Yingshuai Zhao, Assistant Professor, University of Cologne
Supply chain collaboration often involves lies, particularly in the digital era where the cost of lying is low. While researchers have proposed several approaches to this problem such as mechanism design, contract design, and reputation building, these approaches are not effective in detecting lies before transactions, especially if the lies can be denied. To address this issue, we employ process data associated with decisions to detect lies. Process data refer to the information generated during the process of decisions, such as response times and mouse trajectories. By analysing process data, we can gain insights into the intra-choice dynamics that underlie the decision-making process. We set up an experiment to test the approach, in which a retailer has private demand information and a supplier makes production decisions based on the retailer’s self-reported demand. In this scenario, the retailer has the incentive to inflate demand information to maximise profits.
We explore how process data differ between a liar and a truthteller in a one-shot game using laboratory experiments. Specifically, we examine three scenarios in which lies can be identified with varying levels of difficulty: a deniable scenario where lies can be easily concealed, a low-risk scenario where lies have a small chance of being detected, and a high-risk scenario where lies are highly likely to be exposed. The results show that process data provide rich information in assessing agents’ lying behaviour. We find that both response times and mouse trajectories differ between partial liars and truth-tellers, but full liars exhibit similar patterns to truthtellers. Furthermore, the process data do not vary significantly among scenarios, and our findings are robust in a setting with repeated interactions. The agents’ performances are well predicted by the quantal response equilibrium under lying aversion. In summary, our study shows the usefulness of response times and mouse trajectories in detecting potential liars in supply chain collaboration. Given the abundance of process data available in online interactions, our findings are particularly relevant to e-business.
Yingshuai Zhao is an Assistant Professor of Supply Chain Management who joined the University of Cologne, Germany in 2015. She earned her PhD in Industrial Engineering from Tsinghua University, China. Her research interests are focused on the behavioural modelling of supply chain interactions, including inventory management, demand management, and supply chain bargaining. Her most recent research works have been published in European Journal of Operational Research and Manufacturing & Service Operations Management.
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