Z
Zhangxi Lin
Researcher at College of Business Administration
Publications - 83
Citations - 2531
Zhangxi Lin is an academic researcher from College of Business Administration. The author has contributed to research in topics: The Internet & Service (business). The author has an hindex of 25, co-authored 82 publications receiving 2157 citations. Previous affiliations of Zhangxi Lin include Texas Tech University & Xihua University.
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Hope or Hype: On the Viability of Escrow Services as Trusted Third Parties in Online Auction Environments
TL;DR: A dynamic game model for online traders and a profit maximization model for the escrow service provider are proposed and a numerical study based on the theoretical analysis is conducted to provide detailed guidelines of the model application for an escrowService provider and to explore if theEscrow service is a viable business model in C2C auction markets.
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Identifying effective influencers based on trust for electronic word-of-mouth marketing
TL;DR: A novel product review domain-aware (PRDA) approach is conceived that identifies effective influencers and categorizes them into three types, i.e., emerging influencer, holding influencers, and vanishing Influencers, based on their popularity status across the life cycle.
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User comments for news recommendation in forum-based social media
TL;DR: The relationship (duplicate, generalization, or specialization) between recommended news articles and the original posting is investigated and the proposed solutions provide an improved news recommendation service in forum-based social media.
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A trust model for online peer-to-peer lending: a lender's perspective
TL;DR: An integrated trust model specifically for the online P2P lending context is developed to better understand the critical factors that drive lenders’ trust and provides valuable insights for both borrowers and intermediaries.
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Capturing the essence of word-of-mouth for social commerce: Assessing the quality of online e-commerce reviews by a semi-supervised approach
TL;DR: A semi-supervised system that exploits two opportunities: the improvement of classification performance through the use of a few labeled instances and numerous unlabeled instances, and the effectiveness of the social characteristics of e-commerce communities as identifiers of influential reviewers who write high-quality reviews.