Optimizing incident investigation information collection in the construction industry

Conducting effective incident investigation is crucial to learning from incidents and implementing systemic improvements to prevent the reoccurrence of incidents. The information collected in this process holds utmost significance, as the subsequent stages depend on the quality of the data collected. Notably, information obtained through interviews with individuals involved in incidents is particularly susceptible to quality issues due to its subjective nature, posing the potential for biases to influence the collected data. The motivation behind this study arises from a literature gap highlighting the susceptibility of investigations to bias, which can impede effective learning. However, existing studies do not delve into the identification of common biases and how to mitigate these biases to optimize information collection in incident investigations.Therefore, with the aim of optimizing information collection in incident investigations the objectives of this dissertation are to (1) conduct a systematic literature review to identify factors influencing high quality information collection in incident investigations (2) identify the most common biases and how they emerge during incident investigation interviews and (3) validate Check Yourself as a bias awareness tool in mitigating the effects of biases in incident investigators. The systematic literature review revealed that subjectivity in information collection is one of the factors influencing the quality of the information collected, thus pointing out the need for empirical studies to explore what these biases are and how they impact the information collected during interviews. Further, thematic analysis from role play simulations with 34 industry professionals from the construction industry was able to reveal the most common biases in incident investigation interviews including confirmation bias, representativeness bias, hindsight bias, anchoring bias, experience bias, conservatism in belief revision and fundamental attribution error. Another round of simulations with 20 participants revealed that Check Yourself can be used as a bias mitigation tool by creating awareness among investigators. Further research is needed to understand the effects of Check Yourself in real world settings over longer periods of time to evaluate if Check Yourself needs to be used as a standalone tool or in tandem with other types of training, and also to validate if this tool can be utilized in other domains.