Kohei Otake

In recent years, an increasing number of companies have been utilizing social media marketing, a marketing activity that makes use of social media. Among them, “influencer marketing,” which utilizes influencers who have great influence over other users on social media to promote their products, has been attracting attention. On the other hand, there are studies that point out the risk that PR using influencers may be counterproductive in some cases, and the effects given by influencers may vary depending on the subject influencer (sender) and the user (receiver). Therefore, there is a need for more detailed research on the reactions of consumers when they encounter PR postings. This study aims to clarify the effects of influencer attributes and posted content on consumer behaviour based on an evaluation using conjoint analysis and eye-tracking data. First, we examined eight attributes related to the scale of influencers' follower counts and the content of their posts and generated multiple scenario posts based on an orthogonal array. We also generated fictitious influencer account profiles for each scale of influencers' follower counts. These scenario posts and account profiles were combined to generate a total of 16 conjoint cards, which were then used in an experimental study. Furthermore, an eye-tracking experiment was conducted to validate the effects of the factors identified through conjoint analysis. The analysis reveals that the size of the influencer is the most important factor influencing consumer preference. In addition, we found that PR posts by mega-influencers contribute to consumers' impressions of PR posts. Furthermore, the eye tracking data collected in the experiment revealed that the number of followers and self-introductions in the influencer's profile account tended to be watched closely, with minor differences depending on the size of the influencer.

In recent years, the market for online flea markets, which are consumer-to-consumer (C2C) services where goods are bought and sold among users, has been expanding. In such services, sellers (individuals that offer goods or services for sale) create product details, including price, condition, shipping method, and images, when listing their items for sale. We consider the product image to be the first thing users see when selecting a product as a thumbnail, significantly impacting their purchase decisions. In this study, we proposed a discriminant model of purchase decisions for online flea market data to clarify the factors influencing these decisions based on product details. Specifically, we used metadata such as price and delivery method, along with image labels for product thumbnails, as features. We created and compared models with three patterns: metadata only, image labels only, and a combination of metadata and image labels. We created four types of models in this study: logistic regression, decision tree, gradient boosting, and random forest. We selected the models based on their accuracy evaluations. Our analysis revealed that the model using both metadata and image labels as features, combined with the gradient boosting method, had the highest accuracy. The partial dependence plots of the selected models highlighted the features important for users' purchase decisions. Received: 10 August 2024 | Revised: 8 February 2025 | Accepted: 14 March 2025 Conflicts of InterestThe authors declare that they have no conflicts of interest to this work. Data Availability StatementData sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution StatementEmi Iwanade: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Writing – original draft, Writing – review & editing, Visualization. Yoshihisa Shinozawa: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Writing–original draft, Writing – review & editing, Visualization. Kohei Otake: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition.