Asia-Pacific Journal of Business (아태비즈니스연구)

KNU The Institute of Management & Economy Research (강원대학교 경영경제연구소)
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The Truth about Sellers' Lies: Why Dishonesty Loses in Markets under Information Asymmetry

  • Huh, Seung (College of Business Administration, Incheon National University)
  • Received : 2020.11.30
  • Accepted : 2020.12.21
  • Published : 2020.12.30
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Abstract

Purpose - This study analyzes the effect of sellers' dishonesty on various market outcomes such as seller profit, buyer profit, and market welfare, through precisely measuring the level of sellers' information disclosure and its economic impacts. As an explicit observation of sellers' dishonesty is not easy in most other settings, this study is expected to suggest unique and meaningful implications on the effect of sellers' incomplete information disclosure to researchers, managers, and policy makers. Design/methodology/approach - In order to precisely measure the level of sellers' dishonesty under information asymmetry, this study analyzes the data from an incentive-based economic experiment using z-Tree software. This experimental method enables us to focus on the strategic interactions among participants, observe the integrity of seller's information disclosure, and reproduce real market situations. Findings - The analysis of sellers' dishonesty has provided the following important and counterintuitive findings about the reality of buyer-seller interactions under information asymmetry. First, sellers' lies do not affect seller profit even when they are very intensive. Second, sellers' dishonesty negatively affects buyer profit and the entire market welfare. Third, a seller's quality claim has a positive effect on the seller profit only when a seller is being honest. Research implications or Originality - This study analyzes sellers' dishonesty using incentive-based economic experiment using z-Tree software which provides a straightforward examination on dishonest behavior of sellers, that is not readily available with other types of observational or experimental data.

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