• Journal of Distribution Science
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• v.17 no.3
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• pp.85-95
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• 2019

Purpose - The existing marketing studies using Social Network Analysis have assumed that network structure variables are time-invariant. However, a node's network position can fluctuate considerably over time and the node's network structure can be changed dynamically. Hence, if such a dynamic structural network characteristics are not specified for virtual goods purchase model, estimated parameters can be biased. In this paper, by comparing a time-invariant network structure specification model(base model) and time-varying network specification model(proposed model), the authors intend to prove whether the proposed model is superior to the base model. In addition, the authors also intend to investigate whether coefficients of network structure variables are random over time. Research design, data, and methodology - The data of this study are obtained from a Korean social network provider. The authors construct a monthly panel data by calculating the raw data. To fit the panel data, the authors derive random effects panel tobit model and multi-level mixed effects model. Results - First, the proposed model is better than that of the base model in terms of performance. Second, except for constraint, multi-level mixed effects models with random coefficient of every network structure variable(in-degree, out-degree, in-closeness centrality, out-closeness centrality, clustering coefficient) perform better than not random coefficient specification model. Conclusion - The size and importance of virtual goods market has been dramatically increasing. Notwithstanding such a strategic importance of virtual goods, there is little research on social influential factors which impact the intention of virtual good purchase. Even studies which investigated social influence factors have assumed that social network structure variables are time-invariant. However, the authors show that network structure variables are time-variant and coefficients of network structure variables are random over time. Thus, virtual goods purchase model with dynamic network structure variables performs better than that with static network structure model. Hence, if marketing practitioners intend to use social influences to sell virtual goods in social media, they had better consider time-varying social influences of network members. In addition, this study can be also differentiated from other related researches using survey data in that this study deals with actual field data.

• Fashion & Textile Research Journal
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• v.22 no.3
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• pp.271-286
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• 2020

This study evaluated designs via the consumers' function and design preferences survey for using product design images, virtual avatar wearing images and product explanations that identified consumers' function and design preferences for knee protection pads as well as to develop movement assistive knee pad designs. We developed Design A for men and Design B for women. For Design A, the front of the knee supports muscles and alleviates pain with a hole. Mesh material with good ventilation was applied to enhance wearing comfort. The color was achromatic for a modern style, and the hook fastener and loops enabled easy wear and removal of the pad while controlling size and pressure strength. For Design B, taping details seamlessly support muscles in the knee area with fabrics less than 0.1 cm thick and with long sleeves in the diverse sizes. The design's satisfaction assessment showed that potential consumers were satisfied with Design A and Design B for overall design and functional features. Over 77% wanted to use/wear and purchase designs; in addition, over 78% expected it would help with walking and relieve knee pain. The results can be helpful for designers when deciding designs for manufacturing and commercializing kneepad products.

• Journal of Global Scholars of Marketing Science
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• v.17 no.3
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• pp.61-81
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• 2007

