東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Optimizing Incident Investigation In...

Thallapureddy, Sreeja,

Linked to FindBook

Google Book

Amazon

博客來

Optimizing Incident Investigation Information Collection in the Construction Industry /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Optimizing Incident Investigation Information Collection in the Construction Industry // Sreeja Thallapureddy.
Author:	Thallapureddy, Sreeja,
Description:	1 electronic resource (201 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
Contained By:	Dissertations Abstracts International85-11B.
Subject:	Engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31147351
ISBN:	9798382717227

Optimizing Incident Investigation Information Collection in the Construction Industry /
Thallapureddy, Sreeja,

Optimizing Incident Investigation Information Collection in the Construction Industry /Sreeja Thallapureddy. - 1 electronic resource (201 pages)

Source: Dissertations Abstracts International, Volume: 85-11, Section: B.

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.

English

ISBN: 9798382717227Subjects--Topical Terms:

586835
Engineering.
Subjects--Index Terms:

Bias mitigation

Optimizing Incident Investigation Information Collection in the Construction Industry /
LDR:04088nmm a22004573i 4500 001 2428853
005 20250420214336.5
006 m o d
007 cr|nu||||||||
008 261011s2024 miu||||||m |||||||eng d
020 $a 9798382717227
035 $a (MiAaPQD)AAI31147351
035 $a AAI31147351
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Thallapureddy, Sreeja, $e author. $3 3816330
245 1 0 $a Optimizing Incident Investigation Information Collection in the Construction Industry / $c Sreeja Thallapureddy.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 1 electronic resource (201 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
500 $a Advisors: Hallowell, Matthew; Sherratt, Fred Committee members: Torres-Machi, Cristina; Pearson, David; Tymvios, Nicholas; Javernick-Will, Amy.
502 $b Ph.D. $c University of Colorado at Boulder $d 2024.
520 $a 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.
546 $a English
590 $a School code: 0051
650 4 $a Engineering. $3 586835
650 4 $a Architectural engineering. $3 3174102
650 4 $a Environmental engineering. $3 548583
653 $a Bias mitigation
653 $a Incident investigations
653 $a Construction safety
653 $a Common biases
653 $a Incident learning
690 $a 0543
690 $a 0775
690 $a 0537
690 $a 0462
710 2 $a University of Colorado at Boulder. $b Civil, Environmental, and Architectural Engineering. $e degree granting institution. $3 3765764
720 1 $a Hallowell, Matthew $e degree supervisor.
720 1 $a Sherratt, Fred $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 85-11B.
790 $a 0051
791 $a Ph.D.
792 $a 2024
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31147351