Kohei Otake

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.

Social networking services (SNS) have become indispensable communication tools. Consequently, influencer marketing, which leverages users with a significant influence on SNS, has garnered significant attention. Among these influencers, micro-influencers, who have substantial influence within specific domains, are particularly interesting to both academia and industry. This study proposes evaluation indices that can effectively select micro-influencers for product promotion using follower data and past posts from SNS accounts. Specifically, we propose four evaluation indices for micro-influencers: Virality, Commonality, Expertise and Credibility (VC-EC indices). VC indices are based on network features, whereas EC indices are based on language features. In this study, we present the concepts and specific calculation methods for the proposed indices. In addition, we demonstrate how to discover micro-influencers using the proposed methods with practical examples from accounts operated by actual stores.