Recently, use of foreign models and foreign language in advertising is a general trend in Korea even though the effect has not been well-known..Most of the previous research shows rather an opposite effect claiming marketing communication is more effective when higher congruity between marketing communication and consumer's cultural values are achieved. However, the introduction of global culture due to the expansion of new media such as Internet or cable television makes the congruity not the best choice of marketing strategy. In addition, use of highly attractive models in advertising to increase the effect of advertising is general. However, recent studies show that targeted women audience tend to compare themselves to the highly attractive models and do experience negative sentiment. Bower (2001) proved the difference between 'comparer' and 'noncomparer' when women face highly attractive models. The results show that a comparer who has an intention to compare highly attractive model (HAM) with herself has a significantly negative effect on model expertise, product argument, product evaluation and buying intention. Therefore, HAM is not always a good choice and model attractiveness plays a role in the processing other cues or changing the advertising effect from result of processing other cues. The purpose of this study is to investigate the effect of the use of foreign language on the advertising response of the audience with regard of the model attractiveness. For the empirical study, the virtual advertising using foreign models (HAM, NAM), brand names and slogans(Korean, English) were used as stimuli. The respondents of each stimulus were 75('HAM-Korean'), 75('NAM-Korean'), 66('HAM-English') and 66 ('NAM-English') respectively. To establish the effect of marketing communication, the attitude for media(AM), the attitude for product(AP), targetedness(TD), overall quality(OQ), and purchase intention(PI) with 7 point likert scale were measured. The manipulation was verified to check the difference between HAM attractiveness assessment (m=3.27) and NAM attractiveness assessment (m=5.12). The mean difference was statiscally significant (p<.05). As a result, all consequences were significantly changed with model attractiveness, and overall quality evaluation(OQ) were significantly changed with language. The interaction effect from model attractiveness and language was significant on attitude toward the product(AP) and purchase intention(PI). To analyze the difference, the mean values and standard deviation of consequences were compared. The result was more positive when model attractiveness was high for all consequences. For language effect, the assessment was more positive when English was used for OQ. Considering model attractiveness and language simultaneously, HAM-Korean was more positive for AP and PI, and NAM-English was more positive for AP and PI. In other words, the interaction effect was confirmed by model attractiveness and language. As mentioned above, use of foreign models and foreign language in advertising was explained by cultural match up hypothesis (Leclerc et al. 1994) which claimed that culture of origin effect. In other words, in advertising, use of same cultural language with the foreign model could make positive assessment for OQ. But this effect was moderated by model attractiveness. When the model attractiveness was low, the use of English makes PI high because of the effect of foreign language which supported the cultural match up hypothesis. When the model attractiveness was low, the use of Korean made AP and PI high because the effect of foreign language was diluted. It was a general notion that the visual cues got processed before (Holbrook and Moore, 1981; Sholl et al, 1995) compared to linguistic cues. Therefore, when consumers were faced HAM, so much perception was already consumed at processing visual cues making their native language of Korean to strongly and positively connected with the advertising concept. On the contrary, when consumers were faced with NAM, less perception was consumed compared to HAM, making English to accompany cultural halo effect which affected more positively. Therefore, when foreign models were employed in advertising, the language must be carefully selected according to the level of model attractiveness.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.27-42
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• 2020

Traditional companies with offline stores were unable to secure large display space due to the problems of cost. This limitation inevitably allowed limited kinds of products to be displayed on the shelves, which resulted in consumers being deprived of the opportunity to experience various items. Taking advantage of the virtual space called the Internet, online shopping goes beyond the limits of limitations in physical space of offline shopping and is now able to display numerous products on web pages that can satisfy consumers with a variety of needs. Paradoxically, however, this can also cause consumers to experience the difficulty of comparing and evaluating too many alternatives in their purchase decision-making process. As an effort to address this side effect, various kinds of consumer's purchase decision support systems have been studied, such as keyword-based item search service and recommender systems. These systems can reduce search time for items, prevent consumer from leaving while browsing, and contribute to the seller's increased sales. Among those systems, recommender systems based on association rule mining techniques can effectively detect interrelated products from transaction data such as orders. The association between products obtained by statistical analysis provides clues to predicting how interested consumers will be in another product. However, since its algorithm is based on the number of transactions, products not sold enough so far in the early days of launch may not be included in the list of recommendations even though they are highly likely to be sold. Such missing items may not have sufficient opportunities to be exposed to consumers to record sufficient sales, and then fall into a vicious cycle of a vicious cycle of declining sales and omission in the recommendation list. This situation is an inevitable outcome in situations in which recommendations are made based on past transaction histories, rather than on determining potential future sales possibilities. This study started with the idea that reflecting the means by which this potential possibility can be identified indirectly would help to select highly recommended products. In the light of the fact that the attributes of a product affect the consumer's purchasing decisions, this study was conducted to reflect them in the recommender systems. In other words, consumers who visit a product page have shown interest in the attributes of the product and would be also interested in other products with the same attributes. On such assumption, based on these attributes, the recommender system can select recommended products that can show a higher acceptance rate. Given that a category is one of the main attributes of a product, it can be a good indicator of not only direct associations between two items but also potential associations that have yet to be revealed. Based on this idea, the study devised a recommender system that reflects not only associations between products but also categories. Through regression analysis, two kinds of associations were combined to form a model that could predict the hit rate of recommendation. To evaluate the performance of the proposed model, another regression model was also developed based only on associations between products. Comparative experiments were designed to be similar to the environment in which products are actually recommended in online shopping malls. First, the association rules for all possible combinations of antecedent and consequent items were generated from the order data. Then, hit rates for each of the associated rules were predicted from the support and confidence that are calculated by each of the models. The comparative experiments using order data collected from an online shopping mall show that the recommendation accuracy can be improved by further reflecting not only the association between products but also categories in the recommendation of related products. The proposed model showed a 2 to 3 percent improvement in hit rates compared to the existing model. From a practical point of view, it is expected to have a positive effect on improving consumers' purchasing satisfaction and increasing sellers' sales.

