The effect of intention analysis-based fraud detection systems in repeated supply Chain quality inspection : A context of learning and contract

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

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Author(s)

  • Jiaqi Yan
  • Xin Li
  • Yani Shi
  • Sherry Sun
  • Huaiqing Wang

Detail(s)

Original languageEnglish
Article number103177
Journal / PublicationInformation and Management
Online published29 Jun 2019
Publication statusOnline published - 29 Jun 2019

Abstract

As a result of the information asymmetry on product quality, there is a risk of unethical suppliers defrauding buyers in a supply chain. Buyers often conduct quality inspection on shipments and frame supply contracts to punish quality fraud. Due to cost concerns, buyers need to estimate the suppliers’ fraud possibilities and choose appropriate testing methods and frequencies. As suppliers’ fraud intentions depend on their cost-benefit analysis, it is possible to analyze suppliers’ fraud intention with appropriate modeling of their profit-seeking behavior. In this research, we are interested in how fraud intention analysis may affect the quality inspection process. It should be noted that quality inspection can be a repeated process, with suppliers and buyers conducting multiple rounds of transactions (including transactions with frauds) and learning about each other during the process. Their supply contracts may also affect suppliers’ profit-seeking attitude. We conduct a laboratory experiment to examine the effect of fraud intention analysis systems on inspection decision making considering the learning and contract effects. We put the experiment in the context of a dairy supply chain as a critical and interesting example application. The experiment shows that if there are no strong punitive terms for fraud in the contract, fraud intention analysis can improve buyers’ decision-making efficiency after controlling the learning effect, in terms of decision time, inspection cost, and correctness of rejecting suppliers’ fraudulent shipments.

Research Area(s)

  • Deception detection, Decision support, Fraud intention analysis, Laboratory experiment, Learning, Supply chain quality inspection, Supply contract

Citation Format(s)