant. Price appropriateness was significant for each of the three clusters. With respect to Product B, design, price and compatibility with my image factors were important, but different attributes were associated significantly with purchase intention for each of the three groups. The influence of LOHAS characteristics and clothing consumption values on intention to purchase Products A and B were also examined. The LOHAS factor of health concern and the personal value factor were significant in the relationships with the purchase intention; however, the explanatory powers were low in the three segments. Findings showed that each group as classified by clothing disposal behaviors showed differences in the attributes of a product, personal values, and the LOHAS characteristics that influenced their purchase intention of eco-friendly products. Findings would enable organizations to understand eco-friendly behavior and to design appropriate strategic decisions to appeal eco-sumers.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.1-20
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• 2013

Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.