• Asia pacific journal of information systems
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• v.20 no.1
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• pp.81-99
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• 2010

Lately, in consumers' markets the number of new items is rapidly increasing at an overwhelming rate while consumers have limited access to information about those new products in making a sensible, well-informed purchase. Therefore, item providers and customers need a system which recommends right items to right customers. Also, whenever new items are released, for instance, the recommender system specializing in new items can help item providers locate and identify potential customers. Currently, new items are being added to an existing system without being specially noted to consumers, making it difficult for consumers to identify and evaluate new products introduced in the markets. Most of previous approaches for recommender systems have to rely on the usage history of customers. For new items, this content-based (CB) approach is simply not available for the system to recommend those new items to potential consumers. Although collaborative filtering (CF) approach is not directly applicable to solve the new item problem, it would be a good idea to use the basic principle of CF which identifies similar customers, i,e. neighbors, and recommend items to those customers who have liked the similar items in the past. This research aims to suggest a hybrid recommendation procedure based on the preference boundary of target customer. We suggest the hybrid recommendation procedure using the preference boundary in the feature space for recommending new items only. The basic principle is that if a new item belongs within the preference boundary of a target customer, then it is evaluated to be preferred by the customer. Customers' preferences and characteristics of items including new items are represented in a feature space, and the scope or boundary of the target customer's preference is extended to those of neighbors'. The new item recommendation procedure consists of three steps. The first step is analyzing the profile of items, which are represented as k-dimensional feature values. The second step is to determine the representative point of the target customer's preference boundary, the centroid, based on a personal information set. To determine the centroid of preference boundary of a target customer, three algorithms are developed in this research: one is using the centroid of a target customer only (TC), the other is using centroid of a (dummy) big target customer that is composed of a target customer and his/her neighbors (BC), and another is using centroids of a target customer and his/her neighbors (NC). The third step is to determine the range of the preference boundary, the radius. The suggested algorithm Is using the average distance (AD) between the centroid and all purchased items. We test whether the CF-based approach to determine the centroid of the preference boundary improves the recommendation quality or not. For this purpose, we develop two hybrid algorithms, BC and NC, which use neighbors when deciding centroid of the preference boundary. To test the validity of hybrid algorithms, BC and NC, we developed CB-algorithm, TC, which uses target customers only. We measured effectiveness scores of suggested algorithms and compared them through a series of experiments with a set of real mobile image transaction data. We spilt the period between 1st June 2004 and 31st July and the period between 1st August and 31st August 2004 as a training set and a test set, respectively. The training set Is used to make the preference boundary, and the test set is used to evaluate the performance of the suggested hybrid recommendation procedure. The main aim of this research Is to compare the hybrid recommendation algorithm with the CB algorithm. To evaluate the performance of each algorithm, we compare the purchased new item list in test period with the recommended item list which is recommended by suggested algorithms. So we employ the evaluation metric to hit the ratio for evaluating our algorithms. The hit ratio is defined as the ratio of the hit set size to the recommended set size. The hit set size means the number of success of recommendations in our experiment, and the test set size means the number of purchased items during the test period. Experimental test result shows the hit ratio of BC and NC is bigger than that of TC. This means using neighbors Is more effective to recommend new items. That is hybrid algorithm using CF is more effective when recommending to consumers new items than the algorithm using only CB. The reason of the smaller hit ratio of BC than that of NC is that BC is defined as a dummy or virtual customer who purchased all items of target customers' and neighbors'. That is centroid of BC often shifts from that of TC, so it tends to reflect skewed characters of target customer. So the recommendation algorithm using NC shows the best hit ratio, because NC has sufficient information about target customers and their neighbors without damaging the information about the target customers.