Emerging hotspot and trendy areas are formed into alleys and blocks with the help of viral effects among social network services (SNS) users called "Golmogleo." These users search for every corner of the alleys to share and promote their own favorite places through SNS. An analysis of hot places is limited if it is only based on macroeconomic indicators such as commercial area data published by national organizations, large-scale visiting facilities, and commuter figures. Careful analyses based on consumers' actual activities are needed. This study develops a "social big data analysis methodology" using Instagram data, which is one of the most popular SNSs suitable to identify recent consumer trends. We build a spatial analysis model using Local Moran's I. Results show that our model identifies new trend zones on the basis of posting data in Instagram, which are not included in the commercial information prepared by national organizations. The proposed analysis methodology enables better identification of the latest trend areas formulated by SNS user activities. It also provides practical information for start-ups, small business owners, and alley merchants for marketing purposes. This analytical methodology can be applied to future studies on social big data analysis.
The core service of most research portal sites is providing relevant research papers to various researchers that match their research interests. This kind of service may only be effective and easy to use when a user can provide correct and concrete information about a paper such as the title, authors, and keywords. However, unfortunately, most users of this service are not acquainted with concrete bibliographic information. It implies that most users inevitably experience repeated trial and error attempts of keyword-based search. Especially, retrieving a relevant research paper is more difficult when a user is novice in the research domain and does not know appropriate keywords. In this case, a user should perform iterative searches as follows : i) perform an initial search with an arbitrary keyword, ii) acquire related keywords from the retrieved papers, and iii) perform another search again with the acquired keywords. This usage pattern implies that the level of service quality and user satisfaction of a portal site are strongly affected by the level of keyword management and searching mechanism. To overcome this kind of inefficiency, some leading research portal sites adopt the association rule mining-based keyword recommendation service that is similar to the product recommendation of online shopping malls. However, keyword recommendation only based on association analysis has limitation that it can show only a simple and direct relationship between two keywords. In other words, the association analysis itself is unable to present the complex relationships among many keywords in some adjacent research areas. To overcome this limitation, we propose the hybrid approach for establishing association network among keywords used in research papers. The keyword association network can be established by the following phases : i) a set of keywords specified in a certain paper are regarded as co-purchased items, ii) perform association analysis for the keywords and extract frequent patterns of keywords that satisfy predefined thresholds of confidence, support, and lift, and iii) schematize the frequent keyword patterns as a network to show the core keywords of each research area and connecting keywords among two or more research areas. To estimate the practical application of our approach, we performed a simple experiment with 600 keywords. The keywords are extracted from 131 research papers published in five prominent Korean journals in 2009. In the experiment, we used the SAS Enterprise Miner for association analysis and the R software for social network analysis. As the final outcome, we presented a network diagram and a cluster dendrogram for the keyword association network. We summarized the results in Section 4 of this paper. The main contribution of our proposed approach can be found in the following aspects : i) the keyword network can provide an initial roadmap of a research area to researchers who are novice in the domain, ii) a researcher can grasp the distribution of many keywords neighboring to a certain keyword, and iii) researchers can get some idea for converging different research areas by observing connecting keywords in the keyword association network. Further studies should include the following. First, the current version of our approach does not implement a standard meta-dictionary. For practical use, homonyms, synonyms, and multilingual problems should be resolved with a standard meta-dictionary. Additionally, more clear guidelines for clustering research areas and defining core and connecting keywords should be provided. Finally, intensive experiments not only on Korean research papers but also on international papers should be performed in further studies.
