It is important to preempt new technology because the technology competition is getting much tougher. Stakeholders conduct exploration activities continuously for new technology preoccupancy at the right time. Gartner's Hype Cycle has significant implications for stakeholders. The Hype Cycle is a expectation graph for new technologies which is combining the technology life cycle (S-curve) with the Hype Level. Stakeholders such as R&D investor, CTO(Chef of Technology Officer) and technical personnel are very interested in Gartner's Hype Cycle for new technologies. Because high expectation for new technologies can bring opportunities to maintain investment by securing the legitimacy of R&D investment. However, contrary to the high interest of the industry, the preceding researches faced with limitations aspect of empirical method and source data(news, academic papers, search traffic, patent etc.). In this study, we focused on two research questions. The first research question was 'Is there a difference in the characteristics of the network structure at each stage of the hype cycle?'. To confirm the first research question, the structural characteristics of each stage were confirmed through the component cohesion size. The second research question is 'Is there a pattern of diffusion at each stage of the hype cycle?'. This research question was to be solved through centralization index and network density. The centralization index is a concept of variance, and a higher centralization index means that a small number of nodes are centered in the network. Concentration of a small number of nodes means a star network structure. In the network structure, the star network structure is a centralized structure and shows better diffusion performance than a decentralized network (circle structure). Because the nodes which are the center of information transfer can judge useful information and deliver it to other nodes the fastest. So we confirmed the out-degree centralization index and in-degree centralization index for each stage. For this purpose, we confirmed the structural features of the community and the expectation diffusion patterns using Social Network Serice(SNS) data in 'Gartner Hype Cycle for Artificial Intelligence, 2021'. Twitter data for 30 technologies (excluding four technologies) listed in 'Gartner Hype Cycle for Artificial Intelligence, 2021' were analyzed. Analysis was performed using R program (4.1.1 ver) and Cyram Netminer. From October 31, 2021 to November 9, 2021, 6,766 tweets were searched through the Twitter API, and converting the relationship user's tweet(Source) and user's retweets (Target). As a result, 4,124 edgelists were analyzed. As a reult of the study, we confirmed the structural features and diffusion patterns through analyze the component cohesion size and degree centralization and density. Through this study, we confirmed that the groups of each stage increased number of components as time passed and the density decreased. Also 'Innovation Trigger' which is a group interested in new technologies as a early adopter in the innovation diffusion theory had high out-degree centralization index and the others had higher in-degree centralization index than out-degree. It can be inferred that 'Innovation Trigger' group has the biggest influence, and the diffusion will gradually slow down from the subsequent groups. In this study, network analysis was conducted using social network service data unlike methods of the precedent researches. This is significant in that it provided an idea to expand the method of analysis when analyzing Gartner's hype cycle in the future. In addition, the fact that the innovation diffusion theory was applied to the Gartner's hype cycle's stage in artificial intelligence can be evaluated positively because the Gartner hype cycle has been repeatedly discussed as a theoretical weakness. Also it is expected that this study will provide a new perspective on decision-making on technology investment to stakeholdes.
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.
1. Research Purpose Consumers rely on various clues to evaluate their decision to patronize a retail store, and store brand is one of them (Dodds 1991; Grewal et al. 1998). As consumers find ever increasing variety of contact points connecting them to specific store, the value of experiential shopping as a means of increasing store's brand equity warrants greater attention from scholars of retail management. Retail shopping values are credited for creating not only cognitive experiences like brand knowledge but also emotional experiences such as shopping pleasure and pride (Schmitt 1999). This may be because today's consumers place emphasis on emotional values associated with shopping pleasure, lifestyle brought to life, brand relationship, and store atmosphere more than utilitarian values such as product quality and price. Many previous literature found this to be true (Ahn and Lee 2011; Mathwick et al. 2001). This brings forth important research issues and questions regarding the roles of shopping experiential values and brand equity with regard to consumer's retail patronage choice. However, despite this importance, research on this area remains quite inadequate (Hwang 2010). For this reason, this study aims to verify the relationships among experiential shopping values, retail store brand equity and tries to link that with customer loyalty by surveying large-scale discount store shoppers in Korea and China. 2. Research Contents In order to carry out the research objective, this study conducted comprehensive literature survey on previous literature by discussing major findings and implications with regard to shopping values and retail brand equity and store loyalty. For data collection, researcher employed survey-based research method where data were collected in two major cities of Korea (Seoul) and China (Bejing) and sampling frame was based on patrons of large discount stores in both countries. Specific research questions raised in this study are as follows; RQ1: How do Korean and Chinese consumers differently perceive of shopping values regarding shopping at large-sclae discount stores? RQ2: Are there differences in consumers' emotional consumption propensities? RQ3: Do Korean and Chinese consumers display different perceptions of brand equity towards large-scale discount stores? RQ4: Are there differences in relationships between shopping values and brand equity for Korean and Chinese consumers? For statistical analysis, SPSS17.0, AMOS17.0 and SmartPLS were employed. 3. Research Results The data collected through face-to-face survey conducted in Seoul and Bejing revealed appropriate data validity and reliability as a result of exploratory/confirmatory factor analysis and reliability tests, andh SEM model yielding satisfactory model fitness. The result of the study may be summarized by three main points. First, as a result of testing differences in consumption dispositions, Chinese consumers showed higher scores in aesthetic and symbolic dispositions, whereas Korean consumers scored higher in hedonic disposition. Second, testing on perceptions toward brand equity of large discount stores showed that Korean consumers exhibited more positive perceptions of brand awareness and brand image than Chinese counterparts. Third, the result of exploratory factor analysis on the experiential shopping values revealed different factors for each country. On Korean side, consumer interest value, aesthetic value, and hedonic value were prominent, whereas on Chinese side, hedonic value, aesthetic value, consumer interest value, and service excellence value were found salient. 4. Research Implications While many previous studies on inter-country differences in retailing area mainly focused on cultural dispositions or orientations to explain the differences, this study sets itself apart by specifically targeting individual consumer's shopping values from an experiential viewpoint. The study result provides important theoretical as well as practical implications for large-scale discount store, especially the impotance of fully exploring the linkage between shopping values and brand equity, which has significant influence on loyalty. Therefore, the specific implications deriving from the result shed some important insights upon the consumption values based on shopping experiences and brand equity. The differences found in store shoppers between the two countries may also provide useful insights for Korean and Chinese retailers who plan to expand their operations globally. Related strategic implications derived from this study is the importance of localizing retail strategy which is based on the differences found in experiential shopping values between the two country groups. Especially the finding that Chinese consumers value consumer interest and service excellence, whereas Koreans place importance on hedonic or aesthetic values indicates the need to differentiate the consumer's psychographical profiles when it comes to expanding retail operations globally. Particularly important will be to pursue price-orienated strategy in China in consideration of the high emphasis on consumer interests and service excellence, but to emphasize the symbolic aspects of brand equity in Korea by maximizing the brand equity associated with aesthetic values and hedonic orientations. 5. Recommendations This study focused on generic retail branded discount stores in both countries, thus making it difficult to tease out store-specific strategies based on specific retail brands. Future studies may benefit fro employing actual brand names in survey questionnaire to verify relationship between shopping values and brand-based store strategy. As with other studies of this nature, this study needs to strengthen the result's generalizability by selecting respondents from a wider spectrum of respondents.