Keyao (Eden) Li
Keyao (Eden) Li, Australia
|Are we ready for a rational discussion? The existence of biases in construction dispute negotiation
Sunday, May 29 at 12:10 pm – 12:45 pm UTC
Embedded in a network constellation of multi-stakeholders with various professional backgrounds and differing goals, the construction industry is featured with adversarial relationships and inevitable disputes. Therefore, high quality communication and negotiation among the parties are essential in achieving speedy and less costly dispute resolution. As rational evaluation underpins quality negotiation decisions, are construction disputing parties rational as they assumed? Are there any biases that could possibly prohibit them from making prudent judgement?
During this presentation, Dr. Li will share the research findings on the effect of bias in construction dispute negotiation. The existence of bias in construction dispute negotiation was revealed with four types: preconception bias, self-affirmation bias, optimism bias and interest-oriented bias. A validated bias framework in construction dispute negotiation was developed to present the symptoms of each type of bias and their relative magnitudes. This bias framework is innovative in raising the awareness of bias and its harm in construction dispute negotiation. It offers insights in detecting and mitigating psychological barriers against successful construction dispute negotiation. At the end of the presentation, Dr. Li will suggest three categories of bias minimizing approach to improve the rationality of the decisions in construction dispute negotiation. Vast resources would be saved and amicable relationship among the collaborating parties could be developed when the impact of bias is curbed.
Dr. Keyao (Eden) Li is a research fellow at Future of Work Institute, Curtin University. Her research interests include construction negotiation, digital innovation, performance, safety and well-being in the workplace. Eden is also a research fellow at the ARC Training Centre for Transforming Maintenance Through Data Science. Her research at the centre aims to translate advances in data science arising from the research projects into workplace improvement for the future mining industry but also applicable to other industries.
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