Advances in Internet technologies and the proliferation of mobile devices enabled consumers to approach a wide range of goods and services, while causing an adverse effect that they have hard time reaching their congenial items even if they devote much time to searching for them. Accordingly, businesses are using the recommender systems to provide tools for consumers to find the desired items more easily. Association Rule Mining (ARM) technology is advantageous to recommender systems in that ARM provides intuitive form of a rule with interestingness measures (support, confidence, and lift) describing the relationship between items. Given an item, its relevant items can be distinguished with the help of the measures that show the strength of relationship between items. Based on the strength, the most pertinent items can be chosen among other items and exposed to a given item's web page. However, the diversity of the measures may confuse which items are more recommendable. Given two rules, for example, one rule's support and confidence may not be concurrently superior to the other rule's. Such discrepancy of the measures in distinguishing one rule's superiority from other rules may cause difficulty in selecting proper items for recommendation. In addition, in an online environment where a web page or mobile screen can provide a limited number of recommendations that attract consumer interest, the prudent selection of items to be included in the list of recommendations is very important. The exposure of items of little interest may lead consumers to ignore the recommendations. Then, such consumers will possibly not pay attention to other forms of marketing activities. Therefore, the measures should be aligned with the probability of consumer's acceptance of recommendations. For this reason, this study proposes a model-based approach to combine those measures into one unified measure that can consistently determine the ranking of recommended items. A regression model was designed to describe how well the measures (independent variables; i.e., support, confidence, and lift) explain consumer's acceptance of recommendations (dependent variables, hit rate of recommended items). The model is intuitive to understand and easy to use in that the equation consists of the commonly used measures for ARM and can be used in the estimation of hit rates. The experiment using transaction data from one of the Korea's largest online shopping malls was conducted to show that the proposed model can improve the hit rates of recommendations. From the top of the list to 13th place, recommended items in the higher rakings from the proposed model show the higher hit rates than those from the competitive model's. The result shows that the proposed model's performance is superior to the competitive model's in online recommendation environment. In a web page, consumers are provided around ten recommendations with which the proposed model outperforms. Moreover, a mobile device cannot expose many items simultaneously due to its limited screen size. Therefore, the result shows that the newly devised recommendation technique is suitable for the mobile recommender systems. While this study has been conducted to cover the cross-selling in online shopping malls that handle merchandise, the proposed method can be expected to be applied in various situations under which association rules apply. For example, this model can be applied to medical diagnostic systems that predict candidate diseases from a patient's symptoms. To increase the efficiency of the model, additional variables will need to be considered for the elaboration of the model in future studies. For example, price can be a good candidate for an explanatory variable because it has a major impact on consumer purchase decisions. If the prices of recommended items are much higher than the items in which a consumer is interested, the consumer may hesitate to accept the recommendations.
The concept of Corporate Social Responsibility (CSR) was first introduced sixty years ago in the academic field. However, the phrase CSR was not explicitly stated before the 1990s in Korean business and academic researches. Recently CSR is more considered a corporate strategy than a philanthropic donation. CSR comprises contributions to local communities as well as using environmentally beneficial and humane practices. Sponsoring is one available marketing tactic used in order to communicate with the market. This study of sponsorship has concentrated on developing brand asset by accessing potential values of sporting events or star-players. However, sponsorship includes providing funds or goods to non-profit institutions as well as sports or entertainment organizations. Accordingly corporate community-specific sponsorship is defined as firms offering to provide money, goods and/or services to individuals and/or institutions within a particular community, thus establishing an interdependent relationship between the partners aspiring to gain social and economic assets. National sponsorship is typically targeted toward commonly recognized individuals and/or organizations with the intent to maximize exposure of a sponsor's brand, and is known to positively affect brand equity(community-specific sponsorship is committed to a limited local area) that a firm could benefit from by gaining a specific asset. POSCO sponsors the Jukdo Market, locate dinthe city of Pohang, tohelp revive their traditional market. Inreturn, the Jukdo Market merchant suni on display sflags with the POSCO embleminfrontof stores with in the market intending to make shopper sand merchant saware of POSCO's sponsorship. POSCO has succeeded in acquiring public support from the citizens of Pohang. However, the economic effects resulting from the cooperative relationship between POSCO and the Jukdo Market have yet to be measured by any empirical research. The purpose of this study is to assess the economic effects created by the community-specific sponsorship from the groups of merchants and shoppers, measuring its influence on the corporate image and subsequent brand loyalty, as parts of brand equity. The result of the study shows that the community-specific sponsorship of POSCO of the Jukdo Market had different influences on its corporate image and the brand loyalty of shoppers and merchants. First, the merchant group who was more frequently exposed to POSCO's flag recognized the sponsorship of POSCO more than the shopper group, and, therefore, had a better image of the company. Second, the recognition of POSCO's sponsorship had a positive influence on its corporate image, and that positive corporate image had a positive effect on brand loyalty development. However, the recognition of the sponsorship did not have a direct influence on brand loyalty. The friendly corporate image developed by the recognition of the sponsorship consequently could have had an effect on brand loyalty. Therefore, companies should not relinquish investments to corporate image development if they require more brand loyalty. Third, the influence of corporate image on brand loyalty shows stronger results in the shopper group rather than in the merchant group. Psycho-graphic factors of shoppers and merchants might give rise to the difference between the two groups.
