論文

The aim of this paper is to provide a deeper understanding of Shikake through the lens of marketing. "The aim of marketing is to make selling superfluous" is a famous quote by Peter Drucker. Its key message is that marketing is not an effort to control or force consumers to purchase products or services, but an activity to create a situation where consumers engage in consumer behavior spontaneously. The core essence of Shikakeology is a spontaneous behavior change, and it is fair to assume that marketing and Shikakeology share a fundamental philosophy. This paper defines the key concepts in the intersection of marketing and Shikakeology and attempts to classify Shikakes in the field of marketing in accordance with participants' motivation and consciousness. Different types of consumer participation are described in case studies. Finally, attempts are made to reveal the key to success of a Shikake in the field of marketing and discuss future research opportunities.

Previous research on personal influence has focused on the sender, such as influencers or opinion leaders. This paper focuses on the context of personal influence i.e., social relationship between senders and receivers, and attempts to identify the optimal relational context for information cascade. The results of empirical analysis using both offline survey data and online blogosphere data provide evidence of optimal heterophily between the two; i.e., the receiver wants to receive information from someone slightly more knowledgeable, not from a distant expert. The context of personal influence is also examined in terms of the stage of hierarchical consumer behavior and sentiment. Finally, the patterns of influence are presented to depict the diffusion process. This analysis of relational context and the patterns of influence suggest that many grassroots influentials, rather than a few super influentials, play a central role in information cascade.

Previous research on word-of-mouth (WOM) diffusion has focused on the attributes of the sender, such as opinion leadership and innovativeness. This paper focuses on the social relationship between WOM senders and receivers and attempts to identify the optimal heterophily for WOM diffusion. The results of empirical analysis using both offline survey data and online blogosphere data provide evidence of optimal heterophily between the two; i.e., the WOM receiver wants to receive information from someone slightly more knowledgeable, not from a distant expert. The context of WOM communication is also examined in terms of the stage of hierarchical consumer behavior and sentiment. Finally, the patterns of influence are presented to depict the diffusion process. This analysis of optimal heterophily and the patterns of influence suggest that many grassroots influentials, rather than a few super influentials, play a central role in WOM diffusion.

Social media, especially the micro-blogging network Twitter, have gained much popularity among users and have thus attracted attention from firms. Social media can serve as advertising media and platforms of online word-of-mouth, because they enable consumers to share their consumption experiences with others easily. When firms advertise their products and services, they usually do not rely on one single medium. Instead, they combine various media to promote their products and services. Recently, they have started to include social media. The aim of this paper is to capture the dynamic relationships among TV advertising, tweets, offline word-of-mouth, and customer acquisition. We call these dynamics the "marketing ecosystem" and attempt to investigate this model. To achieve this goal, this paper employs a structural equation approach, which allows developing a more complete and accurate picture of the inter-relationships among constructs. We incorporate both the direct effects and the indirect effects of traditional marketing actions such as TV advertising, which in turn increase tweets and new customer acquisition. The results of our analysis show that TV advertising increases buzz on social media and offline word-of-mouth. The results also show that TV advertising, offline-word-of-mouth, and tweets have a strong impact on customer acquisition.

Mobile Social Networking Services (SNS) are an emerging trend in which individuals of similar interests communicate with one another using mobile phones. In this paper, we calculate the monetary value of customers and their networks in mobile SNS using the official data provided by a service provider. The mobile SNS enable users to create their avatars to communicate with each other via blog comments and communities. The company sells various items for these avatars such as apparel and interiors. This service generates consumer purchase history data and user network data. In previous research, the value of the customer is often estimated using the purchase history, but the network value is neglected. In the field of computer science, the value of the network is evaluated via the network structure, but not much attention is paid to the economic aspect of each node. The aim of this paper is to incorporate the social factor with the customer purchase database and calculate the monetary value of each customer and his or her network. The results of empirical analysis show that our approach is useful in finding valuable customers for marketing activities and outperforms conventional metrics such as degree centrality.

Several attempts have been made to analyze customer behavior on online E-commerce sites. Some studies particularly emphasize the social networks of customers. Users' reviews and ratings of a product exert effects on other consumers' purchasing behavior. Whether a user refers to other users' ratings depends on the trust accorded by a user to the reviewer. On the other hand, the trust that is felt by a user for another user correlates with the similarity of two users'ratings. This bidirectional interaction that involves trust and rating is an important aspect of understanding consumer behavior in online communities because it suggests clustering of similar users and the evolution of strong communities. This paper presents a theoretical model along with analyses of an actual online E-commerce site. We analyzed a large community site in Japan: @cosme. The noteworthy characteristics of @cosme are that users can bookmark their trusted users; in addition, they can post their own ratings of products, which facilitates our analyses of the ratings' bidirectional effects on trust and ratings. We describe an overview of the data in @cosme, analyses of effects from trust to rating and vice versa, and our proposition of a measure of of community gravity, which measures how strongly a user might be attracted to a community. Our study is based on the @cosme dataset in addition to the Epinions dataset. It elucidates important insights and proposes a potentially important measure for mining online social networks. Copyright is held by the International World Wide Web Conference Committee (IW3C2).

Marketing communication has recently become increasingly interactive due to the widespread use of social media and requires wider participation by consumers. One method that facilitates this interaction is Shikake, which is defined as a trigger for behavior change. The aim of this paper is to demonstrate the effectiveness of Shikake with respect to enhancing customer engagement behavior, which is defined as customers’ behavioral manifestations that have a brand or firm focus, beyond purchase, resulting from motivational drivers. This paper introduces various approaches that can be used to trigger customer engagement behavior, such as brand relationships, behavioral economics, and human–computer interaction, emphasizes the power of design framing and triggers, and attempts to classify Shikake in the field of marketing according to participants’ motivation and consciousness. Finally, the paper attempts to reveal Shikake’s key success factor in the field of marketing and discusses future research opportunities.

There have been numerous attempts at the aggregation of attributes for relational data mining. Recently, an increasing number of studies have been undertaken to process social network data, partly because of the fact that so much social network data has become available. Among the various tasks in link mining, a popular task is link-based classification, by which samples are classified using the relations or links that are present among them. On the other hand, we sometimes employ traditional analytical methods in the field of social network analysis using e.g., centrality measures, structural holes, and network clustering. Through this study, we seek to bridge the gap between the aggregated features from the network data and traditional indices used in social network analysis. The notable feature of our algorithm is the ability to invent several indices that are well studied in sociology. We first define general operators that are applicable to an adjacent network. Then the combinations of the operators generate new features, some of which correspond to traditional indices, and others which are considered to be new. We apply our method for classification to two different datasets, thereby demonstrating the effectiveness of our approach.