• Journal of Fashion Business
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• v.22 no.3
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• pp.39-56
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• 2018

Since trusting in other consumers may refer to having similar needs and preferences on fashion goods with them, interpersonal trust can be a reliable and practical criterion for market segmentation online. Therefore, this study aims to identify fashion online consumers based on interpersonal trust. This study segments fashion consumers based on interpersonal trust and experience and describes characteristics of each segment by examining demo-psychographic and behavioral variables as well as clothing consumption values. An online survey was conducted to collect data from 426 adult consumers who had bought fashion goods from their patronized e-tailer in the past one month. They completed a self-administered questionnaire inquiring about interpersonal trust, trust in e-tailers, purchase intentions, clothing consumption values, and their purchasing behavior online. Two-step cluster analysis generated four segments: distrustful doers, trusting doers, inactive trusters, and distrusters. They exhibited different characteristics in gender, online experiences, interpersonal trust, clothing consumption values, trust in the e-tailers, and attitude toward the e-tailers. Providing a new effective segmentation base, this study suggests that fashion marketers identify customers with a high level of trust in other customers and develop an encouraging environment that customers can interact with others in order to increase the effectiveness of trust. Because customers with a higher level of interpersonal trust are likely to have stronger trust in e-tailers with, more favorable attitude and purchase intention, and highly perceive the value of clothing consumption than those who have a lower level of interpersonal trust.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.4892-4911
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• 2015

Trust as one of important social relations has attracted much attention from researchers in the field of social network-based recommender systems. In trust network-based recommender systems, there exist normally two roles for users, truster and trustee. Most of trust-based methods generally utilize explicit links between truster and trustee to find similar neighbors for recommendation. However, there possibly exist implicit correlations between users, especially for users with the same role (truster or trustee). In this paper, we propose a novel Collaborative Filtering method called CF-TC, which exploits Trust Context to discover implicit correlation between users with the same role for recommendation. In this method, each user is first represented by the same-role users who are co-occurring with the user. Then, similarities between users with the same role are measured based on obtained user representation. Finally, two variants of our method are proposed to fuse these computed similarities into traditional collaborative filtering for rating prediction. Using two publicly available real-world Epinions and Ciao datasets, we conduct comprehensive experiments to compare the performance of our proposed method with some existing benchmark methods. The results show that CF-TC outperforms other baseline methods in terms of RMSE, MAE, and recall.