Reputation inflation detection in a Chinese C2C market

  • Authors:
  • Weijia You;Lu Liu;Mu Xia;Chenggong Lv

  • Affiliations:
  • School of Economics and Management, Beihang University, Beijing 100191, PR China and School of Economics and Management, Beijing Forestry University, Beijing 100191, PR China;School of Economics and Management, Beihang University, Beijing 100191, PR China;Leavey School of Business, Santa Clara University, Santa Clara, CA 95053, USA;School of Economics and Management, Beihang University, Beijing 100191, PR China

  • Venue:
  • Electronic Commerce Research and Applications
  • Year:
  • 2011

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Abstract

In consumer-to-consumer (C2C) markets, sellers can manipulate their reputation by employing a large number of puppet buyers who offer positive feedback on fake transactions. We present a conceptual framework to identify the characteristics of collusive transactions based on the homo economicus assumption. We hypothesize that transaction-related indicators including price, frequency, comment, and connectedness to the transaction network, and individual-related indicators including reputation and age can be used to identify collusive transactions. The model is empirically tested using a dataset from Taobao, the largest C2C market in China. The results show that the proposed indicators are effective in identifying collusive traders.