The retail trades in many countries have changed recently according to the high quality, diversification, and marked individuality of consumer needs. Under the continually competing system of the WTO agreement, corporations based in the USA and the EU try to raise their market share in other countries so it is inevitable for Korean retail enterprises to compete with them. This paper is aimed at contributing to the efficient growth for Korean retail trade from the analysis of the development process in retail trade and its changing locations comparing Korea and the USA. Retailers in the USA have practiced diversified marketing strategies considerably in order to survive in a rapidly changing retailing environment. American retailing, which has the most advanced marketing system in the world, has been of growing concern to marketing strategies in Korea. The following is a brief summary of this study. 1. Speedy and higher quality consumption is needed in accordance with the great increase in the single-family household and the female labor force participation both in Korea and in the USA. Senior citizens have become a new consumer group due to the aging population. In the future the retail trade will switch over to diversified retail formats and internet shopping as countries are transformed into information and communication societies. 2. In Korea, the former retail system characterized by markets and department stores has been greatly changed since the late 1980s with emphasis on high quality and convenience in consumption behaviors, with large domestic enterprises and foreign distribution corporations participating in Korean retailing. In the USA, retailing mergers and takeovers by major retails, bankruptcies, and extra-large shopping centers have emerged since the late 1980s. Recently, the USA retailing formats have been changed from the lower price-oriented discount types to the large scale theme parks. Much emphasis was put on entertainment, resorts, and convention centers. On the other hand, non-store types, such as the internet shopping, the CATV shopping, as well as catalog and mail-order sales are drastically increasing, although the proportion of their sales is low up to now. 3. In Korea, most of the retail facilities are concentrated in Seoul and the Metropolitan Region, and the distribution ratio of facilities came to 52% in 1997. The periodic markets, traditional markets which open on a periodic basis, are located mainly in Chollanam-do and Kyungsangbuk-do. The large-sized discount stores have expanded their locations to the over-crowded apartment complexes in new towns, located in the Metropolitan Region, and the large provincial cities, unlike the suburban locations in the USA. Therefore we needed to give attention to the locational relations in retail facilities between Seoul & the Metropolitan Region and rural settlement areas. In the USA, urban areas grew quickly with the development of the automobile in the 1920s, and the location of stores changed from a dispersed style centering around rural areas to a centralized one in urban areas. There is an accelerated growth for suburban areas, which have grown rapidly since 1950. As the membership warehouse clubs were introduced in the 1970s, the decentralization of location was more intensified. On the other hand, inner cities were revitalized by rearranging existing facilities to cope with suburban areas. And the location-free virtual retailing & TV shopping are also growing every year. 4. In view of the above, the continuous and desirable development devices in Korean retail trade are summarized as follows: First, the countermeasures against economies of scale, increase in retailing sales, and rise of a employment percentage in retailing are in need. Second, a scheme of lowering the proportion of food retail sales, and increasing a ratio of durable goods sales need to be worked out. Third, the original ideas are needed to apply positively information, communication and technology to retailing, to graft the traditional types on modem ones based on the social culture. Fourth, strategies are needed to strengthen the competitiveness of our retail trade through cooperation and chains of smaller retailers, the large enterprises participating in the distribution industry. Fifth, in order to realize the above, the retail industry, the administration, and the academic world should support the retail segment with concern and a practical strategy plan.
Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used